diff --git a/.azuredevops/rocm-ci.yml b/.azuredevops/rocm-ci.yml new file mode 100644 index 0000000000..06e1fc7f4f --- /dev/null +++ b/.azuredevops/rocm-ci.yml @@ -0,0 +1,42 @@ +resources: + repositories: + - repository: pipelines_repo + type: github + endpoint: ROCm + name: ROCm/ROCm + +variables: +- group: common +- template: /.azuredevops/variables-global.yml@pipelines_repo + +trigger: + batch: true + branches: + include: + - develop + paths: + exclude: + - .githooks + - .github + - docs + - '.*.y*ml' + - '*.md' + - LICENSE.txt + +pr: + autoCancel: true + branches: + include: + - develop + paths: + exclude: + - .githooks + - .github + - docs + - '.*.y*ml' + - '*.md' + - LICENSE.txt + drafts: false + +jobs: + - template: ${{ variables.CI_COMPONENT_PATH }}/MIOpen.yml@pipelines_repo diff --git a/.gitattributes b/.gitattributes index 0429091efd..8e66d7251e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,4 @@ -*.kdb filter=lfs diff=lfs merge=lfs -text +*.db.bz2 binary +*.fdb.txt.bz2 binary *.kdb.bz2 filter=lfs diff=lfs merge=lfs -text +*.ktn.model binary diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index dff4cb61c5..1b79ec9b8c 100755 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,5 +1,6 @@ * @JehandadKhan @junliume # Documentation files -docs/* @ROCm/rocm-documentation +docs/ @ROCm/rocm-documentation *.md @ROCm/rocm-documentation *.rst @ROCm/rocm-documentation +.readthedocs.yaml @ROCm/rocm-documentation diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 9e6678abe5..b3299fa4e8 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -15,4 +15,4 @@ python: build: os: ubuntu-22.04 tools: - python: "3.8" + python: "3.10" diff --git a/CHANGELOG.md b/CHANGELOG.md index f85f66a0cf..40f7136c24 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,25 @@ Full documentation for MIOpen is available [here](https://rocm.docs.amd.com/projects/MIOpen/en/latest/) -## (Unreleased) MIOpen-3.1.0 for ROCm 6.1.0 +## MIOpen-3.2.0 for ROCm 6.2.0 +### Added +- [Conv] bilinear (alpha beta) solvers +- [Conv] enable bf16 for ck-based solvers +- [Conv] Add split_k tuning to 2d wrw ck-based solver +- [MHA] graph API fp8 fwd +- [RNN] multi-stream as default solution. +- TunaNetv2.0 for MI300 +- Added adam and amp adam optimizer + +### Fixed +- Memory access fault caused by GemmBwdRest +- Context configuration in GetWorkSpaceSize +- Fixes to support huge tensors + +### Performance +- Find: Improve precision of benchmarking + +## MIOpen-3.1.0 for ROCm 6.1.0 ### Added - CK-based 2d/3d convolution solvers to support nchw/ncdhw layout - Fused solver for Fwd Convolution with Residual, Bias and activation diff --git a/CMakeLists.txt b/CMakeLists.txt index 44ebb25b75..e53dd871c9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -37,6 +37,19 @@ macro(set_var_to_condition var) endif() endmacro() +macro(set_if_bools_are_different var in1 in2) + set(${var} FALSE) + if(${in1}) + if(NOT ${in2}) + set(${var} TRUE) + endif() + else() + if(${in2}) + set(${var} TRUE) + endif() + endif() +endmacro() + get_property(MIOPEN_GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG) # This has to be initialized before the project() command appears @@ -103,15 +116,16 @@ set(MIOPEN_ENABLE_SQLITE_BACKOFF On CACHE BOOL "") option( BUILD_DEV "Build for development only" OFF) option(MIOPEN_ENABLE_FIN "Enable the fin driver for MIOpen" OFF) +option(MIOPEN_STRIP_SYMBOLS "Strip symbols in release mode" ON) # Strip symbols for release -if(NOT WIN32 AND NOT APPLE) +if(MIOPEN_STRIP_SYMBOLS AND NOT WIN32 AND NOT APPLE) set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -s") set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -s") endif() -rocm_setup_version(VERSION 3.1.0) +rocm_setup_version(VERSION 3.2.0) list( APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake ) include(TargetFlags) @@ -216,7 +230,7 @@ endif() # HIP is always required find_package(hip REQUIRED PATHS /opt/rocm) -message(STATUS "Build with HIP ${hip_VERSION}") +message(STATUS "Build with HIP ${hip_VERSION} ${hip_DIR}") # Override HIP version in config.h, if necessary. # The variables set by find_package() can't be overwritten, @@ -251,9 +265,14 @@ set(MIOPEN_hip_VERSION ${MIOPEN_hip_VERSION_MAJOR}.${MIOPEN_hip_VERSION_MINOR}.$ # Do not enable HIPRTC by default for older ROCm versions in order to avoid # build time errors, because HIPRTC is a relatively new component. -set_var_to_condition(MIOPEN_USE_HIPRTC_DEFAULT (MIOPEN_USE_COMGR AND (MIOPEN_hip_VERSION VERSION_GREATER_EQUAL 5))) +set_var_to_condition(MIOPEN_USE_HIPRTC_DEFAULT MIOPEN_USE_COMGR) option(MIOPEN_USE_HIPRTC "Use HIPRTC to build HIP kernels instead of COMGR" ${MIOPEN_USE_HIPRTC_DEFAULT}) +set_if_bools_are_different(MIOPEN_CONFIGURATION_ERROR_COMGR_HIPRTC MIOPEN_USE_COMGR MIOPEN_USE_HIPRTC) +if(MIOPEN_CONFIGURATION_ERROR_COMGR_HIPRTC) + message(FATAL_ERROR "MIOPEN_USE_COMGR (${MIOPEN_USE_COMGR}) and MIOPEN_USE_HIPRTC (${MIOPEN_USE_HIPRTC}) should be set to the same value") +endif() + # Do not append system include directories to HIP compiler flags when HIPRTC is used set_var_to_condition(MIOPEN_HIP_COMPILER_USE_SYSTEM_INCLUDE_DIRECTORIES_DEFAULT (NOT (MIOPEN_USE_HIPRTC AND (MIOPEN_hip_VERSION VERSION_GREATER_EQUAL 6.1.40091)))) @@ -282,7 +301,7 @@ if(HAS_HIP) else() # CXX compiler is not HIP compiler, let's analyze HIP version. set(MIOPEN_HIP_COMPILER_HAS_OPTION_OFFLOAD_UNIFORM_BLOCK Off) - if(${MIOPEN_hip_VERSION_FLAT} GREATER_EQUAL 500723302) + if(MIOPEN_hip_VERSION_FLAT GREATER_EQUAL 500723302) set(MIOPEN_HIP_COMPILER_HAS_OPTION_OFFLOAD_UNIFORM_BLOCK On) endif() message(STATUS "MIOPEN_HIP_COMPILER_HAS_OPTION_OFFLOAD_UNIFORM_BLOCK: ${MIOPEN_HIP_COMPILER_HAS_OPTION_OFFLOAD_UNIFORM_BLOCK}") @@ -302,18 +321,19 @@ add_compile_definitions($<$:HIP_COMPILER_FLAGS=${HIP_COMPI # HIP if( MIOPEN_BACKEND STREQUAL "HIP" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN_BACKEND STREQUAL "HIPNOGPU") if(MIOPEN_USE_COMPOSABLEKERNEL) - find_package(composable_kernel 1.0.0 COMPONENTS device_other_operations device_gemm_operations device_conv_operations device_contraction_operations device_reduction_operations) + find_package(composable_kernel 1.0.0 COMPONENTS device_other_operations device_gemm_operations device_conv_operations device_reduction_operations) endif() if( MIOPEN_BACKEND STREQUAL "HIPNOGPU") set(MIOPEN_MODE_NOGPU 1) endif() set(MIOPEN_BACKEND_HIP 1) - find_program(HIP_OC_COMPILER amdclang + find_program(HIP_OC_COMPILER NAMES amdclang clang PATH_SUFFIXES bin PATHS /opt/rocm ${CMAKE_INSTALL_PREFIX} + ENV HIP_PATH ) if(HIP_OC_COMPILER) message(STATUS "OpenCL compiler: ${HIP_OC_COMPILER}") @@ -331,10 +351,22 @@ if( MIOPEN_BACKEND STREQUAL "HIP" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN_B set(MIOPEN_USE_ROCBLAS ON CACHE BOOL "") if(MIOPEN_USE_ROCBLAS) find_package(rocblas REQUIRED PATHS /opt/rocm) - message(STATUS "Build with rocblas ${rocblas_VERSION}") + message(STATUS "Build with rocblas ${rocblas_VERSION} ${rocblas_DIR}") else() message(STATUS "Build without rocblas") endif() + + # hipblaslt + set_var_to_condition(MIOPEN_USE_HIPBLASLT_DEFAULT NOT WIN32) + option(MIOPEN_USE_HIPBLASLT "Use hipBlasLt" ${MIOPEN_USE_HIPBLASLT_DEFAULT}) + if(MIOPEN_USE_HIPBLASLT) + find_package(hipblas REQUIRED PATHS /opt/rocm $ENV{HIP_PATH}) + message(STATUS "Build with hipbBLAS ${hipblas_VERSION} ${hipblas_DIR}") + find_package(hipblaslt REQUIRED PATHS /opt/rocm $ENV{HIP_PATH}) + message(STATUS "Build with hipbBLASLt ${hipblaslt_VERSION} ${hipblaslt_DIR}") + else() + message(STATUS "Build without hipbBLASLt") + endif() else() #CK is only enabled when HIP backend is selected set(MIOPEN_USE_COMPOSABLEKERNEL Off) @@ -367,27 +399,37 @@ if(MIOPEN_USE_MLIR) if(NOT ${BUILD_SHARED_LIBS} AND ${MIOPEN_USE_COMGR}) message(FATAL_ERROR "Potential symbol conflict between mlir and comgr in static build") endif() - # Try to find package rocMLIR - # REQUIRED is omitted since we do not want cmake to abort if the package is not found - find_package(rocMLIR 1.0.0 CONFIG) - if(NOT rocMLIR_FOUND) - message(STATUS "Falling back to find library libMLIRMIOpen") - # Backward compatibility with ROCm 5.3 - # If the rocMLIR package is not found, try to find the library libMLIRMIOpen directly - find_library(LIBMLIRMIOPEN MLIRMIOpen REQUIRED) - if(NOT LIBMLIRMIOPEN) - message(FATAL_ERROR "library libMLIRMIOpen not found, please reinstall dependencies. \ - Refer to https://github.com/ROCm/MIOpen#installing-the-dependencies") - else() - message(STATUS "Build with library libMLIRMIOpen: " ${LIBMLIRMIOPEN}) - set(rocMLIR_VERSION 0.0.1) - endif() + if(WIN32) + # Windows does not support earlier ROCm versions hence no fallback to MLIRMIOpen. + find_package(rocMLIR 1.0.0 CONFIG REQUIRED) else() - message(STATUS "Build with rocMLIR::rockCompiler ${rocMLIR_VERSION}") + # Try to find package rocMLIR + # REQUIRED is omitted since we do not want cmake to abort if the package is not found + find_package(rocMLIR 1.0.0 CONFIG) + if(NOT rocMLIR_FOUND) + message(STATUS "Falling back to find library libMLIRMIOpen") + # Backward compatibility with ROCm 5.3 + # If the rocMLIR package is not found, try to find the library libMLIRMIOpen directly + find_library(LIBMLIRMIOPEN MLIRMIOpen REQUIRED) + if(NOT LIBMLIRMIOPEN) + message(FATAL_ERROR "library libMLIRMIOpen not found, please reinstall dependencies. \ + Refer to https://github.com/ROCm/MIOpen#installing-the-dependencies") + else() + message(STATUS "Build with library libMLIRMIOpen: " ${LIBMLIRMIOPEN}) + set(rocMLIR_VERSION 0.0.1) + endif() + endif() endif() + message(STATUS "Build with rocMLIR::rockCompiler ${rocMLIR_VERSION} ${rocMLIR_DIR}") endif() -set(MIOPEN_PACKAGE_REQS "hip-rocclr") +# Update HIP Runtime Package Dependency +if(ENABLE_ASAN_PACKAGING) + set(DEPENDS_HIP_RUNTIME "hip-runtime-amd-asan" ) +else() + set(DEPENDS_HIP_RUNTIME "hip-runtime-amd" ) +endif() +set(MIOPEN_PACKAGE_REQS "${DEPENDS_HIP_RUNTIME}") # Online assembler find_program(MIOPEN_AMDGCN_ASSEMBLER @@ -407,7 +449,7 @@ message(STATUS "AMDGCN assembler: ${MIOPEN_AMDGCN_ASSEMBLER}") if(MIOPEN_USE_COMGR) find_package(amd_comgr REQUIRED CONFIG) - message(STATUS "Build with comgr ${amd_comgr_VERSION}") + message(STATUS "Build with amd_comgr ${amd_comgr_VERSION} ${amd_comgr_DIR}") set(MIOPEN_PACKAGE_REQS "${MIOPEN_PACKAGE_REQS}, comgr") endif() @@ -416,7 +458,7 @@ if(MIOPEN_USE_HIPRTC) message(FATAL_ERROR "HIPRTC can be used only together with COMGR") endif() find_package(hiprtc REQUIRED) - message(STATUS "Build with HIPRTC ${hiprtc_VERSION}") + message(STATUS "Build with hiprtc ${hiprtc_VERSION} ${hiprtc_DIR}") endif() option(Boost_USE_STATIC_LIBS "Use boost static libraries" ON) @@ -447,7 +489,9 @@ endif() if(MIOPEN_ENABLE_AI_KERNEL_TUNING OR MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK) find_package(frugally-deep CONFIG REQUIRED) + message(STATUS "Build with frugally-deep ${frugally-deep_VERSION} ${frugally-deep_DIR}") find_package(Eigen3 REQUIRED) + message(STATUS "Build with Eigen3 ${Eigen3_VERSION} ${Eigen3_DIR}") endif() if(WIN32) @@ -498,6 +542,10 @@ if(MIOPEN_USE_ROCBLAS) set(MIOPEN_PACKAGE_REQS "${MIOPEN_PACKAGE_REQS}, rocblas") endif() +if(MIOPEN_USE_HIPBLASLT) + set(MIOPEN_PACKAGE_REQS "${MIOPEN_PACKAGE_REQS}, hipblaslt") +endif() + if(MIOPEN_OFFLINE_COMPILER_PATHS_V2) set(MIOPEN_PACKAGE_REQS "${MIOPEN_PACKAGE_REQS}, rocm-core") endif() @@ -505,7 +553,7 @@ endif() if(MIOPEN_BUILD_DRIVER) # PR #2785 MIOpenDriver to use rocrand to init buffers find_package(rocrand REQUIRED) - message(STATUS "rocrand_VERSION=${rocrand_VERSION}") + message(STATUS "Build with rocrand ${rocrand_VERSION} ${rocrand_DIR}") set(MIOPEN_PACKAGE_REQS "${MIOPEN_PACKAGE_REQS}, rocrand") endif() @@ -596,8 +644,8 @@ function(install_kdb FILE_NAME COMPONENT_NAME) endfunction() # Both the lists below should be in sync always -set(KDB_BZ2_FILES gfx942.kdb.bz2 gfx90a.kdb.bz2 gfx1030.kdb.bz2 gfx908.kdb.bz2 gfx906.kdb.bz2 gfx900.kdb.bz2) -set(COMPONENT_LST gfx942kdb gfx90akdb gfx1030kdb gfx908kdb gfx906kdb gfx900kdb) +set(KDB_BZ2_FILES gfx90a.kdb.bz2 gfx1030.kdb.bz2 gfx908.kdb.bz2 gfx906.kdb.bz2 gfx900.kdb.bz2) +set(COMPONENT_LST gfx90akdb gfx1030kdb gfx908kdb gfx906kdb gfx900kdb) if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.17) foreach(__file __component IN ZIP_LISTS KDB_BZ2_FILES COMPONENT_LST) @@ -630,7 +678,7 @@ endif() rocm_create_package( NAME MIOpen-${MIOPEN_BACKEND} - DESCRIPTION "AMD's DNN Library" + DESCRIPTION "AMD DNN Library" MAINTAINER "MIOpen Maintainer " LDCONFIG # DEPENDS rocm-opencl hip-rocclr tinygemm diff --git a/Dockerfile b/Dockerfile index 975acda006..89c8065f48 100755 --- a/Dockerfile +++ b/Dockerfile @@ -7,6 +7,7 @@ RUN dpkg --add-architecture i386 # Install preliminary dependencies RUN apt-get update && \ DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ + "linux-headers-$(uname -r)" "linux-modules-extra-$(uname -r)" \ apt-utils \ ca-certificates \ curl \ @@ -18,7 +19,7 @@ DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ ENV APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn RUN curl -fsSL https://repo.radeon.com/rocm/rocm.gpg.key | gpg --dearmor -o /etc/apt/trusted.gpg.d/rocm-keyring.gpg -RUN wget https://repo.radeon.com/amdgpu-install/.6.1/ubuntu/jammy/amdgpu-install_6.1.60100-1_all.deb --no-check-certificate +RUN wget https://repo.radeon.com/amdgpu-install/6.1/ubuntu/jammy/amdgpu-install_6.1.60100-1_all.deb --no-check-certificate RUN apt-get update && \ DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ ./amdgpu-install_6.1.60100-1_all.deb @@ -26,8 +27,8 @@ DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ # Add rocm repository RUN export ROCM_APT_VER=6.1;\ echo $ROCM_APT_VER &&\ -sh -c 'echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] https://repo.radeon.com/amdgpu/.$ROCM_APT_VER/ubuntu jammy main > /etc/apt/sources.list.d/amdgpu.list' &&\ -sh -c 'echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] https://repo.radeon.com/rocm/apt/.apt_$ROCM_APT_VER jammy main > /etc/apt/sources.list.d/rocm.list' +sh -c 'echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] https://repo.radeon.com/amdgpu/$ROCM_APT_VER/ubuntu jammy main > /etc/apt/sources.list.d/amdgpu.list' &&\ +sh -c 'echo deb [arch=amd64 signed-by=/etc/apt/trusted.gpg.d/rocm-keyring.gpg] https://repo.radeon.com/rocm/apt/$ROCM_APT_VER jammy main > /etc/apt/sources.list.d/rocm.list' RUN sh -c "echo deb http://mirrors.kernel.org/ubuntu jammy main universe | tee -a /etc/apt/sources.list" RUN amdgpu-install -y --usecase=rocm --no-dkms @@ -104,10 +105,11 @@ RUN apt-get update && \ DEBIAN_FRONTEND=noninteractive apt-get purge -y --allow-unauthenticated \ composablekernel-dev ARG COMPILER_LAUNCHER="" +# rbuild is used to trigger build of requirements.txt, dev-requirements.txt RUN if [ "$USE_FIN" = "ON" ]; then \ - rbuild prepare -s fin -d $PREFIX -DGPU_TARGETS=${GPU_ARCH} -DCMAKE_CXX_COMPILER_LAUNCHER="${COMPILER_LAUNCHER}"; \ + rbuild prepare -s fin -d $PREFIX -DCMAKE_CXX_COMPILER_LAUNCHER="${COMPILER_LAUNCHER}"; \ else \ - rbuild prepare -s develop -d $PREFIX -DGPU_TARGETS=${GPU_ARCH} -DCMAKE_CXX_COMPILER_LAUNCHER="${COMPILER_LAUNCHER}"; \ + rbuild prepare -s develop -d $PREFIX -DCMAKE_CXX_COMPILER_LAUNCHER="${COMPILER_LAUNCHER}"; \ fi RUN ccache -s diff --git a/Jenkinsfile b/Jenkinsfile index 474dbd378e..424c1d8a2f 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -31,7 +31,7 @@ def cmake_build(Map conf=[:]){ def compiler = conf.get("compiler","/opt/rocm/llvm/bin/clang++") def make_targets = conf.get("make_targets","check") def debug_flags = "-g -fno-omit-frame-pointer -fsanitize=undefined -fno-sanitize-recover=undefined -Wno-option-ignored " + conf.get("extradebugflags", "") - def build_envs = "CTEST_PARALLEL_LEVEL=4 MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 " + conf.get("build_env","") + def build_envs = "CTEST_PARALLEL_LEVEL=4 " + conf.get("build_env","") def prefixpath = conf.get("prefixpath","/opt/rocm") def mlir_args = " -DMIOPEN_USE_MLIR=" + conf.get("mlir_build", "ON") def setup_args = mlir_args + " -DMIOPEN_GPU_SYNC=Off " + conf.get("setup_flags","") @@ -284,7 +284,7 @@ def buildHipClangJob(Map conf=[:]){ } withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { - timeout(time: 150, unit:'MINUTES') + timeout(time: 210, unit:'MINUTES') { if (lfs_pull) { sh "git lfs pull --exclude=" @@ -469,7 +469,7 @@ pipeline { description: "") booleanParam( name: "TARGET_GFX908", - defaultValue: env.BRANCH_NAME == env.NIGHTLY_BRANCH ? true : false, + defaultValue: env.BRANCH_NAME == "develop" ? true : false, description: "") booleanParam( name: "TARGET_GFX90A", @@ -523,12 +523,20 @@ pipeline { name: "PERF_TEST_BRANCH_OVERRIDE", defaultValue: false, description: "Enable performance testing stages") + booleanParam( + name: "DBSYNC_TEST", + defaultValue: true, + description: "Enable database synchronization testing stages") string(name: "PERF_TEST_OVERRIDE", defaultValue: '', description: "Add extra env vars for the MIOpenDriver cmd, comma separated") string(name: "DOCKER_IMAGE_OVERRIDE", defaultValue: '', description: "") + booleanParam( + name: "WORKAROUND__TARGET_GFX94X_MINIMUM_TEST_ENABLE", + defaultValue: false, + description: "") } environment{ @@ -630,28 +638,28 @@ pipeline { expression { params.BUILD_SMOKE_FP32 && params.DATATYPE_FP32 } } parallel{ - stage('Fp32 Hip AnyGPU') { + stage('Fp32 Hip gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } steps{ buildHipClangJobAndReboot(make_targets: Smoke_targets) } } - stage('Fp32 Hip Debug AnyGPU') { + stage('Fp32 Hip Debug gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } steps{ buildHipClangJobAndReboot(build_type: 'debug', make_targets: Smoke_targets) } @@ -669,30 +677,17 @@ pipeline { buildHipClangJobAndReboot(build_type: 'debug', make_targets: Smoke_targets) } } - stage('Fp32 Hip Debug gfx90a') { - when { - beforeAgent true - expression { params.TARGET_GFX90A } - } - options { - retry(2) - } - agent{ label rocmnode("gfx90a") } - steps{ - buildHipClangJobAndReboot(build_type: 'debug', make_targets: Smoke_targets) - } - } stage('Fp32 Hip Debug gfx94X') { when { beforeAgent true - expression { params.TARGET_GFX94X } + expression { params.TARGET_GFX94X || params.WORKAROUND__TARGET_GFX94X_MINIMUM_TEST_ENABLE } } options { retry(2) } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot(build_type: 'debug', make_targets: Smoke_targets) + buildHipClangJobAndReboot(build_type: 'debug', make_targets: Smoke_targets, needs_reboot:false) } } } @@ -702,49 +697,49 @@ pipeline { expression { params.BUILD_SMOKE_AUX1 && params.DATATYPE_FP32 } } parallel{ - stage('Fp32 Hip Debug NOCOMGR AnyGPU') { + stage('Fp32 Hip Debug NOCOMGR gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } environment{ // Can be removed altogether with when WORKAROUND_SWDEV_290754. - NOCOMGR_build_cmd = "CTEST_PARALLEL_LEVEL=4 MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" + NOCOMGR_build_cmd = "CTEST_PARALLEL_LEVEL=4 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" } steps{ buildHipClangJobAndReboot( build_type: 'debug', setup_flags: NOCOMGR_flags, build_cmd: NOCOMGR_build_cmd, test_flags: ' --verbose ') } } - stage('Fp32 Hip Debug NOMLIR AnyGPU') { + stage('Fp32 Hip Debug NOMLIR gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } environment{ // Can be removed altogether with when WORKAROUND_SWDEV_290754. - NOMLIR_build_cmd = "CTEST_PARALLEL_LEVEL=4 MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" + NOMLIR_build_cmd = "CTEST_PARALLEL_LEVEL=4 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" } steps{ buildHipClangJobAndReboot( build_type: 'debug', setup_flags: NOMLIR_flags, build_cmd: NOMLIR_build_cmd, test_flags: ' --verbose ') } } - stage('Fp32 Hip Debug NOCK AnyGPU Build-Only') { + stage('Fp32 Hip Debug NOCK gfx90a Build-Only') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } steps{ buildHipClangJobAndReboot( build_type: 'debug', setup_flags: "-DMIOPEN_USE_COMPOSABLEKERNEL=Off", make_targets: "") } @@ -765,62 +760,62 @@ pipeline { buildHipClangJobAndReboot( build_type: 'debug', setup_flags: Embedded_flags, build_env: extra_log_env, test_flags: ' --verbose ') } } - stage('Fp32 Hip Static AnyGPU') { + stage('Fp32 Hip Static gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } steps{ buildHipClangJobAndReboot( setup_flags: "-DBUILD_SHARED_LIBS=Off", mlir_build: 'OFF') } } - stage('Fp32 Hip Normal-Find AnyGPU') { + stage('Fp32 Hip Normal-Find gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } environment{ make_targets = "test_conv2d" - execute_cmd = "MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 bin/test_conv2d --disable-verification-cache" + execute_cmd = "bin/test_conv2d --disable-verification-cache" } steps{ buildHipClangJobAndReboot(make_targets: make_targets, execute_cmd: execute_cmd, find_mode: "Normal") } } - stage('Fp32 Hip Fast-Find AnyGPU') { + stage('Fp32 Hip Fast-Find gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } environment{ make_targets = "test_conv2d" - execute_cmd = "MIOPEN_FIND_MODE=2 CTEST_PARALLEL_LEVEL=4 MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 bin/test_conv2d --disable-verification-cache" + execute_cmd = "MIOPEN_FIND_MODE=2 CTEST_PARALLEL_LEVEL=4 bin/test_conv2d --disable-verification-cache" } steps{ buildHipClangJobAndReboot( make_targets: make_targets, execute_cmd: execute_cmd) } } - stage('Fp32 Hip AnyGPU') { + stage('Fp32 Hip gfx90a') { when { beforeAgent true - expression { params.TARGET_VEGA20 || params.TARGET_VEGA10 || params.TARGET_GFX908 || params.TARGET_GFX90A } + expression { params.TARGET_GFX90A } } options { retry(2) } - agent{ label rocmnode("vega || gfx908 || gfx90a") } + agent{ label rocmnode("gfx90a") } steps{ buildHipClangJobAndReboot() } @@ -933,7 +928,7 @@ pipeline { } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot( setup_flags: Fp16_flags, make_targets: Smoke_targets) + buildHipClangJobAndReboot( setup_flags: Fp16_flags, make_targets: Smoke_targets, needs_reboot:false) } } stage('Bf16 Hip gfx94X') { @@ -946,7 +941,7 @@ pipeline { } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot(setup_flags: Bf16_flags, make_targets: Smoke_targets) + buildHipClangJobAndReboot(setup_flags: Bf16_flags, make_targets: Smoke_targets, needs_reboot:false) } } } @@ -958,45 +953,64 @@ pipeline { environment{ // WORKAROUND_ISSUE_1148: "CTEST_PARALLEL_LEVEL=2" // WORKAROUND_SWDEV_290754: "LLVM_PATH=/opt/rocm/llvm" - Navi21_build_cmd = "LLVM_PATH=/opt/rocm/llvm CTEST_PARALLEL_LEVEL=2 MIOPEN_CONV_PRECISE_ROCBLAS_TIMING=0 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" + Navi21_build_cmd = "LLVM_PATH=/opt/rocm/llvm CTEST_PARALLEL_LEVEL=2 MIOPEN_LOG_LEVEL=5 make -j\$(nproc) check" } parallel{ - stage('dbsync gfx908') { + stage('Dbsync gfx908') { when { beforeAgent true - expression { params.TARGET_GFX908 } + expression { params.DBSYNC_TEST && params.TARGET_GFX908 } } options { retry(2) } agent{ label rocmnode("gfx908") } - environment{ - setup_flags="-DMIOPEN_TEST_DBSYNC=1" - make_targets='test_db_sync' - execute_cmd='MIOPEN_TEST_DBSYNC=1 ./bin/test_db_sync' - } steps{ - buildHipClangJobAndReboot(lfs_pull: true, setup_flags: setup_flags, make_targets: make_targets, execute_cmd: execute_cmd, - needs_gpu:false, needs_reboot:false, build_install: "true") + buildHipClangJobAndReboot(lfs_pull: true, + setup_flags: "-DMIOPEN_TEST_DBSYNC=1", + make_targets: 'test_db_sync', + execute_cmd: 'MIOPEN_TEST_DBSYNC=1 ./bin/test_db_sync', + needs_gpu:false, + needs_reboot:false, + build_install: "true") } } - stage('dbsync gfx90a') { + stage('Dbsync gfx90a') { when { beforeAgent true - expression { params.TARGET_GFX90A } + expression { params.DBSYNC_TEST && params.TARGET_GFX90A } } options { retry(2) } agent{ label rocmnode("gfx90a") } - environment{ - setup_flags="-DMIOPEN_TEST_DBSYNC=1" - make_targets='test_db_sync' - execute_cmd='MIOPEN_TEST_DBSYNC=1 ./bin/test_db_sync' + steps{ + buildHipClangJobAndReboot(lfs_pull: true, + setup_flags: "-DMIOPEN_TEST_DBSYNC=1", + make_targets: 'test_db_sync', + execute_cmd: 'MIOPEN_TEST_DBSYNC=1 ./bin/test_db_sync', + needs_gpu:false, + needs_reboot:false, + build_install: "true") + } + } + stage('Dbsync gfx942') { + when { + beforeAgent true + expression { params.DBSYNC_TEST && (params.TARGET_GFX94X || params.WORKAROUND__TARGET_GFX94X_MINIMUM_TEST_ENABLE) } + } + options { + retry(2) } + agent{ label rocmnode("gfx942") } steps{ - buildHipClangJobAndReboot(lfs_pull: true, setup_flags: setup_flags, make_targets: make_targets, execute_cmd: execute_cmd, - needs_gpu:false, needs_reboot:false, build_install: "true") + buildHipClangJobAndReboot(lfs_pull: true, + setup_flags: "-DMIOPEN_TEST_DBSYNC=1", + make_targets: 'test_db_sync', + execute_cmd: 'MIOPEN_TEST_DBSYNC=1 ./bin/test_db_sync', + needs_gpu:false, + needs_reboot:false, + build_install: "true") } } stage('Int8 HIP All Vega20') { @@ -1048,7 +1062,7 @@ pipeline { } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot(setup_flags: Bf16_flags + Full_test, build_install: "true") + buildHipClangJobAndReboot(setup_flags: Bf16_flags + Full_test, build_install: "true", needs_reboot:false) } } stage('Fp16 Hip All gfx1030') { @@ -1123,7 +1137,7 @@ pipeline { } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot(setup_flags: Full_test) + buildHipClangJobAndReboot(setup_flags: Full_test, needs_reboot:false) } } stage('Fp16 Hip Install All Vega20') { @@ -1214,7 +1228,7 @@ pipeline { } agent{ label rocmnode("gfx94X") } steps{ - buildHipClangJobAndReboot(setup_flags: Full_test + Fp16_flags, build_install: "true") + buildHipClangJobAndReboot(setup_flags: Full_test + Fp16_flags, build_install: "true", needs_reboot:false) } } } diff --git a/LICENSE.txt b/LICENSE.txt index f46cfc569f..157ccd1ec2 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -19,3 +19,96 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +------------------------------------------------------------------------------ + +The following files + - src/include/miopen/kernel_cache.hpp + - src/kernel_cache.cpp + +are licensed using the MIT license described at the top of this file in +addition to an Apache-2.0 license using the following text: + + +Copyright 2015 Vratis, Ltd. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + + +------------------------------------------------------------------------------ + +driver/mloSoftmaxHost.hpp is available under a BSD-2-Clause license + +src/include/miopen/mlo_internal.hpp is licensed using the MIT described above +and a BSD-2-Clause license + +Both files use the following license text for their BSD license text: + + +Copyright (c)2017 Advanced Micro Devices, Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted +provided that the following conditions are met: + +Redistributions of source code must retain the above copyright notice, this list of conditions and +the following disclaimer. +Redistributions in binary form must reproduce the above copyright notice, this list of conditions +and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR +IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT +SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY + DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS + OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + + +------------------------------------------------------------------------------ + +The file src/md5.cpp is derived from a public domain implementation. The +original license text is as follows: + +Author: +Alexander Peslyak, better known as Solar Designer + +This software was written by Alexander Peslyak in 2001. No copyright is +claimed, and the software is hereby placed in the public domain. +In case this attempt to disclaim copyright and place the software in the +public domain is deemed null and void, then the software is +Copyright (c) 2001 Alexander Peslyak and it is hereby released to the +general public under the following terms: + +Redistribution and use in source and binary forms, with or without +modification, are permitted. + +There's ABSOLUTELY NO WARRANTY, express or implied. + +(This is a heavily cut-down "BSD license".) + +This differs from Colin Plumb's older public domain implementation in that +no exactly 32-bit integer data type is required (any 32-bit or wider +unsigned integer data type will do), there's no compile-time endianness +configuration, and the function prototypes match OpenSSL's. No code from +Colin Plumb's implementation has been reused; this comment merely compares +the properties of the two independent implementations. + +The primary goals of this implementation are portability and ease of use. +It is meant to be fast, but not as fast as possible. Some known +optimizations are not included to reduce source code size and avoid +compile-time configuration. + diff --git a/README.md b/README.md index 5e0f79b425..5858223141 100755 --- a/README.md +++ b/README.md @@ -45,6 +45,9 @@ To install MIOpen, you must first install these prerequisites: (BLAS) on the ROCm platform. * Minimum version branch for pre-ROCm 3.5 [master-rocm-2.10](https://github.com/ROCm/rocBLAS/tree/master-rocm-2.10) * Minimum version branch for post-ROCm 3.5 [master-rocm-3.5](https://github.com/ROCm/rocBLAS/tree/master-rocm-3.5) +* [hipBLASLt](https://github.com/ROCm/hipBLASLt): AMD's flexible Basic Linear Algebra Subprograms + (BLAS) API. +* [hipBLAS](https://github.com/ROCm/hipBLAS): AMD's (BLAS) marshalling library. * [Multi-Level Intermediate Representation (MLIR)](https://github.com/ROCm/rocMLIR) with its MIOpen dialect to support and complement kernel development * [Composable Kernel](https://github.com/ROCm/composable_kernel): A C++ templated device library @@ -121,6 +124,12 @@ MIOpen's HIP backend uses [rocBLAS](https://github.com/ROCm/rocBLAS) by default. rocBLAS' minimum release using `apt-get install rocblas`. To disable rocBLAS, set the configuration flag `-DMIOPEN_USE_ROCBLAS=Off`. rocBLAS is **not** available with OpenCL. +MIOpen's HIP backend can use [hipBLASLt](https://github.com/ROCm/hipBLASLt). You can install hipBLASLt's minimum +release using ``apt-get install hipblaslt``. In addition to needing hipblaslt, you will also need to install [hipBLAS](https://github.com/ROCm/hipBLAS). +You can install hipBLAS's minimum release using ``apt-get install hipblas``. +To disable hipBLASLt, set the configuration flag ``-DMIOPEN_USE_HIPBLASLT=Off``. +hipBLASLt is **not** available with OpenCL. + ## Building MIOpen from source You can build MIOpen form source with a HIP backend or an OpenCL backend. diff --git a/addkernels/addkernels.cpp b/addkernels/addkernels.cpp index d2c5439cdb..ff38bb7523 100644 --- a/addkernels/addkernels.cpp +++ b/addkernels/addkernels.cpp @@ -24,6 +24,7 @@ * *******************************************************************************/ #include "include_inliner.hpp" +#include "miopen/filesystem.hpp" #include #include #include @@ -31,6 +32,9 @@ #include #include #include +#include + +namespace fs = miopen::fs; void Bin2Hex(std::istream& source, std::ostream& target, @@ -47,10 +51,10 @@ void Bin2Hex(std::istream& source, if(variable.length() != 0) { target << "extern const size_t " << variable << "_SIZE;" << std::endl; - target << "extern const unsigned char " << variable << "[];" << std::endl; + target << "extern const char " << variable << "[];" << std::endl; target << "const size_t " << variable << "_SIZE = " << std::setbase(10) << sourceSize << ";" << std::endl; - target << "const unsigned char " << variable << "[] = {" << std::endl; + target << "const char " << variable << "[] = {" << std::endl; } target << std::setbase(16) << std::setfill('0'); @@ -108,16 +112,15 @@ void PrintHelp() << std::endl; } -[[noreturn]] void WrongUsage(const std::string& error) +[[noreturn]] void WrongUsage(std::string_view error) { - std::cout << "Wrong usage: " << error << std::endl; - std::cout << std::endl; + std::cout << "Wrong usage: " << error << "\n" << std::endl; PrintHelp(); // NOLINTNEXTLINE (concurrency-mt-unsafe) std::exit(1); } -[[noreturn]] void UnknownArgument(const std::string& arg) +[[noreturn]] void UnknownArgument(const std::string_view arg) { std::ostringstream ss; ss << "unknown argument - " << arg; @@ -200,81 +203,70 @@ void Process(const fs::path& sourcePath, Bin2Hex(*source, target, variable, true, bufferSize, lineSize); } -int main(int argsn, char** args) +int main(int argc, char* argv[]) { - if(argsn == 1) + if(argc == 1) { PrintHelp(); return 2; } + // The configuration to establish with command line options + // before running the algorithm. + std::string guard; size_t bufferSize = 512; size_t lineSize = 16; - std::ofstream targetFile; - std::ostream* target = &std::cout; - bool recurse = true; - bool as_extern = false; - bool mark_includes = false; + std::string targetFile; + std::vector sourceFiles; + + bool recurse = true; + bool as_extern = false; + bool mark_includes = false; + + // Parse command line options to establish configuration int i = 0; - while(++i < argsn && **args != '-') + while(++i < argc) { - std::string arg(args[i] + 1); + std::string arg(argv[i]); std::transform(arg.begin(), arg.end(), arg.begin(), ::tolower); - if(arg == "s" || arg == "source") + if(arg == "-s" || arg == "-source") { - if(guard.length() > 0) - { - *target << "#ifndef " << guard << std::endl; - *target << "#define " << guard << std::endl; - } - - *target << "#ifndef MIOPEN_USE_CLANG_TIDY" << std::endl; - *target << "#include " << std::endl; - - while(++i < argsn) - { - Process(args[i], *target, bufferSize, lineSize, recurse, as_extern, mark_includes); - } - - *target << "#endif" << std::endl; - - if(guard.length() > 0) - { - *target << "#endif" << std::endl; - } - - return 0; + while(++i < argc && *argv[i] != '-') + sourceFiles.emplace_back(argv[i]); } - else if(arg == "t" || arg == "target") + else if(arg == "-t" || arg == "-target") { - targetFile.open(args[++i], std::ios::out); - target = &targetFile; + std::string outputFile{argv[++i]}; + if(!targetFile.empty()) + std::cerr << "Warning: overriding output file\n '" << targetFile + << "'\nwith\n '" << outputFile << "'\n"; + targetFile = outputFile; } - else if(arg == "l" || arg == "line-size") + else if(arg == "-l" || arg == "-line-size") { - lineSize = std::stol(args[++i]); + lineSize = std::stol(argv[++i]); } - else if(arg == "b" || arg == "buffer") + else if(arg == "-b" || arg == "-buffer") { - bufferSize = std::stol(args[++i]); + bufferSize = std::stol(argv[++i]); } - else if(arg == "g" || arg == "guard") + else if(arg == "-g" || arg == "-guard") { - guard = args[++i]; + guard = argv[++i]; } - else if(arg == "n" || arg == "no-recurse") + else if(arg == "-n" || arg == "-no-recurse") { recurse = false; } - else if(arg == "m" || arg == "mark-includes") + else if(arg == "-m" || arg == "-mark-includes") { mark_includes = true; } - else if(arg == "e" || arg == "extern") + else if(arg == "-e" || arg == "-extern") { as_extern = true; } @@ -284,5 +276,48 @@ int main(int argsn, char** args) } } - WrongUsage("source key is required"); + // Execute the algorithm on the established configuration + + if(sourceFiles.empty()) + WrongUsage("'source' option is required"); + + std::stringstream ss; + if(guard.length() > 0) + { + ss << "#ifndef " << guard << "\n#define " << guard << "\n"; + } + + ss << "#ifndef MIOPEN_USE_CLANG_TIDY\n" + "#include \n"; + + for(const auto& file : sourceFiles) + { + Process(file, ss, bufferSize, lineSize, recurse, as_extern, mark_includes); + } + + ss << "#endif\n"; + + if(guard.length() > 0) + { + ss << "#endif\n"; + } + + auto sourceCode = ss.str(); + + if(targetFile.empty()) + { + std::cout << sourceCode << std::flush; + } + else + { + std::ofstream file{targetFile}; + if(!file.is_open()) + { + std::cerr << "failure opening file: " << targetFile << "\n"; + return 1; + } + file.write(sourceCode.data(), sourceCode.length()); + } + + return 0; } diff --git a/cmake/ClangTidy.cmake b/cmake/ClangTidy.cmake index e03ed6e142..a83a777216 100644 --- a/cmake/ClangTidy.cmake +++ b/cmake/ClangTidy.cmake @@ -69,6 +69,15 @@ else() message( STATUS "Clang tidy found: ${CLANG_TIDY_VERSION}") endif() +set(EXTRA_CHECKS) +# There is a bug in tidy that hangs it in some cases when it encounters optional access +# It can spend 3.5h+ and timeout the CI +# It is fixed in 18.0.0 or worked around by disabling this check: bugprone-unchecked-optional-access +# https://github.com/llvm/llvm-project/issues/59492 +if (CLANG_TIDY_VERSION VERSION_LESS "18.0.0") + list(APPEND EXTRA_CHECKS -bugprone-unchecked-optional-access) +endif() + set(CMAKE_EXPORT_COMPILE_COMMANDS ON) set(CLANG_TIDY_FIXIT_DIR ${CMAKE_BINARY_DIR}/fixits) @@ -81,6 +90,7 @@ macro(enable_clang_tidy) set(multiValueArgs CHECKS ERRORS EXTRA_ARGS) cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + list(APPEND PARSE_CHECKS ${EXTRA_CHECKS}) string(REPLACE ";" "," CLANG_TIDY_CHECKS "${PARSE_CHECKS}") string(REPLACE ";" "," CLANG_TIDY_ERRORS "${PARSE_ERRORS}") set(CLANG_TIDY_EXTRA_ARGS) diff --git a/cmake/embed.cmake b/cmake/embed.cmake index b614a174af..d9e6fd8cc8 100644 --- a/cmake/embed.cmake +++ b/cmake/embed.cmake @@ -1,19 +1,19 @@ ################################################################################ -# +# # MIT License -# +# # Copyright (c) 2020 Advanced Micro Devices, Inc. -# +# # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: -# +# # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. -# +# # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE @@ -21,7 +21,7 @@ # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -# +# ################################################################################ find_program(EMBED_LD ld) @@ -86,7 +86,8 @@ function(generate_embed_source EMBED_NAME) #include #include #include -const std::unordered_map>& ${EMBED_NAME}(); +#include +const std::unordered_map, miopen::FsPathHash>& ${EMBED_NAME}(); ") file(WRITE "${PARSE_SRC}" " @@ -94,9 +95,9 @@ const std::unordered_map>& ${EMB #pragma clang diagnostic ignored \"-Wreserved-identifier\" #include <${EMBED_NAME}.hpp> ${EXTERNS} -const std::unordered_map>& ${EMBED_NAME}() +const std::unordered_map, miopen::FsPathHash>& ${EMBED_NAME}() { - static const std::unordered_map> result = {${INIT_KERNELS}}; + static const std::unordered_map, miopen::FsPathHash> result = {${INIT_KERNELS}}; return result; } #pragma clang diagnostic pop // \"-Wreserved-identifier\" @@ -120,7 +121,7 @@ function(embed_file OUTPUT_FILE OUTPUT_SYMBOL FILE) message(TRACE "Converting ${REL_FILE} to ${REL_FILE}.o") add_custom_command( OUTPUT "${FILE}.o" - COMMAND ${EMBED_LD} -r -o "${FILE}.o" -z noexecstack --format=binary "${REL_FILE}" + COMMAND ${EMBED_LD} -r -o "${FILE}.o" -z noexecstack --format=binary "${REL_FILE}" COMMAND ${EMBED_OBJCOPY} --rename-section .data=.rodata,alloc,load,readonly,data,contents "${FILE}.o" WORKING_DIRECTORY ${WORKING_DIRECTORY} DEPENDS ${FILE} @@ -147,6 +148,6 @@ function(add_embed_library EMBED_NAME) message(STATUS "Generating embedding library ${EMBED_NAME}") generate_embed_source(${EMBED_NAME} SRC ${SRC_FILE} HEADER ${HEADER_FILE} OBJECTS ${OUTPUT_FILES} SYMBOLS ${SYMBOLS}) add_library(${EMBED_NAME} STATIC ${OUTPUT_FILES} "${SRC_FILE}") - target_include_directories(${EMBED_NAME} PUBLIC "${EMBED_DIR}/include") + target_include_directories(${EMBED_NAME} PUBLIC "${EMBED_DIR}/include" $) set_target_properties(${EMBED_NAME} PROPERTIES POSITION_INDEPENDENT_CODE On) endfunction() diff --git a/dev-requirements.txt b/dev-requirements.txt index f5082a1a6a..3d5b4d4bdc 100755 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,4 +1,3 @@ ROCm/rocm-recipes@329203d79f9fe77ae5d0d742af0966bc57f4dfc8 -f requirements.txt danmar/cppcheck@2.12.1 -google/googletest@v1.14.0 diff --git a/docs/conceptual/perfdb.rst b/docs/conceptual/perfdb.rst index 96ce0d74ff..3ecb3c2a5a 100644 --- a/docs/conceptual/perfdb.rst +++ b/docs/conceptual/perfdb.rst @@ -29,7 +29,7 @@ found, they're stored in the User PerfDb. MIOpen then automatically reads and us values. By default, System PerfDb resides within MIOpen's install location, while User PerfDb resides in your -home directory. See :ref:` setting up locations ` for more information. +home directory. See :ref:`setting up locations ` for more information. System PerfDb is not modified during MIOpen installation. diff --git a/docs/conceptual/porting-guide.rst b/docs/conceptual/porting-guide.rst index 928ef14cfa..a61059eba4 100644 --- a/docs/conceptual/porting-guide.rst +++ b/docs/conceptual/porting-guide.rst @@ -33,10 +33,8 @@ Useful MIOpen environment variables include: * ``MIOPEN_ENABLE_LOGGING=1``: Logs all the MIOpen APIs called, including the parameters passed to those APIs -* ``MIOPEN_DEBUG_AMD_ROCM_PRECOMPILED_BINARIES=0``: Disables the Winograd convolution - algorithm -* ``MIOPEN_DEBUG_GCN_ASM_KERNELS=0``: Disables hand-tuned ASM kernels for the direct - convolution algorithm (the fall-back is to kernels written in high-level language) +* ``MIOPEN_DEBUG_GCN_ASM_KERNELS=0``: Disables hand-tuned ASM kernels (the fallback is to use + kernels written in a high-level language) * ``MIOPEN_DEBUG_CONV_FFT=0``: Disables the FFT convolution algorithm * ``MIOPEN_DEBUG_CONV_DIRECT=0``: Disables the direct convolution algorithm diff --git a/docs/how-to/debug-log.rst b/docs/how-to/debug-log.rst index cfd28873ce..f901e0a898 100644 --- a/docs/how-to/debug-log.rst +++ b/docs/how-to/debug-log.rst @@ -102,8 +102,6 @@ Filtering by build method ImplicitGemm algorithm. * ``MIOPEN_DEBUG_OPENCL_CONVOLUTIONS``: Convolution kernels written in OpenCL; this only affects convolutions. -* ``MIOPEN_DEBUG_AMD_ROCM_PRECOMPILED_BINARIES``: Binary kernels. The library does not - currently use binaries. Filtering out all but one solution -------------------------------------------------------------------------------------------------------------- @@ -265,16 +263,25 @@ Implicit GEMM solutions: * ``MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_HIP_WRW_V4R4_PADDED_GEMM_XDLOPS`` -- ``ConvHipImplicitGemmWrwV4R4Xdlops_Padded_Gemm`` -rocBlas logging and behavior +GEMM logging and behavior ========================================================== -The ``ROCBLAS_LAYER`` environmental variable can be set to output GEMM information: +The ``ROCBLAS_LAYER`` environmental variable can be set to output GEMM information when using rocBLAS GEMM backend: * ``ROCBLAS_LAYER=``: Not set--there is no logging * ``ROCBLAS_LAYER=1``: Trace logging * ``ROCBLAS_LAYER=2``: Bench logging * ``ROCBLAS_LAYER=3``: Trace and bench logging +The ``HIPBLASLT_LOG_LEVEL`` environmental variable can be set to output GEMM information when using hipBLASLt GEMM backend: + +* ``HIPBLASLT_LOG_LEVEL=0``: Off -- there is no logging (default) +* ``HIPBLASLT_LOG_LEVEL=1``: Error logging +* ``HIPBLASLT_LOG_LEVEL=2``: Trace - API calls that launch HIP kernels log their parameters and important information +* ``HIPBLASLT_LOG_LEVEL=3``: Hints - Hints that can potentially improve the application’s performance +* ``HIPBLASLT_LOG_LEVEL=4``: Info - Provides general information about the library execution, may contain details about heuristic status +* ``HIPBLASLT_LOG_LEVEL=5``: API Trace - API calls log their parameters and important information + You can also set the ``MIOPEN_GEMM_ENFORCE_BACKEND`` environment variable to override the default GEMM backend (rocBLAS): @@ -282,13 +289,15 @@ default GEMM backend (rocBLAS): * ``MIOPEN_GEMM_ENFORCE_BACKEND=2``: Reserved * ``MIOPEN_GEMM_ENFORCE_BACKEND=3``: No GEMM is called * ``MIOPEN_GEMM_ENFORCE_BACKEND=4``: Reserved +* ``MIOPEN_GEMM_ENFORCE_BACKEND=5``: Use hipBLASLt if enabled * ``MIOPEN_GEMM_ENFORCE_BACKEND=``: Use default behavior To disable using rocBlas entirely, set the `-DMIOPEN_USE_ROCBLAS=Off` configuration flag during +MIOpen configuration. To disable using hipBLASLt entirely, set the `-DMIOPEN_USE_HIPBLASLT=Off` configuration flag during MIOpen configuration. You can find more information on logging with rocBLAS in the -:doc:`rocBLAS programmer guide `. +:doc:`rocBLAS programmer guide `. Numerical checking ========================================================== diff --git a/docs/install/install.rst b/docs/install/install.rst index ab574b9bca..a24a5a6bf9 100644 --- a/docs/install/install.rst +++ b/docs/install/install.rst @@ -111,6 +111,12 @@ MIOpen's HIP backend uses :doc:`rocBLAS ` by default. You can ins minimum release using ``apt-get install rocblas``. To disable rocBLAS, set the configuration flag ``-DMIOPEN_USE_ROCBLAS=Off``. rocBLAS is **not** available with OpenCL. +MIOpen's HIP backend can use :doc:`hipBLASLt `. You can install hipBLASLt's minimum +release using ``apt-get install hipblaslt``. In addition to needing hipblaslt, you will also need to +install :doc:`hipBLAS `. You can install hipBLAS's minimum release using ``apt-get install hipblas``. +To disable hipBLASLt, set the configuration flag ``-DMIOPEN_USE_HIPBLASLT=Off``. +hipBLASLt is **not** available with OpenCL. + Building MIOpen from source ================================================ diff --git a/docs/reference/argmax.rst b/docs/reference/argmax.rst deleted file mode 100644 index 001731b79c..0000000000 --- a/docs/reference/argmax.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. meta:: - :description: MIOpen documentation - :keywords: MIOpen, ROCm, API, documentation - -******************************************************************** -Argmax Layer (experimental) -******************************************************************** - -Find the index of the maximum value of a tensor across dimensions. - -To enable this, define ``MIOPEN_BETA_API`` before including ``miopen.h``. - -miopenArgmaxForward ----------------------------------- - -.. doxygenfunction:: miopenArgmaxForward diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 5a74c95eaa..04f0c01e10 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -31,5 +31,7 @@ The MIOpen API library is structured as follows: * :doc:`Sum <../doxygen/html/group__sum>` (experimental) * :doc:`GroupNorm <../doxygen/html/group__groupnorm>` (experimental) * :doc:`Cat <../doxygen/html/group__cat>` (experimental) - * :doc:`Argmax<./argmax>` (experimental) + * :doc:`SGD <../doxygen/html/group___s_g_d>` (experimental) + * :doc:`ReduceExtreme <../doxygen/html/group__ReduceExtreme>` (experimental) + * :doc:`Getitem <../doxygen/html/group__getitem>` (experimental) * :doc:`Interpolate <../doxygen/html/group__interpolate>` (experimental) diff --git a/docs/sphinx/requirements.in b/docs/sphinx/requirements.in index 5671f15adc..322fa75bd1 100644 --- a/docs/sphinx/requirements.in +++ b/docs/sphinx/requirements.in @@ -1 +1 @@ -rocm-docs-core[api_reference]==0.38.1 +rocm-docs-core[api_reference]==1.6.1 diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index cca748a63a..18db7ee156 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -1,14 +1,14 @@ # -# This file is autogenerated by pip-compile with Python 3.8 +# This file is autogenerated by pip-compile with Python 3.10 # by the following command: # # pip-compile requirements.in # -accessible-pygments==0.0.4 +accessible-pygments==0.0.5 # via pydata-sphinx-theme -alabaster==0.7.13 +alabaster==0.7.16 # via sphinx -babel==2.14.0 +babel==2.15.0 # via # pydata-sphinx-theme # sphinx @@ -16,7 +16,7 @@ beautifulsoup4==4.12.3 # via pydata-sphinx-theme breathe==4.35.0 # via rocm-docs-core -certifi==2024.2.2 +certifi==2024.7.4 # via requests cffi==1.16.0 # via @@ -31,35 +31,29 @@ click==8.1.7 # sphinx-external-toc click-log==0.4.0 # via doxysphinx -cryptography==42.0.5 +cryptography==42.0.7 # via pyjwt deprecated==1.2.14 # via pygithub -docutils==0.19 +docutils==0.21.2 # via # breathe # myst-parser # pydata-sphinx-theme # sphinx -doxysphinx==3.3.7 +doxysphinx==3.3.8 # via rocm-docs-core fastjsonschema==2.19.1 # via rocm-docs-core gitdb==4.0.11 # via gitpython -gitpython==3.1.42 +gitpython==3.1.43 # via rocm-docs-core -idna==3.6 +idna==3.7 # via requests imagesize==1.4.1 # via sphinx -importlib-metadata==7.0.2 - # via sphinx -importlib-resources==6.1.3 - # via - # mpire - # rocm-docs-core -jinja2==3.1.3 +jinja2==3.1.4 # via # myst-parser # sphinx @@ -67,33 +61,35 @@ libsass==0.22.0 # via doxysphinx lxml==4.9.4 # via doxysphinx -markdown-it-py==2.2.0 +markdown-it-py==3.0.0 # via # mdit-py-plugins # myst-parser markupsafe==2.1.5 # via jinja2 -mdit-py-plugins==0.3.5 +mdit-py-plugins==0.4.1 # via myst-parser mdurl==0.1.2 # via markdown-it-py -mpire==2.10.0 +mpire==2.10.2 # via doxysphinx -myst-parser==1.0.0 +myst-parser==3.0.1 # via rocm-docs-core +numpy==1.26.4 + # via doxysphinx packaging==24.0 # via # pydata-sphinx-theme # sphinx -pycparser==2.21 +pycparser==2.22 # via cffi -pydata-sphinx-theme==0.14.4 +pydata-sphinx-theme==0.15.2 # via # rocm-docs-core # sphinx-book-theme -pygithub==2.2.0 +pygithub==2.3.0 # via rocm-docs-core -pygments==2.17.2 +pygments==2.18.0 # via # accessible-pygments # mpire @@ -101,24 +97,22 @@ pygments==2.17.2 # sphinx pyjson5==1.6.6 # via doxysphinx -pyjwt[crypto]==2.6.0 +pyjwt[crypto]==2.8.0 # via pygithub pynacl==1.5.0 # via pygithub pyparsing==3.1.2 # via doxysphinx -pytz==2024.1 - # via babel pyyaml==6.0.1 # via # myst-parser # rocm-docs-core # sphinx-external-toc -requests==2.31.0 +requests==2.32.2 # via # pygithub # sphinx -rocm-docs-core[api-reference]==0.38.1 +rocm-docs-core[api-reference]==1.6.1 # via -r requirements.in smmap==5.0.1 # via gitdb @@ -126,7 +120,7 @@ snowballstemmer==2.2.0 # via sphinx soupsieve==2.5 # via beautifulsoup4 -sphinx==5.3.0 +sphinx==7.3.7 # via # breathe # myst-parser @@ -137,41 +131,39 @@ sphinx==5.3.0 # sphinx-design # sphinx-external-toc # sphinx-notfound-page -sphinx-book-theme==1.0.1 +sphinx-book-theme==1.1.2 # via rocm-docs-core sphinx-copybutton==0.5.2 # via rocm-docs-core sphinx-design==0.5.0 # via rocm-docs-core -sphinx-external-toc==0.3.1 +sphinx-external-toc==1.0.1 # via rocm-docs-core -sphinx-notfound-page==1.0.0 +sphinx-notfound-page==1.0.1 # via rocm-docs-core -sphinxcontrib-applehelp==1.0.4 +sphinxcontrib-applehelp==1.0.8 # via sphinx -sphinxcontrib-devhelp==1.0.2 +sphinxcontrib-devhelp==1.0.6 # via sphinx -sphinxcontrib-htmlhelp==2.0.1 +sphinxcontrib-htmlhelp==2.0.5 # via sphinx sphinxcontrib-jsmath==1.0.1 # via sphinx -sphinxcontrib-qthelp==1.0.3 +sphinxcontrib-qthelp==1.0.7 + # via sphinx +sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sphinxcontrib-serializinghtml==1.1.5 +tomli==2.0.1 # via sphinx -tqdm==4.66.2 +tqdm==4.66.4 # via mpire -typing-extensions==4.10.0 +typing-extensions==4.11.0 # via # pydata-sphinx-theme # pygithub -urllib3==2.2.1 +urllib3==2.2.2 # via # pygithub # requests wrapt==1.16.0 # via deprecated -zipp==3.17.0 - # via - # importlib-metadata - # importlib-resources diff --git a/driver/CBAInferFusion_driver.hpp b/driver/CBAInferFusion_driver.hpp index 84be967d44..7c62a7bc91 100644 --- a/driver/CBAInferFusion_driver.hpp +++ b/driver/CBAInferFusion_driver.hpp @@ -601,7 +601,7 @@ int CBAInferFusionDriver::AllocateBuffersAndCopy() else out_sz = in_sz; // This is for N+A so the output is the same as the input size - if(miopen::IsEnabled(ENV(MIOPEN_DRIVER_PAD_BUFFERS_2M))) + if(env::enabled(MIOPEN_DRIVER_PAD_BUFFERS_2M)) { PadBufferSize(wei_sz, sizeof(Tgpu)); } diff --git a/driver/CMakeLists.txt b/driver/CMakeLists.txt index a824ecd45d..8782a8babd 100644 --- a/driver/CMakeLists.txt +++ b/driver/CMakeLists.txt @@ -30,7 +30,8 @@ add_executable(MIOpenDriver InputFlags.cpp conv_common.cpp dm_activ.cpp - dm_argmax.cpp + dm_adam.cpp + dm_addlayernorm.cpp dm_bnorm.cpp dm_cat.cpp dm_conv.cpp @@ -42,16 +43,20 @@ add_executable(MIOpenDriver dm_dropout.cpp dm_fusion.cpp dm_gemm.cpp + dm_getitem.cpp dm_groupnorm.cpp dm_interpolate.cpp dm_layernorm.cpp dm_lrn.cpp dm_pool.cpp dm_reduce.cpp + dm_reduceextreme.cpp dm_rnn.cpp dm_softmax.cpp dm_sum.cpp + dm_t5layernorm.cpp dm_tensorop.cpp + dm_transformers_adam_w.cpp main.cpp registry_driver_maker.cpp rocrand_wrapper.cpp) diff --git a/driver/InputFlags.cpp b/driver/InputFlags.cpp index 41f872b0e8..12df05cfb5 100644 --- a/driver/InputFlags.cpp +++ b/driver/InputFlags.cpp @@ -292,6 +292,165 @@ TensorParameters InputFlags::GetValueTensor(const std::string& long_name) const MIOPEN_THROW("Too many tensor descriptor parameters."); } + +TensorParametersUint64 InputFlags::GetValueTensorUint64(const std::string& long_name) const +{ + const auto& input = MapInputs.at(FindShortName(long_name)); + const auto components = miopen::SplitDelim(input.value.c_str(), ','); + + if(components.size() < 1) + return {}; + + auto parse = [](auto line) { + auto ret = std::vector{}; + const auto strs = miopen::SplitDelim(line, 'x'); + for(auto&& str : strs) + { + auto elem = uint64_t{}; + auto ss = std::istringstream{str}; + ss >> elem; + + if(ss.bad() || ss.fail()) + MIOPEN_THROW("Invalid tensor component " + str + " in " + line + "."); + + ret.push_back(elem); + } + return ret; + }; + + auto lens = parse(components[0]); + + if(components.size() == 1) + return {lens}; + + auto layout = std::string{}; + auto strides = std::vector{}; + + if(std::isdigit(components[1][0])) + strides = parse(components[1]); + else + layout = components[1]; + + if(components.size() == 2) + return {lens, strides, layout}; + + MIOPEN_THROW("Too many tensor descriptor parameters."); +} + +std::vector InputFlags::GetValueVectorInt(const std::string& long_name) const +{ + const auto& input = MapInputs.at(FindShortName(long_name)); + + auto ret = std::vector{}; + const auto strs = miopen::SplitDelim(input.value.c_str(), ','); + + for(auto&& str : strs) + { + auto elem = int32_t{}; + auto ss = std::istringstream{str}; + ss >> elem; + + if(ss.bad() || ss.fail()) + MIOPEN_THROW("Invalid tensor component " + str + " in " + input.value.c_str() + "."); + + ret.push_back(elem); + } + + return ret; +} + +std::vector InputFlags::GetValueVectorUint64(const std::string& long_name) const +{ + const auto& input = MapInputs.at(FindShortName(long_name)); + + auto ret = std::vector{}; + const auto strs = miopen::SplitDelim(input.value.c_str(), ','); + + for(auto&& str : strs) + { + auto elem = uint64_t{}; + auto ss = std::istringstream{str}; + ss >> elem; + + if(ss.bad() || ss.fail()) + MIOPEN_THROW("Invalid tensor component " + str + " in " + input.value.c_str() + "."); + + ret.push_back(elem); + } + + return ret; +} + +std::vector> +InputFlags::GetValue2dVectorInt(const std::string& long_name) const +{ + const auto& input = MapInputs.at(FindShortName(long_name)); + const auto components = miopen::SplitDelim(input.value.c_str(), ','); + auto output = std::vector>{}; + + if(components.size() < 1) + return {}; + + auto parse = [](auto line) { + auto ret = std::vector{}; + const auto strs = miopen::SplitDelim(line, 'x'); + for(auto&& str : strs) + { + auto elem = int32_t{}; + auto ss = std::istringstream{str}; + ss >> elem; + + if(ss.bad() || ss.fail()) + MIOPEN_THROW("Invalid tensor component " + str + " in " + line + "."); + + ret.push_back(elem); + } + return ret; + }; + + for(auto&& component : components) + { + output.push_back(parse(component)); + } + + return output; +} + +std::vector> +InputFlags::GetValue2dVectorUint64(const std::string& long_name) const +{ + const auto& input = MapInputs.at(FindShortName(long_name)); + const auto components = miopen::SplitDelim(input.value.c_str(), ','); + auto output = std::vector>{}; + + if(components.size() < 1) + return {}; + + auto parse = [](auto line) { + auto ret = std::vector{}; + const auto strs = miopen::SplitDelim(line, 'x'); + for(auto&& str : strs) + { + auto elem = uint64_t{}; + auto ss = std::istringstream{str}; + ss >> elem; + + if(ss.bad() || ss.fail()) + MIOPEN_THROW("Invalid tensor component " + str + " in " + line + "."); + + ret.push_back(elem); + } + return ret; + }; + + for(auto&& component : components) + { + output.push_back(parse(component)); + } + + return output; +} + void InputFlags::SetValue(const std::string& long_name, const std::string& new_value) { char short_name = FindShortName(long_name); diff --git a/driver/InputFlags.hpp b/driver/InputFlags.hpp index 557a895b11..43f7c3a206 100644 --- a/driver/InputFlags.hpp +++ b/driver/InputFlags.hpp @@ -63,6 +63,25 @@ struct TensorParameters void CalculateStrides(); }; +struct TensorParametersUint64 +{ + std::vector lengths = {}; + std::vector strides = {}; + std::string layout = ""; + + TensorParametersUint64 FillMissing(const TensorParametersUint64& other) const + { + return { + (lengths.empty() ? other.lengths : lengths), + (strides.empty() ? other.strides : strides), + (layout.empty() ? other.layout : layout), + }; + } + + uint64_t SetTensordDescriptor(miopenTensorDescriptor_t result, miopenDataType_t data_type); + void CalculateStrides(); +}; + class InputFlags { std::map MapInputs; @@ -90,6 +109,11 @@ class InputFlags uint64_t GetValueUint64(const std::string& _long_name) const; double GetValueDouble(const std::string& _long_name) const; TensorParameters GetValueTensor(const std::string& long_name) const; + TensorParametersUint64 GetValueTensorUint64(const std::string& long_name) const; + std::vector GetValueVectorInt(const std::string& long_name) const; + std::vector GetValueVectorUint64(const std::string& long_name) const; + std::vector> GetValue2dVectorInt(const std::string& long_name) const; + std::vector> GetValue2dVectorUint64(const std::string& long_name) const; void SetValue(const std::string& long_name, const std::string& new_value); void StoreOptionalFlagValue(char short_name, const std::string& input_value); diff --git a/driver/adam_driver.hpp b/driver/adam_driver.hpp new file mode 100644 index 0000000000..0664bc59a7 --- /dev/null +++ b/driver/adam_driver.hpp @@ -0,0 +1,593 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_ADAM_DRIVER_HPP +#define GUARD_MIOPEN_ADAM_DRIVER_HPP + +#include "InputFlags.hpp" +#include "driver.hpp" +#include "random.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" + +#include "../test/verify.hpp" + +#include +#include + +#include +#include +#include +#include +#include +#include + +#ifndef MLO_ADAMHOST_H_ +#define MLO_ADAMHOST_H_ + +template +void mloAdamRunHost(miopenTensorDescriptor_t paramDesc, + Tref* params, + Tref* grads, + Tref* exp_avgs, + Tref* exp_avg_sqs, + Tref* max_exp_avg_sqs, + int32_t step, + float lr, + float beta1, + float beta2, + float weight_decay, + float eps, + bool amsgrad, + bool maximize, + bool adamw, + bool is_amp, + int32_t grad_scale, + bool found_inf) +{ + if(is_amp && found_inf) + return; + + size_t numel = miopen::deref(paramDesc).GetElementSize(); + for(int i = 0; i < numel; i++) + { + Tref exp_avg = exp_avgs[i]; + Tref exp_avg_sq = exp_avg_sqs[i]; + + Tref param = params[i]; + Tref grad = grads[i]; + if(maximize) + grad *= -1; + if(is_amp) + grad /= grad_scale; + + float bias_correction1 = 1 - pow(beta1, step); + float bias_correction2 = 1 - pow(beta2, step); + + if(weight_decay != 0) + { + if(adamw) + param -= lr * weight_decay * param; + else + grad += param * weight_decay; + } + + exp_avg = exp_avg * beta1 + grad * (1 - beta1); + exp_avg_sq = exp_avg_sq * beta2 + grad * grad * (1 - beta2); + + float denom; + if(amsgrad) + { + Tref max_exp_avg_sq = max_exp_avg_sqs[i]; + if(exp_avg_sq > max_exp_avg_sq) + { + max_exp_avg_sq = exp_avg_sq; + max_exp_avg_sqs[i] = max_exp_avg_sq; + } + + denom = sqrt(max_exp_avg_sq) / sqrt(bias_correction2) + eps; + } + else + { + denom = sqrt(exp_avg_sq) / sqrt(bias_correction2) + eps; + } + + params[i] = param - (lr / bias_correction1) * exp_avg / denom; + } +} + +#endif + +template +class AdamDriver : public Driver +{ +public: + AdamDriver(bool adamw_ = false, bool is_amp_ = false) : Driver(), adamw(adamw_), is_amp(is_amp_) + { + miopenCreateTensorDescriptor(¶mDesc); + miopenCreateTensorDescriptor(&gradDesc); + miopenCreateTensorDescriptor(&expAvgDesc); + miopenCreateTensorDescriptor(&expAvgSqDesc); + miopenCreateTensorDescriptor(¶mOutDesc); + miopenCreateTensorDescriptor(&dummyOutDesc); + if(is_amp) + { + miopenCreateTensorDescriptor(&stepDesc); + miopenCreateTensorDescriptor(&gradScaleDesc); + miopenCreateTensorDescriptor(&foundInfDesc); + } + + data_type = miopen_type{}; + grad_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + std::vector GetInputTensorLengthsFromCmdLine(); + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + int RunForwardCPU(); + + int RunBackwardGPU() override; + + Tref GetTolerance(); + int VerifyBackward() override; + int VerifyForward() override; + ~AdamDriver() override + { + miopenDestroyTensorDescriptor(paramDesc); + miopenDestroyTensorDescriptor(gradDesc); + miopenDestroyTensorDescriptor(expAvgDesc); + miopenDestroyTensorDescriptor(expAvgSqDesc); + miopenDestroyTensorDescriptor(paramOutDesc); + miopenDestroyTensorDescriptor(dummyOutDesc); + if(maxExpAvgSqDesc) + miopenDestroyTensorDescriptor(maxExpAvgSqDesc); + if(stepDesc) + miopenDestroyTensorDescriptor(stepDesc); + if(gradScaleDesc) + miopenDestroyTensorDescriptor(gradScaleDesc); + if(stepDesc) + miopenDestroyTensorDescriptor(foundInfDesc); + } + +private: + InputFlags inflags; + + int forw = 1; + + miopenTensorDescriptor_t paramDesc = nullptr; + miopenTensorDescriptor_t gradDesc = nullptr; + miopenTensorDescriptor_t expAvgDesc = nullptr; + miopenTensorDescriptor_t expAvgSqDesc = nullptr; + miopenTensorDescriptor_t maxExpAvgSqDesc = nullptr; + miopenTensorDescriptor_t stepDesc = nullptr; + miopenTensorDescriptor_t gradScaleDesc = nullptr; + miopenTensorDescriptor_t foundInfDesc = nullptr; + miopenTensorDescriptor_t paramOutDesc = nullptr; + miopenTensorDescriptor_t dummyOutDesc = nullptr; + + std::unique_ptr param_dev; + std::unique_ptr param_out_dev; + std::unique_ptr dummy_out_dev; + std::unique_ptr grad_dev; + std::unique_ptr exp_avg_dev; + std::unique_ptr exp_avg_sq_dev; + std::unique_ptr max_exp_avg_sq_dev; + std::unique_ptr step_dev; + std::unique_ptr scale_dev; + std::unique_ptr found_inf_dev; + + std::vector param; + std::vector grad; + std::vector exp_avg; + std::vector exp_avg_sq; + std::vector max_exp_avg_sq; + + std::vector param_host; + std::vector grad_host; + std::vector exp_avg_host; + std::vector exp_avg_sq_host; + std::vector max_exp_avg_sq_host; + + float lr; + float beta1; + float beta2; + float weight_decay; + float eps; + bool amsgrad = false; + bool maximize = false; + bool found_inf = false; + bool adamw = false; + bool is_amp = false; + int grad_scale = 1; + int iter = 0; + + miopenDataType_t grad_type; +}; + +template +int AdamDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + return miopenStatusSuccess; +} + +template +int AdamDriver::GetandSetData() +{ + auto param_len = GetInputTensorLengthsFromCmdLine(); + lr = inflags.GetValueDouble("lr"); + beta1 = inflags.GetValueDouble("beta1"); + beta2 = inflags.GetValueDouble("beta2"); + eps = inflags.GetValueDouble("eps"); + weight_decay = inflags.GetValueDouble("weight_decay"); + amsgrad = inflags.GetValueInt("amsgrad"); + maximize = inflags.GetValueInt("maximize"); + iter = inflags.GetValueInt("iter"); + + if(is_amp) + { + grad_scale = inflags.GetValueInt("scale"); + found_inf = inflags.GetValueInt("found_inf"); + } + + std::vector one_size = {1}; + SetTensorNd(paramDesc, param_len, data_type); + SetTensorNd(paramOutDesc, param_len, data_type); + SetTensorNd(gradDesc, param_len, grad_type); + SetTensorNd(expAvgDesc, param_len, data_type); + SetTensorNd(expAvgSqDesc, param_len, data_type); + SetTensorNd(dummyOutDesc, param_len, data_type); + + if(amsgrad) + { + miopenCreateTensorDescriptor(&maxExpAvgSqDesc); + SetTensorNd(maxExpAvgSqDesc, param_len, data_type); + } + + if(is_amp) + { + SetTensorNd(stepDesc, one_size, miopenInt32); + SetTensorNd(gradScaleDesc, one_size, miopenInt32); + SetTensorNd(foundInfDesc, one_size, miopenInt32); + } + + return 0; +} + +template +int AdamDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "1", "Run only Forward GroupNorm (Default=1)", "int"); + inflags.AddTensorFlag("dims", 'd', "64x32x128", "params tensor dims (Default=64x32x128)"); + + inflags.AddInputFlag("lr", 'l', "0.001", "learning rate (Default=0.001)", "float"); + inflags.AddInputFlag("beta1", '1', "0.9", "beta1 (Default=0.9)", "float"); + inflags.AddInputFlag("beta2", '2', "0.999", "beta2 (Default=0.999)", "float"); + inflags.AddInputFlag("eps", 'e', "0.00000001", "eps (Default=0.00000001)", "float"); + inflags.AddInputFlag("weight_decay", 'W', "0", "weight decay (Default=0)", "float"); + inflags.AddInputFlag("amsgrad", 'a', "0", "whether to use the AMSGrad (Default=0)", "int"); + inflags.AddInputFlag("maximize", 'm', "0", "whether to use the maximize (Default=0)", "int"); + + if(is_amp) + { + inflags.AddInputFlag("scale", 's', "65536", "grad scale factor (Default=65536)", "int"); + inflags.AddInputFlag("found_inf", 'f', "0", "found inf in grad (Default=0)", "int"); + } + + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +std::vector AdamDriver::GetInputTensorLengthsFromCmdLine() +{ + std::vector ret; + auto tensor = inflags.GetValueTensor("dims"); + if(!tensor.lengths.empty()) + return tensor.lengths; + return ret; +} + +template +int AdamDriver::AllocateBuffersAndCopy() +{ + size_t param_sz = GetTensorSize(paramDesc); + + uint32_t ctx = 0; + param_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + grad_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgrad))); + exp_avg_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + exp_avg_sq_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + param_out_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + dummy_out_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + + if(amsgrad) + max_exp_avg_sq_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + + if(is_amp) + { + step_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(int))); + scale_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(int))); + found_inf_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(bool))); + } + + param = std::vector(param_sz, static_cast(0)); + grad = std::vector(param_sz, static_cast(0)); + exp_avg = std::vector(param_sz, static_cast(0)); + exp_avg_sq = std::vector(param_sz, static_cast(0)); + + param_host = std::vector(param_sz, static_cast(0)); + grad_host = std::vector(param_sz, static_cast(0)); + exp_avg_host = std::vector(param_sz, static_cast(0)); + exp_avg_sq_host = std::vector(param_sz, static_cast(0)); + + if(amsgrad) + { + max_exp_avg_sq = std::vector(param_sz, static_cast(0)); + max_exp_avg_sq_host = std::vector(param_sz, static_cast(0)); + } + + for(int i = 0; i < param_sz; i++) + { + param[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); + grad[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(0.1)); + exp_avg[i] = prng::gen_A_to_B(static_cast(0), static_cast(0.1)); + exp_avg_sq[i] = prng::gen_A_to_B(static_cast(0), static_cast(0.1)); + param_host[i] = param[i]; + exp_avg_host[i] = exp_avg[i]; + exp_avg_sq_host[i] = exp_avg_sq[i]; + + if(amsgrad) + { + max_exp_avg_sq[i] = + prng::gen_A_to_B(static_cast(0.5), static_cast(1.0)); + max_exp_avg_sq_host[i] = max_exp_avg_sq[i]; + } + + if(is_amp) + { + grad[i] *= grad_scale; + if(!found_inf && (std::isnan(grad[i]) || std::isinf(grad[i]))) + { + std::cerr << "Error init (grad), idx: " << i << ", value: " << grad[i] << std::endl; + found_inf = true; + } + } + grad_host[i] = grad[i]; + } + + if(param_dev->ToGPU(GetStream(), param.data()) != 0) + std::cerr << "Error copying (param) to GPU, size: " << param_dev->GetSize() << std::endl; + + if(grad_dev->ToGPU(GetStream(), grad.data()) != 0) + std::cerr << "Error copying (grad) to GPU, size: " << grad_dev->GetSize() << std::endl; + + if(exp_avg_dev->ToGPU(GetStream(), exp_avg.data()) != 0) + std::cerr << "Error copying (exp_avg) to GPU, size: " << exp_avg_dev->GetSize() + << std::endl; + + if(exp_avg_sq_dev->ToGPU(GetStream(), exp_avg_sq.data()) != 0) + std::cerr << "Error copying (exp_avg_sq) to GPU, size: " << exp_avg_sq_dev->GetSize() + << std::endl; + + if(amsgrad) + { + if(max_exp_avg_sq_dev->ToGPU(GetStream(), max_exp_avg_sq.data()) != 0) + std::cerr << "Error copying (max_exp_avg_sq) to GPU, size: " + << max_exp_avg_sq_dev->GetSize() << std::endl; + } + + if(is_amp) + { + int step = 0; + if(step_dev->ToGPU(GetStream(), &step) != 0) + std::cerr << "Error copying (step) to GPU, size: " << step_dev->GetSize() << std::endl; + + if(scale_dev->ToGPU(GetStream(), &grad_scale) != 0) + std::cerr << "Error copying (scale) to GPU, size: " << scale_dev->GetSize() + << std::endl; + if(found_inf_dev->ToGPU(GetStream(), &found_inf) != 0) + std::cerr << "Error copying (found_inf) to GPU, size: " << found_inf_dev->GetSize() + << std::endl; + } + + return miopenStatusSuccess; +} + +template +int AdamDriver::RunForwardGPU() +{ + float kernel_total_time = 0; + float kernel_first_time = 0; + + void* max_exp_avg_sq_ptr = amsgrad ? max_exp_avg_sq_dev->GetMem() : nullptr; + void* grad_scale_ptr = is_amp ? scale_dev->GetMem() : nullptr; + void* found_inf_ptr = is_amp ? found_inf_dev->GetMem() : nullptr; + void* state_step_ptr = is_amp ? step_dev->GetMem() : nullptr; + + Timer t; + START_TIME + + for(int i = 0; i < iter; i++) + { + miopenFusedAdamWithOutput(GetHandle(), + paramDesc, + param_dev->GetMem(), + paramOutDesc, + param_out_dev->GetMem(), + nullptr, + nullptr, + gradDesc, + grad_dev->GetMem(), + expAvgDesc, + exp_avg_dev->GetMem(), + dummyOutDesc, + dummy_out_dev->GetMem(), + expAvgSqDesc, + exp_avg_sq_dev->GetMem(), + dummyOutDesc, + dummy_out_dev->GetMem(), + maxExpAvgSqDesc, + max_exp_avg_sq_ptr, + dummyOutDesc, + dummy_out_dev->GetMem(), + stepDesc, + state_step_ptr, + stepDesc, + state_step_ptr, + i + 1, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + gradScaleDesc, + grad_scale_ptr, + foundInfDesc, + found_inf_ptr); + + float time = 0.0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + if(WALL_CLOCK) + printf("Wall-clock Time Forward Adam Elapsed: %f ms\n", t.gettime_ms() / iter); + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + printf("GPU Kernel Time Forward Adam Elapsed: %f ms\n", kernel_average_time); + } + + if(param_out_dev->FromGPU(GetStream(), param.data()) != 0) + std::cerr << "Error copying (param_dev) from GPU, size: " << param_dev->GetSize() + << std::endl; + + return miopenStatusSuccess; +} + +template +int AdamDriver::RunForwardCPU() +{ + mloAdamRunHost(paramDesc, + param_host.data(), + grad_host.data(), + exp_avg_host.data(), + exp_avg_sq_host.data(), + max_exp_avg_sq_host.data(), + iter, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + is_amp, + grad_scale, + found_inf); + + return miopenStatusSuccess; +} + +template +int AdamDriver::RunBackwardGPU() +{ + return miopenStatusSuccess; +} + +template +Tref AdamDriver::GetTolerance() +{ + if(data_type == miopenHalf) + { + return 1e-3; + } + else if(data_type == miopenFloat) + { + return 5e-5; + } + else if(data_type == miopenDouble) + { + return 1e-10; + } + else if(data_type == miopenBFloat16) + { + return 5e-3; + } + return 0; +} + +template +int AdamDriver::VerifyForward() +{ + RunForwardCPU(); + const Tref tolerance = GetTolerance(); + auto error = miopen::rms_range(param_host, param); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Forward Adam FAILED: " << error << std::endl; + return EC_VerifyFwd; + } + + std::cout << "Forward Adam Verifies OK on CPU reference" << std::endl; + + return miopenStatusSuccess; +} + +template +int AdamDriver::VerifyBackward() +{ + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_ADAM_DRIVER_HPP diff --git a/driver/addlayernorm_driver.hpp b/driver/addlayernorm_driver.hpp new file mode 100644 index 0000000000..e74a1548e6 --- /dev/null +++ b/driver/addlayernorm_driver.hpp @@ -0,0 +1,511 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_ADDLAYERNORM_DRIVER_HPP +#define GUARD_MIOPEN_ADDLAYERNORM_DRIVER_HPP + +#include <../test/tensor_holder.hpp> +#include <../test/verify.hpp> +#include "InputFlags.hpp" +#include "driver.hpp" +#include "random.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" +#include +#include +#include +#include +#include +#include +#include + +template +int32_t mloAddLayerNormForwardRunHost(miopenTensorDescriptor_t inputDesc, + Tgpu* input, + Tgpu* input2, + Tgpu* weight, + Tgpu* bias, + Tcheck* outputhost, + Tcheck* meanhost, + Tcheck* rstdhost, + float eps, + int32_t normalized_dim, + miopenNormMode_t mode) +{ + auto dims = miopen::deref(inputDesc).GetLengths(); + size_t outer_size = 1; + size_t inner_size = 1; + size_t norm_dim = static_cast(normalized_dim); + + for(size_t i = 0ULL; i < dims.size(); ++i) + { + if(i < norm_dim) + outer_size *= dims[i]; + else + inner_size *= dims[i]; + } + + int32_t ret = 0; + + for(int32_t o = 0; o < outer_size; o++) + { + Tcheck pmean = 0.0f; + Tcheck pvar = 0.0f; + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck tmp = static_cast(input[o * inner_size + i]) + + static_cast(input2[o * inner_size + i]); + pmean += tmp; + pvar += tmp * tmp; + } + + pmean = pmean / inner_size; + pvar = pvar / inner_size - pmean * pmean; + Tcheck prstd = 1.0f / sqrt(pvar + eps); + + meanhost[o] = pmean; + rstdhost[o] = prstd; + + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck pweight = + (mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? 1 : static_cast(weight[i]); + Tcheck pbias = + (mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? 0 : static_cast(bias[i]); + outputhost[o * inner_size + i] = + (static_cast(input[o * inner_size + i]) + + static_cast(input2[o * inner_size + i]) - pmean) * + prstd * pweight + + pbias; + } + } + return ret; +} + +template +class AddLayerNormDriver : public Driver +{ +public: + AddLayerNormDriver() : Driver() + { + miopenCreateTensorDescriptor(&inputDesc); + miopenCreateTensorDescriptor(&input2Desc); + miopenCreateTensorDescriptor(&weightDesc); + miopenCreateTensorDescriptor(&biasDesc); + miopenCreateTensorDescriptor(&outputDesc); + miopenCreateTensorDescriptor(&meanDesc); + miopenCreateTensorDescriptor(&rstdDesc); + + data_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + std::vector GetInputTensorLengthsFromCmdLine(); + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + int RunForwardCPU(); + + int RunBackwardGPU() override; + + Tref GetTolerance(); + int VerifyBackward() override; + int VerifyForward() override; + ~AddLayerNormDriver() override + { + miopenDestroyTensorDescriptor(inputDesc); + miopenDestroyTensorDescriptor(input2Desc); + miopenDestroyTensorDescriptor(weightDesc); + miopenDestroyTensorDescriptor(biasDesc); + miopenDestroyTensorDescriptor(outputDesc); + miopenDestroyTensorDescriptor(meanDesc); + miopenDestroyTensorDescriptor(rstdDesc); + } + +private: + InputFlags inflags; + + int forw; + int dim_size; + + miopenTensorDescriptor_t inputDesc; + miopenTensorDescriptor_t input2Desc; + miopenTensorDescriptor_t weightDesc; + miopenTensorDescriptor_t biasDesc; + miopenTensorDescriptor_t outputDesc; + miopenTensorDescriptor_t meanDesc; + miopenTensorDescriptor_t rstdDesc; + + std::unique_ptr in_dev; + std::unique_ptr in2_dev; + std::unique_ptr weight_dev; + std::unique_ptr bias_dev; + std::unique_ptr out_dev; + std::unique_ptr mean_dev; + std::unique_ptr rstd_dev; + + std::vector in; + std::vector in2; + std::vector weight; + std::vector bias; + std::vector out; + std::vector mean; + std::vector rstd; + std::vector outhost; + std::vector meanhost; + std::vector rstdhost; + + float eps; + int dim; + miopenNormMode_t mode; +}; + +template +int AddLayerNormDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::GetandSetData() +{ + auto inTensorParam = inflags.GetValueTensor("input"); + + auto in_len = inTensorParam.lengths; + + dim = inflags.GetValueInt("normalized_dim"); + + MIOPEN_THROW_IF(dim < 0 || static_cast(dim) >= in_len.size(), + "normalized_dim out of range"); + + std::vector inner_len; + if(dim == in_len.size()) + inner_len = {1}; + else + inner_len = {in_len.begin() + dim, in_len.end()}; + + std::vector outer_len; + if(dim == 0) + outer_len = {1}; + else + outer_len = {in_len.begin(), in_len.end() - (in_len.size() - dim)}; + + if(SetTensorNd(inputDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing input tensor: " + inflags.GetValueStr("input") + "."); + + if(SetTensorNd(input2Desc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing input2 tensor: " + inflags.GetValueStr("input") + "."); + + if(SetTensorNd(weightDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting weight tensor."); + + if(SetTensorNd(biasDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting bias tensor."); + + if(SetTensorNd(outputDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting doutput tensor."); + + if(SetTensorNd(meanDesc, outer_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting mean tensor."); + + if(SetTensorNd(rstdDesc, outer_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting rstd tensor."); + + eps = static_cast(inflags.GetValueDouble("eps")); + mode = miopenNormMode_t(inflags.GetValueInt("mode")); + + return 0; +} + +template +int AddLayerNormDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "1", "Run only Forward AddLayerNorm (Default=1)", "int"); + inflags.AddTensorFlag("input", 'X', "100x3x32x32", "input tensor descriptor"); + + inflags.AddInputFlag("eps", 'e', "0.00001", "Alpha (Default=0.00001)", "double"); + inflags.AddInputFlag("normalized_dim", 'o', "3", "Nomalized Dim (Default=3)", "int"); + inflags.AddInputFlag( + "mode", 'm', "2", "elemwise affine mode (2), weight and bias mode (3) (Default=0)", "int"); + + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::AllocateBuffersAndCopy() +{ + const Tgpu Tgpu0val = static_cast(0.0); + const Tgpu Tgpu1val = static_cast(1.0); + const Tref Tref0val = static_cast(0.0); + size_t in_sz = GetTensorSize(inputDesc); + size_t in2_sz = GetTensorSize(input2Desc); + size_t weight_sz = GetTensorSize(weightDesc); + size_t bias_sz = GetTensorSize(biasDesc); + size_t out_sz = GetTensorSize(outputDesc); + size_t mean_sz = GetTensorSize(meanDesc); + size_t rstd_sz = GetTensorSize(rstdDesc); + + uint32_t ctx = 0; + + in_dev = std::unique_ptr(new GPUMem(ctx, in_sz, sizeof(Tgpu))); + in2_dev = std::unique_ptr(new GPUMem(ctx, in2_sz, sizeof(Tgpu))); + weight_dev = std::unique_ptr(new GPUMem(ctx, weight_sz, sizeof(Tgpu))); + bias_dev = std::unique_ptr(new GPUMem(ctx, bias_sz, sizeof(Tgpu))); + out_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(Tgpu))); + mean_dev = std::unique_ptr(new GPUMem(ctx, mean_sz, sizeof(Tgpu))); + rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tgpu))); + + in = std::vector(in_sz, Tgpu0val); + in2 = std::vector(in2_sz, Tgpu0val); + weight = std::vector(weight_sz, Tgpu0val); + bias = std::vector(bias_sz, Tgpu0val); + out = std::vector(out_sz, Tgpu0val); + mean = std::vector(mean_sz, Tgpu0val); + rstd = std::vector(rstd_sz, Tgpu0val); + outhost = std::vector(out_sz, Tref0val); + meanhost = std::vector(mean_sz, Tref0val); + rstdhost = std::vector(rstd_sz, Tref0val); + + for(int i = 0; i < in_sz; i++) + { + in[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); + } + + for(int i = 0; i < in2_sz; i++) + { + in2[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); + } + + if(in_dev->ToGPU(GetStream(), in.data()) != 0) + std::cerr << "Error copying (in) to GPU, size: " << in_dev->GetSize() << std::endl; + if(in2_dev->ToGPU(GetStream(), in2.data()) != 0) + std::cerr << "Error copying (in2) to GPU, size: " << in2_dev->GetSize() << std::endl; + + for(int i = 0; i < weight_sz; i++) + { + if(mode == MIOPEN_ELEMENTWISE_AFFINE) + weight[i] = Tgpu1val; + else + weight[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); + } + + if(weight_dev->ToGPU(GetStream(), weight.data()) != 0) + std::cerr << "Error copying (weight) to GPU, size: " << weight_dev->GetSize() << std::endl; + + for(int i = 0; i < bias_sz; i++) + { + if(mode == MIOPEN_ELEMENTWISE_AFFINE) + bias[i] = Tgpu0val; + else + bias[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); + } + if(bias_dev->ToGPU(GetStream(), bias.data()) != 0) + std::cerr << "Error copying (bias) to GPU, size: " << bias_dev->GetSize() << std::endl; + + if(out_dev->ToGPU(GetStream(), out.data()) != 0) + std::cerr << "Error copying (out) to GPU, size: " << out_dev->GetSize() << std::endl; + + if(mean_dev->ToGPU(GetStream(), mean.data()) != 0) + std::cerr << "Error copying (mean) to GPU, size: " << mean_dev->GetSize() << std::endl; + + if(rstd_dev->ToGPU(GetStream(), rstd.data()) != 0) + std::cerr << "Error copying (rstd) to GPU, size: " << rstd_dev->GetSize() << std::endl; + + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::RunForwardGPU() +{ + float kernel_total_time = 0.0; + float kernel_first_time = 0.0; + + Timer t; + START_TIME + + for(int i = 0; i < inflags.GetValueInt("iter"); i++) + { + miopenAddLayerNormForward(GetHandle(), + mode, + inputDesc, + in_dev->GetMem(), + input2Desc, + in2_dev->GetMem(), + weightDesc, + weight_dev->GetMem(), + biasDesc, + bias_dev->GetMem(), + eps, + dim, + outputDesc, + out_dev->GetMem(), + meanDesc, + mean_dev->GetMem(), + rstdDesc, + rstd_dev->GetMem()); + + float time = 0.0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + int iter = inflags.GetValueInt("iter"); + if(WALL_CLOCK) + std::cout << "Wall-clock Time Forward AddLayerNorm Elapsed: " << t.gettime_ms() / iter + << " ms\n"; + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + std::cout << "GPU Kernel Time Forward AddLayerNorm Elapsed: " << kernel_average_time + << " ms\n"; + } + + if(out_dev->FromGPU(GetStream(), out.data()) != 0) + std::cerr << "Error copying (out_dev) from GPU, size: " << out_dev->GetSize() << std::endl; + + if(mean_dev->FromGPU(GetStream(), mean.data()) != 0) + std::cerr << "Error copying (mean_dev) from GPU, size: " << mean_dev->GetSize() + << std::endl; + + if(rstd_dev->FromGPU(GetStream(), rstd.data()) != 0) + std::cerr << "Error copying (rstd_dev) from GPU, size: " << rstd_dev->GetSize() + << std::endl; + + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::RunForwardCPU() +{ + mloAddLayerNormForwardRunHost(inputDesc, + in.data(), + in2.data(), + weight.data(), + bias.data(), + outhost.data(), + meanhost.data(), + rstdhost.data(), + eps, + dim, + mode); + + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::RunBackwardGPU() +{ + return miopenStatusSuccess; +} + +template +Tref AddLayerNormDriver::GetTolerance() +{ + // Computation error of fp16 is ~2^13 (=8192) bigger than + // the one of fp32 because mantissa is shorter by 13 bits. + auto tolerance = std::is_same::value ? 1.5e-6 : 8.2e-3; + + // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. + if(std::is_same::value) + tolerance *= 8.0; + return tolerance; +} + +template +int AddLayerNormDriver::VerifyForward() +{ + RunForwardCPU(); + const Tref tolerance = GetTolerance(); + auto error = miopen::rms_range(outhost, out); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Forward AddLayerNorm FAILED: " << error << " > " << tolerance << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward AddLayerNorm Verifies OK on CPU reference (" << error << " < " + << tolerance << ')' << std::endl; + } + + auto meanerror = miopen::rms_range(meanhost, mean); + if(!std::isfinite(meanerror) || meanerror > tolerance) + { + std::cout << "Forward AddLayerNorm mean FAILED: " << meanerror << " > " << tolerance + << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward AddLayerNorm mean Verifies OK on CPU reference (" << meanerror + << " < " << tolerance << ')' << std::endl; + } + + auto rstderror = miopen::rms_range(rstdhost, rstd); + if(!std::isfinite(rstderror) || rstderror > tolerance) + { + std::cout << "Forward AddLayerNorm rstd FAILED: " << rstderror << " > " << tolerance + << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward AddLayerNorm rstd Verifies OK on CPU reference (" << rstderror + << " < " << tolerance << ')' << std::endl; + } + + return miopenStatusSuccess; +} + +template +int AddLayerNormDriver::VerifyBackward() +{ + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_ADDLAYERNORM_DRIVER_HPP diff --git a/driver/argmax_driver.hpp b/driver/argmax_driver.hpp deleted file mode 100644 index d0ca256433..0000000000 --- a/driver/argmax_driver.hpp +++ /dev/null @@ -1,340 +0,0 @@ -/******************************************************************************* - * - * MIT License - * - * Copyright (c) 2023 Advanced Micro Devices, Inc. - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - * - *******************************************************************************/ -#ifndef GUARD_MIOPEN_ARGMAX_DRIVER_HPP -#define GUARD_MIOPEN_ARGMAX_DRIVER_HPP - -#include "InputFlags.hpp" -#include "driver.hpp" -#include "tensor_driver.hpp" -#include "timer.hpp" -#include "random.hpp" -#include -#include -#include -#include -#include -#include -#include -#include -#include <../test/tensor_holder.hpp> -#include <../test/verify.hpp> - -template -int32_t mloArgmaxForwardRunHost(miopenTensorDescriptor_t inputDesc, - miopenTensorDescriptor_t outputDesc, - Tgpu* input, - int32_t* outputhost, - int32_t dim) -{ - auto input_dims = miopen::deref(inputDesc).GetLengths(); - auto output_dims = miopen::deref(outputDesc).GetLengths(); - - int32_t reduce_size = static_cast(input_dims[dim]); - auto output_numel = - std::accumulate(output_dims.begin(), output_dims.end(), 1L, std::multiplies()); - - auto inner_size = std::accumulate( - input_dims.begin() + dim + 1, input_dims.end(), 1ULL, std::multiplies()); - - int32_t ret = 0; - - for(size_t o = 0; o < output_numel; o++) - { - size_t input_idx = (o / inner_size) * inner_size * reduce_size + o % inner_size; - - int32_t max_idx = 0; - Tcheck max = static_cast(input[input_idx]); - - for(int32_t i = 1; i < reduce_size; i++) - { - input_idx += inner_size; - Tcheck val = static_cast(input[input_idx]); - if(max < val) - { - max = val; - max_idx = i; - } - } - outputhost[o] = max_idx; - } - return ret; -} - -template -class ArgmaxDriver : public Driver -{ -public: - ArgmaxDriver() : Driver() - { - miopenCreateTensorDescriptor(&inputDesc); - miopenCreateTensorDescriptor(&outputDesc); - - data_type = miopen_type{}; - } - - int AddCmdLineArgs() override; - int ParseCmdLineArgs(int argc, char* argv[]) override; - InputFlags& GetInputFlags() override { return inflags; } - - int GetandSetData() override; - std::vector GetInputTensorLengthsFromCmdLine(); - - int AllocateBuffersAndCopy() override; - - int RunForwardGPU() override; - int RunForwardCPU(); - - int RunBackwardGPU() override; - - int VerifyBackward() override; - int VerifyForward() override; - ~ArgmaxDriver() override - { - miopenDestroyTensorDescriptor(inputDesc); - miopenDestroyTensorDescriptor(outputDesc); - } - -private: - InputFlags inflags; - - int forw; - - miopenTensorDescriptor_t inputDesc; - miopenTensorDescriptor_t outputDesc; - - std::unique_ptr in_dev; - std::unique_ptr out_dev; - - std::vector in; - std::vector out; - std::vector outhost; - - int dim; -}; - -template -int ArgmaxDriver::ParseCmdLineArgs(int argc, char* argv[]) -{ - inflags.Parse(argc, argv); - - if(inflags.GetValueInt("time") == 1) - { - miopenEnableProfiling(GetHandle(), true); - } - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::GetandSetData() -{ - std::vector in_len = GetInputTensorLengthsFromCmdLine(); - dim = inflags.GetValueInt("DimToReduce"); - - SetTensorNd(inputDesc, in_len, data_type); - - std::vector out_len; - - for(int i = 0; i < in_len.size(); i++) - { - if(i != dim) - { - out_len.push_back(in_len[i]); - } - } - - if(out_len.empty()) - out_len.push_back(1); - - SetTensorNd(outputDesc, out_len, miopenInt32); - - return 0; -} - -template -int ArgmaxDriver::AddCmdLineArgs() -{ - inflags.AddInputFlag("forw", 'F', "1", "Run only Forward Argmax (Default=1)", "int"); - inflags.AddInputFlag("batchsize", 'n', "21", "Mini-batch size (Default=100)", "int"); - inflags.AddInputFlag("in_channels", 'c', "500", "Number of Input Channels (Default=3)", "int"); - inflags.AddInputFlag("in_d", 'D', "0", "Input Depth (Default=0)", "int"); - inflags.AddInputFlag("in_h", 'H', "0", "Input Height (Default=32)", "int"); - inflags.AddInputFlag("in_w", 'W', "375", "Input Width (Default=32)", "int"); - inflags.AddInputFlag( - "DimToReduce", 'R', "0", "The indice of the dimensions to be reduced(Default=1)", "int"); - inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); - inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); - inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); - inflags.AddInputFlag( - "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); - - return miopenStatusSuccess; -} - -template -std::vector ArgmaxDriver::GetInputTensorLengthsFromCmdLine() -{ - int in_n = inflags.GetValueInt("batchsize"); - int in_c = inflags.GetValueInt("in_channels"); - int in_w = inflags.GetValueInt("in_w"); - int in_h = inflags.GetValueInt("in_h"); - int in_d = inflags.GetValueInt("in_d"); - - if((in_n != 0) && (in_c != 0) && (in_d != 0) && (in_h != 0) && (in_w != 0)) - { - return std::vector({in_n, in_c, in_d, in_h, in_w}); - } - else if((in_n != 0) && (in_c != 0) && (in_h != 0) && (in_w != 0)) - { - return std::vector({in_n, in_c, in_h, in_w}); - } - else if((in_n != 0) && (in_c != 0) && (in_w != 0)) - { - return std::vector({in_n, in_c, in_w}); - } - else if((in_n != 0) && (in_w != 0)) - { - return std::vector({in_n, in_w}); - } - else if(in_n != 0) - { - return std::vector({in_n}); - } - else - { - std::cerr << "Error Input Tensor Lengths\n" << std::endl; - return std::vector({0}); - } -} - -template -int ArgmaxDriver::AllocateBuffersAndCopy() -{ - size_t in_sz = GetTensorSize(inputDesc); - size_t out_sz = GetTensorSize(outputDesc); - - uint32_t ctx = 0; - - in_dev = std::unique_ptr(new GPUMem(ctx, in_sz, sizeof(Tgpu))); - out_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(int))); - - in = std::vector(in_sz, static_cast(0)); - out = std::vector(out_sz, static_cast(0)); - outhost = std::vector(out_sz, static_cast(0)); - - for(int i = 0; i < in_sz; i++) - { - in[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); - } - - if(in_dev->ToGPU(GetStream(), in.data()) != 0) - std::cerr << "Error copying (in) to GPU, size: " << in_dev->GetSize() << std::endl; - - if(out_dev->ToGPU(GetStream(), out.data()) != 0) - std::cerr << "Error copying (out) to GPU, size: " << out_dev->GetSize() << std::endl; - - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::RunForwardGPU() -{ - float kernel_total_time = 0; - float kernel_first_time = 0; - - Timer t; - START_TIME - - for(int i = 0; i < inflags.GetValueInt("iter"); i++) - { - miopenArgmaxForward( - GetHandle(), inputDesc, in_dev->GetMem(), dim, outputDesc, out_dev->GetMem()); - - float time = 0; - miopenGetKernelTime(GetHandle(), &time); - kernel_total_time += time; - if(i == 0) - kernel_first_time = time; - } - - if(inflags.GetValueInt("time") == 1) - { - STOP_TIME - int iter = inflags.GetValueInt("iter"); - if(WALL_CLOCK) - std::cout << "Wall-clock Time Forward Argmax Elapsed: " << t.gettime_ms() / iter - << " ms\n"; - - float kernel_average_time = - iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; - std::cout << "GPU Kernel Time Forward Argmax Elapsed: " << kernel_average_time << " ms\n"; - } - - if(out_dev->FromGPU(GetStream(), out.data()) != 0) - std::cerr << "Error copying (out_dev) from GPU, size: " << out_dev->GetSize() << std::endl; - - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::RunForwardCPU() -{ - mloArgmaxForwardRunHost(inputDesc, outputDesc, in.data(), outhost.data(), dim); - - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::RunBackwardGPU() -{ - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::VerifyForward() -{ - RunForwardCPU(); - auto error = miopen::rms_range(outhost, out); - - if(!std::isfinite(error) || std::abs(static_cast(error)) != 0.0f) - { - std::cout << "Forward Argmax FAILED: Result does not equal" << std::endl; - return EC_VerifyFwd; - } - else - { - std::cout << "Forward Argmax Verifies on CPU and GPU (err=" << error << ")\n"; - } - - return miopenStatusSuccess; -} - -template -int ArgmaxDriver::VerifyBackward() -{ - return miopenStatusSuccess; -} - -#endif // GUARD_MIOPEN_ARGMAX_DRIVER_HPP diff --git a/driver/cat_driver.hpp b/driver/cat_driver.hpp index 51eb16b1c7..3254b5f3bc 100644 --- a/driver/cat_driver.hpp +++ b/driver/cat_driver.hpp @@ -183,8 +183,8 @@ template int CatDriver::AddCmdLineArgs() { inflags.AddInputFlag("forw", 'F', "1", "Run only Forward Cat (Default=1)", "int"); - inflags.AddTensorFlag("input1", '1', "", "input1 tensor descriptor"); - inflags.AddTensorFlag("input2", '2', "", "input2 tensor descriptor"); + inflags.AddTensorFlag("input1", '1', "2x32x128x128x128", "input1 tensor descriptor"); + inflags.AddTensorFlag("input2", '2', "2x32x128x128x128", "input2 tensor descriptor"); inflags.AddTensorFlag("input3", '3', "", "input3 tensor descriptor"); inflags.AddTensorFlag("input4", '4', "", "input4 tensor descriptor"); inflags.AddTensorFlag("input5", '5', "", "input5 tensor descriptor"); diff --git a/driver/conv_common.hpp b/driver/conv_common.hpp index 3b34974c21..7f7bf4fac9 100644 --- a/driver/conv_common.hpp +++ b/driver/conv_common.hpp @@ -39,11 +39,7 @@ #include #include -#if !defined(_WIN32) #include -#else -#include -#endif using half = half_float::half; using hip_bfloat16 = bfloat16; #include diff --git a/driver/conv_driver.hpp b/driver/conv_driver.hpp index e8114bc4c8..8f9e836345 100644 --- a/driver/conv_driver.hpp +++ b/driver/conv_driver.hpp @@ -81,6 +81,12 @@ MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DRIVER_PAD_BUFFERS_2M) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DRIVER_USE_GPU_REFERENCE) MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DRIVER_SUBNORM_PERCENTAGE) +// 0 - Allocate WS size as reported by the library (default) +// 1 - Do not allocate workspace. +// 2...16 - Allocate smaller WS. Size = default/value. +// Other - The driver allocates workspace size equal to the value of the variable (in bytes). +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DRIVER_CONV_WORKSPACE_SIZE_ADJUST) + // Support in the library discontinued, but left in the driver // for reference in the future. #define miopenInt8x4 (static_cast(4)) @@ -137,6 +143,23 @@ struct AutoPrepareForGpuReference bool quiet_prev; }; +static inline void AdjustWorkspacesizeVariableFromEnv(std::size_t& sz) +{ + auto adj = env::value(MIOPEN_DRIVER_CONV_WORKSPACE_SIZE_ADJUST); + if(adj == 0ULL) + return; // nop + auto sz_save = sz; + if(adj == 1ULL) + sz = 0ULL; + else if(1 <= adj && adj <= 16) + sz /= adj; + else + sz = adj; + MIOPEN_LOG_CUSTOM( + miopen::LoggingLevel::Info2, "MIOpenDriver", "From " << sz_save << " to " << sz); + return; +} + static inline miopenDataType_t DataTypeFromShortString(const std::string& type) { static const std::unordered_map conv_map = { @@ -205,7 +228,7 @@ class GpumemTensor return; } - for(int i = 0; i < sz; ++i) + for(size_t i = 0; i < sz; ++i) { /// \anchor move_rand /// Generate random value, even if buffer is unused. This provides the same @@ -524,6 +547,13 @@ class ConvDriver : public Driver Timer2 wrw_auxiliary_gwss; Timer2 warmup_wall_total; // Counts also auxiliary time. + float ComputeAverageTime(const float total_time, const float first_time) const + { + if(num_iterations > 1) + return (total_time - first_time) / (num_iterations - 1); + return total_time; + } + void PrintForwardTime(float kernel_total_time, float kernel_first_time) const; int RunForwardGpuImmed(bool is_transform); int RunForwardGpuFind(bool is_transform); @@ -566,11 +596,21 @@ class ConvDriver : public Driver Tref* data) const; void TrySaveVerificationCache(const Direction& direction, std::vector& data) const; + void DebugPrintWorkspaceDev() const + { + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Info2, + "MIOpenDriver", + "ptr=" << (workspace_dev != nullptr ? workspace_dev->GetMem() : nullptr) + << " size=" + << (workspace_dev != nullptr ? workspace_dev->GetSize() : 0ULL)); + } + void ResizeWorkspaceDev(context_t ctx, std::size_t size) { workspace_dev.reset(); if(size > 0) workspace_dev = std::unique_ptr(new GPUMem(ctx, size, 1)); + DebugPrintWorkspaceDev(); } // Helper functions, can be moved out of class. @@ -942,7 +982,7 @@ int ConvDriver::GetandSetData() static_cast(miopenConvolutionFindModeNormal)); // Repeat via hidden API. miopenSetConvolutionGroupCount(warmupConvDesc, group_count); - int warmup_out_len_size = miopen::deref(warmupInputTensor).GetSize(); + int warmup_out_len_size = miopen::deref(warmupInputTensor).GetNumDims(); std::vector warmup_out_len(warmup_out_len_size); miopenGetConvolutionNdForwardOutputDim(warmupConvDesc, warmupInputTensor, @@ -1297,7 +1337,7 @@ int ConvDriver::SetConvDescriptorFromCmdLineArgs() template std::vector ConvDriver::GetOutputTensorLengths() { - int ndim = miopen::deref(inputTensor).GetSize(); + int ndim = miopen::deref(inputTensor).GetNumDims(); std::vector out_lens(ndim); @@ -1358,12 +1398,12 @@ int ConvDriver::AllocateBuffersAndCopy() size_t in_sz = GetTensorSize(inputTensor); size_t wei_sz = GetTensorSize(weightTensor); size_t out_sz = GetTensorSize(outputTensor); - auto subnorm_percentage = miopen::Value(ENV(MIOPEN_DRIVER_SUBNORM_PERCENTAGE)); + auto subnorm_percentage = env::value(MIOPEN_DRIVER_SUBNORM_PERCENTAGE); if(subnorm_percentage != 0) std::cout << "MIOPEN_DRIVER_SUBNORM_PERCENTAGE = " << subnorm_percentage << std::endl; // Workaround: Pad buffers allocations to be a multiple of 2M - if(miopen::IsEnabled(ENV(MIOPEN_DRIVER_PAD_BUFFERS_2M))) + if(env::enabled(MIOPEN_DRIVER_PAD_BUFFERS_2M)) { // PadBufferSize(in_sz, sizeof(Tgpu)); PadBufferSize(wei_sz, sizeof(Tgpu)); @@ -1386,7 +1426,7 @@ int ConvDriver::AllocateBuffersAndCopy() size_t warmup_in_sz = GetTensorSize(warmupInputTensor); size_t warmup_wei_sz = GetTensorSize(warmupWeightTensor); size_t warmup_out_sz = GetTensorSize(warmupOutputTensor); - if(miopen::IsEnabled(ENV(MIOPEN_DRIVER_PAD_BUFFERS_2M))) + if(env::enabled(MIOPEN_DRIVER_PAD_BUFFERS_2M)) { PadBufferSize(warmup_wei_sz, sizeof(warmup_Tgpu)); PadBufferSize(warmup_out_sz, sizeof(warmup_Tgpu)); @@ -1459,6 +1499,7 @@ int ConvDriver::AllocateBuffersAndCopy() &ws_sizeof_find_wrw); wrw_auxiliary_gwss.pause(wall_enabled); wrw_auxiliary.pause(wall_enabled); + AdjustWorkspacesizeVariableFromEnv(ws_sizeof_find_wrw); } if(is_bwd && rc == miopenStatusSuccess) { @@ -1472,6 +1513,7 @@ int ConvDriver::AllocateBuffersAndCopy() &ws_sizeof_find_bwd); bwd_auxiliary_gwss.pause(wall_enabled); bwd_auxiliary.pause(wall_enabled); + AdjustWorkspacesizeVariableFromEnv(ws_sizeof_find_bwd); } if(is_fwd && rc == miopenStatusSuccess) { @@ -1486,6 +1528,7 @@ int ConvDriver::AllocateBuffersAndCopy() &ws_sizeof_find_fwd); fwd_auxiliary_gwss.pause(wall_enabled); fwd_auxiliary.pause(wall_enabled); + AdjustWorkspacesizeVariableFromEnv(ws_sizeof_find_fwd); } if(rc != miopenStatusSuccess) { @@ -1559,7 +1602,7 @@ int ConvDriver::AllocateBuffersAndCopy() if(!biasFileName.empty()) read = readBufferFromFile(b_int8.data(), b_sz, biasFileName.c_str()); if(!read) - for(int i = 0; i < b_sz; i++) + for(size_t i = 0; i < b_sz; ++i) b_int8[i] = static_cast(i % 8) + prng::gen_canonical(); } std::ignore = b.AllocOnDeviceAndInit(q, ctx, b_sz, b_int8); @@ -1602,15 +1645,20 @@ int ConvDriver::AllocateBuffersAndCopy() if(!is_gpualloc) { - for(int i = 0; i < b_sz; i++) + for(size_t i = 0; i < b_sz; ++i) { if(!b_read) { - b.GetVector()[i] = static_cast(i % 8) // + /// (i % 8) can't be converted to F8 type as there is no suitable + /// conversion, but we have conversions from int and from uint8_t. + /// int is not good as it would produce negative results + /// after truncation of size_t, while we want positive values. + /// uint8_t is fine because (i % 8) fits into 3 bits. + b.GetVector()[i] = static_cast(static_cast(i) % 8) // + (is_fp8 ? prng::gen_A_to_B(Data_min, Data_max) // : prng::gen_canonical()); } - db.GetVector()[i] = static_cast(i % 8) // + db.GetVector()[i] = static_cast(static_cast(i) % 8) // + (is_fp8 ? prng::gen_A_to_B(Data_min, Data_max) // : prng::gen_canonical()); } @@ -1698,7 +1746,7 @@ int ConvDriver::AllocateBuffersAndCopy() template bool ConvDriver::UseGPUReference() { - if(!miopen::IsDisabled(ENV(MIOPEN_DRIVER_USE_GPU_REFERENCE))) + if(!env::disabled(MIOPEN_DRIVER_USE_GPU_REFERENCE)) { if((miopen_type{} == miopenFloat && (miopen_type{} == miopenFloat || miopen_type{} == miopenHalf || @@ -1745,12 +1793,10 @@ template void ConvDriver::PrintForwardTime(const float kernel_total_time, const float kernel_first_time) const { - float kernel_average_time = num_iterations > 1 - ? (kernel_total_time - kernel_first_time) / (num_iterations - 1) - : kernel_first_time; + float kernel_average_time = ComputeAverageTime(kernel_total_time, kernel_first_time); printf("GPU Kernel Time Forward Conv. Elapsed: %f ms (average)\n", kernel_average_time); - const auto num_dim = miopen::deref(inputTensor).GetSize() - 2; + const auto num_dim = miopen::deref(inputTensor).GetNumDims() - 2; if(num_dim != 2 && num_dim != 3) { printf("stats: for conv%dd\n", num_dim); @@ -2108,21 +2154,30 @@ int ConvDriver::RunForwardGpuFind(const bool is_transform) float alpha = static_cast(1), beta = static_cast(0); - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; const auto algo = perf_results[0].fwd_algo; // use the fastest algo const auto ws_size = perf_results[0].memory; is_fwd_igemm = (algo == miopenConvolutionFwdAlgoImplicitGEMM); - ResizeWorkspaceDev(ctx, ws_size); - wall.start(wall_enabled); - auto in_tens = (is_transform ? inputTensor_vect4 : inputTensor); auto in_buff = (is_transform ? in_vect4_dev->GetMem() : in.GetDevicePtr()); auto wei_tens = (is_transform ? weightTensor_vect4 : weightTensor); auto wei_buff = (is_transform ? wei_vect4_dev->GetMem() : wei.GetDevicePtr()); + if(ws_size > ws_sizeof_find_fwd) + { + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Error, + "MIOpenDriver", + "Find returns bigger workspace than provided " << ws_sizeof_find_fwd + << " < " << ws_size); + return miopenStatusInternalError; + } + ResizeWorkspaceDev(ctx, ws_size); + wall.start(wall_enabled); + for(int i = 0; i < num_iterations; i++) { rc = miopenConvolutionForward(GetHandle(), @@ -2141,6 +2196,9 @@ int ConvDriver::RunForwardGpuFind(const bool is_transform) if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; @@ -2157,7 +2215,7 @@ int ConvDriver::RunForwardGpuFind(const bool is_transform) fwd_auxiliary.stop(); fwd_auxiliary_gwss.stop(); std::cout << "Wall-clock Time Forward Conv. Elapsed: " - << (wall.gettime_ms() / num_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << fwd_auxiliary.gettime_ms() << " ms" << " (GWSS: " << fwd_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -2269,8 +2327,9 @@ int ConvDriver::RunForwardGpuImmed(const bool is_transform) if(rc != miopenStatusSuccess) return rc; - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; wall.start(wall_enabled); @@ -2291,17 +2350,16 @@ int ConvDriver::RunForwardGpuImmed(const bool is_transform) if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; miopenGetKernelTime(GetHandle(), &time); kernel_total_time += time; if(i == 0) - { kernel_first_time = time; - if(wall_enabled && num_iterations > 1) - wall.start(); // The 1st is warm-up. Disregard it in wall time. - } } } @@ -2310,9 +2368,8 @@ int ConvDriver::RunForwardGpuImmed(const bool is_transform) wall.stop(); fwd_auxiliary.stop(); fwd_auxiliary_gwss.stop(); - const auto wall_iterations = (num_iterations > 1 ? num_iterations - 1 : 1); std::cout << "Wall-clock Time Forward Conv. Elapsed: " - << (wall.gettime_ms() / wall_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << fwd_auxiliary.gettime_ms() << " ms" << " (GWSS: " << fwd_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -2415,7 +2472,7 @@ int ConvDriver::RunForwardGPUReference() { auto out_tmp = tensor(miopen::deref(outputTensor)); out.CopyFromDeviceToHost(GetStream(), out_tmp); - for(int i = 0; i < out_tmp.data.size(); i++) + for(size_t i = 0; i < out_tmp.data.size(); ++i) { outhost.data[i] = static_cast(out_tmp.data[i]); } @@ -2571,14 +2628,23 @@ int ConvDriver::RunBackwardDataGpuFind() if(ret_algo_count == 0) throw std::runtime_error("Find Backward Data Conv. ret_algo_count == 0"); - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; float alpha = static_cast(1), beta = static_cast(0); const auto algo = perf_results_data[0].bwd_data_algo; const auto ws_size = perf_results_data[0].memory; is_bwd_igemm = (algo == miopenConvolutionBwdDataAlgoImplicitGEMM); + if(ws_size > ws_sizeof_find_bwd) + { + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Error, + "MIOpenDriver", + "Find returns bigger workspace than provided " << ws_sizeof_find_bwd + << " < " << ws_size); + return miopenStatusInternalError; + } ResizeWorkspaceDev(ctx, ws_size); wall.start(wall_enabled); @@ -2601,6 +2667,9 @@ int ConvDriver::RunBackwardDataGpuFind() if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; @@ -2617,7 +2686,7 @@ int ConvDriver::RunBackwardDataGpuFind() bwd_auxiliary.stop(); bwd_auxiliary_gwss.stop(); std::cout << "Wall-clock Time Backward Data Conv. Elapsed: " - << (wall.gettime_ms() / num_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << bwd_auxiliary.gettime_ms() << " ms" << " (GWSS: " << bwd_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -2642,13 +2711,10 @@ int ConvDriver::RunBackwardDataGpuFind() template void ConvDriver::PrintBackwardDataTime(float kernel_total_time, float kernel_first_time) { - float kernel_average_time = num_iterations > 1 - ? (kernel_total_time - kernel_first_time) / (num_iterations - 1) - : kernel_first_time; - + float kernel_average_time = ComputeAverageTime(kernel_total_time, kernel_first_time); printf("GPU Kernel Time Backward Data Conv. Elapsed: %f ms (average)\n", kernel_average_time); - const auto num_dim = miopen::deref(inputTensor).GetSize() - 2; + const auto num_dim = miopen::deref(inputTensor).GetNumDims() - 2; if(num_dim != 2 && num_dim != 3) { printf("stats: for conv%dd\n", num_dim); @@ -2757,8 +2823,9 @@ int ConvDriver::RunBackwardWrwGpuFind() int ret_algo_count; int request_algo_count = 2; - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; float alpha = static_cast(1), beta = static_cast(0); std::vector perf_results_weights(request_algo_count); @@ -2775,14 +2842,19 @@ int ConvDriver::RunBackwardWrwGpuFind() if(ret_algo_count == 0) throw std::runtime_error("Find Backward Weights Conv. ret_algo_count == 0"); - kernel_total_time = 0.0; - kernel_first_time = 0.0; - const auto algo = perf_results_weights[0].bwd_weights_algo; const auto ws_size = perf_results_weights[0].memory; is_wrw_winograd = (algo == miopenConvolutionBwdWeightsAlgoWinograd); is_wrw_igemm = (algo == miopenConvolutionBwdWeightsAlgoImplicitGEMM); + if(ws_size > ws_sizeof_find_wrw) + { + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Error, + "MIOpenDriver", + "Find returns bigger workspace than provided " << ws_sizeof_find_wrw + << " < " << ws_size); + return miopenStatusInternalError; + } ResizeWorkspaceDev(ctx, ws_size); wall.start(wall_enabled); @@ -2805,6 +2877,9 @@ int ConvDriver::RunBackwardWrwGpuFind() if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; @@ -2821,7 +2896,7 @@ int ConvDriver::RunBackwardWrwGpuFind() wrw_auxiliary.stop(); wrw_auxiliary_gwss.stop(); std::cout << "Wall-clock Time Backward Weights Conv. Elapsed: " - << (wall.gettime_ms() / num_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << wrw_auxiliary.gettime_ms() << " ms" << " (GWSS: " << wrw_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -2846,17 +2921,11 @@ int ConvDriver::RunBackwardWrwGpuFind() template void ConvDriver::PrintBackwardWrwTime(float kernel_total_time, float kernel_first_time) { - float time = 0.0; - miopenGetKernelTime(GetHandle(), &time); - - float kernel_average_time = num_iterations > 1 - ? (kernel_total_time - kernel_first_time) / (num_iterations - 1) - : kernel_first_time; - + float kernel_average_time = ComputeAverageTime(kernel_total_time, kernel_first_time); printf("GPU Kernel Time Backward Weights Conv. Elapsed: %f ms (average)\n", kernel_average_time); - const auto num_dim = miopen::deref(inputTensor).GetSize() - 2; + const auto num_dim = miopen::deref(inputTensor).GetNumDims() - 2; if(num_dim != 2 && num_dim != 3) { printf("stats: for conv%dd\n", num_dim); @@ -2923,7 +2992,7 @@ void ConvDriver::PrintBackwardWrwTime(float kernel_total_time, float "stats: name, n, c, do, ho, wo, z, x, y, k, flopCnt, bytesRead, bytesWritten, GFLOPs, " "GB/s, timeMs\n"); printf("stats: %s%dx%dx%du%d, %u, %u, %u, %u, %u, %u, %u, %u, %u, %zu, %zu, %zu, %.0f, " - "%.0f, %f\n ", + "%.0f, %f\n", "bwdw-conv", wei_d, wei_h, @@ -3020,8 +3089,9 @@ int ConvDriver::RunBackwardDataGpuImmed() handle, outputTensor, weightTensor, convDesc, inputTensor, selected->solution_id); bwd_auxiliary.pause(wall_enabled); - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; wall.start(wall_enabled); @@ -3041,17 +3111,16 @@ int ConvDriver::RunBackwardDataGpuImmed() if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; miopenGetKernelTime(GetHandle(), &time); kernel_total_time += time; if(i == 0) - { kernel_first_time = time; - if(wall_enabled && num_iterations > 1) - wall.start(); - } } } @@ -3060,9 +3129,8 @@ int ConvDriver::RunBackwardDataGpuImmed() wall.stop(); bwd_auxiliary.stop(); bwd_auxiliary_gwss.stop(); - const auto wall_iterations = (num_iterations > 1 ? num_iterations - 1 : 1); std::cout << "Wall-clock Time Backward Data Conv. Elapsed: " - << (wall.gettime_ms() / wall_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << bwd_auxiliary.gettime_ms() << " ms" << " (GWSS: " << bwd_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -3150,8 +3218,9 @@ int ConvDriver::RunBackwardWrwGpuImmed() handle, outputTensor, inputTensor, convDesc, weightTensor, selected->solution_id); wrw_auxiliary.pause(wall_enabled); - float kernel_total_time = 0.0; - float kernel_first_time = 0.0; + float kernel_total_time = 0.f; + float kernel_first_time = 0.f; + float wall_first_time = 0.f; wall.start(wall_enabled); @@ -3171,17 +3240,16 @@ int ConvDriver::RunBackwardWrwGpuImmed() if(rc != miopenStatusSuccess) return rc; + if(wall_enabled && i == 0) + wall_first_time = wall.interim_time_ms(); + if(time_enabled) { float time = 0.0; miopenGetKernelTime(GetHandle(), &time); kernel_total_time += time; if(i == 0) - { kernel_first_time = time; - if(wall_enabled && num_iterations > 1) - wall.start(); - } } } @@ -3190,9 +3258,8 @@ int ConvDriver::RunBackwardWrwGpuImmed() wall.stop(); wrw_auxiliary.stop(); wrw_auxiliary_gwss.stop(); - const auto wall_iterations = (num_iterations > 1 ? num_iterations - 1 : 1); std::cout << "Wall-clock Time Backward Weights Conv. Elapsed: " - << (wall.gettime_ms() / wall_iterations) << " ms" + << ComputeAverageTime(wall.gettime_ms(), wall_first_time) << " ms" << ", Auxiliary API calls: " << wrw_auxiliary.gettime_ms() << " ms" << " (GWSS: " << wrw_auxiliary_gwss.gettime_ms() << ')' << std::endl; } @@ -3326,7 +3393,7 @@ int ConvDriver::RunBackwardWeightsGPUReference() { auto dwei_tmp = tensor(miopen::deref(weightTensor)); dwei.CopyFromDeviceToHost(GetStream(), dwei_tmp); - for(int i = 0; i < dwei_tmp.data.size(); i++) + for(size_t i = 0; i < dwei_tmp.data.size(); ++i) { dwei_host.data[i] = static_cast(dwei_tmp.data[i]); } @@ -3377,7 +3444,7 @@ int ConvDriver::RunBackwardDataGPUReference() { auto din_tmp = tensor(miopen::deref(inputTensor)); din.CopyFromDeviceToHost(GetStream(), din_tmp); - for(int i = 0; i < din_tmp.data.size(); i++) + for(size_t i = 0; i < din_tmp.data.size(); ++i) { din_host.data[i] = static_cast(din_tmp.data[i]); } @@ -3433,6 +3500,10 @@ std::string ConvDriver::GetVerificationCacheFileName( { return "int8"; } + if(std::is_same::value) + { + return "int32"; + } else if(std::is_same::value) { return "float16"; diff --git a/driver/ctc_driver.hpp b/driver/ctc_driver.hpp index fe9c27bdb2..c7fa9f02e6 100644 --- a/driver/ctc_driver.hpp +++ b/driver/ctc_driver.hpp @@ -251,7 +251,7 @@ template int CTCDriver::AllocateBuffersAndCopy() { size_t probs_sz = batch_size * (num_class + 1) * max_time_step; - size_t labels_sz = std::accumulate(labelLengths.begin(), labelLengths.end(), 0); + size_t labels_sz = std::accumulate(labelLengths.begin(), labelLengths.end(), 0ULL); size_t workSpaceSize; size_t workSpaceSizeCPU; diff --git a/driver/dm_adam.cpp b/driver/dm_adam.cpp new file mode 100644 index 0000000000..1a76c4b8d5 --- /dev/null +++ b/driver/dm_adam.cpp @@ -0,0 +1,46 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include "adam_driver.hpp" +#include "registry_driver_maker.hpp" + +static Driver* makeDriver(const std::string& base_arg) +{ + if(base_arg == "adam") + return new AdamDriver(); + else if(base_arg == "adamfp16") + return new AdamDriver(); + else if(base_arg == "ampadam") + return new AdamDriver(false, true); + else if(base_arg == "adamw") + return new AdamDriver(true); + else if(base_arg == "adamwfp16") + return new AdamDriver(true); + else if(base_arg == "ampadamw") + return new AdamDriver(true, true); + return nullptr; +} + +REGISTER_DRIVER_MAKER(makeDriver); diff --git a/driver/dm_addlayernorm.cpp b/driver/dm_addlayernorm.cpp new file mode 100644 index 0000000000..af8046dde5 --- /dev/null +++ b/driver/dm_addlayernorm.cpp @@ -0,0 +1,40 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include "addlayernorm_driver.hpp" +#include "registry_driver_maker.hpp" + +static Driver* makeDriver(const std::string& base_arg) +{ + if(base_arg == "addlayernorm") + return new AddLayerNormDriver(); + if(base_arg == "addlayernormfp16") + return new AddLayerNormDriver(); + if(base_arg == "addlayernormbfp16") + return new AddLayerNormDriver(); + return nullptr; +} + +REGISTER_DRIVER_MAKER(makeDriver); diff --git a/driver/dm_argmax.cpp b/driver/dm_getitem.cpp similarity index 83% rename from driver/dm_argmax.cpp rename to driver/dm_getitem.cpp index a6daa5cf94..bfb72be96a 100644 --- a/driver/dm_argmax.cpp +++ b/driver/dm_getitem.cpp @@ -23,17 +23,17 @@ * SOFTWARE. * *******************************************************************************/ -#include "argmax_driver.hpp" +#include "getitem_driver.hpp" #include "registry_driver_maker.hpp" static Driver* makeDriver(const std::string& base_arg) { - if(base_arg == "argmax") - return new ArgmaxDriver(); - if(base_arg == "argmaxfp16") - return new ArgmaxDriver(); - if(base_arg == "argmaxbfp16") - return new ArgmaxDriver(); + if(base_arg == "getitem") + return new GetitemDriver(); + if(base_arg == "getitemfp16") + return new GetitemDriver(); + if(base_arg == "getitembfp16") + return new GetitemDriver(); return nullptr; } diff --git a/driver/dm_reduceextreme.cpp b/driver/dm_reduceextreme.cpp new file mode 100644 index 0000000000..2a94a5b69d --- /dev/null +++ b/driver/dm_reduceextreme.cpp @@ -0,0 +1,40 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include "reduceextreme_driver.hpp" +#include "registry_driver_maker.hpp" + +static Driver* makeDriver(const std::string& base_arg) +{ + if(base_arg == "reduceextreme") + return new ReduceExtremeDriver(); + if(base_arg == "reduceextremefp16") + return new ReduceExtremeDriver(); + if(base_arg == "reduceextremebfp16") + return new ReduceExtremeDriver(); + return nullptr; +} + +REGISTER_DRIVER_MAKER(makeDriver); diff --git a/driver/dm_t5layernorm.cpp b/driver/dm_t5layernorm.cpp new file mode 100644 index 0000000000..7fded668fd --- /dev/null +++ b/driver/dm_t5layernorm.cpp @@ -0,0 +1,40 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include "t5layernorm_driver.hpp" +#include "registry_driver_maker.hpp" + +static Driver* makeDriver(const std::string& base_arg) +{ + if(base_arg == "t5layernorm") + return new T5LayerNormDriver(); + if(base_arg == "t5layernormfp16") + return new T5LayerNormDriver(); + if(base_arg == "t5layernormbfp16") + return new T5LayerNormDriver(); + return nullptr; +} + +REGISTER_DRIVER_MAKER(makeDriver); diff --git a/driver/dm_transformers_adam_w.cpp b/driver/dm_transformers_adam_w.cpp new file mode 100644 index 0000000000..999b9f54d0 --- /dev/null +++ b/driver/dm_transformers_adam_w.cpp @@ -0,0 +1,40 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include "transformers_adam_w_driver.hpp" +#include "registry_driver_maker.hpp" + +static Driver* makeDriver(const std::string& base_arg) +{ + if(base_arg == "transformersadamw") + return new TransformersAdamWDriver(); + else if(base_arg == "transformersadamwfp16") + return new TransformersAdamWDriver(); + else if(base_arg == "transformersampadamw") + return new TransformersAdamWDriver(true); + return nullptr; +} + +REGISTER_DRIVER_MAKER(makeDriver); diff --git a/driver/driver.hpp b/driver/driver.hpp index 40aa59cfa5..bce1d2a0eb 100644 --- a/driver/driver.hpp +++ b/driver/driver.hpp @@ -26,12 +26,7 @@ #ifndef GUARD_MIOPEN_DRIVER_HPP #define GUARD_MIOPEN_DRIVER_HPP -#if !defined(_WIN32) #include -#else -#include -#endif - #include "random.hpp" #include "InputFlags.hpp" @@ -40,6 +35,7 @@ #include #include #include +#include #include #include using half = half_float::half; @@ -108,25 +104,51 @@ struct GPUMem GPUMem(){}; GPUMem(uint32_t ctx, size_t psz, size_t pdata_sz) : _ctx(ctx), sz(psz), data_sz(pdata_sz) { - hipMalloc(static_cast(&buf), data_sz * sz); + auto status = hipMalloc(static_cast(&buf), GetSize()); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status, + "[MIOpenDriver] hipMalloc " + std::to_string(GetSize())); + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Info2, + "MIOpenDriver", + "hipMalloc " << GetSize() << " at " << buf << " Ok"); } int ToGPU(hipStream_t q, void* p) { _q = q; - return static_cast(hipMemcpy(buf, p, data_sz * sz, hipMemcpyHostToDevice)); + return static_cast(hipMemcpy(buf, p, GetSize(), hipMemcpyHostToDevice)); } int FromGPU(hipStream_t q, void* p) { hipDeviceSynchronize(); _q = q; - return static_cast(hipMemcpy(p, buf, data_sz * sz, hipMemcpyDeviceToHost)); + return static_cast(hipMemcpy(p, buf, GetSize(), hipMemcpyDeviceToHost)); } void* GetMem() { return buf; } size_t GetSize() { return sz * data_sz; } - ~GPUMem() { hipFree(buf); } + ~GPUMem() + { + size_t size = 0; + auto status = hipMemPtrGetInfo(buf, &size); + if(status != hipSuccess) + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Warning, + "MIOpenDriver", + "hipMemPtrGetInfo at " << buf << ' ' + << miopen::HIPErrorMessage(status, "")); + status = hipFree(buf); + if(status != hipSuccess) + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Error, + "MIOpenDriver", + "hipFree " << size << " at " << buf << ' ' + << miopen::HIPErrorMessage(status, "")); + else + MIOPEN_LOG_CUSTOM(miopen::LoggingLevel::Info2, + "MIOpenDriver", + "hipFree " << size << " at " << buf << " Ok"); + } + hipStream_t _q; // Place holder for opencl context uint32_t _ctx; void* buf; @@ -147,11 +169,13 @@ inline void PadBufferSize(size_t& sz, int datatype_sz) [[noreturn]] inline void Usage() { printf("Usage: ./driver *base_arg* *other_args*\n"); - printf("Supported Base Arguments: conv[fp16|int8|bfp16|fp8|bfp8], CBAInfer[fp16], " - "pool[fp16], lrn[fp16], " + printf("Supported Base Arguments: conv[fp16|int8|bfp16], pool[fp16], lrn[fp16], " "activ[fp16], softmax[fp16], bnorm[fp16], rnn[fp16], gemm[fp16], ctc, dropout[fp16], " - "tensorop[fp16], reduce[fp16|fp64], layernorm[bfp16|fp16], sum[bfp16|fp16], " - "argmax[bfp16|fp16], groupnorm[bfp16|fp16], cat[bfp16|fp16], interpolate[bfp16|fp16]\n"); + "tensorop, reduce[fp16|fp64], layernorm[bfp16|fp16], sum[bfp16|fp16], " + "groupnorm[bfp16|fp16], cat[bfp16|fp16], addlayernorm[bfp16|fp16], " + "t5layernorm[bfp16|fp16], adam[fp16], ampadam, reduceextreme[bfp16|fp16], " + "adamw[fp16], ampadamw, transformersadamw[fp16], transformersampadamw, " + "getitem[bfp16|fp16], interpolate[bfp16|fp16]\n"); exit(0); // NOLINT (concurrency-mt-unsafe) } @@ -166,18 +190,23 @@ inline std::string ParseBaseArg(int argc, char* argv[]) std::string arg = argv[1]; if(arg != "conv" && arg != "convfp16" && arg != "convint8" && arg != "convbfp16" && - arg != "convfp8" && arg != "convbfp8" && arg != "CBAInfer" && arg != "CBAInferfp16" && arg != "pool" && arg != "poolfp16" && arg != "lrn" && arg != "lrnfp16" && arg != "activ" && arg != "activfp16" && arg != "softmax" && arg != "softmaxfp16" && arg != "bnorm" && arg != "bnormfp16" && arg != "rnn" && arg != "rnnfp16" && arg != "rnn_seq" && arg != "rnn_seqfp16" && arg != "gemm" && arg != "gemmfp16" && arg != "ctc" && - arg != "dropout" && arg != "dropoutfp16" && arg != "tensorop" && arg != "tensoropfp16" && - arg != "reduce" && arg != "reducefp16" && arg != "reducefp64" && arg != "layernorm" && - arg != "layernormfp16" && arg != "layernormbfp16" && arg != "sum" && arg != "sumfp16" && - arg != "sumbfp16" && arg != "argmax" && arg != "argmaxfp16" && arg != "argmaxbfp16" && + arg != "dropout" && arg != "dropoutfp16" && arg != "tensorop" && arg != "reduce" && + arg != "reducefp16" && arg != "reducefp64" && arg != "layernorm" && arg != "layernormfp16" && + arg != "layernormbfp16" && arg != "sum" && arg != "sumfp16" && arg != "sumbfp16" && arg != "groupnorm" && arg != "groupnormfp16" && arg != "groupnormbfp16" && arg != "cat" && - arg != "catfp16" && arg != "catbfp16" && arg != "interpolate" && arg != "interpolatefp16" && - arg != "interpolatebfp16" && arg != "--version") + arg != "catfp16" && arg != "catbfp16" && arg != "addlayernorm" && + arg != "addlayernormfp16" && arg != "addlayernormbfp16" && arg != "t5layernorm" && + arg != "t5layernormfp16" && arg != "t5layernormbfp16" && arg != "adam" && + arg != "adamfp16" && arg != "ampadam" && arg != "reduceextreme" && + arg != "reduceextremefp16" && arg != "reduceextremebfp16" && arg != "adamw" && + arg != "adamwfp16" && arg != "ampadamw" && arg != "transformersadamw" && + arg != "transformersadamwfp16" && arg != "transformersampadamw" && arg != "getitem" && + arg != "getitemfp16" && arg != "getitembfp16" && arg != "interpolate" && + arg != "interpolatefp16" && arg != "interpolatebfp16" && arg != "--version") { printf("FAILED: Invalid Base Input Argument\n"); Usage(); diff --git a/driver/dropout_gpu_emulator.hpp b/driver/dropout_gpu_emulator.hpp index f7ecdd03ea..435d3cba55 100644 --- a/driver/dropout_gpu_emulator.hpp +++ b/driver/dropout_gpu_emulator.hpp @@ -208,8 +208,8 @@ void RunDropoutForwardEmulator(miopenHandle_t handle, size_t rsvsp_offset = 0) { (void)noise_shape; - auto in_dim = miopen::deref(inputTensor).GetSize(); - auto out_dim = miopen::deref(outputTensor).GetSize(); + auto in_dim = miopen::deref(inputTensor).GetNumDims(); + auto out_dim = miopen::deref(outputTensor).GetNumDims(); if(in_dim != out_dim) { printf("CPU verification: Input/Output dimension does not match\n"); @@ -292,8 +292,8 @@ void RunDropoutBackwardEmulator(const miopenDropoutDescriptor_t dropoutDesc, size_t out_offset = 0, size_t rsvsp_offset = 0) { - auto in_dim = miopen::deref(inputTensor).GetSize(); - auto out_dim = miopen::deref(outputTensor).GetSize(); + auto in_dim = miopen::deref(inputTensor).GetNumDims(); + auto out_dim = miopen::deref(outputTensor).GetNumDims(); if(in_dim != out_dim) { printf("CPU verification: Input/Output dimension does not match\n"); diff --git a/driver/gemm_driver.hpp b/driver/gemm_driver.hpp index 85c27e1f95..772104544e 100644 --- a/driver/gemm_driver.hpp +++ b/driver/gemm_driver.hpp @@ -217,11 +217,20 @@ int GemmDriver::GetandSetData() gemm_desc.alpha = inflags.GetValueDouble("alpha"); gemm_desc.beta = inflags.GetValueDouble("beta"); - // we are assuming: row-major, each matrix is saved in continuous memory, no empty memory + // we are assuming: each matrix is saved in continuous memory, no empty memory // between batches of matrices - gemm_desc.lda = gemm_desc.transA == 0 ? gemm_desc.k : gemm_desc.m; - gemm_desc.ldb = gemm_desc.transB == 0 ? gemm_desc.n : gemm_desc.k; - gemm_desc.ldc = gemm_desc.n; // C is never transposed + if(gemm_desc.isColMajor) + { + gemm_desc.lda = gemm_desc.transA == 0 ? gemm_desc.m : gemm_desc.k; + gemm_desc.ldb = gemm_desc.transB == 0 ? gemm_desc.k : gemm_desc.n; + gemm_desc.ldc = gemm_desc.m; // C is never transposed + } + else + { + gemm_desc.lda = gemm_desc.transA == 0 ? gemm_desc.k : gemm_desc.m; + gemm_desc.ldb = gemm_desc.transB == 0 ? gemm_desc.n : gemm_desc.k; + gemm_desc.ldc = gemm_desc.n; // C is never transposed + } gemm_desc.batch_count = inflags.GetValueInt("batch_count"); diff --git a/driver/getitem_driver.hpp b/driver/getitem_driver.hpp new file mode 100644 index 0000000000..c48c9a0520 --- /dev/null +++ b/driver/getitem_driver.hpp @@ -0,0 +1,545 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_GETITEM_DRIVER_HPP +#define GUARD_MIOPEN_GETITEM_DRIVER_HPP + +#include "InputFlags.hpp" +#include "driver.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" +#include "random.hpp" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include <../test/tensor_holder.hpp> +#include <../test/verify.hpp> + +template +int32_t mloGetitemBackwardRunHost(miopenTensorDescriptor_t dyDesc, + uint32_t indexCount, + miopenTensorDescriptor_t* indexDescs, + miopenTensorDescriptor_t dxDesc, + miopenTensorDescriptor_t errorDesc, + Tgpu* dy, + int32_t** indexs, + Tcheck* dxhost, + int32_t* errorhost, + uint32_t dimCount, + int32_t* dims, + uint32_t sliceCount, + int32_t* slices, + uint32_t offset) +{ + auto dy_dims = miopen::deref(dyDesc).GetLengths(); + auto dy_numel = std::accumulate(dy_dims.begin(), dy_dims.end(), 1L, std::multiplies()); + auto dx_dims = miopen::deref(dxDesc).GetLengths(); + auto index_dims = miopen::deref(indexDescs[0]).GetLengths(); + auto index_numel = + std::accumulate(index_dims.begin(), index_dims.end(), 1L, std::multiplies()); + auto element_index = std::vector(indexCount * index_numel + indexCount); + + std::vector output_dims; + for(int32_t i = 0; i < dimCount; i++) + { + output_dims.push_back(dx_dims[dims[i]]); + } + + auto dim_info_offset = indexCount > 0 ? indexCount * index_dims[0] : 0; + auto start_dim = dims[0]; + + auto dy_tv = miopen::get_inner_expanded_tv<5>(miopen::deref(dyDesc)); + auto dxhost_tv = miopen::get_inner_expanded_tv<5>(miopen::deref(dxDesc)); + miopen::slice_tv<5>(dxhost_tv, sliceCount, slices); + + int32_t ret = 0; + + // Get element index form indexs + for(size_t j = 0; j < indexCount; j++) + { + const auto& index_dim = dims[j]; + const auto& dim_size = output_dims[j]; + + for(size_t o = 0; o < index_numel; o++) + { + int32_t getitem_index = indexs[j][o]; + + if(getitem_index >= 0 && getitem_index < dim_size) + { + element_index[(o * indexCount) + j] = getitem_index; + } + else if(getitem_index >= -dim_size && getitem_index < 0) + { + element_index[(o * indexCount) + j] = getitem_index + dim_size; + } + else + { + errorhost[j] = -1; + } + + if(o == 0) + { + element_index[dim_info_offset + j] = index_dim; + } + } + } + + // GetItem + for(size_t o = 0; o < dy_numel; o++) + { + tensor_layout_t<5> ncdhw(dy_tv, o); + tensor_layout_t<5> idx(ncdhw); + + if(indexCount > 0) + { + size_t dim_cursor = ncdhw.layout[start_dim]; + size_t i = start_dim; + size_t j = 0; + + for(; i < start_dim + indexCount; ++i, ++j) + { + size_t dim_idx = element_index[dim_info_offset + j]; + idx.layout[dim_idx] = element_index[(dim_cursor * indexCount) + j]; + } + + i = element_index[dim_info_offset + indexCount - 1] + 1; + dim_cursor = start_dim + 1; + for(; i < 5; ++i, ++dim_cursor) + { + idx.layout[i] = ncdhw.layout[dim_cursor]; + } + } + + dxhost[dxhost_tv.get_tensor_view_idx(idx)] += dy[dy_tv.get_tensor_view_idx(ncdhw)]; + } + + return ret; +} + +template +class GetitemDriver : public Driver +{ +public: + GetitemDriver() : Driver() + { + miopenCreateTensorDescriptor(&dyDesc); + miopenCreateTensorDescriptor(&dxDesc); + miopenCreateTensorDescriptor(&errorDesc); + + data_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + + int RunBackwardGPU() override; + int RunBackwardCPU(); + + Tref GetTolerance(); + + int VerifyBackward() override; + int VerifyForward() override; + ~GetitemDriver() override + { + miopenDestroyTensorDescriptor(dyDesc); + for(auto indexDesc : indexDescs) + { + miopenDestroyTensorDescriptor(indexDesc); + } + miopenDestroyTensorDescriptor(dxDesc); + miopenDestroyTensorDescriptor(errorDesc); + } + +private: + InputFlags inflags; + + miopenTensorDescriptor_t dyDesc; + std::vector indexDescs; + miopenTensorDescriptor_t dxDesc; + miopenTensorDescriptor_t errorDesc; + + std::unique_ptr dy_dev; + std::vector> index_devs; + std::unique_ptr dx_dev; + std::unique_ptr error_dev; + std::unique_ptr workspace_dev; + + std::vector dy; + std::vector> indexs; + std::vector dx; + std::vector error; + std::vector workspace; + std::vector dxhost; + std::vector errorhost; + + size_t ws_sizeInBytes; + + std::vector dims; + std::vector> slices; + std::vector slices_flat; + uint32_t offset; + + std::vector output_dims; + std::vector index_devs_ptr; + std::vector indexs_ptr; +}; + +template +int GetitemDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + + if(inflags.GetValueInt("indexcount") < 0) + MIOPEN_THROW("Index count is negative: " + inflags.GetValueStr("indexcount") + "."); + + if(inflags.GetValueInt("dimcount") < 0) + MIOPEN_THROW("Dim count is negative: " + inflags.GetValueStr("dimcount") + "."); + + if(inflags.GetValueInt("slicecount") < 0) + MIOPEN_THROW("Slice count is negative: " + inflags.GetValueStr("slicecount") + "."); + + return miopenStatusSuccess; +} + +template +int GetitemDriver::GetandSetData() +{ + auto dyTensorParam = inflags.GetValueTensorUint64("doutput"); + auto dxTensorParam = inflags.GetValueTensorUint64("dinput"); + auto indexCountParam = inflags.GetValueInt("indexcount"); + auto dimCountParam = inflags.GetValueInt("dimcount"); + auto sliceCountParam = inflags.GetValueInt("slicecount"); + offset = inflags.GetValueInt("offset"); + + auto indexTensorLengths = inflags.GetValue2dVectorInt("indexs"); + if(indexTensorLengths.size() != indexCountParam) + MIOPEN_THROW("Error parsing indexs tensor: " + inflags.GetValueStr("indexs") + "."); + + dims = inflags.GetValueVectorInt("dims"); + if(dims.size() != dimCountParam) + MIOPEN_THROW("Error parsing dims tensor: " + inflags.GetValueStr("dims") + "."); + + for(auto dim : dims) + { + output_dims.push_back(dxTensorParam.lengths[dim]); + } + + slices = inflags.GetValue2dVectorInt("slices"); + if(slices.size() != sliceCountParam) + MIOPEN_THROW("Error parsing slices: " + inflags.GetValueStr("slices") + "."); + + for(auto slice : slices) + { + for(int32_t i = 0; i < 4; i++) + { + slices_flat.push_back(slice[i]); + } + } + + if(SetTensorNd(dyDesc, dyTensorParam.lengths, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing doutput tensor: " + inflags.GetValueStr("doutput") + "."); + + for(auto indexTensorLength : indexTensorLengths) + { + miopenTensorDescriptor_t indexDesc; + miopenCreateTensorDescriptor(&indexDesc); + if(SetTensorNd(indexDesc, indexTensorLength, miopenInt32) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing indexs tensor: " + inflags.GetValueStr("indexs") + "."); + indexDescs.push_back(indexDesc); + } + + if(SetTensorNd(dxDesc, dxTensorParam.lengths, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing dinput tensor: " + inflags.GetValueStr("dinput") + "."); + + std::vector error_length; + error_length.push_back(indexCountParam); + if(SetTensorNd(errorDesc, error_length, miopen_type{}) != miopenStatusSuccess) + MIOPEN_THROW("Error making error tensor: " + inflags.GetValueStr("indexcount") + "."); + + return 0; +} + +template +int GetitemDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "0", "Run only Forward Getitem (Default=0)", "int"); + inflags.AddTensorFlag("doutput", 'O', "128x128", "doutput tensor descriptor"); + inflags.AddTensorFlag("indexs", 'D', "128", "indexs tensor descriptor"); + inflags.AddTensorFlag("dinput", 'N', "128x128", "dinput tensor descriptor"); + + inflags.AddInputFlag("indexcount", '1', "1", "the number of indexs tensor(Default=1)", "int"); + inflags.AddInputFlag("dimcount", '2', "1", "The dimensions(Default=1)", "int"); + inflags.AddInputFlag("dims", '3', "0", "The dimensions(Default=0)", "vector"); + inflags.AddInputFlag("slicecount", '4', "0", "The number of slices(Default=0)", "int"); + inflags.AddInputFlag("slices", + '5', + "", + "The slices(Default=\'\'" + ")", + "vector>"); + inflags.AddInputFlag("offset", '6', "0", "The offset of output(Default=0)", "int"); + + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +int GetitemDriver::AllocateBuffersAndCopy() +{ + size_t dy_sz = GetTensorSize(dyDesc); + size_t dx_sz = GetTensorSize(dxDesc); + size_t error_sz = GetTensorSize(errorDesc); + + miopenGetGetitemWorkspaceSize( + GetHandle(), indexDescs.size(), indexDescs.data(), &ws_sizeInBytes); + + uint32_t ctx = 0; + + dy_dev = std::unique_ptr(new GPUMem(ctx, dy_sz, sizeof(Tgpu))); + dx_dev = std::unique_ptr(new GPUMem(ctx, dx_sz, sizeof(Tgpu))); + error_dev = std::unique_ptr(new GPUMem(ctx, error_sz, sizeof(int32_t))); + workspace_dev = std::unique_ptr(new GPUMem(ctx, ws_sizeInBytes, sizeof(std::byte))); + + dy = std::vector(dy_sz, static_cast(0)); + dx = std::vector(dx_sz, static_cast(0)); + error = std::vector(error_sz, static_cast(0)); + workspace = std::vector(ws_sizeInBytes / sizeof(int32_t), static_cast(0)); + dxhost = std::vector(dx_sz, static_cast(0)); + errorhost = std::vector(error_sz, static_cast(0)); + + for(int32_t i = 0; i < dy_sz; i++) + { + dy[i] = prng::gen_A_to_B(static_cast(-1), static_cast(1)); + } + + for(int32_t i = 0; i < indexDescs.size(); i++) + { + size_t index_sz = GetTensorSize(indexDescs[i]); + index_devs.push_back(std::unique_ptr(new GPUMem(ctx, index_sz, sizeof(int32_t)))); + indexs.push_back(std::vector(index_sz, static_cast(0))); + auto& index = indexs.back(); + auto index_dev = index_devs.back().get(); + + for(int j = 0; j < index_sz; j++) + { + index[j] = prng::gen_A_to_B(static_cast(0), + static_cast(output_dims[i])); + } + if(index_dev->ToGPU(GetStream(), index.data()) != 0) + std::cerr << "Error copying (index) to GPU, size: " << index_dev->GetSize() + << std::endl; + index_devs_ptr.push_back(index_dev->GetMem()); + indexs_ptr.push_back(index.data()); + } + + if(dy_dev->ToGPU(GetStream(), dy.data()) != 0) + std::cerr << "Error copying (dy) to GPU, size: " << dy_dev->GetSize() << std::endl; + + if(workspace_dev->ToGPU(GetStream(), workspace.data()) != 0) + std::cerr << "Error copying (workspace) to GPU, size: " << workspace_dev->GetSize() + << std::endl; + + if(dx_dev->ToGPU(GetStream(), dx.data()) != 0) + std::cerr << "Error copying (dx) to GPU, size: " << dx_dev->GetSize() << std::endl; + + if(error_dev->ToGPU(GetStream(), error.data()) != 0) + std::cerr << "Error copying (error) to GPU, size: " << error_dev->GetSize() << std::endl; + + return miopenStatusSuccess; +} + +template +int GetitemDriver::RunForwardGPU() +{ + return miopenStatusSuccess; +} + +template +int GetitemDriver::RunBackwardGPU() +{ + float kernel_total_time = 0; + float kernel_first_time = 0; + + Timer t; + START_TIME + + for(int32_t i = 0; i < inflags.GetValueInt("iter"); i++) + { + + if(dx_dev->ToGPU(GetStream(), dx.data()) != 0) + std::cerr << "Error copying (dx) to GPU, size: " << dx_dev->GetSize() << std::endl; + + miopenGetitemBackward(GetHandle(), + workspace_dev->GetMem(), + ws_sizeInBytes, + dyDesc, + dy_dev->GetMem(), + indexDescs.size(), + indexDescs.data(), + index_devs_ptr.data(), + dxDesc, + dx_dev->GetMem(), + errorDesc, + error_dev->GetMem(), + dims.size(), + dims.data(), + slices.size(), + slices_flat.data(), + offset); + + float time = 0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + int32_t iter = inflags.GetValueInt("iter"); + if(WALL_CLOCK) + std::cout << "Wall-clock Time Backward Getitem Elapsed: " << t.gettime_ms() / iter + << " ms" << std::endl; + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + std::cout << "GPU Kernel Time Backward Getitem Elapsed: " << kernel_average_time << " ms" + << std::endl; + } + + if(dx_dev->FromGPU(GetStream(), dx.data()) != 0) + std::cerr << "Error copying (dx_dev) from GPU, size: " << dx_dev->GetSize() << std::endl; + + if(error_dev->FromGPU(GetStream(), error.data()) != 0) + std::cerr << "Error copying (error_dev) from GPU, size: " << error_dev->GetSize() + << std::endl; + + return miopenStatusSuccess; +} + +template +int GetitemDriver::RunBackwardCPU() +{ + mloGetitemBackwardRunHost(dyDesc, + indexDescs.size(), + indexDescs.data(), + dxDesc, + errorDesc, + dy.data(), + indexs_ptr.data(), + dxhost.data(), + errorhost.data(), + dims.size(), + dims.data(), + slices.size(), + slices_flat.data(), + offset); + + return miopenStatusSuccess; +} + +template +Tref GetitemDriver::GetTolerance() +{ + // Computation error of fp16 is ~2^13 (=8192) bigger than + // the one of fp32 because mantissa is shorter by 13 bits. + // In the case of layernorm, there is a cumulative sum operation, and in the case of + // floating point operation, the result value can change if the order of the summed values + // is changed. So apply a threshold that is 10 times larger than other operations. + auto tolerance = std::is_same::value ? 1.5e-4 : 8.2e-1; + + // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. + // If there is an atomic operation on the GPU kernel, a large error occurs depending on the + // calculation order, so it is multiplied by 10 times. + if(std::is_same::value) + tolerance *= 8000.0; + return tolerance; +} + +template +int GetitemDriver::VerifyForward() +{ + return miopenStatusSuccess; +} + +template +int GetitemDriver::VerifyBackward() +{ + RunBackwardCPU(); + const Tref tolerance = GetTolerance(); + + auto error_dx = miopen::rms_range(dxhost, dx); + + if(!std::isfinite(error_dx) || error_dx > tolerance) + { + std::cout << "Backward Getitem FAILED: " << error_dx << " > " << tolerance << std::endl; + return EC_VerifyBwd; + } + else + { + std::cout << "Backward Getitem Verifies OK on CPU reference (" << error_dx << " < " + << tolerance << ')' << std::endl; + } + + auto error_error = miopen::rms_range(errorhost, error); + + if(!std::isfinite(error_error) || std::abs(static_cast(error_error)) != 0.0f) + { + std::cout << "Backward Getitem FAILED: Result does not equal" << std::endl; + return EC_VerifyBwd; + } + else + { + std::cout << "Backward Getitem Verifies OK on CPU and GPU" << std::endl; + } + + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_GETITEM_DRIVER_HPP diff --git a/driver/groupnorm_driver.hpp b/driver/groupnorm_driver.hpp index c143496cdd..1e97f541a0 100644 --- a/driver/groupnorm_driver.hpp +++ b/driver/groupnorm_driver.hpp @@ -110,8 +110,8 @@ class GroupNormDriver : public Driver std::vector weight; std::vector bias; std::vector out; - std::vector mean; - std::vector rstd; + std::vector mean; + std::vector rstd; std::vector outhost; std::vector meanhost; std::vector rstdhost; @@ -158,14 +158,14 @@ template int GroupNormDriver::AddCmdLineArgs() { inflags.AddInputFlag("forw", 'F', "1", "Run only Forward GroupNorm (Default=1)", "int"); - inflags.AddInputFlag("batchsize", 'n', "100", "Mini-batch size (Default=100)", "int"); - inflags.AddInputFlag("in_channels", 'c', "6", "Number of Input Channels (Default=6)", "int"); - inflags.AddInputFlag("in_d", 'D', "0", "Input Depth (Default=0)", "int"); - inflags.AddInputFlag("in_h", 'H', "32", "Input Height (Default=32)", "int"); - inflags.AddInputFlag("in_w", 'W', "32", "Input Width (Default=32)", "int"); + inflags.AddInputFlag("batchsize", 'n', "32", "Mini-batch size (Default=100)", "int"); + inflags.AddInputFlag("in_channels", 'c', "32", "Number of Input Channels (Default=6)", "int"); + inflags.AddInputFlag("in_d", 'D', "14", "Input Depth (Default=0)", "int"); + inflags.AddInputFlag("in_h", 'H', "14", "Input Height (Default=32)", "int"); + inflags.AddInputFlag("in_w", 'W', "14", "Input Width (Default=32)", "int"); inflags.AddInputFlag("eps", 'e', "0.00001", "Alpha (Default=0.00001)", "double"); - inflags.AddInputFlag("num_groups", 'g', "3", "num_groups", "int"); + inflags.AddInputFlag("num_groups", 'g', "4", "num_groups", "int"); inflags.AddInputFlag( "mode", 'm', "0", "elemwise affine mode (0), weight and bias mode (1) (Default=0)", "int"); @@ -224,15 +224,15 @@ int GroupNormDriver::AllocateBuffersAndCopy() weight_dev = std::unique_ptr(new GPUMem(ctx, weight_sz, sizeof(Tgpu))); bias_dev = std::unique_ptr(new GPUMem(ctx, bias_sz, sizeof(Tgpu))); out_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(Tgpu))); - mean_dev = std::unique_ptr(new GPUMem(ctx, mean_sz, sizeof(Tref))); - rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tref))); + mean_dev = std::unique_ptr(new GPUMem(ctx, mean_sz, sizeof(Tgpu))); + rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tgpu))); in = std::vector(in_sz, static_cast(0)); weight = std::vector(weight_sz, static_cast(0)); bias = std::vector(bias_sz, static_cast(0)); out = std::vector(out_sz, static_cast(0)); - mean = std::vector(mean_sz, static_cast(0)); - rstd = std::vector(rstd_sz, static_cast(0)); + mean = std::vector(mean_sz, static_cast(0)); + rstd = std::vector(rstd_sz, static_cast(0)); outhost = std::vector(out_sz, static_cast(0)); meanhost = std::vector(mean_sz, static_cast(0)); rstdhost = std::vector(rstd_sz, static_cast(0)); @@ -347,23 +347,14 @@ int GroupNormDriver::RunBackwardGPU() template Tref GroupNormDriver::GetTolerance() { - if(data_type == miopenHalf) - { - return 1e-3; - } - else if(data_type == miopenFloat) - { - return 5e-5; - } - else if(data_type == miopenDouble) - { - return 1e-10; - } - else if(data_type == miopenBFloat16) - { - return 5e-3; - } - return 0; + // Computation error of fp16 is ~2^13 (=8192) bigger than + // the one of fp32 because mantissa is shorter by 13 bits. + auto tolerance = std::is_same::value ? 1.5e-6 : 8.2e-3; + + // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. + if(std::is_same::value) + tolerance *= 8.0; + return tolerance; } template diff --git a/driver/layernorm_driver.hpp b/driver/layernorm_driver.hpp index 59b19f3029..5bdf82ce85 100644 --- a/driver/layernorm_driver.hpp +++ b/driver/layernorm_driver.hpp @@ -56,10 +56,11 @@ int32_t mloLayerNormForwardRunHost(miopenTensorDescriptor_t inputDesc, auto dims = miopen::deref(inputDesc).GetLengths(); size_t outer_size = 1; size_t inner_size = 1; + size_t norm_dim = static_cast(normalized_dim); for(size_t i = 0ULL; i < dims.size(); ++i) { - if(i < normalized_dim) + if(i < norm_dim) outer_size *= dims[i]; else inner_size *= dims[i]; @@ -87,8 +88,9 @@ int32_t mloLayerNormForwardRunHost(miopenTensorDescriptor_t inputDesc, for(int32_t i = 0; i < inner_size; i++) { - Tcheck pweight = mode ? static_cast(weight[i]) : 1; - Tcheck pbias = mode ? static_cast(bias[i]) : 0; + Tcheck pweight = + (mode == MIOPEN_ELEMENTWISE_AFFINE) ? 1 : static_cast(weight[i]); + Tcheck pbias = (mode == MIOPEN_ELEMENTWISE_AFFINE) ? 0 : static_cast(bias[i]); outputhost[o * inner_size + i] = (static_cast(input[o * inner_size + i]) - pmean) * prstd * pweight + pbias; } @@ -164,8 +166,8 @@ class LayerNormDriver : public Driver std::vector weight; std::vector bias; std::vector out; - std::vector mean; - std::vector rstd; + std::vector mean; + std::vector rstd; std::vector outhost; std::vector meanhost; std::vector rstdhost; @@ -190,10 +192,15 @@ int LayerNormDriver::ParseCmdLineArgs(int argc, char* argv[]) template int LayerNormDriver::GetandSetData() { - std::vector in_len = GetInputTensorLengthsFromCmdLine(); + auto inTensorParam = inflags.GetValueTensor("input"); + + auto in_len = inTensorParam.lengths; dim = inflags.GetValueInt("normalized_dim"); + MIOPEN_THROW_IF(dim < 0 || static_cast(dim) >= in_len.size(), + "normalized_dim out of range"); + std::vector inner_len; if(dim == in_len.size()) inner_len = {1}; @@ -206,12 +213,23 @@ int LayerNormDriver::GetandSetData() else outer_len = {in_len.begin(), in_len.end() - (in_len.size() - dim)}; - SetTensorNd(inputDesc, in_len, data_type); - SetTensorNd(weightDesc, inner_len, data_type); - SetTensorNd(biasDesc, inner_len, data_type); - SetTensorNd(outputDesc, in_len, data_type); - SetTensorNd(meanDesc, outer_len, data_type); - SetTensorNd(rstdDesc, outer_len, data_type); + if(SetTensorNd(inputDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing input tensor: " + inflags.GetValueStr("input") + "."); + + if(SetTensorNd(weightDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting weight tensor."); + + if(SetTensorNd(biasDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting bias tensor."); + + if(SetTensorNd(outputDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting doutput tensor."); + + if(SetTensorNd(meanDesc, outer_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting mean tensor."); + + if(SetTensorNd(rstdDesc, outer_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting rstd tensor."); eps = static_cast(inflags.GetValueDouble("eps")); mode = miopenNormMode_t(inflags.GetValueInt("mode")); @@ -223,11 +241,7 @@ template int LayerNormDriver::AddCmdLineArgs() { inflags.AddInputFlag("forw", 'F', "1", "Run only Forward LayerNorm (Default=1)", "int"); - inflags.AddInputFlag("batchsize", 'n', "100", "Mini-batch size (Default=100)", "int"); - inflags.AddInputFlag("in_channels", 'c', "3", "Number of Input Channels (Default=3)", "int"); - inflags.AddInputFlag("in_d", 'D', "0", "Input Depth (Default=0)", "int"); - inflags.AddInputFlag("in_h", 'H', "32", "Input Height (Default=32)", "int"); - inflags.AddInputFlag("in_w", 'W', "32", "Input Width (Default=32)", "int"); + inflags.AddTensorFlag("input", 'X', "100x3x32x32", "input tensor descriptor"); inflags.AddInputFlag("eps", 'e', "0.00001", "Alpha (Default=0.00001)", "double"); inflags.AddInputFlag("normalized_dim", 'o', "3", "Nomalized Dim (Default=3)", "int"); @@ -243,55 +257,18 @@ int LayerNormDriver::AddCmdLineArgs() return miopenStatusSuccess; } -template -std::vector LayerNormDriver::GetInputTensorLengthsFromCmdLine() -{ - int in_n = inflags.GetValueInt("batchsize"); - int in_c = inflags.GetValueInt("in_channels"); - int in_w = inflags.GetValueInt("in_w"); - int in_h = inflags.GetValueInt("in_h"); - int in_d = inflags.GetValueInt("in_d"); - - if((in_n != 0) && (in_c != 0) && (in_d != 0) && (in_h != 0) && (in_w != 0)) - { - dim_size = 5; - return std::vector({in_n, in_c, in_d, in_h, in_w}); - } - else if((in_n != 0) && (in_c != 0) && (in_h != 0) && (in_w != 0)) - { - dim_size = 4; - return std::vector({in_n, in_c, in_h, in_w}); - } - else if((in_n != 0) && (in_c != 0) && (in_w != 0)) - { - dim_size = 3; - return std::vector({in_n, in_c, in_w}); - } - else if((in_n != 0) && (in_w != 0)) - { - dim_size = 2; - return std::vector({in_n, in_w}); - } - else if(in_n != 0) - { - return std::vector({in_n}); - } - else - { - std::cout << "Error Input Tensor Lengths\n" << std::endl; - return std::vector({0}); - } -} - template int LayerNormDriver::AllocateBuffersAndCopy() { - size_t in_sz = GetTensorSize(inputDesc); - size_t weight_sz = GetTensorSize(weightDesc); - size_t bias_sz = GetTensorSize(biasDesc); - size_t out_sz = GetTensorSize(outputDesc); - size_t mean_sz = GetTensorSize(meanDesc); - size_t rstd_sz = GetTensorSize(rstdDesc); + const Tgpu Tgpu0val = static_cast(0.0); + const Tgpu Tgpu1val = static_cast(1.0); + const Tref Tref0ref = static_cast(0.0); + size_t in_sz = GetTensorSize(inputDesc); + size_t weight_sz = GetTensorSize(weightDesc); + size_t bias_sz = GetTensorSize(biasDesc); + size_t out_sz = GetTensorSize(outputDesc); + size_t mean_sz = GetTensorSize(meanDesc); + size_t rstd_sz = GetTensorSize(rstdDesc); uint32_t ctx = 0; @@ -299,22 +276,22 @@ int LayerNormDriver::AllocateBuffersAndCopy() weight_dev = std::unique_ptr(new GPUMem(ctx, weight_sz, sizeof(Tgpu))); bias_dev = std::unique_ptr(new GPUMem(ctx, bias_sz, sizeof(Tgpu))); out_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(Tgpu))); - mean_dev = std::unique_ptr(new GPUMem(ctx, mean_sz, sizeof(Tref))); - rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tref))); - - in = std::vector(in_sz, static_cast(0)); - weight = std::vector(weight_sz, static_cast(0)); - bias = std::vector(bias_sz, static_cast(0)); - out = std::vector(out_sz, static_cast(0)); - mean = std::vector(mean_sz, static_cast(0)); - rstd = std::vector(rstd_sz, static_cast(0)); - outhost = std::vector(out_sz, static_cast(0)); - meanhost = std::vector(mean_sz, static_cast(0)); - rstdhost = std::vector(rstd_sz, static_cast(0)); + mean_dev = std::unique_ptr(new GPUMem(ctx, mean_sz, sizeof(Tgpu))); + rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tgpu))); + + in = std::vector(in_sz, Tgpu0val); + weight = std::vector(weight_sz, Tgpu0val); + bias = std::vector(bias_sz, Tgpu0val); + out = std::vector(out_sz, Tgpu0val); + mean = std::vector(mean_sz, Tgpu0val); + rstd = std::vector(rstd_sz, Tgpu0val); + outhost = std::vector(out_sz, Tref0ref); + meanhost = std::vector(mean_sz, Tref0ref); + rstdhost = std::vector(rstd_sz, Tref0ref); for(int i = 0; i < in_sz; i++) { - in[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); + in[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); } if(in_dev->ToGPU(GetStream(), in.data()) != 0) @@ -325,7 +302,7 @@ int LayerNormDriver::AllocateBuffersAndCopy() if(mode == MIOPEN_ELEMENTWISE_AFFINE) weight[i] = static_cast(1); else - weight[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); + weight[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); } if(weight_dev->ToGPU(GetStream(), weight.data()) != 0) @@ -334,9 +311,9 @@ int LayerNormDriver::AllocateBuffersAndCopy() for(int i = 0; i < bias_sz; i++) { if(mode == MIOPEN_ELEMENTWISE_AFFINE) - bias[i] = static_cast(0); + bias[i] = Tgpu0val; else - bias[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); + bias[i] = prng::gen_A_to_B(Tgpu0val, Tgpu1val); } if(bias_dev->ToGPU(GetStream(), bias.data()) != 0) std::cerr << "Error copying (bias) to GPU, size: " << bias_dev->GetSize() << std::endl; @@ -479,7 +456,7 @@ int LayerNormDriver::VerifyForward() } else { - std::cout << "Forward LayerNorm mean Verifies OK on CPU reference (" << error << " < " + std::cout << "Forward LayerNorm mean Verifies OK on CPU reference (" << meanerror << " < " << tolerance << ')' << std::endl; } diff --git a/driver/random.hpp b/driver/random.hpp index 538f0820a6..804fc94db4 100644 --- a/driver/random.hpp +++ b/driver/random.hpp @@ -7,7 +7,10 @@ #include #include -MIOPEN_DECLARE_ENV_VAR(MIOPEN_DEBUG_DRIVER_PRNG_SEED, uint64_t, 12345678) +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_DRIVER_PRNG_SEED, 12345678) + +namespace env = miopen::env; + namespace prng { namespace details { using glibc_gen = std::linear_congruential_engine; @@ -15,7 +18,7 @@ using glibc_gen = std::linear_congruential_engine #include -#if !defined(_WIN32) #include -#else -#include -#endif #include #include diff --git a/driver/reduceextreme_driver.hpp b/driver/reduceextreme_driver.hpp new file mode 100644 index 0000000000..7f5fbbc301 --- /dev/null +++ b/driver/reduceextreme_driver.hpp @@ -0,0 +1,461 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP +#define GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP + +#include "InputFlags.hpp" +#include "driver.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" +#include "random.hpp" +#include +#include +#include +#include +#include +#include +#include +#include +#include <../test/tensor_holder.hpp> +#include <../test/verify.hpp> +#include "../src/kernels/MIOpenReduceExtreme.hpp" + +template +bool compare_equal(T r1, T r2) +{ + return r1 == r2; +} + +template +int32_t mloReduceExtremeForwardRunHost(miopenTensorDescriptor_t xDesc, + miopenTensorDescriptor_t yDesc, + miopenTensorDescriptor_t indiceDesc, + Tgpu* x, + Tcheck* yhost, + int32_t* indicehost, + int32_t dim) +{ + auto x_dims = miopen::deref(xDesc).GetLengths(); + std::vector indice_dims; + if(yhost) + indice_dims = miopen::deref(yDesc).GetLengths(); + else + indice_dims = miopen::deref(indiceDesc).GetLengths(); + + int32_t reduce_size = static_cast(x_dims[dim]); + auto indice_numel = + std::accumulate(indice_dims.begin(), indice_dims.end(), 1LL, std::multiplies()); + + auto inner_size = + std::accumulate(x_dims.begin() + dim + 1, x_dims.end(), 1ULL, std::multiplies()); + + int32_t ret = miopenStatusSuccess; + + for(size_t o = 0; o < indice_numel; ++o) + { + size_t x_idx = (o / inner_size) * inner_size * reduce_size + o % inner_size; + + int32_t extreme_idx = 0; + Tcheck extreme = static_cast(x[x_idx]); + + for(int32_t i = 1; i < reduce_size; ++i) + { + x_idx += inner_size; + Tcheck val = static_cast(x[x_idx]); + reduce_func{}.calculate(extreme, val, extreme_idx, i); + } + indicehost[o] = extreme_idx; + if(yhost) + yhost[o] = extreme; + } + return ret; +} + +template +class ReduceExtremeDriver : public Driver +{ +public: + ReduceExtremeDriver() : Driver() + { + miopenCreateTensorDescriptor(&xDesc); + miopenCreateTensorDescriptor(&yDesc); + miopenCreateTensorDescriptor(&indiceDesc); + + data_type = miopen_type{}; + indice_data_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + int RunForwardCPU(); + + int RunBackwardGPU() override; + + Tref GetTolerance(); + int VerifyBackward() override; + int VerifyForward() override; + ~ReduceExtremeDriver() override + { + miopenDestroyTensorDescriptor(xDesc); + miopenDestroyTensorDescriptor(yDesc); + miopenDestroyTensorDescriptor(indiceDesc); + } + +private: + InputFlags inflags; + + int forw; + + miopenTensorDescriptor_t xDesc; + miopenTensorDescriptor_t yDesc; + miopenTensorDescriptor_t indiceDesc; + + std::unique_ptr x_dev; + std::unique_ptr indice_dev; + std::unique_ptr y_dev; + + std::vector x; + std::vector y; + std::vector yhost; + std::vector indice; + std::vector indicehost; + + int dim; + miopenReduceExtremeOp_t reduceExtremeOp; + + miopenDataType_t indice_data_type; +}; + +template +int ReduceExtremeDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + + if((static_cast(inflags.GetValueInt("ReduceExtremeOp")) < + ReduceExtremeOp_t::First_) || + (static_cast(inflags.GetValueInt("ReduceExtremeOp")) > + ReduceExtremeOp_t::Last_)) + { + std::cerr << "Error ReduceExtremeOp(1-4)" << std::endl; + return miopenStatusBadParm; + } + + auto inTensorParam = inflags.GetValueTensor("input"); + + if((inflags.GetValueInt("DimToReduce") < 0) || + (inflags.GetValueInt("DimToReduce") > inTensorParam.lengths.size() - 1)) + { + std::cerr << "Error DimToReduce(0-" << inTensorParam.lengths.size() - 1 << ")" << std::endl; + return miopenStatusBadParm; + } + + return miopenStatusSuccess; +} + +template +int ReduceExtremeDriver::GetandSetData() +{ + auto inTensorParam = inflags.GetValueTensor("input"); + auto in_len = inTensorParam.lengths; + + dim = inflags.GetValueInt("DimToReduce"); + reduceExtremeOp = static_cast(inflags.GetValueInt("ReduceExtremeOp")); + + std::vector out_len; + + for(int i = 0; i < in_len.size(); ++i) + { + if(i != dim) + { + out_len.push_back(in_len[i]); + } + } + + if(out_len.empty()) + out_len.push_back(1); + + if(SetTensorNd(xDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing x tensor: " + inflags.GetValueStr("input") + "."); + + if(SetTensorNd(yDesc, out_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting y tensor."); + + if(SetTensorNd(indiceDesc, out_len, indice_data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting indice tensor."); + + return 0; +} + +template +int ReduceExtremeDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "1", "Run only Forward ReduceExtreme (Default=1)", "int"); + inflags.AddTensorFlag("input", 'X', "21x500x375", "input tensor descriptor"); + inflags.AddInputFlag( + "DimToReduce", 'R', "0", "The indice of the dimensions to be reduced(Default=1)", "int"); + inflags.AddInputFlag("ReduceExtremeOp", + 'O', + "1", + "Reduce Extreme Operation Type (check the enum miopenReduceExtremeOp_t in " + "miopen.h) (Default=1 to Find the the minimum index)", + "int"); + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +int ReduceExtremeDriver::AllocateBuffersAndCopy() +{ + size_t in_sz = GetTensorSize(xDesc); + size_t out_sz = GetTensorSize(yDesc); + + uint32_t ctx = 0; + + x_dev = std::unique_ptr(new GPUMem(ctx, in_sz, sizeof(Tgpu))); + indice_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(int32_t))); + + x = std::vector(in_sz, static_cast(0)); + indice = std::vector(out_sz, static_cast(0)); + indicehost = std::vector(out_sz, static_cast(0)); + + for(int32_t i = 0; i < in_sz; ++i) + { + x[i] = prng::gen_A_to_B(static_cast(-1.0), static_cast(1.0)); + } + + if(x_dev->ToGPU(GetStream(), x.data()) != 0) + { + std::cerr << "Error copying (x) to GPU, size: " << x_dev->GetSize() << std::endl; + return miopenStatusAllocFailed; + } + if(indice_dev->ToGPU(GetStream(), indice.data()) != 0) + { + std::cerr << "Error copying (indice) to GPU, size: " << indice_dev->GetSize() << std::endl; + return miopenStatusAllocFailed; + } + if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || + (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) + { + y_dev = std::unique_ptr(new GPUMem(ctx, out_sz, sizeof(Tgpu))); + y = std::vector(out_sz, static_cast(0)); + yhost = std::vector(out_sz, static_cast(0)); + + if(y_dev->ToGPU(GetStream(), y.data()) != 0) + { + std::cerr << "Error copying (y) to GPU, size: " << y_dev->GetSize() << std::endl; + return miopenStatusAllocFailed; + } + } + + return miopenStatusSuccess; +} + +template +int ReduceExtremeDriver::RunForwardGPU() +{ + float kernel_total_time = 0; + float kernel_first_time = 0; + + Timer t; + START_TIME + + for(int32_t i = 0; i < inflags.GetValueInt("iter"); ++i) + { + if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || + (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) + { + miopenReduceExtremeForward(GetHandle(), + xDesc, + x_dev->GetMem(), + dim, + reduceExtremeOp, + yDesc, + y_dev->GetMem(), + indiceDesc, + indice_dev->GetMem()); + } + else + { + miopenReduceExtremeForward(GetHandle(), + xDesc, + x_dev->GetMem(), + dim, + reduceExtremeOp, + nullptr, + nullptr, + indiceDesc, + indice_dev->GetMem()); + } + + float time = 0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + int32_t iter = inflags.GetValueInt("iter"); + if(WALL_CLOCK) + std::cout << "Wall-clock Time Forward ReduceExtreme Elapsed: " << t.gettime_ms() / iter + << " ms" << std::endl; + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + std::cout << "GPU Kernel Time Forward ReduceExtreme Elapsed: " << kernel_average_time + << " ms" << std::endl; + } + + if(indice_dev->FromGPU(GetStream(), indice.data()) != 0) + { + std::cerr << "Error copying (indice_dev) from GPU, size: " << indice_dev->GetSize() + << std::endl; + return miopenStatusInternalError; + } + if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || + (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) + { + if(y_dev->FromGPU(GetStream(), y.data()) != 0) + { + std::cerr << "Error copying (y_dev) from GPU, size: " << y_dev->GetSize() << std::endl; + return miopenStatusInternalError; + } + } + + return miopenStatusSuccess; +} + +template +int ReduceExtremeDriver::RunForwardCPU() +{ + if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_ARGMIN) + { + return mloReduceExtremeForwardRunHost( + xDesc, nullptr, indiceDesc, x.data(), nullptr, indicehost.data(), dim); + } + else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_ARGMAX) + { + return mloReduceExtremeForwardRunHost( + xDesc, nullptr, indiceDesc, x.data(), nullptr, indicehost.data(), dim); + } + else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) + { + return mloReduceExtremeForwardRunHost( + xDesc, yDesc, indiceDesc, x.data(), yhost.data(), indicehost.data(), dim); + } + else if(reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX) + { + return mloReduceExtremeForwardRunHost( + xDesc, yDesc, indiceDesc, x.data(), yhost.data(), indicehost.data(), dim); + } + + return miopenStatusInternalError; +} + +template +int ReduceExtremeDriver::RunBackwardGPU() +{ + return miopenStatusSuccess; +} + +template +Tref ReduceExtremeDriver::GetTolerance() +{ + // Computation error of fp16 is ~2^13 (=8192) bigger than + // the one of fp32 because mantissa is shorter by 13 bits. + auto tolerance = std::is_same::value ? 1.5e-6 : 8.2e-3; + + // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. + if(std::is_same::value) + tolerance *= 8.0; + return tolerance; +} + +template +int ReduceExtremeDriver::VerifyForward() +{ + RunForwardCPU(); + + if((reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MIN) || + (reduceExtremeOp == MIOPEN_REDUCE_EXTREME_MAX)) + { + const Tref tolerance = GetTolerance(); + auto error = miopen::rms_range(yhost, y); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Forward ReduceExtreme FAILED: " << error << " > " << tolerance + << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward ReduceExtreme Verifies on CPU (" << error << " < " << tolerance + << ')' << std::endl; + } + } + auto error_idx = miopen::mismatch_idx(indicehost, indice, compare_equal); + + if(error_idx < miopen::range_distance(indicehost)) + { + std::cout << "Forward ReduceExtreme FAILED: Indice does not equal at " << error_idx + << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward ReduceExtreme Incide Verifies on CPU and GPU" << std::endl; + } + + return miopenStatusSuccess; +} + +template +int ReduceExtremeDriver::VerifyBackward() +{ + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_REDUCEEXTREME_DRIVER_HPP diff --git a/driver/rnn_driver.hpp b/driver/rnn_driver.hpp index f93b719336..03da917e0a 100644 --- a/driver/rnn_driver.hpp +++ b/driver/rnn_driver.hpp @@ -618,7 +618,7 @@ int RNNDriver::AllocateBuffersAndCopy() int nseq = inflags.GetValueInt("seq_len"); std::vector in_n = GetInputTensorLengthsFromCmdLine(); std::size_t inputBatchLenSum; - inputBatchLenSum = std::accumulate(in_n.begin(), in_n.begin() + nseq, 0); + inputBatchLenSum = std::accumulate(in_n.begin(), in_n.begin() + nseq, 0ULL); int hid_h = inflags.GetValueInt("hid_h"); int layer = inflags.GetValueInt("num_layer"); diff --git a/driver/rnn_seq_driver.hpp b/driver/rnn_seq_driver.hpp index 2d5636e062..8d5b720960 100644 --- a/driver/rnn_seq_driver.hpp +++ b/driver/rnn_seq_driver.hpp @@ -766,7 +766,7 @@ inline size_t Get3DNoVECTensorSize(miopenTensorDescriptor_t& tensor) assert(miopen::deref(tensor).IsPacked() && "GetTensorSize should not be used on an unpacked tensor."); const auto len = GetTensorLengths(tensor); - size_t sz = std::accumulate(len.begin(), len.end(), 1, std::multiplies()); + size_t sz = std::accumulate(len.begin(), len.end(), 1ULL, std::multiplies()); return sz; } @@ -827,7 +827,7 @@ int RNNSeqDriver::AllocateBuffersAndCopy() const std::vector out_lens = GetOutputTensorLengthsFromCmdLine(); const size_t vectors_cnt_host = - std::accumulate(sorted_seq_lens.begin(), sorted_seq_lens.end(), 0); + std::accumulate(sorted_seq_lens.begin(), sorted_seq_lens.end(), 0ULL); const size_t vectors_cnt_gpu = io_layout == miopenRNNDataSeqMajorNotPadded ? vectors_cnt_host : in_lens[0] * in_lens[1]; diff --git a/driver/rocrand_wrapper.hpp b/driver/rocrand_wrapper.hpp index e3202425ef..fc680cf98c 100644 --- a/driver/rocrand_wrapper.hpp +++ b/driver/rocrand_wrapper.hpp @@ -34,11 +34,7 @@ // definitions are different. #include -#if !defined(_WIN32) #include -#else -#include -#endif #include #include diff --git a/driver/sum_driver.hpp b/driver/sum_driver.hpp index b348d3a22f..830b89c1dd 100644 --- a/driver/sum_driver.hpp +++ b/driver/sum_driver.hpp @@ -47,18 +47,18 @@ template int32_t mloSumForwardRunHost(miopenTensorDescriptor_t inputDesc, - miopenTensorDescriptor_t outputDesc, + miopenTensorDescriptor_t yDesc, Tgpu* input, Tcheck* outputhost, int32_t dim, miopenSumNanPropagation_t nanPropagation) { auto input_dims = miopen::deref(inputDesc).GetLengths(); - auto output_dims = miopen::deref(outputDesc).GetLengths(); + auto output_dims = miopen::deref(yDesc).GetLengths(); auto reduce_size = input_dims[dim]; auto output_numel = - std::accumulate(output_dims.begin(), output_dims.end(), 1L, std::multiplies()); + std::accumulate(output_dims.begin(), output_dims.end(), 1LL, std::multiplies()); auto inner_size = 1ULL; for(int32_t i = dim + 1; i < input_dims.size(); i++) @@ -96,7 +96,7 @@ class SumDriver : public Driver SumDriver() : Driver() { miopenCreateTensorDescriptor(&inputDesc); - miopenCreateTensorDescriptor(&outputDesc); + miopenCreateTensorDescriptor(&yDesc); data_type = miopen_type{}; } @@ -121,7 +121,7 @@ class SumDriver : public Driver ~SumDriver() override { miopenDestroyTensorDescriptor(inputDesc); - miopenDestroyTensorDescriptor(outputDesc); + miopenDestroyTensorDescriptor(yDesc); } private: @@ -130,7 +130,7 @@ class SumDriver : public Driver int forw; miopenTensorDescriptor_t inputDesc; - miopenTensorDescriptor_t outputDesc; + miopenTensorDescriptor_t yDesc; std::unique_ptr in_dev; std::unique_ptr out_dev; @@ -179,7 +179,7 @@ int SumDriver::GetandSetData() if(out_len.empty()) out_len.push_back(1); - SetTensorNd(outputDesc, out_len, data_type); + SetTensorNd(yDesc, out_len, data_type); nanPropagation = static_cast(inflags.GetValueInt("NanPropagation")); @@ -253,9 +253,9 @@ template int SumDriver::AllocateBuffersAndCopy() { size_t in_sz = GetTensorSize(inputDesc); - size_t out_sz = GetTensorSize(outputDesc); + size_t out_sz = GetTensorSize(yDesc); - miopenGetSumWorkspaceSize(GetHandle(), inputDesc, dim, outputDesc, &ws_sizeInBytes); + miopenGetSumWorkspaceSize(GetHandle(), inputDesc, dim, yDesc, &ws_sizeInBytes); if(ws_sizeInBytes == static_cast(-1)) return miopenStatusAllocFailed; @@ -301,7 +301,7 @@ int SumDriver::RunForwardGPU() inputDesc, in_dev->GetMem(), dim, - outputDesc, + yDesc, out_dev->GetMem()); float time = 0.0; @@ -334,7 +334,7 @@ template int SumDriver::RunForwardCPU() { mloSumForwardRunHost( - inputDesc, outputDesc, in.data(), outhost.data(), dim, nanPropagation); + inputDesc, yDesc, in.data(), outhost.data(), dim, nanPropagation); return miopenStatusSuccess; } diff --git a/driver/t5layernorm_driver.hpp b/driver/t5layernorm_driver.hpp new file mode 100644 index 0000000000..3d02a2c3f4 --- /dev/null +++ b/driver/t5layernorm_driver.hpp @@ -0,0 +1,633 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_T5LAYERNORM_DRIVER_HPP +#define GUARD_MIOPEN_T5LAYERNORM_DRIVER_HPP + +#include <../test/tensor_holder.hpp> +#include <../test/verify.hpp> +#include "InputFlags.hpp" +#include "driver.hpp" +#include "random.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" +#include +#include +#include +#include +#include +#include +#include + +template +int32_t mloT5LayerNormForwardRunHost(miopenTensorDescriptor_t xDesc, + Tgpu* x, + Tgpu* weight, + Tcheck* yhost, + Tcheck* rstdhost, + float eps, + miopenNormMode_t mode) +{ + auto dims = miopen::deref(xDesc).GetLengths(); + size_t outer_size = 1; + size_t inner_size = dims[dims.size() - 1]; + + for(size_t i = 0ULL; i < dims.size() - 1; ++i) + { + outer_size *= dims[i]; + } + + int32_t ret = 0; + + for(int32_t o = 0; o < outer_size; o++) + { + Tcheck pvar = static_cast(0); + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck tmp = static_cast(x[o * inner_size + i]); + pvar += tmp * tmp; + } + + pvar = pvar / inner_size; + Tcheck prstd = static_cast(1.0) / sqrt(pvar + eps); + + rstdhost[o] = prstd; + + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) + ? static_cast(1) + : static_cast(weight[i]); + yhost[o * inner_size + i] = + (static_cast(x[o * inner_size + i])) * prstd * pweight; + } + } + return ret; +} + +template +int32_t mloT5LayerNormBackwardRunHost(miopenTensorDescriptor_t dyDesc, + Tgpu* dy, + Tgpu* x, + Tgpu* weight, + Tcheck* rstdhost, + Tcheck* dxhost, + miopenNormMode_t mode) +{ + auto dims = miopen::deref(dyDesc).GetLengths(); + size_t outer_size = 1; + size_t inner_size = dims[dims.size() - 1]; + + for(size_t i = 0ULL; i < dims.size() - 1; ++i) + { + outer_size *= dims[i]; + } + + int32_t ret = 0; + + for(int32_t o = 0; o < outer_size; o++) + { + Tcheck sum = static_cast(0); + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) + ? static_cast(1) + : static_cast(weight[i]); + Tcheck pdy = dy ? static_cast(dy[o * inner_size + i]) : static_cast(0); + Tcheck px = static_cast(x[o * inner_size + i]); + sum += pdy * px * pweight; + } + + Tcheck ds = sum; + Tcheck s = static_cast(1) / inner_size; + Tcheck prstd = rstdhost[o]; + Tcheck a = ds * prstd * prstd * prstd * s; + + for(int32_t i = 0; i < inner_size; i++) + { + Tcheck pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) + ? static_cast(1) + : static_cast(weight[i]); + Tcheck pdy = dy ? static_cast(dy[o * inner_size + i]) : static_cast(0); + + Tcheck val = prstd * pdy * pweight - a * static_cast(x[o * inner_size + i]); + dxhost[o * inner_size + i] = static_cast(val); + } + } + return ret; +} + +template +int32_t mloT5LayerNormBackckwardweightRunHost( + miopenTensorDescriptor_t dyDesc, Tgpu* dy, Tgpu* x, Tcheck* rstdhost, Tcheck* dwhost) +{ + auto dims = miopen::deref(dyDesc).GetLengths(); + size_t outer_size = 1; + size_t inner_size = dims[dims.size() - 1]; + + for(size_t i = 0ULL; i < dims.size() - 1; ++i) + { + outer_size *= dims[i]; + } + + int32_t ret = 0; + + for(int32_t o = 0; o < inner_size; o++) + { + Tcheck sum = static_cast(0); + for(uint64_t i = 0; i < outer_size; ++i) + { + Tcheck prstd = static_cast(rstdhost[i]); + Tcheck pdy = dy ? static_cast(dy[i * inner_size + o]) : 0; + Tcheck px = static_cast(x[i * inner_size + o]); + + sum += pdy * px * prstd; + } + + dwhost[o] = sum; + } + return ret; +} + +template +class T5LayerNormDriver : public Driver +{ +public: + T5LayerNormDriver() : Driver() + { + miopenCreateTensorDescriptor(&xDesc); + miopenCreateTensorDescriptor(&weightDesc); + miopenCreateTensorDescriptor(&yDesc); + miopenCreateTensorDescriptor(&rstdDesc); + miopenCreateTensorDescriptor(&dyDesc); + miopenCreateTensorDescriptor(&dxDesc); + miopenCreateTensorDescriptor(&dwDesc); + + data_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + std::vector GetInputTensorLengthsFromCmdLine(); + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + int RunForwardCPU(); + + int RunBackwardGPU() override; + int RunBackwardCPU(); + + Tref GetTolerance(); + int VerifyBackward() override; + int VerifyForward() override; + ~T5LayerNormDriver() override + { + + miopenDestroyTensorDescriptor(xDesc); + miopenDestroyTensorDescriptor(weightDesc); + miopenDestroyTensorDescriptor(yDesc); + miopenDestroyTensorDescriptor(rstdDesc); + miopenDestroyTensorDescriptor(dyDesc); + miopenDestroyTensorDescriptor(dxDesc); + miopenDestroyTensorDescriptor(dwDesc); + } + +private: + InputFlags inflags; + + int forw; + int dim_size; + + miopenTensorDescriptor_t xDesc; + miopenTensorDescriptor_t weightDesc; + miopenTensorDescriptor_t yDesc; + miopenTensorDescriptor_t rstdDesc; + miopenTensorDescriptor_t dyDesc; + miopenTensorDescriptor_t dxDesc; + miopenTensorDescriptor_t dwDesc; + + std::unique_ptr x_dev; + std::unique_ptr weight_dev; + std::unique_ptr y_dev; + std::unique_ptr rstd_dev; + std::unique_ptr dy_dev; + std::unique_ptr dx_dev; + std::unique_ptr dw_dev; + std::unique_ptr workspace_dev; + + std::vector x; + std::vector weight; + std::vector y; + std::vector rstd; + std::vector yhost; + std::vector rstdhost; + std::vector dy; + std::vector dx; + std::vector dw; + std::vector dxhost; + std::vector dwhost; + + size_t ws_sizeInBytes; + + float eps; + miopenNormMode_t mode; +}; + +template +int T5LayerNormDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::GetandSetData() +{ + auto inTensorParam = inflags.GetValueTensor("input"); + + auto in_len = inTensorParam.lengths; + + std::vector inner_len; + + inner_len = {in_len[in_len.size() - 1]}; + + MIOPEN_THROW_IF(inner_len[0] == 0, "Final dimension must be nonzero"); + + std::vector outer_len; + + outer_len = {in_len.begin(), in_len.end() - 1}; + + if(SetTensorNd(xDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error parsing input tensor: " + inflags.GetValueStr("input") + "."); + + if(SetTensorNd(weightDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting weight tensor."); + + if(SetTensorNd(yDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting doutput tensor."); + + if(SetTensorNd(rstdDesc, outer_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting rstd tensor."); + + if(SetTensorNd(dyDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting dy tensor."); + + if(SetTensorNd(dxDesc, in_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting dx tensor."); + + if(SetTensorNd(dwDesc, inner_len, data_type) != miopenStatusSuccess) + MIOPEN_THROW("Error setting dw tensor."); + + eps = static_cast(inflags.GetValueDouble("eps")); + mode = miopenNormMode_t(inflags.GetValueInt("mode")); + + return 0; +} + +template +int T5LayerNormDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "0", "Run only Forward T5LayerNorm (Default=1)", "int"); + inflags.AddTensorFlag("input", 'X', "100x3x32x32", "input tensor descriptor"); + + inflags.AddInputFlag("eps", 'e', "0.00001", "Alpha (Default=0.00001)", "double"); + inflags.AddInputFlag( + "mode", 'm', "5", "elemwise affine mode (5), weight mode (6) (Default=5)", "int"); + + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::AllocateBuffersAndCopy() +{ + const Tgpu Tgpu0val = static_cast(0.0); + const Tgpu Tgpu1val = static_cast(1.0); + const Tgpu Tgpuminus1val = static_cast(-1.0); + const Tref Tref0ref = static_cast(0.0); + size_t x_sz = GetTensorSize(xDesc); + size_t weight_sz = GetTensorSize(weightDesc); + size_t y_sz = GetTensorSize(yDesc); + size_t rstd_sz = GetTensorSize(rstdDesc); + size_t dy_sz = GetTensorSize(dyDesc); + size_t dx_sz = GetTensorSize(dxDesc); + size_t dw_sz = GetTensorSize(dwDesc); + + miopenGetT5LayerNormBackwardWorkspaceSize( + GetHandle(), mode, dyDesc, xDesc, weightDesc, rstdDesc, dxDesc, dwDesc, &ws_sizeInBytes); + if(ws_sizeInBytes == static_cast(-1)) + return miopenStatusAllocFailed; + + uint32_t ctx = 0; + + x_dev = std::unique_ptr(new GPUMem(ctx, x_sz, sizeof(Tgpu))); + weight_dev = std::unique_ptr(new GPUMem(ctx, weight_sz, sizeof(Tgpu))); + y_dev = std::unique_ptr(new GPUMem(ctx, y_sz, sizeof(Tgpu))); + rstd_dev = std::unique_ptr(new GPUMem(ctx, rstd_sz, sizeof(Tgpu))); + dy_dev = std::unique_ptr(new GPUMem(ctx, dy_sz, sizeof(Tgpu))); + dx_dev = std::unique_ptr(new GPUMem(ctx, dx_sz, sizeof(Tgpu))); + dw_dev = std::unique_ptr(new GPUMem(ctx, dw_sz, sizeof(Tgpu))); + workspace_dev = std::unique_ptr(new GPUMem(ctx, ws_sizeInBytes, sizeof(std::byte))); + + x = std::vector(x_sz, Tgpu0val); + weight = std::vector(weight_sz, Tgpu0val); + y = std::vector(y_sz, Tgpu0val); + rstd = std::vector(rstd_sz, Tgpu0val); + dy = std::vector(dy_sz, Tgpu0val); + dx = std::vector(dx_sz, Tgpu0val); + dw = std::vector(dw_sz, Tgpu0val); + yhost = std::vector(y_sz, Tref0ref); + rstdhost = std::vector(rstd_sz, Tref0ref); + dxhost = std::vector(dx_sz, Tref0ref); + dwhost = std::vector(dw_sz, Tref0ref); + + for(int i = 0; i < x_sz; i++) + { + x[i] = prng::gen_A_to_B(Tgpuminus1val, Tgpu1val); + dy[i] = prng::gen_A_to_B(Tgpuminus1val, Tgpu1val); + } + + if(x_dev->ToGPU(GetStream(), x.data()) != 0) + std::cerr << "Error copying (x) to GPU, size: " << x_dev->GetSize() << std::endl; + if(dy_dev->ToGPU(GetStream(), dy.data()) != 0) + std::cerr << "Error copying (dy) to GPU, size: " << x_dev->GetSize() << std::endl; + + for(int i = 0; i < weight_sz; i++) + { + if(mode == MIOPEN_ELEMENTWISE_AFFINE) + weight[i] = Tgpu1val; + else + weight[i] = prng::gen_A_to_B(Tgpuminus1val, Tgpu1val); + } + + if(weight_dev->ToGPU(GetStream(), weight.data()) != 0) + std::cerr << "Error copying (weight) to GPU, size: " << weight_dev->GetSize() << std::endl; + + if(y_dev->ToGPU(GetStream(), y.data()) != 0) + std::cerr << "Error copying (y) to GPU, size: " << y_dev->GetSize() << std::endl; + + if(rstd_dev->ToGPU(GetStream(), rstd.data()) != 0) + std::cerr << "Error copying (rstd) to GPU, size: " << rstd_dev->GetSize() << std::endl; + + if(dx_dev->ToGPU(GetStream(), dx.data()) != 0) + std::cerr << "Error copying (dx) to GPU, size: " << dx_dev->GetSize() << std::endl; + + if(dw_dev->ToGPU(GetStream(), dw.data()) != 0) + std::cerr << "Error copying (dw) to GPU, size: " << dw_dev->GetSize() << std::endl; + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::RunForwardGPU() +{ + float kernel_total_time = 0.0; + float kernel_first_time = 0.0; + + Timer t; + START_TIME + + for(int i = 0; i < inflags.GetValueInt("iter"); i++) + { + miopenT5LayerNormForward(GetHandle(), + mode, + xDesc, + x_dev->GetMem(), + weightDesc, + weight_dev->GetMem(), + eps, + yDesc, + y_dev->GetMem(), + rstdDesc, + rstd_dev->GetMem()); + + float time = 0.0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + int iter = inflags.GetValueInt("iter"); + if(WALL_CLOCK) + std::cout << "Wall-clock Time Forward T5LayerNorm Elapsed: " << t.gettime_ms() / iter + << " ms\n"; + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + std::cout << "GPU Kernel Time Forward T5LayerNorm Elapsed: " << kernel_average_time + << " ms\n"; + } + + if(y_dev->FromGPU(GetStream(), y.data()) != 0) + std::cerr << "Error copying (y_dev) from GPU, size: " << y_dev->GetSize() << std::endl; + + if(rstd_dev->FromGPU(GetStream(), rstd.data()) != 0) + std::cerr << "Error copying (rstd_dev) from GPU, size: " << rstd_dev->GetSize() + << std::endl; + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::RunForwardCPU() +{ + mloT5LayerNormForwardRunHost( + xDesc, x.data(), weight.data(), yhost.data(), rstdhost.data(), eps, mode); + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::RunBackwardGPU() +{ + float kernel_total_time = 0.0; + float kernel_first_time = 0.0; + + Timer t; + START_TIME + + for(int i = 0; i < inflags.GetValueInt("iter"); i++) + { + miopenT5LayerNormBackward(GetHandle(), + mode, + workspace_dev->GetMem(), + ws_sizeInBytes, + dyDesc, + dy_dev->GetMem(), + xDesc, + x_dev->GetMem(), + weightDesc, + weight_dev->GetMem(), + rstdDesc, + rstd_dev->GetMem(), + dxDesc, + dx_dev->GetMem(), + dwDesc, + dw_dev->GetMem()); + + float time = 0.0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + int iter = inflags.GetValueInt("iter"); + if(WALL_CLOCK) + std::cout << "Wall-clock Time Backward T5LayerNorm Elapsed: " << t.gettime_ms() / iter + << " ms\n"; + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + std::cout << "GPU Kernel Time Backward T5LayerNorm Elapsed: " << kernel_average_time + << " ms\n"; + } + + if(dx_dev->FromGPU(GetStream(), dx.data()) != 0) + std::cerr << "Error copying (dx_dev) from GPU, size: " << dx_dev->GetSize() << std::endl; + + if(dw_dev->FromGPU(GetStream(), dw.data()) != 0) + std::cerr << "Error copying (dw_dev) from GPU, size: " << dw_dev->GetSize() << std::endl; + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::RunBackwardCPU() +{ + mloT5LayerNormBackwardRunHost( + dyDesc, dy.data(), x.data(), weight.data(), rstdhost.data(), dxhost.data(), mode); + + mloT5LayerNormBackckwardweightRunHost( + dyDesc, dy.data(), x.data(), rstdhost.data(), dwhost.data()); + + return miopenStatusSuccess; +} + +template +Tref T5LayerNormDriver::GetTolerance() +{ + // Computation error of fp16 is ~2^13 (=8192) bigger than + // the one of fp32 because mantissa is shorter by 13 bits. + auto tolerance = std::is_same::value ? 1.5e-6 : 8.2e-3; + + // bf16 mantissa has 7 bits, by 3 bits shorter than fp16. + if(std::is_same::value) + tolerance *= 8.0; + return tolerance; +} + +template +int T5LayerNormDriver::VerifyForward() +{ + RunForwardCPU(); + const Tref tolerance = GetTolerance(); + + auto error = miopen::rms_range(yhost, y); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Forward T5LayerNorm FAILED: " << error << " > " << tolerance << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward T5LayerNorm Verifies OK on CPU reference (" << error << " < " + << tolerance << ')' << std::endl; + } + + auto rstderror = miopen::rms_range(rstdhost, rstd); + if(!std::isfinite(rstderror) || rstderror > tolerance) + { + std::cout << "Forward T5LayerNorm rstd FAILED: " << rstderror << " > " << tolerance + << std::endl; + return EC_VerifyFwd; + } + else + { + std::cout << "Forward T5LayerNorm rstd Verifies OK on CPU reference (" << rstderror << " < " + << tolerance << ')' << std::endl; + } + + return miopenStatusSuccess; +} + +template +int T5LayerNormDriver::VerifyBackward() +{ + RunBackwardCPU(); + const Tref tolerance = GetTolerance(); + + auto error = miopen::rms_range(dxhost, dx); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Backward T5LayerNorm FAILED: " << error << " > " << tolerance << std::endl; + return EC_VerifyBwd; + } + else + { + std::cout << "Backward T5LayerNorm Verifies OK on CPU reference (" << error << " < " + << tolerance << ')' << std::endl; + } + + auto dwerror = miopen::rms_range(dwhost, dw); + if(!std::isfinite(dwerror) || dwerror > tolerance) + { + std::cout << "Backward T5LayerNorm dw FAILED: " << dwerror << " > " << tolerance + << std::endl; + return EC_VerifyBwd; + } + else + { + std::cout << "Backward T5LayerNorm dw Verifies OK on CPU reference (" << dwerror << " < " + << tolerance << ')' << std::endl; + } + + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_T5LAYERNORM_DRIVER_HPP diff --git a/driver/tensor_driver.hpp b/driver/tensor_driver.hpp index cb3139bf48..c353a6ee11 100644 --- a/driver/tensor_driver.hpp +++ b/driver/tensor_driver.hpp @@ -101,7 +101,7 @@ inline std::vector GetTensorLengths(const miopenTensorDescriptor_t& tensor) } std::vector tensor_len; - tensor_len.resize(miopen::deref(tensor).GetSize()); + tensor_len.resize(miopen::deref(tensor).GetNumDims()); miopenGetTensorDescriptor(tensor, nullptr, tensor_len.data(), nullptr); return tensor_len; @@ -131,7 +131,7 @@ inline std::vector GetTensorStrides(const miopenTensorDescriptor_t& tensor) } std::vector tensor_strides; - tensor_strides.resize(miopen::deref(tensor).GetSize()); + tensor_strides.resize(miopen::deref(tensor).GetNumDims()); miopenGetTensorDescriptor(tensor, nullptr, nullptr, tensor_strides.data()); @@ -173,6 +173,13 @@ inline int SetTensorNd(miopenTensorDescriptor_t t, return miopenSetTensorDescriptor(t, data_type, len.size(), len.data(), nullptr); } +inline int SetTensorNd(miopenTensorDescriptor_t t, + std::vector& len, + miopenDataType_t data_type = miopenFloat) +{ + return miopenSetTensorDescriptorV2(t, data_type, len.size(), len.data(), nullptr); +} + inline int SetTensorNd(miopenTensorDescriptor_t t, std::vector& len, std::vector& strides, @@ -181,6 +188,14 @@ inline int SetTensorNd(miopenTensorDescriptor_t t, return miopenSetTensorDescriptor(t, data_type, len.size(), len.data(), strides.data()); } +inline int SetTensorNd(miopenTensorDescriptor_t t, + std::vector& len, + std::vector& strides, + miopenDataType_t data_type = miopenFloat) +{ + return miopenSetTensorDescriptorV2(t, data_type, len.size(), len.data(), strides.data()); +} + inline int SetTensorNd(miopenTensorDescriptor_t t, std::vector& len, const std::string& layout, @@ -208,10 +223,10 @@ inline int SetTensorNd(miopenTensorDescriptor_t t, return SetTensorNd(t, len, data_type); } - std::vector strides; - miopen::tensor_layout_to_strides(len, len_layout, layout, strides); - - return SetTensorNd(t, len, strides, data_type); + std::vector strides2; + std::vector len2(len.cbegin(), len.cend()); + miopen::tensor_layout_to_strides(len2, len_layout, layout, strides2); + return SetTensorNd(t, len2, strides2, data_type); } // This function ignores tensor strides completely and its result should not be interpreted as @@ -222,8 +237,8 @@ inline size_t GetTensorSize(const miopenTensorDescriptor_t& tensor) { assert(miopen::deref(tensor).IsPacked() && "GetTensorSize should not be used on an unpacked tensor."); - const auto len = GetTensorLengths(tensor); - const auto vectorLength = GetTensorVectorLength(tensor); + const auto len = GetTensorLengths(tensor); + const size_t vectorLength = GetTensorVectorLength(tensor); size_t sz = std::accumulate(len.begin(), len.end(), vectorLength, std::multiplies()); return sz; diff --git a/driver/timer.hpp b/driver/timer.hpp index fd5c277e38..ca5dfdc4e6 100644 --- a/driver/timer.hpp +++ b/driver/timer.hpp @@ -58,15 +58,21 @@ class Timer { if(!enabled) return; - et = std::chrono::steady_clock::now(); + capture(); } float gettime_ms() { return std::chrono::duration_cast>(et - st) .count(); } + float interim_time_ms() + { + capture(); + return gettime_ms(); + } private: + void capture() { et = std::chrono::steady_clock::now(); } std::chrono::time_point st; std::chrono::time_point et; }; diff --git a/driver/transformers_adam_w_driver.hpp b/driver/transformers_adam_w_driver.hpp new file mode 100644 index 0000000000..fd3756e559 --- /dev/null +++ b/driver/transformers_adam_w_driver.hpp @@ -0,0 +1,496 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_MIOPEN_TRANSFORMERS_ADAM_W_DRIVER_HPP +#define GUARD_MIOPEN_TRANSFORMERS_ADAM_W_DRIVER_HPP + +#include "InputFlags.hpp" +#include "driver.hpp" +#include "random.hpp" +#include "tensor_driver.hpp" +#include "timer.hpp" + +#include "../test/verify.hpp" + +#include +#include + +#include +#include +#include +#include +#include +#include + +template +class TransformersAdamWDriver : public Driver +{ +public: + TransformersAdamWDriver(bool is_amp_ = false) : Driver(), is_amp(is_amp_) + { + miopenCreateTensorDescriptor(¶mDesc); + miopenCreateTensorDescriptor(&gradDesc); + miopenCreateTensorDescriptor(&expAvgDesc); + miopenCreateTensorDescriptor(&expAvgSqDesc); + miopenCreateTensorDescriptor(¶mOutDesc); + miopenCreateTensorDescriptor(&dummyOutDesc); + if(is_amp) + { + miopenCreateTensorDescriptor(&stepDesc); + miopenCreateTensorDescriptor(&gradScaleDesc); + miopenCreateTensorDescriptor(&foundInfDesc); + } + + data_type = miopen_type{}; + grad_type = miopen_type{}; + } + + int AddCmdLineArgs() override; + int ParseCmdLineArgs(int argc, char* argv[]) override; + InputFlags& GetInputFlags() override { return inflags; } + + int GetandSetData() override; + std::vector GetInputTensorLengthsFromCmdLine(); + + int AllocateBuffersAndCopy() override; + + int RunForwardGPU() override; + int RunForwardCPU(); + + int RunBackwardGPU() override; + + Tref GetTolerance(); + int VerifyBackward() override; + int VerifyForward() override; + ~TransformersAdamWDriver() override + { + miopenDestroyTensorDescriptor(paramDesc); + miopenDestroyTensorDescriptor(gradDesc); + miopenDestroyTensorDescriptor(expAvgDesc); + miopenDestroyTensorDescriptor(expAvgSqDesc); + miopenDestroyTensorDescriptor(paramOutDesc); + miopenDestroyTensorDescriptor(dummyOutDesc); + if(stepDesc) + miopenDestroyTensorDescriptor(stepDesc); + if(gradScaleDesc) + miopenDestroyTensorDescriptor(gradScaleDesc); + if(stepDesc) + miopenDestroyTensorDescriptor(foundInfDesc); + } + +private: + InputFlags inflags; + + int forw = 1; + + miopenTensorDescriptor_t paramDesc = nullptr; + miopenTensorDescriptor_t gradDesc = nullptr; + miopenTensorDescriptor_t expAvgDesc = nullptr; + miopenTensorDescriptor_t expAvgSqDesc = nullptr; + miopenTensorDescriptor_t stepDesc = nullptr; + miopenTensorDescriptor_t gradScaleDesc = nullptr; + miopenTensorDescriptor_t foundInfDesc = nullptr; + miopenTensorDescriptor_t paramOutDesc = nullptr; + miopenTensorDescriptor_t dummyOutDesc = nullptr; + + std::unique_ptr param_dev; + std::unique_ptr param_out_dev; + std::unique_ptr dummy_out_dev; + std::unique_ptr grad_dev; + std::unique_ptr exp_avg_dev; + std::unique_ptr exp_avg_sq_dev; + std::unique_ptr step_dev; + std::unique_ptr scale_dev; + std::unique_ptr found_inf_dev; + + std::vector param; + std::vector grad; + std::vector exp_avg; + std::vector exp_avg_sq; + + std::vector param_host; + std::vector grad_host; + std::vector exp_avg_host; + std::vector exp_avg_sq_host; + + float lr; + float beta1; + float beta2; + float weight_decay; + float eps; + bool correct_bias = true; + bool found_inf = false; + bool is_amp = false; + int grad_scale = 1; + int iter = 0; + + miopenDataType_t grad_type; +}; + +template +int TransformersAdamWDriver::ParseCmdLineArgs(int argc, char* argv[]) +{ + inflags.Parse(argc, argv); + + if(inflags.GetValueInt("time") == 1) + { + miopenEnableProfiling(GetHandle(), true); + } + return miopenStatusSuccess; +} + +template +int TransformersAdamWDriver::GetandSetData() +{ + auto param_len = GetInputTensorLengthsFromCmdLine(); + lr = inflags.GetValueDouble("lr"); + beta1 = inflags.GetValueDouble("beta1"); + beta2 = inflags.GetValueDouble("beta2"); + eps = inflags.GetValueDouble("eps"); + weight_decay = inflags.GetValueDouble("weight_decay"); + correct_bias = inflags.GetValueInt("correct_bias"); + iter = inflags.GetValueInt("iter"); + + if(is_amp) + { + grad_scale = inflags.GetValueInt("scale"); + found_inf = inflags.GetValueInt("found_inf"); + } + + std::vector one_size = {1}; + SetTensorNd(paramDesc, param_len, data_type); + SetTensorNd(paramOutDesc, param_len, data_type); + SetTensorNd(gradDesc, param_len, grad_type); + SetTensorNd(expAvgDesc, param_len, data_type); + SetTensorNd(expAvgSqDesc, param_len, data_type); + SetTensorNd(dummyOutDesc, param_len, data_type); + + if(is_amp) + { + SetTensorNd(stepDesc, one_size, miopenInt32); + SetTensorNd(gradScaleDesc, one_size, miopenInt32); + SetTensorNd(foundInfDesc, one_size, miopenInt32); + } + + return 0; +} + +template +int TransformersAdamWDriver::AddCmdLineArgs() +{ + inflags.AddInputFlag("forw", 'F', "1", "Run only Forward GroupNorm (Default=1)", "int"); + inflags.AddTensorFlag("dims", 'd', "64x32x128", "params tensor dims (Default=64x32x128)"); + + inflags.AddInputFlag("lr", 'l', "0.001", "learning rate (Default=0.001)", "float"); + inflags.AddInputFlag("beta1", '1', "0.9", "beta1 (Default=0.9)", "float"); + inflags.AddInputFlag("beta2", '2', "0.999", "beta2 (Default=0.999)", "float"); + inflags.AddInputFlag("eps", 'e', "0.00000001", "eps (Default=0.00000001)", "float"); + inflags.AddInputFlag("weight_decay", 'W', "0", "weight decay (Default=0)", "float"); + inflags.AddInputFlag("correct_bias", 'c', "1", " (Default=1)", "int"); + + if(is_amp) + { + inflags.AddInputFlag("scale", 's', "65536", "grad scale factor (Default=65536)", "int"); + inflags.AddInputFlag("found_inf", 'f', "0", "found inf in grad (Default=0)", "int"); + } + + inflags.AddInputFlag("iter", 'i', "10", "Number of Iterations (Default=10)", "int"); + inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int"); + inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int"); + inflags.AddInputFlag( + "wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int"); + + return miopenStatusSuccess; +} + +template +std::vector TransformersAdamWDriver::GetInputTensorLengthsFromCmdLine() +{ + std::vector ret; + auto tensor = inflags.GetValueTensor("dims"); + if(!tensor.lengths.empty()) + return tensor.lengths; + return ret; +} + +template +int TransformersAdamWDriver::AllocateBuffersAndCopy() +{ + size_t param_sz = GetTensorSize(paramDesc); + + uint32_t ctx = 0; + param_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + grad_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgrad))); + exp_avg_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + exp_avg_sq_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + param_out_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + dummy_out_dev = std::unique_ptr(new GPUMem(ctx, param_sz, sizeof(Tgpu))); + + if(is_amp) + { + step_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(int))); + scale_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(int))); + found_inf_dev = std::unique_ptr(new GPUMem(ctx, 1, sizeof(bool))); + } + + param = std::vector(param_sz, static_cast(0)); + grad = std::vector(param_sz, static_cast(0)); + exp_avg = std::vector(param_sz, static_cast(0)); + exp_avg_sq = std::vector(param_sz, static_cast(0)); + + param_host = std::vector(param_sz, static_cast(0)); + grad_host = std::vector(param_sz, static_cast(0)); + exp_avg_host = std::vector(param_sz, static_cast(0)); + exp_avg_sq_host = std::vector(param_sz, static_cast(0)); + + for(int i = 0; i < param_sz; i++) + { + param[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(1.0)); + grad[i] = prng::gen_A_to_B(static_cast(0.0), static_cast(0.1)); + exp_avg[i] = prng::gen_A_to_B(static_cast(0), static_cast(0.1)); + exp_avg_sq[i] = prng::gen_A_to_B(static_cast(0), static_cast(0.1)); + param_host[i] = param[i]; + exp_avg_host[i] = exp_avg[i]; + exp_avg_sq_host[i] = exp_avg_sq[i]; + + if(is_amp) + { + grad[i] *= grad_scale; + if(!found_inf && (std::isnan(grad[i]) || std::isinf(grad[i]))) + { + std::cerr << "Error init (grad), idx: " << i << ", value: " << grad[i] << std::endl; + found_inf = true; + } + } + grad_host[i] = grad[i]; + } + + if(param_dev->ToGPU(GetStream(), param.data()) != 0) + std::cerr << "Error copying (param) to GPU, size: " << param_dev->GetSize() << std::endl; + + if(grad_dev->ToGPU(GetStream(), grad.data()) != 0) + std::cerr << "Error copying (grad) to GPU, size: " << grad_dev->GetSize() << std::endl; + + if(exp_avg_dev->ToGPU(GetStream(), exp_avg.data()) != 0) + std::cerr << "Error copying (exp_avg) to GPU, size: " << exp_avg_dev->GetSize() + << std::endl; + + if(exp_avg_sq_dev->ToGPU(GetStream(), exp_avg_sq.data()) != 0) + std::cerr << "Error copying (exp_avg_sq) to GPU, size: " << exp_avg_sq_dev->GetSize() + << std::endl; + + if(is_amp) + { + int step = 0; + if(step_dev->ToGPU(GetStream(), &step) != 0) + std::cerr << "Error copying (step) to GPU, size: " << step_dev->GetSize() << std::endl; + + if(scale_dev->ToGPU(GetStream(), &grad_scale) != 0) + std::cerr << "Error copying (scale) to GPU, size: " << scale_dev->GetSize() + << std::endl; + if(found_inf_dev->ToGPU(GetStream(), &found_inf) != 0) + std::cerr << "Error copying (found_inf) to GPU, size: " << found_inf_dev->GetSize() + << std::endl; + } + + return miopenStatusSuccess; +} + +template +int TransformersAdamWDriver::RunForwardGPU() +{ + float kernel_total_time = 0; + float kernel_first_time = 0; + + void* grad_scale_ptr = is_amp ? scale_dev->GetMem() : nullptr; + void* found_inf_ptr = is_amp ? found_inf_dev->GetMem() : nullptr; + void* state_step_ptr = is_amp ? step_dev->GetMem() : nullptr; + + Timer t; + START_TIME + + for(int i = 0; i < iter; i++) + { + miopenTransformersAdamWWithOutput(GetHandle(), + paramDesc, + param_dev->GetMem(), + paramOutDesc, + param_out_dev->GetMem(), + nullptr, + nullptr, + gradDesc, + grad_dev->GetMem(), + expAvgDesc, + exp_avg_dev->GetMem(), + dummyOutDesc, + dummy_out_dev->GetMem(), + expAvgSqDesc, + exp_avg_sq_dev->GetMem(), + dummyOutDesc, + dummy_out_dev->GetMem(), + stepDesc, + state_step_ptr, + stepDesc, + state_step_ptr, + i + 1, + lr, + beta1, + beta2, + weight_decay, + eps, + -1, + correct_bias, + gradScaleDesc, + grad_scale_ptr, + foundInfDesc, + found_inf_ptr); + + float time = 0.0; + miopenGetKernelTime(GetHandle(), &time); + kernel_total_time += time; + if(i == 0) + kernel_first_time = time; + } + + if(inflags.GetValueInt("time") == 1) + { + STOP_TIME + if(WALL_CLOCK) + printf("Wall-clock Time Forward Adam Elapsed: %f ms\n", t.gettime_ms() / iter); + + float kernel_average_time = + iter > 1 ? (kernel_total_time - kernel_first_time) / (iter - 1) : kernel_first_time; + printf("GPU Kernel Time Forward Adam Elapsed: %f ms\n", kernel_average_time); + } + + if(param_out_dev->FromGPU(GetStream(), param.data()) != 0) + std::cerr << "Error copying (param_dev) from GPU, size: " << param_dev->GetSize() + << std::endl; + + return miopenStatusSuccess; +} + +template +int TransformersAdamWDriver::RunForwardCPU() +{ + if(is_amp && found_inf) + return miopenStatusSuccess; + + auto params = param_host.data(); + auto grads = grad_host.data(); + auto exp_avgs = exp_avg_host.data(); + auto exp_avg_sqs = exp_avg_sq_host.data(); + auto step = iter; + + size_t numel = miopen::deref(paramDesc).GetElementSize(); + for(int i = 0; i < numel; i++) + { + Tref exp_avg_val = exp_avgs[i]; + Tref exp_avg_sq_val = exp_avg_sqs[i]; + + Tref param_val = params[i]; + Tref grad_val = grads[i]; + if(is_amp) + grad_val /= grad_scale; + + exp_avg_val = exp_avg_val * beta1 + grad_val * (1 - beta1); + exp_avg_sq_val = exp_avg_sq_val * beta2 + grad_val * grad_val * (1 - beta2); + + float denorm = sqrt(exp_avg_sq_val) + eps; + float step_size = lr; + + if(correct_bias) + { + float bias_correction1 = 1 - pow(beta1, step); + float bias_correction2 = 1 - pow(beta2, step); + step_size = step_size * sqrt(bias_correction2) / bias_correction1; + } + + param_val = param_val + exp_avg_val / denorm * -step_size; + + if(weight_decay > 0.0) + { + param_val = param_val - param_val * (lr * weight_decay); + } + + params[i] = param_val; + } + + return miopenStatusSuccess; +} + +template +int TransformersAdamWDriver::RunBackwardGPU() +{ + return miopenStatusSuccess; +} + +template +Tref TransformersAdamWDriver::GetTolerance() +{ + if(data_type == miopenHalf) + { + return 1e-3; + } + else if(data_type == miopenFloat) + { + return 5e-5; + } + else if(data_type == miopenDouble) + { + return 1e-10; + } + else if(data_type == miopenBFloat16) + { + return 5e-3; + } + return 0; +} + +template +int TransformersAdamWDriver::VerifyForward() +{ + RunForwardCPU(); + const Tref tolerance = GetTolerance(); + auto error = miopen::rms_range(param_host, param); + + if(!std::isfinite(error) || error > tolerance) + { + std::cout << "Forward Adam FAILED: " << error << std::endl; + return EC_VerifyFwd; + } + + std::cout << "Forward Adam Verifies OK on CPU reference" << std::endl; + + return miopenStatusSuccess; +} + +template +int TransformersAdamWDriver::VerifyBackward() +{ + return miopenStatusSuccess; +} + +#endif // GUARD_MIOPEN_TRANSFORMERS_ADAM_W_DRIVER_HPP diff --git a/include/miopen/config.h.in b/include/miopen/config.h.in index d77fd7319f..fb96368ce2 100644 --- a/include/miopen/config.h.in +++ b/include/miopen/config.h.in @@ -30,6 +30,7 @@ #cmakedefine01 MIOPEN_BACKEND_HIP #cmakedefine01 MIOPEN_MODE_NOGPU #cmakedefine01 MIOPEN_USE_ROCBLAS +#cmakedefine01 MIOPEN_USE_HIPBLASLT #cmakedefine01 MIOPEN_USE_ROCTRACER #cmakedefine01 MIOPEN_BUILD_DEV #cmakedefine01 MIOPEN_GPU_SYNC @@ -108,7 +109,7 @@ const char* getOffloadBundlerBinPath(); #cmakedefine01 MIOPEN_USE_SQLITE_PERFDB -#define MIOPEN_USE_GEMM (MIOPEN_USE_ROCBLAS) +#define MIOPEN_USE_GEMM (MIOPEN_USE_ROCBLAS || MIOPEN_USE_HIPBLASLT) // Usage of "defined" operator within macro expansion is undefined behavior, // so "defined(NDEBUG)" cannot be used there... unlike the following macro: @@ -168,6 +169,31 @@ const char* getOffloadBundlerBinPath(); MIOPEN_ROCBLAS_VERSION_PATCH) #endif // MIOPEN_USE_ROCBLAS +#if MIOPEN_USE_HIPBLASLT +// clang-format off +#define MIOPEN_HIPBLASLT_VERSION_MAJOR @hipblaslt_VERSION_MAJOR@ +#define MIOPEN_HIPBLASLT_VERSION_MINOR @hipblaslt_VERSION_MINOR@ +#define MIOPEN_HIPBLASLT_VERSION_PATCH @hipblaslt_VERSION_PATCH@ +// clang-format on +#ifndef MIOPEN_HIPBLASLT_VERSION_MAJOR +#define MIOPEN_HIPBLASLT_VERSION_MAJOR 0 +#endif +#ifndef MIOPEN_HIPBLASLT_VERSION_MINOR +#define MIOPEN_HIPBLASLT_VERSION_MINOR 0 +#endif +#ifndef MIOPEN_HIPBLASLT_VERSION_PATCH +#define MIOPEN_HIPBLASLT_VERSION_PATCH 0 +#endif +// 3 decimal digits for each number; max fits into 32 bits. +#if MIOPEN_HIPBLASLT_VERSION_MAJOR > 999 || MIOPEN_HIPBLASLT_VERSION_MAJOR > 999 || \ + MIOPEN_HIPBLASLT_VERSION_PATCH > 999 +#error "Too big HIPBLASLT version number(s)" +#endif +#define MIOPEN_HIPBLASLT_VERSION_FLAT \ + ((MIOPEN_HIPBLASLT_VERSION_MAJOR * 1000 + MIOPEN_HIPBLASLT_VERSION_MINOR) * 1000 + \ + MIOPEN_HIPBLASLT_VERSION_PATCH) +#endif // MIOPEN_USE_HIPBLASLT + /// WORKAROUND_BOOST_ISSUE_392 /// Workaround for https://github.com/boostorg/config/issues/392#issuecomment-1109889533 /// See also https://github.com/ROCm/MIOpen/pull/1490#issuecomment-1109928102, diff --git a/include/miopen/miopen.h b/include/miopen/miopen.h index 5fce7be78e..087e207c32 100644 --- a/include/miopen/miopen.h +++ b/include/miopen/miopen.h @@ -65,9 +65,11 @@ * @defgroup TensorReduce * @defgroup find2 * @defgroup sum - * @defgroup argmax + * @defgroup ReduceExtreme * @defgroup groupnorm * @defgroup cat + * @defgroup SGD + * @defgroup getitem * @defgroup interpolate * */ @@ -378,6 +380,7 @@ typedef enum // miopenReserved1 = 7, // miopenReserved2 = 8, #endif + miopenInt64 = 9, } miopenDataType_t; /*! @ingroup tensor @@ -487,6 +490,14 @@ typedef enum MIOPEN_ELEMENTWISE_AFFINE = 0, /*!< initialized to ones for weights and zeros for biases */ MIOPEN_WEIGHT_BIAS = 1, /*!< learnable weights and biases of the module of shape normalized_shape */ + MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD = + 2, /*!< initialized to ones for weights and zeros for biases in addlayernorm */ + MIOPEN_WEIGHT_BIAS_FUSED_ADD = 3, /*!< learnable weights and biases of the module of shape + normalized_shape in addlayernorm */ + MIOPEN_ELEMENTWISE_AFFINE_T5 = + 4, /*!< initialized to ones for weights and zeros for biases in t5layernorm */ + MIOPEN_WEIGHT_BIAS_T5 = 5, /*!< learnable weights and biases of the module of shape + normalized_shape in t5layernorm */ } miopenNormMode_t; #endif /*! @ingroup batchnorm @@ -750,6 +761,16 @@ MIOPEN_EXPORT miopenStatus_t miopenSetTensorDescriptor(miopenTensorDescriptor_t const int* dimsA, const int* stridesA); +#ifdef MIOPEN_BETA_API +/*! @copydoc miopenSetTensorDescriptor() + */ +MIOPEN_EXPORT miopenStatus_t miopenSetTensorDescriptorV2(miopenTensorDescriptor_t tensorDesc, + miopenDataType_t dataType, + int nbDims, + const size_t* dimsA, + const size_t* stridesA); +#endif + #ifdef MIOPEN_BETA_API /*! @brief Set the tensor cast type * @@ -1657,16 +1678,23 @@ miopenConvolutionBackwardWeightsImmediate(miopenHandle_t handle, size_t workSpaceSize, const uint64_t solution_id); -/*! @brief Query the workspace size required for a forward convolution layer +/*! @brief Query the workspace size required for a forward convolution algorithm. * - * This call is required and must be executed once before running - * miopenFindConvolutionForwardAlgorithm() - * in order to determine the largest required allocation for the algorithm search; i.e., the maximum - * size - * of the memory needed from the set of potential forward convolution algorithm is returned. + * For given tensor and convolution descriptors, this function calculates and returns the minimum + * size of the workspace that must be provided to miopenFindConvolutionForwardAlgorithm() in order + * for the latter to find the best candidate from the available forward data convolution algorithms. * - * If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running - * this. + * WARNING: Providing smaller workspace may result in the selection of a slow convolution + * algorithm, and therefore affect library performance. + * + * It should be assumed that the required workspace size is different for each convolution + * configuration. Therefore, typically this function should be called at least once for each + * convolution configuration used. + * + * Since the convolution configuration is determined by tensor and convolution descriptors, the user + * should ensure that all descriptors contain complete information. For example, if Group/Depthwise + * convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running + * this, and so on. * * @param handle MIOpen handle (input) * @param wDesc Tensor descriptor for weight tensor w (input) @@ -1717,10 +1745,14 @@ miopenConvolutionForwardGetWorkSpaceSize(miopenHandle_t handle, * @param requestAlgoCount Number of algorithms to return kernel times (input) * @param returnedAlgoCount Pointer to number of algorithms returned (output) * @param perfResults Pointer to union of best algorithm for forward and backwards (input) - * @param workSpace Pointer to workspace required for the search (output) - * @param workSpaceSize Size in bytes of the memory needed for find (output) - * @param exhaustiveSearch A boolean to toggle a full search of all algorithms and configurations - * (input) + * @param workSpace Pointer to workspace buffer (input). + * @param workSpaceSize Size in bytes of the workspace buffer (input). + * The buffer must be allocated on the device by the caller. + * The size of the buffer should be determined by calling + * miopenConvolutionForwardGetWorkSpaceSize(), see its + * documentation for details. + * @param exhaustiveSearch A boolean to toggle a full search of all algorithms + * and configurations (input) * @return miopenStatus_t */ MIOPEN_EXPORT miopenStatus_t @@ -1745,7 +1777,7 @@ miopenFindConvolutionForwardAlgorithm(miopenHandle_t handle, * miopenFindConvolutionForwardAlgorithm() must have been executed previously to * determine the required memory needed for the workspace and the best convolutional algorithm. * The scaling parameter alpha (float) and shift parameter beta (float) are only supported for - * alpha = 1 and beta = 0. + * alpha = 1 and beta = 0 in 2D. In 3D, these parameters can take other values. * * The forward convolution is designed to accommodate both packed and non-packed tensor strides for * multiple data types and dimensions across various platforms. This flexibility ensures optimal @@ -1810,16 +1842,24 @@ MIOPEN_EXPORT miopenStatus_t miopenConvolutionForwardBias(miopenHandle_t handle, const miopenTensorDescriptor_t yDesc, void* y); -/*! @brief Get the GPU memory required for the backward data convolution algorithm. +/*! @brief Query the workspace size required for a backward data convolution algorithm. * - * For a provided tensor descriptors and algorithm selection, this function calculates and returns - * the workspace size required for back propagation on data. This call is required and must be - * executed once before running miopenFindConvolutionBackwardDataAlgorithm() in order to determine - * the largest required allocation for the algorithm search; i.e., the maximum size of the memory - * needed from the set of potential backward convolution algorithm is returned. + * For given tensor and convolution descriptors, this function calculates and returns the minimum + * size of the workspace that must be provided to miopenFindConvolutionBackwardDataAlgorithm() in + * order for the latter to find the best candidate from the available backward data convolution + * algorithms. * - * If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running - * this. + * WARNING: Providing smaller workspace may result in the selection of a slow convolution + * algorithm, and therefore affect library performance. + * + * It should be assumed that the required workspace size is different for each convolution + * configuration. Therefore, typically this function should be called at least once for each + * convolution configuration used. + * + * Since the convolution configuration is determined by tensor and convolution descriptors, the user + * should ensure that all descriptors contain complete information. For example, if Group/Depthwise + * convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running + * this, and so on. * * @param handle MIOpen handle (input) * @param dyDesc Tensor descriptor for data input tensor dy (input) @@ -1870,10 +1910,14 @@ miopenConvolutionBackwardDataGetWorkSpaceSize(miopenHandle_t handle, * @param requestAlgoCount Number of algorithms to return kernel times (input) * @param returnedAlgoCount Pointer to number of algorithms returned (output) * @param perfResults Pointer to union of best algorithm for forward and backwards (output) - * @param workSpace Pointer to workspace required for the search (output) - * @param workSpaceSize Size in bytes of the memory needed for find (output) - * @param exhaustiveSearch A boolean to toggle a full search of all algorithms and configurations - * (input) + * @param workSpace Pointer to workspace buffer (input). + * @param workSpaceSize Size in bytes of the workspace buffer (input). + * The buffer must be allocated on the device by the caller. + * The size of the buffer should be determined by calling + * miopenConvolutionBackwardDataGetWorkSpaceSize(), see its + * documentation for details. + * @param exhaustiveSearch A boolean to toggle a full search of all algorithms + * and configurations (input) * @return miopenStatus_t */ MIOPEN_EXPORT miopenStatus_t @@ -1942,16 +1986,22 @@ miopenConvolutionBackwardData(miopenHandle_t handle, /*! @brief Get the GPU memory required for the backward weights convolution algorithm. * + * For given tensor and convolution descriptors, this function calculates and returns the minimum + * size of the workspace that must be provided to miopenFindConvolutionBackwardWeightsAlgorithm() in + * order for the latter to find the best candidate from the available backward weights convolution + * algorithms. * - * For a provided tensor descriptors and algorithm selection, this function calculates and returns - * the workspace size required for back propagation on data. This call is required and must be - * executed once before running miopenFindConvolutionBackwardWeightsAlgorithm() in order to - * determine - * the largest required allocation for the algorithm search; i.e., the maximum size of the memory - * needed from the set of potential backward weights convolution algorithm is returned. + * WARNING: Providing smaller workspace may result in the selection of a slow convolution + * algorithm, and therefore affect library performance. * - * If using Group/Depthwise convolution mode, call miopenSetConvolutionGroupCount() before running - * this. + * It should be assumed that the required workspace size is different for each convolution + * configuration. Therefore, typically this function should be called at least once for each + * convolution configuration used. + * + * Since the convolution configuration is determined by tensor and convolution descriptors, the user + * should ensure that all descriptors contain complete information. For example, if Group/Depthwise + * convolution mode is used, then miopenSetConvolutionGroupCount() should be called before running + * this, and so on. * * @param handle MIOpen handle (input) * @param dyDesc Tensor descriptor for data input tensor dy (input) @@ -2002,10 +2052,14 @@ miopenConvolutionBackwardWeightsGetWorkSpaceSize(miopenHandle_t handle, * @param requestAlgoCount Number of algorithms to return kernel times (input) * @param returnedAlgoCount Pointer to number of algorithms returned (output) * @param perfResults Pointer to union of best algorithm for forward and backwards (output) - * @param workSpace Pointer to workspace required for the search (output) - * @param workSpaceSize Size in bytes of the memory needed for find (output) - * @param exhaustiveSearch A boolean to toggle a full search of all algorithms and configurations - * (input) + * @param workSpace Pointer to workspace buffer (input). + * @param workSpaceSize Size in bytes of the workspace buffer (input). + * The buffer must be allocated on the device by the caller. + * The size of the buffer should be determined by calling + * miopenConvolutionBackwardWeightsGetWorkSpaceSize(), see its + * documentation for details. + * @param exhaustiveSearch A boolean to toggle a full search of all algorithms + * and configurations (input) * @return miopenStatus_t */ MIOPEN_EXPORT miopenStatus_t @@ -5305,6 +5359,9 @@ typedef enum miopenProblemDirectionForward = 0, miopenProblemDirectionBackward = 1, miopenProblemDirectionBackwardWeights = 2, +#ifdef MIOPEN_BETA_API + miopenProblemDirectionInference = 4, +#endif } miopenProblemDirection_t; /*! @enum miopenTensorArgumentId_t @@ -5334,22 +5391,56 @@ typedef enum miopenTensorMhaAmaxS = 18, miopenTensorMhaM = 19, miopenTensorMhaZInv = 20, + miopenTensorMhaDO = 21, + miopenTensorMhaDescaleO = 22, + miopenTensorMhaDescaleDO = 23, + miopenTensorMhaDescaleDS = 24, + miopenTensorMhaScaleDS = 25, + miopenTensorMhaScaleDQ = 26, + miopenTensorMhaScaleDK = 27, + miopenTensorMhaScaleDV = 28, + miopenTensorMhaDQ = 29, + miopenTensorMhaDK = 30, + miopenTensorMhaDV = 31, + miopenTensorMhaAmaxDQ = 32, + miopenTensorMhaAmaxDK = 33, + miopenTensorMhaAmaxDV = 34, + miopenTensorMhaAmaxDS = 35, #ifdef MIOPEN_BETA_API - miopenTensorActivationX = 21, - miopenTensorActivationY = 22, - miopenTensorActivationDX = 23, - miopenTensorActivationDY = 24, - miopenTensorBiasX = 25, - miopenTensorBiasY = 26, - miopenTensorBias = 27, - miopenTensorSoftmaxX = 28, - miopenTensorSoftmaxY = 29, - miopenTensorSoftmaxDX = 30, - miopenTensorSoftmaxDY = 31, + miopenTensorActivationX = 36, + miopenTensorActivationY = 37, + miopenTensorActivationDX = 38, + miopenTensorActivationDY = 39, + miopenTensorBiasX = 40, + miopenTensorBiasY = 41, + miopenTensorBias = 42, + miopenTensorSoftmaxX = 43, + miopenTensorSoftmaxY = 44, + miopenTensorSoftmaxDX = 45, + miopenTensorSoftmaxDY = 46, + miopenTensorBatchnormX = 47, + miopenTensorBatchnormY = 48, + miopenTensorBatchnormRunningMean = 49, + miopenTensorBatchnormRunningVariance = 50, + miopenTensorBatchnormSavedMean = 51, + miopenTensorBatchnormSavedVariance = 52, + miopenTensorBatchnormScale = 53, + miopenTensorBatchnormScaleDiff = 54, + miopenTensorBatchnormEstimatedMean = 55, + miopenTensorBatchnormEstimatedVariance = 56, + miopenTensorBatchnormBias = 57, + miopenTensorBatchnormBiasDiff = 58, + miopenTensorBatchnormDX = 59, + miopenTensorBatchnormDY = 60, #endif miopenTensorArgumentIsScalar = 1U << 31, + +#ifdef MIOPEN_BETA_API + miopenScalarBatchnormExpAvgFactor = miopenTensorArgumentIsScalar | 1, + miopenScalarBatchnormEpsilon = miopenTensorArgumentIsScalar | 2, +#endif } miopenTensorArgumentId_t; /*! @enum miopenTensorArgumentId_t @@ -5543,6 +5634,16 @@ MIOPEN_EXPORT miopenStatus_t miopenSetFindOptionPreallocatedTensor(miopenFindOpt miopenTensorArgumentId_t id, void* buffer); +/*! @brief Forces library to attach kernel binaries to solutions for later saving. This allows zero + * lookup miopenRunSolution calls after miopenLoadSolution. Default value is 0. + * + * @param options Options object to update + * @param attach 1 means attaching, 0 - skipping, any other value - reserved for future use + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenSetFindOptionAttachBinaries(miopenFindOptions_t options, + unsigned attach); + /*! @brief The miopenSolution object describes a prepared solution. */ MIOPEN_DECLARE_OBJECT(miopenSolution); @@ -5686,6 +5787,19 @@ miopenCreateActivationProblem(miopenProblem_t* problem, miopenActivationDescriptor_t operatorDesc, miopenProblemDirection_t direction); +/*! @brief Initializes a problem object describing an activation operation. + * @note As of now there is no way to actually get any solution for this kind of problems. + * + * @param problem Pointer to the problem to initialize + * @param mode Batchnorm mode + * @param direction Direction of the operation + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenCreateBatchnormProblem(miopenProblem_t* problem, + miopenBatchNormMode_t mode, + bool runningMeanVariance, + miopenProblemDirection_t direction); + /*! @brief Fuse two problems into a single one. Problems can be either regular, or fused. No * problems are disposed in the process, so the problem2 should be destroyed manually if it is not * needed anymore. @@ -5756,7 +5870,7 @@ typedef enum * * @param handle MIOpen Handle (input) * @param xDesc Tensor descriptor for data input tensor x (input) - * @param dim Dimensions to sum. (input) + * @param dim Dimension to sum. (input) * @param yDesc Tensor descriptor for output data tensor y (input) * @param sizeInBytes Pointer to data to return the minimum workspace size * @return miopenStatus_t @@ -5775,7 +5889,7 @@ MIOPEN_EXPORT miopenStatus_t miopenGetSumWorkspaceSize(miopenHandle_t handle, * @param workspaceSizeInBytes Size in bytes of the allocated workspace data (input) * @param xDesc Tensor descriptor for data input tensor x (input) * @param x Data tensor x (input) - * @param dim Dimensions to sum. (input) + * @param dim Dimension to sum. (input) * @param yDesc Tensor descriptor for output data tensor y (input) * @param y Data tensor y (output) * @return miopenStatus_t @@ -5796,24 +5910,57 @@ MIOPEN_EXPORT miopenStatus_t miopenSumForward(miopenHandle_t handle, #ifdef MIOPEN_BETA_API -/*! @ingroup argmax - * @brief Find the index of the maximum value of a tensor across dimensions. +/*! @ingroup ReduceExtreme + * @enum miopenReduceExtremeOp_t + * Reduction Extreme operation types + */ +typedef enum +{ + MIOPEN_REDUCE_EXTREME_ARGMIN = + 1, /*!< the operation is getting the minimum index of the reduced elements */ + MIOPEN_REDUCE_EXTREME_ARGMAX = + 2, /*!< the operation is getting the maximum index of the reduced elements */ + MIOPEN_REDUCE_EXTREME_MIN = + 3, /*!< the operation is getting the minimum value and index of the reduced elements */ + MIOPEN_REDUCE_EXTREME_MAX = + 4, /*!< the operation is getting the maximum value and index of the reduced elements */ + MIOPEN_REDUCE_CALCULATION_SUM = + 5, /*!< the operation is multiplying the values of the reduced elements */ +} miopenReduceExtremeOp_t; + +// ReduceExtreme APIs +/** @addtogroup ReduceExtreme + * + * @{ + */ + +/*! @brief Find the the extreme (minimum, maximum) value and index of a tensor across Dimension. * * @param handle MIOpen handle (input) * @param xDesc Tensor descriptor for data input tensor x (input) * @param x Data tensor x (input) - * @param dim Dimensions to reduce argmax. (input) - * @param yDesc Tensor descriptor for output indice data tensor y (input) + * @param dim Dimension to reduce argmax. (input) + * @param reduceExtremeOp Enumerant specifying the operation used by ReduceExtreme (input) + * @param yDesc Tensor descriptor for reduce data tensor y (input) * @param y Data tensor y (output) + * @param indiceDesc Tensor descriptor for reduce data tensor indice only int32_t + * (input) + * @param indice Data tensor indice (output) * @return miopenStatus_t */ -MIOPEN_EXPORT miopenStatus_t miopenArgmaxForward(miopenHandle_t handle, - const miopenTensorDescriptor_t xDesc, - const void* x, - const int32_t dim, - const miopenTensorDescriptor_t yDesc, - void* y); +MIOPEN_EXPORT miopenStatus_t +miopenReduceExtremeForward(miopenHandle_t handle, + const miopenTensorDescriptor_t xDesc, + const void* x, + const int32_t dim, + const miopenReduceExtremeOp_t reduceExtremeOp, + const miopenTensorDescriptor_t yDesc, + void* y, + const miopenTensorDescriptor_t indiceDesc, + void* indice); +/** @} */ +// CLOSEOUT REDUCEEXTREME DOXYGEN GROUP #endif #ifdef MIOPEN_BETA_API @@ -5863,6 +6010,155 @@ MIOPEN_EXPORT miopenStatus_t miopenGroupNormForward(miopenHandle_t handle, // CLOSEOUT groupnorm DOXYGEN GROUP #endif +#ifdef MIOPEN_BETA_API +// LayerNorm APIs +/** @addtogroup layernorm + * + * @{ + */ +/*! @brief Execute a add and layernorm forward layer + * + * @param handle MIOpen handle (input) + * @param mode LayerNorm mode (input) + * @param xDesc Tensor descriptor for data input tensor x (input) + * @param x Data tensor x (input) + * @param x2Desc Tensor descriptor for data input tensor x2 (input) + * @param x2 Data tensor x2 (input) + * @param weightDesc Tensor descriptor for data input tensor weight (input) + * @param weight Data tensor weight (input) + * @param biasDesc Tensor descriptor for data input tensor bias (input) + * @param bias Data tensor bias (input) + * @param epsilon Value to stablize inverse variance calculation (input) + * @param normalized_dim Nomalized dimensions in the input array (input) + * @param yDesc Tensor descriptor for output data tensor y (input) + * @param y Data tensor y (output) + * @param meanDesc Tensor descriptor for output data tensor mean (input) + * @param mean Data tensor mean (output) + * @param rstdDesc Tensor descriptor for output data tensor rstd (input) + * @param rstd Data tensor rstd (output) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenAddLayerNormForward(miopenHandle_t handle, + miopenNormMode_t mode, + const miopenTensorDescriptor_t xDesc, + const void* x, + const miopenTensorDescriptor_t x2Desc, + const void* x2, + const miopenTensorDescriptor_t weightDesc, + const void* weight, + const miopenTensorDescriptor_t biasDesc, + const void* bias, + const float epsilon, + const int32_t normalized_dim, + const miopenTensorDescriptor_t yDesc, + void* y, + const miopenTensorDescriptor_t meanDesc, + void* mean, + const miopenTensorDescriptor_t rstdDesc, + void* rstd); + +/** @} */ +// CLOSEOUT LAYERNORM DOXYGEN GROUP +#endif + +#ifdef MIOPEN_BETA_API +// LayerNorm APIs +/** @addtogroup layernorm + * + * @{ + */ +/*! @brief Execute a T5layernorm forward layer + * + * @param handle MIOpen handle (input) + * @param mode LayerNorm mode (input) + * @param xDesc Tensor descriptor for data input tensor x (input) + * @param x Data tensor x (input) + * @param weightDesc Tensor descriptor for data input tensor weight (input) + * @param weight Data tensor weight (input) + * @param epsilon Value to stablize inverse variance calculation (input) + * @param yDesc Tensor descriptor for output data tensor y (input) + * @param y Data tensor y (output) + * @param rstdDesc Tensor descriptor for output data tensor rstd (input) + * @param rstd Data tensor rstd (output) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenT5LayerNormForward(miopenHandle_t handle, + miopenNormMode_t mode, + const miopenTensorDescriptor_t xDesc, + const void* x, + const miopenTensorDescriptor_t weightDesc, + const void* weight, + const float epsilon, + const miopenTensorDescriptor_t yDesc, + void* y, + const miopenTensorDescriptor_t rstdDesc, + void* rstd); + +/*! @brief Helper function to query the minimum workspace size required by the T5layernorm backward + * call + * + * @param handle MIOpen Handle (input) + * @param mode LayerNorm mode (input) + * @param dyDesc Tensor descriptor for data input tensor dy (input) + * @param xDesc Tensor descriptor for data input tensor x (input) + * @param weightDesc Tensor descriptor for data input tensor weight (input) + * @param rstdDesc Tensor descriptor for data input tensor rstd (input) + * @param dxDesc Tensor descriptor for output data tensor dx (input) + * @param dwDesc Tensor descriptor for output data tensor dw (input) + * @param sizeInBytes Pointer to data to return the minimum workspace size + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t +miopenGetT5LayerNormBackwardWorkspaceSize(miopenHandle_t handle, + miopenNormMode_t mode, + const miopenTensorDescriptor_t dyDesc, + const miopenTensorDescriptor_t xDesc, + const miopenTensorDescriptor_t weightDesc, + const miopenTensorDescriptor_t rstdDesc, + const miopenTensorDescriptor_t dxDesc, + const miopenTensorDescriptor_t dwDesc, + size_t* sizeInBytes); + +/*! @brief Execute a T5layernorm backward layer + * + * @param handle MIOpen handle (input) + * @param mode LayerNorm mode (input) + * @param workspace Address of the allocated workspace data (input) + * @param workspaceSizeInBytes Size in bytes of the allocated workspace data (input) + * @param dyDesc Tensor descriptor for data input tensor dy (input) + * @param dy Data tensor dy (input) + * @param xDesc Tensor descriptor for output data tensor x (input) + * @param x Data tensor x (input) + * @param weightDesc Tensor descriptor for data input tensor weight (input) + * @param weight Data tensor weight (input) + * @param rstdDesc Tensor descriptor for output data tensor rstd (input) + * @param rstd Data tensor rstd (output) + * @param dxDesc Tensor descriptor for output data tensor dx (input) + * @param dx Data tensor dx (output) + * @param dwDesc Tensor descriptor for output data tensor dw (input) + * @param dw Data tensor dw (output) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenT5LayerNormBackward(miopenHandle_t handle, + miopenNormMode_t mode, + void* workspace, + size_t workspaceSizeInBytes, + const miopenTensorDescriptor_t dyDesc, + const void* dy, + const miopenTensorDescriptor_t xDesc, + const void* x, + const miopenTensorDescriptor_t weightDesc, + const void* weight, + const miopenTensorDescriptor_t rstdDesc, + const void* rstd, + const miopenTensorDescriptor_t dxDesc, + void* dx, + const miopenTensorDescriptor_t dwDesc, + void* dw); +/** @} */ +// CLOSEOUT LAYERNORM DOXYGEN GROUP +#endif + #ifdef MIOPEN_BETA_API // Graph API /** @addtogroup GraphAPI @@ -5900,6 +6196,7 @@ typedef enum MIOPEN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR, MIOPEN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR, MIOPEN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR, + MIOPEN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR, MIOPEN_BACKEND_OPERATION_RNG_DESCRIPTOR, MIOPEN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR, MIOPEN_BACKEND_OPERATIONGRAPH_DESCRIPTOR, @@ -5953,6 +6250,7 @@ typedef enum MIOPEN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE = 402, MIOPEN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS = 403, MIOPEN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS = 404, + MIOPEN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION = 405, MIOPEN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID = 500, MIOPEN_ATTR_INTERMEDIATE_INFO_SIZE = 501, @@ -6393,6 +6691,32 @@ typedef enum MIOPEN_RNG_DISTRIBUTION_NORMAL, } miopenRngDistribution_t; +typedef enum +{ + /* IDENTITY alpha = 1.0 and beta = 0.0 */ + /* SCALE alpha = 4.2 and beta = 0.0 */ + /* BILINEAR alpha = 3.2 and beta = 1.1 */ + /* ERROR_STATE alpha = 0.0 and beta = 3.1 */ + + DEFAULT = 0, /* alpha = 1.0 and beta = 0.0.*/ + SCALE = 1, /* alpha with some value and beta 0.0*/ + BILINEAR = 2, /* both alpha and beta with some value*/ + ERROR_STATE = 3, /* alpha 0.0 and beta with some value, this should not occur. + But used to check for errors.*/ +} miopenAlphaBetaCase_t; +/*! @brief Operation mode of CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR + * + * An enumerated type to indicate the operation mode of a CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR + */ +typedef enum +{ + MIOPEN_HEUR_MODE_INSTANT = 0, + MIOPEN_HEUR_MODE_B = 1, + MIOPEN_HEUR_MODE_FALLBACK = 2, + MIOPEN_HEUR_MODE_A = 3, + MIOPEN_HEUR_MODES_COUNT = 4, +} miopenBackendHeurMode_t; + /*! @brief Backend descriptor * * A typedef void pointer to one of many opaque descriptor structures. @@ -6583,6 +6907,707 @@ MIOPEN_EXPORT miopenStatus_t miopenBackendInitialize(miopenBackendDescriptor_t d // CLOSEOUT BackendAPI DOXYGEN GROUP #endif // MIOPEN_BETA_API +#ifdef MIOPEN_BETA_API +// FusedAdam APIs +/** @addtogroup SGD + * + * @{ + */ +/*! @brief Perform Fused Adam optimization for a single tensor (Adaptive Moment Estimation). + * + * This function implements the Fused Adam optimization algorithm. Adam, short for Adaptive Moment + * Estimation, extends the RMSProp optimizer. It combines the advantages of AdaGrad and RMSProp by + * adaptively adjusting learning rates for each parameter using the first and second moments of + * gradients. Fused Adam optimization efficiently combines multiple operations into a single kernel, + * reducing memory access overhead and improving performance. + * + * Additionally, Fused Adam can be utilized in both adam w and Automatic Mixed Precision (AMP), + * enabling accelerated model training and reduced memory consumption. AMP supports FP16 + * computation, optimizing model calculations using a mixture of FP32 and FP16 precision to enhance + * training speed. When utilizing AMP, FoundInf, ScaleGrad, and step tensors should be employed. In + * AMP mode, the execution of Adam is determined based on the FoundInf value. State Step accepts + * both int values and int tensors. If a Step tensor is employed, the step received as an int is + * disregarded, and if Adam is executed, the step tensor is incremented by 1. + * + * @code + * // Execute Adam + * miopenFusedAdam(handle, + * paramDesc, + * param, + * gradDesc, + * grad, + * expAvgDesc, + * expAvg, + * expAvgSqDesc, + * expAvgSq, + * NULL, // Unused maxExpAvgSqDesc because amsgrad is false + * NULL, + * NULL, // Unused stateStep Tensor because use step integer argument + * NULL, + * step, + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * false, // amsgrad + * false, // maximize + * false, // adamw + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * + * // Execute AdamW + * miopenFusedAdam(handle, + * paramDesc, + * param, + * gradDesc, + * grad, + * expAvgDesc, + * expAvg, + * expAvgSqDesc, + * expAvgSq, + * NULL, // Unused maxExpAvgSqDesc because amsgrad is false + * NULL, + * NULL, // Unused stateStep Tensor because use step integer argument + * NULL, + * step, + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * false, // amsgrad + * false, // maximize + * true, // adamw + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * + * // Execute AMP Adam + * miopenFusedAdam(handle, + * paramDesc, + * param, + * gradDesc, + * grad, + * expAvgDesc, + * expAvg, + * expAvgSqDesc, + * expAvgSq, + * NULL, // Unused maxExpAvgSqDesc because amsgrad is false + * NULL, + * stateStepDesc, + * stateStep, + * -1, // Ignore step value because stateStep Tensor is used + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * false, // amsgrad + * false, // maximize + * false, // adamw + * gradScaleDesc, + * gradScale, + * foundInfDesc, + * foundInf); + * @endcode + * + * @param handle MIOpen handle (input) + * @param paramDesc Tensor descriptor for the input parameter tensor (input) + * @param param Input parameter tensor (input) + * @param gradDesc Tensor descriptor for the input gradient tensor (input) + * @param grad Input gradient tensor (input) + * @param expAvgDesc Tensor descriptor for the input exponential moving average tensor + * (input) + * @param expAvg Input exponential moving average tensor (input) + * @param expAvgSqDesc Tensor descriptor for the input exponential moving average squared + * tensor (input) + * @param expAvgSq Input exponential moving average squared tensor (input) + * @param maxExpAvgSqDesc Tensor descriptor for the input maximum exponential moving average + * squared tensor. Used when amsgrad is true (input, optional) + * @param maxExpAvgSq Input maximum exponential moving average squared tensor. Used when + * amsgrad is true (input, optional) + * @param stateStepDesc Tensor descriptor for the input state step tensor (input) + * @param stateStep Input state step tensor (input) + * @param state_step Input state step. used when the step tensor is null (input) + * @param lr Learning rate (input) + * @param beta1 Coefficient used for computing the first moment running average of + * gradient (input) + * @param beta2 Coefficient used for computing the second moment running average of + * gradient (input) + * @param weight_decay Weight decay (input) + * @param eps Term added to the denominator to improve numerical stability (input) + * @param amsgrad Flag indicating whether to use the AMSGrad variant of Adam (input) + * @param maximize Flag indicating whether to maximize the objective with respect to the + * parameters (input) + * @param adamw If true, the operation becomes AdamW (input) + * @param gradScaleDesc Tensor descriptor for the input grad scale tensor (input, optional) + * @param gradScale Input grad scale tensor (input, optional) + * @param foundInfDesc Tensor descriptor for the input found inf tensor (input, optional) + * @param foundInf Tensor indicating the presence of inf or NaN in gradients. If true, + * skips operation and step update (input, optional) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenFusedAdam(miopenHandle_t handle, + const miopenTensorDescriptor_t paramDesc, + void* param, + const miopenTensorDescriptor_t gradDesc, + const void* grad, + const miopenTensorDescriptor_t expAvgDesc, + void* expAvg, + const miopenTensorDescriptor_t expAvgSqDesc, + void* expAvgSq, + const miopenTensorDescriptor_t maxExpAvgSqDesc, + void* maxExpAvgSq, + const miopenTensorDescriptor_t stateStepDesc, + void* stateStep, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf); + +/*! @brief Execute single tensor Adam optimization and receive the result in a separate output + * tensor. + * + * This function is equivalent to miopenFusedAdam but receives the result in a separate output + * tensor. + * @see miopenFusedAdam + * + * @code + * // Execute Adam + * miopenFusedAdamWithOutput(handle, + * paramInDesc, + * paramIn, + * paramOutDesc, + * paramOut, + * NULL, // Unused paramOutFloat16 tensor because is not amp + * NULL, + * gradInDesc, + * gradIn, + * expAvgInDesc, + * expAvgIn, + * expAvgOutDesc, + * expAvgOut, + * expAvgInSqDesc, + * expAvgSqIn, + * expAvgSqOutDesc, + * expAvgSqOut, + * NULL, // Unused maxExpAvgSqIn tensor because amsgrad is false + * NULL, + * NULL, // Unused maxExpAvgSqOut tensor because amsgrad is false + * NULL, + * NULL, // Unused stateStepIn tensor because use step integer argument + * NULL, + * NULL, // Unused stateStepOut tensor because use step integer argument + * NULL, + * step, + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * false, // amsgrad + * false, // maximize + * false, // adamw + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * + * // Execute Amp Adam + * miopenFusedAdamWithOutput(handle, + * paramInDesc, + * paramIn, + * paramOutDesc, + * paramOut, + * paramOutFloat16Desc, // paramOutFloat16 tensor is optional in amp + * paramOutFloat16, + * gradInDesc, + * gradIn, + * expAvgInDesc, + * expAvgIn, + * expAvgOutDesc, + * expAvgOut, + * expAvgInSqDesc, + * expAvgSqIn, + * expAvgSqIn, + * expAvgSqOutDesc, + * expAvgSqOut, + * NULL, // Unused maxExpAvgSqIn tensor because amsgrad is false + * NULL, + * NULL, // Unused maxExpAvgSqOut tensor because amsgrad is false + * NULL, + * stateStepInDesc, + * stateStepIn, + * stateStepOutDesc, + * stateStepOut + * -1, // Ignore step value because stateStep Tensor is used + * lr, beta1, beta2, weight_decay, eps, + * false, // amsgrad + * false, // maximize + * false, // adamw + * gradScaleDesc, + * gradScale, + * foundInfDesc, + * foundInf); + * @endcode + * + * @param handle MIOpen handle (input) + * @param paramInDesc Tensor descriptor for the input parameter tensor (input) + * @param paramIn Input parameter tensor (input) + * @param paramOutDesc Tensor descriptor for the output parameter tensor (input) + * @param paramOut Output parameter tensor (output) + * @param paramOutFloat16Desc Tensor descriptor for the output parameter tensor float16 (input, + * optional) + * @param paramOutFloat16 Output parameter tensor (output, optional) + * @param gradInDesc Tensor descriptor for the input gradient tensor (input) + * @param gradIn Input gradient tensor (input) + * @param expAvgInDesc Tensor descriptor for the input exponential moving average tensor + * (input) + * @param expAvgIn Input exponential moving average tensor (input) + * @param expAvgOutDesc Tensor descriptor for the output exponential moving average tensor + * (input) + * @param expAvgOut Output exponential moving average tensor (output) + * @param expAvgSqInDesc Tensor descriptor for the input exponential moving average squared + * tensor (input) + * @param expAvgSqIn Input exponential moving average squared tensor (input) + * @param expAvgSqOutDesc Tensor descriptor for the output exponential moving average squared + * tensor (input) + * @param expAvgSqOut Output exponential moving average squared tensor (output) + * @param maxExpAvgSqInDesc Tensor descriptor for the input maximum exponential moving average + * squared tensor. Used when amsgrad is true (input, optional) + * @param maxExpAvgSqIn Input maximum exponential moving average squared tensor. Used when + * amsgrad is true (input, optional) + * @param maxExpAvgSqOutDesc Tensor descriptor for the output maximum exponential moving average + * squared tensor. Used when amsgrad is true (input, optional) + * @param maxExpAvgSqOut Output maximum exponential moving average squared tensor. Used when + * amsgrad is true (output, optional) + * @param stateStepInDesc Tensor descriptor for the input state step tensor (input, optional) + * @param stateStepIn Input state step tensor (input, optional) + * @param stateStepOutDesc Tensor descriptor for the output state step tensor (input, optional) + * @param stateStepOut Output state step tensor that stores the updated step value. (output, + * optional) + * @param state_step Input state step, It is used when the step tensor is null. (input) + * @param lr Learning rate (input) + * @param beta1 Coefficient used for computing the first moment running average of + * gradient (input) + * @param beta2 Coefficient used for computing the second moment running average of + * gradient (input) + * @param weight_decay Weight decay (input) + * @param eps Term added to the denominator to improve numerical stability (input) + * @param amsgrad Flag indicating whether to use the AMSGrad variant of Adam (input) + * @param maximize Flag indicating whether to maximize the objective with respect to the + * parameters (input) + * @param adamw If it is true, the operation becomes AdamW (input) + * @param gradScaleDesc Tensor descriptor for the input grad scale tensor (input, optional) + * @param gradScale Input grad scale tensor (input, optional) + * @param foundInfDesc Tensor descriptor for the input found inf tensor (input, optional) + * @param foundInf Tensor indicating presence of inf or nan in gradients. If true, skips + * operation and step update. (input, optional) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t +miopenFusedAdamWithOutput(miopenHandle_t handle, + const miopenTensorDescriptor_t paramInDesc, + void* paramIn, + const miopenTensorDescriptor_t paramOutDesc, + void* paramOut, + const miopenTensorDescriptor_t paramOutFloat16Desc, + void* paramOutFloat16, + const miopenTensorDescriptor_t gradInDesc, + const void* gradIn, + const miopenTensorDescriptor_t expAvgInDesc, + void* expAvgIn, + const miopenTensorDescriptor_t expAvgOutDesc, + void* expAvgOut, + const miopenTensorDescriptor_t expAvgSqInDesc, + void* expAvgSqIn, + const miopenTensorDescriptor_t expAvgSqOutDesc, + void* expAvgSqOut, + const miopenTensorDescriptor_t maxExpAvgSqInDesc, + void* maxExpAvgSqIn, + const miopenTensorDescriptor_t maxExpAvgSqOutDesc, + void* maxExpAvgSqOut, + const miopenTensorDescriptor_t stateStepInDesc, + void* stateStepIn, + const miopenTensorDescriptor_t stateStepOutDesc, + void* stateStepOut, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf); + +/** @} */ +// CLOSEOUT SGD DOXYGEN GROUP +#endif // MIOPEN_BETA_API + +#ifdef MIOPEN_BETA_API +// TransformersAdamW APIs +/** @addtogroup SGD + * + * @{ + */ +/*! @brief Implements Adam algorithm with weight decay fix as introduced in + * Decoupled Weight Decay Regularization. + * This is the fused kernel version of AdamW included in the Hugging Face Transformers module. + * + * @see miopenFusedAdam + * + * @code + * // Execute Adam + * miopenTransformersAdamW(handle, + * paramDesc, + * param, + * gradDesc, + * grad, + * expAvgDesc, + * expAvg, + * expAvgSqDesc, + * expAvgSq, + * NULL, // Unused stateStep Tensor because use step integer argument + * NULL, + * step, + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * true, // correct_bias + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * + * // Execute AMP Adam + * miopenTransformersAdamW(handle, + * paramDesc, + * param, + * gradDesc, + * grad, + * expAvgDesc, + * expAvg, + * expAvgSqDesc, + * expAvgSq, + * stateStepDesc, + * stateStep, + * -1, // Ignore step value because stateStep Tensor is used + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * true, // correct_bias + * gradScaleDesc, + * gradScale, + * foundInfDesc, + * foundInf); + * @endcode + * + * @param handle MIOpen handle (input) + * @param paramDesc Tensor descriptor for the input parameter tensor (input) + * @param param Input parameter tensor (input) + * @param gradDesc Tensor descriptor for the input gradient tensor (input) + * @param grad Input gradient tensor (input) + * @param expAvgDesc Tensor descriptor for the input exponential moving average tensor + * (input) + * @param expAvg Input exponential moving average tensor (input) + * @param expAvgSqDesc Tensor descriptor for the input exponential moving average squared + * tensor (input) + * @param expAvgSq Input exponential moving average squared tensor (input) + * @param stateStepDesc Tensor descriptor for the input state step tensor (input) + * @param stateStep Input state step tensor (input) + * @param state_step Input state step. used when the step tensor is null (input) + * @param lr Learning rate (input) + * @param beta1 Coefficient used for computing the first moment running average of + * gradient (input) + * @param beta2 Coefficient used for computing the second moment running average of + * gradient (input) + * @param weight_decay Weight decay (input) + * @param eps Term added to the denominator to improve numerical stability (input) + * @param correct_bias Whether or not to correct bias in Adam (for instance, in Bert TF + * repository they use False). + * @param gradScaleDesc Tensor descriptor for the input grad scale tensor (input, optional) + * @param gradScale Input grad scale tensor (input, optional) + * @param foundInfDesc Tensor descriptor for the input found inf tensor (input, optional) + * @param foundInf Tensor indicating the presence of inf or NaN in gradients. If true, + * skips operation and step update (input, optional) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenTransformersAdamW(miopenHandle_t handle, + const miopenTensorDescriptor_t paramDesc, + void* param, + const miopenTensorDescriptor_t gradDesc, + const void* grad, + const miopenTensorDescriptor_t expAvgDesc, + void* expAvg, + const miopenTensorDescriptor_t expAvgSqDesc, + void* expAvgSq, + const miopenTensorDescriptor_t stateStepDesc, + void* stateStep, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool correct_bias, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf); + +/*! @brief Execute single tensor Adam optimization and receive the result in a separate output + * tensor. + * + * This function is equivalent to miopenTransformersAdam but receives the result in a separate + * output tensor. + * @see miopenTransformersAdamW + * @see miopenFusedAdamWithOutput + * + * @code + * // Execute Adam + * miopenTransformersAdamWWithOutput(handle, + * paramInDesc, + * paramIn, + * paramOutDesc, + * paramOut, + * NULL, // Unused paramOutFloat16 tensor because is not amp + * NULL, + * gradInDesc, + * gradIn, + * expAvgInDesc, + * expAvgIn, + * expAvgOutDesc, + * expAvgOut, + * expAvgInSqDesc, + * expAvgSqIn, + * expAvgSqOutDesc, + * expAvgSqOut, + * NULL, // Unused stateStepIn tensor because use step int + * NULL, + * NULL, // Unused stateStepOut tensor because use step int + * NULL, + * step, + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * -1, // step_size + * true, // correct_bias + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * + * // Execute Amp Adam + * miopenTransformersAdamWWithOutput(handle, + * paramInDesc, + * paramIn, + * paramOutDesc, + * paramOut, + * paramOutFloat16Desc, // optional in amp + * paramOutFloat16, + * gradInDesc, + * gradIn, + * expAvgInDesc, + * expAvgIn, + * expAvgOutDesc, + * expAvgOut, + * expAvgInSqDesc, + * expAvgSqIn, + * expAvgSqIn, + * expAvgSqOutDesc, + * expAvgSqOut, + * stateStepInDesc, + * stateStepIn, + * stateStepOutDesc, + * stateStepOut + * -1, // Ignore step value because stateStep Tensor is used + * lr, + * beta1, + * beta2, + * weight_decay, + * eps, + * -1, // step_size + * true, // correct_bias + * NULL, // Unused gradScale Tensor because not amp + * NULL, + * NULL, // Unused foundInf Tensor because not amp + * NULL); + * @endcode + * + * @param handle MIOpen handle (input) + * @param paramInDesc Tensor descriptor for the input parameter tensor (input) + * @param paramIn Input parameter tensor (input) + * @param paramOutDesc Tensor descriptor for the output parameter tensor (input) + * @param paramOut Output parameter tensor (output) + * @param paramOutFloat16Desc Tensor descriptor for the output parameter tensor float16 (input, + * optional) + * @param paramOutFloat16 Output parameter tensor (output, optional) + * @param gradInDesc Tensor descriptor for the input gradient tensor (input) + * @param gradIn Input gradient tensor (input) + * @param expAvgInDesc Tensor descriptor for the input exponential moving average tensor + * (input) + * @param expAvgIn Input exponential moving average tensor (input) + * @param expAvgOutDesc Tensor descriptor for the output exponential moving average tensor + * (input) + * @param expAvgOut Output exponential moving average tensor (output) + * @param expAvgSqInDesc Tensor descriptor for the input exponential moving average squared + * tensor (input) + * @param expAvgSqIn Input exponential moving average squared tensor (input) + * @param expAvgSqOutDesc Tensor descriptor for the output exponential moving average squared + * tensor (input) + * @param expAvgSqOut Output exponential moving average squared tensor (output) + * @param stateStepInDesc Tensor descriptor for the input state step tensor (input, optional) + * @param stateStepIn Input state step tensor (input, optional) + * @param stateStepOutDesc Tensor descriptor for the output state step tensor (input, optional) + * @param stateStepOut Output state step tensor that stores the updated step value. (output, + * optional) + * @param state_step Input state step, It is used when the step tensor is null. (input) + * @param lr Learning rate (input) + * @param beta1 Coefficient used for computing the first moment running average of + * gradient (input) + * @param beta2 Coefficient used for computing the second moment running average of + * gradient (input) + * @param weight_decay Weight decay (input) + * @param eps Term added to the denominator to improve numerical stability (input) + * @param step_size Pre-calculated step_size, used for performance enhancement (input) + * @param correct_bias Whether or not to correct bias in Adam (for instance, in Bert TF + * repository they use False) (input) + * @param gradScaleDesc Tensor descriptor for the input grad scale tensor (input, optional) + * @param gradScale Input grad scale tensor (input, optional) + * @param foundInfDesc Tensor descriptor for the input found inf tensor (input, optional) + * @param foundInf Tensor indicating presence of inf or nan in gradients. If true, skips + * operation and step update. (input, optional) + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t +miopenTransformersAdamWWithOutput(miopenHandle_t handle, + const miopenTensorDescriptor_t paramInDesc, + void* paramIn, + const miopenTensorDescriptor_t paramOutDesc, + void* paramOut, + const miopenTensorDescriptor_t paramOutFloat16Desc, + void* paramOutFloat16, + const miopenTensorDescriptor_t gradInDesc, + const void* gradIn, + const miopenTensorDescriptor_t expAvgInDesc, + void* expAvgIn, + const miopenTensorDescriptor_t expAvgOutDesc, + void* expAvgOut, + const miopenTensorDescriptor_t expAvgSqInDesc, + void* expAvgSqIn, + const miopenTensorDescriptor_t expAvgSqOutDesc, + void* expAvgSqOut, + const miopenTensorDescriptor_t stateStepInDesc, + void* stateStepIn, + const miopenTensorDescriptor_t stateStepOutDesc, + void* stateStepOut, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const float step_size, + const bool correct_bias, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf); + +/** @} */ +// CLOSEOUT SGD DOXYGEN GROUP +#endif // MIOPEN_BETA_API + +#ifdef MIOPEN_BETA_API +// GetItem APIs +/** @addtogroup getitem + * + * @{ + */ +/*! @brief Helper function to query the minimum workspace size required by the getitem call + * + * @param [in] handle MIOpen Handle + * @param [in] indexCount Number of input tensor indexs + * @param [in] indexDescs Tensor descriptor of input tensor indexs + * @param [out] sizeInBytes Pointer to data to return the minimum workspace size + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t +miopenGetGetitemWorkspaceSize(miopenHandle_t handle, + uint32_t indexCount, + const miopenTensorDescriptor_t* indexDescs, + size_t* sizeInBytes); + +/*! @brief Execute a getitem backward layer + * + * Backward of getitem for tensor indexing, slicing, masking. + * + * @param [in] handle MIOpen handle + * @param [in] workspace Address of the allocated workspace data + * @param [in] workspaceSizeInBytes Size in bytes of the allocated workspace data + * @param [in] dyDesc Tensor descriptor of input tensor dy + * @param [in] dy Source data tensor dy + * @param [in] indexCount Number of input tensor indexs + * @param [in] indexDescs Tensor descriptor of input tensor indexs(All indexs same + * size) + * @param [in] indexs Source data tensor indexs + * @param [in] dxDesc Tensor descriptor of output tensor dx + * @param [out] dx Data tensor dx(It must be initialized to 0) + * @param [in] errorDesc Tensor descriptor of output tensor error + * @param [out] error Data tensor error(It must be initialized to 0) + * @param [in] dimCount Number of dimensions + * @param [in] dims Dimensions + * @param [in] sliceCount Number of slices + * @param [in] slices Slices + * @param [in] offset Offset of output tensor dx + * @return miopenStatus_t + */ +MIOPEN_EXPORT miopenStatus_t miopenGetitemBackward(miopenHandle_t handle, + void* workspace, + size_t workspaceSizeInBytes, + const miopenTensorDescriptor_t dyDesc, + const void* dy, + uint32_t indexCount, + const miopenTensorDescriptor_t* indexDescs, + const void* const* indexs, + const miopenTensorDescriptor_t dxDesc, + void* dx, + const miopenTensorDescriptor_t errorDesc, + void* error, + uint32_t dimCount, + const int32_t* dims, + uint32_t sliceCount, + const int32_t* slices, + uint32_t offset); + +/** @} */ +// CLOSEOUT GETITEM DOXYGEN GROUP +#endif // MIOPEN_BETA_API + #ifdef MIOPEN_BETA_API /*! @ingroup interpolate diff --git a/requirements.txt b/requirements.txt index 2fe9fe073c..96b87492f7 100755 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,5 @@ nlohmann/json@v3.11.2 -DJSON_MultipleHeaders=ON -DJSON_BuildTests=Off ROCm/FunctionalPlus@v0.2.18-p0 ROCm/eigen@3.4.0 ROCm/frugally-deep@9683d557eb672ee2304f80f6682c51242d748a50 -ROCm/composable_kernel@687d2b7e0fbc8cf5f538ff9752cb6ee4e7e7ddf9 -DCMAKE_BUILD_TYPE=Release -DINSTANCES_ONLY=ON +ROCm/composable_kernel@15baccf2ecad4fb3498b8acb6bbf58fb5359c7a5 -DCMAKE_BUILD_TYPE=Release -DINSTANCES_ONLY=ON +google/googletest@v1.14.0 diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 4e7a0d0530..1cf4c35dbd 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -39,7 +39,7 @@ endif() # remain in the future) perform final conversion (and rounding) of FP32 # to BF16 results. This affects the main functionality of the library. option( MIOPEN_USE_RNE_BFLOAT16 "Sets rounding scheme for bfloat16 type" ON ) -option( MIOPEN_FP8_IEEE_EXPONENT_BIAS "Sets the FP8 exponent bias to IEEE" ON) +option( MIOPEN_FP8_IEEE_EXPONENT_BIAS "Sets the FP8 exponent bias to IEEE" OFF) option( MIOPEN_FP8_CLIPPING "Sets the FP8 clipping" ON) set ( MIOPEN_DEFAULT_FIND_MODE "DynamicHybrid" CACHE STRING "Sets the default find mode") set_property(CACHE MIOPEN_DEFAULT_FIND_MODE PROPERTY STRINGS @@ -75,8 +75,8 @@ function(add_kernels FILE_NAME VAR_PREFIX VAR_SUFFIX KERNEL_FILES) string(TOUPPER "${BASE_NAME}" KEY_NAME) string(MAKE_C_IDENTIFIER "${KEY_NAME}" VAR_NAME) string(APPEND KERNELS_DECLS "extern const size_t ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}_SIZE;\n") - string(APPEND KERNELS_DECLS "extern const unsigned char ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}[];\n") - list(APPEND INIT_KERNELS_LIST " { \"${KERNEL_FILENAME}\", std::string(reinterpret_cast(${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}), ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}_SIZE) }") + string(APPEND KERNELS_DECLS "extern const char ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}[];\n") + list(APPEND INIT_KERNELS_LIST " { \"${KERNEL_FILENAME}\", { ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}, ${VAR_PREFIX}${VAR_NAME}${VAR_SUFFIX}_SIZE } }") endforeach() string(REPLACE ";" ",\n" INIT_KERNELS "${INIT_KERNELS_LIST}") configure_file(kernels/${FILE_NAME}.in ${PROJECT_BINARY_DIR}/${FILE_NAME}) @@ -85,8 +85,10 @@ endfunction() set( MIOpen_Source activ/problem_description.cpp activ_api.cpp + adam/problem_description.cpp + adam_api.cpp + addlayernorm_api.cpp api/find2_0_commons.cpp - argmax_api.cpp batch_norm.cpp batch_norm_api.cpp batchnorm/problem_description.cpp @@ -97,10 +99,12 @@ set( MIOpen_Source conv/invokers/gcn_asm_1x1u.cpp conv/invokers/gcn_asm_1x1u_ss.cpp conv/invokers/gcn_asm_1x1u_us.cpp + conv/invokers/gcn_asm_wino.cpp conv/invokers/gen_x_w_y_pad.cpp conv/invokers/impl_gemm.cpp conv/invokers/impl_gemm_dynamic.cpp conv/invokers/ocl_wrw_rdc.cpp + conv/kernel_interface/winograd_kernel_interface.cpp conv/problem_description.cpp conv/solver_finders.cpp conv_algo_name.cpp @@ -113,6 +117,7 @@ set( MIOpen_Source driver_arguments.cpp dropout.cpp dropout_api.cpp + env.cpp execution_context.cpp expanduser.cpp find_controls.cpp @@ -121,10 +126,19 @@ set( MIOpen_Source fusion.cpp fusion/problem_description.cpp generic_search.cpp + getitem_api.cpp graphapi/convolution.cpp + graphapi/engine.cpp + graphapi/enginecfg.cpp + graphapi/engineheur.cpp + graphapi/execution_plan.cpp + graphapi/find_engine.cpp graphapi/graphapi.cpp + graphapi/matmul.cpp + graphapi/opgraph.cpp graphapi/pointwise.cpp graphapi/reduction.cpp + graphapi/reshape.cpp graphapi/rng.cpp graphapi/tensor.cpp graphapi/variant_pack.cpp @@ -132,6 +146,7 @@ set( MIOpen_Source groupnorm/problem_description.cpp handle_api.cpp invoker_cache.cpp + getitem/problem_description.cpp interpolate_api.cpp interpolate/problem_description.cpp kernel_build_params.cpp @@ -153,6 +168,7 @@ set( MIOpen_Source process.cpp ramdb.cpp readonlyramdb.cpp + reduceextreme_api.cpp reducetensor.cpp reducetensor_api.cpp reduce/problem_description.cpp @@ -160,6 +176,7 @@ set( MIOpen_Source rnn_api.cpp rnn/rnn_util.cpp rnn/Solutions/rnn_transformer.cpp + scalar.cpp softmax.cpp softmax_api.cpp softmax/problem_description.cpp @@ -169,6 +186,8 @@ set( MIOpen_Source solver/activ/bwd_1.cpp solver/activ/fwd_0.cpp solver/activ/fwd_1.cpp + solver/adam/adam.cpp + solver/adam/transformers_adam_w.cpp solver/batchnorm/backward_ck.cpp solver/batchnorm/backward_per_activation.cpp solver/batchnorm/backward_per_activation_fused.cpp @@ -183,85 +202,89 @@ set( MIOpen_Source solver/batchnorm/forward_spatial_single.cpp solver/batchnorm/forward_training_ck.cpp solver/cat/forward_cat.cpp - solver/conv_asm_1x1u.cpp + solver/conv/conv_asm_1x1u.cpp + solver/conv/conv_asm_1x1u_stride2.cpp + solver/conv/conv_asm_3x3u.cpp + solver/conv/conv_asm_5x10u2v2b1.cpp + solver/conv/conv_asm_5x10u2v2f1.cpp + solver/conv/conv_asm_7x7c3h224w224k64u2v2p3q3f1.cpp + solver/conv/conv_asm_dir_BwdWrW1x1.cpp + solver/conv/conv_asm_dir_BwdWrW3x3.cpp + solver/conv/conv_asm_implicit_gemm_bwd_v4r1_dynamic.cpp + solver/conv/conv_asm_implicit_gemm_gtc_bwd.cpp + solver/conv/conv_asm_implicit_gemm_gtc_bwd_nhwc.cpp + solver/conv/conv_asm_implicit_gemm_gtc_fwd.cpp + solver/conv/conv_asm_implicit_gemm_gtc_fwd_nhwc.cpp + solver/conv/conv_asm_implicit_gemm_gtc_fwd_nchwc.cpp + solver/conv/conv_asm_implicit_gemm_gtc_perf_config.cpp + solver/conv/conv_asm_implicit_gemm_gtc_wrw_nhwc.cpp + solver/conv/conv_asm_implicit_gemm_v4r1_dynamic.cpp + solver/conv/conv_asm_implicit_gemm_wrw_gtc_dynamic_xdlops.cpp + solver/conv/conv_asm_implicit_gemm_wrw_v4r1_dynamic.cpp + solver/conv/conv_bin_wino3x3U.cpp + solver/conv/conv_bin_winoRxS.cpp + solver/conv/conv_ck_igemm_fwd_v6r1_dlops_nchw.cpp + solver/conv/conv_direct_naive_conv.cpp + solver/conv/conv_direct_naive_conv_bwd.cpp + solver/conv/conv_direct_naive_conv_fwd.cpp + solver/conv/conv_direct_naive_conv_wrw.cpp + solver/conv/conv_hip_implicit_gemm_bwd_data_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_bwd_v1r1.cpp + solver/conv/conv_hip_implicit_gemm_bwd_v1r1_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_bwd_v4r1.cpp + solver/conv/conv_hip_implicit_gemm_bwd_v4r1_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_fwd_v4r1.cpp + solver/conv/conv_hip_implicit_gemm_fwd_v4r4.cpp + solver/conv/conv_hip_implicit_gemm_fwd_v4r4_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_fwd_v4r4_xdlops_padded_gemm.cpp + solver/conv/conv_hip_implicit_gemm_fwd_v4r5_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_fwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_grouped_fwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_grouped_bwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_grouped_wrw_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_3d_grouped_fwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_3d_grouped_wrw_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_3d_grouped_bwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_f16f8f16_fwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_f16f8f16_bwd_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_f16f8f16_wrw_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_nonxdlops_common.cpp + solver/conv/conv_hip_implicit_gemm_wrw_v4r4.cpp + solver/conv/conv_hip_implicit_gemm_wrw_v4r4_xdlops.cpp + solver/conv/conv_hip_implicit_gemm_wrw_v4r4_xdlops_padded_gemm.cpp + solver/conv/conv_MP_bidirectional_winograd.cpp + solver/conv/conv_mlir_igemm_bwd.cpp + solver/conv/conv_mlir_igemm_bwd_xdlops.cpp + solver/conv/conv_mlir_igemm_fwd.cpp + solver/conv/conv_mlir_igemm_fwd_xdlops.cpp + solver/conv/conv_mlir_igemm_wrw.cpp + solver/conv/conv_mlir_igemm_wrw_xdlops.cpp + solver/conv/conv_multipass_wino3x3WrW.cpp + solver/conv/conv_ocl_dir2D_bwdWrW_1x1.cpp + solver/conv/conv_ocl_dir2D_bwdWrW_2.cpp + solver/conv/conv_ocl_dir2D_bwdWrW_53.cpp + solver/conv/conv_ocl_dir2D11x11.cpp + solver/conv/conv_ocl_dir2Dfwd.cpp + solver/conv/conv_ocl_dir2Dfwd_exhaustive_search.cpp + solver/conv/conv_ocl_dir2Dfwd1x1.cpp + solver/conv/conv_ocl_dir2Dfwdgen.cpp + solver/conv/conv_wino_fury_RxS.cpp + solver/conv/conv_winoRxS.cpp + solver/conv/fft.cpp + solver/conv/gemm.cpp + solver/conv/gemm_bwd.cpp + solver/conv/gemm_common.cpp + solver/conv/gemm_wrw.cpp solver/conv_asm_1x1u_bias_activ_fused.cpp - solver/conv_asm_1x1u_stride2.cpp - solver/conv_asm_3x3u.cpp - solver/conv_asm_5x10u2v2b1.cpp - solver/conv_asm_5x10u2v2f1.cpp - solver/conv_asm_7x7c3h224w224k64u2v2p3q3f1.cpp - solver/conv_asm_dir_BwdWrW1x1.cpp - solver/conv_asm_dir_BwdWrW3x3.cpp - solver/conv_asm_implicit_gemm_bwd_v4r1_dynamic.cpp - solver/conv_asm_implicit_gemm_gtc_bwd.cpp - solver/conv_asm_implicit_gemm_gtc_bwd_nhwc.cpp - solver/conv_asm_implicit_gemm_gtc_fwd.cpp - solver/conv_asm_implicit_gemm_gtc_fwd_nhwc.cpp - solver/conv_asm_implicit_gemm_gtc_fwd_nchwc.cpp - solver/conv_asm_implicit_gemm_gtc_perf_config.cpp - solver/conv_asm_implicit_gemm_gtc_wrw_nhwc.cpp - solver/conv_asm_implicit_gemm_v4r1_dynamic.cpp - solver/conv_asm_implicit_gemm_wrw_gtc_dynamic_xdlops.cpp - solver/conv_asm_implicit_gemm_wrw_v4r1_dynamic.cpp - solver/conv_bin_wino3x3U.cpp - solver/conv_bin_winoRxS.cpp solver/conv_bin_winoRxS_fused.cpp - solver/conv_ck_igemm_fwd_v6r1_dlops_nchw.cpp solver/conv_ck_igemm_fwd_bias_activ_fused.cpp solver/conv_ck_igemm_fwd_bias_res_add_activ_fused.cpp - solver/conv_direct_naive_conv.cpp - solver/conv_direct_naive_conv_bwd.cpp - solver/conv_direct_naive_conv_fwd.cpp - solver/conv_direct_naive_conv_wrw.cpp - solver/conv_hip_implicit_gemm_bwd_data_xdlops.cpp - solver/conv_hip_implicit_gemm_bwd_v1r1.cpp - solver/conv_hip_implicit_gemm_bwd_v1r1_xdlops.cpp - solver/conv_hip_implicit_gemm_bwd_v4r1.cpp - solver/conv_hip_implicit_gemm_bwd_v4r1_xdlops.cpp - solver/conv_hip_implicit_gemm_fwd_v4r1.cpp - solver/conv_hip_implicit_gemm_fwd_v4r4.cpp - solver/conv_hip_implicit_gemm_fwd_v4r4_xdlops.cpp - solver/conv_hip_implicit_gemm_fwd_v4r4_xdlops_padded_gemm.cpp - solver/conv_hip_implicit_gemm_fwd_v4r5_xdlops.cpp - solver/conv_hip_implicit_gemm_fwd_xdlops.cpp - solver/conv_hip_implicit_gemm_grouped_fwd_xdlops.cpp - solver/conv_hip_implicit_gemm_grouped_bwd_xdlops.cpp - solver/conv_hip_implicit_gemm_grouped_wrw_xdlops.cpp - solver/conv_hip_implicit_gemm_3d_grouped_fwd_xdlops.cpp - solver/conv_hip_implicit_gemm_3d_grouped_wrw_xdlops.cpp - solver/conv_hip_implicit_gemm_3d_grouped_bwd_xdlops.cpp - solver/conv_hip_implicit_gemm_f16f8f16_fwd_xdlops.cpp - solver/conv_hip_implicit_gemm_f16f8f16_bwd_xdlops.cpp - solver/conv_hip_implicit_gemm_f16f8f16_wrw_xdlops.cpp - solver/conv_hip_implicit_gemm_nonxdlops_common.cpp - solver/conv_hip_implicit_gemm_wrw_v4r4.cpp - solver/conv_hip_implicit_gemm_wrw_v4r4_xdlops.cpp - solver/conv_hip_implicit_gemm_wrw_v4r4_xdlops_padded_gemm.cpp - solver/conv_mlir_igemm_bwd.cpp - solver/conv_mlir_igemm_bwd_xdlops.cpp - solver/conv_mlir_igemm_fwd.cpp - solver/conv_mlir_igemm_fwd_xdlops.cpp - solver/conv_mlir_igemm_wrw.cpp - solver/conv_mlir_igemm_wrw_xdlops.cpp - solver/conv_MP_bidirectional_winograd.cpp - solver/conv_multipass_wino3x3WrW.cpp - solver/conv_ocl_dir2D_bwdWrW_1x1.cpp - solver/conv_ocl_dir2D_bwdWrW_2.cpp - solver/conv_ocl_dir2D_bwdWrW_53.cpp - solver/conv_ocl_dir2D11x11.cpp - solver/conv_ocl_dir2Dfwd.cpp solver/conv_ocl_dir2Dfwd_fused.cpp - solver/conv_ocl_dir2Dfwd_exhaustive_search.cpp - solver/conv_ocl_dir2Dfwd1x1.cpp - solver/conv_ocl_dir2Dfwdgen.cpp - solver/conv_wino_fury_RxS.cpp - solver/conv_winoRxS.cpp solver/conv_winoRxS_fused.cpp - solver/fft.cpp - solver/gemm.cpp - solver/gemm_bwd.cpp - solver/gemm_wrw.cpp solver/groupnorm/forward_groupnorm.cpp + solver/getitem/backward_getitem.cpp + solver/layernorm/backward_t5layernorm.cpp + solver/layernorm/forward_addlayernorm.cpp solver/interpolate/bwd_bicubic_interpolate.cpp solver/interpolate/bwd_bilinear_interpolate.cpp solver/interpolate/bwd_linear_interpolate.cpp @@ -274,22 +297,29 @@ set( MIOpen_Source solver/layernorm/forward_layernorm.cpp solver/layernorm/forward_layernorm2d_ck.cpp solver/layernorm/forward_layernorm4d_ck.cpp - solver/mha/mha_solver.cpp + solver/layernorm/forward_t5layernorm.cpp + solver/mha/mha_solver_backward.cpp + solver/mha/mha_solver_forward.cpp solver/pooling/forward2d.cpp solver/pooling/forwardNaive.cpp solver/pooling/forwardNd.cpp solver/pooling/backward2d.cpp solver/pooling/backwardNd.cpp solver/reduce/forward_argmax.cpp + solver/reduce/forward_argmin.cpp + solver/reduce/forward_max.cpp + solver/reduce/forward_min.cpp solver/reduce/forward_sum.cpp solver/softmax/attn_softmax.cpp solver/softmax/softmax.cpp subbuffers.cpp sum_api.cpp + t5layernorm_api.cpp target_properties.cpp temp_file.cpp tensor.cpp tensor_api.cpp + transformers_adam_w_api.cpp seq_tensor.cpp ) @@ -416,11 +446,13 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN kernels/Conv_Winograd_v30_3_1_gfx11_fp32_f3x2_stride1.inc kernels/Conv_Winograd_v30_3_1_gfx11_fp32_f3x2_stride2.inc kernels/Conv_Winograd_v30_3_1_metadata.inc + kernels/MIOpenReduceExtreme.hpp kernels/bfloat16_dev.hpp kernels/conv_common.inc kernels/conv_sizes.inc kernels/float_types.h kernels/gpr_alloc.inc + kernels/hip_atomic.hpp kernels/hip_f8_impl.hpp kernels/hip_float8.hpp kernels/inst_wrappers.inc @@ -434,6 +466,10 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN kernels/stride_array.hpp kernels/tensor_view.hpp kernels/utilities.inc + kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c16_stride1.inc + kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c32_stride1.inc + kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1024vgprs_fp16_fp16acc_f2x3_c16_stride1.inc + kernels/winograd/Conv_Winograd_Fury_v2_4_1_metadata.inc kernels/workaround_issue_1431.hpp kernels/xform_bidirect_winograd_code.inc kernels/xform_data_filter.inc @@ -450,7 +486,7 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN ${GPU_REFERENCE_KERNEL_ASM} ${GPU_BATCHED_TRANSPOSE_KERNEL_HIP} ${GPU_GENERAL_TENSOR_REORDER_KERNEL_HIP_SOURCE} - kernels/MIOpenArgmax.cpp + kernels/MIOpenAdam.cpp kernels/MIOpenCat.cpp kernels/MIOpenCheckNumerics.cpp kernels/MIOpenBatchNormActivBwdPerAct.cl @@ -467,6 +503,7 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN kernels/MIOpenConvDirBatchNormActiv.cl kernels/MIOpenConvDirGenFwd.cl kernels/MIOpenGroupNorm.cpp + kernels/MIOpenGetitem.cpp kernels/MIOpenInterpolate.cpp kernels/MIOpenLayerNorm.cpp kernels/MIOpenLRNBwd.cl @@ -480,12 +517,15 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN kernels/MIOpenConv1x1S.cl kernels/MIOpenConv1x1J1.cl kernels/MIOpenConv1x1J1_stride.cl + kernels/MIOpenReduceExtreme.cpp kernels/MIOpenSoftmax.cl kernels/MIOpenSoftmaxAttn.cpp kernels/MIOpenSum.cpp kernels/MIOpenUtilKernels3.cl kernels/MIOpenUtilKernels4.cl kernels/MIOpenUtilKernels5.cl + kernels/MIOpenVecAdd.cpp + kernels/MIOpenVecAddOCL.cl kernels/MIOpenIm2d2Col.cl kernels/MIOpenIm3d2Col.cl kernels/MIOpenCol2Im2d.cl @@ -552,11 +592,12 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN kernels/Conv_Winograd_v30_3_1_fp32_f3x2_dilation2.s kernels/Conv_Winograd_v30_3_1_fp32_f3x2_stride1.s kernels/Conv_Winograd_v30_3_1_fp32_f3x2_stride2.s - kernels/Conv_Winograd_Fury_v1_1_1_fp16_fp16acc_f2x3_stride1.s kernels/MIOpenConvBwdBias.cl kernels/MIOpenBatchNormActivInfer.cl kernels/MIOpenCTCLoss.cl kernels/MIOpenDropout.cl + kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c16_stride1.s + kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c32_stride1.s kernels/xform_data.s kernels/xform_filter.s kernels/xform_bidirect_winograd_data.s @@ -589,12 +630,14 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN configure_file(db_path.cpp.in ${PROJECT_BINARY_DIR}/db_path.cpp) list(APPEND MIOpen_Source activ.cpp - argmax.cpp + adam.cpp + addlayernorm.cpp cat.cpp groupnorm.cpp + getitem.cpp interpolate.cpp kernel_cache.cpp - layer_norm.cpp + layernorm.cpp lrn.cpp mlo_dir_conv.cpp exec_utils.cpp @@ -615,9 +658,12 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN hip/batched_transpose_sol.cpp hip/general_tensor_reorder_sol.cpp pooling.cpp + t5layernorm.cpp ocl/fusionopconvocl.cpp ocl/fusionopbiasbnactivocl.cpp + reduceextreme.cpp sum.cpp + transformers_adam_w.cpp ${PROJECT_BINARY_DIR}/db_path.cpp ) @@ -627,7 +673,7 @@ if( MIOPEN_BACKEND MATCHES "OpenCL" OR MIOPEN_BACKEND STREQUAL "HIPOC" OR MIOPEN ) endif() -if(MIOPEN_USE_ROCBLAS) +if(MIOPEN_USE_ROCBLAS OR MIOPEN_USE_HIPBLASLT) list(APPEND MIOpen_Source gemm_v2.cpp ) @@ -720,9 +766,9 @@ endif() if(MIOPEN_USE_MLIR) list(APPEND MIOpen_Source - mlir_build.cpp - solver/mlir_common.cpp conv/invokers/mlir_impl_gemm.cpp + mlir_build.cpp + solver/conv/mlir_common.cpp ) endif() @@ -742,6 +788,10 @@ rocm_set_soversion(MIOpen ${MIOpen_SOVERSION}) clang_tidy_check(MIOpen) +if(HAS_LIB_STD_FILESYSTEM) + target_link_libraries(MIOpen PRIVATE stdc++fs) +endif() + find_package(zstd) if(zstd_FOUND) target_link_libraries(MIOpen PRIVATE $,zstd::libzstd_shared,zstd::libzstd_static>) @@ -757,7 +807,7 @@ target_include_directories(MIOpen PUBLIC ) if(MIOPEN_USE_COMPOSABLEKERNEL) -set(MIOPEN_CK_LINK_FLAGS composable_kernel::device_other_operations composable_kernel::device_gemm_operations composable_kernel::device_conv_operations composable_kernel::device_contraction_operations composable_kernel::device_reduction_operations hip::host) +set(MIOPEN_CK_LINK_FLAGS composable_kernel::device_other_operations composable_kernel::device_gemm_operations composable_kernel::device_conv_operations composable_kernel::device_reduction_operations hip::host) endif() if(WIN32) @@ -772,12 +822,13 @@ target_link_libraries(MIOpen PRIVATE ${CMAKE_DL_LIBS} Threads::Threads BZip2::BZ miopen_generate_export_header(MIOpen) if(BUILD_TESTING) - # On Windows, export all symbols only when tests are built, too. - # The officially released binaries must not have internals exposed - # because it violates threat model requirements. - # The property applies only to MS-compatible tools on Windows, and - # is ignored for the rest. - set_target_properties(MIOpen PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON) + # On Windows, export selected internal symbols only when tests are built. The officially released + # binaries must not have internals exposed because doing so violates the threats model requirements. + # We cannot use the CMake property CMAKE_EXPORT_ALL_SYMBOLS here because the number of automatically + # exported symbols exceeds the maximum allowed number in a DLL library (64K). + # See details here: https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll?view=msvc-170 + generate_export_header(MIOpen BASE_NAME MIOPEN_INTERNALS EXPORT_FILE_NAME ${CMAKE_BINARY_DIR}/include/miopen/export_internals.h) + target_compile_definitions(MIOpen PUBLIC $) endif() if(MIOPEN_ENABLE_AI_KERNEL_TUNING OR MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK) @@ -793,7 +844,7 @@ if(MIOPEN_ENABLE_AI_KERNEL_TUNING OR MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK) endif() foreach(MODEL_FILE ${MODEL_FILES}) get_filename_component(MODEL_FILE_FILENAME "${MODEL_FILE}" NAME) - configure_file("${MODEL_FILE}" "${PROJECT_BINARY_DIR}/${DATABASE_INSTALL_DIR}/${MODEL_FILE_FILENAME}" COPYONLY) + configure_file("${MODEL_FILE}" "${PROJECT_BINARY_DIR}/${DATABASE_INSTALL_DIR}/${MODEL_FILE_FILENAME}" COPYONLY) endforeach() endif() @@ -839,6 +890,10 @@ if(rocblas_FOUND) list(APPEND PACKAGE_STATIC_DEPENDS PACKAGE rocblas) endif() +if(hipblaslt_FOUND) + target_link_libraries( MIOpen PRIVATE roc::hipblaslt ) +endif() + # For backward compatibility with ROCm 5.3 # Build with library libMLIRMIOpen if(LIBMLIRMIOPEN) diff --git a/src/activ_api.cpp b/src/activ_api.cpp index de5cf25ed5..32b854ed04 100644 --- a/src/activ_api.cpp +++ b/src/activ_api.cpp @@ -36,7 +36,10 @@ extern "C" miopenStatus_t miopenCreateActivationDescriptor(miopenActivationDescr { MIOPEN_LOG_FUNCTION(activDesc); - return miopen::try_([&] { miopen::deref(activDesc) = new miopen::ActivationDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(activDesc); + desc = new miopen::ActivationDescriptor(); + }); } extern "C" miopenStatus_t miopenSetActivationDescriptor(miopenActivationDescriptor_t activDesc, diff --git a/src/adam.cpp b/src/adam.cpp new file mode 100644 index 0000000000..fedc32791d --- /dev/null +++ b/src/adam.cpp @@ -0,0 +1,138 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace miopen { + +miopenStatus_t Adam(Handle& handle, + const TensorDescriptor& paramInDesc, + ConstData_t paramIn, + const TensorDescriptor& paramOutDesc, + Data_t paramOut, + const TensorDescriptor& paramOutFloat16Desc, + Data_t paramOutFloat16, + const TensorDescriptor& gradInDesc, + ConstData_t gradIn, + const TensorDescriptor& expAvgInDesc, + ConstData_t expAvgIn, + const TensorDescriptor& expAvgOutDesc, + Data_t expAvgOut, + const TensorDescriptor& expAvgSqInDesc, + ConstData_t expAvgSqIn, + const TensorDescriptor& expAvgSqOutDesc, + Data_t expAvgSqOut, + const TensorDescriptor& maxExpAvgSqInDesc, + ConstData_t maxExpAvgSqIn, + const TensorDescriptor& maxExpAvgSqOutDesc, + Data_t maxExpAvgSqOut, + const TensorDescriptor& gradScaleDesc, + ConstData_t gradScale, + const TensorDescriptor& foundInfDesc, + ConstData_t foundInf, + const TensorDescriptor& stepInDesc, + ConstData_t stepIn, + const TensorDescriptor& stepOutDesc, + Data_t stepOut, + const uint32_t step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const bool is_amp) +{ + const auto problem = adam::ProblemDescription{paramInDesc, + paramOutDesc, + paramOutFloat16Desc, + gradInDesc, + expAvgInDesc, + expAvgOutDesc, + expAvgSqInDesc, + expAvgSqOutDesc, + maxExpAvgSqInDesc, + maxExpAvgSqOutDesc, + gradScaleDesc, + foundInfDesc, + stepInDesc, + stepOutDesc, + amsgrad, + true, + adamw, + is_amp}; + + const auto invoke_params = [&]() { + auto tmp = adam::AdamInvokeParams{}; + tmp.type = InvokeType::Run; + + tmp.paramDesc = ¶mInDesc; + tmp.gradDesc = &gradInDesc; + tmp.paramIn = paramIn; + tmp.paramOut = paramOut; + tmp.paramOutFloat16 = paramOutFloat16; + tmp.gradIn = gradIn; + tmp.expAvgIn = expAvgIn; + tmp.expAvgOut = expAvgOut; + tmp.expAvgSqIn = expAvgSqIn; + tmp.expAvgSqOut = expAvgSqOut; + tmp.maxExpAvgSqIn = maxExpAvgSqIn; + tmp.maxExpAvgSqOut = maxExpAvgSqOut; + tmp.gradScale = gradScale; + tmp.foundInf = foundInf; + tmp.stepIn = stepIn; + tmp.stepOut = stepOut; + + tmp.step = step; + tmp.lr = lr; + tmp.beta1 = beta1; + tmp.beta2 = beta2; + tmp.weight_decay = weight_decay; + tmp.eps = eps; + tmp.amsgrad = amsgrad; + tmp.maximize = maximize; + tmp.adamw = adamw; + + return tmp; + }(); + + const auto algo = AlgorithmName{"Adam"}; + const auto solvers = solver::SolverContainer{}; + solvers.ExecutePrimitive(handle, problem, algo, invoke_params); + + return miopenStatusSuccess; +} + +} // namespace miopen diff --git a/src/adam/problem_description.cpp b/src/adam/problem_description.cpp new file mode 100644 index 0000000000..8c214cd443 --- /dev/null +++ b/src/adam/problem_description.cpp @@ -0,0 +1,63 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include + +#include + +namespace miopen { + +namespace adam { + +NetworkConfig ProblemDescription::MakeNetworkConfig() const +{ + auto dtype = paramInDesc.GetType(); + auto kernel = IsAmp() ? "ampadam" : "adam"; + auto step_ind = ExistStepTensor() ? "device" : "host"; + + std::ostringstream ss; + + ss << kernel; + if(IsAdamW()) + ss << "w"; + if(IsAllContiguous()) + ss << "cont"; + ss << "step" << step_ind; + ss << "dtype" << dtype; + if(IsAmp()) + { + auto grad_dtype = gradInDesc.GetType(); + ss << "grad_dtype" << grad_dtype; + } + + return NetworkConfig{ss.str()}; +} + +} // namespace adam + +} // namespace miopen diff --git a/src/adam_api.cpp b/src/adam_api.cpp new file mode 100644 index 0000000000..3911b9fc9d --- /dev/null +++ b/src/adam_api.cpp @@ -0,0 +1,306 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include +#include +#include +#include +#include + +static void LogCmdAdam(const miopenTensorDescriptor_t paramDesc, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const bool is_amp) +{ + if(miopen::IsLoggingCmd()) + { + std::stringstream ss; + auto dtype = miopen::deref(paramDesc).GetType(); + if(is_amp) + { + ss << "ampadam"; + } + else + { + ss << "adam"; + } + if(adamw) + { + ss << "w"; + } + + if(dtype == miopenHalf) + { + ss << "fp16"; + } + + std::string batch_sz; + auto dims = miopen::deref(paramDesc).GetLengths(); + for(auto dim : dims) + { + batch_sz += std::to_string(dim); + batch_sz += "x"; + } + batch_sz.pop_back(); + ss << " -d " << batch_sz << " -l " << lr << " -1 " << beta1 << " -2 " << beta2 << " -e " + << eps << " -W " << weight_decay << " -a " << amsgrad << " -m " << maximize; + MIOPEN_LOG_DRIVER_CMD(ss.str()); + } +} + +#define CHECK_DESC_EXIST(desc) (((desc) != nullptr) ? miopen::deref(desc) : dummyDesc) + +extern "C" miopenStatus_t miopenFusedAdam(miopenHandle_t handle, + const miopenTensorDescriptor_t paramDesc, + void* param, + const miopenTensorDescriptor_t gradDesc, + const void* grad, + const miopenTensorDescriptor_t expAvgDesc, + void* expAvg, + const miopenTensorDescriptor_t expAvgSqDesc, + void* expAvgSq, + const miopenTensorDescriptor_t maxExpAvgSqDesc, + void* maxExpAvgSq, + const miopenTensorDescriptor_t stateStepDesc, + void* stateStep, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf) +{ + MIOPEN_LOG_FUNCTION(handle, + paramDesc, + param, + gradDesc, + grad, + expAvgDesc, + expAvg, + expAvgSqDesc, + expAvgSq, + maxExpAvgSqDesc, + maxExpAvgSq, + stateStepDesc, + stateStep, + state_step, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + gradScaleDesc, + gradScale, + foundInfDesc, + foundInf); + + const miopen::TensorDescriptor dummyDesc; + bool is_amp = (foundInfDesc != nullptr || gradScaleDesc != nullptr); + + LogCmdAdam(paramDesc, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, adamw, is_amp); + + return miopen::try_([&] { + miopen::Adam(miopen::deref(handle), + miopen::deref(paramDesc), + DataCast(param), + miopen::deref(paramDesc), + DataCast(param), + dummyDesc, + nullptr, + miopen::deref(gradDesc), + DataCast(grad), + miopen::deref(expAvgDesc), + DataCast(expAvg), + miopen::deref(expAvgDesc), + DataCast(expAvg), + miopen::deref(expAvgSqDesc), + DataCast(expAvgSq), + miopen::deref(expAvgSqDesc), + DataCast(expAvgSq), + CHECK_DESC_EXIST(maxExpAvgSqDesc), + DataCast(maxExpAvgSq), + CHECK_DESC_EXIST(maxExpAvgSqDesc), + DataCast(maxExpAvgSq), + CHECK_DESC_EXIST(gradScaleDesc), + DataCast(gradScale), + CHECK_DESC_EXIST(foundInfDesc), + DataCast(foundInf), + CHECK_DESC_EXIST(stateStepDesc), + DataCast(stateStep), + CHECK_DESC_EXIST(stateStepDesc), + DataCast(stateStep), + state_step, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + is_amp); + }); +} + +extern "C" miopenStatus_t +miopenFusedAdamWithOutput(miopenHandle_t handle, + const miopenTensorDescriptor_t paramInDesc, + void* paramIn, + const miopenTensorDescriptor_t paramOutDesc, + void* paramOut, + const miopenTensorDescriptor_t paramOutFloat16Desc, + void* paramOutFloat16, + const miopenTensorDescriptor_t gradInDesc, + const void* gradIn, + const miopenTensorDescriptor_t expAvgInDesc, + void* expAvgIn, + const miopenTensorDescriptor_t expAvgOutDesc, + void* expAvgOut, + const miopenTensorDescriptor_t expAvgSqInDesc, + void* expAvgSqIn, + const miopenTensorDescriptor_t expAvgSqOutDesc, + void* expAvgSqOut, + const miopenTensorDescriptor_t maxExpAvgSqInDesc, + void* maxExpAvgSqIn, + const miopenTensorDescriptor_t maxExpAvgSqOutDesc, + void* maxExpAvgSqOut, + const miopenTensorDescriptor_t stateStepInDesc, + void* stateStepIn, + const miopenTensorDescriptor_t stateStepOutDesc, + void* stateStepOut, + const unsigned int state_step, + const float lr, + const float beta1, + const float beta2, + const float weight_decay, + const float eps, + const bool amsgrad, + const bool maximize, + const bool adamw, + const miopenTensorDescriptor_t gradScaleDesc, + const void* gradScale, + const miopenTensorDescriptor_t foundInfDesc, + const void* foundInf) +{ + MIOPEN_LOG_FUNCTION(handle, + paramInDesc, + paramIn, + paramOutDesc, + paramOut, + gradInDesc, + gradIn, + expAvgInDesc, + expAvgIn, + expAvgOutDesc, + expAvgOut, + expAvgSqInDesc, + expAvgSqIn, + expAvgSqOutDesc, + expAvgSqOut, + maxExpAvgSqInDesc, + maxExpAvgSqIn, + maxExpAvgSqOutDesc, + maxExpAvgSqOut, + stateStepInDesc, + stateStepIn, + stateStepOutDesc, + stateStepOut, + state_step, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + gradScaleDesc, + gradScale, + foundInfDesc, + foundInf); + + const miopen::TensorDescriptor dummyDesc; + bool is_amp = (foundInfDesc != nullptr || gradScaleDesc != nullptr); + + LogCmdAdam(paramInDesc, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, adamw, is_amp); + + return miopen::try_([&] { + miopen::Adam(miopen::deref(handle), + miopen::deref(paramInDesc), + DataCast(paramIn), + miopen::deref(paramOutDesc), + DataCast(paramOut), + CHECK_DESC_EXIST(paramOutFloat16Desc), + DataCast(paramOutFloat16), + miopen::deref(gradInDesc), + DataCast(gradIn), + miopen::deref(expAvgInDesc), + DataCast(expAvgIn), + miopen::deref(expAvgOutDesc), + DataCast(expAvgOut), + miopen::deref(expAvgSqInDesc), + DataCast(expAvgSqIn), + miopen::deref(expAvgSqOutDesc), + DataCast(expAvgSqOut), + CHECK_DESC_EXIST(maxExpAvgSqInDesc), + DataCast(maxExpAvgSqIn), + CHECK_DESC_EXIST(maxExpAvgSqOutDesc), + DataCast(maxExpAvgSqOut), + CHECK_DESC_EXIST(gradScaleDesc), + DataCast(gradScale), + CHECK_DESC_EXIST(foundInfDesc), + DataCast(foundInf), + CHECK_DESC_EXIST(stateStepInDesc), + DataCast(stateStepIn), + CHECK_DESC_EXIST(stateStepOutDesc), + DataCast(stateStepOut), + state_step, + lr, + beta1, + beta2, + weight_decay, + eps, + amsgrad, + maximize, + adamw, + is_amp); + }); +} diff --git a/src/addlayernorm.cpp b/src/addlayernorm.cpp new file mode 100644 index 0000000000..271c81ca60 --- /dev/null +++ b/src/addlayernorm.cpp @@ -0,0 +1,92 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include +#include +#include +#include +#include + +namespace miopen { + +miopenStatus_t AddLayerNormForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& x2Desc, + ConstData_t x2, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& biasDesc, + ConstData_t bias, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& meanDesc, + Data_t mean, + const TensorDescriptor& rstdDesc, + Data_t rstd, + miopenNormMode_t mode, + float epsilon, + int32_t normalized_dim) +{ + const auto problem = layernorm::ProblemDescription{mode, + xDesc, + x2Desc, + weightDesc, + biasDesc, + yDesc, + meanDesc, + rstdDesc, + epsilon, + normalized_dim}; + + const auto invoke_params = [&]() { + auto tmp = layernorm::AddInvokeParams{}; + tmp.type = InvokeType::Run; + tmp.xDesc = &xDesc; + tmp.x = x; + tmp.x2 = x2; + tmp.weight = weight; + tmp.bias = bias; + tmp.y = y; + tmp.mean = mean; + tmp.rstd = rstd; + tmp.epsilon = epsilon; + tmp.normalized_dim = normalized_dim; + tmp.mode = mode; + return tmp; + }(); + + const auto algo = AlgorithmName{"AddLayerNormForward"}; + const auto solvers = solver::SolverContainer{}; + + solvers.ExecutePrimitive(handle, problem, algo, invoke_params); + + return miopenStatusSuccess; +} + +} // namespace miopen diff --git a/src/addlayernorm_api.cpp b/src/addlayernorm_api.cpp new file mode 100644 index 0000000000..8b9ed7e969 --- /dev/null +++ b/src/addlayernorm_api.cpp @@ -0,0 +1,138 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include +#include +#include + +static void +LogCmdAddLayerNorm(const miopenTensorDescriptor_t xDesc, const miopenNormMode_t mode, bool is_fwd) +{ + if(miopen::IsLoggingCmd()) + { + std::stringstream ss; + auto dtype = miopen::deref(xDesc).GetType(); + if(dtype == miopenHalf) + { + ss << "addlayernormfp16"; + } + else if(dtype == miopenFloat) + { + ss << "addlayernormfp32"; + } + else if(dtype == miopenBFloat16) + { + ss << "addlayernormbfp16"; + } + + int32_t size = {0}; + miopenGetTensorDescriptorSize(xDesc, &size); + ss << " -n " << miopen::deref(xDesc).GetLengths()[0] << " -c " + << miopen::deref(xDesc).GetLengths()[1]; + if(size == 5) + { + ss << " -D " << miopen::deref(xDesc).GetLengths()[2] << " -H " + << miopen::deref(xDesc).GetLengths()[3] << " -W " + << miopen::deref(xDesc).GetLengths()[4]; + } + else if(size == 4) + { + ss << " -H " << miopen::deref(xDesc).GetLengths()[2] << " -W " + << miopen::deref(xDesc).GetLengths()[3]; + } + else if(size == 3) + { + ss << " -W " << miopen::deref(xDesc).GetLengths()[2]; + } + + ss << " -F " << ((is_fwd) ? "1" : "2") << " -m " << mode; + + MIOPEN_LOG_DRIVER_CMD(ss.str()); + } +} + +extern "C" miopenStatus_t miopenAddLayerNormForward(miopenHandle_t handle, + miopenNormMode_t mode, + const miopenTensorDescriptor_t xDesc, + const void* x, + const miopenTensorDescriptor_t x2Desc, + const void* x2, + const miopenTensorDescriptor_t weightDesc, + const void* weight, + const miopenTensorDescriptor_t biasDesc, + const void* bias, + const float epsilon, + const int32_t normalized_dim, + const miopenTensorDescriptor_t yDesc, + void* y, + const miopenTensorDescriptor_t meanDesc, + void* mean, + const miopenTensorDescriptor_t rstdDesc, + void* rstd) +{ + MIOPEN_LOG_FUNCTION(handle, + mode, + xDesc, + x, + x2Desc, + x2, + weightDesc, + weight, + biasDesc, + bias, + epsilon, + normalized_dim, + yDesc, + y, + meanDesc, + mean, + rstdDesc, + rstd); + + LogCmdAddLayerNorm(xDesc, mode, true); + return miopen::try_([&] { + miopen::AddLayerNormForward(miopen::deref(handle), + miopen::deref(xDesc), + DataCast(x), + miopen::deref(x2Desc), + DataCast(x2), + miopen::deref(weightDesc), + DataCast(weight), + miopen::deref(biasDesc), + DataCast(bias), + miopen::deref(yDesc), + DataCast(y), + miopen::deref(meanDesc), + DataCast(mean), + miopen::deref(rstdDesc), + DataCast(rstd), + mode, + epsilon, + normalized_dim); + }); +} diff --git a/src/anyramdb.cpp b/src/anyramdb.cpp index 9032de918a..3d717c962d 100644 --- a/src/anyramdb.cpp +++ b/src/anyramdb.cpp @@ -52,27 +52,18 @@ static std::chrono::seconds GetLockTimeout() { return std::chrono::seconds{60}; using exclusive_lock = std::unique_lock; -AnyRamDb& AnyRamDb::GetCached(const std::string& path) +AnyRamDb& AnyRamDb::GetCached(const fs::path& path) { static std::mutex mutex; const std::lock_guard lock{mutex}; - static auto instances = std::map{}; + static auto instances = std::map>{}; const auto it = instances.find(path); if(it != instances.end()) return *it->second; - // The AnyRamDb objects allocated here by "new" shall be alive during - // the calling app lifetime. Size of each is very small, and there couldn't - // be many of them (max number is number of _different_ GPU board installed - // in the user's system, which is _one_ for now). Therefore the total - // footprint in heap is very small. That is why we can omit deletion of - // these objects thus avoiding bothering with MP/MT syncronization. - // These will be destroyed altogether with heap. - auto instance = new AnyRamDb{path}; - instances.emplace(path, instance); - return *instance; + return *instances.emplace(path, std::make_unique(path)).first->second; } boost::optional AnyRamDb::FindRecord(const std::string& problem) diff --git a/src/api/find2_0_commons.cpp b/src/api/find2_0_commons.cpp index a43bc9b513..7aa5583afc 100644 --- a/src/api/find2_0_commons.cpp +++ b/src/api/find2_0_commons.cpp @@ -44,11 +44,12 @@ static miopenStatus_t MakeProblem(miopenProblem_t* problem, miopenProblemDirection_t direction) { return miopen::try_([&] { - miopen::deref(problem) = new miopen::ProblemContainer(); + auto& in_problem_deref = miopen::deref(problem); + in_problem_deref = new miopen::ProblemContainer(); auto& container_deref = miopen::deref(*problem); container_deref.item = miopen::Problem(); - auto& problem_deref = boost::get(container_deref.item); + auto& problem_deref = std::get(container_deref.item); auto& operator_deref = miopen::deref(operatorDesc); problem_deref.SetOperatorDescriptor(operator_deref); @@ -78,11 +79,12 @@ miopenStatus_t miopenCreateBiasProblem(miopenProblem_t* problem, miopenProblemDi MIOPEN_LOG_FUNCTION(problem, direction); return miopen::try_([&] { - miopen::deref(problem) = new miopen::ProblemContainer(); - auto& container_deref = miopen::deref(*problem); + auto& container_ptr = miopen::deref(problem); + container_ptr = new miopen::ProblemContainer(); + auto& container_deref = miopen::deref(*problem); container_deref.item = miopen::Problem(); - auto& problem_deref = boost::get(container_deref.item); + auto& problem_deref = std::get(container_deref.item); problem_deref.SetOperatorDescriptor(miopen::BiasDescriptor{}); problem_deref.SetDirection(direction); @@ -105,6 +107,26 @@ miopenStatus_t miopenCreateSoftmaxProblem(miopenProblem_t* problem, return MakeProblem(problem, operatorDesc, direction); } +miopenStatus_t miopenCreateBatchnormProblem(miopenProblem_t* problem, + miopenBatchNormMode_t mode, + bool runningMeanVariance, + miopenProblemDirection_t direction) +{ + MIOPEN_LOG_FUNCTION(problem, mode, direction); + + return miopen::try_([&] { + auto& container_ptr = miopen::deref(problem); + container_ptr = new miopen::ProblemContainer(); + auto& container_deref = miopen::deref(*problem); + + container_deref.item = miopen::Problem(); + auto& problem_deref = std::get(container_deref.item); + + problem_deref.SetOperatorDescriptor(miopen::BatchnormDescriptor{mode, runningMeanVariance}); + problem_deref.SetDirection(direction); + }); +} + miopenStatus_t miopenFuseProblems(miopenProblem_t problem1, miopenProblem_t problem2) { MIOPEN_LOG_FUNCTION(problem1, problem2); @@ -121,23 +143,23 @@ miopenStatus_t miopenFuseProblems(miopenProblem_t problem1, miopenProblem_t prob std::back_inserter(problems)); }); - boost::apply_visitor(impl2, miopen::deref(problem2).item); + std::visit(impl2, miopen::deref(problem2).item); }; - boost::apply_visitor(boost::hof::match( - [&](miopen::Problem& problem1_inner) { - auto tmp = miopen::FusedProblem{}; - tmp.problems.reserve(2); - tmp.problems.emplace_back(problem1_inner); - emplace_problem2(tmp.problems); - problem1_deref.item = std::move(tmp); - }, - [&](miopen::FusedProblem& problem1_inner) { - emplace_problem2(problem1_inner.problems); - }), - miopen::deref(problem1).item); - - boost::get(miopen::deref(problem1).item).PropagateDescriptors(); + std::visit(boost::hof::match( + [&](miopen::Problem& problem1_inner) { + auto tmp = miopen::FusedProblem{}; + tmp.problems.reserve(2); + tmp.problems.emplace_back(problem1_inner); + emplace_problem2(tmp.problems); + problem1_deref.item = std::move(tmp); + }, + [&](miopen::FusedProblem& problem1_inner) { + emplace_problem2(problem1_inner.problems); + }), + miopen::deref(problem1).item); + + std::get(miopen::deref(problem1).item).PropagateDescriptors(); }); } @@ -163,14 +185,17 @@ miopenStatus_t miopenSetProblemTensorDescriptor(miopenProblem_t problem, "Attempt to set tensor descriptor of a fused problem"); }); - boost::apply_visitor(impl, miopen::deref(problem).item); + std::visit(impl, miopen::deref(problem).item); }); } miopenStatus_t miopenCreateFindOptions(miopenFindOptions_t* options) { MIOPEN_LOG_FUNCTION(options); - return miopen::try_([&] { miopen::deref(options) = new miopen::FindOptions(); }); + return miopen::try_([&] { + auto& options_ptr = miopen::deref(options); + options_ptr = new miopen::FindOptions(); + }); } miopenStatus_t miopenDestroyFindOptions(miopenFindOptions_t options) @@ -233,6 +258,16 @@ miopenStatus_t miopenSetFindOptionPreallocatedTensor(miopenFindOptions_t options }); } +miopenStatus_t miopenSetFindOptionAttachBinaries(miopenFindOptions_t options, unsigned attach) +{ + MIOPEN_LOG_FUNCTION(options, attach); + + return miopen::try_([&] { + auto& options_deref = miopen::deref(options); + options_deref.attach_binaries = (attach == 1); + }); +} + miopenStatus_t miopenFindSolutions(miopenHandle_t handle, miopenProblem_t problem, miopenFindOptions_t options, @@ -246,19 +281,22 @@ miopenStatus_t miopenFindSolutions(miopenHandle_t handle, auto& handle_deref = miopen::deref(handle); const auto& problem_deref = miopen::deref(problem).item; - boost::apply_visitor([](auto&& problem) { problem.LogDriverCommand(); }, problem_deref); + std::visit([](auto&& problem) { problem.LogDriverCommand(); }, problem_deref); const auto& options_deref = options == nullptr ? miopen::FindOptions{} : miopen::deref(options); - auto solutions_deref = boost::apply_visitor( + auto solutions_deref = std::visit( [&](auto&& problem) { return problem.FindSolutions(handle_deref, options_deref, maxSolutions); }, problem_deref); for(auto i = 0; i < solutions_deref.size(); ++i) - miopen::deref(solutions + i) = new miopen::Solution{std::move(solutions_deref[i])}; + { + auto& theSolution = miopen::deref(solutions + i); + theSolution = new miopen::Solution{std::move(solutions_deref[i])}; + } if(numSolutions != nullptr) *numSolutions = solutions_deref.size(); @@ -296,11 +334,46 @@ inline std::ostream& operator<<(std::ostream& stream, const miopenTensorArgument case miopenTensorMhaAmaxS: stream << "MhaAmaxS"; break; case miopenTensorMhaM: stream << "MhaM"; break; case miopenTensorMhaZInv: stream << "MhaZInv"; break; + case miopenTensorMhaDO: stream << "miopenTensorMhaDO"; break; + case miopenTensorMhaDescaleO: stream << "miopenTensorMhaDescaleO"; break; + case miopenTensorMhaDescaleDO: stream << "miopenTensorMhaDescaleDO"; break; + case miopenTensorMhaDescaleDS: stream << "miopenTensorMhaDescaleDS"; break; + case miopenTensorMhaScaleDS: stream << "miopenTensorMhaScaleDS"; break; + case miopenTensorMhaScaleDQ: stream << "miopenTensorMhaScaleDQ"; break; + case miopenTensorMhaScaleDK: stream << "miopenTensorMhaScaleDK"; break; + case miopenTensorMhaScaleDV: stream << "miopenTensorMhaScaleDV"; break; + case miopenTensorMhaDQ: stream << "miopenTensorMhaDQ"; break; + case miopenTensorMhaDK: stream << "miopenTensorMhaDK"; break; + case miopenTensorMhaDV: stream << "miopenTensorMhaDV"; break; + case miopenTensorMhaAmaxDQ: stream << "miopenTensorMhaAmaxDQ"; break; + case miopenTensorMhaAmaxDK: stream << "miopenTensorMhaAmaxDK"; break; + case miopenTensorMhaAmaxDV: stream << "miopenTensorMhaAmaxDV"; break; + case miopenTensorMhaAmaxDS: stream << "miopenTensorMhaAmaxDS"; break; case miopenTensorSoftmaxX: stream << "SoftmaxX"; break; case miopenTensorSoftmaxY: stream << "SoftmaxY"; break; case miopenTensorSoftmaxDX: stream << "SoftmaxDX"; break; case miopenTensorSoftmaxDY: stream << "SoftmaxDY"; break; case miopenTensorArgumentIsScalar: stream << "ScalarArgument"; break; + case miopenTensorBatchnormX: stream << "miopenTensorBatchnormX"; break; + case miopenTensorBatchnormY: stream << "miopenTensorBatchnormY"; break; + case miopenTensorBatchnormRunningMean: stream << "miopenTensorBatchnormRunningMean"; break; + case miopenTensorBatchnormRunningVariance: + stream << "miopenTensorBatchnormRunningVariance"; + break; + case miopenTensorBatchnormSavedMean: stream << "miopenTensorBatchnormSavedMean"; break; + case miopenTensorBatchnormSavedVariance: stream << "miopenTensorBatchnormSavedVariance"; break; + case miopenTensorBatchnormScale: stream << "miopenTensorBatchnormScale"; break; + case miopenTensorBatchnormScaleDiff: stream << "miopenTensorBatchnormScaleDiff"; break; + case miopenTensorBatchnormEstimatedMean: stream << "miopenTensorBatchnormEstimatedMean"; break; + case miopenTensorBatchnormEstimatedVariance: + stream << "miopenTensorBatchnormEstimatedVariance"; + break; + case miopenTensorBatchnormBias: stream << "miopenTensorBatchnormBias"; break; + case miopenTensorBatchnormBiasDiff: stream << "miopenTensorBatchnormBiasDiff"; break; + case miopenTensorBatchnormDX: stream << "miopenTensorBatchnormDX"; break; + case miopenTensorBatchnormDY: stream << "miopenTensorBatchnormDY"; break; + case miopenScalarBatchnormEpsilon: stream << "miopenScalarBatchnormEpsilon"; break; + case miopenScalarBatchnormExpAvgFactor: stream << "miopenScalarBatchnormExpAvgFactor"; break; case miopenTensorArgumentIdInvalid: stream << "Invalid"; break; } diff --git a/src/batch_norm_api.cpp b/src/batch_norm_api.cpp index 69454b185a..8f184a9508 100644 --- a/src/batch_norm_api.cpp +++ b/src/batch_norm_api.cpp @@ -102,13 +102,6 @@ miopenBatchNormalizationForwardInference(miopenHandle_t handle, estimatedVariance, epsilon); - // bfloat16 not supported for batchnorm operation - if(miopen::deref(yDesc).GetType() == miopenBFloat16 || - miopen::deref(xDesc).GetType() == miopenBFloat16) - { - return miopenStatusNotImplemented; - } - miopen::debug::LogCmdBNorm(xDesc, bn_mode, estimatedMean, @@ -178,14 +171,6 @@ miopenBatchNormalizationForwardTraining(miopenHandle_t handle, resultSaveMean, resultSaveInvVariance); - // bfloat16 not supported for batchnorm operation - if(miopen::deref(xDesc).GetType() == miopenBFloat16 || - miopen::deref(yDesc).GetType() == miopenBFloat16 || - miopen::deref(bnScaleBiasMeanVarDesc).GetType() == miopenBFloat16) - { - return miopenStatusNotImplemented; - } - miopen::debug::LogCmdBNorm(xDesc, bn_mode, resultRunningMean, diff --git a/src/batchnorm/problem_description.cpp b/src/batchnorm/problem_description.cpp index b90d958a1e..39bc909aed 100644 --- a/src/batchnorm/problem_description.cpp +++ b/src/batchnorm/problem_description.cpp @@ -67,17 +67,9 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const size_t ygridsize = 1; bool bfpmixparm = false; - bool bfp16parm = false; - bool bfp32parm = true; - if(xDesc.GetType() == miopenHalf && GetBnScaleBiasMeanVarDesc().GetType() == miopenHalf) - { - bfp16parm = true; - bfp32parm = false; - } - else if(xDesc.GetType() == miopenHalf && GetBnScaleBiasMeanVarDesc().GetType() == miopenFloat) + if(xDesc.GetType() == miopenHalf && GetBnScaleBiasMeanVarDesc().GetType() == miopenFloat) { bfpmixparm = true; - bfp32parm = false; } if(bn_mode == miopenBNSpatial) @@ -123,7 +115,7 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const } // clang-format on - if((n > 768) && (in_cstride > 150) && bfp32parm) + if((n > 768) && (in_cstride > 150) && IsFp32()) { variant = 2; xlocalsize = 1; @@ -142,8 +134,10 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const { ss << "rs" << static_cast(resultsave); ss << "rr" << static_cast(resultrunning); - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "c" << c; } else @@ -156,8 +150,10 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const ss << "ldsgcn" << ldsgcn; ss << "rs" << static_cast(resultsave); ss << "rr" << static_cast(resultrunning); - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "single" << static_cast(single); ss << "n" << n; ss << "c" << c; @@ -172,8 +168,10 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const xgridsize = c; ygridsize = segment * ylocalsize; - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "gx" << xgridsize; ss << "gy" << ygridsize; ss << "lx" << xlocalsize; @@ -185,6 +183,7 @@ NetworkConfig ProblemDescription::MakeForwardTrainingNetworkConfig() const ss << "c" << c; ss << "hw" << in_cstride; } + ss << "layout" << xDesc.GetLayout_str(); return NetworkConfig{ss.str()}; } @@ -193,28 +192,19 @@ NetworkConfig ProblemDescription::MakeForwardInferenceNetworkConfig() const { std::ostringstream ss; - bool bfp16parm = false; - bool bfp32parm = true; - if(xDesc.GetType() == miopenHalf && GetBnScaleBiasMeanVarDesc().GetType() == miopenHalf) - { - bfp16parm = true; - bfp32parm = false; - } - else if(xDesc.GetType() == miopenHalf && GetBnScaleBiasMeanVarDesc().GetType() == miopenFloat) - { - bfp32parm = false; - } - int n, c, h, w; std::tie(n, c, h, w) = tien<4>(xDesc.GetLengths()); const unsigned int in_cstride = h * w; - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "mode" << bn_mode; ss << "HWdims" << in_cstride; ss << "C" << c; + ss << "layout" << xDesc.GetLayout_str(); return NetworkConfig{ss.str()}; } @@ -224,17 +214,9 @@ NetworkConfig ProblemDescription::MakeBackwardNetworkConfig() const std::ostringstream ss; bool bfpmixparm = false; - bool bfp16parm = false; - bool bfp32parm = true; - if(xDesc.GetType() == miopenHalf && GetScaleBiasDiffDesc().GetType() == miopenHalf) - { - bfp16parm = true; - bfp32parm = false; - } - else if(xDesc.GetType() == miopenHalf && GetScaleBiasDiffDesc().GetType() == miopenFloat) + if(xDesc.GetType() == miopenHalf && GetScaleBiasDiffDesc().GetType() == miopenFloat) { bfpmixparm = true; - bfp32parm = false; } int n, c, h, w; @@ -282,7 +264,7 @@ NetworkConfig ProblemDescription::MakeBackwardNetworkConfig() const else { variant = 0; - if(bfp32parm) + if(IsFp32()) { xlocalsize = 1024; xgridsize = 1024 * static_cast(c); @@ -322,8 +304,10 @@ NetworkConfig ProblemDescription::MakeBackwardNetworkConfig() const ss << "lx" << xlocalsize; ss << "ly" << ylocalsize; ss << "us" << static_cast(useSaved); - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "single" << static_cast(single); ss << "gcn" << ldsgcn; } @@ -342,10 +326,13 @@ NetworkConfig ProblemDescription::MakeBackwardNetworkConfig() const ss << "c" << c; ss << "hw" << in_cstride; ss << "u" << static_cast(useSaved); - ss << "fp16" << static_cast(bfp16parm); - ss << "fp32" << static_cast(bfp32parm); + ss << "fp16" << static_cast(IsFp16()); + ss << "fp32" << static_cast(IsFp32()); + ss << "fp64" << static_cast(IsFp64()); + ss << "fbf16" << static_cast(IsBfp16()); ss << "nhw" << in_nhw; } + ss << "layout" << xDesc.GetLayout_str(); return NetworkConfig{ss.str()}; } diff --git a/src/binary_cache.cpp b/src/binary_cache.cpp index 7feb3b079b..3faf994621 100644 --- a/src/binary_cache.cpp +++ b/src/binary_cache.cpp @@ -51,8 +51,7 @@ namespace miopen { static fs::path ComputeSysCachePath() { - const std::string cache_dir = GetSystemDbPath(); - auto p = miopen::ExpandUser(cache_dir); + auto p = miopen::ExpandUser(GetSystemDbPath()); if(!fs::exists(p)) return {}; else @@ -65,7 +64,7 @@ static fs::path ComputeUserCachePath() fs::path p; /// If MIOPEN_CUSTOM_CACHE_DIR is set in the environment, then /// use exactly that path. - const auto& custom = miopen::GetStringEnv(ENV(MIOPEN_CUSTOM_CACHE_DIR)); + const auto& custom = env::value(MIOPEN_CUSTOM_CACHE_DIR); if(!custom.empty()) { p = ExpandUser(custom); @@ -118,7 +117,7 @@ bool IsCacheDisabled() if(MIOPEN_DISABLE_USERDB && MIOPEN_DISABLE_SYSDB) return true; else - return miopen::IsEnabled(ENV(MIOPEN_DISABLE_CACHE)); + return env::enabled(MIOPEN_DISABLE_CACHE); #else return true; #endif @@ -140,11 +139,11 @@ KDb GetDb(const TargetProperties& target, size_t num_cu) if(!fs::exists(sys_path)) sys_path = fs::path{}; #endif - return {DbKinds::KernelDb, sys_path.string(), user_path.string()}; + return {DbKinds::KernelDb, sys_path, user_path}; } #endif -fs::path GetCacheFile(const std::string& device, const std::string& name, const std::string& args) +fs::path GetCacheFile(const std::string& device, const fs::path& name, const std::string& args) { const auto filename = make_object_file_name(name); return GetCachePath(false) / miopen::md5(device + ":" + args) / filename; @@ -153,7 +152,7 @@ fs::path GetCacheFile(const std::string& device, const std::string& name, const #if MIOPEN_ENABLE_SQLITE_KERN_CACHE std::vector LoadBinary(const TargetProperties& target, const size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args) { if(miopen::IsCacheDisabled()) @@ -161,7 +160,7 @@ std::vector LoadBinary(const TargetProperties& target, auto db = GetDb(target, num_cu); - const auto filename = make_object_file_name(name).string(); + const auto filename = make_object_file_name(name); const KernelConfig cfg{filename, args, {}}; MIOPEN_LOG_I2("Loading binary for: " << filename << "; args: " << args); @@ -181,7 +180,7 @@ std::vector LoadBinary(const TargetProperties& target, void SaveBinary(const std::vector& hsaco, const TargetProperties& target, const std::size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args) { if(miopen::IsCacheDisabled()) @@ -189,7 +188,7 @@ void SaveBinary(const std::vector& hsaco, auto db = GetDb(target, num_cu); - const auto filename = make_object_file_name(name).string(); + const auto filename = make_object_file_name(name); KernelConfig cfg{filename, args, hsaco}; MIOPEN_LOG_I2("Saving binary for: " << filename << "; args: " << args); @@ -198,7 +197,7 @@ void SaveBinary(const std::vector& hsaco, #else fs::path LoadBinary(const TargetProperties& target, const size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args) { if(miopen::IsCacheDisabled()) @@ -216,20 +215,22 @@ fs::path LoadBinary(const TargetProperties& target, } } -void SaveBinary(const fs::path& binary_path, - const TargetProperties& target, - const std::string& name, - const std::string& args) +fs::path SaveBinary(const fs::path& binary_path, + const TargetProperties& target, + const fs::path& name, + const std::string& args) { if(miopen::IsCacheDisabled()) { fs::remove(binary_path); + return {}; } else { auto p = GetCacheFile(target.DbId(), name, args); fs::create_directories(p.parent_path()); fs::rename(binary_path, p); + return p; } } #endif diff --git a/src/check_numerics.cpp b/src/check_numerics.cpp index 99d114b0a5..a8fef5e065 100644 --- a/src/check_numerics.cpp +++ b/src/check_numerics.cpp @@ -37,7 +37,7 @@ namespace miopen { bool CheckNumericsEnabled(const int bitMask) { - return (miopen::Value(ENV(MIOPEN_CHECK_NUMERICS)) & bitMask) != 0; + return (env::value(MIOPEN_CHECK_NUMERICS) & bitMask) != 0; } // Must keep this structure synchronized with one in MIOpenCheckNumerics @@ -62,6 +62,7 @@ std::string GetKernelName(miopenDataType_t data_type) case miopenBFloat16: return {"check_numerics_bf16"}; case miopenFloat8: return {"check_numerics_fp8"}; case miopenBFloat8: return {"check_numerics_bf8"}; + case miopenInt64: case miopenInt32: case miopenInt8: case miopenDouble: @@ -139,8 +140,7 @@ bool checkNumericsImpl( // Returns: 1 if abnormal value (inf or nan) detected in specified data, 0 otherwise bool checkNumericsInput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data) { - return checkNumericsImpl( - handle, static_cast(miopen::Value(ENV(MIOPEN_CHECK_NUMERICS))), dDesc, data, true); + return checkNumericsImpl(handle, env::value(MIOPEN_CHECK_NUMERICS), dDesc, data, true); } // Synchronizes to wait for kernel to finish, then checks data for output: @@ -148,9 +148,7 @@ bool checkNumericsInput(const Handle& handle, const TensorDescriptor& dDesc, Con bool checkNumericsOutput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data) { handle.Finish(); - - return checkNumericsImpl( - handle, static_cast(miopen::Value(ENV(MIOPEN_CHECK_NUMERICS))), dDesc, data, false); + return checkNumericsImpl(handle, env::value(MIOPEN_CHECK_NUMERICS), dDesc, data, false); } } // namespace miopen diff --git a/src/comgr.cpp b/src/comgr.cpp index 41af1d0e95..aa53b71bb5 100644 --- a/src/comgr.cpp +++ b/src/comgr.cpp @@ -37,14 +37,9 @@ #include #include -#if !defined(_WIN32) #include -#else -#include -#endif #include #if MIOPEN_USE_HIPRTC -#include #include #endif @@ -60,6 +55,9 @@ /// More info at https://github.com/ROCm/MIOpen/issues/1257. #define WORKAROUND_ISSUE_1257 (HIP_PACKAGE_VERSION_FLAT >= 4003021331ULL) +/// https://github.com/ROCm/ROCm-CompilerSupport/issues/67 about unused -nogpulib. +#define WORKAROUND_ROCMCOMPILERSUPPORT_ISSUE_67 1 + MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMGR_LOG_CALLS) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES) @@ -72,11 +70,6 @@ MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_COMGR_LOG_OPTIONS) /// you would like to log onto console. MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_COMGR_LOG_SOURCE_TEXT) -/// \todo Temporary for debugging: -MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEBUG_COMGR_COMPILER_OPTIONS_INSERT) -/// \todo Temporary for debugging: -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMGR_HIP_BUILD_FATBIN) - /// \todo see issue #1222, PR #1316 MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_SRAM_EDC_DISABLED) @@ -101,16 +94,18 @@ MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP) ((MIOPEN_AMD_COMGR_VERSION_MAJOR * 1000 + MIOPEN_AMD_COMGR_VERSION_MINOR) * 1000 + \ MIOPEN_AMD_COMGR_VERSION_PATCH) +#if COMGR_VERSION < 1007000 +#error "AMD COMgr older than 1.7.0 is not supported" +#endif + #define COMGR_SUPPORTS_PCH (COMGR_VERSION >= 1008000) #if COMGR_SUPPORTS_PCH - #if defined(__HIP_HAS_GET_PCH) && __HIP_HAS_GET_PCH #define HIP_SUPPORTS_PCH 1 #else #define HIP_SUPPORTS_PCH 0 #endif - #endif // COMGR_SUPPORTS_PCH #define PCH_IS_SUPPORTED (COMGR_SUPPORTS_PCH && HIP_SUPPORTS_PCH) @@ -120,10 +115,6 @@ MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP) /// have wavesize != 64 (currently gfx10 with default build settings). #define WORKAROUND_ISSUE_1431 PCH_IS_SUPPORTED -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE) - -#define COMPILER_LC 1 - #define EC_BASE(comgrcall, info, action) \ do \ { \ @@ -134,7 +125,7 @@ MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE) << GetStatusText(status)); \ (action); \ } \ - else if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_LOG_CALLS))) \ + else if(env::enabled(MIOPEN_DEBUG_COMGR_LOG_CALLS)) \ MIOPEN_LOG_I("Ok \'" #comgrcall "\' " << to_string(info)); \ } while(false) @@ -161,7 +152,6 @@ using OptionList = std::vector; /// (minimal compiler abstraction layer). namespace compiler { -#if COMPILER_LC namespace lc { static auto GetOptionsNoSplit() @@ -185,8 +175,6 @@ static void RemoveOptionsUnwanted(OptionList& list) namespace ocl { -#define OCL_COMPILE_SOURCE_WITH_DEVICE_LIBS (COMGR_VERSION >= 1007000) - #define OCL_EARLY_INLINE 1 #define OCL_STANDARD 200 @@ -213,7 +201,7 @@ static void AddCompilerOptions(OptionList& list) #endif list.push_back("-mllvm"); list.push_back("-amdgpu-prelink"); - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP))) + if(env::enabled(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP)) { list.push_back("-mwavefrontsize64"); list.push_back("-mcumode"); @@ -237,83 +225,6 @@ static void RemoveOptionsUnwanted(OptionList& list) } // namespace ocl -namespace hip { - -#if PCH_IS_SUPPORTED -static bool IsPchEnabled() { return !miopen::IsDisabled(ENV(MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE)); } -#endif - -static std::string GetPchEnableStatus() -{ -#if PCH_IS_SUPPORTED - auto rv = std::string{IsPchEnabled() ? "1" : "0"}; - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_COMGR_HIP_PCH_ENFORCE))) - return rv += " (enforced)"; - return rv; -#else - return "0 (not supported)"; -#endif -} - -static bool IsLinkerOption(const std::string& option) -{ - return miopen::StartsWith(option, "-L") || miopen::StartsWith(option, "-Wl,") || - option == "-ldl" || option == "-lm" || option == "--hip-link"; -} - -static void RemoveCommonOptionsUnwanted(OptionList& list) -{ - list.erase( - remove_if( - list.begin(), - list.end(), - [&](const auto& option) { // clang-format off - return miopen::StartsWith(option, "-mcpu=") - || (option == "-hc") - || (option == "-x hip") || (option == "-xhip") - || (option == "--hip-link") - // The following matches current "-lclang_rt.builtins-x86_64" (4.5) as weel as - // upcoming ".../libclang_rt.builtins-x86_64.a" and even future things like - // "...x86_64.../libclang_rt.builtins.a" etc. - || ((option.find("clang_rt.builtins") != std::string::npos) - && (option.find("x86_64") != std::string::npos)) - || miopen::StartsWith(option, "--hip-device-lib-path="); // clang-format on - }), - list.end()); -} - -static void AddCompilerOptions(const OptionList& list) // `const` is for clang-tidy. -{ - // Nothing to do here yet, but let's keep the placeholder for now. - std::ignore = list; -} - -static void RemoveCompilerOptionsUnwanted(OptionList& list) -{ - RemoveCommonOptionsUnwanted(list); - list.erase(remove_if(list.begin(), - list.end(), - [&](const auto& option) { // clang-format off - return (!miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_HIP_BUILD_FATBIN)) - && (IsLinkerOption(option))); // clang-format on - }), - list.end()); -} - -static void RemoveLinkOptionsUnwanted(OptionList& list) -{ - RemoveCommonOptionsUnwanted(list); - list.erase(remove_if(list.begin(), - list.end(), - [&](const auto& option) { // clang-format off - return miopen::StartsWith(option, "-D") - || miopen::StartsWith(option, "-isystem"); // clang-format on - }), - list.end()); -} - -} // namespace hip - /// \todo Get list of supported isa names from comgr and select. static std::string GetIsaName(const miopen::TargetProperties& target, const bool isHlcBuild) { @@ -327,7 +238,6 @@ static std::string GetIsaName(const miopen::TargetProperties& target, const bool } // namespace lc #undef OCL_EARLY_INLINE -#endif // COMPILER_LC } // namespace compiler @@ -340,6 +250,12 @@ static inline std::string to_string(const std::string& v) { return {v}; } static inline std::string to_string(const bool& v) { return v ? "true" : "false"; } static inline auto to_string(const std::size_t& v) { return std::to_string(v); } +/// Convert amd_comgr enum members to strings. +/// +/// \note We need support only for the enum members used in this file. +/// Let's skip unused members in order to simplify maintenance +/// of code between different COMgr versions. +/// /// \todo Request comgr to expose this stuff via API. static std::string to_string(const amd_comgr_language_t val) { @@ -349,7 +265,6 @@ static std::string to_string(const amd_comgr_language_t val) AMD_COMGR_LANGUAGE_NONE, AMD_COMGR_LANGUAGE_OPENCL_1_2, AMD_COMGR_LANGUAGE_OPENCL_2_0, - AMD_COMGR_LANGUAGE_HC, AMD_COMGR_LANGUAGE_HIP); return oss.str(); } @@ -362,58 +277,21 @@ static std::string to_string(const amd_comgr_data_kind_t val) AMD_COMGR_DATA_KIND_UNDEF, AMD_COMGR_DATA_KIND_SOURCE, AMD_COMGR_DATA_KIND_INCLUDE, - AMD_COMGR_DATA_KIND_PRECOMPILED_HEADER, - AMD_COMGR_DATA_KIND_DIAGNOSTIC, AMD_COMGR_DATA_KIND_LOG, - AMD_COMGR_DATA_KIND_BC, - AMD_COMGR_DATA_KIND_RELOCATABLE, - AMD_COMGR_DATA_KIND_EXECUTABLE, - AMD_COMGR_DATA_KIND_BYTES, - AMD_COMGR_DATA_KIND_FATBIN); + AMD_COMGR_DATA_KIND_EXECUTABLE); return oss.str(); } static std::string to_string(const amd_comgr_action_kind_t val) { std::ostringstream oss; -#if COMGR_VERSION >= 1007000 MIOPEN_LOG_ENUM(oss, val, - AMD_COMGR_ACTION_SOURCE_TO_PREPROCESSOR, AMD_COMGR_ACTION_ADD_PRECOMPILED_HEADERS, - AMD_COMGR_ACTION_COMPILE_SOURCE_TO_BC, - AMD_COMGR_ACTION_ADD_DEVICE_LIBRARIES, - AMD_COMGR_ACTION_LINK_BC_TO_BC, - AMD_COMGR_ACTION_OPTIMIZE_BC_TO_BC, AMD_COMGR_ACTION_CODEGEN_BC_TO_RELOCATABLE, - AMD_COMGR_ACTION_CODEGEN_BC_TO_ASSEMBLY, - AMD_COMGR_ACTION_LINK_RELOCATABLE_TO_RELOCATABLE, AMD_COMGR_ACTION_LINK_RELOCATABLE_TO_EXECUTABLE, AMD_COMGR_ACTION_ASSEMBLE_SOURCE_TO_RELOCATABLE, - AMD_COMGR_ACTION_DISASSEMBLE_RELOCATABLE_TO_SOURCE, - AMD_COMGR_ACTION_DISASSEMBLE_EXECUTABLE_TO_SOURCE, - AMD_COMGR_ACTION_DISASSEMBLE_BYTES_TO_SOURCE, - AMD_COMGR_ACTION_COMPILE_SOURCE_TO_FATBIN, AMD_COMGR_ACTION_COMPILE_SOURCE_WITH_DEVICE_LIBS_TO_BC); -#else - MIOPEN_LOG_ENUM(oss, - val, - AMD_COMGR_ACTION_SOURCE_TO_PREPROCESSOR, - AMD_COMGR_ACTION_ADD_PRECOMPILED_HEADERS, - AMD_COMGR_ACTION_COMPILE_SOURCE_TO_BC, - AMD_COMGR_ACTION_ADD_DEVICE_LIBRARIES, - AMD_COMGR_ACTION_LINK_BC_TO_BC, - AMD_COMGR_ACTION_OPTIMIZE_BC_TO_BC, - AMD_COMGR_ACTION_CODEGEN_BC_TO_RELOCATABLE, - AMD_COMGR_ACTION_CODEGEN_BC_TO_ASSEMBLY, - AMD_COMGR_ACTION_LINK_RELOCATABLE_TO_RELOCATABLE, - AMD_COMGR_ACTION_LINK_RELOCATABLE_TO_EXECUTABLE, - AMD_COMGR_ACTION_ASSEMBLE_SOURCE_TO_RELOCATABLE, - AMD_COMGR_ACTION_DISASSEMBLE_RELOCATABLE_TO_SOURCE, - AMD_COMGR_ACTION_DISASSEMBLE_EXECUTABLE_TO_SOURCE, - AMD_COMGR_ACTION_DISASSEMBLE_BYTES_TO_SOURCE, - AMD_COMGR_ACTION_COMPILE_SOURCE_TO_FATBIN); -#endif return oss.str(); } @@ -422,8 +300,7 @@ static bool PrintVersionImpl() std::size_t major = 0; std::size_t minor = 0; (void)amd_comgr_get_version(&major, &minor); - MIOPEN_LOG_NQI("COMgr v." << major << '.' << minor << '.' << MIOPEN_AMD_COMGR_VERSION_PATCH - << ", USE_HIP_PCH: " << compiler::lc::hip::GetPchEnableStatus()); + MIOPEN_LOG_NQI("COMgr v." << major << '.' << minor << '.' << MIOPEN_AMD_COMGR_VERSION_PATCH); return true; } @@ -443,7 +320,7 @@ static std::string GetStatusText(const amd_comgr_status_t status, const bool unk static void LogOptions(const char* options[], size_t count) { - static const auto control = miopen::Value(ENV(MIOPEN_DEBUG_COMGR_LOG_OPTIONS)); + static const auto control = env::value(MIOPEN_DEBUG_COMGR_LOG_OPTIONS); if(!(control != 0 && miopen::IsLogging(miopen::LoggingLevel::Info))) return; if(control == 2) @@ -536,16 +413,10 @@ class Data : ComgrOwner { ECI_THROW(amd_comgr_set_data_name(handle, s.c_str()), s); } - void SetBytes(const std::string& bytes) const + void SetBytes(std::string_view bytes) const { ECI_THROW(amd_comgr_set_data(handle, bytes.size(), bytes.data()), bytes.size()); } -#if PCH_IS_SUPPORTED - void SetFromBuffer(const char* const buffer, const size_t size) const - { - ECI_THROW(amd_comgr_set_data(handle, size, buffer), size); - } -#endif private: std::size_t GetSize() const @@ -583,16 +454,16 @@ class Dataset : ComgrOwner auto GetHandle() const { return handle; } void AddData(const Data& d) const { EC_THROW(amd_comgr_data_set_add(handle, d.GetHandle())); } void AddData(const std::string& name, - const std::string& content, + std::string_view content, const amd_comgr_data_kind_t type) const { const Data d(type); - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES))) + if(env::enabled(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES)) MIOPEN_LOG_I(name << ' ' << content.size() << " bytes"); d.SetName(name); d.SetBytes(content); AddData(d); - const auto show_first = miopen::Value(ENV(MIOPEN_DEBUG_COMGR_LOG_SOURCE_TEXT)); + const auto show_first = env::value(MIOPEN_DEBUG_COMGR_LOG_SOURCE_TEXT); if(show_first > 0 && miopen::IsLogging(miopen::LoggingLevel::Info) && (type == AMD_COMGR_DATA_KIND_SOURCE || type == AMD_COMGR_DATA_KIND_INCLUDE)) { @@ -601,21 +472,6 @@ class Dataset : ComgrOwner MIOPEN_LOG_I(text); } } -#if PCH_IS_SUPPORTED - void AddDataHipPch(const char* const content, const size_t size) const - { - const char name[] = "hip.pch"; - const Data d(AMD_COMGR_DATA_KIND_PRECOMPILED_HEADER); - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES))) - { - MIOPEN_LOG_I(name << ' ' << size - << " bytes, ptr = " << static_cast(content)); - } - d.SetName(name); - d.SetFromBuffer(content, size); - AddData(d); - } -#endif size_t GetDataCount(const amd_comgr_data_kind_t kind) const { std::size_t count = 0; @@ -730,146 +586,16 @@ static void SetIsaName(const ActionInfo& action, action.SetIsaName(isaName); } -static std::string GetDebugCompilerOptionsInsert() -{ - const auto& p = miopen::GetStringEnv(ENV(MIOPEN_DEBUG_COMGR_COMPILER_OPTIONS_INSERT)); - return {p}; -} - +#if WORKAROUND_ISSUE_1431 static inline bool IsWave64Enforced(const OptionList& opts) { return std::any_of( opts.begin(), opts.end(), [](const std::string& s) { return s == "-mwavefrontsize64"; }); } - -void BuildHip(const std::string& name, - const std::string& text, - const std::string& options, - const miopen::TargetProperties& target, - std::vector& binary) -{ - PrintVersion(); - try - { - const Dataset inputs; - inputs.AddData(name, text, AMD_COMGR_DATA_KIND_SOURCE); - - // For OCL and ASM sources, we do insert contents of include - // files directly into the source text during library build phase by means - // of the addkernels tool. We don't do that for HIP sources, and, therefore - // have to export include files prior compilation. - // Note that we do not need any "subdirs" in the include "pathnames" so far. - const auto incNames = miopen::GetHipKernelIncList(); - for(const auto& inc : incNames) - inputs.AddData(inc, miopen::GetKernelInc(inc), AMD_COMGR_DATA_KIND_INCLUDE); - -#if PCH_IS_SUPPORTED - if(compiler::lc::hip::IsPchEnabled()) - { - const char* pch = nullptr; - unsigned int pch_size = 0; - __hipGetPCH(&pch, &pch_size); - inputs.AddDataHipPch(pch, pch_size); - } -#endif - - const ActionInfo action; - action.SetLanguage(AMD_COMGR_LANGUAGE_HIP); - SetIsaName(action, target, true); - action.SetLogging(true); - - const Dataset exe; - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_HIP_BUILD_FATBIN))) - { - auto raw = options // - + " " + GetDebugCompilerOptionsInsert() // - + " " + MIOPEN_STRINGIZE(HIP_COMPILER_FLAGS) + - (" -DHIP_PACKAGE_VERSION_FLAT=") + std::to_string(HIP_PACKAGE_VERSION_FLAT); - if(miopen::solver::support_amd_buffer_atomic_fadd(target.Name())) - raw += " -DCK_AMD_BUFFER_ATOMIC_FADD_RETURNS_FLOAT=1"; - auto optCompile = miopen::SplitSpaceSeparated(raw, compiler::lc::GetOptionsNoSplit()); - compiler::lc::hip::RemoveCompilerOptionsUnwanted(optCompile); - action.SetOptionList(optCompile); - action.Do(AMD_COMGR_ACTION_COMPILE_SOURCE_TO_FATBIN, inputs, exe); - } - else - { - auto raw = std::string(" -O3 ") // Without this, fails in lld. - + options // - + " " + GetDebugCompilerOptionsInsert() // - + " " + MIOPEN_STRINGIZE(HIP_COMPILER_FLAGS) + - (" -DHIP_PACKAGE_VERSION_FLAT=") + std::to_string(HIP_PACKAGE_VERSION_FLAT); - if(miopen::solver::support_amd_buffer_atomic_fadd(target.Name())) - raw += " -DCK_AMD_BUFFER_ATOMIC_FADD_RETURNS_FLOAT=1"; -#if PCH_IS_SUPPORTED - if(compiler::lc::hip::IsPchEnabled()) - { - raw += " -nogpuinc -DMIOPEN_DONT_USE_HIP_RUNTIME_HEADERS"; - } -#endif - auto optCompile = miopen::SplitSpaceSeparated(raw, compiler::lc::GetOptionsNoSplit()); - auto optLink = optCompile; - compiler::lc::hip::RemoveCompilerOptionsUnwanted(optCompile); - compiler::lc::hip::AddCompilerOptions(optCompile); -#if WORKAROUND_ISSUE_1431 - if(compiler::lc::hip::IsPchEnabled()) - { - if((StartsWith(target.Name(), "gfx10") || StartsWith(target.Name(), "gfx11")) && - !IsWave64Enforced(optCompile)) - optCompile.emplace_back("-DWORKAROUND_ISSUE_1431=1"); - } #endif - action.SetOptionList(optCompile); - const Dataset compiledBc; - action.Do(AMD_COMGR_ACTION_COMPILE_SOURCE_TO_BC, inputs, compiledBc); - - OptionList addDevLibs; - // Use device libs for wavefrontsize64 for non-gfx10 targets - // or when enforced via option. - if(!(StartsWith(target.Name(), "gfx10") || StartsWith(target.Name(), "gfx11")) || - IsWave64Enforced(optCompile)) - { - addDevLibs.push_back("wavefrontsize64"); - } - addDevLibs.push_back("daz_opt"); // Assume that it's ok to flush denormals to zero. - addDevLibs.push_back("finite_only"); // No need to handle INF correcly. - addDevLibs.push_back("unsafe_math"); // Prefer speed over correctness for FP math. - action.SetOptionList(addDevLibs); - const Dataset withDevLibs; - action.Do(AMD_COMGR_ACTION_ADD_DEVICE_LIBRARIES, compiledBc, withDevLibs); - - compiler::lc::hip::RemoveLinkOptionsUnwanted(optLink); - action.SetOptionList(optLink); - const Dataset linkedBc; - action.Do(AMD_COMGR_ACTION_LINK_BC_TO_BC, withDevLibs, linkedBc); - - OptionList codegenBcToRel; - codegenBcToRel.push_back("-O3"); // Nothing more is required at this step. - action.SetOptionList(codegenBcToRel); - const Dataset relocatable; - action.Do(AMD_COMGR_ACTION_CODEGEN_BC_TO_RELOCATABLE, linkedBc, relocatable); - - action.SetOptionList(OptionList()); - action.Do(AMD_COMGR_ACTION_LINK_RELOCATABLE_TO_EXECUTABLE, relocatable, exe); - } - - if(exe.GetDataCount(AMD_COMGR_DATA_KIND_EXECUTABLE) < 1) - throw ComgrError{AMD_COMGR_STATUS_ERROR, true, "Executable binary not found"}; - // Assume that the first exec data contains the binary we need. - const auto data = exe.GetData(AMD_COMGR_DATA_KIND_EXECUTABLE, 0); - data.GetBytes(binary); - } - catch(ComgrError& ex) - { - binary.resize(0); // Necessary when "get binary" fails. - MIOPEN_LOG_E("comgr status = " << GetStatusText(ex)); - if(!ex.text.empty()) - MIOPEN_LOG_W(ex.text); - } -} void BuildOcl(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary) @@ -895,41 +621,8 @@ void BuildOcl(const std::string& name, const Dataset addedPch; action.Do(AMD_COMGR_ACTION_ADD_PRECOMPILED_HEADERS, inputs, addedPch); -#if OCL_COMPILE_SOURCE_WITH_DEVICE_LIBS const Dataset linkedBc; action.Do(AMD_COMGR_ACTION_COMPILE_SOURCE_WITH_DEVICE_LIBS_TO_BC, addedPch, linkedBc); -#else - const Dataset compiledBc; - action.Do(AMD_COMGR_ACTION_COMPILE_SOURCE_TO_BC, addedPch, compiledBc); - - OptionList optLink; - // Use device libs for wavefrontsize64 for non-gfx10 targets - // or when enforced via option. - if(!(StartsWith(target.Name(), "gfx10") || StartsWith(target.Name(), "gfx11")) || - IsWave64Enforced(optCompile)) - { - optLink.push_back("wavefrontsize64"); - } - for(const auto& opt : optCompile) - { - if(opt == "-cl-fp32-correctly-rounded-divide-sqrt") - optLink.push_back("correctly_rounded_sqrt"); - else if(opt == "-cl-denorms-are-zero") - optLink.push_back("daz_opt"); - else if(opt == "-cl-finite-math-only" || opt == "-cl-fast-relaxed-math") - optLink.push_back("finite_only"); - else if(opt == "-cl-unsafe-math-optimizations" || opt == "-cl-fast-relaxed-math") - optLink.push_back("unsafe_math"); - else - { - } // nop - } - action.SetOptionList(optLink); - const Dataset addedDevLibs; - action.Do(AMD_COMGR_ACTION_ADD_DEVICE_LIBRARIES, compiledBc, addedDevLibs); - const Dataset linkedBc; - action.Do(AMD_COMGR_ACTION_LINK_BC_TO_BC, addedDevLibs, linkedBc); -#endif action.SetOptionList(optCompile); const Dataset relocatable; @@ -955,7 +648,7 @@ void BuildOcl(const std::string& name, } void BuildAsm(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary) @@ -970,9 +663,14 @@ void BuildAsm(const std::string& name, SetIsaName(action, target); action.SetLogging(true); auto optAsm = miopen::SplitSpaceSeparated(options); +#if WORKAROUND_ISSUE_3001 if(target.Xnack() && !*target.Xnack()) optAsm.emplace_back("-mno-xnack"); +#endif compiler::lc::gcnasm::RemoveOptionsUnwanted(optAsm); +#if WORKAROUND_ROCMCOMPILERSUPPORT_ISSUE_67 + optAsm.push_back("--rocm-path=."); +#endif action.SetOptionList(optAsm); const Dataset relocatable; @@ -1014,7 +712,6 @@ using OptionList = std::vector; /// Compiler implementation-specific functionality namespace compiler { -#if COMPILER_LC namespace lc { static inline void RemoveOptionsUnwanted(OptionList& list) @@ -1026,7 +723,6 @@ static inline void RemoveOptionsUnwanted(OptionList& list) } } // namespace lc -#endif // COMPILER_LC } // namespace compiler @@ -1047,9 +743,9 @@ struct Error : std::exception throw Error{s, text}; } -static inline std::string to_string(const std::string& v) { return {v}; } -static inline std::string to_string(const char* v) { return {v}; } -static inline auto to_string(const std::size_t& v) { return std::to_string(v); } +inline std::string_view to_string(std::string_view v) { return v; } +inline std::string_view to_string(const char* v) { return v; } +inline std::string to_string(const std::size_t& v) { return std::to_string(v); } static std::string GetStatusText(const hiprtcResult status) { @@ -1066,7 +762,7 @@ static std::string GetStatusText(const hiprtcResult status) MIOPEN_LOG_E("\'" #call "\' " << to_string(info) << ": " << GetStatusText(status)); \ (action); \ } \ - else if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_LOG_CALLS))) \ + else if(env::enabled(MIOPEN_DEBUG_COMGR_LOG_CALLS)) \ MIOPEN_LOG_I("Ok \'" #call "\' " << to_string(info)); \ } while(false) @@ -1096,8 +792,8 @@ static void PrintVersion() static void hiprtc_program_destroy(hiprtcProgram prog) { hiprtcDestroyProgram(&prog); } using hiprtc_program_ptr = MIOPEN_MANAGE_PTR(hiprtcProgram, hiprtc_program_destroy); -static hiprtc_program_ptr CreateProgram(const char* src, - const char* name, +static hiprtc_program_ptr CreateProgram(std::string_view src, + std::string_view name, int numHeaders, const char** headers, const char** includeNames) @@ -1105,7 +801,8 @@ static hiprtc_program_ptr CreateProgram(const char* src, hiprtcProgram prog = nullptr; hiprtcResult status; HIPRTC_CALL_INFO_NOSTATUSDEF( - hiprtcCreateProgram(&prog, src, name, numHeaders, headers, includeNames), name); + hiprtcCreateProgram(&prog, src.data(), name.data(), numHeaders, headers, includeNames), + name); hiprtc_program_ptr p{prog}; // To destroy prog even if hiprtcCreateProgram() failed. if(status != HIPRTC_SUCCESS) { @@ -1123,7 +820,7 @@ class HiprtcProgram string_ptr_array(const string_ptr_array&) = delete; std::size_t size() const { return c_strs.size(); } const char** data() { return c_strs.data(); } - void push_back(const std::string* s) { c_strs.push_back(s->c_str()); } + void push_back(std::string_view s) { c_strs.emplace_back(s.data()); } }; struct string_array @@ -1148,28 +845,30 @@ class HiprtcProgram string_ptr_array include_texts{}; // Copying of text is not necessary. string_array include_names{}; - const std::string& src_name; - const std::string& src_text; + std::string_view src_name; + std::string_view src_text; public: - HiprtcProgram(const std::string& src_name_, const std::string& src_text_) + HiprtcProgram(std::string_view src_name_, std::string_view src_text_) : src_name(src_name_), src_text(src_text_) { LogInputFile(src_name, src_text); - const auto inc_names = miopen::GetHipKernelIncList(); + // For OCL and ASM sources, we do insert contents of include + // files directly into the source text during library build phase by means + // of the addkernels tool. We don't do that for HIP sources, and, therefore + // have to export include files prior compilation. + // Note that we do not need any "subdirs" in the include "pathnames" so far. + const auto inc_names = miopen::GetKernelIncList(); include_names.reserve(inc_names.size()); for(const auto& inc_name : inc_names) { - const auto inc_text = miopen::GetKernelIncPtr(inc_name); - LogInputFile(inc_name, *inc_text); - include_names.push_back(inc_name); + const auto inc_text = GetKernelInc(inc_name); + LogInputFile(inc_name, inc_text); + include_names.push_back(inc_name.get().string()); include_texts.push_back(inc_text); } - prog = CreateProgram(src_text.c_str(), - src_name.c_str(), - include_texts.size(), - include_texts.data(), - include_names.data()); + prog = CreateProgram( + src_text, src_name, include_texts.size(), include_texts.data(), include_names.data()); } void Compile(const std::vector& options) @@ -1199,13 +898,13 @@ class HiprtcProgram } private: - void LogInputFile(const std::string& name, const std::string& content) + void LogInputFile(const fs::path& name, std::string_view content) { - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES))) + if(env::enabled(MIOPEN_DEBUG_COMGR_LOG_SOURCE_NAMES)) MIOPEN_LOG_I(name << ' ' << content.size() << " bytes"); if(miopen::IsLogging(miopen::LoggingLevel::Info)) { - const auto show_first = miopen::Value(ENV(MIOPEN_DEBUG_COMGR_LOG_SOURCE_TEXT)); + const auto show_first = env::value(MIOPEN_DEBUG_COMGR_LOG_SOURCE_TEXT); if(show_first > 0) { const auto text_length = @@ -1241,7 +940,7 @@ class HiprtcProgram }; void BuildHip(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary) diff --git a/src/composable_kernel/composable_kernel/include/utility/config.hpp b/src/composable_kernel/composable_kernel/include/utility/config.hpp index 7869a075f2..0ab589f2e8 100644 --- a/src/composable_kernel/composable_kernel/include/utility/config.hpp +++ b/src/composable_kernel/composable_kernel/include/utility/config.hpp @@ -17,7 +17,7 @@ defined(CK_AMD_GPU_GFX940) || defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90A) || \ defined(CK_AMD_GPU_GFX941) || defined(CK_AMD_GPU_GFX942) || defined(CK_AMD_GPU_GFX1030) || \ defined(CK_AMD_GPU_GFX1031) || defined(CK_AMD_GPU_GFX1100) || defined(CK_AMD_GPU_GFX1101) || \ - defined(CK_AMD_GPU_GFX1102)) + defined(CK_AMD_GPU_GFX1102) || defined(CK_AMD_GPU_GFX1200) || defined(CK_AMD_GPU_GFX1201)) #error Need to define (only) one GPU target #endif @@ -35,7 +35,8 @@ defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90A) #define CK_BUFFER_RESOURCE_3RD_DWORD 0x00020000 #elif defined(CK_AMD_GPU_GFX1030) || defined(CK_AMD_GPU_GFX1031) || defined(CK_AMD_GPU_GFX1100) || \ - defined(CK_AMD_GPU_GFX1101) || defined(CK_AMD_GPU_GFX1102) + defined(CK_AMD_GPU_GFX1101) || defined(CK_AMD_GPU_GFX1102) || defined(CK_AMD_GPU_GFX1200) || \ + defined(CK_AMD_GPU_GFX1201) #define CK_BUFFER_RESOURCE_3RD_DWORD 0x31014000 #endif @@ -45,7 +46,8 @@ #elif defined(CK_AMD_GPU_GFX906) || defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90a) || \ defined(CK_AMD_GPU_GFX941) || defined(CK_AMD_GPU_GFX942) || defined(CK_AMD_GPU_GFX940) || \ defined(CK_AMD_GPU_GFX1030) || defined(CK_AMD_GPU_GFX1031) || defined(CK_AMD_GPU_GFX1100) || \ - defined(CK_AMD_GPU_GFX1101) || defined(CK_AMD_GPU_GFX1102) + defined(CK_AMD_GPU_GFX1101) || defined(CK_AMD_GPU_GFX1102) || defined(CK_AMD_GPU_GFX1200) || \ + defined(CK_AMD_GPU_GFX1201) #define CK_USE_AMD_V_FMAC_F32 #define CK_USE_AMD_V_DOT2_F32_F16 #define CK_USE_AMD_V_DOT4_I32_I8 diff --git a/src/conv/heuristics/ai_heuristics.cpp b/src/conv/heuristics/ai_heuristics.cpp index c2ecc16878..a8a456e2d6 100644 --- a/src/conv/heuristics/ai_heuristics.cpp +++ b/src/conv/heuristics/ai_heuristics.cpp @@ -33,7 +33,7 @@ namespace miopen { namespace ai { namespace common { -nlohmann::json LoadJSON(const std::string& path) +nlohmann::json LoadJSON(const fs::path& path) { if(!fs::exists(path)) MIOPEN_THROW(miopenStatusInternalError, "Unable to load file: " + path); @@ -64,7 +64,7 @@ std::vector LookupValues(const std::vector& keys, const std::unordered_map #if MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK namespace immed_mode { Metadata::Metadata(const std::string& arch) - : json(common::LoadJSON(GetSystemDbPath() + "/" + arch + "_metadata.tn.model")), + : json(common::LoadJSON(GetSystemDbPath() / (arch + "_metadata.tn.model"))), direction_encodings(json["encodings"]["Direction"]), precision_encodings(json["encodings"]["Precision"]), layout_encodings(json["encodings"]["Layout"]), @@ -141,10 +141,10 @@ class Model const size_t offset; static std::string ModelPath(const std::string& arch) { - const auto file_path = GetSystemDbPath() + "/" + arch + ".tn.model"; + const auto file_path = GetSystemDbPath() / (arch + ".tn.model"); if(!fs::exists(file_path)) MIOPEN_THROW(miopenStatusInternalError, "Unable to load AI model file:" + file_path); - return file_path; + return file_path.string(); } virtual std::vector ToFeatures(const conv::ProblemDescription& problem) const = 0; }; @@ -433,6 +433,7 @@ class Gfx942Model final : public Model static_cast(problem.GetDilationH()), static_cast(problem.GetDilationW()), static_cast(problem.GetOutBatchSize()), + static_cast(metadata.EncodeLayout(problem.GetInLayout())), static_cast(metadata.EncodePrecision(problem.GetInDataType())), static_cast(metadata.EncodeDirection(problem.GetDirection())), static_cast(problem.GetGroupCount())}; @@ -533,8 +534,10 @@ namespace tuning { Metadata::Metadata(const std::string& arch, const std::string& solver) { const nlohmann::json metadata = - common::LoadJSON(GetSystemDbPath() + "/" + arch + "_" + solver + "_metadata.ktn.model"); - num_tuning_params = metadata["num_tuning_params"].get(); + common::LoadJSON(GetSystemDbPath() / (arch + "_" + solver + "_metadata.ktn.model")); + predict_type = metadata["predict_type"].get(); + num_tuning_params = + metadata["num_tuning_params"].get>(); tuning_decodings = metadata["decodings"]["tunings"].get>(); } @@ -572,19 +575,17 @@ class Model const fdeep::model decoder; static std::string EncoderPath(const std::string& arch, const std::string& solver) { - const std::string path = - GetSystemDbPath() + "/" + arch + "_" + solver + "_encoder.ktn.model"; + const auto path = GetSystemDbPath() / (arch + "_" + solver + "_encoder.ktn.model"); if(!fs::exists(path)) MIOPEN_THROW(miopenStatusInternalError, "Unable to load file: " + path); - return path; + return path.string(); } static std::string DecoderPath(const std::string& arch, const std::string& solver) { - const std::string path = - GetSystemDbPath() + "/" + arch + "_" + solver + "_decoder.ktn.model"; + const auto path = GetSystemDbPath() / (arch + "_" + solver + "_decoder.ktn.model"); if(!fs::exists(path)) MIOPEN_THROW(miopenStatusInternalError, "Unable to load file: " + path); - return path; + return path.string(); } }; @@ -606,6 +607,7 @@ std::shared_ptr GetModel(const std::string& arch, const std::string& solv bool ModelSetParams(const std::string& arch, const std::string& solver, + miopen::conv::Direction direction, const std::vector& features, bool transform_features, std::function validator) @@ -616,10 +618,23 @@ bool ModelSetParams(const std::string& arch, dim = std::sqrt(features.size()); else dim = features.size(); + auto start = std::chrono::high_resolution_clock::now(); fdeep::tensors context = model->Encode(features, dim, transform_features); float decoder_input = 0.0; - for(std::size_t i = 0; i < model->metadata.num_tuning_params; ++i) + std::string dir; + switch(direction) + { + case miopen::conv::Direction::Forward: dir = "fwd"; break; + case miopen::conv::Direction::BackwardData: dir = "bwd"; break; + case miopen::conv::Direction::BackwardWeights: dir = "wrw"; break; + default: return false; + } + + for(size_t i = 0, num_tuning_params = 1; i < num_tuning_params; ++i) { + + if(i == 0 && (model->metadata.predict_type == 0u)) + num_tuning_params = model->metadata.num_tuning_params[dir]; fdeep::tensors decoder_output = model->Decode(decoder_input, context); auto token_scores = decoder_output[0].to_vector(); @@ -635,17 +650,28 @@ bool ModelSetParams(const std::string& arch, std::string value = model->metadata.tuning_decodings[std::to_string(token)]; pq.pop(); if(value == "-1") + { + auto stop = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(stop - start); + MIOPEN_LOG_I2("Model ran for " << duration.count() << " micro-seconds"); return false; + } if(validator(i, value)) { output_token_index = token; // index with largest value that is valid = predicted index + if(i == 0 && model->metadata.predict_type != 0u) + num_tuning_params = model->metadata.num_tuning_params[value]; break; } } decoder_input = float(output_token_index); context = {decoder_output.begin() + 1, decoder_output.end()}; } + + auto stop = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(stop - start); + MIOPEN_LOG_I2("Model ran for " << duration.count() << " micro-seconds"); return true; } diff --git a/src/conv/invokers/gcn_asm_wino.cpp b/src/conv/invokers/gcn_asm_wino.cpp new file mode 100644 index 0000000000..7f38bbeb40 --- /dev/null +++ b/src/conv/invokers/gcn_asm_wino.cpp @@ -0,0 +1,225 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include + +#include +#include +#include +#include +#include + +namespace miopen { + +InvokerFactory MakeGcnAsmWinoV2InvokerFactory(const WinoShaderArgsV2& args, + conv::Direction direction, + std::size_t sync_buffer_size, + bool fused) +{ + const bool is_backWrW = (direction == conv::Direction::BackwardWeights); + const bool coop_launch = (args.sync_period != 0); + const bool do_bias = + ((args.flags64 & WinoShaderFlagsV2::F_BIAS) != static_cast(0)); + + if(fused && (direction != conv::Direction::Forward)) + { + MIOPEN_THROW(miopenStatusInternalError); + } + + return [=](const std::vector& kernels) { + return [=](const Handle& handle, const AnyInvokeParams& primitive_params) { + const auto k = handle.Run(kernels[0], coop_launch); + + // pointers + ConstData_t data_addr; + ConstData_t filter_addr; + Data_t output_addr; + ConstData_t bias_addr = nullptr; + Data_t acc_addr = nullptr; + Data_t sync_addr = nullptr; + + if(fused) + { + const auto& invoke_ctx = primitive_params.CastTo(); + const auto& conv_params = + dynamic_cast(*invoke_ctx.op_args.params[0]); + + data_addr = invoke_ctx.in; + filter_addr = conv_params.weights; + output_addr = invoke_ctx.out; + + if(do_bias) + { + const auto& bias_params = + dynamic_cast(*invoke_ctx.op_args.params[1]); + bias_addr = bias_params.bdata; + } + + if(coop_launch) + sync_addr = invoke_ctx.GetWorkspace(); + } + else if(!is_backWrW) + { + const auto& invoke_ctx = primitive_params.CastTo(); + + data_addr = invoke_ctx.tensors.in; + filter_addr = invoke_ctx.tensors.w; + output_addr = invoke_ctx.tensors.out; + + if(coop_launch) + sync_addr = invoke_ctx.GetWorkspace(); + } + else + { + const auto& invoke_ctx = primitive_params.CastTo(); + + data_addr = invoke_ctx.tensors.x; + filter_addr = invoke_ctx.tensors.dy; + output_addr = invoke_ctx.tensors.dw; + + if(coop_launch) + sync_addr = invoke_ctx.GetWorkspace(); + } + + // offsets + uint64_t d_offset = 0; + uint64_t f_offset = 0; + uint64_t o_offset = 0; + + uint64_t b_offset = 0; + uint64_t a_offset = 0; + + // activation parameters + float alpha = 0.0f; + float beta = 0.0f; + + if(fused && (args.activation_mode != WinoShaderActivationModeV2_t::IDENTITY)) + { + const auto& invoke_ctx = primitive_params.CastTo(); + const int idx = do_bias ? 2 : 1; + const auto& activ_args = + dynamic_cast(*invoke_ctx.op_args.params[idx]); + if(args.activation_mode == WinoShaderActivationModeV2_t::SCALED_TANH) + { + // The kernel uses a different expression in which alpha and beta are swapped + alpha = activ_args.activBeta; + beta = activ_args.activAlpha; + } + else + { + alpha = activ_args.activAlpha; + beta = activ_args.activBeta; + } + } + + // clang-format off + MIOPEN_LOG_I2(" N=" << args.N << " C=" << args.C << " H=" << args.H << " W=" << args.W + << " K=" << args.K << " R=" << args.R << " S=" << args.S + << " pad_H=" << args.pad_h << " pad_W=" << args.pad_w + << " out_H=" << args.out_h << " out_W=" << args.out_w + << " G=" << args.G + << " alpha=" << alpha << " beta=" << beta << " act_mode=" << args.activation_mode + << " d_offset=" << d_offset << " f_offset=" << f_offset + << " o_offset=" << o_offset << " b_offset=" << b_offset + << " d_N_stride=" << args.d_N_stride << " d_C_stride=" << args.d_C_stride + << " d_H_stride=" << args.d_H_stride << " d_G_stride=" << args.d_G_stride + << " f_K_stride=" << args.f_K_stride << " f_C_stride=" << args.f_C_stride + << " f_R_stride=" << args.f_R_stride << " f_G_stride=" << args.f_G_stride + << " o_N_stride=" << args.o_N_stride << " o_K_stride=" << args.o_K_stride + << " o_H_stride=" << args.o_H_stride << " o_G_stride=" << args.o_G_stride + << " n_groups=" << args.n_groups << " flags64=" << args.flags64 + << " sync_limit=" << static_cast(args.sync_limit) + << " sync_period=" << static_cast(args.sync_period)); + // clang-format on + + if(coop_launch) + { + // Sync buffer that has to be zeroed before each shader dispatch +#if MIOPEN_BACKEND_HIP + auto status = hipMemsetAsync(sync_addr, 0, sync_buffer_size, handle.GetStream()); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status, "hipMemsetAsync() failed"); +#else +#error "Unsupported backend" +#endif + } + + // clang-format off + // Any reserved fields should be set to 0 + k(args.N, // uint32_t, batch size + args.C, // uint32_t, number of input channels in each filter group + args.H, // uint32_t, input height + args.W, // uint32_t, input width + args.K, // uint32_t, number of output channels in each filter group + args.n_groups, // uint32_t, number of shader groups + args.flags64, // uint64_t, shader flags + data_addr, // uint64_t, address of input tensor + filter_addr, // uint64_t, address of filter tensor + output_addr, // uint64_t, address of output tensor + static_cast(0), // uint64_t, not used, for backward compatibility only + args.R, // uint32_t, filter height + args.S, // uint32_t, filter width + args.pad_h, // int32_t, padding in h dimension + args.pad_w, // int32_t, padding in w dimension + args.out_h, // uint32_t, output height + args.out_w, // uint32_t, output width + bias_addr, // uint64_t, address of bias buffer + alpha, // fp32, activation parameter alpha + beta, // fp32, activation parameter beta + d_offset, // uint64_t, byte offset for buffer referenced by data_addr + f_offset, // uint64_t, byte offset for buffer referenced by filter_addr + o_offset, // uint64_t, byte offset for buffer referenced by output_addr + b_offset, // uint64_t, byte offset for buffer referenced by bias_addr + args.d_N_stride, // uint32_t, stride in number of elements of the N dimension of the input data buffer + args.d_C_stride, // uint32_t, stride in number of elements of the C dimension of the input data buffer + args.d_H_stride, // uint32_t, stride in number of elements of the H dimension of the input data buffer + static_cast(0), // uint32_t, reserved + args.f_K_stride, // uint32_t, stride in number of elements of the K dimension of the filter buffer + args.f_C_stride, // uint32_t, stride in number of elements of the C dimension of the filter buffer + args.f_R_stride, // uint32_t, stride in number of elements of the R dimension of the filter buffer + static_cast(0), // uint32_t, reserved + args.o_N_stride, // uint32_t, stride in number of elements of the N dimension of the output buffer + args.o_K_stride, // uint32_t, stride in number of elements of the K dimension of the output buffer + args.o_H_stride, // uint32_t, stride in number of elements of the H dimension of the output buffer + static_cast(0), // uint32_t, reserved + args.G, // uint32_t, number of filter groups + args.d_G_stride, // uint32_t, stride in number of elements of the G dimension of the input data buffer + args.f_G_stride, // uint32_t, stride in number of elements of the G dimension of the filter buffer + args.o_G_stride, // uint32_t, stride in number of elements of the G dimension of the output buffer + args.activation_mode, // uint8_t, activation mode + args.sync_limit, // uint8_t, maximum number of sync attempts + args.sync_period, // uint8_t, synchronization period + static_cast(0), // uint8_t, reserved + static_cast(0), // uint32_t, reserved + sync_addr, // uint64_t, address of sync buffer + acc_addr, // uint64_t, address of accumulation buffer + a_offset); // uint64_t, byte offset for buffer referenced by acc_addr + // clang-format on + }; + }; +} + +} // namespace miopen diff --git a/src/conv/invokers/impl_gemm.cpp b/src/conv/invokers/impl_gemm.cpp index 2f07422a30..4ab0829c9d 100644 --- a/src/conv/invokers/impl_gemm.cpp +++ b/src/conv/invokers/impl_gemm.cpp @@ -139,8 +139,9 @@ InvokerFactory MakeImplGemmDataInvokerFactory(const ProblemDescription& problem) { if(tensors.wDesc.GetLengths()[2] == 1 && tensors.wDesc.GetLengths()[3] == 1) { // filter = 1 - if(tensors.wDesc.GetSize() == 4 || - (tensors.wDesc.GetSize() == 5 && tensors.wDesc.GetLengths()[4] == 1)) + if(tensors.wDesc.GetNumDims() == 4 || + (tensors.wDesc.GetNumDims() == 5 && + tensors.wDesc.GetLengths()[4] == 1)) { is_1x1_s1 = true; } diff --git a/src/conv/kernel_interface/winograd_kernel_interface.cpp b/src/conv/kernel_interface/winograd_kernel_interface.cpp new file mode 100644 index 0000000000..1cc2b86f16 --- /dev/null +++ b/src/conv/kernel_interface/winograd_kernel_interface.cpp @@ -0,0 +1,238 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include + +#include +#include + +namespace miopen { + +namespace { + +template +bool AssignAndCheck(Tdst& dst_v, Tsrc src_v) noexcept +{ + static_assert(std::is_integral_v); + static_assert(std::is_integral_v); + + dst_v = src_v; + + if(dst_v != src_v) + return false; + + if constexpr(std::numeric_limits::is_signed) + { + if constexpr(std::numeric_limits::is_signed) + return (dst_v >= 0 && src_v >= 0) || (dst_v < 0 && src_v < 0); + else + return src_v >= 0; + } + else if constexpr(std::numeric_limits::is_signed) + { + return dst_v >= 0; + } + + return true; +} + +} // namespace + +bool WinoShaderArgsV2::SetConvParams(const conv::ProblemDescription& problem) +{ + if(!problem.Is2d()) + return false; + if(problem.GetBias() != 0) + return false; + if(!(problem.GetInStrideW() == 1 && problem.GetWeightsStrideW() == 1 && + problem.GetOutStrideW() == 1)) + { + return false; + } + + if(!AssignAndCheck(G, problem.GetGroupCount())) + return false; + + const auto in_c_per_group = problem.GetInChannels() / G; + const auto out_c_per_group = problem.GetOutChannels() / G; + + if(!problem.IsDirectionBackwardWrW()) + { + if(!AssignAndCheck(N, problem.GetBatchSize())) + return false; + if(!AssignAndCheck(C, in_c_per_group)) + return false; + if(!AssignAndCheck(H, problem.GetInHeight())) + return false; + if(!AssignAndCheck(W, problem.GetInWidth())) + return false; + if(!AssignAndCheck(K, out_c_per_group)) + return false; + if(!AssignAndCheck(R, problem.GetWeightsHeight())) + return false; + if(!AssignAndCheck(S, problem.GetWeightsWidth())) + return false; + if(!AssignAndCheck(out_h, problem.GetOutHeight())) + return false; + if(!AssignAndCheck(out_w, problem.GetOutWidth())) + return false; + } + else + { + if(!AssignAndCheck(N, out_c_per_group)) + return false; + if(!AssignAndCheck(C, problem.GetBatchSize())) + return false; + if(!AssignAndCheck(H, problem.GetOutHeight())) + return false; + if(!AssignAndCheck(W, problem.GetOutWidth())) + return false; + if(!AssignAndCheck(K, in_c_per_group)) + return false; + if(!AssignAndCheck(R, problem.GetInHeight())) + return false; + if(!AssignAndCheck(S, problem.GetInWidth())) + return false; + if(!AssignAndCheck(out_h, problem.GetWeightsHeight())) + return false; + if(!AssignAndCheck(out_w, problem.GetWeightsWidth())) + return false; + } + + if(!problem.IsDirectionBackwardData()) + { + if(!AssignAndCheck(pad_h, problem.GetPadH())) + return false; + if(!AssignAndCheck(pad_w, problem.GetPadW())) + return false; + } + else + { + if(!AssignAndCheck(pad_h, problem.GetBackwardPadH())) + return false; + if(!AssignAndCheck(pad_w, problem.GetBackwardPadW())) + return false; + } + + if(problem.GetInBatchStride() > std::numeric_limits::max() || + problem.GetInChannelStride() > std::numeric_limits::max() || + problem.GetInStrideH() > std::numeric_limits::max()) + return false; + if(problem.GetWeightsStrideK() > std::numeric_limits::max() || + problem.GetWeightsStrideC() > std::numeric_limits::max() || + problem.GetWeightsStrideH() > std::numeric_limits::max()) + return false; + if(problem.GetOutBatchStride() > std::numeric_limits::max() || + problem.GetOutChannelStride() > std::numeric_limits::max() || + problem.GetOutStrideH() > std::numeric_limits::max()) + return false; + + return true; +} + +void WinoShaderArgsV2::SetStrides(const conv::ProblemDescription& problem) +{ + MemLayout_t d_layout, o_layout, f_layout; + + if(!problem.IsDirectionBackwardWrW()) + { + d_layout = GetGroupConvLayout(GetMemLayout_t(problem.GetInLayout()), true); + o_layout = GetGroupConvLayout(GetMemLayout_t(problem.GetOutLayout()), true); + // clang-format off + f_layout = GetGroupConvLayout(problem.IsDirectionForward() ? MemLayout_t::NCHW + : GetSwappedNCLayout(MemLayout_t::NCHW), false); + // clang-format on + } + else + { + d_layout = + GetGroupConvLayout(GetSwappedNCLayout(GetMemLayout_t(problem.GetInLayout())), true); + o_layout = + GetGroupConvLayout(GetSwappedNCLayout(GetMemLayout_t(problem.GetOutLayout())), false); + f_layout = GetGroupConvLayout(GetSwappedNCLayout(MemLayout_t::NCHW), true); + } + + // TODO Make a constructor that takes unsigned int + BuffInfo d_buf(d_layout, N, C, H, W, G, GetTypeSize(problem.GetInDataType())); + BuffInfo o_buf(o_layout, N, K, out_h, out_w, G, GetTypeSize(problem.GetOutDataType())); + BuffInfo f_buf(f_layout, K, C, R, S, G, GetTypeSize(problem.GetWeightsDataType())); + + const auto& d_strides = d_buf.stride; + const auto& f_strides = f_buf.stride; + const auto& o_strides = o_buf.stride; + + d_N_stride = d_strides.nk; + d_C_stride = d_strides.c; + d_H_stride = d_strides.h; + d_G_stride = d_strides.g; + + f_K_stride = f_strides.nk; + f_C_stride = f_strides.c; + f_R_stride = f_strides.h; + f_G_stride = f_strides.g; + + o_N_stride = o_strides.nk; + o_K_stride = o_strides.c; + o_H_stride = o_strides.h; + o_G_stride = o_strides.g; +} + +void WinoShaderArgsV2::SetActivParams(miopenActivationMode_t mode) +{ + // Fused activation parameters + // clang-format off + switch(mode) + { + case miopenActivationPASTHRU: + activation_mode = WinoShaderActivationModeV2_t::IDENTITY; + break; + case miopenActivationLOGISTIC: + activation_mode = WinoShaderActivationModeV2_t::SIGMOID; + break; + case miopenActivationTANH: + activation_mode = WinoShaderActivationModeV2_t::SCALED_TANH; + break; + case miopenActivationLEAKYRELU: + activation_mode = WinoShaderActivationModeV2_t::LEAKY_RELU; + break; + default: + MIOPEN_THROW(miopenStatusInternalError); + } + // clang-format on +} + +void WinoShaderArgsV2::SetShaderParams(uint32_t n_groups_, + WinoShaderFlagsV2 flags_, + uint8_t sync_limit_, + uint8_t sync_period_) noexcept +{ + n_groups = n_groups_; + flags64 = flags_; + sync_limit = sync_limit_; + sync_period = sync_period_; +} + +} // namespace miopen diff --git a/src/conv/problem_description.cpp b/src/conv/problem_description.cpp index 8939c0e7f6..3f4e0abb37 100644 --- a/src/conv/problem_description.cpp +++ b/src/conv/problem_description.cpp @@ -65,6 +65,34 @@ std::ostream& operator<<(std::ostream& stream, std::function #include #include - -MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEVICE_ARCH) +#include MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_GEMM) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_DIRECT) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_WINOGRAD) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_FFT) +MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEVICE_ARCH) +MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMPILE_ONLY) + +MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_FIND_CONV_INSUFFICIENT_WORKSPACE_ALLOW_FINDDB_UPDATE) namespace miopen { @@ -58,7 +61,7 @@ class DirectSolverFinder : public SolversFinderMixin FindImpl(const ExecutionContext& ctx, @@ -87,7 +90,7 @@ class ImplicitGemmSolverFinder : public SolversFinderMixin FindImpl(const ExecutionContext& ctx, @@ -118,7 +121,7 @@ class FftSolverFinder : public SolversFinderMixin FindImpl(const ExecutionContext& ctx, @@ -145,7 +148,7 @@ class GemmSolverFinder : public SolversFinderMixin FindImpl(const ExecutionContext& ctx, @@ -172,7 +175,7 @@ class WinogradSolverFinder : public SolversFinderMixin FindImpl(const ExecutionContext& ctx, @@ -211,51 +214,72 @@ const std::vector>& GetConvSolverFinders() } // namespace conv /// Register invoker only for the best solution within algorithm. -/// Add all solutions to the find-db record. -static void EvaluateInvokers(Handle& handle, - const std::vector& solutions, - const AlgorithmName& algorithm_name, - const NetworkConfig& network_config, - const AnyInvokeParams& invoke_ctx, - DbRecord& record) +static std::vector EvaluateInvokers(Handle& handle, + const std::vector& solutions, + const AlgorithmName& algorithm_name, + const NetworkConfig& network_config, + const AnyInvokeParams& invoke_ctx, + bool& is_result_optimal, + bool force_attach_binary) { - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto arch = env::value(MIOPEN_DEVICE_ARCH); if(!arch.empty()) - return; + return {}; auto selected = miopen::solver::ConvSolution{miopenStatusUnknownError}; auto best = std::numeric_limits::max(); auto best_invoker = Invoker{}; + auto ret = std::vector{}; for(const auto& sol : solutions) { - if(sol.workspace_sz > 0) + if(!conv::IsEnoughWorkspace( + "EvaluateInvokers", solver::Id{sol.solver_id}, sol.workspace_sz, &invoke_ctx)) { - if(invoke_ctx.GetWorkspace() == nullptr) - { - MIOPEN_LOG_I("Warning: skipping solver <" << sol.solver_id - << "> due to no workspace provided (" - << sol.workspace_sz << " required)"); - continue; - } - if(invoke_ctx.GetWorkspaceSize() < sol.workspace_sz) - { - MIOPEN_LOG_I("Warning: skipping solver <" - << sol.solver_id << "> due to insufficient workspace (" - << invoke_ctx.GetWorkspaceSize() << " < " << sol.workspace_sz << ")"); - continue; - } + // Providing smaller workspace may result in the selection of a slow convolution + // algorithm, and therefore affect library performance. Moreover, sub-optimal data may + // be cached in the user's find-db. This means that the performance drop will become + // persistent, i.e. even providing sufficient workspace won't restore the performance. + // To get rid of this problem, the user will need to either remove the user's find-db, + // or repeat miopenFindConvolution*() with affected convolution configs in Normal Find + // Mode (the latter will overwrite sub-optimal user's find-db records). + // + // That is why we do not write sub-optimal results into persistent find-db (on disk) + // unless this is explicitly enabled via environment setting. + if(!env::enabled(MIOPEN_FIND_CONV_INSUFFICIENT_WORKSPACE_ALLOW_FINDDB_UPDATE)) + is_result_optimal = false; + continue; } if(!sol.invoker_factory) MIOPEN_THROW("Invoker is not provided by solver " + sol.solver_id); - const auto invoker = handle.PrepareInvoker(*sol.invoker_factory, sol.construction_params); + std::vector programs; + const auto invoker = handle.PrepareInvoker(*sol.invoker_factory, + sol.construction_params, + force_attach_binary ? &programs : nullptr); + try { - invoker(handle, invoke_ctx); - const auto elapsed = handle.GetKernelTime(); - record.SetValues(sol.solver_id, FindDbData{elapsed, sol.workspace_sz, algorithm_name}); + // Run invoker max 6 times, with ~5 sec time limit. + using elapsed_t = decltype(handle.GetKernelTime()); + constexpr elapsed_t TIME_MS_MAX = 5000.0; + constexpr int N_RUNS_MAX = 6; + auto elapsed = static_cast(0); + auto first_elapsed = static_cast(0); + int i = 0; + while(i < N_RUNS_MAX && elapsed < TIME_MS_MAX) + { + invoker(handle, invoke_ctx); + elapsed += handle.GetKernelTime(); + if(i == 0) + first_elapsed = elapsed; + ++i; + } + // If the execution time was not too long, + // then the 1st run is not counted (assume it's warm-up): + if(i > 1) + elapsed = (elapsed - first_elapsed) / static_cast(i - 1); MIOPEN_LOG_I(sol << ": " << elapsed << (elapsed < best ? " < " : " >= ") << best); if(elapsed < best) @@ -264,6 +288,13 @@ static void EvaluateInvokers(Handle& handle, selected = sol; best_invoker = invoker; } + + auto solution = Solution{solver::Id{selected.solver_id}, best, selected.workspace_sz}; + if(force_attach_binary) + solution.SetInvoker(invoker, programs, selected.construction_params); + else + solution.SetInvoker(invoker, {}, {}); + ret.emplace_back(std::move(solution)); } catch(const miopen::Exception& ex) { @@ -271,30 +302,23 @@ static void EvaluateInvokers(Handle& handle, } } - if(selected.Succeeded()) - { - handle.RegisterInvoker(best_invoker, network_config, selected.solver_id, algorithm_name); - MIOPEN_LOG_I("Selected: " << selected << ": " << best - << ", workspace_sz = " << selected.workspace_sz); - } -} + if(!selected.Succeeded()) + return {}; -static inline void AppendPointersToElements(const std::vector& from, - std::vector& to) -{ - std::transform(from.begin(), - from.end(), - std::back_inserter(to), - [](const miopen::solver::ConvSolution& s) { return &s; }); + handle.RegisterInvoker(best_invoker, network_config, selected.solver_id, algorithm_name); + MIOPEN_LOG_I("Selected: " << selected << ": " << best + << ", workspace_sz = " << selected.workspace_sz); + + return ret; } -void FindCore(const AnyInvokeParams& invoke_ctx, - DbRecord& record, - const ExecutionContext& ctx, - const ProblemDescriptionBase& problem, - const PrimitiveFindParameters& parameters, - const std::vector>& finders, - const std::optional& options) +FindCoreResult FindCore(const AnyInvokeParams& invoke_ctx, + const ExecutionContext& ctx, + const ProblemDescriptionBase& problem, + const PrimitiveFindParameters& parameters, + const std::vector>& finders, + const std::optional& options, + bool force_attach_binary) { auto& handle = ctx.GetStream(); @@ -306,27 +330,61 @@ void FindCore(const AnyInvokeParams& invoke_ctx, f->Find(ctx, problem, invoke_ctx, parameters, options)); }); + std::size_t total = 0; + + for(auto it = solutions.begin(); it != solutions.end();) + { + if(it->second.empty()) + { + it = solutions.erase(it); + continue; + } + + total += it->second.size(); + ++it; + } + // Precompile { auto all = std::vector{}; - all.reserve( - std::accumulate(solutions.begin(), solutions.end(), 0, [](auto&& before, auto&& ss) { - return before + ss.second.size(); - })); + all.reserve(total); for(const auto& ss : solutions) - AppendPointersToElements(ss.second, all); - PrecompileSolutions(handle, all); + std::transform(ss.second.begin(), + ss.second.end(), + std::back_inserter(all), + [](auto&& s) { return &s; }); + PrecompileSolutions(handle, all, force_attach_binary); } + if(env::enabled((MIOPEN_DEBUG_COMPILE_ONLY))) + MIOPEN_THROW( + miopenStatusGpuOperationsSkipped, + "MIOPEN_DEBUG_COMPILE_ONLY is enabled, escaping forward convolution. Search skipped."); + // Evaluate Invokers AutoEnableProfiling enableProfiling{handle}; const auto network_config = problem.MakeNetworkConfig(); + auto ret = FindCoreResult(); + ret.is_optimal = true; + + ret.solutions.reserve(total); for(const auto& ss : solutions) { - if(!ss.second.empty()) - EvaluateInvokers(handle, ss.second, ss.first, network_config, invoke_ctx, record); + auto evaluated = EvaluateInvokers(handle, + ss.second, + ss.first, + network_config, + invoke_ctx, + ret.is_optimal, + force_attach_binary); + + ret.solutions.insert(ret.solutions.end(), + std::make_move_iterator(evaluated.begin()), + std::make_move_iterator(evaluated.end())); } + + return ret; } namespace conv { @@ -335,20 +393,42 @@ bool IsAlgorithmDisabled(miopenConvAlgorithm_t algo) { switch(algo) { // clang-format off +#if MIOPEN_USE_GEMM case miopenConvolutionAlgoGEMM: - return !MIOPEN_USE_GEMM || miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_GEMM)); + return env::disabled(MIOPEN_DEBUG_CONV_GEMM); +#endif case miopenConvolutionAlgoDirect: - return miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_DIRECT)); + return env::disabled(MIOPEN_DEBUG_CONV_DIRECT); case miopenConvolutionAlgoFFT: - return miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_FFT)); + return env::disabled(MIOPEN_DEBUG_CONV_FFT); case miopenConvolutionAlgoWinograd: - return miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_WINOGRAD)); + return env::disabled(MIOPEN_DEBUG_CONV_WINOGRAD); case miopenConvolutionAlgoImplicitGEMM: - return miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM)); + return env::disabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM); default: // Disable future algos by default to enforce explicit handling: return true; } // clang-format on } +bool IsEnoughWorkspace(std::string_view where, + const miopen::solver::Id& solver_id, + const std::size_t required_size, + const miopen::AnyInvokeParams* const invokeParams) +{ + if(invokeParams != nullptr && required_size > 0) + { + const auto provided_size = invokeParams->GetWorkspaceSize(); + const auto provided_ptr = invokeParams->GetWorkspace(); + if(provided_ptr == nullptr || provided_size < required_size) + { + MIOPEN_LOG_W("[" << where << "] Solver <" << solver_id.ToString() << ">" + << ", workspace required: " << required_size + << ", provided ptr: " << provided_ptr << " size: " << provided_size); + return false; + } + } + return true; +} + } // namespace conv } // namespace miopen diff --git a/src/convolution.cpp b/src/convolution.cpp index 94b2a3b3b8..5110bf2974 100644 --- a/src/convolution.cpp +++ b/src/convolution.cpp @@ -80,7 +80,7 @@ std::size_t GetWorkSpaceSizeGEMM(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { #if MIOPEN_USE_GEMM - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_GEMM)) || + if(env::disabled(MIOPEN_DEBUG_CONV_GEMM) || miopen::any_of(problem.GetConv().GetConvDilations(), [](auto v) { return v > 1; })) return 0; @@ -95,7 +95,7 @@ std::size_t GetWorkSpaceSizeGEMM(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeImplicitGemm(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM))) + if(env::disabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM)) return 0; return GetMaxWorkSpaceSize(FindAllImplicitGemmWorkspaceSizes(ctx, problem)); } @@ -103,7 +103,7 @@ std::size_t GetWorkSpaceSizeImplicitGemm(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeDirect(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_DIRECT))) + if(env::disabled(MIOPEN_DEBUG_CONV_DIRECT)) return 0; return GetMaxWorkSpaceSize(AllDirectForwardBackwardDataWorkspaceSize(ctx, problem)); } @@ -111,7 +111,7 @@ std::size_t GetWorkSpaceSizeDirect(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeFFT(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_FFT))) + if(env::disabled(MIOPEN_DEBUG_CONV_FFT)) return 0; return GetMaxWorkSpaceSize(AllFFTForwardBackwardDataWorkspaceSize(ctx, problem)); } @@ -119,7 +119,7 @@ std::size_t GetWorkSpaceSizeFFT(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeWinograd(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_WINOGRAD))) + if(env::disabled(MIOPEN_DEBUG_CONV_WINOGRAD)) return 0; return GetMaxWorkSpaceSize(FindAllWinogradWorkspaceSizes(ctx, problem)); } @@ -127,7 +127,7 @@ std::size_t GetWorkSpaceSizeWinograd(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeDirectWrW(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_DIRECT))) + if(env::disabled(MIOPEN_DEBUG_CONV_DIRECT)) return 0; return GetMaxWorkSpaceSize(AllDirectBwdWrW2DWorkspaceSize(ctx, problem)); } @@ -135,7 +135,7 @@ std::size_t GetWorkSpaceSizeDirectWrW(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeWinogradWrW(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_WINOGRAD))) + if(env::disabled(MIOPEN_DEBUG_CONV_WINOGRAD)) return 0; return GetMaxWorkSpaceSize(FindWinogradWrWWorkspaceSizes(ctx, problem)); } @@ -143,7 +143,7 @@ std::size_t GetWorkSpaceSizeWinogradWrW(const miopen::ExecutionContext& ctx, std::size_t GetWorkSpaceSizeImplicitGemmWrW(const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM))) + if(env::disabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM)) return 0; return GetMaxWorkSpaceSize(FindImplicitGemmWrWWorkspaceSizes(ctx, problem)); } @@ -355,7 +355,7 @@ ConvolutionDescriptor::GetForwardOutputTensorWithLayout(const TensorDescriptor& out_lens[0] = in_n; out_lens[1] = out_c; - const std::string default_layout = tensor_layout_get_default(xDesc.GetSize()); + const std::string default_layout = tensor_layout_get_default(xDesc.GetNumDims()); std::vector out_strides; tensor_layout_to_strides( out_lens, default_layout, yLayout, xDesc.GetVectorLength(), out_strides); @@ -373,7 +373,7 @@ TensorDescriptor ConvolutionDescriptor::GetForwardOutputTensor(const TensorDescr miopenDataType_t yType) const { // output layout same as input - const std::string default_layout = tensor_layout_get_default(xDesc.GetSize()); + const std::string default_layout = tensor_layout_get_default(xDesc.GetNumDims()); const std::string in_layout = xDesc.GetLayout(default_layout); return GetForwardOutputTensorWithLayout(xDesc, wDesc, in_layout, yType); } @@ -386,7 +386,7 @@ TensorDescriptor ConvolutionDescriptor::GetForwardOutputTensor(const TensorDescr bool ConvolutionDescriptor::IsWinograd3x3SupportedAndFast( const miopen::ExecutionContext& ctx, const conv::ProblemDescription& problem) const { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_WINOGRAD))) + if(env::disabled(MIOPEN_DEBUG_CONV_WINOGRAD)) return false; // Disable this performance optimization when we want to run some specific Solver. @@ -425,7 +425,7 @@ std::size_t ConvolutionDescriptor::GetWorkSpaceSize(ExecutionContext ctx, auto fallback = bool{}; const auto solutions = GetSolutions(ctx, problem, 1, &fallback); if(solutions.empty() || ((findMode.IsHybrid(ctx) && fallback) && - !miopen::IsEnabled(ENV(MIOPEN_DEBUG_FORCE_IMMED_MODE_FALLBACK)))) + !env::enabled(MIOPEN_DEBUG_FORCE_IMMED_MODE_FALLBACK))) { ctx.use_dynamic_solutions_only = findMode.IsDynamicHybrid(ctx); break; // Fall down to Normal Find. diff --git a/src/convolution_api.cpp b/src/convolution_api.cpp index 6929512e05..9c166278a8 100644 --- a/src/convolution_api.cpp +++ b/src/convolution_api.cpp @@ -35,6 +35,7 @@ #include #include #include +#include #include #include @@ -113,12 +114,17 @@ static inline auto MakeWrWCtxAndProblem(miopenHandle_t handle, return std::make_tuple(std::move(ctx), std::move(problem)); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenCreateConvolutionDescriptor(miopenConvolutionDescriptor_t* convDesc) { MIOPEN_LOG_FUNCTION(convDesc); - return miopen::try_([&] { miopen::deref(convDesc) = new miopen::ConvolutionDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(convDesc); + desc = new miopen::ConvolutionDescriptor(); + }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenInitConvolutionDescriptor(miopenConvolutionDescriptor_t convDesc, miopenConvolutionMode_t c_mode, int pad_h, @@ -139,6 +145,7 @@ extern "C" miopenStatus_t miopenInitConvolutionDescriptor(miopenConvolutionDescr }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenInitConvolutionNdDescriptor(miopenConvolutionDescriptor_t convDesc, int spatialDim, const int* padA, @@ -163,6 +170,7 @@ extern "C" miopenStatus_t miopenInitConvolutionNdDescriptor(miopenConvolutionDes }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionGroupCount(miopenConvolutionDescriptor_t convDesc, int* groupCount) { @@ -170,6 +178,7 @@ extern "C" miopenStatus_t miopenGetConvolutionGroupCount(miopenConvolutionDescri return miopen::try_([&] { miopen::deref(groupCount) = miopen::deref(convDesc).group_count; }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenSetConvolutionGroupCount(miopenConvolutionDescriptor_t convDesc, int groupCount) { @@ -177,6 +186,7 @@ extern "C" miopenStatus_t miopenSetConvolutionGroupCount(miopenConvolutionDescri return miopen::try_([&] { miopen::deref(convDesc).group_count = groupCount; }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenSetConvolutionFindMode(miopenConvolutionDescriptor_t convDesc, miopenConvolutionFindMode_t findMode) { @@ -186,6 +196,7 @@ extern "C" miopenStatus_t miopenSetConvolutionFindMode(miopenConvolutionDescript }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionFindMode(const miopenConvolutionDescriptor_t convDesc, miopenConvolutionFindMode_t* findMode) { @@ -196,14 +207,77 @@ extern "C" miopenStatus_t miopenGetConvolutionFindMode(const miopenConvolutionDe }); } +MIOPEN_EXPORT extern "C" miopenStatus_t +miopenConvolutionABBackwardWeightsGetWorkSpaceSize(const miopenAlphaBetaCase_t alpha_beta_case, + const miopenTensorDescriptor_t inputTensorDesc, + const miopenTensorDescriptor_t outputTensorDesc, + const miopenConvolutionDescriptor_t convDesc, + size_t* buffer_size) +{ + MIOPEN_LOG_FUNCTION(alpha_beta_case, outputTensorDesc); + return miopen::try_([&] { + miopenDataType_t data_type = miopen::deref(outputTensorDesc).GetType(); + size_t in_spatial_dims = miopen::deref(inputTensorDesc).GetNumDims(); + + assert(in_spatial_dims == miopen::deref(outputTensorDesc).GetNumDims()); + + int G = miopen::deref(convDesc).GetGroupCount(); + size_t C = std::get<1>( + miopen::GetNCDHW(in_spatial_dims, miopen::deref(inputTensorDesc).GetLengths())); + size_t K = std::get<1>( + miopen::GetNCDHW(in_spatial_dims, miopen::deref(outputTensorDesc).GetLengths())); + + auto CKWrwRequireWorkspace = [&](size_t G, + size_t C, + size_t K, + miopenDataType_t data_type, + miopenAlphaBetaCase_t alpha_beta_case) { + auto is_odd = [](int num) { return num % 2 != 0; }; + size_t C_per_group = C / G; + size_t K_per_group = K / G; + + return (alpha_beta_case == BILINEAR || alpha_beta_case == SCALE) || + (data_type == miopenHalf && (is_odd(C_per_group) || is_odd(K_per_group))); + }; + + size_t output_tensor_size = miopen::deref(outputTensorDesc).GetElementSize(); + size_t byte_size = 0; + if(CKWrwRequireWorkspace(G, C, K, data_type, alpha_beta_case)) + { + switch(data_type) + { + case miopenInt32: + case miopenFloat: + case miopenHalf: + case miopenBFloat16: + case miopenInt8: + case miopenFloat8: + case miopenBFloat8: byte_size = 4; break; + case miopenDouble: + case miopenInt64: byte_size = 8; break; + } + *buffer_size = byte_size * output_tensor_size; + } + else + { + *buffer_size = 0; + } + + MIOPEN_LOG_FUNCTION( + alpha_beta_case, data_type, C, K, output_tensor_size, byte_size, *buffer_size); + }); +} + // Hidden C++ functions for MIGraphX. -extern "C" MIOPEN_EXPORT miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenHiddenSetConvolutionFindMode(miopenConvolutionDescriptor_t convDesc, int findMode) { return miopen::try_([&] { miopen::deref(convDesc).findMode.Set(static_cast(findMode)); }); } + +MIOPEN_EXPORT extern "C" miopenStatus_t miopenHiddenGetConvolutionFindMode(miopenConvolutionDescriptor_t convDesc, int* findMode) { @@ -212,7 +286,7 @@ extern "C" miopenStatus_t miopenHiddenGetConvolutionFindMode(miopenConvolutionDe }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenSetTransposeConvOutputPadding(miopenConvolutionDescriptor_t convDesc, int adj_h, int adj_w) { MIOPEN_LOG_FUNCTION(convDesc, adj_h, adj_w); @@ -227,6 +301,7 @@ miopenSetTransposeConvOutputPadding(miopenConvolutionDescriptor_t convDesc, int }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenSetTransposeConvNdOutputPadding( miopenConvolutionDescriptor_t convDesc, int spatialDim, const int* adjA) { @@ -245,6 +320,7 @@ extern "C" miopenStatus_t miopenSetTransposeConvNdOutputPadding( }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionDescriptor(miopenConvolutionDescriptor_t convDesc, miopenConvolutionMode_t* c_mode, int* pad_h, @@ -271,6 +347,7 @@ extern "C" miopenStatus_t miopenGetConvolutionDescriptor(miopenConvolutionDescri }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionNdDescriptor(miopenConvolutionDescriptor_t convDesc, int requestedSpatialDim, int* spatialDim, @@ -301,6 +378,7 @@ extern "C" miopenStatus_t miopenGetConvolutionNdDescriptor(miopenConvolutionDesc }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionSpatialDim(miopenConvolutionDescriptor_t convDesc, int* spatialDim) { @@ -309,7 +387,7 @@ extern "C" miopenStatus_t miopenGetConvolutionSpatialDim(miopenConvolutionDescri [&] { miopen::deref(spatialDim) = miopen::deref(convDesc).GetSpatialDimension(); }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionForwardOutputDim(miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t inputTensorDesc, const miopenTensorDescriptor_t filterDesc, @@ -332,7 +410,7 @@ miopenGetConvolutionForwardOutputDim(miopenConvolutionDescriptor_t convDesc, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionNdForwardOutputDim(miopenConvolutionDescriptor_t convDesc, const miopenTensorDescriptor_t inputTensorDesc, const miopenTensorDescriptor_t filterDesc, @@ -344,22 +422,23 @@ miopenGetConvolutionNdForwardOutputDim(miopenConvolutionDescriptor_t convDesc, auto out_desc = miopen::deref(convDesc).GetForwardOutputTensor( miopen::deref(inputTensorDesc), miopen::deref(filterDesc)); - miopen::deref(nDim) = out_desc.GetSize(); + miopen::deref(nDim) = out_desc.GetNumDims(); - for(int i = 0; i < out_desc.GetSize(); ++i) + for(unsigned i = 0; i < out_desc.GetNumDims(); ++i) { outputTensorDimA[i] = out_desc.GetLengths()[i]; } }); } -extern "C" miopenStatus_t miopenDestroyConvolutionDescriptor(miopenConvolutionDescriptor_t convDesc) +MIOPEN_EXPORT extern "C" miopenStatus_t +miopenDestroyConvolutionDescriptor(miopenConvolutionDescriptor_t convDesc) { MIOPEN_LOG_FUNCTION(convDesc); return miopen::try_([&] { miopen_destroy_object(convDesc); }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardGetWorkSpaceSize(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, @@ -380,7 +459,7 @@ miopenConvolutionForwardGetWorkSpaceSize(miopenHandle_t handle, namespace miopen { namespace debug { -MIOPEN_EXPORT +MIOPEN_INTERNALS_EXPORT void LogCmdConvolution(const miopen::TensorDescriptor& x, const miopen::TensorDescriptor& w, const miopen::ConvolutionDescriptor& conv, @@ -395,7 +474,7 @@ void LogCmdConvolution(const miopen::TensorDescriptor& x, } } -MIOPEN_EXPORT +MIOPEN_INTERNALS_EXPORT void LogCmdFindConvolution(const miopen::TensorDescriptor& x, const miopen::TensorDescriptor& w, const miopen::ConvolutionDescriptor& conv, @@ -410,7 +489,7 @@ void LogCmdFindConvolution(const miopen::TensorDescriptor& x, } } -MIOPEN_EXPORT +MIOPEN_INTERNALS_EXPORT void LogCmdConvolution(const miopenTensorDescriptor_t& xDesc, const miopenTensorDescriptor_t& wDesc, const miopenConvolutionDescriptor_t& convDesc, @@ -427,7 +506,7 @@ void LogCmdConvolution(const miopenTensorDescriptor_t& xDesc, } } -MIOPEN_EXPORT +MIOPEN_INTERNALS_EXPORT void LogCmdFindConvolution(const miopenTensorDescriptor_t& xDesc, const miopenTensorDescriptor_t& wDesc, const miopenConvolutionDescriptor_t& convDesc, @@ -447,7 +526,7 @@ void LogCmdFindConvolution(const miopenTensorDescriptor_t& xDesc, } // namespace debug } // namespace miopen -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenFindConvolutionForwardAlgorithm(miopenHandle_t handle, const miopenTensorDescriptor_t xDesc, const void* x, @@ -525,19 +604,20 @@ miopenFindConvolutionForwardAlgorithm(miopenHandle_t handle, }); } -extern "C" miopenStatus_t miopenConvolutionForward(miopenHandle_t handle, - const void* alpha, - const miopenTensorDescriptor_t xDesc, - const void* x, - const miopenTensorDescriptor_t wDesc, - const void* w, - const miopenConvolutionDescriptor_t convDesc, - miopenConvFwdAlgorithm_t algo, - const void* beta, - const miopenTensorDescriptor_t yDesc, - void* y, - void* workSpace, - size_t workSpaceSize) +MIOPEN_EXPORT extern "C" miopenStatus_t +miopenConvolutionForward(miopenHandle_t handle, + const void* alpha, + const miopenTensorDescriptor_t xDesc, + const void* x, + const miopenTensorDescriptor_t wDesc, + const void* w, + const miopenConvolutionDescriptor_t convDesc, + miopenConvFwdAlgorithm_t algo, + const void* beta, + const miopenTensorDescriptor_t yDesc, + void* y, + void* workSpace, + size_t workSpaceSize) { MIOPEN_LOG_FUNCTION(handle, @@ -592,13 +672,14 @@ extern "C" miopenStatus_t miopenConvolutionForward(miopenHandle_t handle, }); } -extern "C" miopenStatus_t miopenConvolutionForwardBias(miopenHandle_t handle, - const void* alpha, - const miopenTensorDescriptor_t bDesc, - const void* b, - const void* beta, - const miopenTensorDescriptor_t yDesc, - void* y) +MIOPEN_EXPORT extern "C" miopenStatus_t +miopenConvolutionForwardBias(miopenHandle_t handle, + const void* alpha, + const miopenTensorDescriptor_t bDesc, + const void* b, + const void* beta, + const miopenTensorDescriptor_t yDesc, + void* y) { MIOPEN_LOG_FUNCTION(handle, alpha, bDesc, b, beta, yDesc, y); @@ -625,7 +706,7 @@ extern "C" miopenStatus_t miopenConvolutionForwardBias(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardGetSolutionCount(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, @@ -656,7 +737,7 @@ static inline void ReturnSolutions(const std::vector& solu } } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardGetSolution(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, @@ -680,7 +761,7 @@ miopenConvolutionForwardGetSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardGetSolutionWorkspaceSize(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, @@ -712,7 +793,7 @@ miopenConvolutionForwardGetSolutionWorkspaceSize(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardCompileSolution(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const miopenTensorDescriptor_t xDesc, @@ -729,7 +810,7 @@ miopenConvolutionForwardCompileSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionForwardImmediate(miopenHandle_t handle, const miopenTensorDescriptor_t wDesc, const void* w, @@ -777,7 +858,7 @@ miopenConvolutionForwardImmediate(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataGetSolutionCount(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, @@ -795,7 +876,7 @@ miopenConvolutionBackwardDataGetSolutionCount(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataGetSolution(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, @@ -819,7 +900,7 @@ miopenConvolutionBackwardDataGetSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataGetSolutionWorkspaceSize(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, @@ -851,7 +932,7 @@ miopenConvolutionBackwardDataGetSolutionWorkspaceSize(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataCompileSolution(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, @@ -868,7 +949,7 @@ miopenConvolutionBackwardDataCompileSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataImmediate(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void* dy, @@ -915,7 +996,7 @@ miopenConvolutionBackwardDataImmediate(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionCount(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, @@ -933,7 +1014,7 @@ miopenConvolutionBackwardWeightsGetSolutionCount(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetSolution(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, @@ -957,7 +1038,7 @@ miopenConvolutionBackwardWeightsGetSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionWorkspaceSize( +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionWorkspaceSize( miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, @@ -989,7 +1070,7 @@ extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetSolutionWorkspaceSi }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsCompileSolution(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, @@ -1006,7 +1087,7 @@ miopenConvolutionBackwardWeightsCompileSolution(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsImmediate(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void* dy, @@ -1053,7 +1134,7 @@ miopenConvolutionBackwardWeightsImmediate(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenFindConvolutionBackwardDataAlgorithm(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void* dy, @@ -1131,7 +1212,7 @@ miopenFindConvolutionBackwardDataAlgorithm(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardData(miopenHandle_t handle, const void* alpha, const miopenTensorDescriptor_t dyDesc, @@ -1199,7 +1280,7 @@ miopenConvolutionBackwardData(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardDataGetWorkSpaceSize(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t wDesc, @@ -1216,7 +1297,7 @@ miopenConvolutionBackwardDataGetWorkSpaceSize(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeightsGetWorkSpaceSize(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const miopenTensorDescriptor_t xDesc, @@ -1233,7 +1314,7 @@ miopenConvolutionBackwardWeightsGetWorkSpaceSize(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenFindConvolutionBackwardWeightsAlgorithm(miopenHandle_t handle, const miopenTensorDescriptor_t dyDesc, const void* dy, @@ -1286,7 +1367,7 @@ miopenFindConvolutionBackwardWeightsAlgorithm(miopenHandle_t handle, }); } -extern "C" miopenStatus_t +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardWeights(miopenHandle_t handle, const void* alpha, const miopenTensorDescriptor_t dyDesc, @@ -1336,6 +1417,7 @@ miopenConvolutionBackwardWeights(miopenHandle_t handle, }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenConvolutionBackwardBias(miopenHandle_t handle, const void* alpha, const miopenTensorDescriptor_t dyDesc, @@ -1363,6 +1445,7 @@ extern "C" miopenStatus_t miopenConvolutionBackwardBias(miopenHandle_t handle, }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenSetConvolutionAttribute(miopenConvolutionDescriptor_t convDesc, const miopenConvolutionAttrib_t attr, const int value) @@ -1371,6 +1454,7 @@ extern "C" miopenStatus_t miopenSetConvolutionAttribute(miopenConvolutionDescrip return miopen::try_([&] { miopen::deref(convDesc).attribute.Set(attr, value); }); } +MIOPEN_EXPORT extern "C" miopenStatus_t miopenGetConvolutionAttribute(miopenConvolutionDescriptor_t convDesc, const miopenConvolutionAttrib_t attr, int* const value) diff --git a/src/ctc_api.cpp b/src/ctc_api.cpp index a82e713f00..d82deb7083 100644 --- a/src/ctc_api.cpp +++ b/src/ctc_api.cpp @@ -34,7 +34,10 @@ extern "C" miopenStatus_t miopenCreateCTCLossDescriptor(miopenCTCLossDescriptor_t* ctcLossDesc) { MIOPEN_LOG_FUNCTION(ctcLossDesc); - return miopen::try_([&] { miopen::deref(ctcLossDesc) = new miopen::CTCLossDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(ctcLossDesc); + desc = new miopen::CTCLossDescriptor(); + }); } extern "C" miopenStatus_t miopenDestroyCTCLossDescriptor(miopenCTCLossDescriptor_t ctcLossDesc) diff --git a/src/db.cpp b/src/db.cpp index d2e091a078..1361597020 100644 --- a/src/db.cpp +++ b/src/db.cpp @@ -28,9 +28,9 @@ #include #include #include +#include #include -#include #include #include @@ -47,10 +47,10 @@ namespace miopen { -PlainTextDb::PlainTextDb(DbKinds db_kind_, const std::string& filename_, bool is_system) +PlainTextDb::PlainTextDb(DbKinds db_kind_, const fs::path& filename_, bool is_system) : db_kind(db_kind_), filename(filename_), - lock_file(LockFile::Get(LockFilePath(filename_).c_str())), + lock_file(LockFile::Get(LockFilePath(filename_))), warning_if_unreadable(is_system) { if(is_system) @@ -61,10 +61,8 @@ PlainTextDb::PlainTextDb(DbKinds db_kind_, const std::string& filename_, bool is if(!DisableUserDbFileIO) { - auto file = fs::path(filename_); - const auto directory = file.remove_filename(); - - if(!(fs::exists(directory))) + fs::path directory = filename.has_parent_path() ? filename.parent_path() : ""; + if(!fs::exists(directory)) { if(!fs::create_directories(directory)) MIOPEN_LOG_W("Unable to create a directory: " << directory); diff --git a/src/db_path.cpp.in b/src/db_path.cpp.in index 8712218053..5b84b57c50 100644 --- a/src/db_path.cpp.in +++ b/src/db_path.cpp.in @@ -44,14 +44,14 @@ namespace miopen { #ifdef __linux__ fs::path GetLibPath() { - fs::path path = {""}; + fs::path path; Dl_info info; if(dladdr(reinterpret_cast(miopenCreate), &info) != 0) { path = fs::canonical(fs::path{info.dli_fname}); MIOPEN_LOG_I2("Lib Path: " << path); - if(path.empty()) + if(!path.has_parent_path()) return path; path = path.parent_path(); @@ -60,9 +60,9 @@ fs::path GetLibPath() } #endif -std::string GetSystemDbPath() +fs::path GetSystemDbPath() { - auto p = GetStringEnv(ENV(MIOPEN_SYSTEM_DB_PATH)); + auto p = env::value(MIOPEN_SYSTEM_DB_PATH); if(p.empty()) #if MIOPEN_BUILD_DEV || defined(_WIN32) { @@ -73,7 +73,7 @@ std::string GetSystemDbPath() #else { // Get the module path and construct the db path - static const auto lib_path = (GetLibPath().parent_path() / "share/miopen/db/").string(); + static const auto lib_path = GetLibPath().parent_path() / "share/miopen/db"; return lib_path; } #endif @@ -88,7 +88,7 @@ fs::path PrepareUserDbPath() { /// If MIOPEN_USER_DB_PATH is set in the environment, then assume that the user wants /// the library to use exactly that path. - const auto p = GetStringEnv(ENV(MIOPEN_USER_DB_PATH)); + const auto p = env::value(MIOPEN_USER_DB_PATH); if(!p.empty()) return ExpandUser(p); /// \anchor nfs-detection diff --git a/src/driver_arguments.cpp b/src/driver_arguments.cpp index 38bf392858..82b4fb156f 100644 --- a/src/driver_arguments.cpp +++ b/src/driver_arguments.cpp @@ -111,6 +111,9 @@ std::string ConvArgsForMIOpenDriver(const miopen::TensorDescriptor& xDesc, case miopenProblemDirectionForward: return ConvDirection::Fwd; case miopenProblemDirectionBackward: return ConvDirection::Bwd; case miopenProblemDirectionBackwardWeights: return ConvDirection::WrW; + case miopenProblemDirectionInference: + MIOPEN_THROW(miopenStatusInternalError); + return ConvDirection::Fwd; } }(); @@ -236,7 +239,7 @@ std::string BnormArgsForMIOpenDriver(miopenTensorDescriptor_t xDesc, ss << " -H " << miopen::deref(xDesc).GetLengths()[2] << " -W " << miopen::deref(xDesc).GetLengths()[3]; } - ss << " -M " << bn_mode; // clang-format on + ss << " -m " << bn_mode; // clang-format on if(print_for_bn_driver) { BnDriverInfo(ss, diff --git a/src/dropout_api.cpp b/src/dropout_api.cpp index 2b50f7bf80..721340b2f7 100644 --- a/src/dropout_api.cpp +++ b/src/dropout_api.cpp @@ -34,7 +34,10 @@ extern "C" miopenStatus_t miopenCreateDropoutDescriptor(miopenDropoutDescriptor_ { MIOPEN_LOG_FUNCTION(dropoutDesc); - return miopen::try_([&] { miopen::deref(dropoutDesc) = new miopen::DropoutDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(dropoutDesc); + desc = new miopen::DropoutDescriptor(); + }); } extern "C" miopenStatus_t miopenDestroyDropoutDescriptor(miopenDropoutDescriptor_t dropoutDesc) diff --git a/src/env.cpp b/src/env.cpp new file mode 100644 index 0000000000..791f8ba078 --- /dev/null +++ b/src/env.cpp @@ -0,0 +1,86 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include + +#ifndef _WIN32 +#include +#endif + +#include +#include +#include + +#ifdef _WIN32 +#define WIN32_LEAN_AND_MEAN +#include +#endif + +namespace miopen::env { + +void setEnvironmentVariable(std::string_view name, std::string_view value) +{ +#ifdef _WIN32 + if(SetEnvironmentVariable(name.data(), value.data()) == FALSE) +#else + // NOLINTNEXTLINE(concurrency-mt-unsafe) + if(setenv(name.data(), value.data(), 1) != 0) +#endif + MIOPEN_THROW("Setting environment variable failed: " + std::string{name}); +} + +void clearEnvironmentVariable(std::string_view name) +{ +#ifdef _WIN32 + if(SetEnvironmentVariable(name.data(), nullptr) == FALSE) +#else + // NOLINTNEXTLINE(concurrency-mt-unsafe) + if(unsetenv(name.data()) != 0) +#endif + MIOPEN_THROW("Removing environment variable failed: " + std::string{name}); +} + +std::optional getEnvironmentVariable(std::string_view name) +{ +#ifdef _WIN32 + auto required_size = GetEnvironmentVariable(name.data(), nullptr, 0); + if(required_size == 0) // usually ERROR_ENVVAR_NOT_FOUND + { + return std::nullopt; + } + // requires size to hold the string and its terminating null character. + std::string value(required_size - 1, 0); + GetEnvironmentVariable(name.data(), value.data(), required_size); + return {value}; +#else + // NOLINTNEXTLINE(concurrency-mt-unsafe) + auto value = std::getenv(name.data()); + return value == nullptr ? std::nullopt : std::make_optional(value); +#endif +} + +} // namespace miopen::env diff --git a/src/execution_context.cpp b/src/execution_context.cpp index aa95392591..5a48c043a8 100644 --- a/src/execution_context.cpp +++ b/src/execution_context.cpp @@ -39,7 +39,6 @@ MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_OPENCL_CONVOLUTIONS) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_GCN_ASM_KERNELS) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_HIP_KERNELS) -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_AMD_ROCM_PRECOMPILED_BINARIES) MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_AMD_ROCM_METADATA_ENFORCE) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_AMD_ROCM_METADATA_PREFER_OLDER) @@ -86,7 +85,7 @@ static std::ostream& operator<<(std::ostream& os, const rocm_meta_version& rmv) bool rocm_meta_version::UseV3() const { if(val == AMDHSA_COv2_COv3) - return !miopen::IsEnabled(ENV(MIOPEN_DEBUG_AMD_ROCM_METADATA_PREFER_OLDER)); + return !miopen::env::enabled(MIOPEN_DEBUG_AMD_ROCM_METADATA_PREFER_OLDER); return (val == AMDHSA_COv3); } @@ -137,8 +136,7 @@ static bool CalculateIsAmdRocmOpencl(const miopen::ExecutionContext& context) static rocm_meta_version AmdRocmMetadataVersionGetEnv() { - const rocm_meta_version val( - static_cast(miopen::Value(ENV(MIOPEN_DEBUG_AMD_ROCM_METADATA_ENFORCE)))); + const rocm_meta_version val{env::value(MIOPEN_DEBUG_AMD_ROCM_METADATA_ENFORCE)}; if(!val.IsValid()) { MIOPEN_LOG_W("Incorrect MIOPEN_DEBUG_AMD_ROCM_ENFORCE_MDVERSION = " << val.getValue() @@ -208,25 +206,21 @@ static bool IsAmdRocmOpencl(miopen::ExecutionContext& context) bool IsHipKernelsEnabled() { #if MIOPEN_USE_HIP_KERNELS - return !miopen::IsDisabled(ENV(MIOPEN_DEBUG_HIP_KERNELS)); + return !env::disabled(MIOPEN_DEBUG_HIP_KERNELS); #else - return miopen::IsEnabled(ENV(MIOPEN_DEBUG_HIP_KERNELS)); + return env::enabled(MIOPEN_DEBUG_HIP_KERNELS); #endif } void ExecutionContext::DetectRocm() { - use_binaries = false; use_asm_kernels = false; use_hip_kernels = IsHipKernelsEnabled(); - use_opencl_convolutions = !IsDisabled(ENV(MIOPEN_DEBUG_OPENCL_CONVOLUTIONS)); + use_opencl_convolutions = !env::disabled(MIOPEN_DEBUG_OPENCL_CONVOLUTIONS); rmv = rocm_meta_version::Default; if(IsAmdRocmOpencl(*this)) { - use_asm_kernels = !IsDisabled(ENV(MIOPEN_DEBUG_GCN_ASM_KERNELS)) && ValidateGcnAssembler(); -#ifndef HIP_OC_FINALIZER - use_binaries = !IsDisabled(ENV(MIOPEN_DEBUG_AMD_ROCM_PRECOMPILED_BINARIES)); -#endif + use_asm_kernels = !env::disabled(MIOPEN_DEBUG_GCN_ASM_KERNELS) && ValidateGcnAssembler(); } } diff --git a/src/expanduser.cpp b/src/expanduser.cpp index 60f627ba1a..c87d5d10cb 100644 --- a/src/expanduser.cpp +++ b/src/expanduser.cpp @@ -25,10 +25,9 @@ *******************************************************************************/ #include -#include -#include - +#include #include +#include #include #ifdef _WIN32 @@ -37,6 +36,7 @@ #endif #ifdef __linux__ +#include #include #include #include @@ -87,6 +87,12 @@ MIOPEN_DECLARE_ENV_VAR_STR(HOME) +#ifdef _WIN32 +MIOPEN_DECLARE_ENV_VAR_STR(USERPROFILE, miopen::fs::temp_directory_path().string()) +MIOPEN_DECLARE_ENV_VAR_STR(HOMEPATH, miopen::fs::temp_directory_path().string()) +MIOPEN_DECLARE_ENV_VAR_STR(HOMEDRIVE) +#endif + namespace miopen { #ifdef __linux__ @@ -178,7 +184,7 @@ bool IsNetworkedFilesystem(const fs::path& path_) namespace { std::string GetHomeDir() { - const auto p = GetStringEnv(ENV(HOME)); + const auto p = env::value(HOME); if(!(p.empty() || p == std::string("/"))) { return p; @@ -187,52 +193,32 @@ std::string GetHomeDir() // need to figure out what is the correct thing to do here // in tensoflow unit tests run via bazel, $HOME is not set, so this can happen // setting home_dir to the /tmp for now - return {fs::temp_directory_path().string()}; + return fs::temp_directory_path().string(); } } // namespace -fs::path ExpandUser(const std::string& path) +fs::path ExpandUser(const fs::path& path) { - static const std::string home_dir = GetHomeDir(); - return {ReplaceString(path, "~", home_dir)}; + static const auto home_dir = GetHomeDir(); + return {ReplaceString(path.string(), "~", home_dir)}; } #else namespace { -std::optional GetEnvironmentVariable(const std::string_view name) +std::optional> ReplaceVariable( + std::string_view path, const env::detail::EnvVar& t, std::size_t offset = 0) { - std::size_t required_size; - getenv_s(&required_size, nullptr, 0, name.data()); - if(required_size == 0) - { - return std::nullopt; - } - // getenv_s returns the required size of a string including '\0' character. - std::string result(required_size - 1, 'A'); - getenv_s(&required_size, result.data(), required_size, name.data()); - return {result}; -} - -std::optional> -ReplaceVariable(const std::string& path, std::string_view name, std::size_t offset = 0) -{ - std::vector variables{ - "$" + std::string{name}, "$env:" + std::string{name}, "%" + std::string{name} + "%"}; + std::vector variables{"$" + std::string{t.name()}, + "$env:" + std::string{t.name()}, + "%" + std::string{t.name()} + "%"}; for(auto& variable : variables) { auto pos{path.find(variable, offset)}; if(pos != std::string::npos) { - auto result{path}; - auto value{GetEnvironmentVariable(name)}; - if(!value) - { - // TODO: log warning message that the name used - // does not correspond to an environment variable. - value = fs::temp_directory_path().string(); - } - result.replace(pos, variable.length(), *value); + std::string result{path}; + result.replace(pos, variable.length(), t.value()); return {{pos, result}}; } } @@ -240,18 +226,18 @@ ReplaceVariable(const std::string& path, std::string_view name, std::size_t offs } } // namespace -fs::path ExpandUser(const std::string& path) +fs::path ExpandUser(const fs::path& path) { - auto result{ReplaceVariable(path, "USERPROFILE")}; + auto result{ReplaceVariable(path.string(), USERPROFILE)}; if(!result) { - result = ReplaceVariable(path, "HOME"); + result = ReplaceVariable(path.string(), HOME); if(!result) { - result = ReplaceVariable(path, "HOMEDRIVE"); + result = ReplaceVariable(path.string(), HOMEDRIVE); if(result) { - result = ReplaceVariable(std::get<1>(*result), "HOMEPATH", std::get<0>(*result)); + result = ReplaceVariable(result->second, HOMEPATH, result->first); // TODO: if (not result): log warning message that // HOMEDRIVE and HOMEPATH work in conjunction, respectively. } diff --git a/src/find_controls.cpp b/src/find_controls.cpp index 87a3449368..da66644ded 100644 --- a/src/find_controls.cpp +++ b/src/find_controls.cpp @@ -39,10 +39,13 @@ #include #include #include +#include +#include MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_FIND_ENFORCE) MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEBUG_FIND_ONLY_SOLVER) MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_FIND_MODE) +MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_FIND_MODE_FUSION) namespace miopen { @@ -70,7 +73,7 @@ const char* ToCString(const FindEnforceAction mode) FindEnforceAction GetFindEnforceActionImpl() { - auto str = miopen::GetStringEnv(ENV(MIOPEN_FIND_ENFORCE)); + auto str = env::value(MIOPEN_FIND_ENFORCE); if(str.empty()) return FindEnforceAction::Default_; for(auto& c : str) @@ -114,7 +117,7 @@ FindEnforceAction GetFindEnforceAction() boost::optional> GetEnvFindOnlySolverImpl() { static_assert(miopen::solver::Id::invalid_value == 0, "miopen::solver::Id::invalid_value == 0"); - const auto& slv_str = miopen::GetStringEnv(ENV(MIOPEN_DEBUG_FIND_ONLY_SOLVER)); + const auto slv_str = env::value(MIOPEN_DEBUG_FIND_ONLY_SOLVER); std::vector res; if(!slv_str.empty()) { @@ -196,11 +199,12 @@ std::ostream& operator<<(std::ostream& os, const FindMode::Values& v) return os << ToCString(v) << "(" << static_cast(v) << ')'; } -FindMode::Values GetFindModeValueImpl2() +template +std::optional GetFindModeValueImpl2(Variable variable) { - auto str = miopen::GetStringEnv(ENV(MIOPEN_FIND_MODE)); + auto str = env::value(variable); if(str.empty()) - return FindMode::Values::Default_; + return std::nullopt; for(auto& c : str) c = toupper(static_cast(c)); if(str == "NORMAL") @@ -225,26 +229,34 @@ FindMode::Values GetFindModeValueImpl2() const auto val = static_cast(stoul(str)); if(FindMode::Values::Begin_ <= val && val < FindMode::Values::End_) return val; - MIOPEN_LOG_NQE("Wrong MIOPEN_FIND_MODE, using default."); - return FindMode::Values::Default_; + MIOPEN_LOG_NQE("Wrong " << variable.GetName() << ", using default."); + return std::nullopt; } -FindMode::Values GetFindModeValueImpl() +template +FindMode::Values GetFindModeValue(Variable variable, FindMode::Values defaultValue) { - auto rv = GetFindModeValueImpl2(); - MIOPEN_LOG_NQI("MIOPEN_FIND_MODE = " << rv); - return rv; -} - -FindMode::Values GetFindModeValue() -{ - static const FindMode::Values val = GetFindModeValueImpl(); + static const FindMode::Values val = [&]() { + auto rv = GetFindModeValueImpl2(variable).value_or(defaultValue); + MIOPEN_LOG_NQI(variable.GetName() << " = " << rv); + return rv; + }(); return val; } } // namespace -FindMode::FindMode() { value = GetFindModeValue(); } +FindMode::FindMode(solver::Primitive primitive) +{ + switch(primitive) + { + case solver::Primitive::Fusion: + value = GetFindModeValue(MIOPEN_FIND_MODE_FUSION, FindMode::Values::Fast); + break; + default: value = GetFindModeValue(MIOPEN_FIND_MODE, FindMode::Values::Default_); break; + } +} + std::ostream& operator<<(std::ostream& os, const FindMode& obj) { return os << obj.value; } static_assert(miopenConvolutionFindModeNormal == diff --git a/src/find_db.cpp b/src/find_db.cpp index 42cddf991f..6c493a5b62 100644 --- a/src/find_db.cpp +++ b/src/find_db.cpp @@ -44,10 +44,10 @@ namespace debug { MIOPEN_EXPORT bool testing_find_db_enabled = true; /// \todo Remove when #1723 is resolved. -boost::optional& testing_find_db_path_override() +boost::optional& testing_find_db_path_override() { // NOLINTNEXTLINE (cppcoreguidelines-avoid-non-const-global-variables) - static boost::optional data = boost::none; + static boost::optional data = boost::none; return data; } @@ -55,20 +55,19 @@ boost::optional& testing_find_db_path_override() #if MIOPEN_EMBED_DB template -std::string FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, - const std::string& path_suffix) +fs::path FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, const std::string& path_suffix) { static const auto embed_path = [&] { const std::string ext = ".fdb.txt"; - const auto root_path = fs::path(GetSystemDbPath()); + const auto root_path = GetSystemDbPath(); const auto base_name = handle.GetDbBasename(); const auto suffix = GetSystemFindDbSuffix() + path_suffix; const auto filename = base_name + "." + suffix + ext; const auto file_path = root_path / filename; - if(miopen_data().find(make_object_file_name(filename).string()) != miopen_data().end()) + if(miopen_data().find(make_object_file_name(filename)) != miopen_data().end()) { MIOPEN_LOG_I2("Found exact embedded find database file:" << filename); - return file_path.string(); + return file_path; } else { @@ -76,9 +75,9 @@ std::string FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, std::vector all_files; for(const auto& kinder : miopen_data()) { - const auto& fname = kinder.first.substr(0, kinder.first.size() - 2); + const auto& fname = kinder.first.stem(); const auto& filepath = root_path / fname; - if(EndsWith(fname, path_suffix + ".fdb.txt")) + if(EndsWith(fname.string(), path_suffix + ".fdb.txt")) all_files.push_back(filepath); } @@ -93,10 +92,10 @@ std::string FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, // Check for alternate back end same ASIC if(fname.rfind(base_name, 0) == 0) { - return entry.string(); + return entry; } if(db_id.empty() || !miopen::StartsWith(db_id, "gfx") || real_cu_count == 0) - return std::string(); + return fs::path{}; // Check for alternate ASIC any back end if(fname.rfind(db_id, 0) == 0) { @@ -113,7 +112,7 @@ std::string FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, } } } - return best_path.string(); + return best_path; } }(); return embed_path; @@ -122,12 +121,11 @@ std::string FindDbRecord_t::GetInstalledPathEmbed(Handle& handle, #else template -std::string FindDbRecord_t::GetInstalledPathFile(Handle& handle, - const std::string& path_suffix) +fs::path FindDbRecord_t::GetInstalledPathFile(Handle& handle, const std::string& path_suffix) { static const auto installed_path = [&] { const std::string ext = ".fdb.txt"; - const auto root_path = fs::path(GetSystemDbPath()); + const auto root_path = GetSystemDbPath(); const auto base_name = handle.GetDbBasename(); const auto suffix = GetSystemFindDbSuffix() + (path_suffix.empty() ? "" : ('.' + path_suffix)); @@ -135,12 +133,12 @@ std::string FindDbRecord_t::GetInstalledPathFile(Handle& handle, if(fs::exists(file_path)) { MIOPEN_LOG_I2("Found exact find database file: " << file_path); - return file_path.string(); + return file_path; } else { MIOPEN_LOG_I2("inexact find database search"); - if(fs::exists(root_path) && fs::is_directory(root_path)) + if(fs::is_directory(root_path)) { MIOPEN_LOG_I2("Iterating over find db directory " << root_path); std::vector all_files; @@ -166,10 +164,10 @@ std::string FindDbRecord_t::GetInstalledPathFile(Handle& handle, // Check for alternate back end same ASIC if(fname.rfind(base_name, 0) == 0) { - return entry.string(); + return entry; } if(db_id.empty() || !miopen::StartsWith(db_id, "gfx") || real_cu_count == 0) - return std::string(); + return fs::path{}; // Check for alternate ASIC any back end if(fname.rfind(db_id, 0) == 0) { @@ -186,12 +184,12 @@ std::string FindDbRecord_t::GetInstalledPathFile(Handle& handle, } } } - return best_path.string(); + return best_path; } else { MIOPEN_LOG_I("Database directory does not exist"); - return std::string(); + return fs::path{}; } } }(); @@ -199,7 +197,7 @@ std::string FindDbRecord_t::GetInstalledPathFile(Handle& handle, } #endif template -std::string FindDbRecord_t::GetInstalledPath(Handle& handle, const std::string& path_suffix) +fs::path FindDbRecord_t::GetInstalledPath(Handle& handle, const std::string& path_suffix) { #if !MIOPEN_DISABLE_SYSDB #if MIOPEN_EMBED_DB @@ -215,17 +213,11 @@ std::string FindDbRecord_t::GetInstalledPath(Handle& handle, const std::str } template -std::string FindDbRecord_t::GetUserPath(Handle& handle, const std::string& path_suffix) +fs::path FindDbRecord_t::GetUserPath(Handle& handle, const std::string& path_suffix) { #if !MIOPEN_DISABLE_USERDB - std::ostringstream ss; - ss << GetUserDbPath().string() << '/'; - ss << handle.GetDbBasename(); - ss << '.' << GetUserDbSuffix(); - if(!path_suffix.empty()) - ss << '.' << path_suffix; - ss << ".ufdb.txt"; - return ss.str(); + return GetUserDbPath() / (handle.GetDbBasename() + '.' + GetUserDbSuffix() + + (path_suffix.empty() ? "" : '.' + path_suffix) + ".ufdb.txt"); #else std::ignore = handle; std::ignore = path_suffix; @@ -261,12 +253,11 @@ bool FindDbRecord_t::Validate(Handle& handle, const NetworkConfig& config) } template -void FindDbRecord_t::CopyTo(std::vector& to) const +void FindDbRecord_t::CopyTo(std::vector& to) const { const auto range = content->As(); std::transform(range.begin(), range.end(), std::back_inserter(to), [](const auto& pair) { - return PerfField{ - pair.second.algorithm, pair.first, pair.second.time, pair.second.workspace}; + return Solution{solver::Id{pair.first}, pair.second.time, pair.second.workspace}; }); } diff --git a/src/fused_api.cpp b/src/fused_api.cpp index c2e2612e3d..0dbd125710 100644 --- a/src/fused_api.cpp +++ b/src/fused_api.cpp @@ -45,8 +45,8 @@ extern "C" miopenStatus_t miopenCreateFusionPlan(miopenFusionPlanDescriptor_t* f { MIOPEN_LOG_FUNCTION(fusePlanDesc, fuseDirection, inputDesc); return miopen::try_([&] { - miopen::deref(fusePlanDesc) = - new miopen::FusionPlanDescriptor(fuseDirection, miopen::deref(inputDesc)); + auto& desc = miopen::deref(fusePlanDesc); + desc = new miopen::FusionPlanDescriptor(fuseDirection, miopen::deref(inputDesc)); }); } @@ -236,7 +236,10 @@ extern "C" miopenStatus_t miopenCreateOpBatchNormBackward(miopenFusionPlanDescri extern "C" miopenStatus_t miopenCreateOperatorArgs(miopenOperatorArgs_t* args) { MIOPEN_LOG_FUNCTION(args); - return miopen::try_([&] { miopen::deref(args) = new miopen::OperatorArgs(); }); + return miopen::try_([&] { + auto& theArgs = miopen::deref(args); + theArgs = new miopen::OperatorArgs(); + }); } extern "C" miopenStatus_t miopenDestroyOperatorArgs(miopenOperatorArgs_t args) diff --git a/src/fusion.cpp b/src/fusion.cpp index 034ffcb320..d60f025c77 100644 --- a/src/fusion.cpp +++ b/src/fusion.cpp @@ -40,16 +40,13 @@ #include #include #include +#include #include #include #include #include -#if !defined(_WIN32) #include -#else -#include -#endif #define MIOPEN_CHECK(x) \ if(x != miopenStatusSuccess) \ @@ -102,7 +99,7 @@ miopenStatus_t ConvBiasActivFusion(Handle& handle, float falpha1 = alpha1 != nullptr ? *(static_cast(alpha1)) : 1.0f; float falpha2 = alpha2 != nullptr ? *(static_cast(alpha2)) : 1.0f; - // if(z != nullptr || zDesc.GetSize() != 0) + // if(z != nullptr || zDesc.GetNumDims() != 0) // MIOPEN_THROW(miopenStatusNotImplemented, "The addition of z vector is not yet supported"); FusionPlanDescriptor fusePlanDesc{miopenVerticalFusion, xDesc}; OperatorArgs fusionArgs; @@ -408,6 +405,7 @@ std::string LogCmdBnormFusion(const miopenFusionPlanDescriptor_t fusePlanDesc, i return str; } +MIOPEN_INTERNALS_EXPORT void LogCmdFusion(const miopenFusionPlanDescriptor_t fusePlanDesc) { if(miopen::IsLoggingCmd()) @@ -767,7 +765,8 @@ static auto GetFusedIGemmSolvers() static auto GetFusedWinogradSolvers() { return solver::SolverContainer{}; + solver::fusion::ConvBinWinogradRxSf2x3g1Fused, + solver::fusion::ConvWinoFuryRxSFused<2, 3>>{}; } static auto GetAllFusionSolvers() @@ -853,7 +852,7 @@ static const std::vector>& GetFusionSolverFinder return finders; } -static std::vector +static std::vector FindFusion(const ExecutionContext& ctx, const FusionDescription& fusion_problem, const std::function& invoke_params, @@ -862,42 +861,214 @@ FindFusion(const ExecutionContext& ctx, return UserFindDbRecord::TryLoad( ctx.GetStream(), fusion_problem, - [&](DbRecord& record) { + [&]() { // fusion_ctx.use_dynamic_solutions_only = findMode.IsDynamicHybrid(fusion_ctx); // We need buffers for find, thus we lazily get them, possibly allocating. auto fusion_ctx = FusionContext(ctx.GetStream()); - FindCore(invoke_params(), - record, - fusion_ctx, - fusion_problem, - FusionFindParameters{}, - GetFusionSolverFinders(), - options); + return FindCore(invoke_params(), + fusion_ctx, + fusion_problem, + FusionFindParameters{}, + GetFusionSolverFinders(), + options); }, "fusion"); } +namespace { + +// Copy from convolutionocl.cpp +struct SolutionTimeComparator +{ + inline bool operator()(const miopenConvSolution_t& lhs, const miopenConvSolution_t& rhs) const + { + // Negative values are very coarse estimations. + // The more modulus, the "worse" (slower) is solution. + if(lhs.time < 0 && rhs.time < 0) + return !(lhs.time < rhs.time); + // Positive values are always "better" than negative (coarse) estimations. + if(lhs.time > 0 && rhs.time < 0) + return true; + if(lhs.time < 0 && rhs.time > 0) + return false; + // Both values are positive. The less is the better. + return (lhs.time < rhs.time); + } +}; + +std::ostream& operator<<(std::ostream& os, const miopenConvSolution_t& s) +{ + return os << "id: " << s.solution_id // + << ", algo: " << s.algorithm // + << ", time: " << s.time << ", ws: " << s.workspace_size // + << ", name: " << miopen::solver::Id(s.solution_id).ToString(); +} + +// Modified copy from convolutionocl.cpp +std::vector GetSolutions(const FusionContext& ctx, + const FusionDescription& problem, + const size_t maxSolutionCount) +{ + const FindDbRecord fdb_record{ctx.GetStream(), problem, "fusion"}; + + if(fdb_record.empty()) + return {}; + + auto interim = std::vector{}; + interim.reserve(20); // Heuristic for speed. + + for(const auto& pair : fdb_record) + { + const auto solver_id = solver::Id{pair.first}; + + // Wrong IDs can't be used to call IsApplicable(), so let's + // ignore obsolete or invalid IDs read from find-db first. + if(!solver_id.IsValid()) + { + // Do not disturb users with warnings unless detailed log is enabled. + MIOPEN_LOG_I("[Warning] incorrect solver_id: " << pair.first); + continue; + } + + // algorithm doesn't matter for our purpose here, so we stub it out + interim.emplace_back(miopenConvSolution_t{pair.second.time, + pair.second.workspace, + solver_id.Value(), + miopenConvolutionAlgoDirect}); + } + + std::sort(begin(interim), end(interim), SolutionTimeComparator{}); + auto out = std::vector{}; + out.reserve(maxSolutionCount); + auto n_copied = 0; + for(const auto& s : interim) + { + const auto solver_id = solver::Id{s.solution_id}; + bool is_applicable = false; + + GetAllFusionSolvers().FindById( + solver_id, [&](auto solver) { is_applicable = solver.IsApplicable(ctx, problem); }); + + if(!is_applicable) + continue; + out.push_back(s); + if(++n_copied >= maxSolutionCount) + break; + } + + for(const auto& s : out) + MIOPEN_LOG_I2(s); + + return out; +} + +} // namespace + miopenStatus_t FusionPlanDescriptor::Compile(Handle& handle) { std::vector invoke_bufs; miopen::OperatorArgs params; - const auto find_results = Find(handle, [&]() { - return AllocateBuffersAndMakeFusionInvokeParams( - handle, FusionDescription{this}, invoke_bufs, params, *this); - }); + const auto& fusion_problem = FusionDescription{this}; + std::vector find_results; - const auto network_config = FusionDescription{this}.MakeNetworkConfig(); + const auto network_config = fusion_problem.MakeNetworkConfig(); + auto invoker = handle.GetInvoker(network_config, std::nullopt, AlgorithmName{"fusion"}); + + if(invoker) + { + invokers.push_back(*invoker); + return miopenStatusSuccess; + } + + { + FindMode findMode(solver::Primitive::Fusion); + auto sol = boost::optional{}; + + if(findMode.IsFast(fusion_problem) || findMode.IsHybrid(fusion_problem)) + { + const auto ctx = FusionContext{handle}; + auto sols = GetSolutions(ctx, fusion_problem, 1); + const auto fallback = sols.empty(); + + if(fallback) + { + auto fallback_failed = true; + bool found = false; + + GetAllFusionSolvers().Foreach([&](auto solver) { + if(found || !solver.IsApplicable(ctx, fusion_problem)) + return; + const auto id = solver::Id(solver.SolverDbId()); + const auto wti = solver.GetWti(ctx, fusion_problem); + // Assume WTI == 1.0 (100%) is 10 ms. + // Return negative values as is, avoid DIV/0. + const auto time = wti <= 0.0f ? wti : (10.f / wti); + sols.push_back({time, 0, id.Value(), miopenConvolutionAlgoDirect}); + fallback_failed = false; + }); + + if(fallback_failed) + { + MIOPEN_LOG_I("No supported fusion solvers found"); + return miopenStatusUnsupportedOp; + } + } + + // override the normal find with immed mode with env var + if(!sols.empty() && (!(findMode.IsHybrid(fusion_problem) && fallback))) + // || env::enabled(MIOPEN_DEBUG_FORCE_IMMED_MODE_FALLBACK) + { + std::sort(sols.begin(), sols.end(), SolutionTimeComparator()); + sol = sols.front(); + } + // In Hybrid Find mode, we use Normal Find instead of Immediate fallback kernels. + } + + if(sol.has_value()) + { + // We need to create an invoker + + const auto id = solver::Id{sol->solution_id}; + + GetAllFusionSolvers().FindById(id, [&](auto solver) { + const auto ctx = FusionContext{handle}; + auto db = GetDb(ctx); + const auto solution = solver::FindSolution( + solver, ctx, fusion_problem, db, {}); // auto tune is not expected here + auto invoker = + handle.PrepareInvoker(*solution.invoker_factory, solution.construction_params); + // We register the invoker below + + auto ret = Solution{id, sol->time, solver.GetWorkspaceSize(ctx, fusion_problem)}; + ret.SetInvoker(std::move(invoker)); + find_results.push_back(std::move(ret)); + }); + } + else + { + find_results = Find(handle, [&]() { + return AllocateBuffersAndMakeFusionInvokeParams( + handle, fusion_problem, invoke_bufs, params, *this); + }); + } + } for(const auto& result : find_results) { - if(conv_fwd_algo && result.algorithm != "fusion" && - miopen::StringToConvolutionFwdAlgo(result.algorithm) != *conv_fwd_algo) + const auto primitive = result.GetSolver().GetPrimitive(); + const auto algorithm = result.GetSolver().GetAlgo(); + + if(conv_fwd_algo && primitive != solver::Primitive::Fusion && + algorithm != static_cast(*conv_fwd_algo)) continue; - const auto id = solver::Id{result.solver_id}; - const auto invoker = handle.GetInvoker(network_config, id); + const auto id = result.GetSolver(); + invoker = result.GetInvoker(); + + if(!invoker) + invoker = handle.GetInvoker(network_config, id); if(!invoker) { @@ -905,8 +1076,9 @@ miopenStatus_t FusionPlanDescriptor::Compile(Handle& handle) continue; } - invokers.push_back(*invoker); - MIOPEN_LOG_I2(result.algorithm); + handle.RegisterInvoker(*invoker, network_config, id.ToString()); + invokers.push_back(std::move(*invoker)); + MIOPEN_LOG_I2(miopen::ConvolutionAlgoToString(algorithm)); } if(invokers.empty()) @@ -915,10 +1087,12 @@ miopenStatus_t FusionPlanDescriptor::Compile(Handle& handle) return miopenStatusUnsupportedOp; } + handle.SetAsFound1_0( + network_config, AlgorithmName{"fusion"}, find_results.front().GetSolver().ToString()); return miopenStatusSuccess; } -std::vector +std::vector FusionPlanDescriptor::Find(Handle& handle, const std::function& invoke_params, const std::optional& options) const diff --git a/src/gemm_v2.cpp b/src/gemm_v2.cpp index eb78bb11e7..1af243aa4a 100644 --- a/src/gemm_v2.cpp +++ b/src/gemm_v2.cpp @@ -30,9 +30,14 @@ #include #include #include - -#if MIOPEN_BACKEND_HIP #include + +#if MIOPEN_USE_HIPBLASLT +#include +// Only enable BF8 support if hipBLASLt is 0.8 or above +#if MIOPEN_HIPBLASLT_VERSION_FLAT >= 8000 +#define ENABLE_HIPBLASLT_BF8 +#endif #endif #if MIOPEN_USE_ROCBLAS @@ -40,11 +45,7 @@ #pragma clang diagnostic ignored "-Wunused-macros" #define ROCBLAS_BETA_FEATURES_API 1 #pragma clang diagnostic pop -#if !defined(_WIN32) #include -#else -#include -#endif #if MIOPEN_ROCBLAS_VERSION_FLAT < 2045000 #include #else @@ -197,7 +198,8 @@ rocblas_status miopen_rocblas_gemm_ex3(const miopen::Handle& handle, std::ignore = C; std::ignore = c_offset; #endif - MIOPEN_THROW(miopenStatusBadParm, "An appropriate version of rocBLAS is required for this op"); + MIOPEN_THROW(miopenStatusInternalError, + "An appropriate version of rocBLAS is required for this op"); std::ignore = handle; std::ignore = gemm_desc; return rb_status; @@ -311,7 +313,6 @@ inline void SetRocblasAtomics(const miopen::Handle& handle, rocblas_atomics_mode #endif -#if MIOPEN_BACKEND_HIP inline void ProfilingRecordStart(const Handle& handle, HipEventPtr& start, HipEventPtr& stop) { start = make_hip_event(); @@ -328,90 +329,346 @@ inline void ProfilingRecordStop(const Handle& handle, HipEventPtr& start, HipEve handle.ResetKernelTime(); handle.AccumKernelTime(mS); } -#endif -// hacks: control GEMM backend by enviroment variable and build option -// very nasty -static GemmBackend_t enforce_gemm_backend(miopenDataType_t data_type, - GemmBackend_t gemm_backend_preferred) +#if MIOPEN_USE_HIPBLASLT + +struct HipBLASLtMemoryHandles { - GemmBackend_t gemm_backend_enforced = GemmBackend_t::nogemmbackend; - GemmBackend_t gemm_backend_env = GemmBackend_t::nogemmbackend; + hipblasLtMatrixLayout_t matA, matB, matC, matD; + hipblasLtMatmulDesc_t matmul; + hipblasLtMatmulPreference_t pref; - // enforce backend based on env variable - // I have left the commented lines here to preserve values for the enforce and hint at why are - // they 1 and 3 - switch(Value(ENV(MIOPEN_GEMM_ENFORCE_BACKEND))) + HipBLASLtMemoryHandles() + : matA(nullptr), matB(nullptr), matC(nullptr), matD(nullptr), matmul(nullptr), pref(nullptr) { - case 1: gemm_backend_env = GemmBackend_t::rocblas; break; - // case 2: gemm_backend_env = GemmBackend_t::miopengemm; break; - case 3: gemm_backend_env = GemmBackend_t::nogemmbackend; break; - // case 4: gemm_backend_env = GemmBackend_t::miopentensile; break; - default: gemm_backend_env = gemm_backend_preferred; } -// make sure backend chosen based on env variable is suppported -#if MIOPEN_USE_ROCBLAS - (void)data_type; - switch(gemm_backend_env) + ~HipBLASLtMemoryHandles() { - case GemmBackend_t::nogemmbackend: gemm_backend_enforced = GemmBackend_t::nogemmbackend; break; - case GemmBackend_t::rocblas: gemm_backend_enforced = GemmBackend_t::rocblas; break; + hipblasLtMatrixLayoutDestroy(matA); + hipblasLtMatrixLayoutDestroy(matB); + hipblasLtMatrixLayoutDestroy(matC); + hipblasLtMatrixLayoutDestroy(matD); + hipblasLtMatmulDescDestroy(matmul); + hipblasLtMatmulPreferenceDestroy(pref); } -#else - gemm_backend_enforced = GemmBackend_t::nogemmbackend; -#endif +}; - return gemm_backend_enforced; +static inline void check_hipblas_status(hipblasStatus_t status) +{ + if(status != hipblasStatus_t::HIPBLAS_STATUS_SUCCESS) + { + std::cout << "error msg" << std::endl; + MIOPEN_THROW(miopenStatusInternalError, "hipBlasLt error encountered"); + } } -miopenStatus_t CallGemmTimeMeasure(const Handle& handle, - GemmDescriptor gemm_desc, - ConstData_t A, - std::size_t a_offset, - ConstData_t B, - std::size_t b_offset, - Data_t C, - std::size_t c_offset, - bool time_precision, - CallGemmType_t call_gemm_type, - GemmBackend_t gemm_backend) +template +static void miopen_hipblasLt_gemm(const miopen::Handle& handle, + const miopen::GemmDescriptor& gemm_desc, + ConstData_t A, + std::size_t a_offset, + ConstData_t B, + std::size_t b_offset, + hipDataType hip_type_AB, + Data_t C, + std::size_t c_offset, + hipDataType hip_type_C, + bool skip_batches) { - switch(call_gemm_type) + HipBLASLtMemoryHandles hipBLASLtHandles; + + if(gemm_desc.transA) { - case callGemm: { - if(time_precision) - { - // rocBLAS need a warm-up call for accurate timing - CallGemm(handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); - } + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matA, hip_type_AB, gemm_desc.k, gemm_desc.m, gemm_desc.lda)); + } + else + { + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matA, hip_type_AB, gemm_desc.m, gemm_desc.k, gemm_desc.lda)); + } - return CallGemm(handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); + if(gemm_desc.transB) + { + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matB, hip_type_AB, gemm_desc.n, gemm_desc.k, gemm_desc.ldb)); + } + else + { + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matB, hip_type_AB, gemm_desc.k, gemm_desc.n, gemm_desc.ldb)); + } + + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matC, hip_type_C, gemm_desc.m, gemm_desc.n, gemm_desc.ldc)); + check_hipblas_status(hipblasLtMatrixLayoutCreate( + &hipBLASLtHandles.matD, hip_type_C, gemm_desc.m, gemm_desc.n, gemm_desc.ldc)); + + if(gemm_desc.batch_count > 1 && !skip_batches) + { + check_hipblas_status(hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matA, + HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, + &gemm_desc.batch_count, + sizeof(gemm_desc.batch_count))); + check_hipblas_status( + hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matA, + HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, + &gemm_desc.strideA, + sizeof(gemm_desc.strideA))); + check_hipblas_status(hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matB, + HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, + &gemm_desc.batch_count, + sizeof(gemm_desc.batch_count))); + check_hipblas_status( + hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matB, + HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, + &gemm_desc.strideB, + sizeof(gemm_desc.strideB))); + check_hipblas_status(hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matC, + HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, + &gemm_desc.batch_count, + sizeof(gemm_desc.batch_count))); + check_hipblas_status( + hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matC, + HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, + &gemm_desc.strideC, + sizeof(gemm_desc.strideC))); + check_hipblas_status(hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matD, + HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, + &gemm_desc.batch_count, + sizeof(gemm_desc.batch_count))); + check_hipblas_status( + hipblasLtMatrixLayoutSetAttribute(hipBLASLtHandles.matD, + HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, + &gemm_desc.strideC, + sizeof(gemm_desc.strideC))); + } + + check_hipblas_status( + hipblasLtMatmulDescCreate(&hipBLASLtHandles.matmul, HIPBLAS_COMPUTE_32F, HIP_R_32F)); + + hipblasOperation_t opTypeA = gemm_desc.transA ? HIPBLAS_OP_T : HIPBLAS_OP_N; + hipblasOperation_t opTypeB = gemm_desc.transB ? HIPBLAS_OP_T : HIPBLAS_OP_N; + check_hipblas_status(hipblasLtMatmulDescSetAttribute( + hipBLASLtHandles.matmul, HIPBLASLT_MATMUL_DESC_TRANSA, &opTypeA, sizeof(opTypeA))); + check_hipblas_status(hipblasLtMatmulDescSetAttribute( + hipBLASLtHandles.matmul, HIPBLASLT_MATMUL_DESC_TRANSB, &opTypeB, sizeof(opTypeB))); + + hipblasLtEpilogue_t epilogue = HIPBLASLT_EPILOGUE_DEFAULT; + check_hipblas_status(hipblasLtMatmulDescSetAttribute( + hipBLASLtHandles.matmul, HIPBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue))); + + /// \todo Need to request additional workspace for optimal gemm performance, and pass down + /// workspace size & pointer. --BrianHarrisonAMD June 2024 + size_t max_workspace_size = 0; + void* workspace = nullptr; + check_hipblas_status(hipblasLtMatmulPreferenceCreate(&hipBLASLtHandles.pref)); + check_hipblas_status( + hipblasLtMatmulPreferenceSetAttribute(hipBLASLtHandles.pref, + HIPBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, + &max_workspace_size, + sizeof(max_workspace_size))); + + const int requestSolutions = 1; + hipblasLtMatmulHeuristicResult_t heuristicResult[requestSolutions]; + int returnedAlgoCount = 0; + check_hipblas_status(hipblasLtMatmulAlgoGetHeuristic(handle.HipblasLtHandle().get(), + hipBLASLtHandles.matmul, + hipBLASLtHandles.matA, + hipBLASLtHandles.matB, + hipBLASLtHandles.matC, + hipBLASLtHandles.matD, + hipBLASLtHandles.pref, + requestSolutions, + heuristicResult, + &returnedAlgoCount)); + + if(returnedAlgoCount == 0) + { + MIOPEN_THROW(miopenStatusInternalError, + "no solution found for hipBLASLt hipBLASLtHandles.matmul"); + } + + float alpha = gemm_desc.alpha; + float beta = gemm_desc.beta; + const void* aData = static_cast(A) + a_offset; + const void* bData = static_cast(B) + b_offset; + const void* cData = static_cast(C) + c_offset; + void* dData = static_cast(C) + c_offset; + + { + HipEventProfiler profiler(handle); + check_hipblas_status(hipblasLtMatmul(handle.HipblasLtHandle().get(), + hipBLASLtHandles.matmul, + &alpha, + aData, + hipBLASLtHandles.matA, + bData, + hipBLASLtHandles.matB, + &beta, + cData, + hipBLASLtHandles.matC, + dData, + hipBLASLtHandles.matD, + &heuristicResult[0].algo, + workspace, + max_workspace_size, + handle.GetStream())); + } +} + +static void call_miopen_hipblasLt_gemm(const miopen::Handle& handle, + const miopen::GemmDescriptor& gemm_desc, + ConstData_t A, + std::size_t a_offset, + ConstData_t B, + std::size_t b_offset, + Data_t C, + std::size_t c_offset, + bool skip_batches) +{ + switch(gemm_desc.dataType) + { + case miopenInt8: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt8 is not supported for hipBLASLt"); + } + break; + case miopenInt32: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt32 is not supported for hipBLASLt"); + } + break; + case miopenHalf: { + miopen_hipblasLt_gemm(handle, + gemm_desc, + A, + a_offset, + B, + b_offset, + HIP_R_16F, + C, + c_offset, + HIP_R_16F, + skip_batches); } - case callGemmStridedBatched: { - if(time_precision) + break; + case miopenBFloat16: { + miopen_hipblasLt_gemm(handle, + gemm_desc, + A, + a_offset, + B, + b_offset, + HIP_R_16BF, + C, + c_offset, + HIP_R_16BF, + skip_batches); + } + break; + case miopenFloat: { + miopen_hipblasLt_gemm(handle, + gemm_desc, + A, + a_offset, + B, + b_offset, + HIP_R_32F, + C, + c_offset, + HIP_R_32F, + skip_batches); + } + break; + case miopenFloat8: { + const auto is_gfx94x = miopen::StartsWith(handle.GetDeviceName(), "gfx94"); + if(is_gfx94x) { - // rocBLAS need extra warm-up call for accurate timing - CallGemmStridedBatched( - handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); + miopen_hipblasLt_gemm(handle, + gemm_desc, + A, + a_offset, + B, + b_offset, + HIP_R_8F_E4M3_FNUZ, + C, + c_offset, + HIP_R_8F_E4M3_FNUZ, + skip_batches); + } + else + { + MIOPEN_THROW(miopenStatusInternalError, + "miopenFloat8 is only supported for hipBlasLt on gfx94x"); } - - return CallGemmStridedBatched( - handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); } - case callGemmStridedBatchedSequential: { - if(time_precision) + break; + case miopenBFloat8: { + const auto is_gfx94x = miopen::StartsWith(handle.GetDeviceName(), "gfx94"); + if(is_gfx94x) + { +#ifdef ENABLE_HIPBLASLT_BF8 + miopen_hipblasLt_gemm(handle, + gemm_desc, + A, + a_offset, + B, + b_offset, + HIP_R_8F_E5M2_FNUZ, + C, + c_offset, + HIP_R_8F_E5M2_FNUZ, + skip_batches); +#else + MIOPEN_THROW(miopenStatusInternalError, + "miopenBFloat8 is not supported for this version of hipBlasLt on gfx94x"); +#endif + } + else { - // rocBLAS need a warm-up call for accurate timing - CallGemmStridedBatchedSequential( - handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); + MIOPEN_THROW(miopenStatusInternalError, + "miopenBFloat8 is only supported for hipBlasLt on gfx94x"); } - - return CallGemmStridedBatchedSequential( - handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, gemm_backend); } + break; + case miopenDouble: { + MIOPEN_THROW(miopenStatusInternalError, "miopenDouble is not supported for hipBlasLt"); + } + break; + case miopenInt64: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt64 is not supported for hipBlasLt"); + } + break; + } +} +#endif + +// hacks: control GEMM backend by enviroment variable and build option +// very nasty +static GemmBackend_t enforce_gemm_backend(GemmBackend_t gemm_backend_preferred) +{ + GemmBackend_t gemm_backend_env = GemmBackend_t::nogemmbackend; + + // enforce backend based on env variable + // I have left the commented lines here to preserve values for the enforce and hint at why are + // they 1, 3, and 5 + switch(env::value(MIOPEN_GEMM_ENFORCE_BACKEND)) + { +#if MIOPEN_USE_ROCBLAS + case 1: gemm_backend_env = GemmBackend_t::rocblas; break; +#endif + // case 2: gemm_backend_env = GemmBackend_t::miopengemm; break; + case 3: + gemm_backend_env = GemmBackend_t::nogemmbackend; + break; + // case 4: gemm_backend_env = GemmBackend_t::miopentensile; break; +#if MIOPEN_USE_HIPBLASLT + case 5: gemm_backend_env = GemmBackend_t::hipblaslt; break; +#endif + default: gemm_backend_env = gemm_backend_preferred; } - return miopenStatusNotImplemented; + + return gemm_backend_env; } miopenStatus_t CallGemm(const Handle& handle, @@ -426,7 +683,7 @@ miopenStatus_t CallGemm(const Handle& handle, { MIOPEN_LOG_I2("gemm_desc: " << gemm_desc); - gemm_backend = enforce_gemm_backend(gemm_desc.dataType, gemm_backend); + gemm_backend = enforce_gemm_backend(gemm_backend); if(!gemm_desc.isColMajor) { @@ -444,7 +701,7 @@ miopenStatus_t CallGemm(const Handle& handle, case GemmBackend_t::nogemmbackend: return miopenStatusNotImplemented; case GemmBackend_t::rocblas: { #if MIOPEN_USE_ROCBLAS - MIOPEN_LOG_FUNCTION("rocBLAS"); + MIOPEN_LOG_I2("rocBLAS"); HipEventPtr start = nullptr; HipEventPtr stop = nullptr; @@ -523,7 +780,7 @@ miopenStatus_t CallGemm(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } } @@ -638,16 +895,22 @@ miopenStatus_t CallGemm(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } }; break; case miopenDouble: { - MIOPEN_THROW(miopenStatusBadParm, "miopenDouble data type not supported by rocBLAS."); + MIOPEN_THROW(miopenStatusInternalError, + "miopenDouble data type not supported by rocBLAS."); }; break; + + case miopenInt64: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt64 is not currently supported."); + } + break; } if(handle.IsProfilingEnabled()) @@ -661,6 +924,14 @@ miopenStatus_t CallGemm(const Handle& handle, return miopenStatusSuccess; #else return miopenStatusNotImplemented; +#endif + } + case GemmBackend_t::hipblaslt: { +#if MIOPEN_USE_HIPBLASLT + call_miopen_hipblasLt_gemm(handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, true); + return miopenStatusSuccess; +#else + return miopenStatusNotImplemented; #endif } } @@ -680,7 +951,7 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, { MIOPEN_LOG_I2("gemm_desc: " << gemm_desc); - gemm_backend = enforce_gemm_backend(gemm_desc.dataType, gemm_backend); + gemm_backend = enforce_gemm_backend(gemm_backend); if(!gemm_desc.isColMajor) { @@ -699,7 +970,7 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, case GemmBackend_t::nogemmbackend: return miopenStatusNotImplemented; case GemmBackend_t::rocblas: { #if MIOPEN_USE_ROCBLAS - MIOPEN_LOG_FUNCTION("rocBLAS"); + MIOPEN_LOG_I2("rocBLAS"); HipEventPtr start = nullptr; HipEventPtr stop = nullptr; @@ -783,7 +1054,7 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } } @@ -911,7 +1182,7 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } @@ -919,7 +1190,12 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, } case miopenDouble: { - MIOPEN_THROW(miopenStatusBadParm, "miopenDouble data type not supported by rocBLAS."); + MIOPEN_THROW(miopenStatusInternalError, + "miopenDouble data type not supported by rocBLAS."); + } + break; + case miopenInt64: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt64 is not currently supported."); } break; } @@ -937,6 +1213,14 @@ miopenStatus_t CallGemmStridedBatched(const Handle& handle, #else return miopenStatusNotImplemented; #endif + } + case GemmBackend_t::hipblaslt: { +#if MIOPEN_USE_HIPBLASLT + call_miopen_hipblasLt_gemm(handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, false); + return miopenStatusSuccess; +#else + return miopenStatusNotImplemented; +#endif } } @@ -955,7 +1239,7 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, { MIOPEN_LOG_I2("gemm_desc: " << gemm_desc); - gemm_backend = enforce_gemm_backend(gemm_desc.dataType, gemm_backend); + gemm_backend = enforce_gemm_backend(gemm_backend); if(!gemm_desc.isColMajor) { @@ -974,7 +1258,7 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, case GemmBackend_t::nogemmbackend: return miopenStatusNotImplemented; case GemmBackend_t::rocblas: { #if MIOPEN_USE_ROCBLAS - MIOPEN_LOG_FUNCTION("rocBLAS"); + MIOPEN_LOG_I2("rocBLAS"); HipEventPtr start = nullptr; HipEventPtr stop = nullptr; @@ -1058,7 +1342,7 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } } @@ -1183,7 +1467,7 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, } else { - MIOPEN_THROW(miopenStatusBadParm, + MIOPEN_THROW(miopenStatusInternalError, "8-bit floating types are only supported on gfx94x"); } @@ -1191,7 +1475,13 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, } case miopenDouble: { - MIOPEN_THROW(miopenStatusBadParm, "miopenDouble data type not supported by rocBLAS."); + MIOPEN_THROW(miopenStatusInternalError, + "miopenDouble data type not supported by rocBLAS."); + } + break; + + case miopenInt64: { + MIOPEN_THROW(miopenStatusInternalError, "miopenInt64 is not currently supported."); } break; } @@ -1209,6 +1499,16 @@ miopenStatus_t CallGemmStridedBatchedSequential(const Handle& handle, #else return miopenStatusNotImplemented; #endif + } + case GemmBackend_t::hipblaslt: { +#if MIOPEN_USE_HIPBLASLT + // todo bharriso - find out if we need to support iterative variant, or if using regular + // batching is alright. + call_miopen_hipblasLt_gemm(handle, gemm_desc, A, a_offset, B, b_offset, C, c_offset, false); + return miopenStatusSuccess; +#else + return miopenStatusNotImplemented; +#endif } } diff --git a/src/generic_search.cpp b/src/generic_search.cpp index e79550add5..428d5d166a 100644 --- a/src/generic_search.cpp +++ b/src/generic_search.cpp @@ -53,15 +53,15 @@ std::size_t GetTuningIterationsMax() { if(debug::tuning_iterations_limit) return *debug::tuning_iterations_limit; - return Value(ENV(MIOPEN_DEBUG_TUNING_ITERATIONS_MAX)); + return env::value(MIOPEN_DEBUG_TUNING_ITERATIONS_MAX); } std::chrono::milliseconds GetTuningTimeMax() { - return std::chrono::milliseconds{Value(ENV(MIOPEN_TUNING_TIME_MS_MAX))}; + return std::chrono::milliseconds{env::value(MIOPEN_TUNING_TIME_MS_MAX)}; } -std::size_t GetTuningThreadsMax() { return Value(ENV(MIOPEN_COMPILE_PARALLEL_LEVEL)); } +std::size_t GetTuningThreadsMax() { return env::value(MIOPEN_COMPILE_PARALLEL_LEVEL); } } // namespace solver } // namespace miopen diff --git a/src/getitem.cpp b/src/getitem.cpp new file mode 100644 index 0000000000..c3b1b0c3bc --- /dev/null +++ b/src/getitem.cpp @@ -0,0 +1,106 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace miopen { + +std::size_t GetGetitemWorkspaceSize(Handle& handle, + uint32_t indexCount, + const TensorDescriptor* const* indexDescs) +{ + auto ctx = ExecutionContext{&handle}; + const auto problem = getitem::ProblemDescription{indexCount, indexDescs}; + + const auto algo = AlgorithmName{"GetitemBackward"}; + const auto solvers = solver::SolverContainer{}; + + auto pair_size_vector = solvers.GetWorkspaceSizes(ctx, problem, true); + + return pair_size_vector.empty() ? static_cast(0) : pair_size_vector.front().second; +} + +miopenStatus_t GetitemBackward(Handle& handle, + Data_t workspace, + size_t workspaceSizeInBytes, + const TensorDescriptor& dyDesc, + ConstData_t dy, + uint32_t indexCount, + const TensorDescriptor* const* indexDescs, + ConstData_t* indexs, + const TensorDescriptor& dxDesc, + Data_t dx, + const TensorDescriptor& errorDesc, + Data_t error, + uint32_t dimCount, + const int32_t* dims, + uint32_t sliceCount, + const int32_t* slices, + uint32_t offset) +{ + const auto problem = getitem::ProblemDescription{dyDesc, + indexCount, + indexDescs, + dxDesc, + errorDesc, + dimCount, + dims, + sliceCount, + slices, + offset}; + + const auto invoke_params = getitem::GetitemInvokeParams{workspace, + workspaceSizeInBytes, + dyDesc, + dy, + indexCount, + indexDescs, + indexs, + dxDesc, + dx, + errorDesc, + error, + dimCount, + dims, + sliceCount, + slices, + offset}; + + const auto algo = AlgorithmName{"GetitemBackward"}; + const auto solvers = solver::SolverContainer{}; + solvers.ExecutePrimitive(handle, problem, algo, invoke_params); + + return miopenStatusSuccess; +} + +} // namespace miopen diff --git a/src/getitem/problem_description.cpp b/src/getitem/problem_description.cpp new file mode 100644 index 0000000000..b8b32109d6 --- /dev/null +++ b/src/getitem/problem_description.cpp @@ -0,0 +1,69 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include + +#include + +namespace miopen { + +namespace getitem { + +NetworkConfig ProblemDescription::MakeNetworkConfig() const +{ + auto dy_dims = dyDesc.GetLengths(); + auto input_dtype = dyDesc.GetType(); + auto error_dtype = errorDesc.GetType(); + + auto input_size = + std::accumulate(dy_dims.begin(), dy_dims.end(), 1ULL, std::multiplies()); + + std::ostringstream ss; + + ss << "getitembwd"; + ss << "input_size" << input_size; + ss << "input_dtype" << input_dtype; + ss << "error_dtype" << error_dtype; + ss << "indexCount" << indexCount; + + for(int i = 0; i < indexCount; ++i) + { + if(i == 0) + ss << "indexs_size"; + const auto& index_dims = (*indexDescs)[i].GetLengths(); + auto index_size = std::accumulate( + index_dims.begin(), index_dims.begin(), 1ULL, std::multiplies()); + ss << index_size << "_"; + } + + return NetworkConfig{ss.str()}; +} + +} // namespace getitem + +} // namespace miopen diff --git a/src/getitem_api.cpp b/src/getitem_api.cpp new file mode 100644 index 0000000000..094f44620f --- /dev/null +++ b/src/getitem_api.cpp @@ -0,0 +1,206 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include +#include +#include +#include +#include + +static void LogCmdGetitem(const miopenTensorDescriptor_t dyDesc, + uint32_t indexCount, + const miopenTensorDescriptor_t* indexDescs, + const miopenTensorDescriptor_t dxDesc, + uint32_t dimCount, + const int32_t* dims, + uint32_t sliceCount, + const int32_t* slices, + uint32_t offset, + bool is_fwd) +{ + if(miopen::IsLoggingCmd()) + { + std::stringstream ss; + auto dtype = miopen::deref(dyDesc).GetType(); + if(dtype == miopenHalf) + { + ss << "getitemfp16"; + } + else if(dtype == miopenFloat) + { + ss << "getitemfp32"; + } + else if(dtype == miopenBFloat16) + { + ss << "getitemf16"; + } + + std::string dy_s; + auto dy_dims = miopen::deref(dyDesc).GetLengths(); + for(int i = 0; i < dy_dims.size(); i++) + { + dy_s += std::to_string(dy_dims[i]); + if(i != dy_dims.size() - 2) + dy_s += ","; + } + ss << " -doutput " << dy_s; + + for(int i = 0; i < indexCount; i++) + { + std::string index_s; + auto index_dims = miopen::deref(indexDescs[i]).GetLengths(); + for(int j = 0; j < index_dims.size(); j++) + { + index_s += std::to_string(index_dims[j]); + if(j != index_dims.size() - 2) + index_s += ","; + } + ss << " -index" << i + 1 << " " << index_s; + } + + std::string dx_s; + auto dx_dims = miopen::deref(dxDesc).GetLengths(); + + for(int i = 0; i < dx_dims.size(); i++) + { + dx_s += std::to_string(dx_dims[i]); + if(i != dx_dims.size() - 2) + dx_s += ","; + } + + ss << " -dx " << dx_s; + + std::string dims_s; + for(int i = 0; i < dimCount; i++) + { + dims_s += std::to_string(dims[i]); + if(i != dimCount - 2) + dims_s += ","; + } + ss << " -dims" << dims_s; + + std::string slices_s; + for(int i = 0; i < sliceCount; i++) + { + slices_s += std::to_string(slices[i]); + if(i != sliceCount - 2) + slices_s += ","; + } + ss << " -slice" << slices_s; + + ss << " -offset" << offset; + ss << " -F " << ((is_fwd) ? "1" : "2"); + + MIOPEN_LOG_DRIVER_CMD(ss.str()); + } +} + +extern "C" miopenStatus_t miopenGetGetitemWorkspaceSize(miopenHandle_t handle, + uint32_t indexCount, + const miopenTensorDescriptor_t* indexDescs, + size_t* sizeInBytes) +{ + MIOPEN_LOG_FUNCTION(handle, indexCount, indexDescs); + + return miopen::try_([&] { + std::vector indexDescsCast; + std::transform(indexDescs, + indexDescs + indexCount, + std::back_inserter(indexDescsCast), + [](const auto& indexDesc) { return &miopen::deref(indexDesc); }); + miopen::deref(sizeInBytes) = miopen::GetGetitemWorkspaceSize( + miopen::deref(handle), indexCount, indexDescsCast.data()); + }); +}; + +extern "C" miopenStatus_t miopenGetitemBackward(miopenHandle_t handle, + void* workspace, + size_t workspaceSizeInBytes, + const miopenTensorDescriptor_t dyDesc, + const void* dy, + uint32_t indexCount, + const miopenTensorDescriptor_t* indexDescs, + const void* const* indexs, + const miopenTensorDescriptor_t dxDesc, + void* dx, + const miopenTensorDescriptor_t errorDesc, + void* error, + uint32_t dimCount, + const int32_t* dims, + uint32_t sliceCount, + const int32_t* slices, + uint32_t offset) +{ + MIOPEN_LOG_FUNCTION(handle, + workspace, + workspaceSizeInBytes, + dyDesc, + dy, + indexCount, + indexDescs, + indexs, + dxDesc, + dx, + errorDesc, + error, + dimCount, + dims, + sliceCount, + slices, + offset); + + LogCmdGetitem( + dyDesc, indexCount, indexDescs, dxDesc, dimCount, dims, sliceCount, slices, offset, true); + return miopen::try_([&] { + std::vector indexsCast; + std::vector indexDescsCast; + std::transform(indexDescs, + indexDescs + indexCount, + std::back_inserter(indexDescsCast), + [](const auto& indexDesc) { return &miopen::deref(indexDesc); }); + std::transform(indexs, + indexs + indexCount, + std::back_inserter(indexsCast), + [](const void* index) { return DataCast(index); }); + + miopen::GetitemBackward(miopen::deref(handle), + DataCast(workspace), + workspaceSizeInBytes, + miopen::deref(dyDesc), + DataCast(dy), + indexCount, + indexDescsCast.data(), + indexsCast.data(), + miopen::deref(dxDesc), + DataCast(dx), + miopen::deref(errorDesc), + DataCast(error), + dimCount, + dims, + sliceCount, + slices, + offset); + }); +} diff --git a/src/graphapi/convolution.cpp b/src/graphapi/convolution.cpp index d9735a138b..2279f793d7 100644 --- a/src/graphapi/convolution.cpp +++ b/src/graphapi/convolution.cpp @@ -394,9 +394,7 @@ void BackendConvolutionDescriptor::getDilations(miopenBackendAttributeType_t att const auto& dilations = mConvolution.getDilations(); *elementCount = dilations.size(); std::copy_n(dilations.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else @@ -415,9 +413,7 @@ void BackendConvolutionDescriptor::getFilterStrides(miopenBackendAttributeType_t const auto& strides = mConvolution.getFilterStrides(); *elementCount = strides.size(); std::copy_n(strides.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else @@ -436,9 +432,7 @@ void BackendConvolutionDescriptor::getPrePaddings(miopenBackendAttributeType_t a const auto& pads = mConvolution.getPrePaddings(); *elementCount = pads.size(); std::copy_n(pads.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else @@ -457,9 +451,7 @@ void BackendConvolutionDescriptor::getPostPaddings(miopenBackendAttributeType_t const auto& postPads = mConvolution.getPostPaddings(); *elementCount = postPads.size(); std::copy_n(postPads.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else diff --git a/src/graphapi/engine.cpp b/src/graphapi/engine.cpp new file mode 100644 index 0000000000..8dd1674c8f --- /dev/null +++ b/src/graphapi/engine.cpp @@ -0,0 +1,283 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include + +namespace miopen { + +namespace graphapi { + +GraphPatternExecutor::~GraphPatternExecutor() = default; + +size_t GraphExecutorFind20::getWorkspaceSize() const +{ + return miopen::deref(mSolution).GetWorkspaceSize(); +} + +void GraphExecutorFind20::execute(miopenHandle_t handle, const VariantPack& vpk) +{ + + std::vector tens_args; + + auto num = vpk.getTensorIds().size(); + assert(num == vpk.getDataPtrs().size()); + + /// \todo verify that variant pack has all the expected input and output + /// tensors --amberhassaan May, 2024 + for(std::size_t i = 0; i < num; ++i) + { + auto tens_id = vpk.getTensorIds()[i]; + auto* gpu_ptr = vpk.getDataPtrs()[i]; + assert(gpu_ptr); + + auto it = mTensorInfoMap->find(tens_id); + MIOPEN_THROW_IF(it == mTensorInfoMap->cend(), + "couldn't find a variant pack tensor id in the map"); + + auto& v = it->second; + + /// \todo use this code with C++20 --amberhassaan May, 2024 + /* + miopenTensorArgument_t targ{ + .id = v.mEnumId, + // .descriptor = &(v.mTensDesc), + .descriptor = nullptr, + .buffer = gpu_ptr + }; + */ + miopenTensorArgument_t targ{}; + targ.id = v.mEnumId; + targ.descriptor = nullptr; + targ.buffer = gpu_ptr; + + tens_args.emplace_back(targ); + } + + auto s = miopenRunSolution(handle, + mSolution, + tens_args.size(), + tens_args.data(), + vpk.getWorkspace(), + getWorkspaceSize()); + + MIOPEN_THROW_IF(s != miopenStatusSuccess, "Run Solution failed"); + if(s == miopenStatusSuccess) + { + MIOPEN_LOG_I2("Graph API Find 2.0 Solution Ran"); + } +} + +EngineBuilder& EngineBuilder::setGraph(OpGraph* g) +{ + assert(g); + mGraph = checkPtr(g); + mGraphSet = true; + return *this; +} + +EngineBuilder& EngineBuilder::setGlobalIndex(int64_t globalIndex) +{ + MIOPEN_THROW_IF(globalIndex < 0, "globalIndex must be >= 0"); + mGlobalIndex = globalIndex; + mIndexSet = true; + return *this; +} + +EngineBuilder& EngineBuilder::setSmCount(int32_t smCount) +{ + MIOPEN_THROW_IF(smCount <= 0, "SM count must be positive"); + mSmCount = smCount; + return *this; +} + +EngineBuilder& EngineBuilder::setExecutor(const std::shared_ptr& e) +{ + assert(e.get()); + mExecutor = e; + mExecSet = true; + return *this; +} + +Engine EngineBuilder::build() +{ + MIOPEN_THROW_IF(!mGraphSet || !mExecSet || !mIndexSet, + "must set graph, index and executor attributes"); + Engine e; + e.mGraph = mGraph; + e.mGlobalIndex = mGlobalIndex; + e.mExecutor = mExecutor; + e.mSmCount = mSmCount; + return e; +} + +void BackendEngineDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINE_OPERATION_GRAPH: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t& apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendOperationGraphDescriptor& operationGraphDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setGraph(operationGraphDescriptor.getOperationGraph()); + mOpGraphDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINE_GLOBAL_INDEX: + if(attributeType == MIOPEN_TYPE_INT64 && elementCount == 1) + { + mBuilder.setGlobalIndex(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINE_SM_COUNT_TARGET: + if(attributeType == MIOPEN_TYPE_INT32 && elementCount == 1) + { + mBuilder.setSmCount(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendEngineDescriptor::finalize() +{ + if(mFinalized || mBuilder.mGraph == nullptr) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + const auto& engines = mBuilder.mGraph->getEngines(); + + if(static_cast(mBuilder.mGlobalIndex) >= engines.size()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + const auto& candidate_engine = engines.at(mBuilder.mGlobalIndex); + mBuilder.setExecutor(candidate_engine.getExecutor()); + mEngine = mBuilder.build(); + + mFinalized = true; +} + +void BackendEngineDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINE_OPERATION_GRAPH: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mOpGraphDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINE_GLOBAL_INDEX: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngine.getGlobalIndex(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINE_SM_COUNT_TARGET: + if(attributeType == MIOPEN_TYPE_INT32 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngine.getSmCount(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINE_BEHAVIOR_NOTE: + case MIOPEN_ATTR_ENGINE_KNOB_INFO: + case MIOPEN_ATTR_ENGINE_LAYOUT_INFO: + case MIOPEN_ATTR_ENGINE_NUMERICAL_NOTE: + /// \todo figure out what we can return here --Sergei May, 2024 + *elementCount = 0; + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +} // namespace graphapi + +} // namespace miopen diff --git a/src/graphapi/enginecfg.cpp b/src/graphapi/enginecfg.cpp new file mode 100644 index 0000000000..44a583e64a --- /dev/null +++ b/src/graphapi/enginecfg.cpp @@ -0,0 +1,158 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include + +namespace miopen { + +namespace graphapi { + +EngineCfg EngineCfgBuilder::build() & +{ + if(mEngineSet) + { + return mEngineCfg; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +EngineCfg EngineCfgBuilder::build() && +{ + if(mEngineSet) + { + return std::move(mEngineCfg); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendEngineCfgDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINECFG_ENGINE: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendEngineDescriptor& engineDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setEngine(engineDescriptor.getEngine()); + mEngineDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINECFG_KNOB_CHOICES: + if(attributeType != MIOPEN_TYPE_BACKEND_DESCRIPTOR || elementCount < 0) + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendEngineCfgDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + mEngineCfg = mBuilder.build(); + mFinalized = true; +} + +void BackendEngineCfgDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINECFG_ENGINE: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngineDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINECFG_KNOB_CHOICES: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR || requestedElementCount >= 0) + { + *elementCount = 0; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINECFG_INTERMEDIATE_INFO: MIOPEN_THROW(miopenStatusUnsupportedOp); + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +} // namespace graphapi + +} // namespace miopen diff --git a/src/graphapi/engineheur.cpp b/src/graphapi/engineheur.cpp new file mode 100644 index 0000000000..6ac0bd3feb --- /dev/null +++ b/src/graphapi/engineheur.cpp @@ -0,0 +1,270 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include + +#include + +namespace miopen { + +namespace graphapi { + +EngineHeurBuilder& EngineHeurBuilder::setOpGraph(OpGraph* opGraph) +{ + mEngineHeur.mOpGraph = checkPtr(opGraph); + return *this; +} + +EngineHeurBuilder& EngineHeurBuilder::setMode(miopenBackendHeurMode_t mode) +{ + mEngineHeur.mMode = mode; + mModeSet = true; + return *this; +} + +EngineHeurBuilder& EngineHeurBuilder::setSmCount(int32_t smCount) +{ + mEngineHeur.mSmCount = smCount; + return *this; +} + +EngineHeur EngineHeurBuilder::build() +{ + if(mEngineHeur.mOpGraph == nullptr || !mModeSet) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + const auto& engines = mEngineHeur.mOpGraph->getEngines(); + std::for_each(engines.begin(), engines.end(), [this](const Engine& engine) { + mEngineHeur.mResults.emplace_back(engine); + }); + + return mEngineHeur; +} + +void BackendEngineHeurDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINEHEUR_MODE: + if(attributeType == MIOPEN_TYPE_HEUR_MODE && elementCount == 1) + { + mBuilder.setMode(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINEHEUR_OPERATION_GRAPH: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendOperationGraphDescriptor& opGraphDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setOpGraph(opGraphDescriptor.getOperationGraph()); + mOpGraphDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINEHEUR_SM_COUNT_TARGET: + if(attributeType == MIOPEN_TYPE_INT32 && elementCount == 1) + { + mBuilder.setSmCount(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendEngineHeurDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mEngineHeur = mBuilder.build(); + + const auto& engineCfgs = mEngineHeur.getResults(); + mResults.reserve(engineCfgs.size()); + + std::for_each(engineCfgs.begin(), engineCfgs.end(), [this](const EngineCfg& engineCfg) { + mResults.emplace_back(engineCfg, mOpGraphDescriptor); + }); + + mFinalized = true; +} + +void BackendEngineHeurDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_ENGINEHEUR_MODE: + if(attributeType == MIOPEN_TYPE_HEUR_MODE && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngineHeur.getMode(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINEHEUR_OPERATION_GRAPH: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mOpGraphDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINEHEUR_RESULTS: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount >= 0) + { + *elementCount = mResults.size(); + std::transform(mResults.begin(), + mResults.begin() + minimum(*elementCount, requestedElementCount), + static_cast(arrayOfElements), + [](auto& descriptor) { return &descriptor; }); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_ENGINEHEUR_SM_COUNT_TARGET: + if(attributeType == MIOPEN_TYPE_INT32 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngineHeur.getSmCount(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +// TODO(Amber): delete +/* +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor::OwnedEngineCfgDescriptor( + EngineCfg&& engineCfg, miopenBackendDescriptor_t opGraphDescriptor) + : BackendEngineCfgDescriptor(std::move(engineCfg), &mOwnedEngineDescriptorInstance), + mOwnedEngineDescriptorInstance(getEngineCfg().getEngine(), opGraphDescriptor) +{ +} + +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor::OwnedEngineCfgDescriptor( + const OwnedEngineCfgDescriptor& other) + : BackendEngineCfgDescriptor(other.getEngineCfg(), &mOwnedEngineDescriptorInstance), + mOwnedEngineDescriptorInstance(other.mOwnedEngineDescriptorInstance) +{ +} + +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor::OwnedEngineCfgDescriptor( + OwnedEngineCfgDescriptor&& other) noexcept + : BackendEngineCfgDescriptor(std::move(other.getEngineCfg()), &mOwnedEngineDescriptorInstance), + mOwnedEngineDescriptorInstance(std::move(other.mOwnedEngineDescriptorInstance)) +{ +} + +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor& +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor::operator=( + const OwnedEngineCfgDescriptor& other) +{ + if(this != &other) + { + BackendEngineCfgDescriptor::operator=(other); + mEngineDescriptor = &mOwnedEngineDescriptorInstance; + mOwnedEngineDescriptorInstance = other.mOwnedEngineDescriptorInstance; + } + return *this; +} + +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor& +BackendEngineHeurDescriptor::OwnedEngineCfgDescriptor::operator=( + OwnedEngineCfgDescriptor&& other) noexcept +{ + if(this != &other) + { + BackendEngineCfgDescriptor::operator=(std::move(other)); + mEngineDescriptor = &mOwnedEngineDescriptorInstance; + mOwnedEngineDescriptorInstance = std::move(other.mOwnedEngineDescriptorInstance); + } + return *this; +} +*/ + +} // namespace graphapi + +} // namespace miopen diff --git a/src/graphapi/execution_plan.cpp b/src/graphapi/execution_plan.cpp new file mode 100644 index 0000000000..ec45fcf8c7 --- /dev/null +++ b/src/graphapi/execution_plan.cpp @@ -0,0 +1,279 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include + +namespace miopen { + +namespace graphapi { + +std::string ExecutionPlan::getJsonRepresentation() const +{ + /// \todo Implement ExecutionPlan::getJsonRepresentation --Sergei May, 2024 + return {}; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setHandle(miopenHandle_t handle) & +{ + mExecutionPlan.mHandle = checkPtr(handle); + return *this; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setEngineCfg(const EngineCfg& engineCfg) & +{ + mExecutionPlan.mEngineCfg = engineCfg; + mEngineCfgSet = true; + return *this; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setEngineCfg(EngineCfg&& engineCfg) & +{ + mExecutionPlan.mEngineCfg = std::move(engineCfg); + mEngineCfgSet = true; + return *this; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setIntermediateIds(const std::vector& ids) & +{ + mExecutionPlan.mIntermediateIds = ids; + return *this; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setIntermediateIds(std::vector&& ids) & +{ + mExecutionPlan.mIntermediateIds = std::move(ids); + return *this; +} + +ExecutionPlanBuilder& ExecutionPlanBuilder::setJsonRepresentation(const std::string_view& s) & +{ + // TODO: Implement ExecutionPlanBuilder::setJsonRepresentation + (void)s; + return *this; +} + +ExecutionPlan ExecutionPlanBuilder::build() & +{ + if(mExecutionPlan.mHandle != nullptr && mEngineCfgSet) + { + return mExecutionPlan; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +ExecutionPlan ExecutionPlanBuilder::build() && +{ + if(mExecutionPlan.mHandle != nullptr && mEngineCfgSet) + { + return std::move(mExecutionPlan); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendExecutionPlanDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_EXECUTION_PLAN_HANDLE: + if(attributeType == MIOPEN_TYPE_HANDLE && elementCount == 1) + { + mBuilder.setHandle(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendEngineCfgDescriptor& engineCfgDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setEngineCfg(engineCfgDescriptor.getEngineCfg()); + mEngineCfgDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS: + if(attributeType == MIOPEN_TYPE_INT64 && elementCount >= 0) + { + mBuilder.setIntermediateIds({static_cast(arrayOfElements), + static_cast(arrayOfElements) + elementCount}); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION: + if(attributeType == MIOPEN_TYPE_CHAR && elementCount > 0) + { + std::string_view s(static_cast(arrayOfElements), elementCount); + mBuilder.setJsonRepresentation(s); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendExecutionPlanDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + mExecutionPlan = std::move(mBuilder).build(); + mFinalized = true; +} + +void BackendExecutionPlanDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_EXECUTION_PLAN_HANDLE: + if(attributeType == MIOPEN_TYPE_HANDLE && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mExecutionPlan.getHandle(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mEngineCfgDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + case MIOPEN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mExecutionPlan.getWorkspaceSize(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount >= 0) + { + const auto& vec = mExecutionPlan.getIntermediateIds(); + *elementCount = vec.size(); + std::copy_n(vec.begin(), + minimum(requestedElementCount, *elementCount), + static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION: + if(attributeType == MIOPEN_TYPE_CHAR && requestedElementCount > 0) + { + std::string s = mExecutionPlan.getJsonRepresentation(); + *elementCount = s.size() + 1; + std::copy_n(s.c_str(), + minimum(requestedElementCount, *elementCount), + static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendExecutionPlanDescriptor::execute(miopenHandle_t handle, + miopenBackendDescriptor_t variantPack) +{ + BackendDescriptor& bd = deref(variantPack); + auto& bendvp = dynamic_cast(bd); + assert(&bendvp); + mExecutionPlan.execute(handle, *bendvp.getVariantPack()); +} + +} // namespace graphapi + +} // namespace miopen diff --git a/src/graphapi/find_engine.cpp b/src/graphapi/find_engine.cpp new file mode 100644 index 0000000000..2c746b7278 --- /dev/null +++ b/src/graphapi/find_engine.cpp @@ -0,0 +1,340 @@ + +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2023 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace miopen { +namespace graphapi { + +GraphPatternMatcher::~GraphPatternMatcher() = default; + +class MHA_Fwd_F8_Pattern : public GraphPatternMatcher +{ + static const OpGraph& getPatternGraph() + { + static auto graph_gen = PatternGraphGenerator::Make({ + {"OP_MATMUL", {"Q", "K"}, {"T_BMM_0"}}, + // {"OP_POINTWISE:MUL", {"T_BMM_0", "ATTN_S"}, {"PW_S_0"}}, + // replacing the MUL node above with IDENTITY node below for now + // because Find 2.0 mha descriptor expects attention scale as a scalar + // parameter on host side + {"OP_POINTWISE:IDENTITY", {"T_BMM_0"}, {"PW_S_0"}}, + {"OP_POINTWISE:MUL", {"PW_S_0", "DSCL_Q"}, {"PW_S_1"}}, + {"OP_POINTWISE:MUL", {"PW_S_1", "DSCL_K"}, {"PW_S_2"}}, + {"OP_REDUCTION:MAX", {"PW_S_2"}, {"M"}}, + {"OP_POINTWISE:SUB", {"PW_S_2", "M"}, {"T_SUB"}}, + {"OP_POINTWISE:EXP", {"T_SUB"}, {"T_EXP"}}, + {"OP_REDUCTION:ADD", {"T_EXP"}, {"T_SUM"}}, + {"OP_POINTWISE:RECIPROCAL", {"T_SUM"}, {"Z_INV"}}, + {"OP_POINTWISE:MUL", {"Z_INV", "T_EXP"}, {"T_MUL_0"}}, + {"OP_REDUCTION:MAX", {"T_MUL_0"}, {"AMAX_S"}}, + {"OP_RNG", {"SEED", "OFFSET"}, {"T_RND"}}, + {"OP_POINTWISE:MUL", {"T_RND", "T_MUL_0"}, {"T_MUL_1"}}, + {"OP_POINTWISE:MUL", {"T_MUL_1", "I_PROB"}, {"PW_S_3"}}, + {"OP_POINTWISE:MUL", {"PW_S_3", "SCL_S"}, {"PW_S_4"}}, + {"OP_MATMUL", {"PW_S_4", "V"}, {"T_BMM_1"}}, + {"OP_POINTWISE:MUL", {"T_BMM_1", "DSCL_S"}, {"PW_S_5"}}, + {"OP_POINTWISE:MUL", {"PW_S_5", "DSCL_V"}, {"PW_S_6"}}, + {"OP_POINTWISE:MUL", {"PW_S_6", "SCL_O"}, {"O"}}, + {"OP_REDUCTION:MAX", {"PW_S_6"}, {"AMAX_O"}}, + + }); + + return graph_gen->graph(); + } + + std::shared_ptr extractFind20Tensors(const OpGraph& graph, + float* attn_scale) const + { + + assert(attn_scale); + std::vector all1s = { + std::size_t{1}, std::size_t{1}, std::size_t{1}, std::size_t{1}}; + + auto tensor_map = std::make_shared(); + + auto add_mapping = [&](miopenTensorArgumentId_t enum_id, Tensor* tens_ptr) { + assert(tens_ptr); + assert(enum_id != miopenTensorArgumentIdInvalid); + + /* + * leaving to check that correct tensors are picked + MIOPEN_LOG_W("Tensor enum id: " << tensorEnumIdToStr(enum_id) + << " tensor unique id as str: " + << tensorIdAsStr(tens_ptr)); + */ + + tensor_map->try_emplace(tens_ptr->getId(), TensorInfo(enum_id, tens_ptr)); + }; + + for(auto [neigh, tens_ptr] : graph.getOutEdges(graph.getSourceNode())) + { + + if(neigh->signName() == "OP_MATMUL") + { + auto* matmul = dynamic_cast(neigh); + assert(matmul); + + if(auto* pw_prev = graph.findInNeighByName(matmul, "OP_POINTWISE:MUL"); + pw_prev == nullptr) + { + // this is the first matmul node + add_mapping(miopenTensorMhaQ, matmul->getA()); + add_mapping(miopenTensorMhaK, matmul->getB()); + /// \todo dim check on Q and K --amberhassaan May, 2024 + + /// \note old code assuming attn_scale applies to pw_mult + // auto* pw_0 = dynamic_cast( + // graph.findOutNeighByName(matmul, "OP_POINTWISE:MUL")); + auto* pw_0 = dynamic_cast( + graph.findOutNeighByName(matmul, "OP_POINTWISE:IDENTITY")); + assert(pw_0); + + float alpha1 = std::get(pw_0->getAlpha1()); + *attn_scale = alpha1; + /// \note old code assuming attn_scale applies to pw_mult + // auto* attn_scl = pw_0->getB(); + // assert(attn_scl->GetLengths() == all1s); + // add_mapping(miopenTensorMhaAttnScale, attn_scl); + + auto* pw_1 = dynamic_cast( + graph.findOutNeighByName(pw_0, "OP_POINTWISE:MUL")); + assert(pw_1); + auto dscl_q = pw_1->getB(); + assert(dscl_q->GetLengths() == all1s); + add_mapping(miopenTensorMhaDescaleQ, dscl_q); + + auto* pw_2 = dynamic_cast( + graph.findOutNeighByName(pw_1, "OP_POINTWISE:MUL")); + assert(pw_2); + auto* dscl_k = pw_2->getB(); + assert(dscl_k->GetLengths() == all1s); + add_mapping(miopenTensorMhaDescaleK, dscl_k); + + auto* red = dynamic_cast( + graph.findOutNeighByName(pw_2, "OP_REDUCTION:MAX")); + assert(red); + auto* m = red->getY(); + assert(m->GetLengths()[3] == 1LL); + add_mapping(miopenTensorMhaM, m); + } + else + { + // this is the second matmul node + add_mapping(miopenTensorMhaV, matmul->getB()); + + auto* pw_prev_cast = dynamic_cast(pw_prev); + assert(pw_prev_cast); + auto* scl_s = pw_prev_cast->getB(); + assert(scl_s->GetLengths() == all1s); + add_mapping(miopenTensorMhaScaleS, scl_s); + + auto* pw_0 = dynamic_cast( + graph.findOutNeighByName(matmul, "OP_POINTWISE:MUL")); + assert(pw_0); + + auto* dscl_s = pw_0->getB(); + assert(dscl_s->GetLengths() == all1s); + add_mapping(miopenTensorMhaDescaleS, dscl_s); + + auto* pw_1 = dynamic_cast( + graph.findOutNeighByName(pw_0, "OP_POINTWISE:MUL")); + assert(pw_1); + auto* dscl_v = pw_1->getB(); + assert(dscl_v->GetLengths() == all1s); + add_mapping(miopenTensorMhaDescaleV, dscl_v); + + auto* red = dynamic_cast( + graph.findOutNeighByName(pw_1, "OP_REDUCTION:MAX")); + assert(red); + auto* amax_o = red->getY(); + assert(amax_o->GetLengths() == all1s); + add_mapping(miopenTensorMhaAmaxO, amax_o); + + auto* pw_2 = dynamic_cast( + graph.findOutNeighByName(pw_1, "OP_POINTWISE:MUL")); + assert(pw_2); + auto* scl_o = pw_2->getB(); + assert(scl_o->GetLengths() == all1s); + add_mapping(miopenTensorMhaScaleO, scl_o); + + auto* o = pw_2->getY(); + add_mapping(miopenTensorMhaO, o); + } + } + else if(neigh->signName() == "OP_RNG") + { + auto* rng = dynamic_cast(neigh); + assert(rng); + add_mapping(miopenTensorMhaDropoutSeed, std::get(rng->getSeed())); + add_mapping(miopenTensorMhaDropoutOffset, rng->getOffset()); + + auto* pw_mult_0 = dynamic_cast( + graph.findOutNeighByName(rng, "OP_POINTWISE:MUL")); + assert(pw_mult_0); + + auto* pw_mult_1 = dynamic_cast( + graph.findOutNeighByName(pw_mult_0, "OP_POINTWISE:MUL")); + assert(pw_mult_1); + + auto* prob = pw_mult_1->getB(); + add_mapping(miopenTensorMhaDropoutProbability, prob); + } + } + + { // discovering Z_INV and AMAX_S tensors + auto* exp_node = graph.findNodeByName("OP_POINTWISE:EXP"); + assert(exp_node); + + // get exp_node's neighbor that is a Pointwise mult + auto* pw_mult = dynamic_cast( + graph.findOutNeighByName(exp_node, "OP_POINTWISE:MUL")); + assert(pw_mult); + + auto* red = dynamic_cast( + graph.findOutNeighByName(pw_mult, "OP_REDUCTION:MAX")); + assert(red); + + add_mapping(miopenTensorMhaAmaxS, red->getY()); + + auto* inv_node = + dynamic_cast(graph.findNodeByName("OP_POINTWISE:RECIPROCAL")); + add_mapping(miopenTensorMhaZInv, inv_node->getY()); + } + + return tensor_map; + } + +public: + static std::unique_ptr Make() + { + return std::make_unique(); + } + + std::string_view name() const final + { + static const char* n = "mha_fwd_f8"; + return n; + } + + bool matches(const OpGraph* graph_ptr) const final + { + assert(graph_ptr); + return isIsomorphic(*graph_ptr, getPatternGraph()); + } + + std::vector getEngines(OpGraph* graph_ptr) const override + { + + assert(graph_ptr); + assert(matches(graph_ptr)); + auto& graph = *graph_ptr; + + miopenProblem_t mha_prob; + MhaDescriptor mha_desc; + + float attn_scale = std::numeric_limits::quiet_NaN(); + std::shared_ptr tensor_map = extractFind20Tensors(graph, &attn_scale); + assert(attn_scale != std::numeric_limits::quiet_NaN()); + + mha_desc.SetParams(attn_scale); + + auto s = miopenCreateMhaProblem(&mha_prob, &mha_desc, miopenProblemDirectionForward); + MIOPEN_THROW_IF(s != miopenStatusSuccess, "failed while creating problem for mha fwd"); + + for(auto& [k, v] : *tensor_map) + { + s = miopenSetProblemTensorDescriptor(mha_prob, v.mEnumId, v.mGraphTensor); + MIOPEN_THROW_IF(s != miopenStatusSuccess, + "failed while setting tensor descriptor for mha fwd"); + } + + std::vector solutions(10); + size_t num_found = 0; + s = miopenFindSolutions( + graph.getHandle(), mha_prob, nullptr, solutions.data(), &num_found, solutions.size()); + MIOPEN_THROW_IF(s != miopenStatusSuccess, "failed while finding solutions for mha fwd"); + + solutions.resize(num_found); + + std::vector engines; + + size_t i = 0; + for(const auto& sol : solutions) + { + std::shared_ptr exec = GraphExecutorFind20::make(sol, tensor_map); + + engines.emplace_back( + EngineBuilder().setGraph(graph_ptr).setExecutor(exec).setGlobalIndex(i).build()); + ++i; + } + + return engines; + } +}; + +/* +class FwdConvResAddBiasActPattern : public GraphPattern +{ +public: + static std::unique_ptr Make() + { + return std::make_unique(); + } +}; +*/ + +std::vector findEngines(OpGraph* graph) +{ + assert(graph); + + std::vector> patterns; + patterns.emplace_back(MHA_Fwd_F8_Pattern::Make()); + + for(const auto& p : patterns) + { + if(p->matches(graph)) + { + MIOPEN_LOG_I2("Matched against pattern: " << p->name()); + return p->getEngines(graph); + } + } + + return {}; +} + +} // end namespace graphapi +} // end namespace miopen diff --git a/src/graphapi/graphapi.cpp b/src/graphapi/graphapi.cpp index 7ee0f0af6e..7385a6d4dd 100644 --- a/src/graphapi/graphapi.cpp +++ b/src/graphapi/graphapi.cpp @@ -25,13 +25,20 @@ *******************************************************************************/ #include #include +#include +#include +#include +#include #include +#include #include #include +#include #include #include #include #include +#include #include @@ -41,7 +48,7 @@ miopenBackendCreateDescriptor(miopenBackendDescriptorType_t descriptorType, { MIOPEN_LOG_FUNCTION(descriptorType, descriptor); return miopen::try_([&] { - auto& outputDesciptor = miopen::deref(descriptor); + auto& outputDescriptor = miopen::deref(descriptor); switch(descriptorType) { @@ -52,31 +59,66 @@ miopenBackendCreateDescriptor(miopenBackendDescriptorType_t descriptorType, */ // clang-format off case MIOPEN_BACKEND_CONVOLUTION_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendConvolutionDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendConvolutionDescriptor(); break; + + case MIOPEN_BACKEND_ENGINE_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendEngineDescriptor(); break; + + case MIOPEN_BACKEND_ENGINECFG_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendEngineCfgDescriptor(); break; + + case MIOPEN_BACKEND_ENGINEHEUR_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendEngineHeurDescriptor(); break; + + case MIOPEN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendExecutionPlanDescriptor(); break; + + case MIOPEN_BACKEND_MATMUL_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendMatmulDescriptor(); + break; case MIOPEN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendOperationConvolutionForwardDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendOperationConvolutionForwardDescriptor(); break; case MIOPEN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendOperationConvolutionBackwardFilterDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendOperationConvolutionBackwardFilterDescriptor(); break; case MIOPEN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendOperationConvolutionBackwardDataDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendOperationConvolutionBackwardDataDescriptor(); break; + + case MIOPEN_BACKEND_OPERATION_MATMUL_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationMatmulDescriptor(); + break; + + case MIOPEN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationPointwiseDescriptor(); break; + + case MIOPEN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationReductionDescriptor(); break; + + case MIOPEN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationReshapeDescriptor(); break; + + case MIOPEN_BACKEND_OPERATION_RNG_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationRngDescriptor(); break; + + case MIOPEN_BACKEND_OPERATIONGRAPH_DESCRIPTOR: + outputDescriptor = new miopen::graphapi::BackendOperationGraphDescriptor(); break; case MIOPEN_BACKEND_POINTWISE_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendPointwiseDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendPointwiseDescriptor(); break; case MIOPEN_BACKEND_REDUCTION_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendReductionDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendReductionDescriptor(); break; case MIOPEN_BACKEND_RNG_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendRngDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendRngDescriptor(); break; case MIOPEN_BACKEND_TENSOR_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendTensorDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendTensorDescriptor(); break; case MIOPEN_BACKEND_VARIANT_PACK_DESCRIPTOR: - outputDesciptor = new miopen::graphapi::BackendVariantPackDescriptor(); break; + outputDescriptor = new miopen::graphapi::BackendVariantPackDescriptor(); break; default: MIOPEN_THROW(miopenStatusUnsupportedOp); // clang-format on @@ -184,15 +226,31 @@ extern "C" miopenStatus_t miopenBackendInitialize(miopenBackendDescriptor_t desc return miopen::try_([&] { switch(descriptorType) { - /* This part is a common place of changes of about 25 PRs and merge conflicts arise heavily + /** This part is a common place of changes of about 25 PRs and merge conflicts arise heavily * here. Turn off clang-format to keep each line unique to simplify resolving of conflicts. * - * TODO: Turn on clang-format when active phase of development is finished. + * \todo Turn on clang-format when active phase of development is finished. + * --Sergei Apr, 2024 */ // clang-format off case MIOPEN_BACKEND_CONVOLUTION_DESCRIPTOR: initializeBackendDescriptor(descriptor, sizeInBytes); break; + case MIOPEN_BACKEND_ENGINE_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_ENGINECFG_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_ENGINEHEUR_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_MATMUL_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + case MIOPEN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR: initializeBackendDescriptor(descriptor, sizeInBytes); break; @@ -202,6 +260,24 @@ extern "C" miopenStatus_t miopenBackendInitialize(miopenBackendDescriptor_t desc case MIOPEN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR: initializeBackendDescriptor(descriptor, sizeInBytes); break; + case MIOPEN_BACKEND_OPERATION_MATMUL_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_OPERATION_RNG_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + + case MIOPEN_BACKEND_OPERATIONGRAPH_DESCRIPTOR: + initializeBackendDescriptor(descriptor, sizeInBytes); break; + case MIOPEN_BACKEND_POINTWISE_DESCRIPTOR: initializeBackendDescriptor(descriptor, sizeInBytes); break; diff --git a/src/graphapi/matmul.cpp b/src/graphapi/matmul.cpp new file mode 100644 index 0000000000..de0c83c5a4 --- /dev/null +++ b/src/graphapi/matmul.cpp @@ -0,0 +1,448 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include + +namespace miopen { +namespace graphapi { + +Matmul MatmulBuilder::build() const +{ + if(!mComputeTypeSet) + MIOPEN_THROW(miopenStatusBadParm); + return mMatmul; +} + +void BackendMatmulDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_MATMUL_COMP_TYPE: + if(attributeType == MIOPEN_TYPE_DATA_TYPE && elementCount == 1) + { + mBuilder.setComputeType(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendMatmulDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mMatmul = mBuilder.build(); + mFinalized = true; +} + +void BackendMatmulDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_MATMUL_COMP_TYPE: + if(attributeType == MIOPEN_TYPE_DATA_TYPE && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mMatmul.getComputeType(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OperationMatmulBuilder& OperationMatmulBuilder::setA(Tensor* A) +{ + mOperationMatmul.mA = checkPtr(A); + if(mOperationMatmul.mA->GetLengths().size() < 2) + MIOPEN_THROW(miopenStatusBadParm); + mASet = true; + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setB(Tensor* B) +{ + mOperationMatmul.mB = checkPtr(B); + if(mOperationMatmul.mB->GetLengths().size() < 2) + MIOPEN_THROW(miopenStatusBadParm); + mBSet = true; + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setC(Tensor* C) +{ + mOperationMatmul.mC = checkPtr(C); + if(mOperationMatmul.mC->GetLengths().size() < 2) + MIOPEN_THROW(miopenStatusBadParm); + mCSet = true; + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setBatchCount(int64_t count) +{ + mOperationMatmul.mBatchCount = count; + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setGemmMOverride(Tensor* overrideTensor) +{ + mOperationMatmul.mGemmMOverride = checkPtr(overrideTensor); + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setGemmNOverride(Tensor* overrideTensor) +{ + mOperationMatmul.mGemmNOverride = checkPtr(overrideTensor); + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setGemmKOverride(Tensor* overrideTensor) +{ + mOperationMatmul.mGemmKOverride = checkPtr(overrideTensor); + return *this; +}; +OperationMatmulBuilder& OperationMatmulBuilder::setMatmulDescriptor(Matmul* mMatmul) +{ + mOperationMatmul.mMatmul = checkPtr(mMatmul); + mMatmulSet = true; + return *this; +} + +OperationMatmul OperationMatmulBuilder::build() +{ + if(!mASet || !mBSet || !mCSet || !mMatmulSet) + MIOPEN_THROW(miopenStatusBadParm); + + auto& aDimensions = mOperationMatmul.mA->GetLengths(); + auto& bDimensions = mOperationMatmul.mB->GetLengths(); + auto& cDimensions = mOperationMatmul.mC->GetLengths(); + + int aDimensionsCount = aDimensions.size(); + int bDimensionsCount = bDimensions.size(); + int cDimensionsCount = cDimensions.size(); + + if(cDimensionsCount != std::max(aDimensionsCount, bDimensionsCount)) + MIOPEN_THROW(miopenStatusBadParm); + + size_t Am = aDimensions[aDimensionsCount - 2]; + size_t An = aDimensions[aDimensionsCount - 1]; + + size_t Bn = bDimensions[bDimensionsCount - 2]; + size_t Bk = bDimensions[bDimensionsCount - 1]; + + size_t Cm = cDimensions[cDimensionsCount - 2]; + size_t Ck = cDimensions[cDimensionsCount - 1]; + + // non-tranpose case: + bool nt = (Am == Cm && An == Bn && Bk == Ck); + // test for transpose case, allowing [m, n] * [k, n] = [m, k] + bool tt = (Am == Cm && An == Bk && Bn == Ck); + + if(!nt && !tt) + MIOPEN_THROW(miopenStatusBadParm); + + auto correctBroadcastedDims = [](size_t dim1, size_t dim2, size_t dimOut) -> bool { + if(dim1 == dim2 && dim2 == dimOut) + return true; + if(dim1 == 1) + return dimOut == dim2; + if(dim2 == 1) + return dimOut == dim1; + return false; + }; + + auto lengthDiff = + std::max(aDimensionsCount, bDimensionsCount) - std::min(aDimensionsCount, bDimensionsCount); + + auto& longestDims = aDimensionsCount > bDimensionsCount ? aDimensions : bDimensions; + + // For tensors (j×1×n×m) and (k×m×p) second tensor will be virtually extended to (1*k×m×p) + // + for(int i = 0; i < lengthDiff; i++) + { + if(!correctBroadcastedDims(1, longestDims[i], cDimensions[i])) + MIOPEN_THROW(miopenStatusBadParm); + } + // Last 2 dimensions are not batch dimensions + // + for(int i = lengthDiff; i < cDimensionsCount - 2; i++) + { + if(!correctBroadcastedDims(aDimensions[i], bDimensions[i], cDimensions[i])) + MIOPEN_THROW(miopenStatusBadParm); + } + return mOperationMatmul; +} + +void BackendOperationMatmulDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + mMatmul = mBuilder.build(); + mFinalized = true; +} + +void BackendOperationMatmulDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_MATMUL_ADESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mA; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_BDESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mB; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_CDESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mC; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mMatmuDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mGemmMOverride; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mGemmNOverride; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mGemmKOverride; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mMatmul.getBatchCount(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OpNode* BackendOperationMatmulDescriptor::getOperation() { return &mMatmul; } + +void BackendOperationMatmulDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + using TensorSetter = OperationMatmulBuilder& (OperationMatmulBuilder::*)(Tensor*); + + auto callTensorSetter = [=](TensorSetter setter, miopenBackendDescriptor_t& outApiDescriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendTensorDescriptor& tensorDescriptor = + dynamic_cast(backendDescriptor); + (mBuilder.*setter)(tensorDescriptor.getTensor()); + outApiDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_MATMUL_ADESC: + callTensorSetter(&OperationMatmulBuilder::setA, mA); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_BDESC: + callTensorSetter(&OperationMatmulBuilder::setB, mB); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_CDESC: + callTensorSetter(&OperationMatmulBuilder::setC, mC); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + BackendMatmulDescriptor& matmulDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setMatmulDescriptor(matmulDescriptor.getMatmul()); + mMatmuDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC: + callTensorSetter(&OperationMatmulBuilder::setGemmMOverride, mGemmMOverride); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC: + callTensorSetter(&OperationMatmulBuilder::setGemmNOverride, mGemmNOverride); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC: + callTensorSetter(&OperationMatmulBuilder::setGemmKOverride, mGemmKOverride); + break; + + case MIOPEN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT: + if(attributeType == MIOPEN_TYPE_INT64 && elementCount == 1) + { + mBuilder.setBatchCount(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +} // namespace graphapi +} // namespace miopen diff --git a/src/graphapi/opgraph.cpp b/src/graphapi/opgraph.cpp new file mode 100644 index 0000000000..57cbb85ca7 --- /dev/null +++ b/src/graphapi/opgraph.cpp @@ -0,0 +1,451 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2023 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include +#include + +#include +#include + +namespace miopen { +namespace graphapi { + +OpNode::~OpNode() = default; + +OpGraph OpGraphBuilder::build() && +{ + if(mNodes.empty()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + OpGraph graph; + + graph.mHandle = mHandle; + + // key = tensor ptr, value = vec. of dest nodes + /// \todo might eventually move this state to the graph class during build() + std::unordered_map e_map; + + for(OpNode* n : mNodes) + { + + for(Tensor* i : n->getInTensors()) + { + auto [iter, _ignore] = + e_map.try_emplace(i, EdgeInfo{}); // add empty EdgeInfo if not present + + iter->second.mDests.emplace_back(n); + } + + for(Tensor* o : n->getOutTensors()) + { + auto [iter, _ignore] = + e_map.try_emplace(o, EdgeInfo{}); // add empty EdgeInfo if not present + + MIOPEN_THROW_IF(iter->second.mSrc != nullptr, "Output tensor with two source op nodes"); + iter->second.mSrc = n; + } + } + + graph.initNodes(std::move(mNodes)); + + for(const auto& [tens_ptr, edge_info] : e_map) + { + MIOPEN_THROW_IF(edge_info.mSrc == nullptr && edge_info.mDests.empty(), + "Invalid state with null src node and empty dest nodes"); + + if(edge_info.mSrc != nullptr && !edge_info.mDests.empty()) + { + for(const auto& d : edge_info.mDests) + { + graph.addEdge(edge_info.mSrc, tens_ptr, d); + } + } + else if(edge_info.mSrc == nullptr) + { + for(const auto& d : edge_info.mDests) + { + graph.addEdgeFromSrc(d, tens_ptr); + } + } + else if(edge_info.mDests.empty()) + { + graph.addEdgeToSink(edge_info.mSrc, tens_ptr); + } + } + + return graph; +} + +void OpGraph::initEngines() +{ + // cache the engines in the graph. + // NOTE(amber): this may be expensive and there may be benefit in delaying + // findEngines to when the user calls it instead of calling it at graph build + // time, but cudnn graph API has semantics that suggest that graph knows its + // engines or engine count at least. + // + /// \todo findEngines takes pointer to the graph and uses it to construct + // engines. This pointer may become invalid when the graph object is moved. Fix + // by using shared_ptr or not storing graph inside engine + // --amberhassaan May, 2024 + mEngines = findEngines(this); +} + +VecOfPaths OpGraph::getAllPaths() const +{ + /// \todo does not check for cycles. Use DFS to first check for cycles + /// at construction time perhaps. --amberhassaan May, 2024 + VecOfPaths all_paths; + + std::deque paths_to_explore; + paths_to_explore.emplace_back(Path{mSrcNode.get()}); + + while(!paths_to_explore.empty()) + { + Path path = paths_to_explore.front(); + paths_to_explore.pop_front(); + + assert(!path.empty()); + const OpNode* last_node = path.back(); + assert(last_node); + if(last_node->getOutEdges().empty()) + { + // all paths should terminate at the sink + assert(last_node == mSinkNode.get()); + all_paths.emplace_back(std::move(path)); + } + else + { + for(const auto& [dst, tens_ptr] : last_node->getOutEdges()) + { + Path newPath{path}; + newPath.emplace_back(dst); + paths_to_explore.emplace_back(std::move(newPath)); + } + } + } // end while + + return all_paths; +} + +std::string pathToStr(const Path& path) +{ + std::ostringstream oss; + for(const OpNode* n : path) + { + oss << n->signName() << ","; + } + return oss.str(); +} + +namespace internal { + +using MapSizeToPathVec = std::unordered_map; + +bool checkSameNodesByName(const OpGraph& left, const OpGraph& right) +{ + auto l_names = left.getNodeNames(); + auto r_names = right.getNodeNames(); + if(l_names.size() != r_names.size()) + { + return false; + } + + std::sort(l_names.begin(), l_names.end()); + std::sort(r_names.begin(), r_names.end()); + + return l_names == r_names; +} + +bool checkSameDegreeVecs(const OpGraph& left, const OpGraph& right) +{ + auto l_degs = left.getInOutDegrees(); + auto r_degs = right.getInOutDegrees(); + + std::sort(l_degs.begin(), l_degs.end()); + std::sort(r_degs.begin(), r_degs.end()); + return l_degs == r_degs; +} + +auto groupBySize(VecOfPaths&& all_paths) +{ + MapSizeToPathVec paths_by_size; + + for(auto& p : all_paths) + { + auto [it, _ignore] = paths_by_size.emplace(p.size(), VecOfPaths{}); + it->second.emplace_back(std::move(p)); + } + + return paths_by_size; +} + +bool checkSamePathVecs(const VecOfPaths& left, const VecOfPaths& right) +{ + if(left.size() != right.size()) + { + return false; + } + + using VecOfStr = std::vector; + + auto pathvec_to_strvec = [](const VecOfPaths& pathvec) { + VecOfStr ret; + for(const Path& path : pathvec) + { + ret.emplace_back(pathToStr(path)); + } + return ret; + }; + + VecOfStr l_paths_as_str = pathvec_to_strvec(left); + VecOfStr r_paths_as_str = pathvec_to_strvec(right); + + std::sort(l_paths_as_str.begin(), l_paths_as_str.end()); + std::sort(r_paths_as_str.begin(), r_paths_as_str.end()); + + return l_paths_as_str == r_paths_as_str; +} + +bool checkSamePaths(const OpGraph& left, const OpGraph& right) +{ + + auto l_paths = left.getAllPaths(); + auto r_paths = right.getAllPaths(); + + if(l_paths.size() != r_paths.size()) + { + return false; + } + + auto l_paths_by_sz = groupBySize(std::move(l_paths)); + auto r_paths_by_sz = groupBySize(std::move(r_paths)); + + auto get_keys = [](const MapSizeToPathVec& paths_by_size) { + std::vector keys{}; + for(const auto& [k, v] : paths_by_size) + { + keys.emplace_back(k); + } + return keys; + }; + + auto l_keys = get_keys(l_paths_by_sz); + auto r_keys = get_keys(r_paths_by_sz); + + if(l_keys != r_keys) + { + return false; + } + + for(size_t k : l_keys) + { + if(!checkSamePathVecs(l_paths_by_sz[k], r_paths_by_sz[k])) + { + return false; + } + } + + return true; +} + +} // end namespace internal + +bool isIsomorphic(const OpGraph& left, const OpGraph& right) +{ + if(left.numNodes() != right.numNodes()) + { + MIOPEN_LOG_I2("test failed due to num nodes being different"); + return false; + } + + if(left.numEdges() != right.numEdges()) + { + MIOPEN_LOG_I2("test failed due to num edges being different"); + return false; + } + + if(!internal::checkSameNodesByName(left, right)) + { + MIOPEN_LOG_I2("test failed due to node names being different"); + return false; + } + + if(!internal::checkSameDegreeVecs(left, right)) + { + MIOPEN_LOG_I2("test failed due to node degrees being different"); + return false; + } + + if(!internal::checkSamePaths(left, right)) + { + MIOPEN_LOG_I2("test failed due to paths being different"); + return false; + } + + return true; +} + +void BackendOperationGraphDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATIONGRAPH_HANDLE: + if(attributeType == MIOPEN_TYPE_HANDLE && elementCount == 1) + { + mBuilder.setHandle(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATIONGRAPH_OPS: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount > 0) + { + std::vector descriptors; + descriptors.reserve(elementCount); + std::vector nodes; + nodes.reserve(elementCount); + + // for_each_n is not available on RHEL/SLES, see issue #2973 + std::for_each(static_cast(arrayOfElements), + static_cast(arrayOfElements) + elementCount, + [&descriptors, &nodes](miopenBackendDescriptor_t apiDescriptor) { + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + if(backendDescriptor.isFinalized()) + { + descriptors.push_back(apiDescriptor); + nodes.push_back(backendDescriptor.getOperation()); + } + else + { + MIOPEN_THROW(miopenStatusBadParm, "descriptor not finalized"); + } + }); + + if(!internal::noRepetitions(nodes)) + { + MIOPEN_THROW(miopenStatusBadParm, "Repeated node pointer found"); + } + + mBuilder.setNodes(std::move(nodes)); + mOps = std::move(descriptors); + } + else + { + MIOPEN_THROW(miopenStatusBadParm, "Invalid attribute type or count"); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendOperationGraphDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + if(mBuilder.getHandle() == nullptr) // this is not checked by build() so far but API requires + { + MIOPEN_THROW(miopenStatusBadParm); + } + mOpGraph = std::move(mBuilder).build(); + mOpGraph.initEngines(); + mFinalized = true; +} + +void BackendOperationGraphDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATIONGRAPH_HANDLE: + if(attributeType == MIOPEN_TYPE_HANDLE && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mOpGraph.getHandle(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATIONGRAPH_OPS: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount >= 0) + { + *elementCount = mOps.size(); + std::copy_n(mOps.cbegin(), + minimum(*elementCount, requestedElementCount), + static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = mOpGraph.getEngines().size(); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +} // end namespace graphapi +} // end namespace miopen diff --git a/src/graphapi/pointwise.cpp b/src/graphapi/pointwise.cpp index cccf74f22e..bf49d18121 100644 --- a/src/graphapi/pointwise.cpp +++ b/src/graphapi/pointwise.cpp @@ -252,6 +252,214 @@ void BackendPointwiseDescriptor::getAttribute(miopenBackendAttributeName_t attri } } +const std::string& OperationPointwise::signName() const +{ + switch(mPointwise->getMode()) + { + case MIOPEN_POINTWISE_ADD: { + static const std::string name = "OP_POINTWISE:ADD"; + return name; + } + case MIOPEN_POINTWISE_ADD_SQUARE: { + static const std::string name = "OP_POINTWISE:ADD_SQUARE"; + return name; + } + case MIOPEN_POINTWISE_DIV: { + static const std::string name = "OP_POINTWISE:DIV"; + return name; + } + case MIOPEN_POINTWISE_MAX: { + static const std::string name = "OP_POINTWISE:MAX"; + return name; + } + case MIOPEN_POINTWISE_MIN: { + static const std::string name = "OP_POINTWISE:MIN"; + return name; + } + case MIOPEN_POINTWISE_MOD: { + static const std::string name = "OP_POINTWISE:MOD"; + return name; + } + case MIOPEN_POINTWISE_MUL: { + static const std::string name = "OP_POINTWISE:MUL"; + return name; + } + case MIOPEN_POINTWISE_POW: { + static const std::string name = "OP_POINTWISE:POW"; + return name; + } + case MIOPEN_POINTWISE_SUB: { + static const std::string name = "OP_POINTWISE:SUB"; + return name; + } + case MIOPEN_POINTWISE_ABS: { + static const std::string name = "OP_POINTWISE:ABS"; + return name; + } + case MIOPEN_POINTWISE_CEIL: { + static const std::string name = "OP_POINTWISE:CEIL"; + return name; + } + case MIOPEN_POINTWISE_COS: { + static const std::string name = "OP_POINTWISE:COS"; + return name; + } + case MIOPEN_POINTWISE_EXP: { + static const std::string name = "OP_POINTWISE:EXP"; + return name; + } + case MIOPEN_POINTWISE_FLOOR: { + static const std::string name = "OP_POINTWISE:FLOOR"; + return name; + } + case MIOPEN_POINTWISE_LOG: { + static const std::string name = "OP_POINTWISE:LOG"; + return name; + } + case MIOPEN_POINTWISE_NEG: { + static const std::string name = "OP_POINTWISE:NEG"; + return name; + } + case MIOPEN_POINTWISE_RSQRT: { + static const std::string name = "OP_POINTWISE:RSQRT"; + return name; + } + case MIOPEN_POINTWISE_SIN: { + static const std::string name = "OP_POINTWISE:SIN"; + return name; + } + case MIOPEN_POINTWISE_SQRT: { + static const std::string name = "OP_POINTWISE:SQRT"; + return name; + } + case MIOPEN_POINTWISE_TAN: { + static const std::string name = "OP_POINTWISE:TAN"; + return name; + } + case MIOPEN_POINTWISE_ERF: { + static const std::string name = "OP_POINTWISE:ERF"; + return name; + } + case MIOPEN_POINTWISE_IDENTITY: { + static const std::string name = "OP_POINTWISE:IDENTITY"; + return name; + } + case MIOPEN_POINTWISE_RELU_FWD: { + static const std::string name = "OP_POINTWISE:RELU_FWD"; + return name; + } + case MIOPEN_POINTWISE_TANH_FWD: { + static const std::string name = "OP_POINTWISE:TANH_FWD"; + return name; + } + case MIOPEN_POINTWISE_SIGMOID_FWD: { + static const std::string name = "OP_POINTWISE:SIGMOID_FWD"; + return name; + } + case MIOPEN_POINTWISE_ELU_FWD: { + static const std::string name = "OP_POINTWISE:ELU_FWD"; + return name; + } + case MIOPEN_POINTWISE_GELU_FWD: { + static const std::string name = "OP_POINTWISE:GELU_FWD"; + return name; + } + case MIOPEN_POINTWISE_SOFTPLUS_FWD: { + static const std::string name = "OP_POINTWISE:SOFTPLUS_FWD"; + return name; + } + case MIOPEN_POINTWISE_SWISH_FWD: { + static const std::string name = "OP_POINTWISE:SWISH_FWD"; + return name; + } + case MIOPEN_POINTWISE_GELU_APPROX_TANH_FWD: { + static const std::string name = "OP_POINTWISE:APPROX_TANH_FWD"; + return name; + } + case MIOPEN_POINTWISE_RELU_BWD: { + static const std::string name = "OP_POINTWISE:RELU_BWD"; + return name; + } + case MIOPEN_POINTWISE_TANH_BWD: { + static const std::string name = "OP_POINTWISE:TANH_BWD"; + return name; + } + case MIOPEN_POINTWISE_SIGMOID_BWD: { + static const std::string name = "OP_POINTWISE:SIGMOID_BWD"; + return name; + } + case MIOPEN_POINTWISE_ELU_BWD: { + static const std::string name = "OP_POINTWISE:ELU_BWD"; + return name; + } + case MIOPEN_POINTWISE_GELU_BWD: { + static const std::string name = "OP_POINTWISE:GELU_BWD"; + return name; + } + case MIOPEN_POINTWISE_SOFTPLUS_BWD: { + static const std::string name = "OP_POINTWISE:SOFTPLUS_BWD"; + return name; + } + case MIOPEN_POINTWISE_SWISH_BWD: { + static const std::string name = "OP_POINTWISE:SWISH_BWD"; + return name; + } + case MIOPEN_POINTWISE_GELU_APPROX_TANH_BWD: { + static const std::string name = "OP_POINTWISE:APPROX_TANH_BWD"; + return name; + } + case MIOPEN_POINTWISE_CMP_EQ: { + static const std::string name = "OP_POINTWISE:CMP_EQ"; + return name; + } + case MIOPEN_POINTWISE_CMP_NEQ: { + static const std::string name = "OP_POINTWISE:CMP_NEQ"; + return name; + } + case MIOPEN_POINTWISE_CMP_GT: { + static const std::string name = "OP_POINTWISE:CMP_GT"; + return name; + } + case MIOPEN_POINTWISE_CMP_GE: { + static const std::string name = "OP_POINTWISE:CMP_GE"; + return name; + } + case MIOPEN_POINTWISE_CMP_LT: { + static const std::string name = "OP_POINTWISE:CMP_LT"; + return name; + } + case MIOPEN_POINTWISE_CMP_LE: { + static const std::string name = "OP_POINTWISE:CMP_LE"; + return name; + } + case MIOPEN_POINTWISE_LOGICAL_AND: { + static const std::string name = "OP_POINTWISE:LOGICAL_AND"; + return name; + } + case MIOPEN_POINTWISE_LOGICAL_OR: { + static const std::string name = "OP_POINTWISE:LOGICAL_OR"; + return name; + } + case MIOPEN_POINTWISE_LOGICAL_NOT: { + static const std::string name = "OP_POINTWISE:LOGICAL_NOT"; + return name; + } + case MIOPEN_POINTWISE_GEN_INDEX: { + static const std::string name = "OP_POINTWISE:GEN_INDEX"; + return name; + } + case MIOPEN_POINTWISE_BINARY_SELECT: { + static const std::string name = "OP_POINTWISE:BINARY_SELECT"; + return name; + } + case MIOPEN_POINTWISE_RECIPROCAL: { + static const std::string name = "OP_POINTWISE:RECIPROCAL"; + return name; + } + default: MIOPEN_THROW(miopenStatusNotImplemented); + } +} + std::vector OperationPointwise::getInTensors() const { switch(mPointwise->getMode()) @@ -420,62 +628,45 @@ std::vector OperationPointwise::getOutTensors() const } } -namespace { - -template -void assignPtr(Ptr src, Ptr& dst) -{ - if(src != nullptr) - { - dst = src; - } - else - { - MIOPEN_THROW(miopenStatusBadParm); - } -} - -} // namespace - OperationPointwiseBuilder& OperationPointwiseBuilder::setPointwise(Pointwise* pointwise) { - assignPtr(pointwise, mOperationPointwise.mPointwise); + mOperationPointwise.mPointwise = checkPtr(pointwise); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setX(Tensor* x) { - assignPtr(x, mOperationPointwise.mX); + mOperationPointwise.mX = checkPtr(x); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setB(Tensor* b) { - assignPtr(b, mOperationPointwise.mB); + mOperationPointwise.mB = checkPtr(b); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setY(Tensor* y) { - assignPtr(y, mOperationPointwise.mY); + mOperationPointwise.mY = checkPtr(y); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setT(Tensor* t) { - assignPtr(t, mOperationPointwise.mT); + mOperationPointwise.mT = checkPtr(t); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setDx(Tensor* dX) { - assignPtr(dX, mOperationPointwise.mDx); + mOperationPointwise.mDx = checkPtr(dX); return *this; } OperationPointwiseBuilder& OperationPointwiseBuilder::setDy(Tensor* dY) { - assignPtr(dY, mOperationPointwise.mDy); + mOperationPointwise.mDy = checkPtr(dY); return *this; } @@ -554,9 +745,9 @@ OperationPointwise OperationPointwiseBuilder::build() if(mOperationPointwise.mX == nullptr || mOperationPointwise.mB == nullptr || mOperationPointwise.mY == nullptr || mOperationPointwise.mT != nullptr || mOperationPointwise.mDx != nullptr || mOperationPointwise.mDy != nullptr || - !checkDimsWithPossibleBroadcasting(mOperationPointwise.mX->getDimensions(), - mOperationPointwise.mB->getDimensions(), - mOperationPointwise.mY->getDimensions())) + !checkDimsWithPossibleBroadcasting(mOperationPointwise.mX->GetLengths(), + mOperationPointwise.mB->GetLengths(), + mOperationPointwise.mY->GetLengths())) { MIOPEN_THROW(miopenStatusBadParm); } @@ -591,10 +782,10 @@ OperationPointwise OperationPointwiseBuilder::build() if(mOperationPointwise.mX == nullptr || mOperationPointwise.mY == nullptr || mOperationPointwise.mB != nullptr || mOperationPointwise.mT != nullptr || mOperationPointwise.mDx != nullptr || mOperationPointwise.mDy != nullptr || mAlpha2Set || - !std::equal(mOperationPointwise.mX->getDimensions().cbegin(), - mOperationPointwise.mX->getDimensions().cend(), - mOperationPointwise.mY->getDimensions().cbegin(), - mOperationPointwise.mY->getDimensions().cend())) + !std::equal(mOperationPointwise.mX->GetLengths().cbegin(), + mOperationPointwise.mX->GetLengths().cend(), + mOperationPointwise.mY->GetLengths().cbegin(), + mOperationPointwise.mY->GetLengths().cend())) { MIOPEN_THROW(miopenStatusBadParm); } @@ -610,18 +801,18 @@ OperationPointwise OperationPointwiseBuilder::build() if(mOperationPointwise.mX == nullptr || mOperationPointwise.mB == nullptr || mOperationPointwise.mT == nullptr || mOperationPointwise.mY == nullptr || mOperationPointwise.mDx != nullptr || mOperationPointwise.mDy != nullptr || - !std::equal(mOperationPointwise.mX->getDimensions().cbegin(), - mOperationPointwise.mX->getDimensions().cend(), - mOperationPointwise.mB->getDimensions().cbegin(), - mOperationPointwise.mB->getDimensions().cend()) || - !std::equal(mOperationPointwise.mX->getDimensions().cbegin(), - mOperationPointwise.mX->getDimensions().cend(), - mOperationPointwise.mT->getDimensions().cbegin(), - mOperationPointwise.mT->getDimensions().cend()) || - !std::equal(mOperationPointwise.mX->getDimensions().cbegin(), - mOperationPointwise.mX->getDimensions().cend(), - mOperationPointwise.mY->getDimensions().cbegin(), - mOperationPointwise.mY->getDimensions().cend())) + !std::equal(mOperationPointwise.mX->GetLengths().cbegin(), + mOperationPointwise.mX->GetLengths().cend(), + mOperationPointwise.mB->GetLengths().cbegin(), + mOperationPointwise.mB->GetLengths().cend()) || + !std::equal(mOperationPointwise.mX->GetLengths().cbegin(), + mOperationPointwise.mX->GetLengths().cend(), + mOperationPointwise.mT->GetLengths().cbegin(), + mOperationPointwise.mT->GetLengths().cend()) || + !std::equal(mOperationPointwise.mX->GetLengths().cbegin(), + mOperationPointwise.mX->GetLengths().cend(), + mOperationPointwise.mY->GetLengths().cbegin(), + mOperationPointwise.mY->GetLengths().cend())) { MIOPEN_THROW(miopenStatusBadParm); } @@ -643,15 +834,15 @@ OperationPointwise OperationPointwiseBuilder::build() if(mOperationPointwise.mY == nullptr || mOperationPointwise.mDy == nullptr || mOperationPointwise.mDx == nullptr || mOperationPointwise.mX != nullptr || mOperationPointwise.mB != nullptr || mOperationPointwise.mT != nullptr || - !checkDimsWithPossibleBroadcasting(mOperationPointwise.mY->getDimensions(), - mOperationPointwise.mDy->getDimensions(), - mOperationPointwise.mDx->getDimensions())) + !checkDimsWithPossibleBroadcasting(mOperationPointwise.mY->GetLengths(), + mOperationPointwise.mDy->GetLengths(), + mOperationPointwise.mDx->GetLengths())) { MIOPEN_THROW(miopenStatusBadParm); } break; - /* TODO: Implement the remaining cases + /** \todo Implement the remaining cases --Sergei Apr, 2024 */ case MIOPEN_POINTWISE_ERF: case MIOPEN_POINTWISE_GEN_INDEX: MIOPEN_THROW(miopenStatusNotImplemented); @@ -662,6 +853,190 @@ OperationPointwise OperationPointwiseBuilder::build() return mOperationPointwise; } +void BackendOperationPointwiseDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + using TensorSetter = OperationPointwiseBuilder& (OperationPointwiseBuilder::*)(Tensor * tensor); + + auto callTensorSetter = [=](TensorSetter setter, miopenBackendDescriptor_t& outApiDescriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendTensorDescriptor& tensorDescriptor = + dynamic_cast(backendDescriptor); + (mBuilder.*setter)(tensorDescriptor.getTensor()); + outApiDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + using FloatSetter = + OperationPointwiseBuilder& (OperationPointwiseBuilder::*)(OperationPointwise::Alpha alpha); + + auto callFloatSetter = [=](FloatSetter setter) { + if(attributeType == MIOPEN_TYPE_FLOAT && elementCount == 1) + { + (mBuilder.*setter)(*static_cast(arrayOfElements)); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendPointwiseDescriptor& pointwiseDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setPointwise(pointwiseDescriptor.getPointwise()); + mPointwiseDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_XDESC: + callTensorSetter(&OperationPointwiseBuilder::setX, mXDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_BDESC: + callTensorSetter(&OperationPointwiseBuilder::setB, mBDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_YDESC: + callTensorSetter(&OperationPointwiseBuilder::setY, mYDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_TDESC: + callTensorSetter(&OperationPointwiseBuilder::setT, mTDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_DXDESC: + callTensorSetter(&OperationPointwiseBuilder::setDx, mDxDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_DYDESC: + callTensorSetter(&OperationPointwiseBuilder::setDy, mDyDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_ALPHA1: + callFloatSetter(&OperationPointwiseBuilder::setAlpha1); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_ALPHA2: + callFloatSetter(&OperationPointwiseBuilder::setAlpha2); + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendOperationPointwiseDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mOperationPointwise = mBuilder.build(); + mFinalized = true; +} + +void BackendOperationPointwiseDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + auto storeDescriptor = [=](miopenBackendDescriptor_t descriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = descriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + auto storeFloat = [=](OperationPointwise::Alpha alpha) { + if(attributeType == MIOPEN_TYPE_FLOAT && requestedElementCount == 1 && alpha.index() == 0) + { + *elementCount = 1; + *static_cast(arrayOfElements) = std::get(alpha); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR: + storeDescriptor(mPointwiseDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_XDESC: storeDescriptor(mXDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_BDESC: storeDescriptor(mBDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_YDESC: storeDescriptor(mYDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_TDESC: storeDescriptor(mTDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_DXDESC: storeDescriptor(mDxDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_DYDESC: storeDescriptor(mDyDescriptor); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_ALPHA1: storeFloat(mOperationPointwise.getAlpha1()); break; + + case MIOPEN_ATTR_OPERATION_POINTWISE_ALPHA2: storeFloat(mOperationPointwise.getAlpha2()); break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OpNode* BackendOperationPointwiseDescriptor::getOperation() { return &mOperationPointwise; } + } // namespace graphapi } // namespace miopen diff --git a/src/graphapi/reduction.cpp b/src/graphapi/reduction.cpp index 145977547e..d143e4e69e 100644 --- a/src/graphapi/reduction.cpp +++ b/src/graphapi/reduction.cpp @@ -27,6 +27,8 @@ #include #include +#include + namespace miopen { namespace graphapi { @@ -134,6 +136,208 @@ void BackendReductionDescriptor::getAttribute(miopenBackendAttributeName_t attri } } +const std::string& OperationReduction::signName() const +{ + switch(mReduction->getReductionOperator()) + { + case MIOPEN_REDUCE_TENSOR_ADD: { + static const std::string name = "OP_REDUCTION:ADD"; + return name; + } + case MIOPEN_REDUCE_TENSOR_MUL: { + static const std::string name = "OP_REDUCTION:MUL"; + return name; + } + case MIOPEN_REDUCE_TENSOR_MIN: { + static const std::string name = "OP_REDUCTION:MIN"; + return name; + } + case MIOPEN_REDUCE_TENSOR_MAX: { + static const std::string name = "OP_REDUCTION:MAX"; + return name; + } + case MIOPEN_REDUCE_TENSOR_AMAX: { + static const std::string name = "OP_REDUCTION:AMAX"; + return name; + } + case MIOPEN_REDUCE_TENSOR_AVG: { + static const std::string name = "OP_REDUCTION:AVG"; + return name; + } + case MIOPEN_REDUCE_TENSOR_NORM1: { + static const std::string name = "OP_REDUCTION:NORM1"; + return name; + } + case MIOPEN_REDUCE_TENSOR_NORM2: { + static const std::string name = "OP_REDUCTION:NORM2"; + return name; + } + default: MIOPEN_THROW(miopenStatusNotImplemented); + } +} + +std::vector OperationReduction::getInTensors() const { return {mX}; } + +std::vector OperationReduction::getOutTensors() const { return {mY}; } + +OperationReductionBuilder& OperationReductionBuilder::setReduction(Reduction* reduction) +{ + mOperationReduction.mReduction = checkPtr(reduction); + return *this; +} + +OperationReductionBuilder& OperationReductionBuilder::setX(Tensor* x) +{ + mOperationReduction.mX = checkPtr(x); + return *this; +} + +OperationReductionBuilder& OperationReductionBuilder::setY(Tensor* y) +{ + mOperationReduction.mY = checkPtr(y); + return *this; +} + +OperationReduction OperationReductionBuilder::build() +{ + if(mOperationReduction.mReduction != nullptr && mOperationReduction.mX != nullptr && + mOperationReduction.mY != nullptr && + std::equal(mOperationReduction.mX->GetLengths().cbegin(), + mOperationReduction.mX->GetLengths().cend(), + mOperationReduction.mY->GetLengths().cbegin(), + mOperationReduction.mY->GetLengths().cend(), + [](auto inputDim, auto outputDim) { + return outputDim == 1 || outputDim == inputDim || outputDim % inputDim == 0; + })) + { + return mOperationReduction; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendOperationReductionDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + using Setter = OperationReductionBuilder& (OperationReductionBuilder::*)(Tensor * tensor); + + auto callSetter = [=](Setter setter, miopenBackendDescriptor_t& outDescriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendTensorDescriptor& tensorDescriptor = + dynamic_cast(backendDescriptor); + (mBuilder.*setter)(tensorDescriptor.getTensor()); + outDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_REDUCTION_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendReductionDescriptor& reductionDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setReduction(reductionDescriptor.getReduction()); + mReductionDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_REDUCTION_XDESC: + callSetter(&OperationReductionBuilder::setX, mXDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_REDUCTION_YDESC: + callSetter(&OperationReductionBuilder::setY, mYDescriptor); + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendOperationReductionDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mOperationReduction = mBuilder.build(); + mFinalized = true; +} + +void BackendOperationReductionDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + auto storeDescriptor = [=](miopenBackendDescriptor_t descriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = descriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_REDUCTION_DESC: storeDescriptor(mReductionDescriptor); break; + + case MIOPEN_ATTR_OPERATION_REDUCTION_XDESC: storeDescriptor(mXDescriptor); break; + + case MIOPEN_ATTR_OPERATION_REDUCTION_YDESC: storeDescriptor(mYDescriptor); break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OpNode* BackendOperationReductionDescriptor::getOperation() { return &mOperationReduction; } + } // namespace graphapi } // namespace miopen diff --git a/src/graphapi/reshape.cpp b/src/graphapi/reshape.cpp new file mode 100644 index 0000000000..69fb764e47 --- /dev/null +++ b/src/graphapi/reshape.cpp @@ -0,0 +1,192 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#include +#include + +#include + +namespace miopen { + +namespace graphapi { + +const std::string& OperationReshape::signName() const +{ + static const std::string name = "OP_RESHAPE"; + return name; +} + +std::vector OperationReshape::getInTensors() const { return {mX}; } + +std::vector OperationReshape::getOutTensors() const { return {mY}; } + +OperationReshapeBuilder& OperationReshapeBuilder::setX(Tensor* x) +{ + mOperationReshape.mX = checkPtr(x); + return *this; +} + +OperationReshapeBuilder& OperationReshapeBuilder::setY(Tensor* y) +{ + mOperationReshape.mY = checkPtr(y); + return *this; +} + +OperationReshape OperationReshapeBuilder::build() +{ + const bool valid = mOperationReshape.mX != nullptr && mOperationReshape.mY != nullptr; + + if(!valid) + MIOPEN_THROW(miopenStatusBadParm); + + const auto& inputDims = mOperationReshape.mX->GetLengths(); + const auto& inputStrides = mOperationReshape.mX->GetStrides(); + const auto& outputDims = mOperationReshape.mY->GetLengths(); + const auto& outputStrides = mOperationReshape.mY->GetStrides(); + const auto size = inputDims.size(); + + /* Detect a case of transpose operation for the last 2 dimensions: + * [b, h, s, d] -> [b, h, d, s] + * which is important for MHA graphs + */ + const bool transpose = + outputDims.size() == size && size >= 2 && inputDims[size - 1] == outputDims[size - 2] && + inputStrides[size - 1] == outputStrides[size - 2] && + inputDims[size - 2] == outputDims[size - 1] && + inputStrides[size - 2] == outputStrides[size - 1] && + std::equal(inputDims.cbegin(), inputDims.cbegin() + size - 2, outputDims.cbegin()) && + std::equal(inputStrides.cbegin(), inputStrides.cbegin() + size - 2, outputStrides.cbegin()); + + if(transpose) + { + mOperationReshape.mOpKind = OperationReshape::OpKind::TRANSPOSE; + } + else + { + mOperationReshape.mOpKind = OperationReshape::OpKind::GENERIC; + } + + return mOperationReshape; +} + +void BackendOperationReshapeDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + using Setter = OperationReshapeBuilder& (OperationReshapeBuilder::*)(Tensor * tensor); + + auto callSetter = [=](Setter setter, miopenBackendDescriptor_t& outDescriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendTensorDescriptor& tensorDescriptor = + dynamic_cast(backendDescriptor); + (mBuilder.*setter)(tensorDescriptor.getTensor()); + outDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_RESHAPE_XDESC: + callSetter(&OperationReshapeBuilder::setX, mXDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_RESHAPE_YDESC: + callSetter(&OperationReshapeBuilder::setY, mYDescriptor); + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + }; +} + +void BackendOperationReshapeDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mOperationReshape = mBuilder.build(); + mFinalized = true; +} + +void BackendOperationReshapeDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + auto storeDescriptor = [=](miopenBackendDescriptor_t descriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = descriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_RESHAPE_XDESC: storeDescriptor(mXDescriptor); break; + + case MIOPEN_ATTR_OPERATION_RESHAPE_YDESC: storeDescriptor(mYDescriptor); break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OpNode* BackendOperationReshapeDescriptor::getOperation() { return &mOperationReshape; } + +} // namespace graphapi + +} // namespace miopen diff --git a/src/graphapi/rng.cpp b/src/graphapi/rng.cpp index fa1a39b63b..33d4e4e657 100644 --- a/src/graphapi/rng.cpp +++ b/src/graphapi/rng.cpp @@ -24,6 +24,7 @@ * *******************************************************************************/ +#include #include #include @@ -193,6 +194,238 @@ void BackendRngDescriptor::getAttribute(miopenBackendAttributeName_t attributeNa } } +const std::string& OperationRng::signName() const +{ + static const std::string name = "OP_RNG"; + return name; +} + +std::vector OperationRng::getInTensors() const +{ + if(mSeed.index() == 0) + { + return {mOffset}; + } + else + { + return {std::get(mSeed), mOffset}; + } +} + +std::vector OperationRng::getOutTensors() const { return {mOutput}; } + +OperationRngBuilder& OperationRngBuilder::setRng(Rng* rng) +{ + mOperationRng.mRng = checkPtr(rng); + return *this; +} + +OperationRngBuilder& OperationRngBuilder::setOutput(Tensor* output) +{ + mOperationRng.mOutput = checkPtr(output); + return *this; +} + +OperationRngBuilder& OperationRngBuilder::setSeed(int64_t seed) noexcept +{ + mOperationRng.mSeed = seed; + return *this; +} + +OperationRngBuilder& OperationRngBuilder::setSeed(Tensor* seed) +{ + bool valid = seed != nullptr; + + valid = valid && miopen::all_of(seed->GetLengths(), [](auto v) { return v == 1; }) && + miopen::all_of(seed->GetStrides(), [](auto v) { return v == 1; }); + + if(valid) + { + mOperationRng.mSeed = seed; + return *this; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +OperationRngBuilder& OperationRngBuilder::setOffset(Tensor* offset) +{ + bool valid = offset != nullptr; + + valid = valid && miopen::all_of(offset->GetLengths(), [](auto v) { return v == 1; }) && + miopen::all_of(offset->GetStrides(), [](auto v) { return v == 1; }); + + if(valid) + { + mOperationRng.mOffset = offset; + return *this; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } +} + +OperationRng OperationRngBuilder::build() +{ + if(mOperationRng.mRng == nullptr || mOperationRng.mOutput == nullptr || + mOperationRng.mOffset == nullptr) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + return mOperationRng; +} + +void BackendOperationRngDescriptor::setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + using TensorSetter = OperationRngBuilder& (OperationRngBuilder::*)(Tensor*); + + auto callTensorSetter = [=](TensorSetter setter, miopenBackendDescriptor_t& outApiDescriptor) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendTensorDescriptor& tensorDescriptor = + dynamic_cast(backendDescriptor); + (mBuilder.*setter)(tensorDescriptor.getTensor()); + outApiDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_RNG_DESC: + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && elementCount == 1) + { + miopenBackendDescriptor_t apiDescriptor = + deref(static_cast(arrayOfElements)); + BackendDescriptor& backendDescriptor = deref(apiDescriptor); + + if(!backendDescriptor.isFinalized()) + { + MIOPEN_THROW(miopenStatusBadParm); + } + + BackendRngDescriptor& rngDescriptor = + dynamic_cast(backendDescriptor); + mBuilder.setRng(rngDescriptor.getRng()); + mRngDescriptor = apiDescriptor; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_RNG_YDESC: + callTensorSetter(&OperationRngBuilder::setOutput, mOutputDescriptor); + break; + + case MIOPEN_ATTR_OPERATION_RNG_SEED: + if(attributeType == MIOPEN_TYPE_INT64 && elementCount == 1) + { + mBuilder.setSeed(*static_cast(arrayOfElements)); + } + else + { + callTensorSetter(&OperationRngBuilder::setSeed, mSeedDescriptor); + } + break; + + case MIOPEN_ATTR_OPERATION_RNG_OFFSET_DESC: + callTensorSetter(&OperationRngBuilder::setOffset, mOffsetDescriptor); + break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +void BackendOperationRngDescriptor::finalize() +{ + if(mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + mOperationRng = mBuilder.build(); + mFinalized = true; +} + +void BackendOperationRngDescriptor::getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) +{ + if(!mFinalized) + { + MIOPEN_THROW(miopenStatusNotInitialized); + } + + auto retrieveDescriptor = [=](miopenBackendDescriptor_t source) { + if(attributeType == MIOPEN_TYPE_BACKEND_DESCRIPTOR && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = source; + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + }; + + switch(attributeName) + { + case MIOPEN_ATTR_OPERATION_RNG_DESC: retrieveDescriptor(mRngDescriptor); break; + + case MIOPEN_ATTR_OPERATION_RNG_YDESC: retrieveDescriptor(mOutputDescriptor); break; + + case MIOPEN_ATTR_OPERATION_RNG_SEED: + if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount == 1) + { + *elementCount = 1; + *static_cast(arrayOfElements) = std::get(mOperationRng.getSeed()); + } + else if(mOperationRng.getSeed().index() == 1) + { + retrieveDescriptor(mSeedDescriptor); + } + else + { + MIOPEN_THROW(miopenStatusBadParm); + } + break; + + case MIOPEN_ATTR_OPERATION_RNG_OFFSET_DESC: retrieveDescriptor(mOffsetDescriptor); break; + + default: MIOPEN_THROW(miopenStatusBadParm); + } +} + +OpNode* BackendOperationRngDescriptor::getOperation() { return &mOperationRng; } + } // namespace graphapi } // namespace miopen diff --git a/src/graphapi/tensor.cpp b/src/graphapi/tensor.cpp index 5cb7785b56..6fa5da8f15 100644 --- a/src/graphapi/tensor.cpp +++ b/src/graphapi/tensor.cpp @@ -39,9 +39,9 @@ TensorBuilder& TensorBuilder::setDataType(miopenDataType_t dataType) & return *this; } -TensorBuilder& TensorBuilder::setDim(const std::vector& dimensions) & +TensorBuilder& TensorBuilder::setDim(const std::vector& dimensions) & { - if(dimensions.empty() || miopen::any_of(dimensions, [](int64_t val) { return val <= 0; })) + if(dimensions.empty() || miopen::any_of(dimensions, [](std::size_t val) { return val <= 0; })) { MIOPEN_THROW(miopenStatusBadParm); } @@ -51,9 +51,9 @@ TensorBuilder& TensorBuilder::setDim(const std::vector& dimensions) & return *this; } -TensorBuilder& TensorBuilder::setDim(std::vector&& dimensions) & +TensorBuilder& TensorBuilder::setDim(std::vector&& dimensions) & { - if(dimensions.empty() || miopen::any_of(dimensions, [](int64_t val) { return val <= 0; })) + if(dimensions.empty() || miopen::any_of(dimensions, [](std::size_t val) { return val <= 0; })) { MIOPEN_THROW(miopenStatusBadParm); } @@ -63,9 +63,9 @@ TensorBuilder& TensorBuilder::setDim(std::vector&& dimensions) & return *this; } -TensorBuilder& TensorBuilder::setStride(const std::vector& strides) & +TensorBuilder& TensorBuilder::setStride(const std::vector& strides) & { - if(strides.empty() || miopen::any_of(strides, [](int64_t val) { return val <= 0; })) + if(strides.empty() || miopen::any_of(strides, [](std::size_t val) { return val <= 0; })) { MIOPEN_THROW(miopenStatusBadParm); } @@ -75,9 +75,9 @@ TensorBuilder& TensorBuilder::setStride(const std::vector& strides) & return *this; } -TensorBuilder& TensorBuilder::setStride(std::vector&& strides) & +TensorBuilder& TensorBuilder::setStride(std::vector&& strides) & { - if(strides.empty() || miopen::any_of(strides, [](int64_t val) { return val <= 0; })) + if(strides.empty() || miopen::any_of(strides, [](std::size_t val) { return val <= 0; })) { MIOPEN_THROW(miopenStatusBadParm); } @@ -162,8 +162,8 @@ void BackendTensorDescriptor::setAttribute(miopenBackendAttributeName_t attribut if(attributeType == MIOPEN_TYPE_INT64 && elementCount > 0) { mBuilder.setDim( - std::vector(static_cast(arrayOfElements), - static_cast(arrayOfElements) + elementCount)); + std::vector(static_cast(arrayOfElements), + static_cast(arrayOfElements) + elementCount)); return; } else @@ -175,8 +175,8 @@ void BackendTensorDescriptor::setAttribute(miopenBackendAttributeName_t attribut if(attributeType == MIOPEN_TYPE_INT64 && elementCount > 0) { mBuilder.setStride( - std::vector(static_cast(arrayOfElements), - static_cast(arrayOfElements) + elementCount)); + std::vector(static_cast(arrayOfElements), + static_cast(arrayOfElements) + elementCount)); return; } else @@ -243,7 +243,7 @@ void BackendTensorDescriptor::getAttribute(miopenBackendAttributeName_t attribut case MIOPEN_ATTR_TENSOR_DATA_TYPE: if(attributeType == MIOPEN_TYPE_DATA_TYPE && requestedElementCount == 1) { - *static_cast(arrayOfElements) = mDescriptor.getDataType(); + *static_cast(arrayOfElements) = mDescriptor.GetType(); *elementCount = 1; return; } @@ -255,13 +255,10 @@ void BackendTensorDescriptor::getAttribute(miopenBackendAttributeName_t attribut case MIOPEN_ATTR_TENSOR_DIMENSIONS: if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount >= 0) { - const auto& dimensions = mDescriptor.getDimensions(); + const auto& dimensions = mDescriptor.GetLengths(); *elementCount = dimensions.size(); std::copy_n(dimensions.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount - : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); return; } @@ -273,13 +270,10 @@ void BackendTensorDescriptor::getAttribute(miopenBackendAttributeName_t attribut case MIOPEN_ATTR_TENSOR_STRIDES: if(attributeType == MIOPEN_TYPE_INT64 && requestedElementCount >= 0) { - const auto& strides = mDescriptor.getStrides(); + const auto& strides = mDescriptor.GetStrides(); *elementCount = strides.size(); std::copy_n(strides.begin(), - // WORKAROUND: building on Windows is failing due to conflicting definitions - // of std::min() between the MSVC standard library and HIP Clang wrappers. - *elementCount < requestedElementCount ? *elementCount - : requestedElementCount, + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); return; } @@ -291,7 +285,7 @@ void BackendTensorDescriptor::getAttribute(miopenBackendAttributeName_t attribut case MIOPEN_ATTR_TENSOR_IS_VIRTUAL: if(attributeType == MIOPEN_TYPE_BOOLEAN && requestedElementCount == 1) { - *static_cast(arrayOfElements) = mDescriptor.getVirtual(); + *static_cast(arrayOfElements) = mDescriptor.isVirtual(); *elementCount = 1; return; } diff --git a/src/graphapi/variant_pack.cpp b/src/graphapi/variant_pack.cpp index 9034ab1b88..0be7e15eef 100644 --- a/src/graphapi/variant_pack.cpp +++ b/src/graphapi/variant_pack.cpp @@ -115,7 +115,7 @@ void BackendVariantPackDescriptor::getAttribute(miopenBackendAttributeName_t att { *elementCount = mVariantPack.mTensorIds.size(); std::copy_n(mVariantPack.mTensorIds.cbegin(), - std::min(*elementCount, requestedElementCount), + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else @@ -129,7 +129,7 @@ void BackendVariantPackDescriptor::getAttribute(miopenBackendAttributeName_t att { *elementCount = mVariantPack.mDataPointers.size(); std::copy_n(mVariantPack.mDataPointers.cbegin(), - std::min(*elementCount, requestedElementCount), + minimum(*elementCount, requestedElementCount), static_cast(arrayOfElements)); } else diff --git a/src/handle_api.cpp b/src/handle_api.cpp index 10fe54c8ff..abcab18ca7 100644 --- a/src/handle_api.cpp +++ b/src/handle_api.cpp @@ -72,14 +72,20 @@ extern "C" miopenStatus_t miopenGetVersion(size_t* major, size_t* minor, size_t* extern "C" miopenStatus_t miopenCreate(miopenHandle_t* handle) { - return miopen::try_([&] { miopen::deref(handle) = new miopen::Handle(); }); + return miopen::try_([&] { + auto& h = miopen::deref(handle); + h = new miopen::Handle(); + }); } extern "C" miopenStatus_t miopenCreateWithStream(miopenHandle_t* handle, miopenAcceleratorQueue_t stream) { - return miopen::try_([&] { miopen::deref(handle) = new miopen::Handle(stream); }); + return miopen::try_([&] { + auto& h = miopen::deref(handle); + h = new miopen::Handle(stream); + }); } extern "C" miopenStatus_t miopenSetStream(miopenHandle_t handle, miopenAcceleratorQueue_t streamID) diff --git a/src/hip/batched_transpose_sol.cpp b/src/hip/batched_transpose_sol.cpp index 6f1353bd24..efad6cb667 100644 --- a/src/hip/batched_transpose_sol.cpp +++ b/src/hip/batched_transpose_sol.cpp @@ -25,6 +25,7 @@ *******************************************************************************/ #include +#include #include #include #include @@ -413,4 +414,25 @@ size_t BatchedTransposeSolution::GetOutputTensorSize() const return miopen::GetTypeSize(data_type) * batch * height * width; } +InvokerFactory BatchedTransposeSolution::MakeBatchedTransposeInvokerFactory() const +{ + std::vector opArgs = GetKernelArg(); + + miopen::InvokerFactory invoker_factory([=](const std::vector& kernels) mutable { + return [=](const miopen::Handle& _handle, + const miopen::AnyInvokeParams& primitive_param) mutable { + decltype(auto) invoke_params = primitive_param.CastTo(); + + const auto k = _handle.Run(kernels[0]); + + opArgs[0] = OpKernelArg(invoke_params.dst); + opArgs[1] = OpKernelArg(invoke_params.src); + + k(opArgs); + }; + }); + + return invoker_factory; +} + } // namespace miopen diff --git a/src/hip/general_tensor_reorder_sol.cpp b/src/hip/general_tensor_reorder_sol.cpp index ae85048f63..291326431d 100644 --- a/src/hip/general_tensor_reorder_sol.cpp +++ b/src/hip/general_tensor_reorder_sol.cpp @@ -52,15 +52,13 @@ static inline std::string GetKernelNameType(std::size_t type_size) MIOPEN_THROW("data type not supported"); } -static inline std::string GetKernelFileName(std::size_t data_size, - const GeneralReorderParam* kparam) +static inline fs::path GetKernelFileName(std::size_t data_size, const GeneralReorderParam* kparam) { if(kparam == nullptr) MIOPEN_THROW("Memory access fault, kparam is a nullptr"); - std::ostringstream kernel_file_name; - kernel_file_name << "general_tensor_reorder_" << kparam->tile_x << "x" << kparam->tile_y << "_"; - kernel_file_name << GetKernelNameType(data_size) << ".cpp"; - return kernel_file_name.str(); + + return "general_tensor_reorder_" + std::to_string(kparam->tile_x) + "x" + + std::to_string(kparam->tile_y) + "_" + GetKernelNameType(data_size) + ".cpp"; } static inline std::string GetKernelName(std::size_t data_size, @@ -149,8 +147,8 @@ solver::KernelInfo GenericReorderSolutionImpl::GetKernelInfo() const (block_size * kernel_param_heuristic.tile_x); std::size_t grid_size = dim_total; - std::string kernel_name = GetKernelName(); - std::string kernel_file_name = GetKernelFileName(); + std::string kernel_name = GetKernelName(); + fs::path kernel_file_name = GetKernelFileName(); solver::KernelInfo kernel; kernel.kernel_file = kernel_file_name; kernel.kernel_name = kernel_name; @@ -201,7 +199,7 @@ std::vector GenericReorderSolutionImpl::GetKernelArg() const return opArgs; } -std::string GenericReorderSolutionImpl::GetKernelFileName() const +fs::path GenericReorderSolutionImpl::GetKernelFileName() const { return tensor_reorder::GetKernelFileName(miopen::GetTypeSize(data_type), &kernel_param_heuristic); diff --git a/src/hip/handlehip.cpp b/src/hip/handlehip.cpp index a43e102c5c..956556af17 100644 --- a/src/hip/handlehip.cpp +++ b/src/hip/handlehip.cpp @@ -57,6 +57,10 @@ #include #include +#if MIOPEN_USE_HIPBLASLT +#include +#endif + /// hipMemGetInfo constantly fails on gfx906/900 and Navi21. /// Brute-force W/A: return fixed values. #define WORKAROUND_FAULTY_HIPMEMGETINFO_VEGA_NAVI2X (HIP_PACKAGE_VERSION_FLAT >= 5007000000ULL) @@ -230,6 +234,10 @@ struct HandleImpl rocblas_handle_ptr rhandle_; using RocblasHandlePtrPool = std::vector; #endif +#if MIOPEN_USE_HIPBLASLT + hipblasLt_handle_ptr hip_blasLt_handle; + using HipblasLtHandlePtrPool = std::vector; +#endif StreamPtr root_stream = nullptr; @@ -237,16 +245,32 @@ struct HandleImpl struct MultiStreamResourses { -#if MIOPEN_USE_ROCBLAS +#if MIOPEN_USE_ROCBLAS && MIOPEN_USE_HIPBLASLT // (rocBLAS doc):rocBLAS handle contains allocated device memory that must not be shared by // multiple // asynchronous streams at the same time. // Each parallel thread must use its own rocblas_handle. RocblasHandlePtrPool rhandle_pool; - void add_resours(StreamPtr s_ptr, rocblas_handle_ptr r_ptr) + HipblasLtHandlePtrPool hhandle_pool; + void add_resours(StreamPtr s_ptr, rocblas_handle_ptr r_ptr, hipblasLt_handle_ptr h_ptr) { stream_pool.push_back(std::move(s_ptr)); rhandle_pool.push_back(std::move(r_ptr)); + hhandle_pool.push_back(std::move(h_ptr)); + } +#elif MIOPEN_USE_ROCBLAS + RocblasHandlePtrPool rhandle_pool; + void add_resours(StreamPtr s_ptr, rocblas_handle_ptr r_ptr) + { + stream_pool.push_back(s_ptr); + rhandle_pool.push_back(std::move(r_ptr)); + } +#elif MIOPEN_USE_HIPBLASLT + HipblasLtHandlePtrPool hhandle_pool; + void add_resours(StreamPtr s_ptr, hipblasLt_handle_ptr h_ptr) + { + stream_pool.push_back(s_ptr); + hhandle_pool.push_back(std::move(h_ptr)); } #else void add_stream(StreamPtr s_ptr) { stream_pool.push_back(s_ptr); } @@ -284,6 +308,9 @@ Handle::Handle(miopenAcceleratorQueue_t stream) : impl(std::make_uniqueimpl->rhandle_ = CreateRocblasHandle(stream); +#endif +#if MIOPEN_USE_HIPBLASLT + this->impl->hip_blasLt_handle = CreateHipblasLtHandle(); #endif this->impl->target_properties.Init(this); MIOPEN_LOG_NQI(*this); @@ -308,6 +335,9 @@ Handle::Handle() : impl(std::make_unique()) #if MIOPEN_USE_ROCBLAS this->impl->rhandle_ = CreateRocblasHandle(root_stream); +#endif +#if MIOPEN_USE_HIPBLASLT + this->impl->hip_blasLt_handle = CreateHipblasLtHandle(); #endif this->impl->target_properties.Init(this); MIOPEN_LOG_NQI(*this); @@ -346,9 +376,17 @@ void Handle::ReserveExtraStreamsInPool(int cnt) const for(; last_stream_id < cnt; last_stream_id++) { auto new_stream = this->impl->create_stream_non_blocking(); -#if MIOPEN_USE_ROCBLAS +#if MIOPEN_USE_ROCBLAS && MIOPEN_USE_HIPBLASLT + auto new_rhandle = CreateRocblasHandle(new_stream.get()); + auto new_hhandle = CreateHipblasLtHandle(); + this->impl->ms_resourse_ptr->add_resours( + std::move(new_stream), std::move(new_rhandle), std::move(new_hhandle)); +#elif MIOPEN_USE_ROCBLAS auto new_rhandle = CreateRocblasHandle(new_stream.get()); this->impl->ms_resourse_ptr->add_resours(std::move(new_stream), std::move(new_rhandle)); +#elif MIOPEN_USE_HIPBLASLT + auto new_hhandle = CreateHipblasLtHandle(); + this->impl->ms_resourse_ptr->add_resours(std::move(new_stream), std::move(new_hhandle)); #else this->impl->ms_resourse_ptr->add_stream(std::move(new_stream)); #endif @@ -422,7 +460,7 @@ void Handle::Copy(ConstData_t src, Data_t dest, std::size_t size) const KernelInvoke Handle::AddKernel(const std::string& algorithm, const std::string& network_config, - const std::string& program_name, + const fs::path& program_name, const std::string& kernel_name, const std::vector& vld, const std::vector& vgd, @@ -444,12 +482,21 @@ KernelInvoke Handle::AddKernel(const std::string& algorithm, } Invoker Handle::PrepareInvoker(const InvokerFactory& factory, - const std::vector& kernels) const + const std::vector& kernels, + std::vector* programs_out) const { std::vector built; - for(auto& k : kernels) + built.reserve(kernels.size()); + if(programs_out != nullptr) + programs_out->resize(kernels.size()); + + for(auto i = 0; i < kernels.size(); ++i) { + const auto& k = kernels[i]; + Program* program_out = programs_out != nullptr ? &(*programs_out)[i] : nullptr; + MIOPEN_LOG_I2("Preparing kernel: " << k.kernel_name); + const auto kernel = this->impl->cache.AddKernel(*this, "", "", @@ -458,7 +505,9 @@ Invoker Handle::PrepareInvoker(const InvokerFactory& factory, k.l_wk, k.g_wk, k.comp_options, - kernels.size()); + kernels.size(), + "", + program_out); built.push_back(kernel); } return factory(built); @@ -475,26 +524,41 @@ const std::vector& Handle::GetKernelsImpl(const std::string& algorithm, return this->impl->cache.GetKernels(algorithm, network_config); } -KernelInvoke Handle::Run(Kernel k) const +KernelInvoke Handle::Run(Kernel k, bool coop_launch) const { this->impl->set_ctx(); - if(this->impl->enable_profiling || MIOPEN_GPU_SYNC) - return k.Invoke(this->GetStream(), this->impl->elapsed_time_handler()); - else - return k.Invoke(this->GetStream()); + auto callback = (this->impl->enable_profiling || MIOPEN_GPU_SYNC) + ? this->impl->elapsed_time_handler() + : nullptr; + return k.Invoke(this->GetStream(), callback, coop_launch); } -Program Handle::LoadProgram(const std::string& program_name, +Program Handle::LoadProgram(const fs::path& program_name, std::string params, - const std::string& kernel_src) const + const std::string& kernel_src, + bool force_attach_binary) const { this->impl->set_ctx(); std::string arch_name = this->GetTargetProperties().Name(); std::string orig_params = params; // make a copy for target ID fallback - if(!miopen::EndsWith(program_name, ".mlir")) +#if WORKAROUND_ISSUE_3001 + if(program_name.extension() != ".mlir") params = params + " -mcpu=" + this->GetTargetProperties().Name(); +#else + if(program_name.extension() == ".mlir") + { // no -mcpu + } + else if(program_name.extension() == ".s") + { + params += " -mcpu=" + LcOptionTargetStrings{this->GetTargetProperties()}.targetId; + } + else + { + params += " -mcpu=" + this->GetTargetProperties().Name(); + } +#endif auto hsaco = miopen::LoadBinary( this->GetTargetProperties(), this->GetMaxComputeUnits(), program_name, params); @@ -517,47 +581,87 @@ Program Handle::LoadProgram(const std::string& program_name, if(hsaco.empty()) { CompileTimer ct; - auto p = HIPOCProgram{program_name, params, this->GetTargetProperties(), kernel_src}; - ct.Log("Kernel", program_name); + auto p = + HIPOCProgram{program_name.string(), params, this->GetTargetProperties(), kernel_src}; + ct.Log("Kernel", program_name.string()); -// Save to cache + // Save to cache #if MIOPEN_ENABLE_SQLITE_KERN_CACHE - miopen::SaveBinary(p.IsCodeObjectInMemory() ? p.GetCodeObjectBlob() - : miopen::LoadFile(p.GetCodeObjectPathname()), + std::vector binary; + if(!p.IsCodeObjectInMemory()) + binary = miopen::LoadFile(p.GetCodeObjectPathname()); + + miopen::SaveBinary(p.IsCodeObjectInMemory() ? p.GetCodeObjectBlob() : binary, this->GetTargetProperties(), this->GetMaxComputeUnits(), program_name, params); -#else - auto path = miopen::GetCachePath(false) / boost::filesystem::unique_path().string(); - if(p.IsCodeObjectInMemory()) - miopen::WriteFile(p.GetCodeObjectBlob(), path); + + if(force_attach_binary && p.IsCodeObjectInTempFile()) + { + MIOPEN_LOG_I2("Attaching a binary to the program for future serialization"); + p.AttachBinary(std::vector{binary.data(), binary.data() + binary.size()}); + } else - fs::copy_file(p.GetCodeObjectPathname(), path); - miopen::SaveBinary(path, this->GetTargetProperties(), program_name, params); -#endif + { + MIOPEN_LOG_I2("Skipped attaching a binary to the program for future serialization as " + "it is in permanent file storage"); + } + + p.FreeCodeObjectFileStorage(); +#else + boost::filesystem::path cache_path; + + // If cache is disabled we don't need to dump binary and move it there + if(!miopen::IsCacheDisabled()) + { + auto path = miopen::GetCachePath(false) / boost::filesystem::unique_path(); + if(p.IsCodeObjectInMemory()) + miopen::WriteFile(p.GetCodeObjectBlob(), path); + else + boost::filesystem::copy_file(p.GetCodeObjectPathname(), path); + cache_path = miopen::SaveBinary( + path, this->GetTargetProperties(), program_name, params, is_kernel_str); + } + + if(force_attach_binary && p.IsCodeObjectInTempFile()) + { + MIOPEN_LOG_I2("Attaching a binary to the program for future serialization"); + if(cache_path.empty()) + p.AttachBinary(LoadFileAsVector(p.GetCodeObjectPathname())); + else + p.AttachBinary(std::move(cache_path)); + } + p.FreeCodeObjectFileStorage(); +#endif return p; } else { - return HIPOCProgram{program_name, hsaco}; + auto p = HIPOCProgram{program_name, hsaco}; +#if MIOPEN_ENABLE_SQLITE_KERN_CACHE + if(force_attach_binary) + { + MIOPEN_LOG_I2("Attaching a binary to the program for future serialization"); + p.AttachBinary(std::vector{hsaco.data(), hsaco.data() + hsaco.size()}); + } +#endif + return p; } } -bool Handle::HasProgram(const std::string& program_name, const std::string& params) const +bool Handle::HasProgram(const fs::path& program_name, const std::string& params) const { return this->impl->cache.HasProgram(program_name, params); } -void Handle::AddProgram(Program prog, - const std::string& program_name, - const std::string& params) const +void Handle::AddProgram(Program prog, const fs::path& program_name, const std::string& params) const { this->impl->cache.AddProgram(prog, program_name, params); } -void Handle::ClearProgram(const std::string& program_name, const std::string& params) const +void Handle::ClearProgram(const fs::path& program_name, const std::string& params) const { this->impl->cache.ClearProgram(program_name, params); } @@ -618,7 +722,7 @@ std::size_t Handle::GetGlobalMemorySize() const std::size_t Handle::GetMaxComputeUnits() const { - const std::size_t num_cu = Value(ENV(MIOPEN_DEVICE_CU)); + const std::size_t num_cu = env::value(MIOPEN_DEVICE_CU); if(num_cu > 0) return num_cu; @@ -665,6 +769,17 @@ std::size_t Handle::GetMaxMemoryAllocSize() return m_MaxMemoryAllocSizeCached; } +bool Handle::CooperativeLaunchSupported() const +{ + int result; + auto status = + hipDeviceGetAttribute(&result, hipDeviceAttributeCooperativeLaunch, this->impl->device); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status); + + return result == 1; +} + std::string Handle::GetDeviceNameImpl() const { return this->impl->get_device_name(); } std::string Handle::GetDeviceName() const { return this->impl->target_properties.Name(); } @@ -712,4 +827,26 @@ rocblas_handle_ptr Handle::CreateRocblasHandle(miopenAcceleratorQueue_t stream) return result; } #endif + +#if MIOPEN_USE_HIPBLASLT +const hipblasLt_handle_ptr& Handle::HipblasLtHandle() const +{ + if(meopenHandle_current_stream_id == 0) + return this->impl->hip_blasLt_handle; + // locking only if handle in multistream mode + std::shared_lock lock(this->impl->stream_pool_mutex); + return this->impl->ms_resourse_ptr->hhandle_pool.at(meopenHandle_current_stream_id - 1); +} + +hipblasLt_handle_ptr Handle::CreateHipblasLtHandle() const +{ + hipblasLtHandle_t handle = nullptr; + if(hipblasLtCreate(&handle) != hipblasStatus_t::HIPBLAS_STATUS_SUCCESS) + { + MIOPEN_THROW(miopenStatusUnknownError, "failed creating hipBLASLt handle"); + } + + return hipblasLt_handle_ptr{handle}; +} +#endif } // namespace miopen diff --git a/src/hip/hip_build_utils.cpp b/src/hip/hip_build_utils.cpp index 9c1c48f326..0110885971 100644 --- a/src/hip/hip_build_utils.cpp +++ b/src/hip/hip_build_utils.cpp @@ -107,36 +107,31 @@ const char* getOffloadBundlerBinPath() namespace miopen { -static fs::path HipBuildImpl(boost::optional& tmp_dir, - const std::string& filename, +static fs::path HipBuildImpl(const TmpDir& tmp_dir, + const fs::path& filename, std::string src, std::string params, const TargetProperties& target, const bool testing_mode) { -#ifdef __linux__ // Write out the include files // Let's assume includes are overkill for feature tests & optimize'em out. if(!testing_mode) { - auto inc_list = GetHipKernelIncList(); - auto inc_path = tmp_dir->path; - fs::create_directories(inc_path); + auto inc_list = GetKernelIncList(); + fs::create_directories(tmp_dir); for(const auto& inc_file : inc_list) { auto inc_src = GetKernelInc(inc_file); - WriteFile(inc_src, inc_path / inc_file); + WriteFile(inc_src, tmp_dir / inc_file); } } src += "\nint main() {}\n"; - WriteFile(src, tmp_dir->path / filename); + WriteFile(src, tmp_dir / filename); - // cppcheck-suppress unreadVariable const LcOptionTargetStrings lots(target); - auto env = std::string(""); - if(params.find("-std=") == std::string::npos) params += " --std=c++17"; @@ -162,11 +157,11 @@ static fs::path HipBuildImpl(boost::optional& tmp_dir, params += MIOPEN_STRINGIZE(HIP_COMPILER_FLAGS); #if MIOPEN_BUILD_DEV - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_HIP_VERBOSE))) + if(env::enabled(MIOPEN_DEBUG_HIP_VERBOSE)) { params += " -v"; } - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_HIP_DUMP))) + if(env::enabled(MIOPEN_DEBUG_HIP_DUMP)) { params += " -gline-tables-only"; params += " -save-temps"; @@ -174,29 +169,30 @@ static fs::path HipBuildImpl(boost::optional& tmp_dir, #endif // hip version - params += - std::string(" -DHIP_PACKAGE_VERSION_FLAT=") + std::to_string(HIP_PACKAGE_VERSION_FLAT); - - params += " "; - auto bin_file = make_object_file_name(tmp_dir.get() / filename); + params += " -DHIP_PACKAGE_VERSION_FLAT=" + std::to_string(HIP_PACKAGE_VERSION_FLAT) + " "; + auto bin_file = make_object_file_name(tmp_dir / filename); // compile { - const std::string redirector = testing_mode ? " 1>/dev/null 2>&1" : ""; - const std::string cmd = env + std::string(" ") + MIOPEN_HIP_COMPILER; - const std::string args = params + filename + " -o " + bin_file + redirector; - tmp_dir->Execute(cmd, args); + std::string args = params + filename + " -o " + bin_file; +#ifndef _WIN32 + // Windows uses WIN32 API to execute a subprocess. None command shell is spawned. + if(testing_mode) + args += " 1>/dev/null 2>&1"; +#endif + std::ignore = tmp_dir.Execute(MIOPEN_HIP_COMPILER, args); if(!fs::exists(bin_file)) - MIOPEN_THROW("Failed cmd: '" + cmd + "', args: '" + args + '\''); + MIOPEN_THROW("Failed cmd: '" + std::string(MIOPEN_HIP_COMPILER) + "', args: '" + args + + '\''); } #if defined(MIOPEN_OFFLOADBUNDLER_BIN) && !MIOPEN_BACKEND_HIP // Unbundling is not required for HIP runtime && hip-clang - tmp_dir->Execute(MIOPEN_OFFLOADBUNDLER_BIN, - "--type=o " - "--targets=hipv4-amdgcn-amd-amdhsa-" + - (std::string{'-'} + lots.device + lots.xnack) + " --inputs=" + bin_file + - " --outputs=" + bin_file + ".hsaco --unbundle"); + std::ignore = tmp_dir.Execute(MIOPEN_OFFLOADBUNDLER_BIN, + "--type=o " + "--targets=hipv4-amdgcn-amd-amdhsa-" + + (std::string{'-'} + lots.device + lots.xnack) + " --inputs=" + + bin_file + " --outputs=" + bin_file + ".hsaco --unbundle"); auto hsaco = std::find_if(fs::directory_iterator{tmp_dir->path}, {}, [](auto entry) { return (entry.path().extension() == ".hsaco"); @@ -207,24 +203,20 @@ static fs::path HipBuildImpl(boost::optional& tmp_dir, MIOPEN_LOG_E("failed to find *.hsaco in " << hsaco->path()); } return hsaco->path(); -#endif - return bin_file; #else - (void)filename; - (void)params; - MIOPEN_THROW("HIP kernels are only supported in Linux"); + return bin_file; #endif } -fs::path HipBuild(boost::optional& tmp_dir, - const std::string& filename, - std::string src, +fs::path HipBuild(const TmpDir& tmp_dir, + const fs::path& filename, + std::string_view src, std::string params, const TargetProperties& target) { if(miopen::solver::support_amd_buffer_atomic_fadd(target.Name())) params += " -DCK_AMD_BUFFER_ATOMIC_FADD_RETURNS_FLOAT=1"; - return HipBuildImpl(tmp_dir, filename, src, params, target, false); + return HipBuildImpl(tmp_dir, filename, std::string{src}, params, target, false); } } // namespace miopen diff --git a/src/hipoc/hipoc_kernel.cpp b/src/hipoc/hipoc_kernel.cpp index 1b72ebfc7c..c5067953ee 100644 --- a/src/hipoc/hipoc_kernel.cpp +++ b/src/hipoc/hipoc_kernel.cpp @@ -27,6 +27,7 @@ #include #include #include +#include #include #include @@ -36,10 +37,36 @@ #include #include +#define WORKAROUND_SWDEV_448157 1 + MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEVICE_ARCH) namespace miopen { +HipEventProfiler::HipEventProfiler(const Handle& handle_) + : handle(handle_), start(nullptr), stop(nullptr) +{ + if(handle.IsProfilingEnabled()) + { + start = make_hip_event(); + stop = make_hip_event(); + hipEventRecord(start.get(), handle.GetStream()); + } +} + +HipEventProfiler::~HipEventProfiler() +{ + if(start) + { + hipEventRecord(stop.get(), handle.GetStream()); + hipEventSynchronize(stop.get()); + float event_time = 0.0f; + hipEventElapsedTime(&event_time, start.get(), stop.get()); + handle.ResetKernelTime(); + handle.AccumKernelTime(event_time); + } +} + static std::string DimToFormattedString(const size_t* dims, size_t count) { std::stringstream ss; @@ -79,7 +106,7 @@ void HIPOCKernelInvoke::run(void* args, std::size_t size) const stop = make_hip_event(); } - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); if(!arch.empty()) { MIOPEN_THROW("MIOPEN_DEVICE_ARCH used, escaping launching kernel"); @@ -123,9 +150,83 @@ void HIPOCKernelInvoke::run(void* args, std::size_t size) const } } +void HIPOCKernelInvoke::run_cooperative(void** kern_args) const +{ + hipError_t status; + + MIOPEN_LOG_I2("kernel_name = " + << GetName() << ", global_work_dim = " << DimToFormattedString(gdims.data(), 3) + << ", local_work_dim = " << DimToFormattedString(ldims.data(), 3)); + + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); + if(!arch.empty()) + { + MIOPEN_THROW("MIOPEN_DEVICE_ARCH used, escaping launching kernel"); + } + + HipEventPtr start = nullptr; + HipEventPtr stop = nullptr; + + if(callback) + { + start = make_hip_event(); + stop = make_hip_event(); + } + +#if WORKAROUND_SWDEV_448157 + if(gdims[0] >= (1ULL << 32) || gdims[1] >= (1ULL << 32) || gdims[2] >= (1ULL << 32)) + MIOPEN_THROW("gridDim x blockDim >= 2^32"); + + if(gdims[0] % ldims[0] != 0 || gdims[1] % ldims[1] != 0 || gdims[2] % ldims[2] != 0) + MIOPEN_THROW(miopenStatusInternalError); + + unsigned grid_dim_x = gdims[0] / ldims[0]; + unsigned grid_dim_y = gdims[1] / ldims[1]; + unsigned grid_dim_z = gdims[2] / ldims[2]; + + MIOPEN_HANDLE_LOCK + + if(callback) + { + status = hipEventRecord(start.get(), stream); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status, "hipEventRecord() failed"); + } + + status = hipModuleLaunchCooperativeKernel(fun, + grid_dim_x, + grid_dim_y, + grid_dim_z, + ldims[0], + ldims[1], + ldims[2], + 0, + stream, + kern_args); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status, "Failed to launch kernel"); + + if(callback) + { + status = hipEventRecord(stop.get(), stream); + if(status != hipSuccess) + MIOPEN_THROW_HIP_STATUS(status, "hipEventRecord() failed"); + } +#else +#error "Doesn't work without workaround" +#endif // WORKAROUND_SWDEV_448157 + + if(callback) + { + hipEventSynchronize(stop.get()); + callback(start.get(), stop.get()); + } +} + HIPOCKernelInvoke HIPOCKernel::Invoke(hipStream_t stream, - std::function callback) const + std::function callback, + bool coop_launch) const { - return HIPOCKernelInvoke{stream, fun, ldims, gdims, name, callback}; + return HIPOCKernelInvoke{stream, fun, ldims, gdims, name, callback, coop_launch}; } } // namespace miopen diff --git a/src/hipoc/hipoc_program.cpp b/src/hipoc/hipoc_program.cpp index 3373c66b2a..ec090455e8 100644 --- a/src/hipoc/hipoc_program.cpp +++ b/src/hipoc/hipoc_program.cpp @@ -60,7 +60,6 @@ MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_VERSION) MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DEVICE_ARCH) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP) -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_USE_HIPRTC) #if MIOPEN_USE_COMGR #define MIOPEN_WORKAROUND_ROCM_COMPILER_SUPPORT_ISSUE_27 1 @@ -73,7 +72,7 @@ namespace { int DetectCodeObjectOptionSyntax() { - auto syntax = miopen::Value(ENV(MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_OPTION)); + auto syntax = env::value(MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_OPTION); if(syntax > 4) { MIOPEN_LOG_E("Bad MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_OPTION, using default"); @@ -90,7 +89,7 @@ int DetectCodeObjectOptionSyntax() int DetectCodeObjectVersion() { - auto co_version = miopen::Value(ENV(MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_VERSION)); + auto co_version = env::value(MIOPEN_DEBUG_OPENCL_ENFORCE_CODE_OBJECT_VERSION); // Very basic syntax check: if(co_version == 1 || co_version > 4) { @@ -166,22 +165,32 @@ hipModulePtr CreateModuleInMem(const T& blob) return m; } -HIPOCProgramImpl::HIPOCProgramImpl(const std::string& program_name, const fs::path& filespec) +HIPOCProgramImpl::HIPOCProgramImpl(const fs::path& program_name, const fs::path& filespec) : program(program_name), hsaco_file(filespec) { module = CreateModule(hsaco_file); } -HIPOCProgramImpl::HIPOCProgramImpl(const std::string& program_name, const std::vector& blob) +HIPOCProgramImpl::HIPOCProgramImpl(const fs::path& program_name, const std::vector& blob) : program(program_name) ///, module(CreateModuleInMem(blob)) { - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); if(!arch.empty()) return; module = CreateModuleInMem(blob); } -HIPOCProgramImpl::HIPOCProgramImpl(const std::string& program_name, +HIPOCProgramImpl::HIPOCProgramImpl(const fs::path& program_name, + const std::vector& blob) + : program(program_name) ///, module(CreateModuleInMem(blob)) +{ + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); + if(!arch.empty()) + return; + module = CreateModuleInMem(blob); +} + +HIPOCProgramImpl::HIPOCProgramImpl(const fs::path& program_name, std::string params, const TargetProperties& target_, const std::string& kernel_src) @@ -194,7 +203,7 @@ HIPOCProgramImpl::HIPOCProgramImpl(const std::string& program_name, } else { - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); if(arch.empty()) { module = CreateModule(hsaco_file); @@ -204,28 +213,27 @@ HIPOCProgramImpl::HIPOCProgramImpl(const std::string& program_name, #if !MIOPEN_USE_COMGR void HIPOCProgramImpl::BuildCodeObjectInFile(std::string& params, - const std::string& src, - const std::string& filename) + std::string_view src, + const fs::path& filename) { - - dir.emplace(filename); + dir.emplace(filename.filename().string()); hsaco_file = make_object_file_name(dir.get() / filename); - if(miopen::EndsWith(filename, ".so")) + if(filename.extension() == dynamic_library_postfix) // ".so" or ".dll" { WriteFile(src, hsaco_file); } - else if(miopen::EndsWith(filename, ".s")) + else if(filename.extension() == ".s") { const auto assembled = AmdgcnAssemble(src, params, target); WriteFile(assembled, hsaco_file); } - else if(miopen::EndsWith(filename, ".cpp")) + else if(filename.extension() == ".cpp") { - hsaco_file = HipBuild(dir, filename, src, params, target); + hsaco_file = HipBuild(dir.get(), filename, src, params, target); } #if MIOPEN_USE_MLIR - else if(miopen::EndsWith(filename, ".mlir")) + else if(filename.extension() == ".mlir") { std::vector buffer; MiirGenBin(params, buffer); @@ -235,15 +243,15 @@ void HIPOCProgramImpl::BuildCodeObjectInFile(std::string& params, else { params += " " + GetCodeObjectVersionOption(); - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP))) + if(env::enabled(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP)) params += " -mwavefrontsize64 -mcumode"; - WriteFile(src, dir->path / filename); + WriteFile(src, dir.get() / filename); params += " -target amdgcn-amd-amdhsa -x cl -D__AMD__=1 -O3"; params += " -cl-kernel-arg-info -cl-denorms-are-zero"; params += " -cl-std=CL2.0 -mllvm -amdgpu-early-inline-all"; params += " -mllvm -amdgpu-internalize-symbols "; params += " " + filename + " -o " + hsaco_file; - dir->Execute(HIP_OC_COMPILER, params); + std::ignore = dir->Execute(HIP_OC_COMPILER, params); } if(!fs::exists(hsaco_file)) MIOPEN_THROW("Cant find file: " + hsaco_file); @@ -251,14 +259,13 @@ void HIPOCProgramImpl::BuildCodeObjectInFile(std::string& params, #else // MIOPEN_USE_COMGR void HIPOCProgramImpl::BuildCodeObjectInMemory(const std::string& params, - const std::string& src, - const std::string& filename) + const std::string_view src, + const fs::path& filename) { - if(miopen::EndsWith(filename, ".so")) + if(filename.extension() == dynamic_library_postfix) // ".so" or ".dll" { - std::size_t sz = src.length(); - binary.resize(sz); - std::memcpy(&binary[0], src.c_str(), sz); + binary.resize(src.size()); + std::memcpy(&binary[0], src.data(), src.size()); } else { @@ -266,28 +273,23 @@ void HIPOCProgramImpl::BuildCodeObjectInMemory(const std::string& params, static std::mutex mutex; std::lock_guard lock(mutex); #endif - if(miopen::EndsWith(filename, ".cpp")) + if(filename.extension() == ".cpp") { -#if MIOPEN_USE_HIPRTC - if(!miopen::IsDisabled(ENV(MIOPEN_DEBUG_USE_HIPRTC))) - hiprtc::BuildHip(filename, src, params, target, binary); - else -#endif // MIOPEN_USE_HIPRTC - comgr::BuildHip(filename, src, params, target, binary); + hiprtc::BuildHip(filename.string(), src, params, target, binary); } - else if(miopen::EndsWith(filename, ".s")) + else if(filename.extension() == ".s") { - comgr::BuildAsm(filename, src, params, target, binary); + comgr::BuildAsm(filename.string(), src, params, target, binary); } #if MIOPEN_USE_MLIR - else if(miopen::EndsWith(filename, ".mlir")) + else if(filename.extension() == ".mlir") { MiirGenBin(params, binary); } #endif else { - comgr::BuildOcl(filename, src, params, target, binary); + comgr::BuildOcl(filename.string(), src, params, target, binary); } } if(binary.empty()) @@ -297,9 +299,8 @@ void HIPOCProgramImpl::BuildCodeObjectInMemory(const std::string& params, void HIPOCProgramImpl::BuildCodeObject(std::string params, const std::string& kernel_src) { - std::string filename = program; - const auto src = [&]() -> std::string { - if(miopen::EndsWith(filename, ".mlir")) + const auto src = [&]() -> std::string_view { + if(program.extension() == ".mlir") return {}; // MLIR solutions do not use source code. if(!kernel_src.empty()) return kernel_src; @@ -307,28 +308,28 @@ void HIPOCProgramImpl::BuildCodeObject(std::string params, const std::string& ke }(); #if MIOPEN_BUILD_DEV - if(miopen::EndsWith(filename, ".cpp")) + if(program.extension() == ".cpp") { params += " -Werror" + HipKernelWarningsString(); } - else if(miopen::EndsWith(filename, ".cl")) + else if(program.extension() == ".cl") { params += " -Werror" + OclKernelWarningsString(); } #else - if(miopen::EndsWith(filename, ".cpp") || miopen::EndsWith(filename, ".cl")) + if(program.extension() == ".cpp" || program.extension() == ".cl") params += " -Wno-everything"; #endif #if MIOPEN_USE_COMGR /// \todo Refactor when functionality stabilize. - BuildCodeObjectInMemory(params, src, filename); + BuildCodeObjectInMemory(params, src, program); #else - BuildCodeObjectInFile(params, src, filename); + BuildCodeObjectInFile(params, src, program); #endif } HIPOCProgram::HIPOCProgram() {} -HIPOCProgram::HIPOCProgram(const std::string& program_name, +HIPOCProgram::HIPOCProgram(const fs::path& program_name, std::string params, const TargetProperties& target, const std::string& kernel_src) @@ -336,12 +337,17 @@ HIPOCProgram::HIPOCProgram(const std::string& program_name, { } -HIPOCProgram::HIPOCProgram(const std::string& program_name, const fs::path& hsaco) +HIPOCProgram::HIPOCProgram(const fs::path& program_name, const fs::path& hsaco) + : impl(std::make_shared(program_name, hsaco)) +{ +} + +HIPOCProgram::HIPOCProgram(const fs::path& program_name, const std::vector& hsaco) : impl(std::make_shared(program_name, hsaco)) { } -HIPOCProgram::HIPOCProgram(const std::string& program_name, const std::vector& hsaco) +HIPOCProgram::HIPOCProgram(const fs::path& program_name, const std::vector& hsaco) : impl(std::make_shared(program_name, hsaco)) { } @@ -360,14 +366,30 @@ fs::path HIPOCProgram::GetCodeObjectPathname() const } } -std::vector HIPOCProgram::GetCodeObjectBlob() const { return impl->binary; } +const std::vector& HIPOCProgram::GetCodeObjectBlob() const { return impl->binary; } void HIPOCProgram::FreeCodeObjectFileStorage() { - impl->dir = boost::none; - impl->hsaco_file.clear(); + if(impl->dir.has_value()) + { + impl->dir.reset(); + impl->hsaco_file.clear(); + } } bool HIPOCProgram::IsCodeObjectInMemory() const { return !impl->binary.empty(); }; +bool HIPOCProgram::IsCodeObjectInFile() const { return !impl->hsaco_file.empty(); } + +bool HIPOCProgram::IsCodeObjectInTempFile() const { return impl->dir.has_value(); } + +void HIPOCProgram::AttachBinary(std::vector binary) { impl->binary = std::move(binary); } + +void HIPOCProgram::AttachBinary(fs::path binary) +{ + if(impl->hsaco_file != binary) + impl->dir = boost::none; + impl->hsaco_file = std::move(binary); +} + } // namespace miopen diff --git a/src/include/miopen/activ.hpp b/src/include/miopen/activ.hpp index 9e02037ffe..20c5f2ba2b 100644 --- a/src/include/miopen/activ.hpp +++ b/src/include/miopen/activ.hpp @@ -39,7 +39,7 @@ namespace miopen { struct Handle; struct TensorDescriptor; -struct ActivationDescriptor : miopenActivationDescriptor +struct MIOPEN_INTERNALS_EXPORT ActivationDescriptor : miopenActivationDescriptor { ActivationDescriptor(); ActivationDescriptor(miopenActivationMode_t m, const double* pparms); diff --git a/src/include/miopen/activ/problem_description.hpp b/src/include/miopen/activ/problem_description.hpp index 823f692630..1b3f5e46e3 100644 --- a/src/include/miopen/activ/problem_description.hpp +++ b/src/include/miopen/activ/problem_description.hpp @@ -44,7 +44,7 @@ enum class Direction Backward, }; -struct ProblemDescription : ProblemDescriptionBase +struct MIOPEN_INTERNALS_EXPORT ProblemDescription : ProblemDescriptionBase { // Forward constructor ProblemDescription(const ActivationDescriptor& activ, diff --git a/src/include/miopen/adam.hpp b/src/include/miopen/adam.hpp new file mode 100644 index 0000000000..05e22aa5be --- /dev/null +++ b/src/include/miopen/adam.hpp @@ -0,0 +1,112 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_ADAM_HPP_ +#define MIOPEN_ADAM_HPP_ + +#include + +namespace miopen { + +struct Handle; +struct TensorDescriptor; + +MIOPEN_INTERNALS_EXPORT miopenStatus_t Adam(Handle& handle, + const TensorDescriptor& paramInDesc, + ConstData_t paramIn, + const TensorDescriptor& paramOutDesc, + Data_t paramOut, + const TensorDescriptor& paramOutFloat16Desc, + Data_t paramOutFloat16, + const TensorDescriptor& gradInDesc, + ConstData_t gradIn, + const TensorDescriptor& expAvgInDesc, + ConstData_t expAvgIn, + const TensorDescriptor& expAvgOutDesc, + Data_t expAvgOut, + const TensorDescriptor& expAvgSqInDesc, + ConstData_t expAvgSqIn, + const TensorDescriptor& expAvgSqOutDesc, + Data_t expAvgSqOut, + const TensorDescriptor& maxExpAvgSqInDesc, + ConstData_t maxExpAvgSqIn, + const TensorDescriptor& maxExpAvgSqOutDesc, + Data_t maxExpAvgSqOut, + const TensorDescriptor& gradScaleDescPtr, + ConstData_t gradScale, + const TensorDescriptor& foundInfDescPtr, + ConstData_t foundInf, + const TensorDescriptor& stepInDesc, + ConstData_t stepIn, + const TensorDescriptor& stepOutDesc, + Data_t stepOut, + uint32_t step, + float lr, + float beta1, + float beta2, + float weight_decay, + float eps, + bool amsgrad, + bool maximize, + bool adamw, + bool is_amp); + +MIOPEN_INTERNALS_EXPORT miopenStatus_t +TransformersAdamW(Handle& handle, + const TensorDescriptor& paramInDesc, + ConstData_t paramIn, + const TensorDescriptor& paramOutDesc, + Data_t paramOut, + const TensorDescriptor& paramOutFloat16Desc, + Data_t paramOutFloat16, + const TensorDescriptor& gradInDesc, + ConstData_t gradIn, + const TensorDescriptor& expAvgInDesc, + ConstData_t expAvgIn, + const TensorDescriptor& expAvgOutDesc, + Data_t expAvgOut, + const TensorDescriptor& expAvgSqInDesc, + ConstData_t expAvgSqIn, + const TensorDescriptor& expAvgSqOutDesc, + Data_t expAvgSqOut, + const TensorDescriptor& gradScaleDescPtr, + ConstData_t gradScale, + const TensorDescriptor& foundInfDescPtr, + ConstData_t foundInf, + const TensorDescriptor& stepInDesc, + ConstData_t stepIn, + const TensorDescriptor& stepOutDesc, + Data_t stepOut, + uint32_t step, + float lr, + float beta1, + float beta2, + float eps, + float lr_weight_decay, + float step_size, + bool correct_bias, + bool is_amp); +} // namespace miopen +#endif // _MIOPEN_ADAM_HPP_ diff --git a/src/include/miopen/adam/invoke_params.hpp b/src/include/miopen/adam/invoke_params.hpp new file mode 100644 index 0000000000..2842a2cb04 --- /dev/null +++ b/src/include/miopen/adam/invoke_params.hpp @@ -0,0 +1,105 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +namespace miopen { +namespace adam { + +struct AdamInvokeParams : public miopen::InvokeParams +{ + AdamInvokeParams() = default; + + const TensorDescriptor* paramDesc = nullptr; + const TensorDescriptor* gradDesc = nullptr; + + ConstData_t paramIn = nullptr; + Data_t paramOut = nullptr; + Data_t paramOutFloat16 = nullptr; + ConstData_t gradIn = nullptr; + ConstData_t expAvgIn = nullptr; + Data_t expAvgOut = nullptr; + ConstData_t expAvgSqIn = nullptr; + Data_t expAvgSqOut = nullptr; + ConstData_t maxExpAvgSqIn = nullptr; + Data_t maxExpAvgSqOut = nullptr; + ConstData_t gradScale = nullptr; + ConstData_t foundInf = nullptr; + ConstData_t stepIn = nullptr; + Data_t stepOut = nullptr; + + uint32_t step = 0; + float lr = 0.0; + float beta1 = 0.0; + float beta2 = 0.0; + float weight_decay = 0.0; + float eps = 0.0; + bool amsgrad = false; + bool maximize = false; + bool adamw = false; + + std::size_t GetWorkspaceSize() const { return 0; } + Data_t GetWorkspace() const { return nullptr; } +}; + +struct TransformersAdamWInvokeParams : public miopen::InvokeParams +{ + TransformersAdamWInvokeParams() = default; + + const TensorDescriptor* paramDesc = nullptr; + const TensorDescriptor* gradDesc = nullptr; + + ConstData_t paramIn = nullptr; + Data_t paramOut = nullptr; + Data_t paramOutFloat16 = nullptr; + ConstData_t gradIn = nullptr; + ConstData_t expAvgIn = nullptr; + Data_t expAvgOut = nullptr; + ConstData_t expAvgSqIn = nullptr; + Data_t expAvgSqOut = nullptr; + ConstData_t gradScale = nullptr; + ConstData_t foundInf = nullptr; + ConstData_t stepIn = nullptr; + Data_t stepOut = nullptr; + + uint32_t step = 0; + float lr = 0.0; + float beta1 = 0.0; + float beta2 = 0.0; + float eps = 0.0; + float weight_decay = 0.0; + float step_size = 0.0; + bool correct_bias = true; + + std::size_t GetWorkspaceSize() const { return 0; } + Data_t GetWorkspace() const { return nullptr; } +}; + +} // namespace adam +} // namespace miopen diff --git a/src/include/miopen/adam/problem_description.hpp b/src/include/miopen/adam/problem_description.hpp new file mode 100644 index 0000000000..12599f2735 --- /dev/null +++ b/src/include/miopen/adam/problem_description.hpp @@ -0,0 +1,166 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +#include + +namespace miopen { + +struct NetworkConfig; + +namespace adam { + +struct MIOPEN_INTERNALS_EXPORT ProblemDescription : ProblemDescriptionBase +{ + ProblemDescription(const TensorDescriptor& paramInDesc_, + const TensorDescriptor& paramOutDesc_, + const TensorDescriptor& paramOutFloat16Desc_, + const TensorDescriptor& gradInDesc_, + const TensorDescriptor& expAvgInDesc_, + const TensorDescriptor& expAvgOutDesc_, + const TensorDescriptor& expAvgSqInDesc_, + const TensorDescriptor& expAvgSqOutDesc_, + const TensorDescriptor& maxExpAvgSqInDesc_, + const TensorDescriptor& maxExpAvgSqOutDesc_, + const TensorDescriptor& gradScaleDesc_, + const TensorDescriptor& foundInfDesc_, + const TensorDescriptor& stepInDesc_, + const TensorDescriptor& stepOutDesc_, + bool amsgrad_, + bool correct_bias_, + bool adamw_, + bool is_amp_) + : paramInDesc(paramInDesc_), + paramOutDesc(paramOutDesc_), + gradInDesc(gradInDesc_), + expAvgInDesc(expAvgInDesc_), + expAvgOutDesc(expAvgOutDesc_), + expAvgSqInDesc(expAvgSqInDesc_), + expAvgSqOutDesc(expAvgSqOutDesc_), + paramOutFloat16Desc(paramOutFloat16Desc_), + maxExpAvgSqInDesc(maxExpAvgSqInDesc_), + maxExpAvgSqOutDesc(maxExpAvgSqOutDesc_), + gradScaleDesc(gradScaleDesc_), + foundInfDesc(foundInfDesc_), + stepInDesc(stepInDesc_), + stepOutDesc(stepOutDesc_), + amsgrad(amsgrad_), + correct_bias(correct_bias_), + adamw(adamw_), + is_amp(is_amp_) + { + if(amsgrad && + (maxExpAvgSqInDesc.GetLengths().empty() || maxExpAvgSqOutDesc.GetLengths().empty())) + { + MIOPEN_THROW(miopenStatusBadParm, + "Adam: In the amsgrad, the max_exp_avg_sq tensor is required."); + } + + auto dtype = paramInDesc.GetType(); + + if((dtype == miopenBFloat16) || (gradInDesc.GetType() == miopenBFloat16)) + { + MIOPEN_THROW(miopenStatusBadParm, "Adam: bfloat16 type is not supported."); + } + + if((paramOutDesc.GetType() != dtype) || (!is_amp && gradInDesc.GetType() != dtype) || + (expAvgInDesc.GetType() != dtype) || (expAvgOutDesc.GetType() != dtype) || + (expAvgSqInDesc.GetType() != dtype) || (expAvgSqOutDesc.GetType() != dtype) || + (!maxExpAvgSqInDesc.GetLengths().empty() && maxExpAvgSqInDesc.GetType() != dtype) || + (!maxExpAvgSqOutDesc.GetLengths().empty() && maxExpAvgSqOutDesc.GetType() != dtype)) + { + MIOPEN_THROW(miopenStatusBadParm, "Adam: Tensor types do not match."); + } + + if(is_amp && !paramOutFloat16Desc.GetLengths().empty() && + (paramOutFloat16Desc.GetType() != miopenHalf)) + { + MIOPEN_THROW(miopenStatusBadParm, "Adam: Invalid type of param_out_float16."); + } + + auto numel = paramInDesc.GetElementSize(); + if((paramOutDesc.GetElementSize() != numel) || (gradInDesc.GetElementSize() != numel) || + (expAvgInDesc.GetElementSize() != numel) || (expAvgOutDesc.GetElementSize() != numel) || + (expAvgSqInDesc.GetElementSize() != numel) || + (expAvgSqOutDesc.GetElementSize() != numel) || + (is_amp && !paramOutFloat16Desc.GetLengths().empty() && + paramOutFloat16Desc.GetElementSize() != numel) || + (!maxExpAvgSqInDesc.GetLengths().empty() && + maxExpAvgSqInDesc.GetElementSize() != numel) || + (!maxExpAvgSqOutDesc.GetLengths().empty() && + maxExpAvgSqOutDesc.GetElementSize() != numel)) + { + MIOPEN_THROW(miopenStatusBadParm, "Adam: Tensor dimension lengths do not match."); + } + } + + const TensorDescriptor& GetParamDesc() const { return paramInDesc; } + const TensorDescriptor& GetGradDesc() const { return gradInDesc; } + bool ExistStepTensor() const { return !stepInDesc.GetLengths().empty(); } + bool IsAmp() const { return is_amp; } + bool IsAdamW() const { return adamw; } + bool IsCorrectBias() const { return correct_bias; } + bool IsAllContiguous() const + { + if(!(paramInDesc.IsContiguous() && gradInDesc.IsContiguous() && + expAvgInDesc.IsContiguous() && expAvgSqInDesc.IsContiguous())) + return false; + return true; + } + + NetworkConfig MakeNetworkConfig() const override; + +private: + TensorDescriptor paramInDesc; + TensorDescriptor paramOutDesc; + TensorDescriptor gradInDesc; + TensorDescriptor expAvgInDesc; + TensorDescriptor expAvgOutDesc; + TensorDescriptor expAvgSqInDesc; + TensorDescriptor expAvgSqOutDesc; + TensorDescriptor paramOutFloat16Desc; + TensorDescriptor maxExpAvgSqInDesc; + TensorDescriptor maxExpAvgSqOutDesc; + TensorDescriptor gradScaleDesc; + TensorDescriptor foundInfDesc; + TensorDescriptor stepInDesc; + TensorDescriptor stepOutDesc; + + bool amsgrad = false; + bool correct_bias = true; + bool adamw = false; + bool is_amp = false; + + NetworkConfig MakeForwardNetworkConfig() const; +}; + +} // namespace adam + +} // namespace miopen diff --git a/src/include/miopen/adam/solvers.hpp b/src/include/miopen/adam/solvers.hpp new file mode 100644 index 0000000000..399e2f6219 --- /dev/null +++ b/src/include/miopen/adam/solvers.hpp @@ -0,0 +1,80 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +#include + +namespace miopen { + +namespace solver { + +namespace adam { + +using AdamSolver = NonTunableSolverBase; + +struct Adam final : AdamSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::adam::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::adam::ProblemDescription& problem) const override; + std::size_t GetWorkspaceSize( + [[maybe_unused]] const ExecutionContext& context, + [[maybe_unused]] const miopen::adam::ProblemDescription& problem) const override + { + return 0; + } + bool MayNeedWorkspace() const override { return false; } +}; + +struct TransformersAdamW final : AdamSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::adam::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::adam::ProblemDescription& problem) const override; + std::size_t GetWorkspaceSize( + [[maybe_unused]] const ExecutionContext& context, + [[maybe_unused]] const miopen::adam::ProblemDescription& problem) const override + { + return 0; + } + bool MayNeedWorkspace() const override { return false; } +}; + +} // namespace adam + +} // namespace solver + +} // namespace miopen diff --git a/src/include/miopen/addlayernorm.hpp b/src/include/miopen/addlayernorm.hpp new file mode 100644 index 0000000000..d70587193c --- /dev/null +++ b/src/include/miopen/addlayernorm.hpp @@ -0,0 +1,56 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_ADDLAYERNORM_HPP_ +#define MIOPEN_ADDLAYERNORM_HPP_ + +#include + +namespace miopen { + +struct Handle; +struct TensorDescriptor; + +MIOPEN_INTERNALS_EXPORT miopenStatus_t AddLayerNormForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& x2Desc, + ConstData_t x2, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& biasDesc, + ConstData_t bias, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& meanDesc, + Data_t mean, + const TensorDescriptor& rstdDesc, + Data_t rstd, + miopenNormMode_t mode, + float epsilon, + int32_t normalized_dim); + +} // namespace miopen +#endif // MIOPEN_ADDLAYERNORM_HPP_ diff --git a/src/include/miopen/any_solver.hpp b/src/include/miopen/any_solver.hpp index 7edbdf7f03..0908c2823f 100644 --- a/src/include/miopen/any_solver.hpp +++ b/src/include/miopen/any_solver.hpp @@ -95,6 +95,13 @@ struct AnySolver assert(ptr_value != nullptr); return ptr_value->FindSolution(ctx, problem, db, invoke_ctx, perf_cfg); }; + InvokerFactory GetInvokeFactory(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem, + const std::string& perf_cfg = "") const + { + assert(ptr_value != nullptr); + return ptr_value->GetInvokeFactory(ctx, problem, perf_cfg); + }; std::string GetPerfCfgParams(const ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, PerformanceDb& db) const @@ -146,6 +153,9 @@ struct AnySolver PerformanceDb& db, const miopen::AnyInvokeParams& invoke_ctx, const std::string& perf_cfg) const = 0; + virtual InvokerFactory GetInvokeFactory(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem, + const std::string& perf_cfg) const = 0; virtual std::string GetPerfCfgParams(const ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, PerformanceDb& db) const = 0; @@ -299,6 +309,13 @@ struct AnySolver return miopen::solver::FindSolution(value, ctx, problem, db, invoke_ctx, perf_cfg); }; + InvokerFactory GetInvokeFactory(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem, + const std::string& perf_cfg) const override + { + return miopen::solver::GetInvokeFactory(value, ctx, problem, perf_cfg); + } + std::string GetPerfCfgParams(const ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, PerformanceDb& db, diff --git a/src/include/miopen/anyramdb.hpp b/src/include/miopen/anyramdb.hpp index 9fe4596f20..df5ec0eb55 100644 --- a/src/include/miopen/anyramdb.hpp +++ b/src/include/miopen/anyramdb.hpp @@ -41,20 +41,20 @@ namespace miopen { class LockFile; -struct AnyRamDb +struct MIOPEN_INTERNALS_EXPORT AnyRamDb { using TRecord = std::vector; public: - AnyRamDb(std::string filename_) - : filename(filename_), lock_file(LockFile::Get(LockFilePath(filename_).c_str())){}; + AnyRamDb(const fs::path& filename_) + : filename(filename_), lock_file(LockFile::Get(LockFilePath(filename_))){}; AnyRamDb(const AnyRamDb&) = delete; AnyRamDb(AnyRamDb&&) = delete; AnyRamDb& operator=(const AnyRamDb&) = delete; AnyRamDb& operator=(AnyRamDb&&) = delete; - static AnyRamDb& GetCached(const std::string& path); + static AnyRamDb& GetCached(const fs::path& path); boost::optional FindRecord(const std::string& problem); bool RemoveRecord(const std::string& key); @@ -88,7 +88,7 @@ struct AnyRamDb private: std::map> cache; - std::string filename; + fs::path filename; LockFile& lock_file; boost::optional FindRecordUnsafe(const std::string& problem); void UpdateCacheEntryUnsafe(const std::string& key, const TRecord& value); diff --git a/src/include/miopen/batch_norm.hpp b/src/include/miopen/batch_norm.hpp index 0582a7d335..50c309550c 100644 --- a/src/include/miopen/batch_norm.hpp +++ b/src/include/miopen/batch_norm.hpp @@ -42,11 +42,12 @@ namespace miopen { struct Handle; struct TensorDescriptor; -void DeriveBNTensorDescriptor(TensorDescriptor& derivedBnDesc, - const TensorDescriptor& xDesc, - miopenBatchNormMode_t bn_mode); +MIOPEN_INTERNALS_EXPORT void DeriveBNTensorDescriptor(TensorDescriptor& derivedBnDesc, + const TensorDescriptor& xDesc, + miopenBatchNormMode_t bn_mode); -TensorDescriptor BuildReshaped4DTensorDescriptor(const miopen::TensorDescriptor& tDesc); +MIOPEN_INTERNALS_EXPORT TensorDescriptor +BuildReshaped4DTensorDescriptor(const miopen::TensorDescriptor& tDesc); void bnBwdTrainSelectSingle(const Handle& handle, miopenDataType_t dtype, @@ -162,58 +163,60 @@ void bnFwdTrainSelectMulti(const Handle& handle, void profileSequence(const Handle& handle, unsigned char select, float* ctime); -void BatchNormForwardInference(Handle& handle, - miopenBatchNormMode_t bn_mode, - const void* alpha, - const void* beta, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& yDesc, - Data_t y, - const TensorDescriptor& bnScaleBiasMeanVarDesc, - ConstData_t bnScale, - ConstData_t bnBias, - ConstData_t estimatedMean, - ConstData_t estimatedVariance, - double epsilon); - -void BatchNormForwardTraining(Handle& handle, - miopenBatchNormMode_t bn_mode, - const void* alpha, /* these don't seem to be used in conv */ - const void* beta, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& yDesc, - Data_t y, - const TensorDescriptor& bnScaleBiasMeanVarDesc, - ConstData_t bnScale, - ConstData_t bnBias, - double expAvgFactor, - Data_t resultRunningMean, - Data_t resultRunningVariance, - double epsilon, - Data_t resultSaveMean, - Data_t resultSaveInvVariance); - -void BatchNormBackward(Handle& handle, - miopenBatchNormMode_t bn_mode, - const void* alphaDataDiff, - const void* betaDataDiff, - const void* alphaParamDiff, - const void* betaParamDiff, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& dyDesc, - ConstData_t dy, - const TensorDescriptor& dxDesc, - Data_t dx, - const TensorDescriptor& bnScaleBiasDiffDesc, - ConstData_t bnScale, - Data_t resultBnScaleDiff, - Data_t resultBnBiasDiff, - double epsilon, - ConstData_t savedMean, - ConstData_t savedInvVariance); +MIOPEN_INTERNALS_EXPORT void +BatchNormForwardInference(Handle& handle, + miopenBatchNormMode_t bn_mode, + const void* alpha, + const void* beta, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& bnScaleBiasMeanVarDesc, + ConstData_t bnScale, + ConstData_t bnBias, + ConstData_t estimatedMean, + ConstData_t estimatedVariance, + double epsilon); + +MIOPEN_INTERNALS_EXPORT void +BatchNormForwardTraining(Handle& handle, + miopenBatchNormMode_t bn_mode, + const void* alpha, /* these don't seem to be used in conv */ + const void* beta, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& bnScaleBiasMeanVarDesc, + ConstData_t bnScale, + ConstData_t bnBias, + double expAvgFactor, + Data_t resultRunningMean, + Data_t resultRunningVariance, + double epsilon, + Data_t resultSaveMean, + Data_t resultSaveInvVariance); + +MIOPEN_INTERNALS_EXPORT void BatchNormBackward(Handle& handle, + miopenBatchNormMode_t bn_mode, + const void* alphaDataDiff, + const void* betaDataDiff, + const void* alphaParamDiff, + const void* betaParamDiff, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& dyDesc, + ConstData_t dy, + const TensorDescriptor& dxDesc, + Data_t dx, + const TensorDescriptor& bnScaleBiasDiffDesc, + ConstData_t bnScale, + Data_t resultBnScaleDiff, + Data_t resultBnBiasDiff, + double epsilon, + ConstData_t savedMean, + ConstData_t savedInvVariance); } // namespace miopen diff --git a/src/include/miopen/batched_transpose_sol.hpp b/src/include/miopen/batched_transpose_sol.hpp index e117afb808..32d338776c 100644 --- a/src/include/miopen/batched_transpose_sol.hpp +++ b/src/include/miopen/batched_transpose_sol.hpp @@ -28,11 +28,27 @@ #include #include +#include #include #include #include #include +struct transpose_invoke_param : public miopen::InvokeParams +{ + ConstData_t src = nullptr; + Data_t dst = nullptr; + + transpose_invoke_param(ConstData_t src_, Data_t dst_) : InvokeParams{}, src(src_), dst(dst_) {} + transpose_invoke_param(miopen::InvokeType type_, ConstData_t src_, Data_t dst_) + : InvokeParams{type_}, src(src_), dst(dst_) + { + } + + Data_t GetWorkspace() const { return nullptr; } + std::size_t GetWorkspaceSize() const { return 0; } +}; + namespace miopen { struct BatchedTransposeParam @@ -45,7 +61,7 @@ struct BatchedTransposeParam int ediv_y{0}; }; -struct BatchedTransposeSolution +struct MIOPEN_INTERNALS_EXPORT BatchedTransposeSolution { BatchedTransposeSolution(const ExecutionContext& ctx_, miopenDataType_t data_type_, @@ -65,6 +81,8 @@ struct BatchedTransposeSolution int num_cu; BatchedTransposeParam kernel_param_heuristic; + + InvokerFactory MakeBatchedTransposeInvokerFactory() const; }; struct TransposeSolutionDefault2Nhwc : public BatchedTransposeSolution diff --git a/src/include/miopen/batchnorm/problem_description.hpp b/src/include/miopen/batchnorm/problem_description.hpp index 45aa9302be..b00473dfc0 100644 --- a/src/include/miopen/batchnorm/problem_description.hpp +++ b/src/include/miopen/batchnorm/problem_description.hpp @@ -29,6 +29,7 @@ #include #include #include +#include #include #include @@ -36,6 +37,7 @@ namespace miopen { struct NetworkConfig; +struct ExecutionContext; namespace batchnorm { @@ -46,9 +48,13 @@ enum class Direction Backward, }; -struct ProblemDescription : ProblemDescriptionBase +struct ProblemDescriptionTag { - // Forward +}; + +struct MIOPEN_INTERNALS_EXPORT ProblemDescription : ProblemDescriptionBase, ProblemDescriptionTag +{ + // Forward Training ProblemDescription(miopenBatchNormMode_t bn_mode_, const TensorDescriptor& xDesc_, const TensorDescriptor& yDesc_, @@ -176,8 +182,20 @@ struct ProblemDescription : ProblemDescriptionBase : ((in_layout == "NDHWC") && (out_layout == "NDHWC")); } + bool Is2D() const { return xDesc.GetLengths().size() == 4; } + + bool IsFp64() const { return xDesc.GetType() == miopenDouble; } + bool IsFp32() const { return xDesc.GetType() == miopenFloat; } + bool IsFp16() const { return xDesc.GetType() == miopenHalf; } + bool IsBfp16() const { return xDesc.GetType() == miopenBFloat16; } + NetworkConfig MakeNetworkConfig() const override; + // This declaration marks batchnorm as a primitive with tuning enabled. + // Any tunable solver would be able pick it and fetch a db instance in ExecutePrimitive. + // It has to be discoverable via ADL from problem description. + friend auto GetDb(const ExecutionContext& ctx, const ProblemDescriptionTag&) -> PerformanceDb; + private: Direction direction; miopenBatchNormMode_t bn_mode; diff --git a/src/include/miopen/binary_cache.hpp b/src/include/miopen/binary_cache.hpp index c5704c44eb..7ef9bd297f 100644 --- a/src/include/miopen/binary_cache.hpp +++ b/src/include/miopen/binary_cache.hpp @@ -27,7 +27,7 @@ #ifndef GUARD_MLOPEN_BINARY_CACHE_HPP #define GUARD_MLOPEN_BINARY_CACHE_HPP -#include +#include #include #include #include @@ -36,30 +36,31 @@ namespace miopen { bool IsCacheDisabled(); -fs::path GetCacheFile(const std::string& device, const std::string& name, const std::string& args); +MIOPEN_INTERNALS_EXPORT fs::path +GetCacheFile(const std::string& device, const fs::path& name, const std::string& args); -fs::path GetCachePath(bool is_system); +MIOPEN_INTERNALS_EXPORT fs::path GetCachePath(bool is_system); #if !MIOPEN_ENABLE_SQLITE_KERN_CACHE fs::path LoadBinary(const TargetProperties& target, std::size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args); -void SaveBinary(const fs::path& binary_path, - const TargetProperties& target, - const std::string& name, - const std::string& args); +fs::path SaveBinary(const fs::path& binary_path, + const TargetProperties& target, + const fs::path& name, + const std::string& args); #else std::vector LoadBinary(const TargetProperties& target, std::size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args); void SaveBinary(const std::vector& hsaco, const TargetProperties& target, std::size_t num_cu, - const std::string& name, + const fs::path& name, const std::string& args); #endif diff --git a/src/include/miopen/bz2.hpp b/src/include/miopen/bz2.hpp index 707c7032b9..546b28efa9 100644 --- a/src/include/miopen/bz2.hpp +++ b/src/include/miopen/bz2.hpp @@ -26,13 +26,15 @@ #ifndef GUARD_MIOPEN_BZ2_HPP_ #define GUARD_MIOPEN_BZ2_HPP_ +#include #include #include namespace miopen { -void check_bz2_error(int e, const std::string& name); -std::vector compress(const std::vector& v, bool* compressed = nullptr); -std::vector decompress(const std::vector& v, unsigned int size); +MIOPEN_INTERNALS_EXPORT void check_bz2_error(int e, const std::string& name); +MIOPEN_INTERNALS_EXPORT std::vector compress(const std::vector& v, + bool* compressed = nullptr); +MIOPEN_INTERNALS_EXPORT std::vector decompress(const std::vector& v, unsigned int size); } // namespace miopen diff --git a/src/include/miopen/cat.hpp b/src/include/miopen/cat.hpp index dfedd50a03..70e4f7361a 100644 --- a/src/include/miopen/cat.hpp +++ b/src/include/miopen/cat.hpp @@ -33,13 +33,13 @@ namespace miopen { struct Handle; struct TensorDescriptor; -miopenStatus_t CatForward(Handle& handle, - int32_t xCount, - const TensorDescriptor* const* xDescs, - ConstData_t* xs, - const TensorDescriptor& yDesc, - Data_t y, - int32_t dim); +MIOPEN_INTERNALS_EXPORT miopenStatus_t CatForward(Handle& handle, + int32_t xCount, + const TensorDescriptor* const* xDescs, + ConstData_t* xs, + const TensorDescriptor& yDesc, + Data_t y, + int32_t dim); } // namespace miopen #endif // _MIOPEN_CAT_HPP_ diff --git a/src/include/miopen/check_numerics.hpp b/src/include/miopen/check_numerics.hpp index 991db7535d..67a9e6d339 100644 --- a/src/include/miopen/check_numerics.hpp +++ b/src/include/miopen/check_numerics.hpp @@ -16,11 +16,14 @@ struct CheckNumerics static const int Abort = 0x08; // abort on abnormal result (to drop into debugger) static const int ComputeStats = 0x10; // Print mean/absmean/min/max (slow) }; -bool CheckNumericsEnabled(int bitMask = -1); -bool checkNumericsInput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data); -bool checkNumericsOutput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data); -bool checkNumericsImpl( +MIOPEN_INTERNALS_EXPORT bool CheckNumericsEnabled(int bitMask = -1); + +MIOPEN_INTERNALS_EXPORT bool +checkNumericsInput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data); +MIOPEN_INTERNALS_EXPORT bool +checkNumericsOutput(const Handle& handle, const TensorDescriptor& dDesc, ConstData_t data); +MIOPEN_INTERNALS_EXPORT bool checkNumericsImpl( const Handle& handle, int mode, const TensorDescriptor& dDesc, ConstData_t data, bool isInput); } // namespace miopen diff --git a/src/include/miopen/comgr.hpp b/src/include/miopen/comgr.hpp index 193f3190e9..ccb3c58549 100644 --- a/src/include/miopen/comgr.hpp +++ b/src/include/miopen/comgr.hpp @@ -36,20 +36,14 @@ namespace miopen { namespace comgr { -void BuildHip(const std::string& name, - const std::string& text, - const std::string& options, - const miopen::TargetProperties& target, - std::vector& binary); - void BuildOcl(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary); void BuildAsm(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary); @@ -65,7 +59,7 @@ namespace miopen { namespace hiprtc { void BuildHip(const std::string& name, - const std::string& text, + std::string_view text, const std::string& options, const miopen::TargetProperties& target, std::vector& binary); diff --git a/src/include/miopen/common.hpp b/src/include/miopen/common.hpp index 924dca87b0..a4b64b4871 100644 --- a/src/include/miopen/common.hpp +++ b/src/include/miopen/common.hpp @@ -28,6 +28,7 @@ #include #include +#include #if MIOPEN_BACKEND_OPENCL diff --git a/src/kernels/Conv_Winograd_Fury_v1_1_1_fp16_fp16acc_f2x3_stride1.s b/src/include/miopen/config.hpp similarity index 77% rename from src/kernels/Conv_Winograd_Fury_v1_1_1_fp16_fp16acc_f2x3_stride1.s rename to src/include/miopen/config.hpp index 5a6e517993..dcf005fbc3 100644 --- a/src/kernels/Conv_Winograd_Fury_v1_1_1_fp16_fp16acc_f2x3_stride1.s +++ b/src/include/miopen/config.hpp @@ -2,7 +2,7 @@ * * MIT License * - * Copyright (c) 2023 Advanced Micro Devices, Inc. + * Copyright (c) 2017 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal @@ -23,15 +23,16 @@ * SOFTWARE. * *******************************************************************************/ -.include "Conv_Winograd_Fury_v1_1_1_metadata.inc" +#ifndef GUARD_MIOPEN_CONFIG_HPP +#define GUARD_MIOPEN_CONFIG_HPP -.if (.amdgcn.gfx_generation_number == 11) - KERNEL_PROLOG fp16_fp16acc_f2x3_stride1 +#include +#include - .include "Conv_Winograd_Fury_v1_1_1_gfx11_fp16_fp16acc_f2x3_stride1.inc" +#ifdef MIOPEN_BUILD_TESTING +#include +#else +#define MIOPEN_INTERNALS_EXPORT +#endif - KERNEL_EPILOG fp16_fp16acc_f2x3_stride1 -.else - .error "Unsupported gfx generation" - .end -.endif +#endif // GUARD_MIOPEN_CONFIG_HPP diff --git a/src/include/miopen/conv/data_invoke_params.hpp b/src/include/miopen/conv/data_invoke_params.hpp index 006dd2a63e..892c84ff38 100644 --- a/src/include/miopen/conv/data_invoke_params.hpp +++ b/src/include/miopen/conv/data_invoke_params.hpp @@ -26,6 +26,7 @@ #pragma once +#include #include #include @@ -38,15 +39,21 @@ struct DataInvokeParams : InvokeParams Data_t workSpace; std::size_t workSpaceSize; bool gfx90aFp16alt; + Scalar alpha; + Scalar beta; DataInvokeParams(ConvDataTensors tensors_, Data_t workSpace_, std::size_t workSpaceSize_, - bool gfx90aFp16alt_) + bool gfx90aFp16alt_, + const Scalar& alpha_ = Scalar(1.0), + const Scalar& beta_ = Scalar(0.0)) : tensors(tensors_), workSpace(workSpace_), workSpaceSize(workSpaceSize_), - gfx90aFp16alt(gfx90aFp16alt_) + gfx90aFp16alt(gfx90aFp16alt_), + alpha(alpha_), + beta(beta_) { } @@ -54,12 +61,16 @@ struct DataInvokeParams : InvokeParams ConvDataTensors tensors_, Data_t workSpace_, std::size_t workSpaceSize_, - bool gfx90aFp16alt_) + bool gfx90aFp16alt_, + const Scalar& alpha_ = Scalar(1.0), + const Scalar& beta_ = Scalar(0.0)) : InvokeParams{type_}, tensors(tensors_), workSpace(workSpace_), workSpaceSize(workSpaceSize_), - gfx90aFp16alt(gfx90aFp16alt_) + gfx90aFp16alt(gfx90aFp16alt_), + alpha(alpha_), + beta(beta_) { } diff --git a/src/include/miopen/conv/heuristics/ai_heuristics.hpp b/src/include/miopen/conv/heuristics/ai_heuristics.hpp index 209a05791e..b7d2cdeb23 100644 --- a/src/include/miopen/conv/heuristics/ai_heuristics.hpp +++ b/src/include/miopen/conv/heuristics/ai_heuristics.hpp @@ -72,9 +72,9 @@ struct Metadata size_t EncodeLayout(const std::string& layout) const; }; class Model; -std::vector PredictSolver(const conv::ProblemDescription& problem, - const ExecutionContext& ctx, - const std::string& device); +MIOPEN_INTERNALS_EXPORT std::vector PredictSolver(const conv::ProblemDescription& problem, + const ExecutionContext& ctx, + const std::string& device); } // namespace immed_mode #endif // MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK @@ -82,13 +82,15 @@ std::vector PredictSolver(const conv::ProblemDescription& problem, namespace tuning { struct Metadata { - std::size_t num_tuning_params; + std::size_t predict_type; + std::unordered_map num_tuning_params; std::unordered_map tuning_decodings; Metadata(const std::string& arch, const std::string& solver); }; bool ModelSetParams(const std::string& arch, const std::string& solver, + conv::Direction direction, const std::vector& features, bool transform_features, std::function validator); diff --git a/src/include/miopen/graphapi/operation.hpp b/src/include/miopen/conv/invokers/gcn_asm_wino.hpp similarity index 75% rename from src/include/miopen/graphapi/operation.hpp rename to src/include/miopen/conv/invokers/gcn_asm_wino.hpp index a7a97812ae..3c0b224477 100644 --- a/src/include/miopen/graphapi/operation.hpp +++ b/src/include/miopen/conv/invokers/gcn_asm_wino.hpp @@ -23,32 +23,21 @@ * SOFTWARE. * *******************************************************************************/ -#pragma once -#include -#include +#pragma once -#include -#include +#include +#include namespace miopen { -namespace graphapi { - -class OpNode -{ -public: - virtual ~OpNode() = default; - - virtual std::vector getInTensors() const = 0; - - virtual std::vector getOutTensors() const = 0; - - /* TODO: The remaining part of the class is being - * developed separately. Needs merging after finished. - */ -}; +namespace conv { +enum class Direction; +} // namespace conv -} // namespace graphapi +InvokerFactory MakeGcnAsmWinoV2InvokerFactory(const WinoShaderArgsV2& args, + conv::Direction direction, + std::size_t sync_buffer_size, + bool fused); } // namespace miopen diff --git a/src/include/miopen/conv/kernel_interface/winograd_kernel_interface.hpp b/src/include/miopen/conv/kernel_interface/winograd_kernel_interface.hpp new file mode 100644 index 0000000000..602bd695a8 --- /dev/null +++ b/src/include/miopen/conv/kernel_interface/winograd_kernel_interface.hpp @@ -0,0 +1,150 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include +#include + +#include + +namespace miopen { + +namespace conv { +struct ProblemDescription; +} // namespace conv + +enum class WinoShaderFlagsV2 : uint64_t +{ + F_REVERSE_R = 1ULL << 0, + F_REVERSE_S = 1ULL << 1, + F_FLIP_K_C = 1ULL << 2, // Deprecated + F_DENORMS_RND_ENABLE = 1ULL << 3, + F_MALL_READ_CACHE_ENABLE = 1ULL << 4, + F_ACC_PRE_ACTIVATION_MODE = 1ULL << 5, + F_ADDR_INDIRECT = 1ULL << 6, + F_BIAS = 1ULL << 7, + F_LEAKY_RELU = 1ULL << 8, // Deprecated + F_NKCHR_STRIDES = 1ULL << 9, + F_GROUPED_CONVOLUTION = 1ULL << 10, + F_FORCE_FILTER_TRAVERSE_MODE = 1ULL << 11, + F_FILTER_TRAVERSE_DUAL = 1ULL << 12, + F_TENSOR_OFFSETS = 1ULL << 13, + F_USE_ACTIVATION_MODE = 1ULL << 14, + F_USE_EXTENDED_FLAGS_64 = 1ULL << 15, +}; + +inline WinoShaderFlagsV2 operator&(WinoShaderFlagsV2 lhs, WinoShaderFlagsV2 rhs) +{ + using T = std::underlying_type_t; + return static_cast(static_cast(lhs) & static_cast(rhs)); +} + +inline WinoShaderFlagsV2 operator|(WinoShaderFlagsV2 lhs, WinoShaderFlagsV2 rhs) +{ + using T = std::underlying_type_t; + return static_cast(static_cast(lhs) | static_cast(rhs)); +} + +inline WinoShaderFlagsV2 operator|=(WinoShaderFlagsV2& lhs, WinoShaderFlagsV2 rhs) +{ + lhs = lhs | rhs; + return lhs; +} + +inline std::ostream& operator<<(std::ostream& s, WinoShaderFlagsV2 flags) +{ + using T = std::underlying_type_t; + s << "0x" << std::hex << static_cast(flags) << std::dec; + return s; +} + +enum class WinoShaderActivationModeV2_t : uint8_t +{ + IDENTITY = 0, // no activation, alpha and beta are ignored + LEAKY_RELU = 1, // ReLU, beta field is ignored + SIGMOID = 2, // sigmoid, alpha and beta fields are ignored + SCALED_TANH = 3, // parametric tanh function +}; + +inline std::ostream& operator<<(std::ostream& s, const WinoShaderActivationModeV2_t& mode) +{ + s << static_cast(mode); + return s; +} + +struct WinoShaderArgsV2 +{ + // Main convolution parameters + uint32_t N; // batch size + uint32_t C; // number of input channels in each filter group + uint32_t H; // input height + uint32_t W; // input width + uint32_t K; // number of output channels in each filter group + uint32_t R; // filter height + uint32_t S; // filter width + int32_t pad_h; // padding in h dimension + int32_t pad_w; // padding in w dimension + uint32_t out_h; // output height + uint32_t out_w; // output width + uint32_t G; // number of filter groups + + // Data layout related parameters + uint32_t d_N_stride; // stride in number of elements of the N dimension of the input data buffer + uint32_t d_C_stride; // stride in number of elements of the C dimension of the input data buffer + uint32_t d_H_stride; // stride in number of elements of the H dimension of the input data buffer + uint32_t d_G_stride; // stride in number of elements of the G dimension of the input data buffer + + uint32_t f_K_stride; // stride in number of elements of the K dimension of the filter buffer + uint32_t f_C_stride; // stride in number of elements of the C dimension of the filter buffer + uint32_t f_R_stride; // stride in number of elements of the R dimension of the filter buffer + uint32_t f_G_stride; // stride in number of elements of the G dimension of the filter buffer + + uint32_t o_N_stride; // stride in number of elements of the N dimension of the output buffer + uint32_t o_K_stride; // stride in number of elements of the K dimension of the output buffer + uint32_t o_H_stride; // stride in number of elements of the H dimension of the output buffer + uint32_t o_G_stride; // stride in number of elements of the G dimension of the output buffer + + // Fused activation parameters + WinoShaderActivationModeV2_t activation_mode; // activation mode + + // Other shader parameters + uint32_t n_groups; // number of shader groups + WinoShaderFlagsV2 flags64; // shader flags + uint8_t sync_limit; // maximum number of sync attempts + uint8_t sync_period; // synchronization period + + bool SetConvParams(const conv::ProblemDescription& problem); + void SetStrides(const conv::ProblemDescription& problem); + void SetActivParams(miopenActivationMode_t mode); + void SetShaderParams(uint32_t n_groups, + WinoShaderFlagsV2 flags, + uint8_t sync_limit, + uint8_t sync_period) noexcept; +}; + +} // namespace miopen diff --git a/src/include/miopen/conv/problem_description.hpp b/src/include/miopen/conv/problem_description.hpp index 42c558bbe4..9447d7a5ca 100644 --- a/src/include/miopen/conv/problem_description.hpp +++ b/src/include/miopen/conv/problem_description.hpp @@ -29,6 +29,7 @@ #include #include #include +#include #include #include @@ -42,7 +43,7 @@ namespace miopen { struct ExecutionContext; -std::string +MIOPEN_INTERNALS_EXPORT std::string EncodeDataTypesForKey(miopenDataType_t in, miopenDataType_t weights, miopenDataType_t out); template @@ -132,10 +133,13 @@ constexpr TElement GetW5(unsigned spatial_dims, const std::vector& dat namespace conv { -struct ProblemDescription : ProblemDescriptionBase +MIOPEN_INTERNALS_EXPORT miopenAlphaBetaCase_t ClassifyAlphaBeta(const Scalar& alpha, + const Scalar& beta); + +struct MIOPEN_INTERNALS_EXPORT ProblemDescription : ProblemDescriptionBase #if MIOPEN_ENABLE_SQLITE , - SQLiteSerializable + SQLiteSerializable #endif { ProblemDescription() = default; @@ -146,7 +150,9 @@ struct ProblemDescription : ProblemDescriptionBase const TensorDescriptor& out_, // y for Forward, x for Backward* const ConvolutionDescriptor& conv_, Direction direction_, - int bias_ = 0) + int bias_ = 0, + const Scalar& alpha_ = Scalar(1.0), + const Scalar& beta_ = Scalar(0.0)) : in(in_), weights(weights_), out(out_), @@ -155,7 +161,10 @@ struct ProblemDescription : ProblemDescriptionBase weights_layout(ComputeWeightsLayout()), out_layout(ComputeOutLayout()), direction(direction_), - bias(bias_) + bias(bias_), + alpha(alpha_), + beta(beta_), + alpha_beta_case(ClassifyAlphaBeta(alpha, beta)) { HeuristicUpdateLayouts(); } @@ -189,24 +198,8 @@ struct ProblemDescription : ProblemDescriptionBase std::size_t GetInStrideH() const { return GetH5(GetSpatialDims(), in.GetStrides()); } std::size_t GetInStrideW() const { return GetW5(GetSpatialDims(), in.GetStrides()); } std::string GetInLayout() const { return in_layout; } - std::string ComputeInLayout() const - { - if(GetSpatialDims() == 2) - { - return in.GetLayout(in.GetLayout_str()); - } - else - { - return in.GetLayout("NCDHW"); - } - } std::size_t GetInElementSize() const { return GetTypeSize(GetInDataType()); } - - std::size_t GetInSize() const - { - return GetInBatchSize() * GetInChannels() * GetInDepth() * GetInHeight() * GetInWidth() * - GetInElementSize(); - } + std::size_t GetInSize() const { return in.GetNumBytes(); } // Out getters miopenDataType_t GetOutDataType() const { return out.GetType(); } @@ -222,24 +215,8 @@ struct ProblemDescription : ProblemDescriptionBase std::size_t GetOutStrideH() const { return GetH5(GetSpatialDims(), out.GetStrides()); } std::size_t GetOutStrideW() const { return GetW5(GetSpatialDims(), out.GetStrides()); } std::string GetOutLayout() const { return out_layout; } - std::string ComputeOutLayout() const - { - if(GetSpatialDims() == 2) - { - return out.GetLayout(out.GetLayout_str()); - } - else - { - return out.GetLayout("NCDHW"); - } - } std::size_t GetOutElementSize() const { return GetTypeSize(GetOutDataType()); } - - std::size_t GetOutSize() const - { - return GetOutBatchSize() * GetOutChannels() * GetOutDepth() * GetOutHeight() * - GetOutWidth() * GetOutElementSize(); - } + std::size_t GetOutSize() const { return out.GetNumBytes(); } // Weights getters miopenDataType_t GetWeightsDataType() const { return weights.GetType(); } @@ -259,31 +236,14 @@ struct ProblemDescription : ProblemDescriptionBase else return GetW5(GetSpatialDims(), weights.GetLengths()); } - // std::size_t GetWeightsStrideD() const { return GetD5(GetSpatialDims(), weights.GetStrides()); - // } - // std::size_t GetWeightsStrideH() const { return GetH5(GetSpatialDims(), weights.GetStrides()); - // } - // std::size_t GetWeightsStrideW() const { return GetW5(GetSpatialDims(), weights.GetStrides()); - // } + std::size_t GetWeightsStrideK() const { return GetN5(GetSpatialDims(), weights.GetStrides()); } + std::size_t GetWeightsStrideC() const { return GetC5(GetSpatialDims(), weights.GetStrides()); } + std::size_t GetWeightsStrideD() const { return GetD5(GetSpatialDims(), weights.GetStrides()); } + std::size_t GetWeightsStrideH() const { return GetH5(GetSpatialDims(), weights.GetStrides()); } + std::size_t GetWeightsStrideW() const { return GetW5(GetSpatialDims(), weights.GetStrides()); } std::string GetWeightsLayout() const { return weights_layout; } - std::string ComputeWeightsLayout() const - { - if(GetSpatialDims() == 2) - { - return weights.GetLayout(weights.GetLayout_str()); - } - else - { - return weights.GetLayout("NCDHW"); - } - } std::size_t GetWeightsElementSize() const { return GetTypeSize(GetWeightsDataType()); } - - std::size_t GetWeightsSize() const - { - return GetInChannels() * GetOutChannels() * GetWeightsDepth() * GetWeightsHeight() * - GetWeightsWidth() * GetWeightsElementSize(); - } + std::size_t GetWeightsSize() const { return weights.GetNumBytes(); } const TensorDescriptor& GetIn() const { return in; } const TensorDescriptor& GetWeights() const { return weights; } @@ -296,6 +256,13 @@ struct ProblemDescription : ProblemDescriptionBase bool IsDirectionBackwardWrW() const { return direction == conv::Direction::BackwardWeights; } std::string GetDirectionStr() const; + const Scalar& GetAlpha() const { return alpha; } + const Scalar& GetBeta() const { return beta; } + + miopenAlphaBetaCase_t GetAlphaBetaCase() const { return alpha_beta_case; } + + std::string GetAlphaBetaCaseStr() const; + int GetBias() const { return bias; } std::size_t GetBiasSize() const @@ -396,6 +363,12 @@ struct ProblemDescription : ProblemDescriptionBase return in.AllDimsFitIntoInt() && weights.AllDimsFitIntoInt() && out.AllDimsFitIntoInt(); } + bool AllTensorsLengthsFitIntoInt() const + { + return in.AllLengthsFitIntoInt() && weights.AllLengthsFitIntoInt() && + out.AllLengthsFitIntoInt(); + } + void HeuristicUpdateLayouts(); void MakeNetworkConfig(std::string& conf_key) const; @@ -470,6 +443,42 @@ struct ProblemDescription : ProblemDescriptionBase void SetupFloats(ExecutionContext& ctx) const; private: + std::string ComputeInLayout() const + { + if(GetSpatialDims() == 2) + { + return in.GetLayout(in.GetLayout_str()); + } + else + { + return in.GetLayout("NCDHW"); + } + } + + std::string ComputeOutLayout() const + { + if(GetSpatialDims() == 2) + { + return out.GetLayout(out.GetLayout_str()); + } + else + { + return out.GetLayout("NCDHW"); + } + } + + std::string ComputeWeightsLayout() const + { + if(GetSpatialDims() == 2) + { + return weights.GetLayout(weights.GetLayout_str()); + } + else + { + return weights.GetLayout("NCDHW"); + } + } + TensorDescriptor in; TensorDescriptor weights; TensorDescriptor out; @@ -477,8 +486,11 @@ struct ProblemDescription : ProblemDescriptionBase std::string in_layout; std::string weights_layout; std::string out_layout; - Direction direction = Direction::Forward; - int bias = 0; + Direction direction = Direction::Forward; + int bias = 0; + Scalar alpha = Scalar(1.0); + Scalar beta = Scalar(0.0); + miopenAlphaBetaCase_t alpha_beta_case = DEFAULT; }; } // namespace conv diff --git a/src/include/miopen/conv/solver_finders.hpp b/src/include/miopen/conv/solver_finders.hpp index e24b40c7b5..23ffb90077 100644 --- a/src/include/miopen/conv/solver_finders.hpp +++ b/src/include/miopen/conv/solver_finders.hpp @@ -29,15 +29,24 @@ #include #include #include +#include #include #include +#include #include +#include #include #include namespace miopen { +namespace conv { +struct ProblemDescription; +} // namespace conv + +struct Solution; + class DbRecord; // This can be used to pass some primitive-specific pre-computed data to finders. @@ -149,17 +158,26 @@ const std::vector>& GetConvSolverFinders(); } // namespace conv -void FindCore(const AnyInvokeParams& invoke_ctx, - DbRecord& record, - const ExecutionContext& ctx, - const ProblemDescriptionBase& problem, - const PrimitiveFindParameters& parameters, - const std::vector>& finders, - const std::optional& options = std::nullopt); +struct FindCoreResult +{ + std::vector solutions; + bool is_optimal; +}; -namespace conv { +FindCoreResult FindCore(const AnyInvokeParams& invoke_ctx, + const ExecutionContext& ctx, + const ProblemDescriptionBase& problem, + const PrimitiveFindParameters& parameters, + const std::vector>& finders, + const std::optional& options = std::nullopt, + bool force_attach_binary = false); +namespace conv { bool IsAlgorithmDisabled(miopenConvAlgorithm_t algo); +bool IsEnoughWorkspace(std::string_view where, + const miopen::solver::Id& solver_id, + std::size_t required_size, + const miopen::AnyInvokeParams* invokeParams); struct ConvFindParameters : PrimitiveFindParameters { diff --git a/src/include/miopen/conv/wrw_invoke_params.hpp b/src/include/miopen/conv/wrw_invoke_params.hpp index 62eba1e4e6..de36a4fe2b 100644 --- a/src/include/miopen/conv/wrw_invoke_params.hpp +++ b/src/include/miopen/conv/wrw_invoke_params.hpp @@ -26,6 +26,7 @@ #pragma once +#include #include #include @@ -38,15 +39,21 @@ struct WrWInvokeParams : InvokeParams Data_t workSpace; std::size_t workSpaceSize; bool gfx90aFp16alt; + Scalar alpha; + Scalar beta; WrWInvokeParams(ConvWrwTensors tensors_, Data_t workSpace_, std::size_t workSpaceSize_, - bool gfx90aFp16alt_) + bool gfx90aFp16alt_, + const Scalar& alpha_ = Scalar(1.0), + const Scalar& beta_ = Scalar(0.0)) : tensors(tensors_), workSpace(workSpace_), workSpaceSize(workSpaceSize_), - gfx90aFp16alt(gfx90aFp16alt_) + gfx90aFp16alt(gfx90aFp16alt_), + alpha(alpha_), + beta(beta_) { } @@ -54,12 +61,16 @@ struct WrWInvokeParams : InvokeParams ConvWrwTensors tensors_, Data_t workSpace_, std::size_t workSpaceSize_, - bool gfx90aFp16alt_) + bool gfx90aFp16alt_, + const Scalar& alpha_ = Scalar(1.0), + const Scalar& beta_ = Scalar(0.0)) : InvokeParams{type_}, tensors(tensors_), workSpace(workSpace_), workSpaceSize(workSpaceSize_), - gfx90aFp16alt(gfx90aFp16alt_) + gfx90aFp16alt(gfx90aFp16alt_), + alpha(alpha_), + beta(beta_) { } diff --git a/src/include/miopen/conv_solution.hpp b/src/include/miopen/conv_solution.hpp index 5e3561f087..897a55e9bf 100644 --- a/src/include/miopen/conv_solution.hpp +++ b/src/include/miopen/conv_solution.hpp @@ -65,7 +65,6 @@ struct ConvSolution int n_out_pix_tiles; // # output pixel tiles per wk-item (ALU) int n_in_data_tiles; // # of blocks of different inputs in LDS int n_stacks; // # of diff stacks (part of batch). - float weight = 0.0f; ConvSolution(miopenStatus_t status_ = miopenStatusSuccess) : status(status_), @@ -89,7 +88,9 @@ struct ConvSolution std::ostream& operator<<(std::ostream& os, const ConvSolution& s); -void PrecompileSolutions(const Handle& h, const std::vector& sols); +void PrecompileSolutions(const Handle& h, + const std::vector& sols, + bool force_attach_binary = false); } // namespace solver } // namespace miopen diff --git a/src/include/miopen/convolution.hpp b/src/include/miopen/convolution.hpp index 640f52e99a..88368ac95e 100644 --- a/src/include/miopen/convolution.hpp +++ b/src/include/miopen/convolution.hpp @@ -67,7 +67,7 @@ struct ExecutionContext; struct Handle; struct TensorDescriptor; -struct ConvolutionAttribute +struct MIOPEN_INTERNALS_EXPORT ConvolutionAttribute { class Gfx90aFp16alt { @@ -76,9 +76,7 @@ struct ConvolutionAttribute inline int Get() const { - if(!miopen::IsUnset(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP16_ALT_IMPL))) - return miopen::Value(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP16_ALT_IMPL)); - return value; + return env::value_or(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP16_ALT_IMPL, value); } public: @@ -105,18 +103,13 @@ struct ConvolutionAttribute inline miopenF8RoundingMode_t Get() const { - if(!miopen::IsUnset(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_MODE))) - return static_cast( - miopen::Value(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_MODE))); - return rounding_mode; + return env::value_or(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_MODE, rounding_mode); } inline uint32_t GetSeed() const { // assert(rounding_mode == miopenF8RoundingModeStochastic); - if(!miopen::IsUnset(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_SEED))) - return miopen::Value(ENV(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_SEED)); - return seed; + return env::value_or(MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP8_ROUNDING_SEED, seed); } inline void SetSeed(const uint32_t s) { seed = s; } @@ -130,10 +123,7 @@ struct ConvolutionAttribute public: inline int Get() const { - if(!miopen::IsUnset(ENV(MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC))) - return static_cast( - miopen::IsEnabled(ENV(MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC))); - return value; + return env::value_or(MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC, value); } operator bool() const { @@ -153,7 +143,15 @@ struct ConvolutionAttribute friend void from_json(const nlohmann::json& json, ConvolutionAttribute& conv); }; -struct MIOPEN_EXPORT ConvolutionDescriptor : miopenConvolutionDescriptor +struct Solution; + +std::vector FindConvolution(const ExecutionContext& ctx, + const conv::ProblemDescription& problem, + const AnyInvokeParams& invoke_ctx, + int requestAlgoCount, + bool force_attach_binary); + +struct MIOPEN_INTERNALS_EXPORT ConvolutionDescriptor : miopenConvolutionDescriptor { ConvolutionDescriptor(std::size_t spatial_dim, miopenConvolutionMode_t c_mode, @@ -229,10 +227,12 @@ struct MIOPEN_EXPORT ConvolutionDescriptor : miopenConvolutionDescriptor std::size_t GetSolutionCount(const ExecutionContext& ctx, const conv::ProblemDescription& problem) const; - std::vector GetSolutions(const ExecutionContext& ctx, - const conv::ProblemDescription& problem, - size_t maxSolutionCount, - bool* fallbackPathTaken) const; + std::vector + GetSolutions(const ExecutionContext& ctx, + const conv::ProblemDescription& problem, + size_t maxSolutionCount, + bool* fallbackPathTaken, + const AnyInvokeParams* invokeParams = nullptr) const; void CompileSolution(const ExecutionContext& ctx, const conv::ProblemDescription& problem, @@ -355,9 +355,11 @@ struct MIOPEN_EXPORT ConvolutionDescriptor : miopenConvolutionDescriptor FindMode findMode; ConvolutionAttribute attribute; - std::vector GetSolutionsFallback(const ExecutionContext& ctx, - const conv::ProblemDescription& problem, - size_t maxSolutionCount) const; + std::vector + GetSolutionsFallback(const ExecutionContext& ctx, + const conv::ProblemDescription& problem, + size_t maxSolutionCount, + const AnyInvokeParams* invokeParams = nullptr) const; std::size_t GetSolutionCountFallback(const ExecutionContext& ctx, const conv::ProblemDescription& problem) const; @@ -369,24 +371,25 @@ struct MIOPEN_EXPORT ConvolutionDescriptor : miopenConvolutionDescriptor void ValidateTensors(const ConvTensors& conv_tensors) const; }; -void ConvolutionBackwardBias(const Handle& handle, - const void* alpha, - const TensorDescriptor& dyDesc, - ConstData_t dy, - const void* beta, - const TensorDescriptor& dbDesc, - Data_t db); - -Invoker LoadOrPrepareInvoker(const ExecutionContext& ctx, - const conv::ProblemDescription& problem, - solver::Id solver_id); - -std::ostream& operator<<(std::ostream& stream, const ConvolutionDescriptor& c); - -void DumpTensorToFileFromDevice(const miopen::Handle& handle, - const miopen::TensorDescriptor& tDesc, - ConstData_t dData, - const std::string& filename); +MIOPEN_INTERNALS_EXPORT void ConvolutionBackwardBias(const Handle& handle, + const void* alpha, + const TensorDescriptor& dyDesc, + ConstData_t dy, + const void* beta, + const TensorDescriptor& dbDesc, + Data_t db); + +MIOPEN_INTERNALS_EXPORT Invoker LoadOrPrepareInvoker(const ExecutionContext& ctx, + const conv::ProblemDescription& problem, + solver::Id solver_id); + +MIOPEN_INTERNALS_EXPORT std::ostream& operator<<(std::ostream& stream, + const ConvolutionDescriptor& c); + +MIOPEN_INTERNALS_EXPORT void DumpTensorToFileFromDevice(const miopen::Handle& handle, + const miopen::TensorDescriptor& tDesc, + ConstData_t dData, + const fs::path& filename); } // namespace miopen diff --git a/src/include/miopen/ctc.hpp b/src/include/miopen/ctc.hpp index 546c22de6f..f57db4b609 100644 --- a/src/include/miopen/ctc.hpp +++ b/src/include/miopen/ctc.hpp @@ -40,7 +40,7 @@ namespace miopen { -struct CTCLossDescriptor : miopenCTCLossDescriptor +struct MIOPEN_INTERNALS_EXPORT CTCLossDescriptor : miopenCTCLossDescriptor { CTCLossDescriptor(); diff --git a/src/include/miopen/datatype.hpp b/src/include/miopen/datatype.hpp index 7c6043f472..749d87e364 100644 --- a/src/include/miopen/datatype.hpp +++ b/src/include/miopen/datatype.hpp @@ -73,6 +73,10 @@ inline std::string GetDataType(miopenDataType_t type) type_str = "bfloat8"; } break; + case miopenInt64: { + type_str = "int64"; + } + break; } return type_str; } diff --git a/src/include/miopen/db.hpp b/src/include/miopen/db.hpp index b89f1ca16e..cd90ec84f4 100644 --- a/src/include/miopen/db.hpp +++ b/src/include/miopen/db.hpp @@ -28,6 +28,7 @@ #include #include +#include #include #include @@ -49,10 +50,10 @@ class LockFile; constexpr bool DisableUserDbFileIO = MIOPEN_DISABLE_USERDB; /// No instance of this class should be used from several threads at the same time. -class PlainTextDb +class MIOPEN_INTERNALS_EXPORT PlainTextDb { public: - PlainTextDb(DbKinds db_kind_, const std::string& filename_, bool is_system = false); + PlainTextDb(DbKinds db_kind_, const fs::path& filename_, bool is_system = false); /// Searches db for provided key and returns found record or none if key not found in database boost::optional FindRecord(const std::string& key); @@ -141,7 +142,7 @@ class PlainTextDb const DbKinds db_kind; LockFile& GetLockFile() { return lock_file; } - const std::string& GetFileName() const { return filename; } + const fs::path& GetFileName() const { return filename; } bool IsWarningIfUnreadable() const { return warning_if_unreadable; } boost::optional FindRecordUnsafe(const std::string& key, RecordPositions* pos); bool StoreRecordUnsafe(const DbRecord& record); @@ -149,7 +150,7 @@ class PlainTextDb bool RemoveRecordUnsafe(const std::string& key); private: - std::string filename; + fs::path filename; LockFile& lock_file; const bool warning_if_unreadable; @@ -164,20 +165,20 @@ class PlainTextDb }; template -auto GetDbInstance(rank<1>, DbKinds db_kind, const std::string& path, bool is_system) +auto GetDbInstance(rank<1>, DbKinds db_kind, const fs::path& path, bool is_system) -> decltype(TDb::GetCached({}, {}, {})) { return TDb::GetCached(db_kind, path, is_system); }; template -auto GetDbInstance(rank<0>, DbKinds db_kind, const std::string& path, bool is_system) +auto GetDbInstance(rank<0>, DbKinds db_kind, const fs::path& path, bool is_system) { return TDb{db_kind, path, is_system}; }; template -decltype(auto) GetDbInstance(DbKinds db_kind, const std::string& path, bool is_system = true) +decltype(auto) GetDbInstance(DbKinds db_kind, const fs::path& path, bool is_system = true) { return GetDbInstance(rank<1>{}, db_kind, path, is_system); } @@ -186,7 +187,7 @@ template class MultiFileDb { public: - MultiFileDb(DbKinds db_kind, const std::string& installed_path, const std::string& user_path) + MultiFileDb(DbKinds db_kind, const fs::path& installed_path, const fs::path& user_path) : _installed(GetDbInstance(db_kind, installed_path, true)) #if !MIOPEN_DISABLE_USERDB , @@ -261,20 +262,20 @@ class MultiFileDb private: template static TRet - GetDbInstance(rank<1>, DbKinds db_kind, const std::string& path, bool warn_if_unreadable) + GetDbInstance(rank<1>, DbKinds db_kind, const fs::path& path, bool warn_if_unreadable) { return TDb::GetCached(db_kind, path, warn_if_unreadable); }; template static TDb - GetDbInstance(rank<0>, DbKinds db_kind, const std::string& path, bool warn_if_unreadable) + GetDbInstance(rank<0>, DbKinds db_kind, const fs::path& path, bool warn_if_unreadable) { return {db_kind, path, warn_if_unreadable}; }; template (rank<1>{}, {}, {}, {}))> - static TRet GetDbInstance(DbKinds db_kind, const std::string& path, bool warn_if_unreadable) + static TRet GetDbInstance(DbKinds db_kind, const fs::path& path, bool warn_if_unreadable) { return GetDbInstance(rank<1>{}, db_kind, path, warn_if_unreadable); } diff --git a/src/include/miopen/db_path.hpp b/src/include/miopen/db_path.hpp index 6fad802d15..bb7b189af6 100644 --- a/src/include/miopen/db_path.hpp +++ b/src/include/miopen/db_path.hpp @@ -26,15 +26,16 @@ #ifndef GUARD_MIOPEN_DB_PATH_HPP #define GUARD_MIOPEN_DB_PATH_HPP +#include #include #include namespace miopen { -std::string GetSystemDbPath(); -const fs::path& GetUserDbPath(); -std::string GetUserDbSuffix(); -std::string GetSystemFindDbSuffix(); +MIOPEN_INTERNALS_EXPORT fs::path GetSystemDbPath(); +MIOPEN_INTERNALS_EXPORT const fs::path& GetUserDbPath(); +MIOPEN_INTERNALS_EXPORT std::string GetUserDbSuffix(); +MIOPEN_INTERNALS_EXPORT std::string GetSystemFindDbSuffix(); } // namespace miopen diff --git a/src/include/miopen/db_record.hpp b/src/include/miopen/db_record.hpp index d136190f86..073d0c3989 100644 --- a/src/include/miopen/db_record.hpp +++ b/src/include/miopen/db_record.hpp @@ -26,8 +26,7 @@ #ifndef GUARD_MIOPEN_DB_RECORD_HPP_ #define GUARD_MIOPEN_DB_RECORD_HPP_ -#include - +#include #include #include @@ -73,7 +72,7 @@ enum class DbKinds : std::uint8_t /// Upon construction, allows getting and modifying contents of a record (IDs and VALUES). /// /// All operations are MP- and MT-safe. -class DbRecord +class MIOPEN_INTERNALS_EXPORT DbRecord { public: template diff --git a/src/include/miopen/dropout.hpp b/src/include/miopen/dropout.hpp index 380704d5e5..985972edb2 100644 --- a/src/include/miopen/dropout.hpp +++ b/src/include/miopen/dropout.hpp @@ -54,7 +54,7 @@ using prngStates = xorwowStates; namespace miopen { -struct DropoutDescriptor : miopenDropoutDescriptor +struct MIOPEN_INTERNALS_EXPORT DropoutDescriptor : miopenDropoutDescriptor { DropoutDescriptor(); diff --git a/src/include/miopen/env.hpp b/src/include/miopen/env.hpp index 768496c985..4233a1c50c 100644 --- a/src/include/miopen/env.hpp +++ b/src/include/miopen/env.hpp @@ -26,195 +26,194 @@ #ifndef GUARD_MIOPEN_ENV_HPP #define GUARD_MIOPEN_ENV_HPP +#include #include #include +#include #include #include #include #include -namespace miopen { +namespace miopen::env { -namespace internal { +MIOPEN_EXPORT std::optional getEnvironmentVariable(std::string_view name); +MIOPEN_EXPORT void setEnvironmentVariable(std::string_view name, std::string_view value); +MIOPEN_EXPORT void clearEnvironmentVariable(std::string_view name); + +namespace detail { template -struct ParseEnvVal -{ -}; +using remove_cvref_t = std::remove_cv_t>; -template <> -struct ParseEnvVal -{ - static bool go(const char* vp) - { - std::string value_env_str{vp}; +template +inline constexpr bool is_same_v = std::is_same_v, U>; - for(auto& c : value_env_str) - { - if(std::isalpha(c) != 0) - { - c = std::tolower(static_cast(c)); - } - } +template || is_same_v || + is_same_v || is_same_v, + bool> = true> +struct EnvVar +{ + using value_type = T; - if(value_env_str == "disable" || value_env_str == "disabled" || value_env_str == "0" || - value_env_str == "no" || value_env_str == "off" || value_env_str == "false") - { - return false; - } - else if(value_env_str == "enable" || value_env_str == "enabled" || value_env_str == "1" || - value_env_str == "yes" || value_env_str == "on" || value_env_str == "true") + explicit EnvVar(std::string_view name, T default_value = {}, bool create_if_missing = false) + : name_{name}, default_value_{std::forward(default_value)} + { + auto value = getEnvironmentVariable(name); + if(!value.has_value()) { - return true; + if(create_if_missing) + update(default_value); } else { - MIOPEN_THROW(miopenStatusInvalidValue, "Invalid value for env variable"); + if constexpr(is_same_v) + { + value_ = std::strtoull(value->c_str(), nullptr, 0); + } + if constexpr(is_same_v) + { + if(value == "0") + { + value_ = false; + } + else if(value == "1") + { + value_ = true; + } + else + { + std::string value_env_str(value->length(), '\0'); + std::transform(value->begin(), value->end(), value_env_str.begin(), [](int ch) { + return std::tolower(ch); + }); + + if(value_env_str == "disable" || value_env_str == "disabled" || + value_env_str == "no" || value_env_str == "off" || value_env_str == "false") + { + value_ = false; + } + else if(value_env_str == "enable" || value_env_str == "enabled" || + value_env_str == "yes" || value_env_str == "on" || + value_env_str == "true") + { + value_ = true; + } + else + { + MIOPEN_THROW(miopenStatusInvalidValue, + "Invalid value for env variable (value='" + value_env_str + + "')"); + } + } + } + if constexpr(is_same_v || is_same_v) + { + value_ = value.value(); + } } - - return false; // shouldn't reach here } -}; - -// Supports hexadecimals (with leading "0x"), octals (if prefix is "0") and decimals (default). -// Returns 0 if environment variable is in wrong format (strtoull fails to parse the string). -template <> -struct ParseEnvVal -{ - static uint64_t go(const char* vp) { return std::strtoull(vp, nullptr, 0); } -}; - -template <> -struct ParseEnvVal -{ - static std::string go(const char* vp) { return std::string{vp}; } -}; -template -struct EnvVar -{ -private: - T value{}; - bool is_unset = true; - -public: - const T& GetValue() const { return value; } - - bool IsUnset() const { return is_unset; } - - void Unset() { is_unset = true; } - - void UpdateValue(const T& val) + template + U value(std::optional alternate_value = std::nullopt) const + { + return static_cast( + value_.value_or(alternate_value.value_or(static_cast(default_value_)))); + } + void clear() { - is_unset = false; - value = val; + clearEnvironmentVariable(name_); + value_.reset(); } - explicit EnvVar(const char* const name, const T& def_val) + bool exist() const { return value_.has_value(); } + + template + void update(U value) { - // NOLINTNEXTLINE (concurrency-mt-unsafe) - const char* vp = std::getenv(name); - if(vp != nullptr) // a value was provided + if constexpr(is_same_v || is_same_v) { - is_unset = false; - value = ParseEnvVal::go(vp); + setEnvironmentVariable(name_, value); } - else // no value provided, use default value + if constexpr(is_same_v) { - value = def_val; + setEnvironmentVariable(name_, value ? "1" : "0"); } + if constexpr(std::is_integral_v> && !is_same_v) + { + setEnvironmentVariable(name_, std::to_string(value)); + } + value_ = value; } + std::string_view name() const { return name_; } + +private: + std::string_view name_; + T default_value_; + std::optional value_{std::nullopt}; }; -} // end namespace internal +} // end namespace detail -// static inside function hides the variable and provides -// thread-safety/locking -// Used in global namespace -#define MIOPEN_DECLARE_ENV_VAR(name, type, default_val) \ - namespace miopen::env { \ - struct name \ +#define MIOPEN_DECLARE_ENV_VAR(_name, _type, ...) \ + [[maybe_unused]] inline constexpr struct __struct_##_name \ { \ - static_assert(std::is_same_v, \ + static_assert(std::is_same_v<__struct_##_name, ::__struct_##_name>, \ "MIOPEN_DECLARE_ENV* must be used in the global namespace"); \ - using value_type = type; \ - static miopen::internal::EnvVar& Ref() \ + using value_type = _type; \ + static ::miopen::env::detail::EnvVar<_type>& ref() \ { \ - static miopen::internal::EnvVar var{#name, default_val}; \ + static ::miopen::env::detail::EnvVar<_type> var{#_name, __VA_ARGS__}; \ return var; \ } \ - }; \ - } + operator ::miopen::env::detail::EnvVar<_type>&() const { return ref(); } \ + operator bool() const { return ref().exist(); } \ + constexpr std::string_view GetName() const { return #_name; } \ + } _name; -#define MIOPEN_DECLARE_ENV_VAR_BOOL(name) MIOPEN_DECLARE_ENV_VAR(name, bool, false) +#define MIOPEN_DECLARE_ENV_VAR_BOOL(name, ...) MIOPEN_DECLARE_ENV_VAR(name, bool, __VA_ARGS__) -#define MIOPEN_DECLARE_ENV_VAR_UINT64(name) MIOPEN_DECLARE_ENV_VAR(name, uint64_t, 0) +#define MIOPEN_DECLARE_ENV_VAR_UINT64(name, ...) \ + MIOPEN_DECLARE_ENV_VAR(name, unsigned long long, __VA_ARGS__) -#define MIOPEN_DECLARE_ENV_VAR_STR(name) MIOPEN_DECLARE_ENV_VAR(name, std::string, "") +#define MIOPEN_DECLARE_ENV_VAR_STR(name, ...) MIOPEN_DECLARE_ENV_VAR(name, std::string, __VA_ARGS__) -#define ENV(name) \ - miopen::env::name {} +inline bool disabled(const detail::EnvVar& t) { return !t.template value(true); } -/// \todo the following functions should be renamed to either include the word Env -/// or put inside a namespace 'env'. Right now we have a function named Value() -/// that returns env var value as only 64-bit ints +inline bool enabled(const detail::EnvVar& t) { return t.template value(); } -template -inline const std::string& GetStringEnv(EnvVar) +template +inline U value(T t) { - static_assert(std::is_same_v); - return EnvVar::Ref().GetValue(); + return std::forward(t).ref().template value(); } -template -inline bool IsEnabled(EnvVar) +template +inline U value_or(T t, U k) { - static_assert(std::is_same_v); - return !EnvVar::Ref().IsUnset() && EnvVar::Ref().GetValue(); + return std::forward(t).ref().template value(std::forward(k)); } -template -inline bool IsDisabled(EnvVar) -{ - static_assert(std::is_same_v); - return !EnvVar::Ref().IsUnset() && !EnvVar::Ref().GetValue(); -} - -template -inline uint64_t Value(EnvVar) -{ - static_assert(std::is_same_v); - return EnvVar::Ref().GetValue(); -} - -template -inline bool IsUnset(EnvVar) -{ - return EnvVar::Ref().IsUnset(); -} - -template -void Unset(EnvVar) +template +inline std::string name(T t) { - EnvVar::Ref().Unset(); + return std::string{std::forward(t).ref().name()}; } -/// updates the cached value of an environment variable -template -void UpdateEnvVar(EnvVar, const ValueType& val) +template +void clear(T t) { - static_assert(std::is_same_v); - EnvVar::Ref().UpdateValue(val); + std::forward(t).ref().clear(); } -template -void UpdateEnvVar(EnvVar, const std::string_view& val) +template +inline void update(T t, U k) { - EnvVar::Ref().UpdateValue( - miopen::internal::ParseEnvVal::go(val.data())); + std::forward(t).ref().template update(std::forward(k)); } -} // namespace miopen +} // namespace miopen::env -#endif +#endif // GUARD_MIOPEN_ENV_HPP diff --git a/src/include/miopen/errors.hpp b/src/include/miopen/errors.hpp index a82db5a0f4..a2558455d3 100644 --- a/src/include/miopen/errors.hpp +++ b/src/include/miopen/errors.hpp @@ -54,8 +54,8 @@ struct Exception : std::exception const char* what() const noexcept override { return message.c_str(); } }; -std::string OpenCLErrorMessage(int error, const std::string& msg = ""); -std::string HIPErrorMessage(int error, const std::string& msg = ""); +MIOPEN_EXPORT std::string OpenCLErrorMessage(int error, const std::string& msg = ""); +MIOPEN_EXPORT std::string HIPErrorMessage(int error, const std::string& msg = ""); template [[noreturn]] void MIOpenThrow(const std::string& file, int line, Params&&... args) @@ -127,6 +127,19 @@ auto deref(T&& x, [[maybe_unused]] miopenStatus_t err = miopenStatusBadParm) template auto tie_deref(Ts&... xs) MIOPEN_RETURNS(std::tie(miopen::deref(xs)...)); +template +Ptr checkPtr(Ptr ptr, [[maybe_unused]] miopenStatus_t err = miopenStatusBadParm) +{ + if(ptr != nullptr) + { + return ptr; + } + else + { + MIOPEN_THROW(err, "Passing nullptr"); + } +} + } // namespace miopen #endif diff --git a/src/include/miopen/execution_context.hpp b/src/include/miopen/execution_context.hpp index 84a4178488..32660006ae 100644 --- a/src/include/miopen/execution_context.hpp +++ b/src/include/miopen/execution_context.hpp @@ -37,6 +37,7 @@ #include #include +#include class rocm_meta_version { @@ -76,7 +77,7 @@ MIOPEN_EXPORT extern bool } // namespace debug -struct ExecutionContext +struct MIOPEN_INTERNALS_EXPORT ExecutionContext { // Solution-specific std::string general_compile_options; @@ -84,11 +85,9 @@ struct ExecutionContext // Operation modes & environment bool do_search = false; bool db_update = false; - bool save_srch_req = false; bool use_asm_kernels = false; bool use_hip_kernels = true; bool use_opencl_convolutions = true; - bool use_binaries = true; rocm_meta_version rmv = rocm_meta_version::Default; bool disable_search_enforce = false; // Skip perf-db reads and use the default performance configuration. This is used, for example, @@ -112,11 +111,13 @@ struct ExecutionContext ExecutionContext& operator=(ExecutionContext&&) = default; #if MIOPEN_EMBED_DB - std::string GetPerfDbPathEmbed() const + fs::path GetPerfDbPathEmbed(std::string_view prefix = "") const { static const auto result = [&] { - fs::path pdb_path(GetSystemDbPath()); + auto pdb_path(GetSystemDbPath()); std::ostringstream filename; + if(!prefix.empty()) + filename << prefix << '_'; // clang-format off filename << GetStream().GetDbBasename(); #if MIOPEN_ENABLE_SQLITE && MIOPEN_USE_SQLITE_PERFDB @@ -126,10 +127,10 @@ struct ExecutionContext #endif filename << ext; // clang-format on - if(miopen_data().find(filename.str() + ".o") != miopen_data().end()) + if(miopen_data().find(fs::path(filename.str() + ".o")) != miopen_data().end()) { MIOPEN_LOG_I("Found exact embedded perf database file"); - return (pdb_path / filename.str()).string(); + return pdb_path / filename.str(); } else { @@ -141,7 +142,7 @@ struct ExecutionContext for(auto const& entry : miopen_data()) { // string the .o from the filename - const auto fname = entry.first.substr(0, entry.first.size() - 2); + const auto fname = entry.first.stem().string(); MIOPEN_LOG_I2("Testing embedded file:" << fname); const auto& filepath = pdb_path / fname; if(filepath.extension() == ext && @@ -172,38 +173,40 @@ struct ExecutionContext } } } - return best_path.string(); + return best_path; } - return std::string(); + return fs::path(); }(); return result; } #else - std::string GetPerfDbPathFile() const + fs::path GetPerfDbPathFile(std::string_view prefix = "") const { static const auto result = [&] { - const fs::path pdb_path(GetSystemDbPath()); - std::ostringstream filename; - // clang-format off - filename << GetStream().GetDbBasename(); + const auto pdb_path(GetSystemDbPath()); #if MIOPEN_ENABLE_SQLITE && MIOPEN_USE_SQLITE_PERFDB - const std::string ext = ".db"; + constexpr std::string_view ext = ".db"; #else - const std::string ext = ".db.txt"; + constexpr std::string_view ext = ".db.txt"; #endif - filename << ext; + std::string filename{prefix}; + if(!prefix.empty()) + filename.append("_"); + filename.append(GetStream().GetDbBasename()); + filename.append(ext); + // clang-format on - if(fs::exists(pdb_path / filename.str())) + if(fs::exists(pdb_path / filename)) { MIOPEN_LOG_I("Found exact perf database file"); - return (pdb_path / filename.str()).string(); + return pdb_path / filename; } else { MIOPEN_LOG_I2("inexact perf database search"); const auto db_id = GetStream().GetTargetProperties().DbId(); const int real_cu_count = GetStream().GetMaxComputeUnits(); - if(fs::exists(pdb_path) && fs::is_directory(pdb_path)) + if(fs::is_directory(pdb_path)) { MIOPEN_LOG_I2("Iterating over perf db directory " << pdb_path); int closest_cu = std::numeric_limits::max(); @@ -216,7 +219,7 @@ struct ExecutionContext { const auto fname = filepath.stem().string(); if(fs::is_regular_file(filepath) && filepath.extension() == ext && - fname.rfind(db_id, 0) == 0) // starts with db_id + fname.rfind(db_id, 0) == 0) { MIOPEN_LOG_I2("Checking perf db file: " << fname); const auto pos = fname.find('_'); @@ -248,43 +251,45 @@ struct ExecutionContext } } } - return best_path.string(); + return best_path; } else { MIOPEN_LOG_I("Database directory does not exist"); } } - return std::string(); + return fs::path{}; }(); return result; } #endif - std::string GetPerfDbPath() const + fs::path GetPerfDbPath(std::string_view prefix = "") const { #if MIOPEN_EMBED_DB - return GetPerfDbPathEmbed(); + return GetPerfDbPathEmbed(prefix); #else - return GetPerfDbPathFile(); + return GetPerfDbPathFile(prefix); #endif } - std::string GetUserPerfDbPath() const + fs::path GetUserPerfDbPath(std::string_view prefix = "") const { // an empty user-db path indicates user intent to disable // the database. Default in when dev builds are on const auto& udb = GetUserDbPath(); if(udb.empty()) return ""; - std::ostringstream filename; - filename << GetStream().GetDbBasename(); + std::string filename{prefix}; + if(!prefix.empty()) + filename.append("_"); + filename.append(GetStream().GetDbBasename()); #if MIOPEN_ENABLE_SQLITE && MIOPEN_USE_SQLITE_PERFDB - filename << "_" << SQLitePerfDb::MIOPEN_PERFDB_SCHEMA_VER << ".udb"; + filename.append("_" + std::string{SQLitePerfDb::MIOPEN_PERFDB_SCHEMA_VER} + ".udb"); #else - filename << "." << GetUserDbSuffix() << ".udb.txt"; + filename.append("." + GetUserDbSuffix() + ".udb.txt"); #endif - return (udb / filename.str()).string(); + return udb / filename; } private: @@ -297,6 +302,6 @@ struct [[deprecated]] ConvolutionContext : ExecutionContext { }; -bool IsHipKernelsEnabled(); +MIOPEN_INTERNALS_EXPORT bool IsHipKernelsEnabled(); } // namespace miopen diff --git a/src/include/miopen/expanduser.hpp b/src/include/miopen/expanduser.hpp index 0fd47d2c3f..07e56110fc 100644 --- a/src/include/miopen/expanduser.hpp +++ b/src/include/miopen/expanduser.hpp @@ -26,12 +26,13 @@ #ifndef MIOPEN_GUARD_MLOPEN_EXPANDUSER_HPP #define MIOPEN_GUARD_MLOPEN_EXPANDUSER_HPP +#include #include #include namespace miopen { -fs::path ExpandUser(const std::string& path); +MIOPEN_INTERNALS_EXPORT fs::path ExpandUser(const fs::path& path); bool IsNetworkedFilesystem(const fs::path&); } // namespace miopen diff --git a/src/include/miopen/filesystem.hpp b/src/include/miopen/filesystem.hpp index d1e4296fde..9d65a2630e 100644 --- a/src/include/miopen/filesystem.hpp +++ b/src/include/miopen/filesystem.hpp @@ -145,6 +145,10 @@ inline fs::path make_static_library_name(const fs::path& path) return path.parent_path() / (library_prefix + path.filename() + static_library_postfix); } +struct FsPathHash +{ + std::size_t operator()(const fs::path& path) const { return fs::hash_value(path); } +}; } // namespace miopen #endif // GUARD_MIOPEN_FILESYSTEM_HPP_ diff --git a/src/include/miopen/find_controls.hpp b/src/include/miopen/find_controls.hpp index d62937b89b..50d3ec5ee4 100644 --- a/src/include/miopen/find_controls.hpp +++ b/src/include/miopen/find_controls.hpp @@ -60,7 +60,7 @@ enum class FindEnforceAction EnforcedLast_ = DbClean, }; -class FindEnforce +class MIOPEN_INTERNALS_EXPORT FindEnforce { FindEnforceAction action; @@ -102,12 +102,12 @@ class FindEnforce action <= FindEnforceAction::EnforcedLast_); } - friend std::ostream& operator<<(std::ostream&, const FindEnforce&); + MIOPEN_INTERNALS_EXPORT friend std::ostream& operator<<(std::ostream&, const FindEnforce&); }; -boost::optional> GetEnvFindOnlySolver(); +MIOPEN_INTERNALS_EXPORT boost::optional> GetEnvFindOnlySolver(); -class FindMode +class MIOPEN_INTERNALS_EXPORT FindMode { public: enum class Values @@ -137,7 +137,8 @@ class FindMode } public: - FindMode(); + // Todo: remove default value of primitive + FindMode(solver::Primitive primitive = solver::Primitive::Convolution); Values Get() const { return value; } void Set(Values const v) { value = v; } @@ -159,7 +160,7 @@ class FindMode return value == Values::DynamicHybrid && IsEnabled(context); } - friend std::ostream& operator<<(std::ostream&, const FindMode&); + MIOPEN_INTERNALS_EXPORT friend std::ostream& operator<<(std::ostream&, const FindMode&); }; } // namespace miopen diff --git a/src/include/miopen/find_db.hpp b/src/include/miopen/find_db.hpp index 2e6418aeab..a5c1b806bd 100644 --- a/src/include/miopen/find_db.hpp +++ b/src/include/miopen/find_db.hpp @@ -35,6 +35,8 @@ #include #include #include +#include +#include #include @@ -68,7 +70,7 @@ namespace debug { // For unit tests. MIOPEN_EXPORT extern bool testing_find_db_enabled; // NOLINT (cppcoreguidelines-avoid-non-const-global-variables) -MIOPEN_EXPORT extern boost::optional& +MIOPEN_EXPORT extern boost::optional& testing_find_db_path_override(); /// \todo Remove when #1723 is resolved. } // namespace debug @@ -98,7 +100,7 @@ class FindDbRecord_t ? *debug::testing_find_db_path_override() : GetInstalledPath(handle, path_suffix)), db(boost::make_optional>( - debug::testing_find_db_enabled && !IsEnabled(ENV(MIOPEN_DEBUG_DISABLE_FIND_DB)), + debug::testing_find_db_enabled && !env::enabled(MIOPEN_DEBUG_DISABLE_FIND_DB), DbTimer{DbKinds::FindDb, installed_path, path})) { if(!db.is_initialized()) @@ -119,7 +121,7 @@ class FindDbRecord_t db(boost::optional>{DbKinds::FindDb}) #else db(boost::make_optional>(debug::testing_find_db_enabled && - !IsEnabled(ENV(MIOPEN_DEBUG_DISABLE_FIND_DB)), + !env::enabled(MIOPEN_DEBUG_DISABLE_FIND_DB), DbTimer{DbKinds::FindDb, path, false})) #endif { @@ -132,7 +134,7 @@ class FindDbRecord_t ~FindDbRecord_t() { - if(!db.is_initialized() || !content.is_initialized() || in_sync) + if(dont_store || !db.is_initialized() || !content.is_initialized() || in_sync) return; if(!db->StoreRecord(content.get())) MIOPEN_LOG_E("Failed to store record to find-db at <" << path << ">"); @@ -145,47 +147,59 @@ class FindDbRecord_t bool empty() const { return !content.is_initialized(); } template - static std::vector TryLoad(Handle& handle, - const TProblemDescription& problem, - const std::function& regenerator, - const std::string& path_suffix = "") + static std::vector TryLoad(Handle& handle, + const TProblemDescription& problem, + const std::function& regenerator, + const std::string& path_suffix = "") { - auto ret = std::vector{}; FindDbRecord_t record{handle, problem, path_suffix}; const auto network_config = problem.MakeNetworkConfig(); if(record.in_sync && !record.Validate(handle, network_config)) { - record.CopyTo(ret); - return ret; + auto solutions = std::vector{}; + record.CopyTo(solutions); + return solutions; } MIOPEN_LOG_I("Find-db regenerating."); - ret.clear(); record.in_sync = false; record.content.emplace(DbKinds::FindDb, problem); - regenerator(*record.content); - record.CopyTo(ret); - return ret; + const auto result = regenerator(); + record.dont_store = !result.is_optimal; + + if(record.dont_store) + return result.solutions; + + for(const auto& solution : result.solutions) + { + const auto algo = solution.GetSolver().GetAlgo(problem.GetDirection()); + record.content->SetValues( + solution.GetSolver().ToString(), + FindDbData{solution.GetTime(), solution.GetWorkspaceSize(), algo}); + } + + return result.solutions; } private: - std::string path; - std::string installed_path; + fs::path path; + fs::path installed_path; boost::optional> db; boost::optional content{boost::none}; - bool in_sync = false; + bool in_sync = false; + bool dont_store = false; // E.g. to skip writing sub-optimal find-db records to disk. - static std::string GetInstalledPath(Handle& handle, const std::string& path_suffix); - static std::string GetInstalledPathEmbed(Handle& handle, const std::string& path_suffix); - static std::string GetInstalledPathFile(Handle& handle, const std::string& path_suffix); - static std::string GetUserPath(Handle& handle, const std::string& path_suffix); + static fs::path GetInstalledPath(Handle& handle, const std::string& path_suffix); + static fs::path GetInstalledPathEmbed(Handle& handle, const std::string& path_suffix); + static fs::path GetInstalledPathFile(Handle& handle, const std::string& path_suffix); + static fs::path GetUserPath(Handle& handle, const std::string& path_suffix); // Returns true if rebuild is required bool Validate(Handle& handle, const NetworkConfig& config) const; - void CopyTo(std::vector& to) const; + void CopyTo(std::vector& to) const; void LogFindDbItem(const std::pair& item) const; }; diff --git a/src/include/miopen/find_solution.hpp b/src/include/miopen/find_solution.hpp index b8d4e08af6..30735cd38a 100644 --- a/src/include/miopen/find_solution.hpp +++ b/src/include/miopen/find_solution.hpp @@ -27,16 +27,21 @@ #ifndef MIOPEN_GUARD_MLOPEN_FIND_SOLUTION_HPP #define MIOPEN_GUARD_MLOPEN_FIND_SOLUTION_HPP +#include "miopen/miopen.h" #include +#include #include #include #include #include +#include #include #include #include #include +#include +#include #include namespace miopen { @@ -50,12 +55,15 @@ auto FindSolutionImpl(rank<1>, Solver s, const Context& context, const Problem& problem, - Db& db, + Db&& db, const AnyInvokeParams& invoke_ctx, const std::string& perf_cfg, const std::optional& options) -> decltype(s.GetSolution(context, problem, s.Search(context, problem, invoke_ctx))) { + static_assert(std::is_invocable_v, + "db is meant to be a functor returning a reference to perfdb"); + const FindEnforce enforce = options && options->find_enforce ? *options->find_enforce : FindEnforce{}; if(context.disable_perfdb_access) @@ -66,7 +74,7 @@ auto FindSolutionImpl(rank<1>, MIOPEN_LOG_I(s.SolverDbId()); if(enforce.IsDbClean(context)) { - if(db.Remove(problem, s.SolverDbId())) + if(db().Remove(problem, s.SolverDbId())) MIOPEN_LOG_W("Perf Db: record removed: " << s.SolverDbId() << ", enforce: " << enforce); } else @@ -91,7 +99,7 @@ auto FindSolutionImpl(rank<1>, MIOPEN_LOG_WE("Invalid config loaded from Perf Db: " << s.SolverDbId() << ": " << config << ". Performance may degrade."); } - else if(db.Load(problem, s.SolverDbId(), config)) + else if(db().Load(problem, s.SolverDbId(), config)) { MIOPEN_LOG_I2("Perf Db: record loaded: " << s.SolverDbId()); if(s.IsValidPerformanceConfig(context, problem, config)) @@ -101,7 +109,7 @@ auto FindSolutionImpl(rank<1>, MIOPEN_LOG_WE("Invalid config loaded from Perf Db: " << s.SolverDbId() << ": " << config << ". Performance may degrade."); } - else if(!s.AltSolverDbId().empty() && db.Load(problem, s.AltSolverDbId(), config)) + else if(!s.AltSolverDbId().empty() && db().Load(problem, s.AltSolverDbId(), config)) { MIOPEN_LOG_I("Perf Db: alternate record loaded: " << s.AltSolverDbId()); if(s.IsValidPerformanceConfig(context, problem, config)) @@ -124,7 +132,7 @@ auto FindSolutionImpl(rank<1>, try { auto c = s.Search(context, problem, invoke_ctx); - db.Update(problem, s.SolverDbId(), c); + db().Update(problem, s.SolverDbId(), c); return s.GetSolution(context, problem, c); } catch(const miopen::Exception& ex) @@ -143,7 +151,7 @@ auto FindSolutionImpl(rank<0>, Solver s, const Context& context, const Problem& problem, - Db&, + Db&&, const AnyInvokeParams&, const std::string&, const std::optional&) @@ -153,6 +161,38 @@ auto FindSolutionImpl(rank<0>, return s.GetSolution(context, problem); } +template +auto GetInvokeFactoryImpl( + rank<1>, Solver s, const Context& context, const Problem& problem, const std::string& perf_cfg) + -> decltype(s.GetInvokerFactory(context, + problem, + s.GetDefaultPerformanceConfig(context, problem))) +{ + if(!perf_cfg.empty()) + { + using PerformanceConfig = decltype(s.GetDefaultPerformanceConfig(context, problem)); + PerformanceConfig config{}; + config.Deserialize(perf_cfg); + if(s.IsValidPerformanceConfig(context, problem, config)) + { + return s.GetInvokerFactory(context, problem, config); + } + MIOPEN_LOG_WE("Invalid config loaded from Perf Db: " << s.SolverDbId() << ": " << config + << ". Performance may degrade."); + } + + return s.GetInvokerFactory(context, problem, s.GetDefaultPerformanceConfig(context, problem)); +} + +template +auto GetInvokeFactoryImpl( + rank<0>, Solver s, const Context& context, const Problem& problem, const std::string&) + -> decltype(s.GetInvokerFactory(context, problem)) +{ + MIOPEN_LOG_I(s.SolverDbId() << " (not searchable)"); + return s.GetInvokerFactory(context, problem); +} + /// Finds optimized Solution. Generic method. /// /// Given the specific problem config, finds (hopefully) optimal @@ -163,20 +203,40 @@ template ConvSolution FindSolution(Solver s, const Context& context, const Problem& problem, - Db& db, + Db&& db, const AnyInvokeParams& invoke_ctx, const std::string& perf_cfg = "", const std::optional& options = std::nullopt) { static_assert(sizeof(Solver) == sizeof(SolverBase), "Solver must be stateless"); static_assert(std::is_base_of{}, "Not derived class of SolverBase"); + + decltype(auto) db_getter = [&]() -> decltype(auto) { + if constexpr(std::is_invocable_v) + return db; + else + return [&]() -> std::decay_t& { return db; }; + }(); + // TODO: This assumes all solutions are ConvSolution auto solution = - FindSolutionImpl(rank<1>{}, s, context, problem, db, invoke_ctx, perf_cfg, options); + FindSolutionImpl(rank<1>{}, s, context, problem, db_getter, invoke_ctx, perf_cfg, options); solution.solver_id = s.SolverDbId(); return solution; } +template +InvokerFactory GetInvokeFactory(Solver s, + const Context& context, + const Problem& problem, + const std::string& perf_cfg) +{ + static_assert(sizeof(Solver) == sizeof(SolverBase), "Solver must be stateless"); + static_assert(std::is_base_of{}, "Not derived class of SolverBase"); + // TODO: This assumes all solutions are ConvSolution + return GetInvokeFactoryImpl(rank<1>{}, s, context, problem, perf_cfg); +} + template struct SolverContainer { @@ -203,6 +263,13 @@ struct SolverContainer Solvers{}...); } + ///\todo: remove when AnySolver would be able to work with non-conv solvers + template + void Foreach(Functor&& receiver) + { + miopen::each_args([&](auto solver) { receiver(solver); }, Solvers{}...); + } + // Search for all applicable solutions among many solvers template std::vector @@ -266,8 +333,10 @@ struct SolverContainer std::vector SearchForSolutions(const ExecutionContext& ctx, const Problem& problem, - std::size_t limit = std::numeric_limits::max()) const + std::size_t limit = std::numeric_limits::max(), + const AnyInvokeParams& invoke_params = {}) const { + auto db_container = std::optional{}; std::vector ss; std::size_t count = 0; const auto find_only = GetEnvFindOnlySolver(); @@ -290,12 +359,29 @@ struct SolverContainer } else { - auto s = solver.GetSolution(ctx, problem); - s.solver_id = solver.SolverDbId(); + auto db = [&]() -> PerformanceDb& { + constexpr auto db_getter = + []([[maybe_unused]] const ExecutionContext& ctx, + [[maybe_unused]] const auto& problem) -> PerformanceDb { + if constexpr(IsTunable()) + return GetDb(ctx, problem); + else + MIOPEN_THROW(miopenStatusInternalError); + }; + + if(!db_container) + db_container.emplace(std::move(db_getter(ctx, problem))); + + return *db_container; + }; + + auto s = + FindSolution(solver, ctx, problem, db, invoke_params, "", std::nullopt); + if(s.Succeeded()) { ++count; - ss.push_back(s); + ss.emplace_back(std::move(s)); MIOPEN_LOG_I2(solver.SolverDbId() << ": Success."); } else @@ -309,25 +395,25 @@ struct SolverContainer } template - std::vector> - GetWorkspaceSizes(const Context& ctx, - const Problem& problem, - std::size_t limit = std::numeric_limits::max()) const + std::vector> GetWorkspaceSizes( + const Context& ctx, const Problem& problem, const bool simple_primitive = false) const { std::vector> res; const auto find_only = GetEnvFindOnlySolver(); - std::size_t count = 0; miopen::each_args( [&](auto solver) { - if(count >= limit) - return; - if(find_only && (std::find(find_only->begin(), find_only->end(), Id{solver.SolverDbId()}) == find_only->end())) { // Do nothing (and keep silence for the sake of Tuna), just skip. } - else if(!solver.MayNeedWorkspace()) + // The following optimization is required to avoid checks + // for solvers that have slow IsApplicable() and do not + // require workspace (like MLIR convolutions). However we + // do not want to use it for simple primitives, for example, + // the ones that ExecutePrimitive() which uses the first applicable + // solver: + else if(!simple_primitive && !solver.MayNeedWorkspace()) { MIOPEN_LOG_I2(solver.SolverDbId() << ": Skipped (no workspace required)"); } @@ -343,7 +429,6 @@ struct SolverContainer } else { - ++count; auto sz = solver.GetWorkspaceSize(ctx, problem); res.push_back(std::make_pair(solver.SolverDbId(), sz)); MIOPEN_LOG_I2(solver.SolverDbId() << ": " << sz); @@ -389,21 +474,21 @@ struct SolverContainer } template - void ExecutePrimitive(Handle& handle, + void ExecutePrimitive(const ExecutionContext& ctx, const Problem& problem, const AlgorithmName& algo, const AnyInvokeParams& invoke_params) const { const auto network_config = problem.MakeNetworkConfig(); - if(const auto existingInvoker = handle.GetInvoker(network_config, boost::none, algo)) + if(const auto existingInvoker = + ctx.GetStream().GetInvoker(network_config, std::nullopt, algo)) { - (*existingInvoker)(handle, invoke_params); + (*existingInvoker)(ctx.GetStream(), invoke_params); return; } - auto ctx = ExecutionContext{&handle}; - const auto slns = SearchForSolutions(ctx, problem, 1); + const auto slns = SearchForSolutions(ctx, problem, 1, invoke_params); if(slns.empty()) MIOPEN_THROW(miopenStatusNotImplemented, "No solver found."); @@ -411,9 +496,19 @@ struct SolverContainer const auto& sln = slns.front(); if(!sln.invoker_factory) MIOPEN_THROW(miopenStatusInternalError, "Invoker missing in solver " + sln.solver_id); - const auto invoker = handle.PrepareInvoker(*sln.invoker_factory, sln.construction_params); - handle.RegisterInvoker(invoker, network_config, sln.solver_id, algo); - invoker(handle, invoke_params); + const auto invoker = + ctx.GetStream().PrepareInvoker(*sln.invoker_factory, sln.construction_params); + ctx.GetStream().RegisterInvoker(invoker, network_config, sln.solver_id, algo); + invoker(ctx.GetStream(), invoke_params); + } + + template + void ExecutePrimitive(Handle& handle, + const Problem& problem, + const AlgorithmName& algo, + const AnyInvokeParams& invoke_params) const + { + return ExecutePrimitive(&handle, problem, algo, invoke_params); } }; diff --git a/src/include/miopen/fusion.hpp b/src/include/miopen/fusion.hpp index 907a3b3874..70d306deeb 100644 --- a/src/include/miopen/fusion.hpp +++ b/src/include/miopen/fusion.hpp @@ -52,7 +52,7 @@ enum FusionKernelSourceType Binary, /// \todo Unused, consider removing. }; -struct FusionOpDescriptor : miopenFusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT FusionOpDescriptor : miopenFusionOpDescriptor { virtual ~FusionOpDescriptor() = default; FusionOpDescriptor(const FusionOpDescriptor&) = delete; @@ -70,7 +70,7 @@ struct FusionOpDescriptor : miopenFusionOpDescriptor int plan_idx = 0; }; -struct BiasFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT BiasFusionOpDescriptor : FusionOpDescriptor { BiasFusionOpDescriptor(const TensorDescriptor& desc) : base_desc(desc) {} miopenStatus_t GetOutputDesc(TensorDescriptor& output_desc) const override; @@ -81,7 +81,7 @@ struct BiasFusionOpDescriptor : FusionOpDescriptor TensorDescriptor base_desc; }; -struct TensorScaleAddOpDescriptor : public FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT TensorScaleAddOpDescriptor : public FusionOpDescriptor { TensorScaleAddOpDescriptor(const TensorDescriptor& desc) : tensor_desc(desc) {} miopenStatus_t GetOutputDesc(TensorDescriptor& output_desc) const override; @@ -91,7 +91,7 @@ struct TensorScaleAddOpDescriptor : public FusionOpDescriptor TensorDescriptor tensor_desc; }; -struct ActivFwdFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT ActivFwdFusionOpDescriptor : FusionOpDescriptor { ActivFwdFusionOpDescriptor(miopenActivationMode_t mode) : activMode(mode) {} miopenStatus_t GetOutputDesc(TensorDescriptor& output_desc) const override; @@ -106,7 +106,7 @@ struct ActivFwdFusionOpDescriptor : FusionOpDescriptor miopenActivationMode_t activMode; }; -struct ActivBwdFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT ActivBwdFusionOpDescriptor : FusionOpDescriptor { ActivBwdFusionOpDescriptor(miopenActivationMode_t mode) : activMode(mode) {} miopenStatus_t GetOutputDesc(TensorDescriptor& output_desc) const override; @@ -123,7 +123,7 @@ struct ActivBwdFusionOpDescriptor : FusionOpDescriptor miopenActivationMode_t activMode; }; -struct BatchNormInferenceFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT BatchNormInferenceFusionOpDescriptor : FusionOpDescriptor { BatchNormInferenceFusionOpDescriptor(miopenBatchNormMode_t bn_mode, const TensorDescriptor& desc) @@ -148,7 +148,7 @@ struct BatchNormInferenceFusionOpDescriptor : FusionOpDescriptor TensorDescriptor base_desc; }; -struct BatchNormFwdTrainFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT BatchNormFwdTrainFusionOpDescriptor : FusionOpDescriptor { BatchNormFwdTrainFusionOpDescriptor(miopenBatchNormMode_t bn_mode, bool runningMeanVariance) : mode(bn_mode), runningMeanVar(runningMeanVariance) @@ -182,7 +182,7 @@ struct BatchNormFwdTrainFusionOpDescriptor : FusionOpDescriptor bool runningMeanVar; }; -struct BatchNormBwdTrainFusionOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT BatchNormBwdTrainFusionOpDescriptor : FusionOpDescriptor { BatchNormBwdTrainFusionOpDescriptor(miopenBatchNormMode_t bn_mode) : mode(bn_mode), useBatchStats(true) @@ -215,7 +215,7 @@ struct BatchNormBwdTrainFusionOpDescriptor : FusionOpDescriptor bool useBatchStats; }; -struct ConvForwardOpDescriptor : FusionOpDescriptor +struct MIOPEN_INTERNALS_EXPORT ConvForwardOpDescriptor : FusionOpDescriptor { ConvForwardOpDescriptor(const ConvolutionDescriptor& conv_descriptor, const TensorDescriptor& filter_descriptor) @@ -240,11 +240,7 @@ struct ConvForwardOpDescriptor : FusionOpDescriptor conv::ProblemDescription GetConvProblem(); }; -namespace fusion { - -bool IsWinograd(const std::vector& ss); - -} // namespace fusion +MIOPEN_INTERNALS_EXPORT miopenStatus_t ConvBiasActivFusion(Handle& handle, const void* alpha1, const TensorDescriptor& xDesc, @@ -264,6 +260,7 @@ miopenStatus_t ConvBiasActivFusion(Handle& handle, const TensorDescriptor& yDesc, Data_t y); +MIOPEN_INTERNALS_EXPORT solver::ConvSolution MakeFusedSolution(const struct FusionContext& ctx, solver::Id id, const std::optional& perf_cfg_override, diff --git a/src/include/miopen/fusion/problem_description.hpp b/src/include/miopen/fusion/problem_description.hpp index 5a9d7554d0..bcb37878d9 100644 --- a/src/include/miopen/fusion/problem_description.hpp +++ b/src/include/miopen/fusion/problem_description.hpp @@ -41,6 +41,8 @@ struct FusionDescription : ProblemDescriptionBase #endif { const miopen::FusionPlanDescriptor* fusion_plan_desc; + bool disable_search_enforce = false; + FusionDescription(const miopen::FusionPlanDescriptor* ptr_desc) : fusion_plan_desc(ptr_desc) {} [[nodiscard]] NetworkConfig MakeNetworkConfig() const override @@ -86,6 +88,8 @@ struct FusionDescription : ProblemDescriptionBase } #endif + conv::Direction GetDirection() const { return conv::Direction::Forward; } + // This and the following method should be moved to the Ops once the return type can be unified conv::ProblemDescription GetConvProblem(size_t idx, conv::Direction dir, int bias = 0) const { diff --git a/src/include/miopen/fusion/solvers.hpp b/src/include/miopen/fusion/solvers.hpp index 781eba5a89..74aff5d9d5 100644 --- a/src/include/miopen/fusion/solvers.hpp +++ b/src/include/miopen/fusion/solvers.hpp @@ -40,89 +40,10 @@ namespace fusion { using FusionSolverBase = NonTunableSolverBase; -struct FusionTunableSolverBase : SolverMixin -{ - /// Initializes performance config to the default values. - /// The function may involve some heuristic to guess the best solution - /// configuration. It is assumed that the function takes constant time - /// to finish and does not run kernels to measure performance etc. - /// The function shall always return valid config. - /// - /// The int parameter is needed only to not change the name of the - /// function in the derived class. Function declarations that differ - /// only by its return type cannot be overloaded. - virtual boost::any GetDefaultPerformanceConfig(const FusionContext& ctx, - const FusionDescription& problem, - int) const = 0; - - /// Should return false if performance config is wrong for a problem. - /// Main use is validation of values read from the perf db. - virtual bool IsValidPerformanceConfig(const FusionContext& ctx, - const FusionDescription& problem, - const PerfConfig& config) const = 0; - - /// Search - /// - /// The int parameter is needed only to not change the name of the - /// function in the derived class. Function declarations that differ - /// only by its return type cannot be overloaded. - virtual boost::any Search(const FusionContext& ctx, - const FusionDescription& problem, - const AnyInvokeParams& invoke_ctx, - int) const = 0; - - /// Tunable solvers provide a GetSolution that takes a Context and PerformanceConfig - virtual ConvSolution GetSolution(const FusionContext& ctx, - const FusionDescription& problem, - const PerfConfig& config) const = 0; -}; - template -struct FusionTunableSolver : FusionTunableSolverBase -{ - static_assert(std::is_base_of{}, - "PerformanceConfig must be derived of PerfConfig"); - - virtual PerformanceConfig GetDefaultPerformanceConfig(const FusionContext&, - const FusionDescription&) const = 0; - virtual bool IsValidPerformanceConfig(const FusionContext&, - const FusionDescription&, - const PerformanceConfig&) const = 0; - virtual PerformanceConfig - Search(const FusionContext&, const FusionDescription&, const AnyInvokeParams&) const = 0; - virtual ConvSolution - GetSolution(const FusionContext&, const FusionDescription&, const PerformanceConfig&) const = 0; - - boost::any GetDefaultPerformanceConfig(const FusionContext& ctx, - const FusionDescription& problem, - int) const final - { - return GetDefaultPerformanceConfig(ctx, problem); - } - - bool IsValidPerformanceConfig(const FusionContext& ctx, - const FusionDescription& problem, - const PerfConfig& config) const final - { - return IsValidPerformanceConfig( - ctx, problem, dynamic_cast(config)); - } - - boost::any Search(const FusionContext& ctx, - const FusionDescription& problem, - const AnyInvokeParams& invoke_ctx, - int) const final - { - return Search(ctx, problem, invoke_ctx); - } - - ConvSolution GetSolution(const FusionContext& ctx, - const FusionDescription& problem, - const PerfConfig& config) const final - { - return GetSolution(ctx, problem, dynamic_cast(config)); - } -}; +using FusionTunableSolver = + TunableSolverMixin; +; struct PerformanceConfigConvBiasActivAsm1x1U : conv::PerformanceConfigConvAsm1x1U { @@ -131,34 +52,38 @@ struct PerformanceConfigConvBiasActivAsm1x1U : conv::PerformanceConfigConvAsm1x1 : PerformanceConfigConvAsm1x1U(-1, -1, -1, -1, -1, -1, -1, -1, false) { } - void HeuristicInit(const FusionContext& ctx, const FusionDescription& problem); - bool SetNextValue(const FusionDescription& problem); + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const FusionContext& ctx, + const FusionDescription& problem); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const FusionDescription& problem); bool IsValid(const FusionContext&, const FusionDescription& problem) const { return IsValid(problem); } - bool IsValid(const FusionDescription& problem) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const FusionDescription& problem) const; }; struct ConvBiasActivAsm1x1U : FusionTunableSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const FusionContext& context, const FusionDescription& problem, const PerformanceConfigConvBiasActivAsm1x1U& /*config*/) const override; - PerformanceConfigConvBiasActivAsm1x1U + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvBiasActivAsm1x1U GetDefaultPerformanceConfig(const FusionContext&, const FusionDescription&) const override; - PerformanceConfigConvBiasActivAsm1x1U + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvBiasActivAsm1x1U Search(const FusionContext& context, const FusionDescription& problem, const AnyInvokeParams& invoke_params) const override; - bool IsValidPerformanceConfig(const FusionContext&, - const FusionDescription&, - const PerformanceConfigConvBiasActivAsm1x1U&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const FusionContext&, + const FusionDescription&, + const PerformanceConfigConvBiasActivAsm1x1U&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const FusionContext&, + const FusionDescription&) const override; }; using PerformanceConfigConvOclDirectFwdFused = LegacyPerformanceConfig; @@ -169,20 +94,24 @@ struct ConvOclDirectFwdFused final : FusionTunableSolver(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& problem, - const PerformanceConfigConvOclDirectFwdFused&) const override; - PerformanceConfigConvOclDirectFwdFused + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const FusionContext& context, + const FusionDescription& problem, + const PerformanceConfigConvOclDirectFwdFused&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvOclDirectFwdFused GetDefaultPerformanceConfig(const FusionContext&, const FusionDescription&) const override; - PerformanceConfigConvOclDirectFwdFused + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvOclDirectFwdFused Search(const FusionContext&, const FusionDescription&, const AnyInvokeParams& invoke_params) const override; - bool IsValidPerformanceConfig(const FusionContext&, - const FusionDescription&, - const PerformanceConfigConvOclDirectFwdFused&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const FusionContext&, + const FusionDescription&, + const PerformanceConfigConvOclDirectFwdFused&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const FusionContext&, + const FusionDescription& problem) const override; }; struct PerformanceConfigConvCKIgemmFwdBiasActivFused @@ -203,17 +132,19 @@ struct PerformanceConfigConvCKIgemmFwdBiasActivFused : PerformanceConfigConvCKIgemmFwdBiasActivFused(0, "") { } - void HeuristicInit(const FusionDescription& fdesc_problem); - bool SetNextValue(const FusionDescription& fdesc_problem); - bool IsValidValue() const; - bool IsValid(const FusionContext&, const FusionDescription& fdesc_problem) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const FusionDescription& fdesc_problem); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const FusionDescription& fdesc_problem); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const FusionContext&, + const FusionDescription& fdesc_problem) const; template static void Visit(Self&& s, F f) { f(s.kernel_id, "kernel_id"); } - bool operator==(const PerformanceConfigConvCKIgemmFwdBiasActivFused& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigConvCKIgemmFwdBiasActivFused& other) const; private: template @@ -230,20 +161,20 @@ struct ConvCKIgemmFwdBiasActivFused final return GetSolverDbId(); } - PerformanceConfigConvCKIgemmFwdBiasActivFused + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvCKIgemmFwdBiasActivFused GetDefaultPerformanceConfig(const FusionContext& ctx, const FusionDescription& fdesc_problem) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const FusionContext& ctx, const FusionDescription& fdesc_problem, const PerformanceConfigConvCKIgemmFwdBiasActivFused& config) const override; - PerformanceConfigConvCKIgemmFwdBiasActivFused + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvCKIgemmFwdBiasActivFused Search(const FusionContext& ctx, const FusionDescription& fdesc_problem, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const FusionContext& ctx, - const FusionDescription& fdesc_problem) const override; - ConvSolution + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const FusionContext& ctx, const FusionDescription& fdesc_problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const FusionContext& ctx, const FusionDescription& fdesc_problem, const PerformanceConfigConvCKIgemmFwdBiasActivFused& config) const override; @@ -271,17 +202,19 @@ struct PerfConfigConvCKIgemmFwdBiasResAddActivFused : PerfConfigConvCKIgemmFwdBiasResAddActivFused(0, "") { } - void HeuristicInit(const FusionDescription& fdesc_problem); - bool SetNextValue(const FusionDescription& fdesc_problem); - bool IsValidValue() const; - bool IsValid(const FusionContext&, const FusionDescription& fdesc_problem) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const FusionDescription& fdesc_problem); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const FusionDescription& fdesc_problem); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const FusionContext&, + const FusionDescription& fdesc_problem) const; template static void Visit(Self&& s, F f) { f(s.kernel_id, "kernel_id"); } - bool operator==(const PerfConfigConvCKIgemmFwdBiasResAddActivFused& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerfConfigConvCKIgemmFwdBiasResAddActivFused& other) const; private: template @@ -298,20 +231,20 @@ struct ConvCKIgemmFwdBiasResAddActivFused final return GetSolverDbId(); } - PerfConfigConvCKIgemmFwdBiasResAddActivFused + MIOPEN_INTERNALS_EXPORT PerfConfigConvCKIgemmFwdBiasResAddActivFused GetDefaultPerformanceConfig(const FusionContext& ctx, const FusionDescription& fdesc_problem) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const FusionContext& ctx, const FusionDescription& fdesc_problem, const PerfConfigConvCKIgemmFwdBiasResAddActivFused& config) const override; - PerfConfigConvCKIgemmFwdBiasResAddActivFused + MIOPEN_INTERNALS_EXPORT PerfConfigConvCKIgemmFwdBiasResAddActivFused Search(const FusionContext& ctx, const FusionDescription& fdesc_problem, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const FusionContext& ctx, - const FusionDescription& fdesc_problem) const override; - ConvSolution + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const FusionContext& ctx, const FusionDescription& fdesc_problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const FusionContext& ctx, const FusionDescription& fdesc_problem, const PerfConfigConvCKIgemmFwdBiasResAddActivFused& config) const override; @@ -320,6 +253,7 @@ struct ConvCKIgemmFwdBiasResAddActivFused final template bool CheckCKApplicability(const miopen::conv::ProblemDescription&) const; }; + struct ConvBinWinogradRxSFused final : FusionSolverBase { const std::string& SolverDbId() const override @@ -327,10 +261,13 @@ struct ConvBinWinogradRxSFused final : FusionSolverBase return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& fdesc_problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& fdesc_problem) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const FusionContext& context, + const FusionDescription& fdesc_problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution( + const FusionContext& context, const FusionDescription& fdesc_problem) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const FusionContext&, + const FusionDescription&) const override; }; struct ConvBinWinogradRxSf2x3g1Fused final : FusionSolverBase @@ -340,12 +277,36 @@ struct ConvBinWinogradRxSf2x3g1Fused final : FusionSolverBase return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const FusionContext& context, const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const FusionContext&, + const FusionDescription&) const override; }; +template +struct ConvWinoFuryRxSFused final : FusionSolverBase +{ + const std::string& SolverDbId() const override + { + return GetSolverDbId>(); + } + + bool IsApplicable(const FusionContext&, const FusionDescription&) const override; + bool IsDynamic() const override { return true; } + float GetWti(const FusionContext&, const FusionDescription&) const override; + size_t GetWorkspaceSize(const FusionContext&, const FusionDescription&) const override; + bool MayNeedWorkspace() const override { return true; } + + ConvSolution GetSolution(const FusionContext&, const FusionDescription&) const override; +}; + +#ifndef CONV_WINO_FURY_RXS_CPP +extern template struct ConvWinoFuryRxSFused<2, 3>; +// extern template struct ConvWinoFuryRxSFused<3, 2>; +#endif + struct BnFwdInferActivationFused final : FusionSolverBase { const std::string& SolverDbId() const override @@ -353,10 +314,10 @@ struct BnFwdInferActivationFused final : FusionSolverBase return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const FusionContext& context, const FusionDescription& problem) const override; }; struct BnFwdTrgActivationFused final : FusionSolverBase @@ -366,10 +327,10 @@ struct BnFwdTrgActivationFused final : FusionSolverBase return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const FusionContext& context, const FusionDescription& problem) const override; }; struct BnBwdTrgActivationFused final : FusionSolverBase @@ -379,10 +340,10 @@ struct BnBwdTrgActivationFused final : FusionSolverBase return GetSolverDbId(); } - bool IsApplicable(const FusionContext& context, - const FusionDescription& problem) const override; - ConvSolution GetSolution(const FusionContext& context, - const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool IsApplicable(const FusionContext& context, + const FusionDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const FusionContext& context, const FusionDescription& problem) const override; }; } // namespace fusion diff --git a/src/include/miopen/fusion_ops.hpp b/src/include/miopen/fusion_ops.hpp index c458a1c55d..88fe6f094d 100644 --- a/src/include/miopen/fusion_ops.hpp +++ b/src/include/miopen/fusion_ops.hpp @@ -27,9 +27,7 @@ #ifndef MIOPEN_GUARD_MLOPEN_FUSION_OPS_HPP #define MIOPEN_GUARD_MLOPEN_FUSION_OPS_HPP -#include -#include -#include +#include #include namespace miopen { @@ -47,34 +45,8 @@ enum miopenFusionOp_t miopenFusionOpTensorScaleAdd = 7, }; -enum MDGraph_op_t -{ - OpEqual, // of the same type and equal - OpNotEqual, // Not Equal - OpAny, // Dont care, used for metadata - OpModulo, // op_val.val % edg_val.val == edg_val.result (only supported for ints) - OpGTE, // op_val.val >= edg_val.val (only supported for ints) - OpLTE, // op_val.val <= edg_val.val (only supported for ints) - OpEval, // Evaluate the string expression - OpAdd, - OpSub, - OpMul, - OpDiv, - OpPow, - OpAnd, - OpOr, - OpCeil, - OpAssign, - OpGT, - OpLT, -}; - -std::ostream& operator<<(std::ostream& stream, const MDGraph_op_t& o); std::ostream& operator<<(std::ostream& stream, const boost::any& a); -// using FusionMDGraph_Op_Map = std::unordered_map; -using FusionMDGraph_Edge_Map = std::unordered_map>; -using FusionMDGraph_Edge_Map_Vec = std::vector; } // namespace miopen #endif diff --git a/src/include/miopen/fusion_plan.hpp b/src/include/miopen/fusion_plan.hpp index e78cdbeda5..439e58a80d 100644 --- a/src/include/miopen/fusion_plan.hpp +++ b/src/include/miopen/fusion_plan.hpp @@ -2,6 +2,7 @@ #ifndef MIOPEN_GUARD_MLOPEN_FUSION_PLAN_HPP #define MIOPEN_GUARD_MLOPEN_FUSION_PLAN_HPP +#include #include #include #include @@ -15,7 +16,7 @@ namespace solver { struct ConvSolution; } // namespace solver -enum Exec_Arg_Type_t +enum class Exec_Arg_Type_t { Scalar, Input_Ptr, @@ -42,7 +43,7 @@ struct Exec_arg_t }; struct FusionContext; -struct FusionPlanDescriptor : miopenFusionPlanDescriptor +struct MIOPEN_INTERNALS_EXPORT FusionPlanDescriptor : miopenFusionPlanDescriptor { FusionPlanDescriptor() {} FusionPlanDescriptor(miopenFusionDirection_t dir, const TensorDescriptor& inDesc); @@ -58,10 +59,9 @@ struct FusionPlanDescriptor : miopenFusionPlanDescriptor Data_t output, const OperatorArgs& op_args); miopenStatus_t Compile(Handle& handle); - std::vector - Find(Handle& handle, - const std::function& invoke_params, - const std::optional& options = std::nullopt) const; + std::vector Find(Handle& handle, + const std::function& invoke_params, + const std::optional& options = std::nullopt) const; friend std::ostream& operator<<(std::ostream& stream, const FusionPlanDescriptor& fpd); miopenStatus_t diff --git a/src/include/miopen/gcn_asm_utils.hpp b/src/include/miopen/gcn_asm_utils.hpp index e6fc094b10..6f14b15356 100644 --- a/src/include/miopen/gcn_asm_utils.hpp +++ b/src/include/miopen/gcn_asm_utils.hpp @@ -33,8 +33,8 @@ bool ValidateGcnAssembler(); #if !MIOPEN_USE_COMGR -std::string AmdgcnAssemble(const std::string& source, - const std::string& params, +std::string AmdgcnAssemble(std::string_view source, + std::string_view params, const miopen::TargetProperties& target); #endif diff --git a/src/include/miopen/gemm_v2.hpp b/src/include/miopen/gemm_v2.hpp index 57882b3cf6..4e741403fc 100644 --- a/src/include/miopen/gemm_v2.hpp +++ b/src/include/miopen/gemm_v2.hpp @@ -40,13 +40,7 @@ enum GemmBackend_t { nogemmbackend = 0, rocblas = 1, -}; - -enum CallGemmType_t -{ - callGemm = 0, - callGemmStridedBatched = 1, - callGemmStridedBatchedSequential = 2, + hipblaslt = 2, }; // GEMM operation: C = alpha * op(A) * op(B) + beta * C. @@ -122,18 +116,6 @@ struct GemmDescriptor friend std::ostream& operator<<(std::ostream& stream, const GemmDescriptor& gemm_desc); }; -miopenStatus_t CallGemmTimeMeasure(const Handle& handle, - GemmDescriptor gemm_desc, - ConstData_t A, - std::size_t a_offset, - ConstData_t B, - std::size_t b_offset, - Data_t C, - std::size_t c_offset, - bool time_precision, - CallGemmType_t call_gemm_type, - GemmBackend_t gemm_backend = GemmBackend_t::rocblas); - MIOPEN_EXPORT miopenStatus_t CallGemm(const Handle& handle, GemmDescriptor gemm_desc, diff --git a/src/include/miopen/general_tensor_reorder_sol.hpp b/src/include/miopen/general_tensor_reorder_sol.hpp index 0df1d9cd0c..723b14115f 100644 --- a/src/include/miopen/general_tensor_reorder_sol.hpp +++ b/src/include/miopen/general_tensor_reorder_sol.hpp @@ -44,7 +44,7 @@ struct GeneralReorderParam int ediv_y{0}; }; -struct GenericReorderSolutionImpl +struct MIOPEN_INTERNALS_EXPORT GenericReorderSolutionImpl { GenericReorderSolutionImpl(miopenDataType_t data_type_, uint32_t dim_0_, @@ -58,7 +58,7 @@ struct GenericReorderSolutionImpl // TODO batched transpose API solver::KernelInfo GetKernelInfo() const; std::vector GetKernelArg() const; - std::string GetKernelFileName() const; + fs::path GetKernelFileName() const; std::string GetKernelName() const; bool IsSkippable() const; size_t GetOutputTensorSize() const; diff --git a/src/include/miopen/generic_search.hpp b/src/include/miopen/generic_search.hpp index c3aeea319f..78acaaac6c 100644 --- a/src/include/miopen/generic_search.hpp +++ b/src/include/miopen/generic_search.hpp @@ -28,7 +28,7 @@ #define GUARD_MIOPEN_GENERIC_SEARCH_HPP_ #include -#include +#include #include #include #include @@ -55,7 +55,7 @@ namespace solver { namespace debug { // This struct is not MT-safe, meaning one should use it before starting threads, thus avoiding // constructing it inside a worker thread. -struct TuningIterationScopedLimiter +struct MIOPEN_INTERNALS_EXPORT TuningIterationScopedLimiter { TuningIterationScopedLimiter(std::size_t new_limit); ~TuningIterationScopedLimiter(); @@ -440,7 +440,7 @@ auto GenericSearch(const Solver s, std::ref(solution_queue)); } - if(!IsEnabled(ENV(MIOPEN_DEBUG_COMPILE_ONLY))) + if(!env::enabled(MIOPEN_DEBUG_COMPILE_ONLY)) { size_t n_current = 0; auto threads_remaining = total_threads; diff --git a/src/include/miopen/generic_search_controls.hpp b/src/include/miopen/generic_search_controls.hpp index 97092e3981..00d5697d14 100644 --- a/src/include/miopen/generic_search_controls.hpp +++ b/src/include/miopen/generic_search_controls.hpp @@ -30,18 +30,16 @@ #include #include -MIOPEN_DECLARE_ENV_VAR(MIOPEN_DEBUG_TUNING_ITERATIONS_MAX, - uint64_t, - std::numeric_limits::max()) -MIOPEN_DECLARE_ENV_VAR( +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_DEBUG_TUNING_ITERATIONS_MAX, + std::numeric_limits::max()) +MIOPEN_DECLARE_ENV_VAR_UINT64( MIOPEN_TUNING_TIME_MS_MAX, - uint64_t, - (std::chrono::duration_cast(std::chrono::hours{2})).count()) + std::chrono::duration_cast(std::chrono::hours{2}).count()) + #if MIOPEN_USE_COMGR -MIOPEN_DECLARE_ENV_VAR(MIOPEN_COMPILE_PARALLEL_LEVEL, uint64_t, 1) // COMGR is not parallelizable +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_COMPILE_PARALLEL_LEVEL, 1) // COMGR is not parallelizable #else -MIOPEN_DECLARE_ENV_VAR(MIOPEN_COMPILE_PARALLEL_LEVEL, - uint64_t, - std::thread::hardware_concurrency() / 2) +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_COMPILE_PARALLEL_LEVEL, + std::thread::hardware_concurrency() / 2) #endif MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_COMPILE_ONLY) diff --git a/src/include/miopen/getitem.hpp b/src/include/miopen/getitem.hpp new file mode 100644 index 0000000000..191b1dba97 --- /dev/null +++ b/src/include/miopen/getitem.hpp @@ -0,0 +1,58 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_GETITEM_HPP_ +#define MIOPEN_GETITEM_HPP_ + +#include + +namespace miopen { + +struct Handle; +struct TensorDescriptor; + +MIOPEN_INTERNALS_EXPORT std::size_t GetGetitemWorkspaceSize( + Handle& handle, uint32_t indexCount, const TensorDescriptor* const* indexDescs); + +MIOPEN_INTERNALS_EXPORT miopenStatus_t GetitemBackward(Handle& handle, + Data_t workspace, + size_t workspaceSizeInBytes, + const TensorDescriptor& dyDesc, + ConstData_t dy, + uint32_t indexCount, + const TensorDescriptor* const* indexDescs, + ConstData_t* indexs, + const TensorDescriptor& dxDesc, + Data_t dx, + const TensorDescriptor& errorDesc, + Data_t error, + uint32_t dimCount, + const int32_t* dims, + uint32_t sliceCount, + const int32_t* slices, + uint32_t offset); + +} // namespace miopen +#endif // _MIOPEN_GETITEM_HPP_ diff --git a/src/include/miopen/getitem/invoke_params.hpp b/src/include/miopen/getitem/invoke_params.hpp new file mode 100644 index 0000000000..e663482271 --- /dev/null +++ b/src/include/miopen/getitem/invoke_params.hpp @@ -0,0 +1,97 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +namespace miopen { +namespace getitem { + +struct GetitemInvokeParams : public miopen::InvokeParams +{ + + GetitemInvokeParams(Data_t workspace_, + std::size_t workspace_size_, + const TensorDescriptor& dyDesc_, + ConstData_t dy_, + uint32_t indexCount_, + const TensorDescriptor* const* indexDescs_, + ConstData_t* indexs_, + const TensorDescriptor& dxDesc_, + Data_t dx_, + const TensorDescriptor& errorDesc_, + Data_t error_, + uint32_t dimCount_, + const int32_t* dims_, + uint32_t sliceCount_, + const int32_t* slices_, + uint32_t offset_) + : workspace(workspace_), + workspace_size(workspace_size_), + dyDesc(dyDesc_), + dy(dy_), + indexCount(indexCount_), + indexDescs(indexDescs_), + indexs(indexs_), + dxDesc(dxDesc_), + dx(dx_), + errorDesc(errorDesc_), + error(error_), + dimCount(dimCount_), + dims(dims_), + sliceCount(sliceCount_), + slices(slices_), + offset(offset_) + { + } + + Data_t workspace = nullptr; + std::size_t workspace_size = 0; + const TensorDescriptor dyDesc{}; + ConstData_t dy = nullptr; + uint32_t indexCount = 0; + const TensorDescriptor* const* indexDescs = nullptr; + ConstData_t* indexs = nullptr; + const TensorDescriptor dxDesc{}; + Data_t dx = nullptr; + const TensorDescriptor errorDesc{}; + Data_t error = nullptr; + + uint32_t dimCount = 0; + const int32_t* dims = nullptr; + uint32_t sliceCount = 0; + const int32_t* slices = nullptr; + uint32_t offset = 0; + + std::size_t GetWorkspaceSize() const { return workspace_size; } + Data_t GetWorkspace() const { return workspace; } +}; + +} // namespace getitem + +} // namespace miopen diff --git a/src/include/miopen/getitem/problem_description.hpp b/src/include/miopen/getitem/problem_description.hpp new file mode 100644 index 0000000000..fed4e78d22 --- /dev/null +++ b/src/include/miopen/getitem/problem_description.hpp @@ -0,0 +1,181 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +#include +#include +#include +#include +#include + +namespace miopen { + +struct NetworkConfig; + +namespace getitem { + +struct ProblemDescription : ProblemDescriptionBase +{ + ProblemDescription(const TensorDescriptor& dyDesc_, + uint32_t indexCount_, + const TensorDescriptor* const* indexDescs_, + const TensorDescriptor& dxDesc_, + const TensorDescriptor& errorDesc_, + uint32_t dimCount_, + const int32_t* dims_, + uint32_t sliceCount_, + const int32_t* slices_, + uint32_t offset_) + : dyDesc(dyDesc_), + indexCount(indexCount_), + indexDescs(indexDescs_), + dxDesc(dxDesc_), + errorDesc(errorDesc_), + dimCount(dimCount_), + dims(dims_), + sliceCount(sliceCount_), + slices(slices_), + offset(offset_) + { + IsValidIndexsLength(); + IsValidIndexs(); + IsValidDims(); + IsValidSlices(); + } + + ProblemDescription(const int32_t indexCount_, const TensorDescriptor* const* indexDescs_) + : indexCount(indexCount_), indexDescs(indexDescs_) + { + IsValidIndexsLength(); + IsValidIndexs(); + } + + const TensorDescriptor& GetDYDesc() const { return dyDesc; } + int32_t GetIndexCount() const { return indexCount; } + const TensorDescriptor& GetIndexDesc(int i) const + { + if(i >= indexCount) + { + MIOPEN_THROW(miopenStatusInternalError, "Item: Invalid tensor index."); + } + return (*indexDescs)[i]; + } + const TensorDescriptor& GetDXDesc() const { return dxDesc; } + const TensorDescriptor& GetErrorDesc() const { return errorDesc; } + int32_t GetDimCount() const { return dimCount; } + int32_t GetDim(int i) const + { + if(i >= indexCount) + { + MIOPEN_THROW(miopenStatusInternalError, "Item: Invalid dim index."); + } + return dims[i]; + } + int32_t GetSliceCount() const { return sliceCount; } + int32_t GetSlice(int i) const + { + if(i >= sliceCount) + { + MIOPEN_THROW(miopenStatusInternalError, "Item: Invalid slice index."); + } + return slices[i]; + } + int32_t GetOffset() const { return offset; } + + bool IsValidIndexsLength() const + { + if(indexCount > 0) + { + auto firstlength = (*indexDescs)[0]; + for(int32_t i = 1; i < indexCount; ++i) + { + if(firstlength != (*indexDescs)[i]) + MIOPEN_THROW(miopenStatusBadParm, + "Getitem: Indexs dimension lengths do not match."); + } + } + return true; + } + + bool IsValidIndexs() const + { + if(indexCount > 0) + { + if(indexDescs == nullptr) + MIOPEN_THROW(miopenStatusBadParm, "Getitem: indexDesc is nullptr."); + } + return true; + } + + bool IsValidDims() const + { + if(dimCount > 0) + + if(dims == nullptr) + MIOPEN_THROW(miopenStatusBadParm, "Getitem: dims is nullptr."); + return true; + } + + bool IsValidSlices() const + { + if(sliceCount > 0) + { + if(slices == nullptr) + MIOPEN_THROW(miopenStatusBadParm, "Getitem: slices is nullptr."); + } + return true; + } + + bool IsSameType() const + { + if(dyDesc.GetType() != dxDesc.GetType()) + { + return false; + } + return true; + } + + NetworkConfig MakeNetworkConfig() const override; + +private: + TensorDescriptor dyDesc{}; + uint32_t indexCount = 0; + const TensorDescriptor* const* indexDescs = nullptr; + TensorDescriptor dxDesc{}; + TensorDescriptor errorDesc{}; + + uint32_t dimCount = 0; + const int32_t* dims = nullptr; + uint32_t sliceCount = 0; + const int32_t* slices = nullptr; + uint32_t offset = 0; + + NetworkConfig MakeForwardNetworkConfig() const; +}; + +} // namespace getitem + +} // namespace miopen diff --git a/src/include/miopen/getitem/solvers.hpp b/src/include/miopen/getitem/solvers.hpp new file mode 100644 index 0000000000..f2edcbe437 --- /dev/null +++ b/src/include/miopen/getitem/solvers.hpp @@ -0,0 +1,57 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +#include +#include +#include + +namespace miopen { + +namespace solver { + +namespace getitem { + +using ItemSolver = NonTunableSolverBase; + +struct GetitemBackward final : ItemSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::getitem::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::getitem::ProblemDescription& problem) const override; + std::size_t GetWorkspaceSize(const ExecutionContext& context, + const miopen::getitem::ProblemDescription& problem) const override; + bool MayNeedWorkspace() const override { return true; } +}; + +} // namespace getitem + +} // namespace solver + +} // namespace miopen diff --git a/src/include/miopen/graphapi/convolution.hpp b/src/include/miopen/graphapi/convolution.hpp index c3cb7743c5..1e826972ac 100644 --- a/src/include/miopen/graphapi/convolution.hpp +++ b/src/include/miopen/graphapi/convolution.hpp @@ -26,7 +26,7 @@ #pragma once #include -#include +#include #include #include @@ -97,7 +97,7 @@ class Convolution friend class ConvolutionBuilder; }; -class ConvolutionBuilder +class MIOPEN_INTERNALS_EXPORT ConvolutionBuilder { private: Convolution mConvolution; @@ -174,7 +174,7 @@ class ConvolutionBuilder bool validate() const; }; -class BackendConvolutionDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendConvolutionDescriptor : public BackendDescriptor { private: ConvolutionBuilder mBuilder; @@ -291,6 +291,11 @@ class OperationConvolutionForward : public OperationConvolution : OperationConvolution(convolution, x, w, y, alpha, beta) { } + virtual const std::string& signName() const override + { + static const std::string name = "OP_CONVOLUTION_FORWARD"; + return name; + } virtual std::vector getInTensors() const override { return {getX(), getW()}; } virtual std::vector getOutTensors() const override { return {getY()}; } }; @@ -390,7 +395,7 @@ class OperationConvolutionForwardBuilder : public OperationConvolutionBuilder } }; -class BackendOperationConvolutionDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendOperationConvolutionDescriptor : public BackendDescriptor { protected: miopenBackendDescriptor_t mConvolutionDescriptor = nullptr; @@ -442,7 +447,8 @@ class BackendOperationConvolutionDescriptor : public BackendDescriptor void* arrayOfElements); }; -class BackendOperationConvolutionForwardDescriptor : public BackendOperationConvolutionDescriptor +class MIOPEN_INTERNALS_EXPORT BackendOperationConvolutionForwardDescriptor + : public BackendOperationConvolutionDescriptor { private: OperationConvolutionForwardBuilder mBuilder; @@ -479,6 +485,11 @@ class OperationConvolutionBackwardData : public OperationConvolution : OperationConvolution(convolution, x, w, y, alpha, beta) { } + virtual const std::string& signName() const override + { + static const std::string name = "OP_CONVOLUTION_BACKWARD_DATA"; + return name; + } virtual std::vector getInTensors() const override { return {getW(), getY()}; } virtual std::vector getOutTensors() const override { return {getX()}; } }; @@ -528,7 +539,7 @@ class OperationConvolutionBackwardDataBuilder : public OperationConvolutionBuild } }; -class BackendOperationConvolutionBackwardDataDescriptor +class MIOPEN_INTERNALS_EXPORT BackendOperationConvolutionBackwardDataDescriptor : public BackendOperationConvolutionDescriptor { private: @@ -566,6 +577,11 @@ class OperationConvolutionBackwardFilter : public OperationConvolution : OperationConvolution(convolution, x, w, y, alpha, beta) { } + virtual const std::string& signName() const override + { + static const std::string name = "OP_CONVOLUTION_BACKWARD_FILTER"; + return name; + } virtual std::vector getInTensors() const override { return {getX(), getY()}; } virtual std::vector getOutTensors() const override { return {getW()}; } }; @@ -615,7 +631,7 @@ class OperationConvolutionBackwardFilterBuilder : public OperationConvolutionBui } }; -class BackendOperationConvolutionBackwardFilterDescriptor +class MIOPEN_INTERNALS_EXPORT BackendOperationConvolutionBackwardFilterDescriptor : public BackendOperationConvolutionDescriptor { private: diff --git a/src/include/miopen/graphapi/engine.hpp b/src/include/miopen/graphapi/engine.hpp new file mode 100644 index 0000000000..97590461d0 --- /dev/null +++ b/src/include/miopen/graphapi/engine.hpp @@ -0,0 +1,198 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include +#include +#include + +#include +#include + +namespace miopen { + +namespace graphapi { + +class Engine; +class OpGraph; + +// Pattern is a family of solvers for the same graph shape +class MIOPEN_INTERNALS_EXPORT GraphPatternMatcher +{ + +public: + virtual bool matches(const OpGraph* graph) const = 0; + virtual std::vector getEngines(OpGraph* graph) const = 0; + virtual std::string_view name() const = 0; + + virtual ~GraphPatternMatcher(); +}; + +struct TensorInfo +{ + miopenTensorArgumentId_t mEnumId = miopenTensorArgumentIdInvalid; + Tensor* mGraphTensor = nullptr; + Data_t mDevBuf = nullptr; + + TensorInfo(miopenTensorArgumentId_t enum_id, Tensor* tens_ptr) + : mEnumId(enum_id), mGraphTensor(tens_ptr) + { + assert(tens_ptr); + assert(mEnumId != miopenTensorArgumentIdInvalid); + } + + void setDevBuf(Data_t ptr) + { + assert(ptr); + mDevBuf = ptr; + } +}; + +// int64_t is the graph tensor id +using TensorInfoMap = std::unordered_map; + +class MIOPEN_INTERNALS_EXPORT GraphPatternExecutor +{ + +public: + virtual void execute(miopenHandle_t handle, const VariantPack& vpk) = 0; + virtual size_t getWorkspaceSize() const = 0; + virtual ~GraphPatternExecutor(); +}; + +// generic executor that uses Find 2.0 Solution +class GraphExecutorFind20 : public GraphPatternExecutor +{ + miopenSolution_t mSolution; + std::shared_ptr mTensorInfoMap; + +public: + GraphExecutorFind20(miopenSolution_t sol, const std::shared_ptr& tmap) + : GraphPatternExecutor(), mSolution(sol), mTensorInfoMap(tmap) + { + } + + void execute(miopenHandle_t handle, const VariantPack& vpk) final; + + size_t getWorkspaceSize() const final; + + static std::unique_ptr make(miopenSolution_t sol, + const std::shared_ptr& tmap) + { + GraphPatternExecutor* p = new GraphExecutorFind20(sol, tmap); + return std::unique_ptr(p); + } +}; + +class Engine +{ +private: + std::shared_ptr mExecutor; + OpGraph* mGraph = nullptr; + int64_t mGlobalIndex = -1; + int32_t mSmCount = 0; + friend class EngineBuilder; + +public: + Engine() = default; + Engine(const Engine&) = default; + Engine(Engine&&) = default; + Engine& operator=(const Engine&) = default; + Engine& operator=(Engine&&) = default; + + GraphPatternExecutor* getExecutor() noexcept { return mExecutor.get(); } + + const std::shared_ptr& getExecutor() const noexcept { return mExecutor; } + + int64_t getGlobalIndex() const noexcept { return mGlobalIndex; } + int32_t getSmCount() const noexcept { return mSmCount; } + + const OpGraph* getOpGraph() const { return mGraph; } + OpGraph* getOpGraph() { return mGraph; } +}; + +class MIOPEN_INTERNALS_EXPORT EngineBuilder +{ + friend class BackendEngineDescriptor; + + std::shared_ptr mExecutor = nullptr; + OpGraph* mGraph = nullptr; + int64_t mGlobalIndex = -1; + int32_t mSmCount = 0; + bool mGraphSet = false; + bool mExecSet = false; + bool mIndexSet = false; + +public: + EngineBuilder& setGraph(OpGraph* g); + + EngineBuilder& setGlobalIndex(int64_t globalIndex); + + EngineBuilder& setSmCount(int32_t smCount); + + EngineBuilder& setExecutor(const std::shared_ptr& e); + + Engine build(); +}; + +class MIOPEN_INTERNALS_EXPORT BackendEngineDescriptor : public BackendDescriptor +{ +private: + EngineBuilder mBuilder; + Engine mEngine; + + miopenBackendDescriptor_t mOpGraphDescriptor = nullptr; + +public: + BackendEngineDescriptor() = default; + + BackendEngineDescriptor(const Engine& engine, miopenBackendDescriptor_t opGraphDescriptor) + : mEngine(engine), mOpGraphDescriptor(opGraphDescriptor) + { + } + + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + + const Engine& getEngine() const noexcept { return mEngine; } + Engine& getEngine() noexcept { return mEngine; } +}; + +MIOPEN_INTERNALS_EXPORT std::vector findEngines(OpGraph*); + +} // namespace graphapi + +} // namespace miopen diff --git a/src/include/miopen/graphapi/enginecfg.hpp b/src/include/miopen/graphapi/enginecfg.hpp new file mode 100644 index 0000000000..34c3483be2 --- /dev/null +++ b/src/include/miopen/graphapi/enginecfg.hpp @@ -0,0 +1,131 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +namespace miopen { + +namespace graphapi { + +class EngineCfg +{ +private: + /* we don't use a pointer here to allow a user + * to have several configs for an Engine. Each + * config might modify its Engine in future so + * their instance of Engine shouldn't be shared + */ + Engine mEngine; + + friend class EngineCfgBuilder; + +public: + EngineCfg() = default; + EngineCfg(const EngineCfg&) = default; + EngineCfg(EngineCfg&&) = default; + EngineCfg& operator=(const EngineCfg&) = default; + EngineCfg& operator=(EngineCfg&&) = default; + + EngineCfg(const Engine& engine) : mEngine(engine) {} + EngineCfg(Engine&& engine) : mEngine(std::move(engine)) {} + + const Engine& getEngine() const noexcept { return mEngine; } + Engine& getEngine() noexcept { return mEngine; } +}; + +/* For now we don't support tuning and a builder is not needed, + * but in future it will be needed. + */ +class MIOPEN_INTERNALS_EXPORT EngineCfgBuilder +{ + EngineCfg mEngineCfg; + bool mEngineSet = false; + +public: + EngineCfgBuilder& setEngine(const Engine& engine) & + { + mEngineCfg.mEngine = engine; + mEngineSet = true; + return *this; + } + EngineCfgBuilder& setEngine(Engine&& engine) & + { + mEngineCfg.mEngine = std::move(engine); + mEngineSet = true; + return *this; + } + EngineCfgBuilder&& setEngine(const Engine& engine) && { return std::move(setEngine(engine)); } + EngineCfgBuilder&& setEngine(Engine&& engine) && + { + return std::move(setEngine(std::move(engine))); + } + EngineCfg build() &; + EngineCfg build() &&; +}; + +class MIOPEN_INTERNALS_EXPORT BackendEngineCfgDescriptor : public BackendDescriptor +{ +protected: + EngineCfgBuilder mBuilder; + EngineCfg mEngineCfg; + + miopenBackendDescriptor_t mEngineDescriptor = nullptr; + + BackendEngineCfgDescriptor(const EngineCfg& engineCfg, + miopenBackendDescriptor_t engineDescriptor) + : mEngineCfg(engineCfg), mEngineDescriptor(engineDescriptor) + { + mFinalized = true; + } + BackendEngineCfgDescriptor(EngineCfg&& engineCfg, miopenBackendDescriptor_t engineDescriptor) + : mEngineCfg(std::move(engineCfg)), mEngineDescriptor(engineDescriptor) + { + mFinalized = true; + } + +public: + BackendEngineCfgDescriptor() = default; + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + + const EngineCfg& getEngineCfg() const { return mEngineCfg; } + EngineCfg& getEngineCfg() { return mEngineCfg; } +}; + +} // namespace graphapi + +} // namespace miopen diff --git a/src/include/miopen/graphapi/engineheur.hpp b/src/include/miopen/graphapi/engineheur.hpp new file mode 100644 index 0000000000..ca34f4f600 --- /dev/null +++ b/src/include/miopen/graphapi/engineheur.hpp @@ -0,0 +1,122 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include +#include + +#include +#include + +namespace miopen { + +namespace graphapi { + +class EngineHeur +{ +private: + OpGraph* mOpGraph; + std::vector mResults; + int32_t mSmCount = 0; + miopenBackendHeurMode_t mMode = miopenBackendHeurMode_t(0); + + friend class EngineHeurBuilder; + +public: + EngineHeur() noexcept = default; + EngineHeur(const EngineHeur&) = default; + EngineHeur(EngineHeur&&) noexcept = default; + EngineHeur& operator=(const EngineHeur&) = default; + EngineHeur& operator=(EngineHeur&&) noexcept = default; + + OpGraph* getOpgraph() const noexcept { return mOpGraph; } + miopenBackendHeurMode_t getMode() const noexcept { return mMode; } + const std::vector& getResults() const noexcept { return mResults; } + std::vector& getResults() noexcept { return mResults; } + int32_t getSmCount() const noexcept { return mSmCount; } +}; + +class MIOPEN_INTERNALS_EXPORT EngineHeurBuilder +{ +private: + EngineHeur mEngineHeur; + bool mModeSet = false; + +public: + EngineHeurBuilder& setOpGraph(OpGraph* opGraph); + EngineHeurBuilder& setMode(miopenBackendHeurMode_t mode); + EngineHeurBuilder& setSmCount(int32_t smCount); + EngineHeur build(); +}; + +class BackendEngineHeurDescriptor : public BackendDescriptor +{ +private: + EngineHeurBuilder mBuilder; + EngineHeur mEngineHeur; + + miopenBackendDescriptor_t mOpGraphDescriptor = nullptr; + + class OwnedEngineCfgDescriptor : public BackendEngineCfgDescriptor + { + using Base = BackendEngineCfgDescriptor; + BackendEngineDescriptor mOwnedEngineDescriptorInstance; + + public: + OwnedEngineCfgDescriptor(const EngineCfg& engineCfg, + miopenBackendDescriptor_t opGraphDescriptor) + : Base(engineCfg, &mOwnedEngineDescriptorInstance), + mOwnedEngineDescriptorInstance(Base::getEngineCfg().getEngine(), opGraphDescriptor) + { + } + + OwnedEngineCfgDescriptor(const OwnedEngineCfgDescriptor& other) = default; + OwnedEngineCfgDescriptor(OwnedEngineCfgDescriptor&& other) noexcept = default; + ; + OwnedEngineCfgDescriptor& operator=(const OwnedEngineCfgDescriptor& other) = default; + OwnedEngineCfgDescriptor& operator=(OwnedEngineCfgDescriptor&& other) noexcept = default; + }; + + std::vector mResults; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; +}; + +} // namespace graphapi + +} // namespace miopen diff --git a/src/include/miopen/graphapi/execution_plan.hpp b/src/include/miopen/graphapi/execution_plan.hpp new file mode 100644 index 0000000000..8ad9dbd31e --- /dev/null +++ b/src/include/miopen/graphapi/execution_plan.hpp @@ -0,0 +1,145 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +#include +#include +#include + +namespace miopen { + +namespace graphapi { + +class MIOPEN_INTERNALS_EXPORT ExecutionPlan +{ +private: + /* we don't use a pointer for mEngineCfg + * because we need to support serialization + * and deserialization + */ + EngineCfg mEngineCfg; + miopenHandle_t mHandle = nullptr; + std::vector mIntermediateIds; + + friend class ExecutionPlanBuilder; + +public: + ExecutionPlan() = default; + ExecutionPlan(const ExecutionPlan&) = default; + ExecutionPlan(ExecutionPlan&&) = default; + ExecutionPlan& operator=(const ExecutionPlan&) = default; + ExecutionPlan& operator=(ExecutionPlan&&) = default; + + miopenHandle_t getHandle() const noexcept { return mHandle; } + const EngineCfg& getEngineCfg() const noexcept { return mEngineCfg; } + EngineCfg& getEngineCfg() noexcept { return mEngineCfg; } + const std::vector& getIntermediateIds() const noexcept { return mIntermediateIds; } + std::string getJsonRepresentation() const; + + void execute(miopenHandle_t handle, const VariantPack& variantPack) + { + checkPtr(handle); + mEngineCfg.getEngine().getExecutor()->execute(handle, variantPack); + } + + size_t getWorkspaceSize() const + { + return mEngineCfg.getEngine().getExecutor()->getWorkspaceSize(); + } +}; + +class MIOPEN_INTERNALS_EXPORT ExecutionPlanBuilder +{ +private: + ExecutionPlan mExecutionPlan; + bool mEngineCfgSet = false; + +public: + ExecutionPlanBuilder& setHandle(miopenHandle_t handle) &; + ExecutionPlanBuilder& setEngineCfg(const EngineCfg& engineCfg) &; + ExecutionPlanBuilder& setEngineCfg(EngineCfg&& engineCfg) &; + ExecutionPlanBuilder& setIntermediateIds(const std::vector& ids) &; + ExecutionPlanBuilder& setIntermediateIds(std::vector&& ids) &; + ExecutionPlanBuilder& setJsonRepresentation(const std::string_view& s) &; + + ExecutionPlanBuilder&& setHandle(miopenHandle_t handle) && + { + return std::move(setHandle(handle)); + } + ExecutionPlanBuilder&& setEngineCfg(const EngineCfg& engineCfg) && + { + return std::move(setEngineCfg(engineCfg)); + } + ExecutionPlanBuilder&& setEngineCfg(EngineCfg&& engineCfg) && + { + return std::move(setEngineCfg(std::move(engineCfg))); + } + ExecutionPlanBuilder&& setIntermediateIds(const std::vector& ids) && + { + return std::move(setIntermediateIds(ids)); + } + ExecutionPlanBuilder&& setIntermediateIds(std::vector&& ids) && + { + return std::move(setIntermediateIds(std::move(ids))); + } + ExecutionPlanBuilder&& setJsonRepresentation(const std::string_view& s) && + { + return std::move(setJsonRepresentation(s)); + } + + ExecutionPlan build() &; + ExecutionPlan build() &&; +}; + +class MIOPEN_INTERNALS_EXPORT BackendExecutionPlanDescriptor : public BackendDescriptor +{ +private: + ExecutionPlanBuilder mBuilder; + ExecutionPlan mExecutionPlan; + + miopenBackendDescriptor_t mEngineCfgDescriptor = nullptr; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + void execute(miopenHandle_t handle, miopenBackendDescriptor_t variantPack) override; +}; + +} // namespace graphapi + +} // namespace miopen diff --git a/src/include/miopen/graphapi/graphapi.hpp b/src/include/miopen/graphapi/graphapi.hpp index 69ed96b1f4..9670d0bc32 100644 --- a/src/include/miopen/graphapi/graphapi.hpp +++ b/src/include/miopen/graphapi/graphapi.hpp @@ -26,6 +26,7 @@ #pragma once #include +#include #include #include @@ -34,9 +35,17 @@ namespace miopen { namespace graphapi { +#ifdef _WIN32 +// WORKAROUND: building on Windows is failing due to conflicting definitions of std::min() +// between the MSVC standard library and HIP Clang wrappers for int64_t data type. +constexpr std::int64_t minimum(std::int64_t a, std::int64_t b) { return a < b ? a : b; } +#else +#define minimum std::min +#endif + class OpNode; -class BackendDescriptor : public miopenBackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendDescriptor : public miopenBackendDescriptor { public: virtual ~BackendDescriptor(); @@ -58,9 +67,7 @@ class BackendDescriptor : public miopenBackendDescriptor protected: bool mFinalized = false; }; - } // namespace graphapi - } // namespace miopen MIOPEN_DEFINE_OBJECT(miopenBackendDescriptor, miopen::graphapi::BackendDescriptor) diff --git a/src/include/miopen/graphapi/matmul.hpp b/src/include/miopen/graphapi/matmul.hpp new file mode 100644 index 0000000000..7215e9d346 --- /dev/null +++ b/src/include/miopen/graphapi/matmul.hpp @@ -0,0 +1,198 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#include + +#include +#include + +namespace miopen { +namespace graphapi { + +class Matmul +{ +private: + miopenDataType_t mCompType; + +public: + Matmul() = default; + Matmul(miopenDataType_t computeType) : mCompType(computeType) {} + miopenDataType_t getComputeType() { return mCompType; } + +private: + friend class MatmulBuilder; +}; + +class MIOPEN_INTERNALS_EXPORT MatmulBuilder +{ + +private: + Matmul mMatmul; + bool mComputeTypeSet = false; + +public: + MatmulBuilder& setComputeType(miopenDataType_t computeType) + { + mMatmul.mCompType = computeType; + mComputeTypeSet = true; + return *this; + } + + Matmul build() const; +}; + +class MIOPEN_INTERNALS_EXPORT BackendMatmulDescriptor : public BackendDescriptor +{ +private: + MatmulBuilder mBuilder; + Matmul mMatmul; + +public: + virtual void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + virtual void finalize() override; + virtual void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + + const Matmul* getMatmul() const noexcept { return &mMatmul; } + Matmul* getMatmul() noexcept { return &mMatmul; } +}; + +class OperationMatmul : public OpNode +{ +private: + Tensor* mA; + Tensor* mB; + Tensor* mC; + int64_t mBatchCount = 1; + Tensor* mGemmMOverride; + Tensor* mGemmNOverride; + Tensor* mGemmKOverride; + Matmul* mMatmul; + +public: + OperationMatmul(Tensor* A, + Tensor* B, + Tensor* C, + int batchCount, + Tensor* MOverride, + Tensor* NOverride, + Tensor* KOverride, + Matmul* matmul) noexcept + : mA(A), + mB(B), + mC(C), + mBatchCount(batchCount), + mGemmMOverride(MOverride), + mGemmNOverride(NOverride), + mGemmKOverride(KOverride), + mMatmul(matmul) + { + } + + OperationMatmul() = default; + Tensor* getA() const { return mA; } + Tensor* getB() const { return mB; } + Tensor* getC() const { return mC; } + int64_t getBatchCount() const { return mBatchCount; } + Tensor* getMOverride() { return mGemmMOverride; } + Tensor* getNOverride() { return mGemmNOverride; } + Tensor* getKOverride() { return mGemmKOverride; } + Matmul* getMatmul() { return mMatmul; } + virtual std::vector getInTensors() const override { return {getA(), getB()}; } + virtual std::vector getOutTensors() const override { return {getC()}; } + virtual const std::string& signName() const override + { + static const std::string name = "OP_MATMUL"; + return name; + } + +private: + friend class OperationMatmulBuilder; +}; + +class MIOPEN_INTERNALS_EXPORT OperationMatmulBuilder +{ +private: + OperationMatmul mOperationMatmul; + bool mASet = false; + bool mBSet = false; + bool mCSet = false; + bool mMatmulSet = false; + +public: + OperationMatmulBuilder& setA(Tensor* A); + + OperationMatmulBuilder& setB(Tensor* B); + + OperationMatmulBuilder& setC(Tensor* C); + + OperationMatmulBuilder& setBatchCount(int64_t count); + + OperationMatmulBuilder& setGemmMOverride(Tensor* overrideTensor); + + OperationMatmulBuilder& setGemmNOverride(Tensor* overrideTensor); + + OperationMatmulBuilder& setGemmKOverride(Tensor* overrideTensor); + + OperationMatmulBuilder& setMatmulDescriptor(Matmul* mMatmul); + + OperationMatmul build(); +}; + +class MIOPEN_INTERNALS_EXPORT BackendOperationMatmulDescriptor : public BackendDescriptor +{ +private: + OperationMatmulBuilder mBuilder; + OperationMatmul mMatmul; + miopenBackendDescriptor_t mA = nullptr; + miopenBackendDescriptor_t mB = nullptr; + miopenBackendDescriptor_t mC = nullptr; + miopenBackendDescriptor_t mGemmMOverride = nullptr; + miopenBackendDescriptor_t mGemmNOverride = nullptr; + miopenBackendDescriptor_t mGemmKOverride = nullptr; + miopenBackendDescriptor_t mMatmuDescriptor = nullptr; + +public: + virtual void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + virtual void finalize() override; + virtual void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + OpNode* getOperation() override; +}; + +} // namespace graphapi +} // namespace miopen diff --git a/src/include/miopen/graphapi/opgraph.hpp b/src/include/miopen/graphapi/opgraph.hpp new file mode 100644 index 0000000000..4a25352297 --- /dev/null +++ b/src/include/miopen/graphapi/opgraph.hpp @@ -0,0 +1,458 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +#include +#include + +#include +#include +#include +#include +#include + +#include + +namespace miopen { +namespace graphapi { + +namespace internal { +template +bool contains(const C& container, const T& val) noexcept +{ + return std::find(container.cbegin(), container.cend(), val) != container.cend(); +} + +template +bool noRepetitions(const R& r) +{ + auto begin = r.cbegin(); + auto end = r.cend(); + + if(std::distance(begin, end) < threshold) + { + // time = O(n^2) mem = O(1) + bool result = true; + while(result && begin != end) + { + const auto& val = *begin++; + result = std::find(begin, end, val) == end; + } + return result; + } + else + { + // time = O(n) mem = O(n) + std::unordered_set>> seen; + for(; begin != end; ++begin) + { + const auto& val = *begin; + if(seen.find(val) != seen.end()) + { + return false; + } + seen.insert(val); + } + return true; + } +} +} // end namespace internal + +class OpGraphBuilder; +class OpGraph; + +class MIOPEN_INTERNALS_EXPORT OpNode +{ +public: + using Edge = std::pair; + virtual ~OpNode(); + + virtual const std::string& signName() const = 0; + +private: + std::vector mInEdges; + std::vector mOutEdges; + + friend class OpGraphBuilder; + friend class OpGraph; + +protected: + static Edge makeEdge(OpNode* n, Tensor* t) { return Edge{n, t}; } + static Edge makeEdge(const OpNode* n, const Tensor* t) + { + return Edge{ + const_cast(n), // NOLINT (cppcoreguidelines-pro-type-const-cast) + const_cast(t) // NOLINT (cppcoreguidelines-pro-type-const-cast) + }; + } + virtual std::vector getInTensors() const = 0; + + virtual std::vector getOutTensors() const = 0; + + const auto& getInEdges() const { return mInEdges; } + + const auto& getOutEdges() const { return mOutEdges; } + + bool hasInEdge(const OpNode* src, const Tensor* tens_ptr) const + { + assert(src); + assert(tens_ptr); + auto e = makeEdge(src, tens_ptr); + return internal::contains(mInEdges, e); + } + + bool hasOutEdge(const OpNode* dst, const Tensor* tens_ptr) const + { + assert(dst); + assert(tens_ptr); + auto e = makeEdge(dst, tens_ptr); + return internal::contains(mOutEdges, e); + } + + void addOutEdge(OpNode* dst, Tensor* tens_ptr) + { + assert(dst); + assert(tens_ptr); + if(!hasOutEdge(dst, tens_ptr)) + { + mOutEdges.emplace_back(dst, tens_ptr); + } + } + + void addInEdge(OpNode* src, Tensor* tens_ptr) + { + assert(src); + assert(tens_ptr); + if(!hasInEdge(src, tens_ptr)) + { + mInEdges.emplace_back(src, tens_ptr); + } + } + + size_t getInDegree() const { return mInEdges.size(); } + size_t getOutDegree() const { return mOutEdges.size(); } +}; + +using Edge = OpNode::Edge; + +class MIOPEN_INTERNALS_EXPORT SourceOpNode : public OpNode +{ +protected: + std::vector mOutTensors; + friend class OpGraph; + + const std::string& signName() const final + { + static const std::string s = "INTERNAL::SRC"; + return s; + } + + std::vector getInTensors() const final { return {}; } + + std::vector getOutTensors() const final { return mOutTensors; } + + bool hasOutTensor(const Tensor* tensor) const + { + return internal::contains(mOutTensors, tensor); + } + + void addOutTensor(Tensor* tens_ptr) + { + assert(!hasOutTensor(tens_ptr)); + mOutTensors.emplace_back(tens_ptr); + } +}; + +class MIOPEN_INTERNALS_EXPORT SinkOpNode : public OpNode +{ +protected: + std::vector mInTensors; + friend class OpGraph; + + const std::string& signName() const final + { + static const std::string s = "INTERNAL::SINK"; + return s; + } + + std::vector getInTensors() const final { return mInTensors; } + + std::vector getOutTensors() const final { return {}; } + + bool hasInTensor(Tensor* tensor) const { return internal::contains(mInTensors, tensor); } + + void addInTensor(Tensor* tens_ptr) + { + assert(!hasInTensor(tens_ptr)); + mInTensors.emplace_back(tens_ptr); + } +}; + +using Path = std::vector; +using VecOfPaths = std::vector; + +class Engine; + +class MIOPEN_INTERNALS_EXPORT OpGraph +{ + // NOTE: mSrcNode and mSinkNode need to reside on the heap because the graph may move + // to a new memory location after building, while the nodes maintain address + // of SourceOpNode and SinkOpNode in their in and out edge lists + std::unique_ptr mSrcNode = std::make_unique(); + std::unique_ptr mSinkNode = std::make_unique(); + std::vector mNodes{}; + + // Descriptor related members + miopenHandle_t mHandle = nullptr; + std::vector mEngines{}; + +public: + OpGraph(const OpGraph&) = delete; + OpGraph& operator=(const OpGraph&) = delete; + + OpGraph() = default; + OpGraph(OpGraph&&) = default; + OpGraph& operator=(OpGraph&&) = default; + ~OpGraph() = default; + + SourceOpNode* getSourceNode() const noexcept { return mSrcNode.get(); } + + SinkOpNode* getSinkNode() const noexcept { return mSinkNode.get(); } + + bool hasNode(const OpNode* n) const { return internal::contains(mNodes, n); } + + bool hasEdge(const OpNode* src, const Tensor* tens_ptr, const OpNode* dst) const + { + assert(src); + assert(dst); + return src->hasOutEdge(dst, tens_ptr) && dst->hasInEdge(src, tens_ptr); + } + + size_t numNodes() const { return mNodes.size(); } + + size_t numEdges() const + { + size_t ret = 0; + for(OpNode* n : mNodes) + { + ret += n->getOutDegree(); + } + // ignore the edges that lead to mSinkNode + assert(ret >= mSinkNode->getInDegree()); + ret -= mSinkNode->getInDegree(); + + return ret; + } + + const std::vector& getNodes() const noexcept { return mNodes; } + + const std::vector& getOutEdges(const OpNode* n) const noexcept + { + assert(n); + return n->getOutEdges(); + } + + const std::vector& getInEdges(const OpNode* n) const noexcept + { + assert(n); + return n->getInEdges(); + } + + OpNode* findNodeByName(const std::string& name) const noexcept + { + for(auto* n : getNodes()) + { + if(n->signName() == name) + { + return n; + } + } + return nullptr; + } + + OpNode* findOutNeighByName(const OpNode* node, const std::string& neigh_name) const noexcept + { + assert(node); + for(auto [m, t] : getOutEdges(node)) + { + std::ignore = t; + if(m->signName() == neigh_name) + { + return m; + } + } + return nullptr; + } + + OpNode* findInNeighByName(const OpNode* node, const std::string& neigh_name) const + { + assert(node); + for(auto [m, t] : getInEdges(node)) + { + std::ignore = t; + if(m->signName() == neigh_name) + { + return m; + } + } + return nullptr; + } + + std::vector getNodeNames() const + { + std::vector names(mNodes.size()); + for(size_t i = 0; i < mNodes.size(); ++i) + { + names[i] = mNodes[i]->signName(); + } + return names; + } + + std::vector> getInOutDegrees() const + { + std::vector> ret(mNodes.size()); + for(size_t i = 0; i < mNodes.size(); ++i) + { + ret[i] = {mNodes[i]->getInDegree(), mNodes[i]->getOutDegree()}; + } + return ret; + } + + VecOfPaths getAllPaths() const; + + // NOTE: for testing only. May remove in the future + bool hasEdgeFromSource(OpNode* dst, Tensor* tens_ptr) const + { + return hasEdge(mSrcNode.get(), tens_ptr, dst); + } + + // NOTE: for testing only. May remove in the future + bool hasEdgeToSink(OpNode* src, Tensor* tens_ptr) const + { + return hasEdge(src, tens_ptr, mSinkNode.get()); + } + + miopenHandle_t getHandle() const noexcept { return mHandle; } + const std::vector& getEngines() const noexcept { return mEngines; } + + void initEngines(); /// \todo make private. Called in finalize, but also + /// from C++ tests --amberhassaan May, 2024 + +private: + friend class OpGraphBuilder; + + void initNodes(std::vector&& nodes) { mNodes = std::move(nodes); } + + void addEdge(OpNode* src, Tensor* tens_ptr, OpNode* dst) + { + assert(src); + assert(dst); + src->addOutEdge(dst, tens_ptr); + dst->addInEdge(src, tens_ptr); + } + + void addEdgeFromSrc(OpNode* dst, Tensor* tens_ptr) + { + mSrcNode->addOutTensor(tens_ptr); + addEdge(mSrcNode.get(), tens_ptr, dst); + } + + void addEdgeToSink(OpNode* src, Tensor* tens_ptr) + { + mSinkNode->addInTensor(tens_ptr); + addEdge(src, tens_ptr, mSinkNode.get()); + } +}; + +class MIOPEN_INTERNALS_EXPORT OpGraphBuilder +{ +private: + std::vector mNodes; + miopenHandle_t mHandle = nullptr; + +public: + void setHandle(miopenHandle_t handle) { mHandle = checkPtr(handle); } + miopenHandle_t getHandle() const noexcept { return mHandle; } + + bool hasNode(OpNode* node) const { return internal::contains(mNodes, node); } + + void addNode(OpNode* node) + { + assert(!hasNode(node)); + mNodes.emplace_back(node); + } + + void setNodes(const std::vector& nodes) + { + assert(internal::noRepetitions(nodes)); + mNodes = nodes; + } + + void setNodes(std::vector&& nodes) + { + assert(internal::noRepetitions(nodes)); + mNodes = std::move(nodes); + } + + struct EdgeInfo + { + OpNode* mSrc = nullptr; + std::vector mDests{}; + }; + + // r-value method that consumes *this + OpGraph build() &&; +}; + +MIOPEN_INTERNALS_EXPORT bool isIsomorphic(const OpGraph& left, const OpGraph& right); + +MIOPEN_INTERNALS_EXPORT std::string pathToStr(const Path& path); + +class MIOPEN_INTERNALS_EXPORT BackendOperationGraphDescriptor : public BackendDescriptor +{ +private: + OpGraphBuilder mBuilder; + OpGraph mOpGraph; + std::vector mOps; // to return them in getAttribute + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + + const OpGraph* getOperationGraph() const noexcept { return &mOpGraph; } + OpGraph* getOperationGraph() noexcept { return &mOpGraph; } +}; + +} // end namespace graphapi +} // end namespace miopen diff --git a/src/include/miopen/graphapi/pointwise.hpp b/src/include/miopen/graphapi/pointwise.hpp index f4236a0131..c92db8d037 100644 --- a/src/include/miopen/graphapi/pointwise.hpp +++ b/src/include/miopen/graphapi/pointwise.hpp @@ -28,7 +28,7 @@ #include #include -#include +#include #include #include @@ -96,7 +96,7 @@ class Pointwise friend class PointwiseBuilder; }; -class PointwiseBuilder +class MIOPEN_INTERNALS_EXPORT PointwiseBuilder { private: Pointwise mPointwise; @@ -160,7 +160,7 @@ class PointwiseBuilder Pointwise build(); }; -class BackendPointwiseDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendPointwiseDescriptor : public BackendDescriptor { private: PointwiseBuilder mBuilder; @@ -182,7 +182,7 @@ class BackendPointwiseDescriptor : public BackendDescriptor Pointwise* getPointwise() { return &mPointwise; } }; -class OperationPointwise : public OpNode +class MIOPEN_INTERNALS_EXPORT OperationPointwise : public OpNode { public: using Alpha = std::variant; @@ -249,11 +249,12 @@ class OperationPointwise : public OpNode Alpha getAlpha1() const noexcept { return mAlpha1; } Alpha getAlpha2() const noexcept { return mAlpha2; } + const std::string& signName() const override; std::vector getInTensors() const override; std::vector getOutTensors() const override; }; -class OperationPointwiseBuilder +class MIOPEN_INTERNALS_EXPORT OperationPointwiseBuilder { private: OperationPointwise mOperationPointwise; @@ -273,6 +274,37 @@ class OperationPointwiseBuilder OperationPointwise build(); }; +class MIOPEN_INTERNALS_EXPORT BackendOperationPointwiseDescriptor : public BackendDescriptor +{ +private: + OperationPointwiseBuilder mBuilder; + OperationPointwise mOperationPointwise; + + miopenBackendDescriptor_t mPointwiseDescriptor = nullptr; + miopenBackendDescriptor_t mXDescriptor = nullptr; + miopenBackendDescriptor_t mBDescriptor = nullptr; + miopenBackendDescriptor_t mYDescriptor = nullptr; + miopenBackendDescriptor_t mTDescriptor = nullptr; + miopenBackendDescriptor_t mDxDescriptor = nullptr; + miopenBackendDescriptor_t mDyDescriptor = nullptr; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + OpNode* getOperation() override; + + const OperationPointwise* getOperationPointwise() const { return &mOperationPointwise; } + OperationPointwise* getOperationPointwise() { return &mOperationPointwise; } +}; + } // namespace graphapi } // namespace miopen diff --git a/src/include/miopen/graphapi/reduction.hpp b/src/include/miopen/graphapi/reduction.hpp index ce8836254a..5323e16d5d 100644 --- a/src/include/miopen/graphapi/reduction.hpp +++ b/src/include/miopen/graphapi/reduction.hpp @@ -27,6 +27,7 @@ #include #include +#include namespace miopen { @@ -51,7 +52,7 @@ class Reduction miopenDataType_t getCompType() const { return mCompType; } }; -class ReductionBuilder +class MIOPEN_INTERNALS_EXPORT ReductionBuilder { private: Reduction mReduction; @@ -76,7 +77,7 @@ class ReductionBuilder Reduction build(); }; -class BackendReductionDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendReductionDescriptor : public BackendDescriptor { private: ReductionBuilder mBuilder; @@ -98,6 +99,70 @@ class BackendReductionDescriptor : public BackendDescriptor Reduction* getReduction() { return &mReduction; } }; +class MIOPEN_INTERNALS_EXPORT OperationReduction : public OpNode +{ +private: + Reduction* mReduction = nullptr; + Tensor* mX = nullptr; + Tensor* mY = nullptr; + + friend class OperationReductionBuilder; + +public: + OperationReduction() noexcept = default; + OperationReduction(Reduction* reduction, Tensor* x, Tensor* y) noexcept + : mReduction(reduction), mX(x), mY(y) + { + } + + Reduction* getReduction() const noexcept { return mReduction; } + Tensor* getX() const noexcept { return mX; } + Tensor* getY() const noexcept { return mY; } + + const std::string& signName() const override; + std::vector getInTensors() const override; + std::vector getOutTensors() const override; +}; + +class MIOPEN_INTERNALS_EXPORT OperationReductionBuilder +{ +private: + OperationReduction mOperationReduction; + +public: + OperationReductionBuilder& setReduction(Reduction* reduction); + OperationReductionBuilder& setX(Tensor* x); + OperationReductionBuilder& setY(Tensor* y); + OperationReduction build(); +}; + +class MIOPEN_INTERNALS_EXPORT BackendOperationReductionDescriptor : public BackendDescriptor +{ +private: + OperationReductionBuilder mBuilder; + OperationReduction mOperationReduction; + + miopenBackendDescriptor_t mReductionDescriptor = nullptr; + miopenBackendDescriptor_t mXDescriptor = nullptr; + miopenBackendDescriptor_t mYDescriptor = nullptr; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + OpNode* getOperation() override; + + const OperationReduction* getOperationReduction() const { return &mOperationReduction; } + OperationReduction* getOperationReduction() { return &mOperationReduction; } +}; + } // namespace graphapi } // namespace miopen diff --git a/src/include/miopen/graphapi/reshape.hpp b/src/include/miopen/graphapi/reshape.hpp new file mode 100644 index 0000000000..bf4bcf0ab9 --- /dev/null +++ b/src/include/miopen/graphapi/reshape.hpp @@ -0,0 +1,104 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +#include +#include +#include + +namespace miopen { + +namespace graphapi { + +class MIOPEN_INTERNALS_EXPORT OperationReshape : public OpNode +{ +public: + enum class OpKind + { + GENERIC, + TRANSPOSE + }; + +private: + Tensor* mX = nullptr; + Tensor* mY = nullptr; + OpKind mOpKind = OpKind::GENERIC; + + friend class OperationReshapeBuilder; + +public: + OperationReshape() noexcept = default; + OperationReshape(Tensor* x, Tensor* y) : mX(x), mY(y) {} + + Tensor* getX() const noexcept { return mX; } + Tensor* getY() const noexcept { return mY; } + OpKind getOpKind() const noexcept { return mOpKind; } + + const std::string& signName() const override; + std::vector getInTensors() const override; + std::vector getOutTensors() const override; +}; + +class MIOPEN_INTERNALS_EXPORT OperationReshapeBuilder +{ +private: + OperationReshape mOperationReshape; + +public: + OperationReshapeBuilder& setX(Tensor* x); + OperationReshapeBuilder& setY(Tensor* y); + OperationReshape build(); +}; + +class MIOPEN_INTERNALS_EXPORT BackendOperationReshapeDescriptor : public BackendDescriptor +{ +private: + OperationReshapeBuilder mBuilder; + OperationReshape mOperationReshape; + + miopenBackendDescriptor_t mXDescriptor = nullptr; + miopenBackendDescriptor_t mYDescriptor = nullptr; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + OpNode* getOperation() override; + + const OperationReshape* getOperationReshape() const { return &mOperationReshape; } + OperationReshape* getOperationReshape() { return &mOperationReshape; } +}; + +} // namespace graphapi + +} // namespace miopen diff --git a/src/include/miopen/graphapi/rng.hpp b/src/include/miopen/graphapi/rng.hpp index cc1e5dd24b..e56b43e589 100644 --- a/src/include/miopen/graphapi/rng.hpp +++ b/src/include/miopen/graphapi/rng.hpp @@ -27,8 +27,11 @@ #include #include +#include +#include #include +#include namespace miopen { @@ -72,7 +75,7 @@ class Rng friend class RngBuilder; }; -class RngBuilder +class MIOPEN_INTERNALS_EXPORT RngBuilder { private: Rng mRng; @@ -107,7 +110,7 @@ class RngBuilder Rng build() const; }; -class BackendRngDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendRngDescriptor : public BackendDescriptor { private: RngBuilder mBuilder; @@ -129,6 +132,80 @@ class BackendRngDescriptor : public BackendDescriptor Rng* getRng() noexcept { return &mRng; } }; +class MIOPEN_INTERNALS_EXPORT OperationRng : public OpNode +{ +private: + Rng* mRng = nullptr; + Tensor* mOutput = nullptr; + std::variant mSeed = 0; // Don't change the order of variant alternatives + Tensor* mOffset = nullptr; + + friend class OperationRngBuilder; + +public: + OperationRng() noexcept = default; + OperationRng(Rng* rng, Tensor* output, int64_t seed, Tensor* offset) noexcept + : mRng(rng), mOutput(output), mSeed(seed), mOffset(offset) + { + } + OperationRng(Rng* rng, Tensor* output, Tensor* seed, Tensor* offset) noexcept + : mRng(rng), mOutput(output), mSeed(seed), mOffset(offset) + { + } + + Rng* getRng() const noexcept { return mRng; } + Tensor* getOutput() const noexcept { return mOutput; } + std::variant getSeed() const noexcept { return mSeed; } + Tensor* getOffset() const noexcept { return mOffset; } + + virtual const std::string& signName() const override; + virtual std::vector getInTensors() const override; + virtual std::vector getOutTensors() const override; +}; + +class MIOPEN_INTERNALS_EXPORT OperationRngBuilder +{ +private: + OperationRng mOperationRng; + +public: + OperationRngBuilder& setRng(Rng* rng); + OperationRngBuilder& setOutput(Tensor* output); + OperationRngBuilder& setSeed(int64_t seed) noexcept; + OperationRngBuilder& setSeed(Tensor* seed); + OperationRngBuilder& setOffset(Tensor* offset); + + OperationRng build(); +}; + +class MIOPEN_INTERNALS_EXPORT BackendOperationRngDescriptor : public BackendDescriptor +{ +private: + OperationRngBuilder mBuilder; + OperationRng mOperationRng; + miopenBackendDescriptor_t mRngDescriptor = nullptr; + miopenBackendDescriptor_t mOutputDescriptor = nullptr; // sometimes called Y + miopenBackendDescriptor_t mSeedDescriptor = nullptr; + miopenBackendDescriptor_t mOffsetDescriptor = nullptr; + +public: + void setAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t elementCount, + void* arrayOfElements) override; + void finalize() override; + void getAttribute(miopenBackendAttributeName_t attributeName, + miopenBackendAttributeType_t attributeType, + int64_t requestedElementCount, + int64_t* elementCount, + void* arrayOfElements) override; + + OpNode* getOperation() override; + + const OperationRng* getRng() const { return &mOperationRng; } + OperationRng* getRng() { return &mOperationRng; } +}; + } // namespace graphapi } // namespace miopen diff --git a/src/include/miopen/graphapi/tensor.hpp b/src/include/miopen/graphapi/tensor.hpp index 754db0830f..fe6b53cbf1 100644 --- a/src/include/miopen/graphapi/tensor.hpp +++ b/src/include/miopen/graphapi/tensor.hpp @@ -35,14 +35,11 @@ namespace miopen { namespace graphapi { -class Tensor +class Tensor : public TensorDescriptor { private: - std::vector mDimensions; - std::vector mStrides; - int64_t mId = 0; - miopenDataType_t mDataType = miopenFloat; - bool mVirtual = false; + int64_t mId = 0; + bool mVirtual = false; public: Tensor() noexcept = default; @@ -50,50 +47,70 @@ class Tensor Tensor(Tensor&&) noexcept = default; Tensor& operator=(const Tensor&) = default; Tensor& operator=(Tensor&&) noexcept = default; + Tensor(const TensorDescriptor& other, int64_t id, bool isVirtual) + : TensorDescriptor(other), mId(id), mVirtual(isVirtual) + { + } + Tensor(TensorDescriptor&& other, int64_t id, bool isVirtual) + : TensorDescriptor(std::move(other)), mId(id), mVirtual(isVirtual) + { + } Tensor(miopenDataType_t dataType, - const std::vector& dimensions, - const std::vector& strides, + const std::vector& dimensions, + const std::vector& strides, int64_t id, bool isVirtual) - : mDimensions(dimensions), - mStrides(strides), + : TensorDescriptor(dataType, getLayout(strides), dimensions, strides), mId(id), - mDataType(dataType), mVirtual(isVirtual) { } Tensor(miopenDataType_t dataType, - std::vector&& dimensions, - std::vector&& strides, + std::vector&& dimensions, + std::vector&& strides, int64_t id, bool isVirtual) noexcept - : mDimensions(std::move(dimensions)), - mStrides(std::move(strides)), + : TensorDescriptor(dataType, getLayout(strides), std::move(dimensions), std::move(strides)), mId(id), - mDataType(dataType), mVirtual(isVirtual) { } - operator miopen::TensorDescriptor() const + int64_t getId() const noexcept { return mId; } + bool isVirtual() const noexcept { return mVirtual; } + +private: + static miopenTensorLayout_t getLayout(const std::vector& strides) { - return {mDataType, - std::vector(mDimensions.cbegin(), mDimensions.cbegin()), - std::vector(mStrides.cbegin(), mStrides.cbegin())}; + if(strides.size() >= 4) + { + int stride_c = strides[1]; + + // If channels have the smallest stride, or are tied for smallest stride, then we are + // assuming NHWC format. Otherwise, assume NCHW format. + if(std::all_of(strides.cbegin(), strides.cend(), [stride_c](std::size_t x) { + return x >= stride_c; + })) + { + return strides.size() == 4 ? miopenTensorLayout_t::miopenTensorNHWC + : miopenTensorLayout_t::miopenTensorNDHWC; + } + else + { + return strides.size() == 4 ? miopenTensorLayout_t::miopenTensorNCHW + : miopenTensorLayout_t::miopenTensorNCDHW; + } + } + + return GetDefaultLayout(); } - - miopenDataType_t getDataType() const noexcept { return mDataType; } - const std::vector& getDimensions() const noexcept { return mDimensions; } - const std::vector& getStrides() const noexcept { return mStrides; } - int64_t getId() const noexcept { return mId; } - bool getVirtual() const noexcept { return mVirtual; } }; -class TensorBuilder +class MIOPEN_INTERNALS_EXPORT TensorBuilder { private: - std::vector mDimensions; - std::vector mStrides; + std::vector mDimensions; + std::vector mStrides; int64_t mId = 0; miopenDataType_t mDataType = miopenFloat; bool mVirtual = false; @@ -104,10 +121,10 @@ class TensorBuilder public: TensorBuilder& setDataType(miopenDataType_t dataType) &; - TensorBuilder& setDim(const std::vector& dimensions) &; - TensorBuilder& setDim(std::vector&& dimensions) &; - TensorBuilder& setStride(const std::vector& strides) &; - TensorBuilder& setStride(std::vector&& strides) &; + TensorBuilder& setDim(const std::vector& dimensions) &; + TensorBuilder& setDim(std::vector&& dimensions) &; + TensorBuilder& setStride(const std::vector& strides) &; + TensorBuilder& setStride(std::vector&& strides) &; TensorBuilder& setId(int64_t id) &; TensorBuilder& setVirtual(bool isVirtual) &; @@ -115,19 +132,19 @@ class TensorBuilder { return std::move(setDataType(dataType)); } - TensorBuilder&& setDim(const std::vector& dimensions) && + TensorBuilder&& setDim(const std::vector& dimensions) && { return std::move(setDim(dimensions)); } - TensorBuilder&& setDim(std::vector&& dimensions) && + TensorBuilder&& setDim(std::vector&& dimensions) && { return std::move(setDim(std::move(dimensions))); } - TensorBuilder&& setStride(const std::vector& strides) && + TensorBuilder&& setStride(const std::vector& strides) && { return std::move(setStride(strides)); } - TensorBuilder&& setStride(std::vector&& strides) && + TensorBuilder&& setStride(std::vector&& strides) && { return std::move(setStride(std::move(strides))); } @@ -138,7 +155,7 @@ class TensorBuilder Tensor build() &&; }; -class BackendTensorDescriptor : public BackendDescriptor +class MIOPEN_INTERNALS_EXPORT BackendTensorDescriptor : public BackendDescriptor { private: TensorBuilder mBuilder; diff --git a/src/include/miopen/graphapi/util.hpp b/src/include/miopen/graphapi/util.hpp new file mode 100644 index 0000000000..f9415a3630 --- /dev/null +++ b/src/include/miopen/graphapi/util.hpp @@ -0,0 +1,270 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +#include +#include + +#include +#include +#include +#include + +namespace miopen { +namespace graphapi { + +inline std::string tensorIdAsStr(const Tensor* tens_ptr) +{ + + int64_t id = tens_ptr->getId(); + char* b = reinterpret_cast(&id); + + return {b, sizeof(id)}; +} + +template +Tensor makeTensor(std::string_view name, miopenDataType_t dt, const Vec& dims, const Vec& strides) +{ + int64_t id = 0; + MIOPEN_THROW_IF(name.size() > sizeof(id), "tensor name exceeds 8 chars"); + std::copy_n(name.begin(), std::min(sizeof(id), name.size()), reinterpret_cast(&id)); + + return TensorBuilder{} + .setDataType(dt) + .setDim(dims) + .setStride(strides) + .setId(id) + .setVirtual(isVirtual) + .build(); +} + +template +Tensor makeTensor(std::string_view name, miopenDataType_t dt, const Vec& dims) +{ + TensorDescriptor desc{dt, dims}; + return makeTensor(name, dt, desc.GetLengths(), desc.GetStrides()); +} + +/// An RAII style class that captures a pointer to an object on heap and frees it +/// upon destruction. It's different from std::unique_ptr in that it allows +/// capturing multiple types of pointers +struct HeapPtrDeleter +{ + using Fn = std::function; + Fn mFn = {}; + + template + explicit HeapPtrDeleter(T* ptr) + : mFn([ptr]() { delete ptr; }) // NOLINT (cppcoreguidelines-owning-memory) + { + } + + HeapPtrDeleter(const HeapPtrDeleter&) = delete; + HeapPtrDeleter& operator=(const HeapPtrDeleter&) = delete; + + friend void swap(HeapPtrDeleter& left, HeapPtrDeleter& right) noexcept + { + std::swap(left.mFn, right.mFn); + } + + HeapPtrDeleter(HeapPtrDeleter&& that) noexcept : mFn(std::move(that.mFn)) { that.mFn = {}; } + + HeapPtrDeleter& operator=(HeapPtrDeleter&& that) noexcept + { + if(this != &that) + { + HeapPtrDeleter tmp{std::move(that)}; + swap(*this, tmp); + } + return *this; + } + + ~HeapPtrDeleter() + { + // default initialized std::function cannot be invoked + if(mFn) + mFn(); + } +}; + +/// an automatically deleting allocator that frees the allocated objects upon +/// destruction +struct AutoDeleteAllocator +{ + std::vector mPtrsToFree; + + AutoDeleteAllocator() = default; + AutoDeleteAllocator(const AutoDeleteAllocator&) = delete; + AutoDeleteAllocator& operator=(const AutoDeleteAllocator&) = delete; + + AutoDeleteAllocator(AutoDeleteAllocator&&) = default; + AutoDeleteAllocator& operator=(AutoDeleteAllocator&&) = default; + ~AutoDeleteAllocator() = default; + + template + T* allocate(T&& val) + { + T* ret = new T(std::forward(val)); // NOLINT (cppcoreguidelines-owning-memory) + mPtrsToFree.emplace_back(ret); + return ret; + } +}; + +struct PatternGraphGenerator +{ + + struct DummyNode : public OpNode + { + std::string mName; + std::vector mInTensors; + std::vector mOutTensors; + + DummyNode(const std::string& name, + const std::vector& ins, + const std::vector& outs) + : mName(name), mInTensors(ins), mOutTensors(outs) + { + } + + const std::string& signName() const final { return mName; } + + std::vector getInTensors() const final { return mInTensors; } + + std::vector getOutTensors() const final { return mOutTensors; } + }; + + struct DummyNodeGenSpec + { + std::string mName; + std::vector mInTensors; + std::vector mOutTensors; + }; + + inline Tensor* makeDummyTensor(std::string_view name) + { + + return mAlloc.allocate(makeTensor(name, miopenFloat, std::vector({1}))); + } + +private: + AutoDeleteAllocator mAlloc{}; + OpGraph mGraph{}; + + PatternGraphGenerator(const std::vector& node_specs) + { + + std::unordered_map tensor_map; + OpGraphBuilder builder; + + for(const auto& ns : node_specs) + { + std::vector in_tensors; + + for(const auto& ti : ns.mInTensors) + { + auto [it, flag] = tensor_map.try_emplace(ti, makeDummyTensor(ti)); + in_tensors.emplace_back(it->second); + } + + std::vector out_tensors; + for(const auto& to : ns.mOutTensors) + { + auto [it, flag] = tensor_map.try_emplace(to, makeDummyTensor(to)); + out_tensors.emplace_back(it->second); + } + + builder.addNode(mAlloc.allocate(DummyNode{ns.mName, in_tensors, out_tensors})); + } + + mGraph = std::move(builder).build(); + } + +public: + PatternGraphGenerator() = default; + PatternGraphGenerator(const PatternGraphGenerator&) = delete; + PatternGraphGenerator& operator=(const PatternGraphGenerator&) = delete; + PatternGraphGenerator(PatternGraphGenerator&&) = default; + PatternGraphGenerator& operator=(PatternGraphGenerator&&) = default; + ~PatternGraphGenerator() = default; + + static std::unique_ptr + Make(const std::vector& node_specs) + { + return std::unique_ptr(new PatternGraphGenerator(node_specs)); + } + + const auto& graph() const { return mGraph; } +}; + +/// \todo move this function out so that other find 2.0 code can use it +/// --amberhassaan May, 2024 +inline std::string_view tensorEnumIdToStr(miopenTensorArgumentId_t id) +{ + +#define ENUM_CASE(k) \ + case k: return #k; + + switch(id) + { + ENUM_CASE(miopenTensorMhaK) + ENUM_CASE(miopenTensorMhaQ) + ENUM_CASE(miopenTensorMhaV) + ENUM_CASE(miopenTensorMhaDescaleK) + ENUM_CASE(miopenTensorMhaDescaleQ) + ENUM_CASE(miopenTensorMhaDescaleV) + ENUM_CASE(miopenTensorMhaDescaleS) + ENUM_CASE(miopenTensorMhaScaleS) + ENUM_CASE(miopenTensorMhaScaleO) + ENUM_CASE(miopenTensorMhaDropoutProbability) + ENUM_CASE(miopenTensorMhaDropoutSeed) + ENUM_CASE(miopenTensorMhaDropoutOffset) + ENUM_CASE(miopenTensorMhaO) + ENUM_CASE(miopenTensorMhaAmaxO) + ENUM_CASE(miopenTensorMhaAmaxS) + ENUM_CASE(miopenTensorMhaM) + ENUM_CASE(miopenTensorMhaZInv) + ENUM_CASE(miopenTensorMhaDO) + ENUM_CASE(miopenTensorMhaDescaleO) + ENUM_CASE(miopenTensorMhaDescaleDO) + ENUM_CASE(miopenTensorMhaDescaleDS) + ENUM_CASE(miopenTensorMhaScaleDS) + ENUM_CASE(miopenTensorMhaScaleDQ) + ENUM_CASE(miopenTensorMhaScaleDK) + ENUM_CASE(miopenTensorMhaScaleDV) + ENUM_CASE(miopenTensorMhaDQ) + ENUM_CASE(miopenTensorMhaDK) + ENUM_CASE(miopenTensorMhaDV) + ENUM_CASE(miopenTensorMhaAmaxDQ) + ENUM_CASE(miopenTensorMhaAmaxDK) + ENUM_CASE(miopenTensorMhaAmaxDV) + ENUM_CASE(miopenTensorMhaAmaxDS) + default: MIOPEN_THROW(miopenStatusInternalError, "unknown tensor enum id"); + } +#undef ENUM_CASE +} + +} // end namespace graphapi +} // end namespace miopen diff --git a/src/include/miopen/graphapi/variant_pack.hpp b/src/include/miopen/graphapi/variant_pack.hpp index e56d5365d6..6239b4d2f3 100644 --- a/src/include/miopen/graphapi/variant_pack.hpp +++ b/src/include/miopen/graphapi/variant_pack.hpp @@ -83,6 +83,9 @@ class VariantPack { } + const auto& getTensorIds() const noexcept { return mTensorIds; } + const auto& getDataPtrs() const noexcept { return mDataPointers; } + void* getDataPointer(int64_t tensorId) const { assert(mTensorIds.size() == mDataPointers.size()); @@ -225,6 +228,7 @@ class BackendVariantPackDescriptor : public BackendDescriptor int64_t* elementCount, void* arrayOfElements) override; + /// \todo return const ref and ref --amberhassaan May, 2024 const VariantPack* getVariantPack() const { return &mVariantPack; } VariantPack* getVariantPack() { return &mVariantPack; } }; diff --git a/src/include/miopen/groupnorm.hpp b/src/include/miopen/groupnorm.hpp index 837df25013..68ed8a866c 100644 --- a/src/include/miopen/groupnorm.hpp +++ b/src/include/miopen/groupnorm.hpp @@ -23,10 +23,10 @@ * SOFTWARE. * *******************************************************************************/ -#include #ifndef MIOPEN_GROUPNORM_HPP_ #define MIOPEN_GROUPNORM_HPP_ +#include #include namespace miopen { @@ -34,22 +34,22 @@ namespace miopen { struct Handle; struct TensorDescriptor; -miopenStatus_t GroupNormForward(Handle& handle, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& weightDesc, - ConstData_t weight, - const TensorDescriptor& biasDesc, - ConstData_t bias, - const TensorDescriptor& yDesc, - Data_t y, - const TensorDescriptor& meanDesc, - Data_t mean, - const TensorDescriptor& rstdDesc, - Data_t rstd, - miopenNormMode_t mode, - uint64_t num_groups, - float epsilon); +MIOPEN_INTERNALS_EXPORT miopenStatus_t GroupNormForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& biasDesc, + ConstData_t bias, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& meanDesc, + Data_t mean, + const TensorDescriptor& rstdDesc, + Data_t rstd, + miopenNormMode_t mode, + uint64_t num_groups, + float epsilon); } // namespace miopen #endif // _MIOPEN_GROUPNORM_HPP_ diff --git a/src/include/miopen/groupnorm/solvers.hpp b/src/include/miopen/groupnorm/solvers.hpp index 70ede100d0..0bb6111fa4 100644 --- a/src/include/miopen/groupnorm/solvers.hpp +++ b/src/include/miopen/groupnorm/solvers.hpp @@ -44,10 +44,12 @@ struct GroupNormForward final : NormalizationSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext& context, - const miopen::groupnorm::ProblemDescription& problem) const override; - ConvSolution GetSolution(const ExecutionContext& context, - const miopen::groupnorm::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext& context, + const miopen::groupnorm::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext& context, + const miopen::groupnorm::ProblemDescription& problem) const override; }; } // namespace groupnorm diff --git a/src/include/miopen/handle.hpp b/src/include/miopen/handle.hpp index 5b82e88d3d..57c90331b1 100644 --- a/src/include/miopen/handle.hpp +++ b/src/include/miopen/handle.hpp @@ -63,6 +63,13 @@ #endif #endif +#if MIOPEN_USE_HIPBLASLT +#include + +using hipblasLtHandle_t = void*; +extern "C" hipblasStatus_t hipblasLtDestroy(hipblasLtHandle_t handle); +#endif + namespace miopen { struct HandleImpl; @@ -71,6 +78,10 @@ struct HandleImpl; using rocblas_handle_ptr = MIOPEN_MANAGE_PTR(rocblas_handle, rocblas_destroy_handle); #endif +#if MIOPEN_USE_HIPBLASLT +using hipblasLt_handle_ptr = MIOPEN_MANAGE_PTR(hipblasLtHandle_t, hipblasLtDestroy); +#endif + struct MIOPEN_EXPORT Handle : miopenHandle { friend struct TargetProperties; @@ -99,7 +110,7 @@ struct MIOPEN_EXPORT Handle : miopenHandle KernelInvoke AddKernel(const std::string& algorithm, const std::string& network_config, - const std::string& program_name, + const fs::path& program_name, const std::string& kernel_name, const std::vector& vld, const std::vector& vgd, @@ -125,17 +136,18 @@ struct MIOPEN_EXPORT Handle : miopenHandle return this->Run(ks.front()); } - KernelInvoke Run(Kernel k) const; + KernelInvoke Run(Kernel k, bool coop_launch = false) const; const std::vector& GetKernelsImpl(const std::string& algorithm, const std::string& network_config) const; - Program LoadProgram(const std::string& program_name, + Program LoadProgram(const fs::path& program_name, std::string params, - const std::string& kernel_src) const; + const std::string& kernel_src, + bool force_attach_binary = false) const; - bool HasProgram(const std::string& program_name, const std::string& params) const; - void ClearProgram(const std::string& program_name, const std::string& params) const; - void AddProgram(Program prog, const std::string& program_name, const std::string& params) const; + bool HasProgram(const fs::path& program_name, const std::string& params) const; + void ClearProgram(const fs::path& program_name, const std::string& params) const; + void AddProgram(Program prog, const fs::path& program_name, const std::string& params) const; void Finish() const; void Flush() const; @@ -154,6 +166,7 @@ struct MIOPEN_EXPORT Handle : miopenHandle std::size_t m_MaxMemoryAllocSizeCached = 0; std::size_t GetMaxMemoryAllocSize(); + bool CooperativeLaunchSupported() const; std::string GetDeviceName() const; const TargetProperties& GetTargetProperties() const; @@ -231,22 +244,28 @@ struct MIOPEN_EXPORT Handle : miopenHandle std::unordered_map> find_map; Invoker PrepareInvoker(const InvokerFactory& factory, - const std::vector& kernels) const; + const std::vector& kernels, + std::vector* programs_out = nullptr) const; void RegisterInvoker(const Invoker& invoker, const NetworkConfig& config, const std::string& solver, - const boost::optional& algo = boost::none) + const std::optional& algo = std::nullopt) { invokers.Register({config, solver}, invoker); if(algo.has_value()) - invokers.SetAsFound1_0(config, *algo, solver); + SetAsFound1_0(config, *algo, solver); } - boost::optional - GetInvoker(const NetworkConfig& config, - const boost::optional& solver, - const boost::optional& algo = boost::none) const + void + SetAsFound1_0(const NetworkConfig& config, const AlgorithmName& algo, const std::string& solver) + { + invokers.SetAsFound1_0(config, algo, solver); + } + + std::optional GetInvoker(const NetworkConfig& config, + const std::optional& solver, + const std::optional& algo = std::nullopt) const { assert(solver || algo); assert(!(solver && algo)); @@ -256,25 +275,36 @@ struct MIOPEN_EXPORT Handle : miopenHandle << solver->ToString()); return invokers[std::make_pair(config.ToString(), solver->ToString())]; } + + if(!algo) + MIOPEN_THROW(miopenStatusInternalError); + MIOPEN_LOG_I2("Returning an invoker for problem " << config.ToString() << " and algorithm " << algo->ToString()); return invokers.GetFound1_0(config, *algo); } - boost::optional GetFound1_0SolverId(const NetworkConfig& config, - const AlgorithmName& algo) const + std::optional GetFound1_0SolverId(const NetworkConfig& config, + const AlgorithmName& algo) const { return invokers.GetFound1_0SolverId(config, algo); } #if MIOPEN_USE_ROCBLAS const rocblas_handle_ptr& rhandle() const; +#endif +#if MIOPEN_USE_HIPBLASLT + const hipblasLt_handle_ptr& HipblasLtHandle() const; +#endif private: +#if MIOPEN_USE_ROCBLAS rocblas_handle_ptr CreateRocblasHandle(miopenAcceleratorQueue_t streamID) const; -#else -private: #endif +#if MIOPEN_USE_HIPBLASLT + hipblasLt_handle_ptr CreateHipblasLtHandle() const; +#endif + InvokerCache invokers; }; diff --git a/src/include/miopen/handle_lock.hpp b/src/include/miopen/handle_lock.hpp index ff3cad5674..c55109ba11 100644 --- a/src/include/miopen/handle_lock.hpp +++ b/src/include/miopen/handle_lock.hpp @@ -39,10 +39,10 @@ namespace miopen { -#define MIOPEN_DECLARE_HANDLE_MUTEX(x) \ - struct x \ - { \ - static const char* value() { return ".miopen-" #x ".lock"; } \ +#define MIOPEN_DECLARE_HANDLE_MUTEX(x) \ + struct x \ + { \ + static const char* env::value() { return ".miopen-" #x ".lock"; } \ }; #if MIOPEN_GPU_SYNC diff --git a/src/include/miopen/hip_build_utils.hpp b/src/include/miopen/hip_build_utils.hpp index 68db3dfced..16124527ba 100644 --- a/src/include/miopen/hip_build_utils.hpp +++ b/src/include/miopen/hip_build_utils.hpp @@ -36,9 +36,9 @@ namespace miopen { -fs::path HipBuild(boost::optional& tmp_dir, - const std::string& filename, - std::string src, +fs::path HipBuild(const TmpDir& tmp_dir, + const fs::path& filename, + std::string_view src, std::string params, const TargetProperties& target); diff --git a/src/include/miopen/hipoc_kernel.hpp b/src/include/miopen/hipoc_kernel.hpp index cb42faf3a1..a82d02fdf9 100644 --- a/src/include/miopen/hipoc_kernel.hpp +++ b/src/include/miopen/hipoc_kernel.hpp @@ -26,6 +26,7 @@ #ifndef GUARD_MIOPEN_HIPOC_KERNEL_HPP #define GUARD_MIOPEN_HIPOC_KERNEL_HPP +#include #include #include #include @@ -46,6 +47,16 @@ inline HipEventPtr make_hip_event() return HipEventPtr{result}; } +struct HipEventProfiler +{ + const Handle& handle; + HipEventPtr start; + HipEventPtr stop; + + HipEventProfiler(const Handle& handle_); + ~HipEventProfiler(); +}; + #if 1 // Keep around other storage techinques -- @pfultz2 27.03.2017 template @@ -108,28 +119,31 @@ struct KernelArgs uint64_t hidden[6] = {}; }; -struct HIPOCKernelInvoke +struct MIOPEN_INTERNALS_EXPORT HIPOCKernelInvoke { - hipStream_t stream = nullptr; - hipFunction_t fun = nullptr; - std::array ldims = {}; - std::array gdims = {}; - std::string name; - std::function callback; - - // Workaround for aggregate types in c++11 HIPOCKernelInvoke() {} HIPOCKernelInvoke(hipStream_t pstream, hipFunction_t pfun, std::array pldims, std::array pgdims, std::string pname, - std::function pcallback) - : stream(pstream), fun(pfun), ldims(pldims), gdims(pgdims), name(pname), callback(pcallback) + std::function pcallback, + bool pcoop_launch) + : stream(pstream), + fun(pfun), + ldims(pldims), + gdims(pgdims), + name(pname), + callback(pcallback), + coop_launch(pcoop_launch) { } + void operator()(std::vector& any_args) const { + if(coop_launch) + MIOPEN_THROW(miopenStatusNotImplemented); + char hip_args[256] = {0}; auto sz_left = any_args[0].size(); @@ -152,16 +166,38 @@ struct HIPOCKernelInvoke template void operator()(Ts... xs) const { - KernelArgs args{xs...}; - run(&args, sizeof(args)); + if(coop_launch) + { + auto args = std::array{(&xs)...}; + run_cooperative(args.data()); + } + else + { + KernelArgs args{xs...}; + run(&args, sizeof(args)); + } } - void run(void* args, std::size_t size) const; + void SetLocalDims(size_t dim_x, size_t dim_y, size_t dim_z) { ldims = {dim_x, dim_y, dim_z}; } + + void SetGlobalDims(size_t dim_x, size_t dim_y, size_t dim_z) { gdims = {dim_x, dim_y, dim_z}; } const std::string& GetName() const { return name; } + +private: + void run(void* args, std::size_t size) const; + void run_cooperative(void** kern_args) const; + + hipStream_t stream = nullptr; + hipFunction_t fun = nullptr; + std::array ldims = {}; + std::array gdims = {}; + std::string name; + std::function callback; + bool coop_launch; }; -struct HIPOCKernel +struct MIOPEN_INTERNALS_EXPORT HIPOCKernel { HIPOCProgram program; std::string name; @@ -196,7 +232,8 @@ struct HIPOCKernel } HIPOCKernelInvoke Invoke(hipStream_t stream, - std::function callback = nullptr) const; + std::function callback = nullptr, + bool coop_launch = false) const; }; } // namespace miopen diff --git a/src/include/miopen/hipoc_program.hpp b/src/include/miopen/hipoc_program.hpp index b8a04ae63c..31d10b5851 100644 --- a/src/include/miopen/hipoc_program.hpp +++ b/src/include/miopen/hipoc_program.hpp @@ -45,23 +45,33 @@ struct HIPOCProgram /// is initialized. GetModule(), GetCodeObjectPathname(), /// GetCodeObjectBlob() return appropriate data after this ctor. /// Other ctors only guarantee to initialize module. - HIPOCProgram(const std::string& program_name, + HIPOCProgram(const fs::path& program_name, std::string params, const TargetProperties& target, const std::string& kernel_src); - HIPOCProgram(const std::string& program_name, const fs::path& hsaco); - HIPOCProgram(const std::string& program_name, const std::vector& hsaco); + HIPOCProgram(const fs::path& program_name, const fs::path& hsaco); + HIPOCProgram(const fs::path& program_name, const std::vector& hsaco); + HIPOCProgram(const fs::path& program_name, const std::vector& hsaco); std::shared_ptr impl; hipModule_t GetModule() const; /// \return Pathname of CO file, if it resides on the filesystem. /// This function should not be called after FreeCodeObjectFileStorage(). fs::path GetCodeObjectPathname() const; - /// \return Copy of in-memory CO blob. - std::vector GetCodeObjectBlob() const; + /// \return In-memory CO blob. + const std::vector& GetCodeObjectBlob() const; /// \return True if CO blob resides in-memory. /// False if CO resides on filesystem. bool IsCodeObjectInMemory() const; + bool IsCodeObjectInFile() const; + bool IsCodeObjectInTempFile() const; void FreeCodeObjectFileStorage(); + void AttachBinary(std::vector binary); + void AttachBinary(fs::path binary); + + friend bool operator==(const HIPOCProgram& l, const HIPOCProgram& r) + { + return l.impl == r.impl; + } }; } // namespace miopen diff --git a/src/include/miopen/hipoc_program_impl.hpp b/src/include/miopen/hipoc_program_impl.hpp index 7af4a1b7cc..96423df05a 100644 --- a/src/include/miopen/hipoc_program_impl.hpp +++ b/src/include/miopen/hipoc_program_impl.hpp @@ -26,6 +26,7 @@ #ifndef GUARD_MIOPEN_HIPOC_PROGRAM_IMPL_HPP #define GUARD_MIOPEN_HIPOC_PROGRAM_IMPL_HPP +#include #include #include #include @@ -33,22 +34,27 @@ #include #include +#include +#include + namespace miopen { using hipModulePtr = MIOPEN_MANAGE_PTR(hipModule_t, hipModuleUnload); struct HIPOCProgramImpl { HIPOCProgramImpl(){}; - HIPOCProgramImpl(const std::string& program_name, const fs::path& filespec); + HIPOCProgramImpl(const fs::path& program_name, const fs::path& filespec); + + HIPOCProgramImpl(const fs::path& program_name, const std::vector& blob); - HIPOCProgramImpl(const std::string& program_name, const std::vector& blob); + HIPOCProgramImpl(const fs::path& program_name, const std::vector& blob); - HIPOCProgramImpl(const std::string& program_name, + HIPOCProgramImpl(const fs::path& program_name, std::string params, const TargetProperties& target_, const std::string& kernel_src); - std::string program; + fs::path program; TargetProperties target; fs::path hsaco_file; hipModulePtr module; @@ -56,12 +62,11 @@ struct HIPOCProgramImpl std::vector binary; #if !MIOPEN_USE_COMGR - void - BuildCodeObjectInFile(std::string& params, const std::string& src, const std::string& filename); + void BuildCodeObjectInFile(std::string& params, std::string_view src, const fs::path& filename); #else void BuildCodeObjectInMemory(const std::string& params, - const std::string& src, - const std::string& filename); + std::string_view src, + const fs::path& filename); #endif void BuildCodeObject(std::string params, const std::string& kernel_src); diff --git a/src/include/miopen/invoke_params.hpp b/src/include/miopen/invoke_params.hpp index 8b39bb6e57..b62e618ca5 100644 --- a/src/include/miopen/invoke_params.hpp +++ b/src/include/miopen/invoke_params.hpp @@ -39,7 +39,6 @@ namespace miopen { enum class InvokeType { Run, - Evaluate, AutoTune, }; diff --git a/src/include/miopen/invoker_cache.hpp b/src/include/miopen/invoker_cache.hpp index 30bb489c78..4fd791efd0 100644 --- a/src/include/miopen/invoker_cache.hpp +++ b/src/include/miopen/invoker_cache.hpp @@ -29,12 +29,11 @@ #include #include -#include - #include #include #include #include +#include namespace miopen { @@ -44,12 +43,12 @@ class InvokerCache // network_config, solver_id using Key = std::pair; - boost::optional operator[](const Key& key) const; + std::optional operator[](const Key& key) const; // For find 1.0 - boost::optional GetFound1_0(const std::string& network_config, - const std::string& algorithm) const; - boost::optional GetFound1_0SolverId(const std::string& network_config, - const std::string& algorithm) const; + std::optional GetFound1_0(const std::string& network_config, + const std::string& algorithm) const; + std::optional GetFound1_0SolverId(const std::string& network_config, + const std::string& algorithm) const; void Register(const Key& key, const Invoker& invoker); // For find 1.0 diff --git a/src/include/miopen/kern_db.hpp b/src/include/miopen/kern_db.hpp index 14bb03a46f..db6eef12b6 100644 --- a/src/include/miopen/kern_db.hpp +++ b/src/include/miopen/kern_db.hpp @@ -38,6 +38,7 @@ #include #include +#include #include #include #include @@ -46,7 +47,7 @@ namespace miopen { struct KernelConfig { static std::string table_name() { return "kern_db"; } - std::string kernel_name; + fs::path kernel_name; std::string kernel_args; std::vector kernel_blob; static std::vector FieldNames() @@ -72,7 +73,7 @@ struct KernelConfig std::string Where() const { std::ostringstream ss; - ss << "(kernel_name = '" << kernel_name << "')" + ss << "(kernel_name = '" << kernel_name.string() << "')" << " AND (kernel_args = '" << kernel_args << "')"; return ss.str(); } @@ -84,10 +85,11 @@ class KernDb : public SQLiteBase std::function(const std::vector&, unsigned int)> decompress_fn; public: - KernDb(DbKinds db_kind, const std::string& filename_, bool is_system); + MIOPEN_INTERNALS_EXPORT KernDb(DbKinds db_kind, const fs::path& filename_, bool is_system); // This constructor is only intended for testing + MIOPEN_INTERNALS_EXPORT KernDb(DbKinds db_kind, - const std::string& filename_, + const fs::path& filename_, bool is_system_, std::function(const std::vector&, bool*)> compress_fn_, std::function(const std::vector&, unsigned int)> decompress_fn_); @@ -162,7 +164,7 @@ class KernDb : public SQLiteBase bool success = false; auto compressed_blob = compress_fn(problem_config.kernel_blob, &success); auto stmt = SQLite::Statement{sql, insert_query}; - stmt.BindText(1, problem_config.kernel_name); + stmt.BindPath(1, problem_config.kernel_name); stmt.BindText(2, problem_config.kernel_args); if(!success) { diff --git a/src/include/miopen/kernel.hpp b/src/include/miopen/kernel.hpp index 005b6d78de..1dccceaca6 100644 --- a/src/include/miopen/kernel.hpp +++ b/src/include/miopen/kernel.hpp @@ -26,17 +26,16 @@ #ifndef GUARD_MIOPEN_KERNEL_HPP #define GUARD_MIOPEN_KERNEL_HPP -#include +#include #include #include +#include namespace miopen { -std::string GetKernelSrc(std::string name); -std::string GetKernelInc(std::string key); -const std::string* GetKernelIncPtr(std::string key); -std::vector GetKernelIncList(); -std::vector GetHipKernelIncList(); +std::string_view GetKernelSrc(const fs::path& name); +std::string_view GetKernelInc(const fs::path& name); +const std::vector>& GetKernelIncList(); } // namespace miopen #if MIOPEN_BACKEND_OPENCL diff --git a/src/include/miopen/kernel_build_params.hpp b/src/include/miopen/kernel_build_params.hpp index 946dcac597..822ac91e1a 100644 --- a/src/include/miopen/kernel_build_params.hpp +++ b/src/include/miopen/kernel_build_params.hpp @@ -32,6 +32,8 @@ #include #include +#include + namespace miopen { namespace kbp { @@ -133,17 +135,17 @@ class KernelBuildParameters }; namespace kbp { -struct OpenCL +struct MIOPEN_INTERNALS_EXPORT OpenCL { static std::string Generate(const std::vector& options); }; -struct GcnAsm +struct MIOPEN_INTERNALS_EXPORT GcnAsm { static std::string Generate(const std::vector& options); }; -struct HIP +struct MIOPEN_INTERNALS_EXPORT HIP { static std::string Generate(const std::vector& options); }; diff --git a/src/include/miopen/kernel_cache.hpp b/src/include/miopen/kernel_cache.hpp index 3b2909b27c..cb3eeed527 100644 --- a/src/include/miopen/kernel_cache.hpp +++ b/src/include/miopen/kernel_cache.hpp @@ -48,6 +48,7 @@ #include #include #include +#include #include namespace miopen { @@ -60,20 +61,21 @@ class KernelCache { public: - using Key = std::pair; + using Key = std::pair; using KernelMap = std::unordered_map, SimpleHash>; using ProgramMap = std::unordered_map; Kernel AddKernel(const Handle& h, const std::string& algorithm, const std::string& network_config, - const std::string& program_name, + const fs::path& program_name, const std::string& kernel_name, const std::vector& vld, const std::vector& vgd, std::string params = "", std::size_t cache_index = 0, - const std::string& kernel_src = ""); + const std::string& kernel_src = "", + Program* program_out = nullptr); void AddKernel(Key key, Kernel k, std::size_t cache_index); @@ -82,10 +84,10 @@ class KernelCache const std::vector& GetKernels(const std::string& algorithm, const std::string& network_config); - bool HasProgram(const std::string& name, const std::string& params) const; - void ClearProgram(const std::string& name, const std::string& params); + bool HasProgram(const fs::path& name, const std::string& params) const; + void ClearProgram(const fs::path& name, const std::string& params); - void AddProgram(Program prog, const std::string& program_name, std::string params); + void AddProgram(Program prog, const fs::path& program_name, std::string params); KernelCache(); diff --git a/src/include/miopen/kernel_info.hpp b/src/include/miopen/kernel_info.hpp index 17d34d6ac8..d2571afa32 100644 --- a/src/include/miopen/kernel_info.hpp +++ b/src/include/miopen/kernel_info.hpp @@ -45,12 +45,14 @@ struct KernelInfo std::string comp_options; std::vector l_wk; std::vector g_wk; - std::string kernel_file; + fs::path kernel_file; std::string kernel_name; friend std::ostream& operator<<(std::ostream& os, const KernelInfo& k); }; -std::vector PrecompileKernels(const Handle& h, const std::vector& kernels); +std::vector PrecompileKernels(const Handle& h, + const std::vector& kernels, + bool force_attach_binary = false); } // namespace solver } // namespace miopen diff --git a/src/include/miopen/layernorm.hpp b/src/include/miopen/layernorm.hpp index 3a8bf54a90..dc695140fc 100644 --- a/src/include/miopen/layernorm.hpp +++ b/src/include/miopen/layernorm.hpp @@ -33,22 +33,22 @@ namespace miopen { struct Handle; struct TensorDescriptor; -miopenStatus_t LayerNormForward(Handle& handle, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& weightDesc, - ConstData_t weight, - const TensorDescriptor& biasDesc, - ConstData_t bias, - const TensorDescriptor& yDesc, - Data_t y, - const TensorDescriptor& meanDesc, - Data_t mean, - const TensorDescriptor& rstdDesc, - Data_t rstd, - miopenNormMode_t mode, - float epsilon, - int32_t normalized_dim); +MIOPEN_INTERNALS_EXPORT miopenStatus_t LayerNormForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& biasDesc, + ConstData_t bias, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& meanDesc, + Data_t mean, + const TensorDescriptor& rstdDesc, + Data_t rstd, + miopenNormMode_t mode, + float epsilon, + int32_t normalized_dim); } // namespace miopen -#endif // _MIOPEN_LAYERNORM_HPP_ +#endif // MIOPEN_LAYERNORM_HPP_ diff --git a/src/include/miopen/layernorm/invoke_params.hpp b/src/include/miopen/layernorm/invoke_params.hpp index b97bac7d08..5cdff22dcc 100644 --- a/src/include/miopen/layernorm/invoke_params.hpp +++ b/src/include/miopen/layernorm/invoke_params.hpp @@ -52,6 +52,64 @@ struct InvokeParams : public miopen::InvokeParams Data_t GetWorkspace() const { return nullptr; } }; +struct AddInvokeParams : public miopen::InvokeParams +{ + AddInvokeParams() = default; + + const TensorDescriptor* xDesc = nullptr; + + ConstData_t x = nullptr; + ConstData_t x2 = nullptr; + ConstData_t weight = nullptr; + ConstData_t bias = nullptr; + Data_t y = nullptr; + Data_t mean = nullptr; + Data_t rstd = nullptr; + float epsilon = 0; + int32_t normalized_dim = 0; + miopenNormMode_t mode = MIOPEN_ELEMENTWISE_AFFINE; + + std::size_t GetWorkspaceSize() const { return 0; } + Data_t GetWorkspace() const { return nullptr; } +}; + +struct T5InvokeParams : public miopen::InvokeParams +{ + T5InvokeParams() = default; + + const TensorDescriptor* xDesc = nullptr; + + ConstData_t x = nullptr; + ConstData_t weight = nullptr; + Data_t y = nullptr; + Data_t rstd = nullptr; + float epsilon = 0; + miopenNormMode_t mode = MIOPEN_ELEMENTWISE_AFFINE; + + std::size_t GetWorkspaceSize() const { return 0; } + Data_t GetWorkspace() const { return nullptr; } +}; + +struct T5BwdInvokeParams : public miopen::InvokeParams +{ + T5BwdInvokeParams() = default; + + const TensorDescriptor* dyDesc = nullptr; + + ConstData_t dy = nullptr; + ConstData_t x = nullptr; + ConstData_t weight = nullptr; + ConstData_t rstd = nullptr; + Data_t dx = nullptr; + Data_t dw = nullptr; + Data_t workspace = nullptr; + std::size_t workspace_size = 0; + miopenNormMode_t mode = MIOPEN_ELEMENTWISE_AFFINE; + + std::size_t GetWorkspaceSize() const { return workspace_size; } + Data_t GetWorkspace() const { return workspace; } +}; + } // namespace layernorm } // namespace miopen diff --git a/src/include/miopen/layernorm/problem_description.hpp b/src/include/miopen/layernorm/problem_description.hpp index 78a631b292..2c09f7cc40 100644 --- a/src/include/miopen/layernorm/problem_description.hpp +++ b/src/include/miopen/layernorm/problem_description.hpp @@ -37,6 +37,12 @@ struct NetworkConfig; namespace layernorm { +enum class Direction +{ + Forward, + Backward, +}; + struct ProblemDescription : ProblemDescriptionBase { ProblemDescription(miopenNormMode_t mode_, @@ -60,41 +66,133 @@ struct ProblemDescription : ProblemDescriptionBase { } + ProblemDescription(miopenNormMode_t mode_, + const TensorDescriptor& xDesc_, + const TensorDescriptor& x2Desc_, + const TensorDescriptor& weightDesc_, + const TensorDescriptor& biasDesc_, + const TensorDescriptor& yDesc_, + const TensorDescriptor& meanDesc_, + const TensorDescriptor& rstdDesc_, + float epsilon_, + int32_t normalized_dim_) + : mode(mode_), + xDesc(xDesc_), + x2Desc(x2Desc_), + weightDesc(weightDesc_), + biasDesc(biasDesc_), + yDesc(yDesc_), + meanDesc(meanDesc_), + rstdDesc(rstdDesc_), + epsilon(epsilon_), + normalized_dim(normalized_dim_) + { + } + + ProblemDescription(miopenNormMode_t mode_, + const TensorDescriptor& xDesc_, + const TensorDescriptor& weightDesc_, + const TensorDescriptor& yDesc_, + const TensorDescriptor& rstdDesc_, + float epsilon_) + : direction(Direction::Forward), + mode(mode_), + xDesc(xDesc_), + weightDesc(weightDesc_), + yDesc(yDesc_), + rstdDesc(rstdDesc_), + epsilon(epsilon_) + { + } + + ProblemDescription(miopenNormMode_t mode_, + const TensorDescriptor& dyDesc_, + const TensorDescriptor& xDesc_, + const TensorDescriptor& weightDesc_, + const TensorDescriptor& rstdDesc_, + const TensorDescriptor& dxDesc_, + const TensorDescriptor& dwDesc_) + : direction(Direction::Backward), + mode(mode_), + xDesc(xDesc_), + weightDesc(weightDesc_), + rstdDesc(rstdDesc_), + dyDesc(dyDesc_), + dxDesc(dxDesc_), + dwDesc(dwDesc_) + { + } + + Direction GetDirection() const { return direction; } miopenNormMode_t GetMode() const { return mode; } const TensorDescriptor& GetXDesc() const { return xDesc; } + const TensorDescriptor& GetX2Desc() const { return x2Desc; } const TensorDescriptor& GetWeightDesc() const { return weightDesc; } const TensorDescriptor& GetBiasDesc() const { return biasDesc; } const TensorDescriptor& GetYDesc() const { return yDesc; } const TensorDescriptor& GetMeanDesc() const { return meanDesc; } const TensorDescriptor& GetRstdDesc() const { return rstdDesc; } + const TensorDescriptor& GetDYDesc() const { return dyDesc; } + const TensorDescriptor& GetDXDesc() const { return dxDesc; } + const TensorDescriptor& GetDWDesc() const { return dwDesc; } float GetEpsilon() const { return epsilon; } int32_t GetNormalizedDim() const { return normalized_dim; } bool IsSameType() const { - if(xDesc.GetType() != yDesc.GetType()) + if(direction == Direction::Forward) { + if(xDesc.GetType() != yDesc.GetType()) + { #if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG - MIOPEN_THROW(miopenStatusBadParm, "LayerNormForward: Tensor types do not match."); + MIOPEN_THROW(miopenStatusBadParm, "LayerNormForward: Tensor types do not match."); #else - return false; + return false; +#endif + } + } + else + { + if(dyDesc.GetType() != dxDesc.GetType()) + { +#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG + MIOPEN_THROW(miopenStatusBadParm, "LayerNormBackward: Tensor types do not match."); +#else + return false; #endif + } } return true; } bool IsSameLength() const { - if(xDesc.GetLengths() != yDesc.GetLengths()) + if(direction == Direction::Forward) { + if(xDesc.GetLengths() != yDesc.GetLengths()) + { #if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG - MIOPEN_THROW(miopenStatusBadParm, - "LayerNormForward: Tensor dimension lengths do not match."); + MIOPEN_THROW(miopenStatusBadParm, + "LayerNormForward: Tensor dimension lengths do not match."); #else - return false; + return false; #endif + } + return true; + } + else + { + if(dyDesc.GetLengths() != dxDesc.GetLengths()) + { +#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG + MIOPEN_THROW(miopenStatusBadParm, + "LayerNormBackward: Tensor dimension lengths do not match."); +#else + return false; +#endif + } + return true; } - return true; } bool IsRightNormDim() const @@ -115,14 +213,31 @@ struct ProblemDescription : ProblemDescriptionBase bool IsAllPacked() const { - if(!(xDesc.IsPacked() && weightDesc.IsPacked() && biasDesc.IsPacked() && yDesc.IsPacked() && - meanDesc.IsPacked() && rstdDesc.IsPacked())) + if(direction == Direction::Forward) { + if(!(xDesc.IsPacked() && weightDesc.IsPacked() && biasDesc.IsPacked() && + yDesc.IsPacked() && meanDesc.IsPacked() && rstdDesc.IsPacked())) + { #if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG - MIOPEN_THROW(miopenStatusBadParm, "LayerNormForward: Unpacked tensors not supported."); + MIOPEN_THROW(miopenStatusBadParm, + "LayerNormForward: Unpacked tensors not supported."); #else - return false; + return false; +#endif + } + } + else + { + if(!(dyDesc.IsPacked() && xDesc.IsPacked() && weightDesc.IsPacked() && + rstdDesc.IsPacked() && dxDesc.IsPacked() && dwDesc.IsPacked())) + { +#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG + MIOPEN_THROW(miopenStatusBadParm, + "LayerNormBackward: Unpacked tensors not supported."); +#else + return false; #endif + } } return true; } @@ -143,13 +258,18 @@ struct ProblemDescription : ProblemDescriptionBase NetworkConfig MakeNetworkConfig() const override; private: + Direction direction; miopenNormMode_t mode; TensorDescriptor xDesc; + TensorDescriptor x2Desc; TensorDescriptor weightDesc; TensorDescriptor biasDesc; TensorDescriptor yDesc; TensorDescriptor meanDesc; TensorDescriptor rstdDesc; + TensorDescriptor dyDesc; + TensorDescriptor dxDesc; + TensorDescriptor dwDesc; float epsilon; int32_t normalized_dim; diff --git a/src/include/miopen/layernorm/solvers.hpp b/src/include/miopen/layernorm/solvers.hpp index 503bb87fb6..f386e456b2 100644 --- a/src/include/miopen/layernorm/solvers.hpp +++ b/src/include/miopen/layernorm/solvers.hpp @@ -68,6 +68,40 @@ struct Layernorm4DCKForward final : NormalizationSolver const miopen::layernorm::ProblemDescription& problem) const override; }; +struct AddLayernormForward final : NormalizationSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; +}; + +struct T5LayernormForward final : NormalizationSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; +}; + +struct T5LayernormBackward final : NormalizationSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + bool IsApplicable(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; + std::size_t + GetWorkspaceSize(const ExecutionContext& context, + const miopen::layernorm::ProblemDescription& problem) const override; + bool MayNeedWorkspace() const override { return true; } +}; + } // namespace layernorm } // namespace solver diff --git a/src/include/miopen/layernorm/utils.hpp b/src/include/miopen/layernorm/utils.hpp new file mode 100644 index 0000000000..1e40ea1e0b --- /dev/null +++ b/src/include/miopen/layernorm/utils.hpp @@ -0,0 +1,89 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_LAYERNORM_UTILS_HPP_ +#define MIOPEN_LAYERNORM_UTILS_HPP_ + +#include + +namespace miopen { +namespace solver { +namespace layernorm { + +#define LOCAL_SIZE 256 + +inline std::size_t sizeof_kernel_FLOAT(const miopen::layernorm::ProblemDescription& problem) +{ + const auto datatype = problem.GetXDesc().GetType(); + return get_data_size(datatype); +} + +inline std::size_t sizeof_local_memory(const miopen::layernorm::ProblemDescription& problem) +{ + std::size_t rv = 0; + rv += LOCAL_SIZE * sizeof_kernel_FLOAT(problem) * 2; + return rv; +} + +inline std::size_t sizeof_local_memory_t5(const miopen::layernorm::ProblemDescription& problem) +{ + std::size_t rv = 0; + rv += LOCAL_SIZE * sizeof_kernel_FLOAT(problem); + return rv; +} + +inline size_t get_reqd_work_item_cnt(const ExecutionContext& context) +{ + // At least 4 WGs per one CU + return static_cast(LOCAL_SIZE * context.GetStream().GetMaxComputeUnits() * 4); +} + +inline size_t get_reqd_work_item_cnt(const Handle& handle) +{ + // At least 4 WGs per one CU + return static_cast(LOCAL_SIZE * handle.GetMaxComputeUnits() * 4); +} + +inline size_t get_parallelism_size(size_t reqd_work_item_cnt, size_t inner_size, size_t outer_size) +{ + size_t parallelism_size = 1ULL; + while(parallelism_size * inner_size < reqd_work_item_cnt && + parallelism_size < std::sqrt(outer_size)) + { + parallelism_size *= 2ULL; + } + return parallelism_size; +} + +inline bool is_parallelism(size_t reqd_work_item_cnt, size_t inner_size, size_t outer_size) +{ + return !(inner_size > reqd_work_item_cnt) && (inner_size * outer_size > reqd_work_item_cnt); +} + +} // namespace layernorm +} // namespace solver +} // namespace miopen + +#endif // _MIOPEN_LAYERNORM_UTILS_HPP_ diff --git a/src/include/miopen/load_file.hpp b/src/include/miopen/load_file.hpp index 6394f43196..04c0a62f2f 100644 --- a/src/include/miopen/load_file.hpp +++ b/src/include/miopen/load_file.hpp @@ -28,6 +28,7 @@ #define MIOPEN_GUARD_MLOPEN_LOAD_FILE_HPP #include +#include #include namespace miopen { diff --git a/src/include/miopen/lock_file.hpp b/src/include/miopen/lock_file.hpp index 77313e5052..2f1655cd7f 100644 --- a/src/include/miopen/lock_file.hpp +++ b/src/include/miopen/lock_file.hpp @@ -46,12 +46,12 @@ namespace miopen { -std::string LockFilePath(const fs::path& filename_); +MIOPEN_INTERNALS_EXPORT fs::path LockFilePath(const fs::path& filename_); // LockFile class is a wrapper around boost::interprocess::file_lock providing MT-safety. // One process should never have more than one instance of this class with same path at the same // time. It may lead to undefined behaviour on Windows. // Also on windows mutex can be removed because file locks are MT-safe there. -class LockFile +class MIOPEN_INTERNALS_EXPORT LockFile { private: class PassKey @@ -59,7 +59,7 @@ class LockFile }; public: - LockFile(const char* path_, PassKey); + LockFile(const fs::path&, PassKey); LockFile(const LockFile&) = delete; LockFile operator=(const LockFile&) = delete; @@ -122,7 +122,7 @@ class LockFile access_mutex.unlock_shared(); } - static LockFile& Get(const char* path); + static LockFile& Get(const fs::path& file); template bool try_lock_for(TDuration duration) @@ -165,14 +165,14 @@ class LockFile } private: - const char* path; // For logging purposes + fs::path path; // For logging purposes std::shared_timed_mutex access_mutex; boost::interprocess::file_lock flock; - static std::map& LockFiles() + static std::map& LockFiles() { // NOLINTNEXTLINE (cppcoreguidelines-avoid-non-const-global-variables) - static std::map lock_files; + static std::map lock_files; return lock_files; } diff --git a/src/include/miopen/logger.hpp b/src/include/miopen/logger.hpp index 3d87049090..6d9e1932fe 100644 --- a/src/include/miopen/logger.hpp +++ b/src/include/miopen/logger.hpp @@ -36,8 +36,7 @@ #include #include -#include -#include +#include #if MIOPEN_USE_ROCTRACER #include @@ -218,12 +217,14 @@ MIOPEN_EXPORT extern bool } // namespace debug -MIOPEN_EXPORT const char* LoggingLevelToCString(LoggingLevel level); -MIOPEN_EXPORT std::string LoggingPrefix(); +MIOPEN_INTERNALS_EXPORT std::string LoggingLevelToCustomString(LoggingLevel level, + const char* custom); +MIOPEN_INTERNALS_EXPORT const char* LoggingLevelToCString(LoggingLevel level); +MIOPEN_INTERNALS_EXPORT std::string LoggingPrefix(); /// \return true if level is enabled. /// \param level - one of the values defined in LoggingLevel. -MIOPEN_EXPORT bool IsLogging(LoggingLevel level, bool disableQuieting = false); +MIOPEN_INTERNALS_EXPORT bool IsLogging(LoggingLevel level, bool disableQuieting = false); bool IsLoggingCmd(); bool IsLoggingFunctionCalls(); #if MIOPEN_USE_ROCTRACER @@ -378,8 +379,12 @@ constexpr std::string_view LoggingParseFunction(const std::string_view func, #define MIOPEN_LOG_XQ_(level, disableQuieting, fn_name, ...) \ MIOPEN_LOG_XQ_CUSTOM(level, disableQuieting, LoggingLevelToCString(level), fn_name, __VA_ARGS__) -#define MIOPEN_LOG_CUSTOM(level, category, ...) \ - MIOPEN_LOG_XQ_CUSTOM(level, false, category, MIOPEN_GET_FN_NAME, __VA_ARGS__) +#define MIOPEN_LOG_CUSTOM(level, category, ...) \ + MIOPEN_LOG_XQ_CUSTOM(level, \ + false, \ + LoggingLevelToCustomString(level, category), \ + MIOPEN_GET_FN_NAME, \ + __VA_ARGS__) #define MIOPEN_LOG(level, ...) MIOPEN_LOG_XQ_(level, false, MIOPEN_GET_FN_NAME, __VA_ARGS__) #define MIOPEN_LOG_NQ_(level, ...) MIOPEN_LOG_XQ_(level, true, MIOPEN_GET_FN_NAME, __VA_ARGS__) diff --git a/src/include/miopen/lrn.hpp b/src/include/miopen/lrn.hpp index 74280c082e..5238495cd1 100644 --- a/src/include/miopen/lrn.hpp +++ b/src/include/miopen/lrn.hpp @@ -35,7 +35,7 @@ namespace miopen { -struct LRNDescriptor : miopenLRNDescriptor +struct MIOPEN_INTERNALS_EXPORT LRNDescriptor : miopenLRNDescriptor { LRNDescriptor(); LRNDescriptor(miopenLRNMode_t m, unsigned int pn, const double* pparms); diff --git a/src/include/miopen/md5.hpp b/src/include/miopen/md5.hpp index 21dad5cd64..9822c300c4 100644 --- a/src/include/miopen/md5.hpp +++ b/src/include/miopen/md5.hpp @@ -1,13 +1,14 @@ #ifndef GUARD_MLOPEN_MD5_HPP #define GUARD_MLOPEN_MD5_HPP +#include #include #include namespace miopen { -std::string md5(const std::string&); -std::string md5(const std::vector&); +MIOPEN_INTERNALS_EXPORT std::string md5(const std::string&); +MIOPEN_INTERNALS_EXPORT std::string md5(const std::vector&); } // namespace miopen diff --git a/src/include/miopen/mha/invoke_params.hpp b/src/include/miopen/mha/invoke_params.hpp index c81453e3f8..1597344854 100644 --- a/src/include/miopen/mha/invoke_params.hpp +++ b/src/include/miopen/mha/invoke_params.hpp @@ -36,17 +36,52 @@ namespace mha { struct InvokeParams : public miopen::InvokeParams { InvokeParams(const MhaDataForward& dataForward, Data_t ws, std::size_t wsSize) - : mhaDataForward(dataForward), workSpace(ws), workSpaceSize(wsSize) + : mhaDataForwardPtr(std::make_shared(dataForward)), + workSpace(ws), + workSpaceSize(wsSize) { } - const MhaDataForward& GetData() const { return mhaDataForward; } + InvokeParams(const MhaDataBackward& dataBackward, Data_t ws, std::size_t wsSize) + : mhaDataBackwardPtr(std::make_shared(dataBackward)), + workSpace(ws), + workSpaceSize(wsSize) + { + } + + const MhaDataForward& GetDataForward() const + { + assert(mhaDataForwardPtr); + + if(mhaDataForwardPtr == nullptr) + { + MIOPEN_THROW("Mha InvokeParams GetDataForward() failed: InvokeParams was initialized " + "with a backward direction ctor"); + } + + return *mhaDataForwardPtr; + } + + const MhaDataBackward& GetDataBackward() const + { + assert(mhaDataBackwardPtr); + + if(mhaDataBackwardPtr == nullptr) + { + MIOPEN_THROW("Mha InvokeParams GetDataBackward() failed: InvokeParams was initialized " + "with a forward direction ctor"); + } + + return *mhaDataBackwardPtr; + } std::size_t GetWorkspaceSize() const { return workSpaceSize; } Data_t GetWorkspace() const { return workSpace; } private: - const MhaDataForward mhaDataForward; + std::shared_ptr mhaDataForwardPtr; + std::shared_ptr mhaDataBackwardPtr; + const Data_t workSpace; const std::size_t workSpaceSize; }; diff --git a/src/include/miopen/mha/mha.hpp b/src/include/miopen/mha/mha.hpp index 2a9d1aee0d..296faa6892 100644 --- a/src/include/miopen/mha/mha.hpp +++ b/src/include/miopen/mha/mha.hpp @@ -64,6 +64,52 @@ struct MhaInputDescsForward TensorDescriptor zInvDesc; }; +struct MhaInputDescsBackward +{ + // input tensors + TensorDescriptor kDesc; + TensorDescriptor qDesc; + TensorDescriptor vDesc; + + TensorDescriptor oDesc; + TensorDescriptor doDesc; + + // input tensors from fwd pass + TensorDescriptor mDesc; + TensorDescriptor zInvDesc; + + // input scaling tensors + TensorDescriptor descaleKDesc; + TensorDescriptor descaleQDesc; + TensorDescriptor descaleVDesc; + TensorDescriptor descaleSDesc; + TensorDescriptor descaleODesc; + TensorDescriptor descaleDODesc; + TensorDescriptor descaleDSDesc; + + TensorDescriptor scaleSDesc; + TensorDescriptor scaleDSDesc; + TensorDescriptor scaleDQDesc; + TensorDescriptor scaleDKDesc; + TensorDescriptor scaleDVDesc; + + // input scalars + float scale; + + TensorDescriptor dropoutProbabilityDesc; + TensorDescriptor dropoutSeedDesc; + TensorDescriptor dropoutOffsetDesc; + + // output tensors + TensorDescriptor dqDesc; + TensorDescriptor dkDesc; + TensorDescriptor dvDesc; + TensorDescriptor amaxDQDesc; + TensorDescriptor amaxDKDesc; + TensorDescriptor amaxDVDesc; + TensorDescriptor amaxDSDesc; +}; + struct MhaDataForward { // input tensors @@ -93,5 +139,47 @@ struct MhaDataForward Data_t zInvData; }; +struct MhaDataBackward +{ + // input tensors + ConstData_t kData; + ConstData_t qData; + ConstData_t vData; + + ConstData_t oData; + ConstData_t doData; + + // input tensors from fwd pass + ConstData_t mData; + ConstData_t zInvData; + + // input scaling tensors + ConstData_t descaleKData; + ConstData_t descaleQData; + ConstData_t descaleVData; + ConstData_t descaleSData; + ConstData_t descaleOData; + ConstData_t descaleDOData; + ConstData_t descaleDSData; + ConstData_t scaleSData; + ConstData_t scaleDSData; + ConstData_t scaleDQData; + ConstData_t scaleDKData; + ConstData_t scaleDVData; + + ConstData_t dropoutProbabilityData; + ConstData_t dropoutSeedData; + ConstData_t dropoutOffsetData; + + // output tensors + Data_t dqData; + Data_t dkData; + Data_t dvData; + Data_t amaxDQData; + Data_t amaxDKData; + Data_t amaxDVData; + Data_t amaxDSData; +}; + } // namespace mha } // namespace miopen diff --git a/src/include/miopen/mha/problem_description.hpp b/src/include/miopen/mha/problem_description.hpp index 4ef95ab6c6..2136dd81a8 100644 --- a/src/include/miopen/mha/problem_description.hpp +++ b/src/include/miopen/mha/problem_description.hpp @@ -36,23 +36,54 @@ struct NetworkConfig; namespace mha { -struct ProblemDescription : ProblemDescriptionBase +struct MIOPEN_INTERNALS_EXPORT ProblemDescription : ProblemDescriptionBase { // softmax forward constructor ProblemDescription(const MhaInputDescsForward& descs) - : isForward(true), mhaInputDescsForward(descs) + : isForward(true), mhaInputDescsForwardPtr(std::make_shared(descs)) + { + } + + // softmax backward constructor + ProblemDescription(const MhaInputDescsBackward& descs) + : isForward(false), mhaInputDescsBackwardPtr(std::make_shared(descs)) { } bool IsForward() const { return isForward; } - const MhaInputDescsForward& GetDescs() const { return mhaInputDescsForward; } + const MhaInputDescsForward& GetDescsForward() const + { + assert(mhaInputDescsForwardPtr && isForward); + + if(mhaInputDescsForwardPtr == nullptr) + { + MIOPEN_THROW("Mha ProblemDescription GetDescsForward() failed: PD was initialized with " + "a backward direction ctor"); + } + + return *mhaInputDescsForwardPtr; + } + + const MhaInputDescsBackward& GetDescsBackward() const + { + assert(mhaInputDescsBackwardPtr && !isForward); + + if(mhaInputDescsBackwardPtr == nullptr) + { + MIOPEN_THROW("Mha ProblemDescription GetDescsBackward() failed: PD was initialized " + "with a forward direction ctor"); + } + + return *mhaInputDescsBackwardPtr; + } NetworkConfig MakeNetworkConfig() const override; private: const bool isForward; - MhaInputDescsForward mhaInputDescsForward; + std::shared_ptr mhaInputDescsForwardPtr; + std::shared_ptr mhaInputDescsBackwardPtr; }; } // namespace mha diff --git a/src/include/miopen/mha/solvers.hpp b/src/include/miopen/mha/solvers.hpp index 6dea6bb213..6bac473a71 100644 --- a/src/include/miopen/mha/solvers.hpp +++ b/src/include/miopen/mha/solvers.hpp @@ -39,20 +39,42 @@ namespace mha { using MhaSolver = NonTunableSolverBase; -struct Mha final : MhaSolver +struct MhaForward final : MhaSolver { - const std::string& SolverDbId() const override { return GetSolverDbId(); } + const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext& context, - const miopen::mha::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; - ConvSolution GetSolution(const ExecutionContext& context, - const miopen::mha::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; - std::size_t GetWorkspaceSize(const ExecutionContext& context, - const miopen::mha::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT std::size_t + GetWorkspaceSize(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; - bool MayNeedWorkspace() const override; + MIOPEN_INTERNALS_EXPORT bool MayNeedWorkspace() const override; +}; + +struct MhaBackward final : MhaSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; + + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; + + MIOPEN_INTERNALS_EXPORT std::size_t + GetWorkspaceSize(const ExecutionContext& context, + const miopen::mha::ProblemDescription& problem) const override; + + MIOPEN_INTERNALS_EXPORT bool MayNeedWorkspace() const override; }; } // namespace mha diff --git a/src/include/miopen/miopen_internal.h b/src/include/miopen/miopen_internal.h index ed152a5d9d..2cb47c4ee3 100644 --- a/src/include/miopen/miopen_internal.h +++ b/src/include/miopen/miopen_internal.h @@ -34,6 +34,8 @@ #pragma clang diagnostic ignored "-Wextern-c-compat" #endif +#include + #ifdef __cplusplus extern "C" { #endif @@ -104,6 +106,20 @@ MIOPEN_EXPORT miopenStatus_t miopenGetConvolutionFindMode( /* End of Find Mode API */ +/*! @brief Get extra workspace for backward weight kernel. + * + * @param alpha_beta_case type of alpha beta case + * @param inputTensorDesc Input data tensor descriptor (output) + * @param outputTensorDesc Output data tensor descriptor (output) + * @param buffer_size buffer size for CK Backward weights work space + */ +MIOPEN_EXPORT extern "C" miopenStatus_t +miopenConvolutionABBackwardWeightsGetWorkSpaceSize(const miopenAlphaBetaCase_t alpha_beta_case, + const miopenTensorDescriptor_t inputTensorDesc, + const miopenTensorDescriptor_t outputTensorDesc, + const miopenConvolutionDescriptor_t convDesc, + size_t* buffer_size); + #ifdef __cplusplus } #endif diff --git a/src/include/miopen/mlo_internal.hpp b/src/include/miopen/mlo_internal.hpp index f8732f8e62..6252df3087 100644 --- a/src/include/miopen/mlo_internal.hpp +++ b/src/include/miopen/mlo_internal.hpp @@ -55,7 +55,7 @@ POSSIBILITY OF SUCH DAMAGE. #define NOMINMAX // stupid windows.h confused with min() macros in std namespace #endif -#include +#include #if MIOPEN_BACKEND_OPENCL #include @@ -119,6 +119,8 @@ inline int AlignUp(int val, unsigned step) return static_cast(((static_cast(val) + step - 1) / step) * step); } +inline size_t AlignUp(size_t val, size_t step) { return (val + step - 1) / step * step; } + namespace miopen { struct TensorDescriptor; @@ -139,7 +141,7 @@ using PerformanceDb = DbTimer>; #else using PerformanceDb = DbTimer>; #endif -miopen::PerformanceDb GetDb(const miopen::ExecutionContext& ctx); +MIOPEN_INTERNALS_EXPORT miopen::PerformanceDb GetDb(const miopen::ExecutionContext& ctx); template size_t setTopDescFromMLDesc(int spatial_dims, TTo& to, const TensorDescriptor& tensor) @@ -179,74 +181,74 @@ auto mloConstruct(T& x) -> decltype(x.mloConstruct(), void()) x.mloConstruct(); } -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllGemmSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> AllGemmWorkspaceSize(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> AllDirectForwardBackwardDataWorkspaceSize(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> FindAllImplicitGemmWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> FindAllWinogradWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> FindWinogradWrWWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> FindImplicitGemmWrWWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> AllDirectBwdWrW2DWorkspaceSize(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector> +MIOPEN_INTERNALS_EXPORT std::vector> AllFFTForwardBackwardDataWorkspaceSize(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllDirectSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllImplicitGemmSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllWinogradSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindWinogradWrWAllSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindImplicitGemmWrWAllSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllBwdWrW2DSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); -std::vector +MIOPEN_INTERNALS_EXPORT std::vector FindAllFFTSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx); diff --git a/src/include/miopen/nogpu/handle_impl.hpp b/src/include/miopen/nogpu/handle_impl.hpp index 920115e21c..36e3aa5b76 100644 --- a/src/include/miopen/nogpu/handle_impl.hpp +++ b/src/include/miopen/nogpu/handle_impl.hpp @@ -48,6 +48,7 @@ struct HandleImpl bool enable_profiling = false; StreamPtr stream = nullptr; rocblas_handle_ptr rhandle_; + hipblasLt_handle_ptr hip_blasLt_handle; float profiling_result = 0.0; int device = -1; std::string device_name; diff --git a/src/include/miopen/oclkernel.hpp b/src/include/miopen/oclkernel.hpp index 8c00c64640..6b42aa2b3b 100644 --- a/src/include/miopen/oclkernel.hpp +++ b/src/include/miopen/oclkernel.hpp @@ -149,7 +149,7 @@ class OCLKernel { assert(!gdims.empty() && gdims.size() <= 3); assert(!ldims.empty() && ldims.size() <= 3); - if(std::accumulate(ldims.begin(), ldims.end(), 1, std::multiplies{}) > + if(std::accumulate(ldims.begin(), ldims.end(), 1ULL, std::multiplies{}) > 256) // FIXME: get ldims limit from runtime { std::fill(ldims.begin(), ldims.end(), 0); diff --git a/src/include/miopen/op_kernel_args.hpp b/src/include/miopen/op_kernel_args.hpp index e2ec47c195..5bb4041e46 100644 --- a/src/include/miopen/op_kernel_args.hpp +++ b/src/include/miopen/op_kernel_args.hpp @@ -3,11 +3,7 @@ #include #include -#if !defined(_WIN32) #include -#else -#include -#endif #include struct OpKernelArg diff --git a/src/include/miopen/performance_config.hpp b/src/include/miopen/performance_config.hpp index db6512c01e..5d181fed55 100644 --- a/src/include/miopen/performance_config.hpp +++ b/src/include/miopen/performance_config.hpp @@ -29,6 +29,7 @@ #include #include +#include #include #include @@ -38,7 +39,7 @@ namespace miopen { namespace solver { -struct PerfConfig +struct MIOPEN_INTERNALS_EXPORT PerfConfig { virtual ~PerfConfig() = default; @@ -52,7 +53,7 @@ struct PerfConfig PerfConfig& operator=(const PerfConfig&) = default; }; -std::ostream& operator<<(std::ostream& os, const PerfConfig& c); +MIOPEN_INTERNALS_EXPORT std::ostream& operator<<(std::ostream& os, const PerfConfig& c); template struct PerfConfigBase : PerfConfig diff --git a/src/include/miopen/problem.hpp b/src/include/miopen/problem.hpp index 66b12c8e70..110e69299d 100644 --- a/src/include/miopen/problem.hpp +++ b/src/include/miopen/problem.hpp @@ -40,7 +40,7 @@ #include -#include +#include #include #include @@ -70,12 +70,22 @@ struct BiasDescriptor { }; +struct BatchnormDescriptor +{ + miopenBatchNormMode_t mode; + bool runningMeanVariance; + + friend void to_json(nlohmann::json& j, const BatchnormDescriptor& descriptor); + friend void from_json(const nlohmann::json& j, BatchnormDescriptor& descriptor); +}; + // The order of types is important for deserialization and should be preserved between releases. -using OperatorDescriptor = boost::variant; +using OperatorDescriptor = std::variant; struct Problem { @@ -145,6 +155,17 @@ struct Problem Problem MakeTransposed() const; + void TransposeImpl(const ConvolutionDescriptor& conv_desc); + + AnyInvokeParams MakeConvInvokeParams(const TensorDescriptor& x_desc, + Data_t x, + const TensorDescriptor& w_desc, + Data_t w, + const TensorDescriptor& y_desc, + Data_t y, + Data_t workspace, + size_t workspace_size) const; + static void ValidateGroupCount(const TensorDescriptor& xDesc, const TensorDescriptor& wDesc, const ConvolutionDescriptor& conv); @@ -181,9 +202,13 @@ struct Problem void LogDriverCommand(const ConvolutionDescriptor& conv_desc) const; void LogDriverCommand(const ActivationDescriptor& descriptor) const; + void LogDriverCommand(const BiasDescriptor& descriptor) const; + void LogDriverCommand(const MhaDescriptor& descriptor) const; + void LogDriverCommand(const SoftmaxDescriptor& descriptor) const; + void LogDriverCommand(const BatchnormDescriptor& descriptor) const; }; -struct FusedProblem +struct MIOPEN_INTERNALS_EXPORT FusedProblem { std::vector problems; @@ -227,7 +252,7 @@ struct FusedProblem struct ProblemContainer : miopenProblem { // The order of types is important for deserialization and should be preserved between releases. - using Item = boost::variant; + using Item = std::variant; Item item; diff --git a/src/include/miopen/problem_description_base.hpp b/src/include/miopen/problem_description_base.hpp index aff1c047e4..3e19f85e8c 100644 --- a/src/include/miopen/problem_description_base.hpp +++ b/src/include/miopen/problem_description_base.hpp @@ -45,6 +45,7 @@ inline std::string GetDataTypeName(miopenDataType_t data_type) case miopenDouble: return "FP64"; case miopenFloat8: return "FP8"; case miopenBFloat8: return "BF8"; + case miopenInt64: return "INT64"; } return "Unknown(" + std::to_string(data_type) + ")"; diff --git a/src/include/miopen/process.hpp b/src/include/miopen/process.hpp index 3b74d0f04d..84ea88ddd0 100644 --- a/src/include/miopen/process.hpp +++ b/src/include/miopen/process.hpp @@ -27,28 +27,38 @@ #ifndef MIOPEN_GUARD_MLOPEN_PROCESS_HPP #define MIOPEN_GUARD_MLOPEN_PROCESS_HPP +#include #include #include #include +#include namespace miopen { struct ProcessImpl; +using ProcessEnvironmentMap = std::map; -struct Process +struct MIOPEN_INTERNALS_EXPORT Process { Process(const fs::path& cmd); ~Process() noexcept; - int operator()(std::string_view args = "", const fs::path& cwd = ""); + int operator()(std::string_view args = "", + const fs::path& cwd = "", + std::ostream* out = nullptr, + const ProcessEnvironmentMap& additionalEnvironmentVariables = {}); private: std::unique_ptr impl; }; -struct ProcessAsync +struct MIOPEN_INTERNALS_EXPORT ProcessAsync { - ProcessAsync(const fs::path& cmd, std::string_view args = "", const fs::path& cwd = ""); + ProcessAsync(const fs::path& cmd, + std::string_view args = "", + const fs::path& cwd = "", + std::ostream* out = nullptr, + const ProcessEnvironmentMap& additionalEnvironmentVariables = {}); ~ProcessAsync() noexcept; ProcessAsync(ProcessAsync&&) noexcept; diff --git a/src/include/miopen/ramdb.hpp b/src/include/miopen/ramdb.hpp index 4fea6e82d7..99b6839cbb 100644 --- a/src/include/miopen/ramdb.hpp +++ b/src/include/miopen/ramdb.hpp @@ -45,11 +45,11 @@ using ramdb_clock = std::chrono::steady_clock; class LockFile; -class RamDb : protected PlainTextDb +class MIOPEN_INTERNALS_EXPORT RamDb : protected PlainTextDb { public: RamDb(DbKinds db_kind_, - std::string path, + const fs::path& path, bool is_system, const std::string& /*arch*/, std::size_t /*num_cu*/) @@ -57,18 +57,18 @@ class RamDb : protected PlainTextDb { } - RamDb(DbKinds db_kind_, std::string path, bool is_system = false); + RamDb(DbKinds db_kind_, const fs::path& path, bool is_system = false); RamDb(const RamDb&) = delete; RamDb(RamDb&&) = delete; RamDb& operator=(const RamDb&) = delete; RamDb& operator=(RamDb&&) = delete; - static std::string GetTimeFilePath(const std::string& path); - static RamDb& GetCached(DbKinds db_kind_, const std::string& path, bool is_system); + static fs::path GetTimeFilePath(const fs::path& path); + static RamDb& GetCached(DbKinds db_kind_, const fs::path& path, bool is_system); static RamDb& GetCached(DbKinds db_kind_, - const std::string& path, + const fs::path& path, bool is_system, const std::string& /*arch*/, std::size_t /*num_cu*/) diff --git a/src/include/miopen/readonlyramdb.hpp b/src/include/miopen/readonlyramdb.hpp index 589d7adbfd..ca9ac6df3f 100644 --- a/src/include/miopen/readonlyramdb.hpp +++ b/src/include/miopen/readonlyramdb.hpp @@ -27,6 +27,7 @@ #define MIOPEN_GUARD_MLOPEN_READONLYRAMDB_HPP #include +#include #include @@ -37,16 +38,16 @@ namespace miopen { namespace debug { -extern bool& rordb_embed_fs_override(); +MIOPEN_INTERNALS_EXPORT bool& rordb_embed_fs_override(); } // namespace debug -class ReadonlyRamDb +class MIOPEN_INTERNALS_EXPORT ReadonlyRamDb { public: - ReadonlyRamDb(DbKinds db_kind_, const std::string& path) : db_kind(db_kind_), db_path(path) {} + ReadonlyRamDb(DbKinds db_kind_, const fs::path& path) : db_kind(db_kind_), db_path(path) {} static ReadonlyRamDb& - GetCached(DbKinds db_kind_, const std::string& path, bool warn_if_unreadable); + GetCached(DbKinds db_kind_, const fs::path& path, bool warn_if_unreadable); boost::optional FindRecord(const std::string& problem) const { @@ -98,7 +99,7 @@ class ReadonlyRamDb private: DbKinds db_kind; - std::string db_path; + fs::path db_path; std::unordered_map cache; ReadonlyRamDb(const ReadonlyRamDb&) = default; diff --git a/src/include/miopen/reduce/invoke_params.hpp b/src/include/miopen/reduce/invoke_params.hpp index 6ad0884dfd..11923fdd79 100644 --- a/src/include/miopen/reduce/invoke_params.hpp +++ b/src/include/miopen/reduce/invoke_params.hpp @@ -36,11 +36,13 @@ struct InvokeParams : public miopen::InvokeParams { InvokeParams() = default; - const TensorDescriptor* xDesc = nullptr; - const TensorDescriptor* yDesc = nullptr; + const TensorDescriptor* xDesc = nullptr; + const TensorDescriptor* yDesc = nullptr; + const TensorDescriptor* indiceDesc = nullptr; ConstData_t x = nullptr; Data_t y = nullptr; + Data_t indice = nullptr; Data_t workspace = nullptr; std::size_t workspace_size = 0; int32_t dim = 0; diff --git a/src/include/miopen/reduce/problem_description.hpp b/src/include/miopen/reduce/problem_description.hpp index 0f69b06bab..b48bd3b3ce 100644 --- a/src/include/miopen/reduce/problem_description.hpp +++ b/src/include/miopen/reduce/problem_description.hpp @@ -47,65 +47,102 @@ struct ProblemDescription : ProblemDescriptionBase { } - ProblemDescription(const TensorDescriptor& xDesc_, const TensorDescriptor& yDesc_, int32_t dim_) - : xDesc(xDesc_), yDesc(yDesc_), dim(dim_) + ProblemDescription(const TensorDescriptor& xDesc_, + const TensorDescriptor& yDesc_, + const TensorDescriptor& indiceDesc_, + int32_t dim_, + miopenReduceExtremeOp_t reduceExtremeOp_) + : xDesc(xDesc_), + yDesc(yDesc_), + indiceDesc(indiceDesc_), + dim(dim_), + reduceExtremeOp(reduceExtremeOp_) + { + } + + ProblemDescription(const TensorDescriptor& xDesc_, + const TensorDescriptor& indiceDesc_, + int32_t dim_, + miopenReduceExtremeOp_t reduceExtremeOp_) + : xDesc(xDesc_), indiceDesc(indiceDesc_), dim(dim_), reduceExtremeOp(reduceExtremeOp_) { } miopenSumNanPropagation_t GetNanPropagation_() const { return nanPropagation; } const TensorDescriptor& GetXDesc() const { return xDesc; } const TensorDescriptor& GetYDesc() const { return yDesc; } + const TensorDescriptor& GetIndiceDesc() const { return indiceDesc; } int32_t GetDim() const { return dim; } - bool IsSameType() const + bool IsValidLength() const { - if(xDesc.GetType() != yDesc.GetType()) + if(xDesc.GetLengths().size() == 1) + return true; + + int32_t posy = 0; + for(int32_t i = 0; i < xDesc.GetLengths().size(); ++i) { -#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG - MIOPEN_THROW(miopenStatusBadParm, "Reduce: Tensor types do not match."); -#else - return false; -#endif + if(i == dim) + continue; + + if(xDesc.GetLengths()[i] != yDesc.GetLengths()[posy]) + { + MIOPEN_THROW(miopenStatusBadParm, "Reduce: Tensor dimension lengths do not match."); + } + + ++posy; } return true; } - bool IsRightLength() const + bool IsValidLengthIndice() const { if(xDesc.GetLengths().size() == 1) return true; int32_t posy = 0; - for(int32_t i = 0; i < xDesc.GetLengths().size(); i++) + for(int32_t i = 0; i < xDesc.GetLengths().size(); ++i) { if(i == dim) continue; - if(xDesc.GetLengths()[i] != yDesc.GetLengths()[posy]) + if(xDesc.GetLengths()[i] != indiceDesc.GetLengths()[posy]) { -#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG MIOPEN_THROW(miopenStatusBadParm, "Reduce: Tensor dimension lengths do not match."); -#else - return false; -#endif } - posy++; + ++posy; } return true; } - bool IsRightDim() const + bool IsValidDim() const { if((dim < 0) || (dim > xDesc.GetLengths().size())) { -#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG MIOPEN_THROW( miopenStatusBadParm, "Reduce: is greater than 0 and less than or equal tensor dimension length."); -#else + } + return true; + } + + bool IsValidInputNumel() const + { + auto xdims = xDesc.GetLengths(); + auto input_numel = + std::accumulate(xdims.begin(), xdims.end(), 1ULL, std::multiplies()); + if(input_numel > INT32_MAX) + MIOPEN_THROW(miopenStatusBadParm, "Reduce: input numel is bigger than INT_MAX."); + + return true; + } + + bool IsSameType() const + { + if(xDesc.GetType() != yDesc.GetType()) + { return false; -#endif } return true; } @@ -114,12 +151,29 @@ struct ProblemDescription : ProblemDescriptionBase { if(!(xDesc.IsPacked() && yDesc.IsPacked())) { -#if MIOPEN_BUILD_DEV || !MIOPEN_NDEBUG - MIOPEN_THROW(miopenStatusBadParm, "Reduce: Unpacked tensors not supported."); -#else return false; -#endif } + + return true; + } + + bool IsAllPackedWithIndice() const + { + if(!(xDesc.IsPacked() && yDesc.IsPacked() && indiceDesc.IsPacked())) + { + return false; + } + + return true; + } + + bool IsAllPackedIndice() const + { + if(!(xDesc.IsPacked() && indiceDesc.IsPacked())) + { + return false; + } + return true; } @@ -130,15 +184,25 @@ struct ProblemDescription : ProblemDescriptionBase return true; } + bool IsLargeReduceSize() const + { + if(xDesc.GetLengths()[dim] > 64) + return false; + return true; + } + NetworkConfig MakeNetworkConfig() const override; private: miopenSumNanPropagation_t nanPropagation; TensorDescriptor xDesc; TensorDescriptor yDesc; + TensorDescriptor indiceDesc; int32_t dim; + miopenReduceExtremeOp_t reduceExtremeOp = MIOPEN_REDUCE_CALCULATION_SUM; + NetworkConfig MakeForwardNetworkConfig() const; }; diff --git a/src/include/miopen/reduce/solvers.hpp b/src/include/miopen/reduce/solvers.hpp index 9aa584233d..e17f93124d 100644 --- a/src/include/miopen/reduce/solvers.hpp +++ b/src/include/miopen/reduce/solvers.hpp @@ -53,6 +53,48 @@ struct SumForward final : ReduceSolver struct ArgmaxForward final : ReduceSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } + size_t XGridSize(std::vector indicedims) const; + bool OverMaxGridSize(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const; + + bool IsApplicable(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; +}; + +struct ArgminForward final : ReduceSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + size_t XGridSize(std::vector indicedims) const; + bool OverMaxGridSize(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const; + + bool IsApplicable(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; +}; + +struct MaxForward final : ReduceSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + size_t XGridSize(std::vector ydims) const; + bool OverMaxGridSize(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const; + + bool IsApplicable(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; + ConvSolution GetSolution(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const override; +}; + +struct MinForward final : ReduceSolver +{ + const std::string& SolverDbId() const override { return GetSolverDbId(); } + size_t XGridSize(std::vector ydims) const; + bool OverMaxGridSize(const ExecutionContext& context, + const miopen::reduce::ProblemDescription& problem) const; bool IsApplicable(const ExecutionContext& context, const miopen::reduce::ProblemDescription& problem) const override; diff --git a/src/argmax.cpp b/src/include/miopen/reduce/utils.hpp similarity index 53% rename from src/argmax.cpp rename to src/include/miopen/reduce/utils.hpp index 35288a6bc1..a3280c062c 100644 --- a/src/argmax.cpp +++ b/src/include/miopen/reduce/utils.hpp @@ -2,7 +2,7 @@ * * MIT License * - * Copyright (c) 2023 Advanced Micro Devices, Inc. + * Copyright (c) 2024 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal @@ -23,44 +23,49 @@ * SOFTWARE. * *******************************************************************************/ +#ifndef MIOPEN_REDUCE_UTILS_HPP_ +#define MIOPEN_REDUCE_UTILS_HPP_ -#include -#include -#include -#include -#include -#include #include -#include namespace miopen { +namespace solver { +namespace reduce { -miopenStatus_t ArgmaxForward(Handle& handle, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& yDesc, - Data_t y, - int32_t dim) -{ - const auto problem = reduce::ProblemDescription{xDesc, yDesc, dim}; +#define LOCAL_SIZE 256 - const auto invoke_params = [&]() { - auto tmp = reduce::InvokeParams{}; - tmp.type = InvokeType::Run; - tmp.xDesc = &xDesc; - tmp.yDesc = &yDesc; - tmp.x = x; - tmp.y = y; - tmp.dim = dim; - return tmp; - }(); +inline size_t get_reqd_work_item_cnt(const ExecutionContext& context) +{ + // At least 4 WGs per one CU + return static_cast(LOCAL_SIZE * context.GetStream().GetMaxComputeUnits() * 4); +} - const auto algo = AlgorithmName{"ArgmaxForward"}; - const auto solvers = solver::SolverContainer{}; +inline size_t get_reqd_work_item_cnt(const Handle& handle) +{ + // At least 4 WGs per one CU + return static_cast(LOCAL_SIZE * handle.GetMaxComputeUnits() * 4); +} - solvers.ExecutePrimitive(handle, problem, algo, invoke_params); +inline size_t +get_parallelism_size(size_t reqd_work_item_cnt, size_t output_numel, size_t reduce_size) +{ + size_t parallelism_size = 1ULL; + while(parallelism_size * output_numel < reqd_work_item_cnt && + parallelism_size < std::sqrt(reduce_size)) + { + parallelism_size *= 2ULL; + } + return parallelism_size; +} - return miopenStatusSuccess; +inline bool is_parallelism(size_t reqd_work_item_cnt, size_t output_numel, size_t reduce_size) +{ + return !(output_numel > reqd_work_item_cnt) && + (output_numel * reduce_size > reqd_work_item_cnt); } +} // namespace reduce +} // namespace solver } // namespace miopen + +#endif // _MIOPEN_REDUCE_UTILS_HPP_ diff --git a/src/include/miopen/reduce_common.hpp b/src/include/miopen/reduce_common.hpp index 162ad64319..37b92e727d 100644 --- a/src/include/miopen/reduce_common.hpp +++ b/src/include/miopen/reduce_common.hpp @@ -26,11 +26,7 @@ #ifndef GUARD_MIOPEN_REDUCE_COMMON_HPP #define GUARD_MIOPEN_REDUCE_COMMON_HPP -#if !defined(_WIN32) #include -#else -#include -#endif #include namespace reduce { diff --git a/test/env_utils.hpp b/src/include/miopen/reduceextreme.hpp similarity index 54% rename from test/env_utils.hpp rename to src/include/miopen/reduceextreme.hpp index fed965f5af..5976aa7707 100644 --- a/test/env_utils.hpp +++ b/src/include/miopen/reduceextreme.hpp @@ -2,7 +2,7 @@ * * MIT License * - * Copyright (c) 2017 Advanced Micro Devices, Inc. + * Copyright (c) 2024 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal @@ -23,40 +23,35 @@ * SOFTWARE. * *******************************************************************************/ +#ifndef MIOPEN_REDUCEEXTREME_HPP_ +#define MIOPEN_REDUCEEXTREME_HPP_ -#ifndef GUARD_MIOPEN_ENV_UTILS_HPP -#define GUARD_MIOPEN_ENV_UTILS_HPP +#include -#ifdef _WIN32 -#define WIN32_LEAN_AND_MEAN -#include -#endif +namespace miopen { -#include "test.hpp" -#include +struct Handle; +struct TensorDescriptor; -inline void setEnvironmentVariable(std::string_view name, std::string_view value) -{ -#ifdef _WIN32 - BOOL ret = SetEnvironmentVariable(name.data(), value.data()); - EXPECT_EQUAL(ret, TRUE); -#else - // NOLINTNEXTLINE(concurrency-mt-unsafe) - int ret = setenv(name.data(), value.data(), 1); - EXPECT_EQUAL(ret, 0); -#endif -} +MIOPEN_INTERNALS_EXPORT miopenStatus_t +ReduceExtremeForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& indiceDesc, + Data_t indice, + int32_t dim, + miopenReduceExtremeOp_t reduceExtremeOp); -inline void unsetEnvironmentVariable(std::string_view name) -{ -#ifdef _WIN32 - BOOL ret = SetEnvironmentVariable(name.data(), nullptr); - EXPECT_EQUAL(ret, TRUE); -#else - // NOLINTNEXTLINE(concurrency-mt-unsafe) - int ret = unsetenv(name.data()); - EXPECT_EQUAL(ret, 0); -#endif -} +MIOPEN_INTERNALS_EXPORT miopenStatus_t +ReduceExtremeForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& indiceDesc, + Data_t indice, + int32_t dim, + miopenReduceExtremeOp_t reduceExtremeOp); -#endif // GUARD_MIOPEN_ENV_UTILS_HPP +} // namespace miopen +#endif // MIOPEN_REDUCEEXTREME_HPP_ diff --git a/src/include/miopen/reducetensor.hpp b/src/include/miopen/reducetensor.hpp index 4ac5ad042d..8b0d0337e5 100644 --- a/src/include/miopen/reducetensor.hpp +++ b/src/include/miopen/reducetensor.hpp @@ -41,7 +41,7 @@ namespace miopen { -struct ReduceTensorDescriptor : miopenReduceTensorDescriptor +struct MIOPEN_INTERNALS_EXPORT ReduceTensorDescriptor : miopenReduceTensorDescriptor { ReduceTensorDescriptor() = default; ReduceTensorDescriptor(miopenReduceTensorOp_t reduceTensorOp, diff --git a/src/include/miopen/rnn.hpp b/src/include/miopen/rnn.hpp index b5f5275981..18d0b5028e 100644 --- a/src/include/miopen/rnn.hpp +++ b/src/include/miopen/rnn.hpp @@ -65,7 +65,7 @@ struct c_array_view void profileRNNkernels(const Handle& handle, unsigned char select, float& ctime); -struct RNNDescriptor : miopenRNNDescriptor +struct MIOPEN_INTERNALS_EXPORT RNNDescriptor : miopenRNNDescriptor { RNNDescriptor(); @@ -552,7 +552,7 @@ struct RNNDescriptor : miopenRNNDescriptor size_t reserveSpaceSize) const; }; -std::ostream& operator<<(std::ostream& stream, const RNNDescriptor& r); +MIOPEN_INTERNALS_EXPORT std::ostream& operator<<(std::ostream& stream, const RNNDescriptor& r); } // namespace miopen MIOPEN_DEFINE_OBJECT(miopenRNNDescriptor, miopen::RNNDescriptor); diff --git a/src/include/miopen/rnn_util.hpp b/src/include/miopen/rnn_util.hpp index d3a95b598f..92876b8a9a 100644 --- a/src/include/miopen/rnn_util.hpp +++ b/src/include/miopen/rnn_util.hpp @@ -193,7 +193,7 @@ struct RNNTensorPaddingConverter size_t total_batch = std::accumulate( desc_array.data, desc_array.data + desc_array.size(), - 0, + 0ULL, [](size_t x, miopenTensorDescriptor_t y) { return x + deref(y).GetLengths()[0]; }); return GetTempPackedBuffersSpace(rnn_desc, total_batch, desc_array[0].GetLengths()[1]); @@ -255,7 +255,7 @@ struct RNNTensorBaseLayoutConverter size_t total_batch = std::accumulate( desc_array.data, desc_array.data + desc_array.size(), - 0, + 0ULL, [](size_t x, miopenTensorDescriptor_t y) { return x + deref(y).GetLengths()[0]; }); return GetTempPackedBuffersSpace(rnn_desc, total_batch, desc_array[0].GetLengths()[1]); diff --git a/src/include/miopen/scalar.hpp b/src/include/miopen/scalar.hpp new file mode 100644 index 0000000000..e0308102a0 --- /dev/null +++ b/src/include/miopen/scalar.hpp @@ -0,0 +1,52 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#pragma once + +#include +#include + +#include + +namespace miopen { +// Class store value in double for higher precision. +struct MIOPEN_INTERNALS_EXPORT Scalar +{ + explicit Scalar(double val) : mVal(val), mType(miopenDouble) {} + + Scalar(ConstData_t ptr, miopenDataType_t type); + + float GetAsFloat() const { return static_cast(mVal); } + double GetAsDouble() const { return mVal; } + + miopenDataType_t GetType() const { return mType; } + +private: + double mVal; + miopenDataType_t mType; +}; + +} // namespace miopen diff --git a/src/include/miopen/search_options.hpp b/src/include/miopen/search_options.hpp index c2feef5eb0..e7b77ef8d3 100644 --- a/src/include/miopen/search_options.hpp +++ b/src/include/miopen/search_options.hpp @@ -52,6 +52,7 @@ struct FindOptions : miopenFindOptions std::unordered_map preallocated_tensors; std::optional preallocated_workspace; std::optional find_enforce; + bool attach_binaries = false; }; } // namespace miopen diff --git a/src/include/miopen/seq_tensor.hpp b/src/include/miopen/seq_tensor.hpp index 5655224778..57fbd4bcb4 100644 --- a/src/include/miopen/seq_tensor.hpp +++ b/src/include/miopen/seq_tensor.hpp @@ -44,7 +44,7 @@ namespace miopen { -struct SeqTensorDescriptor : miopenSeqTensorDescriptor +struct MIOPEN_INTERNALS_EXPORT SeqTensorDescriptor : miopenSeqTensorDescriptor { SeqTensorDescriptor(); diff --git a/src/include/miopen/sequences.hpp b/src/include/miopen/sequences.hpp index 91177810d1..9071a56a09 100644 --- a/src/include/miopen/sequences.hpp +++ b/src/include/miopen/sequences.hpp @@ -142,10 +142,10 @@ bin/speedtest_sequences --mode tmpl for field and template argument stored pointers respectively. Examples of real life usages may be found in: - src/solver/conv_asm_1x1u.cpp - src/solver/conv_asm_3x3u.cpp - src/solver/conv_asm_dir_BwdWrW1x1.cpp - src/solver/conv_asm_dir_BwdWrW3x3.cpp + src/solver/conv/conv_asm_1x1u.cpp + src/solver/conv/conv_asm_3x3u.cpp + src/solver/conv/conv_asm_dir_BwdWrW1x1.cpp + src/solver/conv/conv_asm_dir_BwdWrW3x3.cpp Rule class is not required to use RuleSet via MakeRuleSet or sequences directly mostly it's like a sequence but operating a specified field of a class and contains several helper methods. diff --git a/src/include/miopen/simple_hash.hpp b/src/include/miopen/simple_hash.hpp index 3781f2eb21..0b8c2e5523 100644 --- a/src/include/miopen/simple_hash.hpp +++ b/src/include/miopen/simple_hash.hpp @@ -27,15 +27,17 @@ #ifndef GUARD_MLOPEN_SIMPLE_HASH_HPP #define GUARD_MLOPEN_SIMPLE_HASH_HPP +#include +#include #include +#include namespace miopen { struct SimpleHash { - size_t operator()(const std::pair& p) const + size_t operator()(const std::pair& p) const { - using std::hash; - return (hash()(p.first) ^ hash()(p.second)); + return (fs::hash_value(p.first) ^ std::hash()(p.second)); } }; diff --git a/src/include/miopen/softmax.hpp b/src/include/miopen/softmax.hpp index 0ed0f300be..f9c5ac9596 100644 --- a/src/include/miopen/softmax.hpp +++ b/src/include/miopen/softmax.hpp @@ -37,7 +37,7 @@ namespace miopen { struct Handle; struct TensorDescriptor; -struct SoftmaxDescriptor : miopenSoftmaxDescriptor +struct MIOPEN_INTERNALS_EXPORT SoftmaxDescriptor : miopenSoftmaxDescriptor { SoftmaxDescriptor() {} @@ -57,7 +57,8 @@ struct SoftmaxDescriptor : miopenSoftmaxDescriptor mode = mode_; } - friend std::ostream& operator<<(std::ostream& stream, const SoftmaxDescriptor& x); + MIOPEN_INTERNALS_EXPORT friend std::ostream& operator<<(std::ostream& stream, + const SoftmaxDescriptor& x); friend void to_json(nlohmann::json& json, const SoftmaxDescriptor& descriptor); friend void from_json(const nlohmann::json& json, SoftmaxDescriptor& descriptor); @@ -69,32 +70,32 @@ struct SoftmaxDescriptor : miopenSoftmaxDescriptor miopenSoftmaxMode_t mode; }; -miopenStatus_t SoftmaxForward(Handle& handle, - const void* alpha, - const void* beta, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& yDesc, - Data_t y, - miopenSoftmaxAlgorithm_t algorithm, - miopenSoftmaxMode_t mode, - int x_offset = 0, - int y_offset = 0); +MIOPEN_INTERNALS_EXPORT miopenStatus_t SoftmaxForward(Handle& handle, + const void* alpha, + const void* beta, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& yDesc, + Data_t y, + miopenSoftmaxAlgorithm_t algorithm, + miopenSoftmaxMode_t mode, + int x_offset = 0, + int y_offset = 0); -miopenStatus_t SoftmaxBackward(Handle& handle, - const void* alpha, - const TensorDescriptor& yDesc, - ConstData_t y, - const TensorDescriptor& dyDesc, - ConstData_t dy, - const void* beta, - const TensorDescriptor& dxDesc, - Data_t dx, - miopenSoftmaxAlgorithm_t algorithm, - miopenSoftmaxMode_t mode, - int y_offset = 0, - int dy_offset = 0, - int dx_offset = 0); +MIOPEN_INTERNALS_EXPORT miopenStatus_t SoftmaxBackward(Handle& handle, + const void* alpha, + const TensorDescriptor& yDesc, + ConstData_t y, + const TensorDescriptor& dyDesc, + ConstData_t dy, + const void* beta, + const TensorDescriptor& dxDesc, + Data_t dx, + miopenSoftmaxAlgorithm_t algorithm, + miopenSoftmaxMode_t mode, + int y_offset = 0, + int dy_offset = 0, + int dx_offset = 0); } // namespace miopen diff --git a/src/include/miopen/solution.hpp b/src/include/miopen/solution.hpp index e60d085ee3..78daacc2d1 100644 --- a/src/include/miopen/solution.hpp +++ b/src/include/miopen/solution.hpp @@ -28,7 +28,9 @@ #include +#include #include +#include #include #include #include @@ -45,11 +47,16 @@ namespace miopen { struct Handle; -struct Solution : miopenSolution +struct MIOPEN_INTERNALS_EXPORT Solution : miopenSolution { std::vector serialization_cache; - Solution() = default; + Solution(solver::Id solver_, float time_, std::size_t workspace_required_) + : time(time_), workspace_required(workspace_required_), solver(solver_) + { + } + + Solution() {} struct SerializationMetadata final { @@ -78,6 +85,20 @@ struct Solution : miopenSolution inline RunInput(Data_t buffer_) : buffer(buffer_) {} }; + struct KernelInfo + { + Program program; + std::vector local_work_dims; + std::vector global_work_dims; + std::string kernel_name; + fs::path program_name; + + operator Kernel() const + { + return Kernel{program, kernel_name, local_work_dims, global_work_dims}; + } + }; + float GetTime() const { return time; } void SetTime(float value) { time = value; } std::size_t GetWorkspaceSize() const { return workspace_required; } @@ -98,12 +119,43 @@ struct Solution : miopenSolution friend void to_json(nlohmann::json& json, const Solution& solution); friend void from_json(const nlohmann::json& json, Solution& solution); + void SetInvoker(Invoker invoker_, + const std::vector& programs = {}, + const std::vector& kernels_ = {}) + { +#if MIOPEN_BACKEND_HIP + invoker = std::move(invoker_); + + kernels.reserve(programs.size()); + + for(int i = 0; i < programs.size(); ++i) + { + auto kernel = KernelInfo{}; + kernel.program = programs[i]; + kernel.kernel_name = kernels_[i].kernel_name; + kernel.program_name = kernels_[i].kernel_file; + kernel.global_work_dims = kernels_[i].g_wk; + kernel.local_work_dims = kernels_[i].l_wk; + kernels.emplace_back(std::move(kernel)); + } +#else + std::ignore = invoker_; + std::ignore = programs; + std::ignore = kernels_; +#endif + } + + const std::optional& GetInvoker() const { return invoker; } + const std::vector& GetKernels() const { return kernels; } + private: float time = 0; std::size_t workspace_required = 0; solver::Id solver; ProblemContainer problem; std::optional perf_cfg = std::nullopt; + std::optional invoker; + std::vector kernels; void RunImpl(Handle& handle, const std::unordered_map& inputs, @@ -129,10 +181,19 @@ struct Solution : miopenSolution std::size_t workspace_size, const FusedProblem& problem_); + static AnyInvokeParams MakeInvokeParams(const Problem& problem_, + const ConvolutionDescriptor& conv_desc, + const RunInput& x, + const RunInput& w, + const RunInput& y, + Data_t workspace, + size_t workspace_size); + static Problem Transpose(const Problem& problem, RunInput* x, const RunInput& w, RunInput* y); void LogDriverCommand(const ConvolutionDescriptor& desc) const; void LogDriverCommand(const ActivationDescriptor& desc) const; + void LogDriverCommand(const BatchnormDescriptor& desc) const; void LogDriverCommand(const Problem& problem_) const; void LogDriverCommand(const FusedProblem& problem_) const; diff --git a/src/include/miopen/solver.hpp b/src/include/miopen/solver.hpp index 2d68b79e7b..b840f4ec03 100644 --- a/src/include/miopen/solver.hpp +++ b/src/include/miopen/solver.hpp @@ -27,21 +27,24 @@ #ifndef GUARD_MIOPEN_SOLVER_HPP_ #define GUARD_MIOPEN_SOLVER_HPP_ -#include +#include +#include +#include #include -#include -#include +#include #include -#include +#include #include -#include +#include #include +#include #include #include #include +#include #include #include #include @@ -105,8 +108,10 @@ struct SolverBase /// run-time parameters. virtual bool IsDynamic() const { return false; } + static constexpr float wti_approximate_worst = -2; + /// [Informative as of Sep 2020] Returns an approximated value of the expected - /// WTI or -2.0 when this value can't be computed. Tips: + /// WTI or wti_approximate_worst when this value can't be computed. Tips: /// * Value 1.0 corresponds to the 100% utilization of HW capabilities as /// if Direct computational algorithm is used. /// * [Notice] WTI may exceed 1.0 for highly optimized algorithms like Winograd. @@ -149,7 +154,7 @@ struct SolverMixin : SolverBase "Context must be derived of ExecutionContext"); virtual bool IsApplicable(const Context&, const Problem&) const = 0; - virtual float GetWti(const Context&, const Problem&) const { return -2.0f; }; + virtual float GetWti(const Context&, const Problem&) const { return wti_approximate_worst; }; virtual size_t GetWorkspaceSize(const Context&, const Problem&) const { return 0; }; bool IsApplicable(const ExecutionContext& ctx, const boost::any& problem) const final @@ -177,15 +182,19 @@ struct NonTunableSolverBase : SolverMixin /// Takes problem config, optimization parameters and other info /// and computes information required to build and run the kernel(s). virtual ConvSolution GetSolution(const Context&, const Problem&) const = 0; + virtual InvokerFactory GetInvokerFactory(const Context& ctx, const Problem& problem) const + { + return *GetSolution(ctx, problem).invoker_factory; + } }; -namespace conv { - -/// Typedef for convolution solvers -using ConvSolver = NonTunableSolverBase; +struct TunableSolverTrait +{ +}; /// Base class for tunable solvers -struct ConvTunableSolverBase : SolverMixin +template +struct TunableSolverBase : SolverMixin, TunableSolverTrait { /// Initializes performance config to the default values. /// The function may involve some heuristic to guess the best solution @@ -196,14 +205,13 @@ struct ConvTunableSolverBase : SolverMixin -struct ConvTunableSolver : ConvTunableSolverBase +template +struct TunableSolverMixin : TunableSolverBase { static_assert(std::is_base_of{}, "PerformanceConfig must be derived of PerfConfig"); + virtual PerformanceConfig GetDefaultPerformanceConfig(const Context&, const Problem&) const = 0; + virtual bool + IsValidPerformanceConfig(const Context&, const Problem&, const PerformanceConfig&) const = 0; virtual PerformanceConfig - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const = 0; - virtual bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfig&) const = 0; - virtual PerformanceConfig Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams&) const = 0; - virtual ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfig&) const = 0; - - boost::any GetDefaultPerformanceConfig(const ExecutionContext& ctx, - const miopen::conv::ProblemDescription& problem, - int) const final + Search(const Context&, const Problem&, const AnyInvokeParams&) const = 0; + virtual ConvSolution + GetSolution(const Context&, const Problem&, const PerformanceConfig&) const = 0; + + boost::any + GetDefaultPerformanceConfig(const Context& ctx, const Problem& problem, int) const final { return GetDefaultPerformanceConfig(ctx, problem); } - bool IsValidPerformanceConfig(const ExecutionContext& ctx, - const miopen::conv::ProblemDescription& problem, + bool IsValidPerformanceConfig(const Context& ctx, + const Problem& problem, const PerfConfig& config) const final { return IsValidPerformanceConfig( ctx, problem, dynamic_cast(config)); } - boost::any Search(const ExecutionContext& ctx, - const miopen::conv::ProblemDescription& problem, + boost::any Search(const Context& ctx, + const Problem& problem, const AnyInvokeParams& invoke_ctx, int) const final { return Search(ctx, problem, invoke_ctx); } - ConvSolution GetSolution(const ExecutionContext& ctx, - const miopen::conv::ProblemDescription& problem, - const PerfConfig& config) const final + ConvSolution + GetSolution(const Context& ctx, const Problem& problem, const PerfConfig& config) const final { return GetSolution(ctx, problem, dynamic_cast(config)); } }; +template +struct IsTunable : std::is_base_of +{ + static_assert(!std::is_same_v, + "Raw trait shouldn't be passed, explicit type is needed"); +}; + +namespace conv { + +/// Typedef for convolution non-tunable solvers +using ConvSolver = NonTunableSolverBase; + +/// Typedef for convolution tunable solvers +template +using ConvTunableSolver = + TunableSolverMixin; + struct PerformanceConfigConvAsm3x3U : PerfConfigBase { int limit_wave_cnt; // [0..9] int filters_per_wave; // [1..8] int output_lines_per_wave; // [1..8] - PerformanceConfigConvAsm3x3U(int lwc, int fpw, int olpw); + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm3x3U(int lwc, int fpw, int olpw); PerformanceConfigConvAsm3x3U() : PerformanceConfigConvAsm3x3U(-1, -1, -1) {} PerformanceConfigConvAsm3x3U(bool) : PerformanceConfigConvAsm3x3U(0, 1, 1) {} @@ -290,35 +312,37 @@ struct PerformanceConfigConvAsm3x3U : PerfConfigBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConfigConvAsm3x3U - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigConvAsm3x3U&) const override; - PerformanceConfigConvAsm3x3U Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm3x3U GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigConvAsm3x3U&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm3x3U + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigConvAsm3x3U&) const override; }; struct PerformanceConfigConvAsm1x1U : PerfConfigBase @@ -335,12 +359,13 @@ struct PerformanceConfigConvAsm1x1U : PerfConfigBase bool use_spare_set; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1U(int, int, int, int, int, int, int, int, bool); PerformanceConfigConvAsm1x1U() : PerformanceConfigConvAsm1x1U(-1, -1, -1, -1, -1, -1, -1, -1, false) { } - PerformanceConfigConvAsm1x1U(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1U(bool spare); template static void Visit(Self&& self, F f) @@ -367,12 +392,14 @@ struct PerformanceConfigConvAsm1x1U : PerfConfigBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - PerformanceConfigConvAsm1x1U - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigConvAsm1x1U&) const override; - PerformanceConfigConvAsm1x1U Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1U GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigConvAsm1x1U&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1U + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + bool MayNeedWorkspace() const override { return true; } + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigConvAsm1x1U&) const override; }; struct PerformanceConfigConvAsm1x1UV2 : PerfConfigBase @@ -441,12 +470,13 @@ struct PerformanceConfigConvAsm1x1UV2 : PerfConfigBase bool use_spare_set; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1UV2(int, int, int, int, int, int, int, int, int, int, bool); PerformanceConfigConvAsm1x1UV2() : PerformanceConfigConvAsm1x1UV2(-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, false) { } - PerformanceConfigConvAsm1x1UV2(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1UV2(bool spare); template static void Visit(Self&& self, F f) @@ -477,55 +507,57 @@ struct PerformanceConfigConvAsm1x1UV2 : PerfConfigBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - PerformanceConfigConvAsm1x1UV2 - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigConvAsm1x1UV2&) const override; - PerformanceConfigConvAsm1x1UV2 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1UV2 GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigConvAsm1x1UV2&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsm1x1UV2 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigConvAsm1x1UV2&) const override; }; struct ConvAsm5x10u2v2f1 final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsm5x10u2v2b1 final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsm7x7c3h224w224k64u2v2p3q3f1 final : ConvSolver @@ -535,10 +567,10 @@ struct ConvAsm7x7c3h224w224k64u2v2p3q3f1 final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvOclDirectFwd11x11 final : ConvSolver @@ -548,20 +580,20 @@ struct ConvOclDirectFwd11x11 final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvOclDirectFwdGen final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct PerformanceImplicitGemm : PerfConfigBase @@ -599,7 +631,7 @@ struct PerformanceImplicitGemm : PerfConfigBase { } - PerformanceImplicitGemm(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemm(bool spare); template static void Visit(Self&& self, F f) @@ -622,11 +654,13 @@ struct PerformanceImplicitGemm : PerfConfigBase f(self.WeiBlockCopyClusterLengths_K, "WeiBlockCopyClusterLengths_K"); } - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceImplicitGemm& other) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool operator==(const PerformanceImplicitGemm& other) const; }; struct PerformanceImplicitGemmV4R1 : public PerformanceImplicitGemm @@ -660,7 +694,8 @@ struct PerformanceImplicitGemmV4R1 : public PerformanceImplicitGemm PerformanceImplicitGemmV4R1(bool spare) : PerformanceImplicitGemm(spare) {} - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; }; struct PerformanceImplicitGemmV4R4Fwd : PerfConfigBase @@ -676,7 +711,7 @@ struct PerformanceImplicitGemmV4R4Fwd : PerfConfigBase static void Visit(Self&& self, F f) @@ -702,23 +737,27 @@ struct PerformanceImplicitGemmV4R4Fwd : PerfConfigBase CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockGemmPerformanceParameters() const; - std::tuple CalculateGemmABlockCopyPerformanceParameters() const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateBlockGemmPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGemmABlockCopyPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmCThreadCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); }; struct PerformanceImplicitGemmV4R4WrW : PerfConfigBase @@ -734,7 +773,7 @@ struct PerformanceImplicitGemmV4R4WrW : PerfConfigBase static void Visit(Self&& self, F f) @@ -760,23 +799,27 @@ struct PerformanceImplicitGemmV4R4WrW : PerfConfigBase CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockGemmPerformanceParameters() const; - std::tuple CalculateGemmABlockCopyPerformanceParameters() const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateBlockGemmPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGemmABlockCopyPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmCThreadCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); }; struct PerformanceImplicitGemmBwdDataV1R1 : PerfConfigBase @@ -792,7 +835,7 @@ struct PerformanceImplicitGemmBwdDataV1R1 : PerfConfigBase static void Visit(Self&& self, F f) @@ -819,24 +862,27 @@ struct PerformanceImplicitGemmBwdDataV1R1 : PerfConfigBase CalculateGridSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockGemmPerformanceParameters() const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateBlockGemmPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmCThreadCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); }; struct PerformanceImplicitGemmBwdDataV4R1 : PerfConfigBase @@ -852,7 +898,7 @@ struct PerformanceImplicitGemmBwdDataV4R1 : PerfConfigBase static void Visit(Self&& self, F f) @@ -879,24 +925,27 @@ struct PerformanceImplicitGemmBwdDataV4R1 : PerfConfigBase CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockGemmPerformanceParameters() const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateBlockGemmPerformanceParameters() const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmCThreadCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple MIOPEN_INTERNALS_EXPORT CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); }; struct PerformanceImplicitGemmBwdDataV4R1Xdlops @@ -916,17 +965,19 @@ struct PerformanceImplicitGemmBwdDataV4R1Xdlops bool GemmBThreadCopyMoreGemmKPack; bool use_spare_set; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1Xdlops(int, int, int, int, int, int, bool, bool, bool); - PerformanceImplicitGemmBwdDataV4R1Xdlops(); - PerformanceImplicitGemmBwdDataV4R1Xdlops(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1Xdlops(); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1Xdlops(bool spare); PerformanceImplicitGemmBwdDataV4R1Xdlops( int a, int b, int c, int d, int e, int f, bool g, bool h) : PerformanceImplicitGemmBwdDataV4R1Xdlops(a, b, c, d, e, f, g, h, false) { } - bool operator==(const PerformanceImplicitGemmBwdDataV4R1Xdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceImplicitGemmBwdDataV4R1Xdlops& other) const; template static void Visit(Self&& self, F f) @@ -941,20 +992,23 @@ struct PerformanceImplicitGemmBwdDataV4R1Xdlops f(self.GemmBThreadCopyMoreGemmKPack, "GemmBThreadCopyMoreGemmKPack"); } - std::tuple CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsReallyValid(const miopen::conv::ProblemDescription&) const; - bool IsFastToBeUsedForTuning(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); }; struct ConvHipImplicitGemmV4R1Fwd final : ConvTunableSolver @@ -964,20 +1018,22 @@ struct ConvHipImplicitGemmV4R1Fwd final : ConvTunableSolver(); } - PerformanceImplicitGemmV4R1 - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmV4R1&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R1 GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmV4R1&) const override; - PerformanceImplicitGemmV4R1 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmV4R1&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R1 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; }; struct ConvHipImplicitGemmV4R4Fwd final : ConvTunableSolver @@ -987,20 +1043,22 @@ struct ConvHipImplicitGemmV4R4Fwd final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceImplicitGemmV4R4Fwd - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmV4R4Fwd&) const override; - PerformanceImplicitGemmV4R4Fwd Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R4Fwd GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmV4R4Fwd&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R4Fwd + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmV4R4Fwd&) const override; private: static std::tuple CalculateGemmSize(const miopen::conv::ProblemDescription&); @@ -1026,7 +1084,7 @@ struct PerformanceConvMlirIgemm : PerfConfigBase return heur; } - PerformanceConvMlirIgemm(int, int, int, int, int, int, bool); + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm(int, int, int, int, int, int, bool); PerformanceConvMlirIgemm(int a, int b, int c, int d, int e, int f) : PerformanceConvMlirIgemm(a, b, c, d, e, f, false) @@ -1035,9 +1093,9 @@ struct PerformanceConvMlirIgemm : PerfConfigBase PerformanceConvMlirIgemm() : PerformanceConvMlirIgemm(-1, -1, -1, -1, -1, -1, false) {} - PerformanceConvMlirIgemm(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm(bool spare); - bool operator==(const PerformanceConvMlirIgemm& other) const; + MIOPEN_INTERNALS_EXPORT bool operator==(const PerformanceConvMlirIgemm& other) const; template static void Visit(Self&& self, F f) @@ -1050,8 +1108,9 @@ struct PerformanceConvMlirIgemm : PerfConfigBase f(self.GemmNPerThread, "GemmNPerThread"); } - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); private: void SetMlirHeuristicInitRequest(); @@ -1061,20 +1120,22 @@ struct ConvMlirIgemmFwd final : ConvTunableSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemm - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemm&) const override; - PerformanceConvMlirIgemm Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemm&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemm&) const override; }; struct PerformanceConvMlirIgemmXdlops : PerfConfigBase @@ -1100,16 +1161,17 @@ struct PerformanceConvMlirIgemmXdlops : PerfConfigBase static void Visit(Self&& self, F f) @@ -1124,8 +1186,9 @@ struct PerformanceConvMlirIgemmXdlops : PerfConfigBase(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemmXdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemmXdlops&) const override; - PerformanceConvMlirIgemmXdlops Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemmXdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemmXdlops&) const override; }; struct ConvHipImplicitGemmV4R4WrW final : ConvTunableSolver @@ -1161,20 +1226,22 @@ struct ConvHipImplicitGemmV4R4WrW final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceImplicitGemmV4R4WrW - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmV4R4WrW&) const override; - PerformanceImplicitGemmV4R4WrW Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R4WrW GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmV4R4WrW&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R4WrW + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmV4R4WrW&) const override; private: static std::tuple CalculateGemmSize(const miopen::conv::ProblemDescription&); @@ -1186,20 +1253,22 @@ struct ConvMlirIgemmWrW final : ConvTunableSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemm - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemm&) const override; - PerformanceConvMlirIgemm Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemm&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemm&) const override; }; struct ConvMlirIgemmWrWXdlops final : ConvTunableSolver @@ -1209,23 +1278,25 @@ struct ConvMlirIgemmWrWXdlops final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemmXdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemmXdlops&) const override; - PerformanceConvMlirIgemmXdlops Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemmXdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool MayNeedWorkspace() const override { return true; } + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemmXdlops&) const override; }; struct PerformanceImplicitGemmForwardV4R4Xdlops @@ -1241,8 +1312,9 @@ struct PerformanceImplicitGemmForwardV4R4Xdlops bool GemmBThreadCopyMoreGemmKPack; int GemmBThreadDataPerRead_GemmN; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops(int, int, int, int, int, int, bool, bool, int); - PerformanceImplicitGemmForwardV4R4Xdlops(); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops(); PerformanceImplicitGemmForwardV4R4Xdlops(bool) : PerformanceImplicitGemmForwardV4R4Xdlops() {} template @@ -1259,23 +1331,27 @@ struct PerformanceImplicitGemmForwardV4R4Xdlops f(self.GemmBThreadDataPerRead_GemmN, "GemmBThreadDataPerRead_GemmN"); } - bool operator==(const PerformanceImplicitGemmForwardV4R4Xdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceImplicitGemmForwardV4R4Xdlops& other) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsReallyValid(const miopen::conv::ProblemDescription&) const; - bool IsFastToBeUsedForTuning(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - - std::tuple CalculateBlockSize() const; - std::tuple CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -1294,9 +1370,10 @@ struct PerformanceImplicitGemmForwardV4R5Xdlops bool use_spare_set; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R5Xdlops(int, int, int, int, int, int, bool, bool, int, bool); - PerformanceImplicitGemmForwardV4R5Xdlops(); - PerformanceImplicitGemmForwardV4R5Xdlops(bool spare); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R5Xdlops(); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R5Xdlops(bool spare); PerformanceImplicitGemmForwardV4R5Xdlops( int a, int b, int c, int d, int e, int f, bool g, bool h, int i) @@ -1318,23 +1395,27 @@ struct PerformanceImplicitGemmForwardV4R5Xdlops f(self.GemmBThreadDataPerRead_GemmN, "GemmBThreadDataPerRead_GemmN"); } - bool operator==(const PerformanceImplicitGemmForwardV4R5Xdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceImplicitGemmForwardV4R5Xdlops& other) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsReallyValid(const miopen::conv::ProblemDescription&) const; - bool IsFastToBeUsedForTuning(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - - std::tuple CalculateBlockSize() const; - std::tuple CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -1354,9 +1435,9 @@ struct PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm bool GemmBThreadCopyMoreGemmKPack; int GemmBThreadDataPerRead_GemmN; - PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm( + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm( int, int, int, int, int, int, int, int, int, bool, bool, int); - PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm(); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm(); PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm(bool) : PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm() { @@ -1379,23 +1460,27 @@ struct PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm f(self.GemmBThreadDataPerRead_GemmN, "GemmBThreadDataPerRead_GemmN"); } - bool operator==(const PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm& other) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsReallyValid(const miopen::conv::ProblemDescription&) const; - bool IsFastToBeUsedForTuning(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - - std::tuple CalculateBlockSize() const; - std::tuple CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -1410,8 +1495,9 @@ struct PerformanceImplicitGemmBwdV1R1Xdlops : PerfConfigBase @@ -1427,23 +1513,27 @@ struct PerformanceImplicitGemmBwdV1R1Xdlops : PerfConfigBase CalculateBlockSize() const; - std::tuple CalculateGridSize(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -1455,18 +1545,19 @@ struct ConvHipImplicitGemmForwardV4R4Xdlops final return GetSolverDbId(); } - PerformanceImplicitGemmForwardV4R4Xdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmForwardV4R4Xdlops&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmForwardV4R4Xdlops&) const override; - PerformanceImplicitGemmForwardV4R4Xdlops + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmForwardV4R4Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; @@ -1486,20 +1577,20 @@ struct ConvHipImplicitGemmForwardV4R4Xdlops_Padded_Gemm final return GetSolverDbId(); } - PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm&) const override; - PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops_Padded_Gemm Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; @@ -1519,18 +1610,19 @@ struct ConvHipImplicitGemmForwardV4R5Xdlops final return GetSolverDbId(); } - PerformanceImplicitGemmForwardV4R5Xdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmForwardV4R5Xdlops&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R5Xdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmForwardV4R5Xdlops&) const override; - PerformanceImplicitGemmForwardV4R5Xdlops + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmForwardV4R5Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R5Xdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; @@ -1543,20 +1635,22 @@ struct ConvHipImplicitGemmV4R1WrW final : ConvTunableSolver(); } - PerformanceImplicitGemmV4R1 - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmV4R1&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R1 GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmV4R1&) const override; - PerformanceImplicitGemmV4R1 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmV4R1&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmV4R1 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; }; struct ConvHipImplicitGemmBwdDataV1R1 final : ConvTunableSolver @@ -1566,23 +1660,25 @@ struct ConvHipImplicitGemmBwdDataV1R1 final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceImplicitGemmBwdDataV1R1 - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmBwdDataV1R1&) const override; - PerformanceImplicitGemmBwdDataV1R1 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV1R1 GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmBwdDataV1R1&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool MayNeedWorkspace() const override { return true; } + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV1R1 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmBwdDataV1R1&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool MayNeedWorkspace() const override { return true; } private: static std::tuple CalculateGemmSize(const ExecutionContext&, @@ -1595,20 +1691,22 @@ struct ConvMlirIgemmBwd final : ConvTunableSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemm - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemm&) const override; - PerformanceConvMlirIgemm Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemm&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemm + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemm&) const override; }; struct ConvMlirIgemmBwdXdlops final : ConvTunableSolver @@ -1618,20 +1716,22 @@ struct ConvMlirIgemmBwdXdlops final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceConvMlirIgemmXdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvMlirIgemmXdlops&) const override; - PerformanceConvMlirIgemmXdlops Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvMlirIgemmXdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvMlirIgemmXdlops + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvMlirIgemmXdlops&) const override; }; struct ConvHipImplicitGemmBwdDataV4R1 final : ConvTunableSolver @@ -1641,20 +1741,22 @@ struct ConvHipImplicitGemmBwdDataV4R1 final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - PerformanceImplicitGemmBwdDataV4R1 - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmBwdDataV4R1&) const override; - PerformanceImplicitGemmBwdDataV4R1 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1 GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmBwdDataV4R1&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmBwdDataV4R1&) const override; private: static int CalculateNumberOfGemm(const miopen::conv::ProblemDescription&); @@ -1672,18 +1774,19 @@ struct ConvHipImplicitGemmBwdDataV4R1Xdlops final return GetSolverDbId(); } - PerformanceImplicitGemmBwdDataV4R1Xdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmBwdDataV4R1Xdlops&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1Xdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmBwdDataV4R1Xdlops&) const override; - PerformanceImplicitGemmBwdDataV4R1Xdlops + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmBwdDataV4R1Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdDataV4R1Xdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; @@ -1704,23 +1807,25 @@ struct ConvHipImplicitGemmBwdDataV1R1Xdlops final return GetSolverDbId(); } - PerformanceImplicitGemmBwdV1R1Xdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmBwdV1R1Xdlops&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool MayNeedWorkspace() const override { return true; } - PerformanceImplicitGemmBwdV1R1Xdlops Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdV1R1Xdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmBwdV1R1Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + bool MayNeedWorkspace() const override { return true; } + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmBwdV1R1Xdlops + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmBwdV1R1Xdlops&) const override; private: static std::tuple @@ -1736,13 +1841,13 @@ struct ConvAsmImplicitGemmV4R1DynamicFwd final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmV4R1DynamicFwd_1x1 final : ConvSolver @@ -1752,13 +1857,13 @@ struct ConvAsmImplicitGemmV4R1DynamicFwd_1x1 final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmV4R1DynamicWrw final : ConvSolver @@ -1768,18 +1873,18 @@ struct ConvAsmImplicitGemmV4R1DynamicWrw final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmGTCDynamicWrwXdlops final : ConvSolver @@ -1789,18 +1894,18 @@ struct ConvAsmImplicitGemmGTCDynamicWrwXdlops final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmV4R1DynamicBwd final : ConvSolver @@ -1810,13 +1915,13 @@ struct ConvAsmImplicitGemmV4R1DynamicBwd final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmGTCDynamicFwdXdlops final : ConvSolver @@ -1826,13 +1931,13 @@ struct ConvAsmImplicitGemmGTCDynamicFwdXdlops final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvAsmImplicitGemmGTCDynamicBwdXdlops final : ConvSolver @@ -1842,25 +1947,25 @@ struct ConvAsmImplicitGemmGTCDynamicBwdXdlops final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; /// Holds common member functions for the Solvers which share the same /// "legacy exhaustive search" machinery. struct ConvOclDirectFwdLegacyExhaustiveSearch : ConvTunableSolver { - LegacyPerformanceConfig - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - LegacyPerformanceConfig Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT LegacyPerformanceConfig GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT LegacyPerformanceConfig + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; private: template @@ -1873,29 +1978,31 @@ struct ConvOclDirectFwd : ConvOclDirectFwdLegacyExhaustiveSearch { const std::string& SolverDbId() const override { return GetSolverDbId(); } - static ConvSolution BaseGetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const LegacyPerformanceConfig&); - - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT static ConvSolution + BaseGetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const LegacyPerformanceConfig&); + + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const LegacyPerformanceConfig&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const LegacyPerformanceConfig&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const LegacyPerformanceConfig&) const override; }; struct ConvOclDirectFwd1x1 final : ConvOclDirectFwdLegacyExhaustiveSearch { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const LegacyPerformanceConfig&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const LegacyPerformanceConfig&) const override; bool IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, @@ -1909,32 +2016,32 @@ struct ConvBinWinograd3x3U final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvBinWinogradRxS final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct PerformanceConfigConvBinWinogradRxS : PerfConfigBase { int n_groups; - PerformanceConfigConvBinWinogradRxS(int n_groups_); + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvBinWinogradRxS(int n_groups_); PerformanceConfigConvBinWinogradRxS() : PerformanceConfigConvBinWinogradRxS(-1) {} PerformanceConfigConvBinWinogradRxS(bool) : PerformanceConfigConvBinWinogradRxS(1) {} @@ -1947,14 +2054,14 @@ struct PerformanceConfigConvBinWinogradRxS : PerfConfigBase void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); bool IsValid(const ExecutionContext& ctx, const miopen::conv::ProblemDescription&) const { return IsValid(ctx); } - bool IsValid(const ExecutionContext&) const; - bool operator==(const PerformanceConfigConvBinWinogradRxS& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&) const; + MIOPEN_INTERNALS_EXPORT bool operator==(const PerformanceConfigConvBinWinogradRxS& other) const; }; template @@ -1971,21 +2078,23 @@ struct ConvBinWinoRxS final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; template @@ -2032,21 +2142,21 @@ struct ConvMPBidirectWinograd final : ConvSolver ConvMPBidirectWinograd>(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; // kernel_file_name for solver identification - static std::string GetSolverFileNames(int id) + static fs::path GetSolverFileNames(int id) { - static const std::string names[3] = {"xform_bidirect_winograd_data.s", - "xform_bidirect_winograd_filter.s", - "xform_bidirect_winograd_out.s"}; + static const fs::path names[3] = {"xform_bidirect_winograd_data.s", + "xform_bidirect_winograd_filter.s", + "xform_bidirect_winograd_out.s"}; return names[id]; } @@ -2091,8 +2201,8 @@ struct ConvMPBidirectWinograd_xdlops final ConvMPBidirectWinograd_xdlops>(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { @@ -2138,13 +2248,14 @@ struct ConvMPBidirectWinograd_xdlops final bool MayNeedWorkspace() const override { return true; } - PerformanceImplicitGemmForwardV4R4Xdlops + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmForwardV4R4Xdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmForwardV4R4Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmForwardV4R4Xdlops&) const override; private: ExecutionContext @@ -2154,7 +2265,7 @@ struct ConvMPBidirectWinograd_xdlops final GetTransformedProblem(const miopen::conv::ProblemDescription& problem) const; // kernel_file_name for solver identification - static std::string GetSolverFileNames(int id) + static fs::path GetSolverFileNames(int id) { return ConvMPBidirectWinograd:: GetSolverFileNames(id); @@ -2200,23 +2311,23 @@ struct ConvWinograd3x3MultipassWrW final : ConvSolver ConvWinograd3x3MultipassWrW>(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; // kernel_file_name for solver identification - static std::string GetSolverFileNames(int id) + static fs::path GetSolverFileNames(int id) { - static const std::string names[3] = {"xform_data.s", "xform_filter.s", "xform_out.s"}; + static const fs::path names[3] = {"xform_data.s", "xform_filter.s", "xform_out.s"}; return names[id]; } @@ -2313,31 +2424,35 @@ struct PerformanceConfigAsmDirect3x3WrW : PerfConfigBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - PerformanceConfigAsmDirect3x3WrW - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigAsmDirect3x3WrW&) const override; - PerformanceConfigAsmDirect3x3WrW Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmDirect3x3WrW GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, - const PerformanceConfigAsmDirect3x3WrW& config) const override; + const PerformanceConfigAsmDirect3x3WrW&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmDirect3x3WrW + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigAsmDirect3x3WrW& config) const override; }; template @@ -2348,16 +2463,17 @@ struct ConvWinoFuryRxS final : ConvSolver return GetSolverDbId>(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + bool MayNeedWorkspace() const override { return true; } - static constexpr bool is2x3() { return Winodata == 2 && Winofilter == 3; } - static constexpr bool is3x2() { return Winodata == 3 && Winofilter == 2; } + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; // Suppress misleading clang warnings @@ -2406,17 +2522,17 @@ struct PerformanceConfigConvAsmBwdWrW1x1 : PerfConfigBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - PerformanceConfigConvAsmBwdWrW1x1 + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsmBwdWrW1x1 MIOPEN_INTERNALS_EXPORT GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigConvAsmBwdWrW1x1&) const override; - PerformanceConfigConvAsmBwdWrW1x1 Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigConvAsmBwdWrW1x1&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvAsmBwdWrW1x1 + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + bool MayNeedWorkspace() const override { return true; } + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigConvAsmBwdWrW1x1&) const override; }; /// N_BATCH_LOOPS - {1,2,4,8,16} Num batches processed in single workitem. @@ -2539,11 +2660,13 @@ struct PerformanceConfigConvOclBwdWrw2 int GetNumOutChannelTiles() const { return n_out_channels_tiles; } int GetNumOutRowsPerIterPerWork() const { return n_out_rows_in_lcl; } // clang-format on - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigConvOclBwdWrw2& other) const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigConvOclBwdWrw2& other) const; }; template @@ -2554,25 +2677,26 @@ struct ConvOclBwdWrW2 : ConvTunableSolvertemplate GetSolverDbId>(); } - PerformanceConfigConvOclBwdWrw2 + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvOclBwdWrw2 GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigConvOclBwdWrw2&) const override; - PerformanceConfigConvOclBwdWrw2 + MIOPEN_INTERNALS_EXPORT PerformanceConfigConvOclBwdWrw2 Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigConvOclBwdWrw2&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigConvOclBwdWrw2&) const override; protected: bool IsApplicableBase(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; @@ -2611,58 +2735,64 @@ struct ConvOclBwdWrW2NonTunable final : ConvOclBwdWrW2<1> return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + InvokerFactory GetInvokerFactory(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem) const + { + return *GetSolution(ctx, problem).invoker_factory; + } private: // This function dervied from ConvOclBwdWrW2 is declared private // so that this solver is not marked searchable/tunable. using ConvOclBwdWrW2<1>::GetDefaultPerformanceConfig; using ConvOclBwdWrW2<1>::GetSolution; + using ConvOclBwdWrW2<1>::GetInvokerFactory; }; struct ConvOclBwdWrW53 final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvOclBwdWrW1x1 final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct fft final : ConvSolver { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct PerformanceImplicitGemmWrwV4R4Xdlops : PerfConfigBase @@ -2677,9 +2807,10 @@ struct PerformanceImplicitGemmWrwV4R4Xdlops : PerfConfigBase + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmSizeAndGemmKBlock(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockSize() const; - std::tuple CalculateGridSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple + CalculateGridSize(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -2730,23 +2865,25 @@ struct ConvHipImplicitGemmWrwV4R4Xdlops final return GetSolverDbId(); } - PerformanceImplicitGemmWrwV4R4Xdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceImplicitGemmWrwV4R4Xdlops&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmWrwV4R4Xdlops&) const override; - PerformanceImplicitGemmWrwV4R4Xdlops Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceImplicitGemmWrwV4R4Xdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; }; struct PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm @@ -2764,9 +2901,9 @@ struct PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm bool GemmAThreadCopyMoreGemmK; bool GemmBThreadCopyMoreGemmK; - PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm( + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm( int, int, int, int, int, int, int, int, int, bool, bool); - PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm(); + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm(); PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm(bool) : PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm() { @@ -2788,27 +2925,31 @@ struct PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm f(self.GemmBThreadCopyMoreGemmK, "GemmBThreadCopyMoreGemmK"); } - bool operator==(const PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm& other) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm& other) const; - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; - bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsReallyValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - bool IsFastToBeUsedForTuning(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const; - - std::tuple + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsReallyValid(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + IsFastToBeUsedForTuning(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; + + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmSizeAndGemmKBlock(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - std::tuple CalculateBlockSize() const; + MIOPEN_INTERNALS_EXPORT std::tuple CalculateBlockSize() const; std::tuple CalculateGridSize(const ExecutionContext&, const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmABlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateGemmBBlockCopyPerformanceParameters(const miopen::conv::ProblemDescription&) const; - std::tuple + MIOPEN_INTERNALS_EXPORT std::tuple CalculateLdsNumberOfByte(const miopen::conv::ProblemDescription&) const; }; @@ -2820,23 +2961,23 @@ struct ConvHipImplicitGemmWrwV4R4Xdlops_Padded_Gemm final return GetSolverDbId(); } - PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm&) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - ConvSolution + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm&) const override; - PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm + MIOPEN_INTERNALS_EXPORT PerformanceImplicitGemmWrwV4R4Xdlops_Padded_Gemm Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; @@ -2859,12 +3000,12 @@ struct PerformanceConvCkIgemmFwdV6r1DlopsNchw f(self.ck_tunable_list_id, "ck_tunable_list_id"); } - bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; bool operator==(const PerformanceConvCkIgemmFwdV6r1DlopsNchw& config) const { return ck_tunable_list_id == config.ck_tunable_list_id; @@ -2878,24 +3019,26 @@ struct ConvCkIgemmFwdV6r1DlopsNchw final : ConvTunableSolver(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } bool IsDynamic() const override { return false; } - PerformanceConvCkIgemmFwdV6r1DlopsNchw - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConvCkIgemmFwdV6r1DlopsNchw&) const override; - PerformanceConvCkIgemmFwdV6r1DlopsNchw Search(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const AnyInvokeParams& invoke_ctx) const override; - ConvSolution GetSolution(const ExecutionContext&, + MIOPEN_INTERNALS_EXPORT PerformanceConvCkIgemmFwdV6r1DlopsNchw GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConvCkIgemmFwdV6r1DlopsNchw&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConvCkIgemmFwdV6r1DlopsNchw + Search(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const AnyInvokeParams& invoke_ctx) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConvCkIgemmFwdV6r1DlopsNchw&) const override; }; struct ConvDirectNaiveConvFwd final : ConvSolver @@ -2905,8 +3048,8 @@ struct ConvDirectNaiveConvFwd final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } /// Use very small fixed value enough to backup GEMM for cases when /// GEMM is disabled. @@ -2914,8 +3057,8 @@ struct ConvDirectNaiveConvFwd final : ConvSolver { return 0.01f; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvDirectNaiveConvBwd final : ConvSolver @@ -2925,8 +3068,8 @@ struct ConvDirectNaiveConvBwd final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } /// Use very small fixed value enough to backup GEMM for cases when /// GEMM is disabled. @@ -2934,8 +3077,8 @@ struct ConvDirectNaiveConvBwd final : ConvSolver { return 0.01f; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct ConvDirectNaiveConvWrw final : ConvSolver @@ -2945,8 +3088,8 @@ struct ConvDirectNaiveConvWrw final : ConvSolver return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } /// Use very small fixed value enough to backup GEMM for cases when /// GEMM is disabled. @@ -2954,18 +3097,19 @@ struct ConvDirectNaiveConvWrw final : ConvSolver { return 0.01f; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct GemmFwdBase : ConvSolver { bool IsDynamic() const override { return true; } - float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const override; private: - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmFwd1x1_0_2; friend struct GemmFwd1x1_0_1_int8; @@ -2977,16 +3121,16 @@ struct GemmFwd1x1_0_2 final : GemmFwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmFwdRest; }; @@ -2995,16 +3139,16 @@ struct GemmFwd1x1_0_1_int8 final : GemmFwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmFwdRest; }; @@ -3013,16 +3157,16 @@ struct GemmFwd1x1_0_1 final : GemmFwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmFwdRest; }; @@ -3031,26 +3175,27 @@ struct GemmFwdRest final : GemmFwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct GemmBwdBase : ConvSolver { bool IsDynamic() const override { return true; } - float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const override; private: - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmBwd1x1_stride2; friend struct GemmBwd1x1_stride1; @@ -3061,16 +3206,16 @@ struct GemmBwd1x1_stride2 final : GemmBwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmBwdRest; }; @@ -3079,16 +3224,17 @@ struct GemmBwd1x1_stride1 final : GemmBwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, + const miopen::conv::ProblemDescription& problem) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription& problem) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution( + const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const override; friend struct GemmBwdRest; }; @@ -3097,26 +3243,27 @@ struct GemmBwdRest final : GemmBwdBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct GemmWrwBase : ConvSolver { bool IsDynamic() const override { return true; } - float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT float GetWti(const ExecutionContext&, + const miopen::conv::ProblemDescription&) const override; private: - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmWrw1x1_stride1; friend struct GemmWrwUniversal; @@ -3126,11 +3273,11 @@ struct GemmWrw1x1_stride1 final : GemmWrwBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; friend struct GemmWrwUniversal; }; @@ -3139,16 +3286,16 @@ struct GemmWrwUniversal final : GemmWrwBase { const std::string& SolverDbId() const override { return GetSolverDbId(); } - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; }; struct PerformanceConfigAsmImplicitGemmGTC : PerfConfigBase @@ -3185,56 +3332,56 @@ struct PerformanceConfigAsmImplicitGemmGTC : PerfConfigBase ta_t, - std::initializer_list ta_c, - std::initializer_list tb_t, - std::initializer_list tb_c, - bool spare = false); - PerformanceConfigAsmImplicitGemmGTC(std::string dir, - std::string layout, - miopenDataType_t prec, - int b, - int e, - int mpb, - int npb, - int kpb, - int wtm, - int wtn, - int wtk, - int wsm, - int wsn, - int wrm, - int wrn, - int mh, - int vs, - int gks, - int me, - int pta, - std::initializer_list ta_t, - std::initializer_list ta_c, - std::initializer_list tb_t, - std::initializer_list tb_c, - bool spare = false); + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTC(std::string dir, + std::string layout, + std::string prec, + int b, + int e, + int mpb, + int npb, + int kpb, + int wtm, + int wtn, + int wtk, + int wsm, + int wsn, + int wrm, + int wrn, + int mh, + int vs, + int gks, + int me, + int pta, + std::initializer_list ta_t, + std::initializer_list ta_c, + std::initializer_list tb_t, + std::initializer_list tb_c, + bool spare = false); + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTC(std::string dir, + std::string layout, + miopenDataType_t prec, + int b, + int e, + int mpb, + int npb, + int kpb, + int wtm, + int wtn, + int wtk, + int wsm, + int wsn, + int wrm, + int wrn, + int mh, + int vs, + int gks, + int me, + int pta, + std::initializer_list ta_t, + std::initializer_list ta_c, + std::initializer_list tb_t, + std::initializer_list tb_c, + bool spare = false); PerformanceConfigAsmImplicitGemmGTC() : PerformanceConfigAsmImplicitGemmGTC("fwd", "nchw", @@ -3346,12 +3493,12 @@ struct PerformanceConfigAsmImplicitGemmGTC : PerfConfigBase(); } - PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC&) const override; - PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCFwdXdlopsNHWC&) const override; @@ -3720,14 +3868,15 @@ struct PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC : PerformanceConfigAsmIm spare) { } - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; }; struct ConvAsmImplicitGemmGTCDynamicBwdXdlopsNHWC final @@ -3738,24 +3887,24 @@ struct ConvAsmImplicitGemmGTCDynamicBwdXdlopsNHWC final return GetSolverDbId(); } - PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC&) const override; - PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCBwdXdlopsNHWC&) const override; @@ -3924,15 +4073,16 @@ struct PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC : PerformanceConfigAsmIm { } - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - size_t ComputeKernelOccupancy() const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT size_t ComputeKernelOccupancy() const; private: void SetParamsForKSplit(const miopen::conv::ProblemDescription& problem, @@ -3947,24 +4097,24 @@ struct ConvAsmImplicitGemmGTCDynamicWrwXdlopsNHWC final return GetSolverDbId(); } - PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC&) const override; - PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCWrwXdlopsNHWC&) const override; @@ -4000,6 +4150,7 @@ struct PerformanceConfigAsmImplicitGemmGTCvector bool use_spare_set; int index; + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCvector(std::string dir, std::string layout, std::string prec, @@ -4021,6 +4172,7 @@ struct PerformanceConfigAsmImplicitGemmGTCvector std::initializer_list tb_c, bool spare = false); + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCvector(std::string dir, std::string layout, miopenDataType_t prec, @@ -4138,12 +4290,14 @@ struct PerformanceConfigAsmImplicitGemmGTCvector bool IsValidValue() const = delete; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription&) const = delete; - bool IsDefaultConstructed() const; - bool operator==(const PerformanceConfigAsmImplicitGemmGTCvector& other) const; - void CopyParameters(const PerformanceConfigAsmImplicitGemmGTCvector& other); - std::string ToString() const override; - std::string ToKernelName(const ExecutionContext&) const; - int BlockSize() const; + MIOPEN_INTERNALS_EXPORT bool IsDefaultConstructed() const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigAsmImplicitGemmGTCvector& other) const; + MIOPEN_INTERNALS_EXPORT void + CopyParameters(const PerformanceConfigAsmImplicitGemmGTCvector& other); + MIOPEN_INTERNALS_EXPORT std::string ToString() const override; + MIOPEN_INTERNALS_EXPORT std::string ToKernelName(const ExecutionContext&) const; + MIOPEN_INTERNALS_EXPORT int BlockSize() const; }; struct PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC : PerformanceConfigAsmImplicitGemmGTCvector { @@ -4281,14 +4435,14 @@ struct PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC : PerformanceConfigAsmIm { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; }; struct ConvAsmImplicitGemmGTCDynamicFwdDlopsNCHWC final @@ -4298,21 +4452,21 @@ struct ConvAsmImplicitGemmGTCDynamicFwdDlopsNCHWC final { return GetSolverDbId(); } - PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC&) const override; - PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC + MIOPEN_INTERNALS_EXPORT PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigAsmImplicitGemmGTCFwdDlopsNCHWC&) const override; @@ -4336,15 +4490,16 @@ struct PerformanceConfigHipImplicitGemmFwdXdlops : PerformanceConfigHipImplicitGemmFwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmFwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmFwdXdlops& other) const; private: template @@ -4361,22 +4516,23 @@ struct ConvHipImplicitGemmFwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmFwdXdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmFwdXdlops&) const override; - PerformanceConfigHipImplicitGemmFwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmFwdXdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmFwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmFwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmFwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmFwdXdlops&) const override; /// \anchor igemm_get_wti_magic_number // Magic Number Alert: // Naive convolutions have GetWti() that return very small value (0.01f). @@ -4416,15 +4572,16 @@ struct PerformanceConfigHipImplicitGemmBwdXdlops : PerformanceConfigHipImplicitGemmBwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmBwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmBwdXdlops& other) const; private: template @@ -4441,22 +4598,23 @@ struct ConvHipImplicitGemmBwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmBwdXdlops - GetDefaultPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmBwdXdlops&) const override; - PerformanceConfigHipImplicitGemmBwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmBwdXdlops GetDefaultPerformanceConfig( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsValidPerformanceConfig(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmBwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmBwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmBwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmBwdXdlops&) const override; /// \ref igemm_get_wti_magic_number float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override { @@ -4486,27 +4644,30 @@ struct PerformanceConfigHipImplicitGemmGroupFwdXdlops : PerformanceConfigHipImplicitGemmGroupFwdXdlops(0, "") { } - void HeuristicInit(const ExecutionContext&, const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmGroupFwdXdlops& other) const; - bool IsModelApplicable(const ExecutionContext& ctx, - const miopen::conv::ProblemDescription& problem) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmGroupFwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + IsModelApplicable(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem) const; private: #if MIOPEN_ENABLE_AI_KERNEL_TUNING std::vector heuristic_indexes; - std::vector> heuristic_kernels; + std::unordered_map> heuristic_kernels; template bool RunParameterPredictionModel(const ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem); - void InitHeuristicKernelIDs(); - bool ModelApplyToken(int idx, std::string value); + void InitHeuristicKernelIDs(const std::string& type); + bool ModelApplyToken(int idx, std::string value, const std::string& arch); #endif template void Init(const miopen::conv::ProblemDescription&); @@ -4522,31 +4683,32 @@ struct ConvHipImplicitGemmGroupFwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmGroupFwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupFwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmGroupFwdXdlops&) const override; - PerformanceConfigHipImplicitGemmGroupFwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupFwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmGroupFwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmGroupFwdXdlops&) const override; /// \ref igemm_get_wti_magic_number float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override { return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4572,15 +4734,16 @@ struct PerformanceConfigHipImplicitGemm3DGroupFwdXdlops : PerformanceConfigHipImplicitGemm3DGroupFwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemm3DGroupFwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemm3DGroupFwdXdlops& other) const; private: template @@ -4597,21 +4760,21 @@ struct ConvHipImplicitGemm3DGroupFwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemm3DGroupFwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupFwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupFwdXdlops&) const override; - PerformanceConfigHipImplicitGemm3DGroupFwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupFwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupFwdXdlops&) const override; @@ -4621,8 +4784,8 @@ struct ConvHipImplicitGemm3DGroupFwdXdlops final return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4648,15 +4811,16 @@ struct PerformanceConfigHipImplicitGemm3DGroupWrwXdlops : PerformanceConfigHipImplicitGemm3DGroupWrwXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops& other) const; private: template @@ -4673,21 +4837,21 @@ struct ConvHipImplicitGemm3DGroupWrwXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemm3DGroupWrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupWrwXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops&) const override; - PerformanceConfigHipImplicitGemm3DGroupWrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupWrwXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops&) const override; @@ -4697,8 +4861,8 @@ struct ConvHipImplicitGemm3DGroupWrwXdlops final return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4724,15 +4888,16 @@ struct PerformanceConfigHipImplicitGemm3DGroupBwdXdlops : PerformanceConfigHipImplicitGemm3DGroupBwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemm3DGroupBwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemm3DGroupBwdXdlops& other) const; private: template @@ -4749,21 +4914,21 @@ struct ConvHipImplicitGemm3DGroupBwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemm3DGroupBwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupBwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupBwdXdlops&) const override; - PerformanceConfigHipImplicitGemm3DGroupBwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemm3DGroupBwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemm3DGroupBwdXdlops&) const override; @@ -4773,8 +4938,8 @@ struct ConvHipImplicitGemm3DGroupBwdXdlops final return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4800,17 +4965,32 @@ struct PerformanceConfigHipImplicitGemmGroupBwdXdlops : PerformanceConfigHipImplicitGemmGroupBwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmGroupBwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmGroupBwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + IsModelApplicable(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem) const; private: +#if MIOPEN_ENABLE_AI_KERNEL_TUNING + std::vector heuristic_indexes; + std::unordered_map> heuristic_kernels; + template + bool RunParameterPredictionModel(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem); + void InitHeuristicKernelIDs(); + bool ModelApplyToken(int idx, std::string value); +#endif template void Init(const miopen::conv::ProblemDescription&); template @@ -4825,31 +5005,32 @@ struct ConvHipImplicitGemmGroupBwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmGroupBwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupBwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmGroupBwdXdlops&) const override; - PerformanceConfigHipImplicitGemmGroupBwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupBwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmGroupBwdXdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmGroupBwdXdlops&) const override; /// \ref igemm_get_wti_magic_number float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override { return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4861,6 +5042,7 @@ struct PerformanceConfigHipImplicitGemmGroupWrwXdlops : PerfConfigBaseCK { int index; + int split_k; std::string kernel_id; std::vector valid_kernels; PerformanceConfigHipImplicitGemmGroupWrwXdlops(int idx, std::string kernl_id) @@ -4875,17 +5057,34 @@ struct PerformanceConfigHipImplicitGemmGroupWrwXdlops : PerformanceConfigHipImplicitGemmGroupWrwXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const ExecutionContext&, + const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmGroupWrwXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmGroupWrwXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool + IsModelApplicable(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem) const; private: +#if MIOPEN_ENABLE_AI_KERNEL_TUNING + std::vector heuristic_indexes; + std::unordered_map> heuristic_kernels; + template + bool RunParameterPredictionModel(const ExecutionContext& ctx, + const miopen::conv::ProblemDescription& problem); + void InitHeuristicKernelIDs(const std::string& type); + bool ModelApplyToken(int idx, + std::string value, + const std::string& arch, + const miopen::conv::ProblemDescription& problem); +#endif template void Init(const miopen::conv::ProblemDescription&); template @@ -4900,31 +5099,32 @@ struct ConvHipImplicitGemmGroupWrwXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmGroupWrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupWrwXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmGroupWrwXdlops&) const override; - PerformanceConfigHipImplicitGemmGroupWrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmGroupWrwXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution GetSolution(const ExecutionContext&, - const miopen::conv::ProblemDescription&, - const PerformanceConfigHipImplicitGemmGroupWrwXdlops&) const override; + MIOPEN_INTERNALS_EXPORT ConvSolution + GetSolution(const ExecutionContext&, + const miopen::conv::ProblemDescription&, + const PerformanceConfigHipImplicitGemmGroupWrwXdlops&) const override; /// \ref igemm_get_wti_magic_number float GetWti(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override { return 0.02f; }; - size_t GetWorkspaceSize(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT size_t GetWorkspaceSize( + const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool MayNeedWorkspace() const override { return true; } private: @@ -4950,15 +5150,16 @@ struct PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops : PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops& other) const; private: template @@ -4975,21 +5176,21 @@ struct ConvHipImplicitGemmF16F8F16FwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops&) const override; - PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16FwdXdlops&) const override; @@ -5022,15 +5223,16 @@ struct PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops : PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops& other) const; private: template @@ -5047,21 +5249,21 @@ struct ConvHipImplicitGemmF16F8F16BwdXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops&) const override; - PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16BwdXdlops&) const override; @@ -5094,15 +5296,16 @@ struct PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops : PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops(0, "") { } - void HeuristicInit(const miopen::conv::ProblemDescription&); - bool SetNextValue(const miopen::conv::ProblemDescription&); - bool IsValidValue() const; + MIOPEN_INTERNALS_EXPORT void HeuristicInit(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool SetNextValue(const miopen::conv::ProblemDescription&); + MIOPEN_INTERNALS_EXPORT bool IsValidValue() const; bool IsValid(const ExecutionContext&, const miopen::conv::ProblemDescription& problem) const { return IsValid(problem); } - bool IsValid(const miopen::conv::ProblemDescription&) const; - bool operator==(const PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops& other) const; + MIOPEN_INTERNALS_EXPORT bool IsValid(const miopen::conv::ProblemDescription&) const; + MIOPEN_INTERNALS_EXPORT bool + operator==(const PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops& other) const; private: template @@ -5119,21 +5322,21 @@ struct ConvHipImplicitGemmF16F8F16WrwXdlops final return GetSolverDbId(); } - PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops GetDefaultPerformanceConfig(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; - bool IsValidPerformanceConfig( + MIOPEN_INTERNALS_EXPORT bool IsValidPerformanceConfig( const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops&) const override; - PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops + MIOPEN_INTERNALS_EXPORT PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops Search(const ExecutionContext&, const miopen::conv::ProblemDescription&, const AnyInvokeParams& invoke_ctx) const override; - bool IsApplicable(const ExecutionContext&, - const miopen::conv::ProblemDescription&) const override; + MIOPEN_INTERNALS_EXPORT bool + IsApplicable(const ExecutionContext&, const miopen::conv::ProblemDescription&) const override; bool IsDynamic() const override { return true; } - ConvSolution + MIOPEN_INTERNALS_EXPORT ConvSolution GetSolution(const ExecutionContext&, const miopen::conv::ProblemDescription&, const PerformanceConfigHipImplicitGemmF16F8F16WrwXdlops&) const override; @@ -5153,7 +5356,7 @@ struct ConvHipImplicitGemmF16F8F16WrwXdlops final // Use struct as a syntactic sugar to make the intent as clear as possible. struct ThisSolverIsDeprecatedStatic { - static bool IsDisabled(const ExecutionContext& ctx); + MIOPEN_INTERNALS_EXPORT static bool IsDisabled(const ExecutionContext& ctx); }; } // namespace solver diff --git a/src/include/miopen/solver/ck_utility_common.hpp b/src/include/miopen/solver/ck_utility_common.hpp index 03457f2b0e..57f4de1a0d 100644 --- a/src/include/miopen/solver/ck_utility_common.hpp +++ b/src/include/miopen/solver/ck_utility_common.hpp @@ -61,7 +61,9 @@ static inline bool is_ck_supported_hardware(const Handle& handle) StartsWith(handle.GetDeviceName(), "gfx1031") || StartsWith(handle.GetDeviceName(), "gfx1100") || StartsWith(handle.GetDeviceName(), "gfx1101") || - StartsWith(handle.GetDeviceName(), "gfx1102"); + StartsWith(handle.GetDeviceName(), "gfx1102") || + StartsWith(handle.GetDeviceName(), "gfx1200") || + StartsWith(handle.GetDeviceName(), "gfx1201"); } // MI100 : gfx908 @@ -121,6 +123,10 @@ static inline auto get_ck_common_compiler_flag(const Handle& handle) compiler_flag << " -DCK_AMD_GPU_GFX1101"; else if(StartsWith(device_name, "gfx1102")) compiler_flag << " -DCK_AMD_GPU_GFX1102"; + else if(StartsWith(device_name, "gfx1200")) + compiler_flag << " -DCK_AMD_GPU_GFX1200"; + else if(StartsWith(device_name, "gfx1201")) + compiler_flag << " -DCK_AMD_GPU_GFX1201"; // NOLINTEND(*-braces-around-statements) // buffer atomic-fadd @@ -129,14 +135,11 @@ static inline auto get_ck_common_compiler_flag(const Handle& handle) // sync LDS compiler_flag << " -DCK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM=" - << (miopen::IsDisabled(ENV(MIOPEN_DEBUG_CK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM)) - ? '0' - : '1'); + << (env::disabled(MIOPEN_DEBUG_CK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM) ? '0' : '1'); // buffer addressing compiler_flag << " -DCK_USE_AMD_BUFFER_ADDRESSING=" - << (miopen::IsDisabled(ENV(MIOPEN_DEBUG_CK_USE_AMD_BUFFER_ADDRESSING)) ? '0' - : '1'); + << (env::disabled(MIOPEN_DEBUG_CK_USE_AMD_BUFFER_ADDRESSING) ? '0' : '1'); return compiler_flag.str(); } diff --git a/src/include/miopen/solver/conv_direct_naive_conv.hpp b/src/include/miopen/solver/conv_direct_naive_conv.hpp index f5f1062ea1..4afc1b5999 100644 --- a/src/include/miopen/solver/conv_direct_naive_conv.hpp +++ b/src/include/miopen/solver/conv_direct_naive_conv.hpp @@ -25,6 +25,9 @@ *******************************************************************************/ #pragma once +#include +#include +#include #include #include #include "miopen/../../kernels/stride_array.hpp" @@ -147,6 +150,29 @@ auto MakeStrideArray(V vec) return ret; } +::miopen::solver::ConvSolution +GetConv2DFWDSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); + +::miopen::solver::ConvSolution +GetConv3DFWDSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); + +::miopen::solver::ConvSolution +GetConv2DWRWSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); + +::miopen::solver::ConvSolution +GetConv3DWRWSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); + +::miopen::solver::ConvSolution +GetConv2DBWDSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); + +::miopen::solver::ConvSolution +GetConv3DBWDSolution(const ExecutionContext& ctx, + const ::miopen::conv::ProblemDescription& problem); } // end namespace conv_internal } // namespace conv diff --git a/src/include/miopen/solver/gemm_common.hpp b/src/include/miopen/solver/gemm_common.hpp new file mode 100644 index 0000000000..d4b4568da9 --- /dev/null +++ b/src/include/miopen/solver/gemm_common.hpp @@ -0,0 +1,63 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once +#ifndef GUARD_MIOPEN_SOLVER_GEMM_COMMON_HPP +#define GUARD_MIOPEN_SOLVER_GEMM_COMMON_HPP + +#include +#include +#include +#include + +#include + +namespace miopen { +namespace solver { +namespace conv { +namespace gemm { + +std::size_t MaxMemAllocSz(Handle& h, + const miopen::conv::ProblemDescription& problem, + bool double_limit_for_fp32 = false); + +constexpr bool IsBf16Supported = MIOPEN_USE_ROCBLAS; +constexpr bool IsFp16Supported = MIOPEN_USE_ROCBLAS; + +bool IsAnyBufferBf16(const TensorDescriptor& xDesc, + const TensorDescriptor& yDesc, + const TensorDescriptor& wDesc); +bool IsAnyBufferFp16(const TensorDescriptor& xDesc, + const TensorDescriptor& yDesc, + const TensorDescriptor& wDesc); + +double SlowdownFactor(int n_oper, double oper_factor, double multiple_oper_factor); + +} // namespace gemm +} // namespace conv +} // namespace solver +} // namespace miopen + +#endif // GUARD_MIOPEN_SOLVER_GEMM_COMMON_HPP diff --git a/src/include/miopen/solver/implicitgemm_ck_util.hpp b/src/include/miopen/solver/implicitgemm_ck_util.hpp index efb0d16b96..ff25d5f622 100644 --- a/src/include/miopen/solver/implicitgemm_ck_util.hpp +++ b/src/include/miopen/solver/implicitgemm_ck_util.hpp @@ -30,9 +30,11 @@ #include #include #include +#include -#if MIOPEN_USE_COMPOSABLEKERNEL +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL #include +#include #endif // MIOPEN_USE_COMPOSABLEKERNEL namespace miopen { @@ -42,9 +44,57 @@ struct ProblemDescription; } // namespace conv namespace solver { +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL +namespace conv { +template +using DeviceOpGWrw = ck::tensor_operation::device::DeviceGroupedConvBwdWeight< + 2, + ck::tensor_layout::convolution::NHWGC, + ck::tensor_layout::convolution::GKYXC, + ck::tensor_layout::convolution::NHWGK, + DataType, + DataType, + DataType, + ck::tensor_operation::element_wise::PassThrough, + ck::tensor_operation::element_wise::PassThrough, + ck::tensor_operation::element_wise::PassThrough>; +template +using DeviceOpGWrwPtrs = + ck::tensor_operation::device::instance::DeviceOperationInstanceFactory>; +} // namespace conv +#endif + +inline bool IsLinear(int L, int H, const int v) +{ + assert(L <= H); + return L <= v && v <= H; +} + +inline bool NextLinear(int L, int H, int& v) +{ + assert((IsLinear(L, H, v))); + if(H == v) + { + v = L; + return true; + } + ++v; + return false; +} struct ConvSolution; +struct CKBWDWeightBufferDescriptor +{ + size_t ck_size; + size_t ck_offset; + + CKBWDWeightBufferDescriptor(size_t _ck_size, size_t _ck_offset) + : ck_size(_ck_size), ck_offset(_ck_offset) + { + } +}; + template typename ConvPtrsType::iterator FindConvPtrByID(ConvPtrsType& conv_ptrs, const std::string& kernel_id) @@ -79,10 +129,29 @@ template bool IsCKArgsSupported(const ProblemDescriptionType& problem, const std::string& kernel_id) { - auto conv_ptrs = DeviceOpType::GetInstances(); - auto ptr_iter = FindConvPtrByID(conv_ptrs, kernel_id); - - return (ptr_iter != conv_ptrs.end()) && CKArgsType{problem}.IsSupportedBy(*ptr_iter); +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL + if(!kernel_id.empty()) + { + auto conv_ptrs = DeviceOpType::GetInstances(); + if constexpr(std::is_same_v> || + std::is_same_v> || + std::is_same_v> || + std::is_same_v>) + { + auto pos = kernel_id.find_last_of('+'); + int split_k = std::stoi(kernel_id.substr(pos + 1)); + auto ptr_iter = FindConvPtrByID(conv_ptrs, kernel_id.substr(0, pos)); + return (ptr_iter != conv_ptrs.end()) && + CKArgsType{problem}.IsSupportedBySplitK(*ptr_iter, split_k); + } + else + { + auto ptr_iter = FindConvPtrByID(conv_ptrs, kernel_id); + return (ptr_iter != conv_ptrs.end()) && CKArgsType{problem}.IsSupportedBy(*ptr_iter); + } + } +#endif + return false; } template -ConvSolution InitInvokerFactoryNHWC(const ExecutionContext&, - const ProblemDescriptionType& problem, - const std::string& kernel_id) +inline bool isDataTypeHalfAndChannelsEven(const miopen::conv::ProblemDescription& problem) { - auto conv_ptrs = DeviceOpType::GetInstances(); - auto ptr_iter = FindConvPtrByID(conv_ptrs, kernel_id); - - if(ptr_iter == conv_ptrs.end()) - { - MIOPEN_LOG_E("PerformanceConfig kernel '" + kernel_id + "' does not exist."); - return {miopenStatusInvalidValue}; - } + return (problem.GetOutDataType() == miopenHalf) && + ((problem.GetInChannels() & 1) != 0 || + (problem.GetOutChannels() & 1) != 0 /* Test if odd*/); +} - ConvSolution result; - result.invoker_factory = - [ck_args = CKArgsType{problem}, - sh_conv_ptr = std::shared_ptr{std::move(*ptr_iter)}](const std::vector&) mutable { - return [ck_args = std::move(ck_args), sh_conv_ptr = std::move(sh_conv_ptr)]( - const Handle& handle, const AnyInvokeParams& primitive_parameters) { - const auto& data_ctx = primitive_parameters.CastTo(); - auto argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, data_ctx.tensors); - auto invoker_ptr = sh_conv_ptr->MakeInvokerPointer(); - { - HipEventProfiler pfr(handle); - if constexpr(std::is_same::value) - { - auto zero = 0.0f; - const auto& tensors = data_ctx.tensors; - SetTensor(handle, tensors.dwDesc, tensors.dw, &zero); - } - invoker_ptr->Run(argument_ptr.get(), {handle.GetStream(), false}); - } - }; - }; - return result; +inline bool ShouldAllocateWorkSpaceBufferForWRW(const miopen::conv::ProblemDescription& problem) +{ + return (problem.GetAlphaBetaCase() != DEFAULT || isDataTypeHalfAndChannelsEven(problem)); } template & ck_buff_des) { auto input1_solver = input1_op.MakeTransposeSolver(ctx, problem, ck_args); @@ -477,10 +498,23 @@ auto MakeTaggedTransposeInstances(ConvSolution& result, output_solver.GetKernelInfo(), output_init_solver.GetKernelInfo()}); + if(ck_buff_des.has_value()) + { + MultiBufferWorkspaceTraits wt({input1_solver.GetOutputTensorSize(), + input2_solver.GetOutputTensorSize(), + output_solver.GetOutputTensorSize(), + ck_buff_des->ck_size}); + ck_buff_des->ck_offset = wt.GetOffset(3); + return std::make_tuple( + TransposeInstanceTagged{input1_solver, 0, wt, 0, Input1TposeOp::CONV_OP_TAG}, + TransposeInstanceTagged{input2_solver, 1, wt, 1, Input2TposeOp::CONV_OP_TAG}, + TransposeInstanceTagged{output_solver, 2, wt, 2, OutputTposeOp::CONV_OP_TAG}, + TransposeInstanceTagged{output_init_solver, 3, wt, 2, OutputTposeOp::CONV_OP_TAG}); + } + MultiBufferWorkspaceTraits wt({input1_solver.GetOutputTensorSize(), input2_solver.GetOutputTensorSize(), output_solver.GetOutputTensorSize()}); - return std::make_tuple( TransposeInstanceTagged{input1_solver, 0, wt, 0, Input1TposeOp::CONV_OP_TAG}, TransposeInstanceTagged{input2_solver, 1, wt, 1, Input2TposeOp::CONV_OP_TAG}, @@ -539,24 +573,62 @@ inline void DebugPrintConvTensors(const ConvTensors& conv_tensors) #endif // NDEBUG } // end namespace internal +// packed size in bytes +inline size_t GetPackedSize(const TensorDescriptor& td) +{ + return td.GetElementSize() * GetTypeSize(td.GetType()); +} + +inline size_t GetCKAlphaBetaWorkspace(const miopen::conv::ProblemDescription& problem) +{ + std::size_t buff_size; + + TensorDescriptor input = problem.GetIn(); + TensorDescriptor output = problem.GetOut(); + ConvolutionDescriptor conv_desc = problem.GetConv(); + + miopenConvolutionABBackwardWeightsGetWorkSpaceSize( + problem.GetAlphaBetaCase(), &input, &output, &conv_desc, &buff_size); + return buff_size; +} + +inline bool CKWrwRequireWorkspace( + size_t G, size_t C, size_t K, miopenDataType_t data_type, miopenAlphaBetaCase_t alpha_beta_case) +{ + auto is_odd = [](size_t num) { return num % 2 != 0; }; + size_t C_per_group = C / G; + size_t K_per_group = K / G; + + return (alpha_beta_case == BILINEAR || alpha_beta_case == SCALE) || + (data_type == miopenHalf && (is_odd(C_per_group) || is_odd(K_per_group))); +} + /// \todo move to a cpp file inline size_t GetWorkspaceSizeLayoutTransformConv(const miopen::conv::ProblemDescription& problem) { if(problem.IsLayoutNHWC()) { + if(problem.GetDirection() == ::miopen::conv::Direction::BackwardWeights) + { + return GetCKAlphaBetaWorkspace(problem); + } return 0; } assert(problem.IsLayoutDefault()); - // packed size in bytes - auto GetPackedSize = [](const TensorDescriptor& td) { - return td.GetElementSize() * GetTypeSize(td.GetType()); - }; + + if(problem.GetDirection() == ::miopen::conv::Direction::BackwardWeights) + { + MultiBufferWorkspaceTraits wt({GetPackedSize(problem.GetIn()), + GetPackedSize(problem.GetWeights()), + GetPackedSize(problem.GetOut()), + GetCKAlphaBetaWorkspace(problem)}); + return wt.GetSize(); + } MultiBufferWorkspaceTraits wt({GetPackedSize(problem.GetIn()), GetPackedSize(problem.GetWeights()), GetPackedSize(problem.GetOut())}); - return wt.GetSize(); } @@ -573,40 +645,60 @@ ConvSolution InitInvokerFactoryNCHW(const ExecutionContext& ctx, const Input2TposeOp& input2_op, const OutputTposeOp& output_op) { - assert(problem.IsLayoutDefault()); ConvSolution result; +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL auto ck_args = CKArgsType{problem}; - auto [_input1_tr_inst, _input2_tr_inst, _output_tr_inst, _output_init_tr_inst] = - internal::MakeTaggedTransposeInstances( - result, ctx, problem, ck_args, input1_op, input2_op, output_op); - auto conv_ptrs = DeviceOpType::GetInstances(); - auto ptr_iter = FindConvPtrByID(conv_ptrs, kernel_id); + std::optional split_k = std::nullopt; + std::string id_string = kernel_id; + auto pos = kernel_id.find_last_of('+'); + if(pos != std::string::npos) + { + split_k = std::stoi(kernel_id.substr(pos + 1)); + id_string = kernel_id.substr(0, pos); + } + + std::optional _ck_buff_des; + + if(problem.IsDirectionBackwardWrW()) + { + _ck_buff_des.emplace(GetCKAlphaBetaWorkspace(problem), 0); + } + + auto ptr_iter = FindConvPtrByID(conv_ptrs, id_string); if(ptr_iter == conv_ptrs.end()) { MIOPEN_LOG_E("PerformanceConfig kernel '" + kernel_id + "' does not exist."); return {miopenStatusInvalidValue}; } - result.invoker_factory = [ck_args = std::move(ck_args), + auto [_input1_tr_inst, _input2_tr_inst, _output_tr_inst, _output_init_tr_inst] = + internal::MakeTaggedTransposeInstances( + result, ctx, problem, ck_args, input1_op, input2_op, output_op, _ck_buff_des); + + result.invoker_factory = [split_k = split_k, + ck_args = std::move(ck_args), sh_conv_ptr = std::shared_ptr{std::move(*ptr_iter)}, input1_tr_inst = std::move(_input1_tr_inst), input2_tr_inst = std::move(_input2_tr_inst), output_tr_inst = std::move(_output_tr_inst), - output_init_tr_inst = std::move(_output_init_tr_inst)]( - const std::vector& kernels) mutable { - return [kernels, + output_init_tr_inst = std::move(_output_init_tr_inst), + ck_buff_des = + _ck_buff_des](const std::vector& kernels) mutable { + return [split_k = split_k, + kernels, ck_args = std::move(ck_args), sh_conv_ptr = std::move(sh_conv_ptr), input1_tr_inst = std::move(input1_tr_inst), input2_tr_inst = std::move(input2_tr_inst), output_tr_inst = std::move(output_tr_inst), - output_init_tr_inst = std::move(output_init_tr_inst)]( - const Handle& handle, const AnyInvokeParams& primitive_parameters) mutable { + output_init_tr_inst = std::move(output_init_tr_inst), + ck_buff_des = ck_buff_des](const Handle& handle, + const AnyInvokeParams& primitive_parameters) mutable { handle.ResetKernelTime(); const auto& data_ctx = primitive_parameters.CastTo(); @@ -632,7 +724,7 @@ ConvSolution InitInvokerFactoryNCHW(const ExecutionContext& ctx, std::swap(conv_tensors.x, conv_tensors.y); std::swap(conv_tensors.xDesc, conv_tensors.yDesc); } - HipEventProfiler pfr(handle); + WorkAroundHipEventProfiler pfr(handle); input1_tr_inst.ConvertFrom(handle, kernels, conv_tensors); input2_tr_inst.ConvertFrom(handle, kernels, conv_tensors); @@ -653,21 +745,168 @@ ConvSolution InitInvokerFactoryNCHW(const ExecutionContext& ctx, return left->GetConvOperandTagAsInt() < right->GetConvOperandTagAsInt(); }); - auto invoker_ptr = sh_conv_ptr->MakeInvokerPointer(); - auto argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, - tr_ptrs[0]->GetBufferPtr(), - tr_ptrs[1]->GetBufferPtr(), - tr_ptrs[2]->GetBufferPtr()); + auto invoker_ptr = sh_conv_ptr->MakeInvokerPointer(); + std::unique_ptr argument_ptr; + if constexpr(std::is_same_v> || + std::is_same_v> || + std::is_same_v> || + std::is_same_v>) + { + if(split_k.has_value()) + { + argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, + tr_ptrs[0]->GetBufferPtr(), + tr_ptrs[1]->GetBufferPtr(), + tr_ptrs[2]->GetBufferPtr(), + data_ctx.alpha.GetAsFloat(), + data_ctx.beta.GetAsFloat(), + split_k.value()); + } + } + else + { + std::ignore = split_k; + argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, + tr_ptrs[0]->GetBufferPtr(), + tr_ptrs[1]->GetBufferPtr(), + tr_ptrs[2]->GetBufferPtr(), + data_ctx.alpha.GetAsFloat(), + data_ctx.beta.GetAsFloat()); + } + + if(ck_buff_des.has_value() && ck_buff_des->ck_size) + { + auto buf_handle = + handle.CreateSubBuffer(data_ctx.workSpace, ck_buff_des->ck_offset, 0); + assert(buf_handle.get()); + sh_conv_ptr->SetWorkSpacePointer(argument_ptr.get(), buf_handle.get()); + } invoker_ptr->Run(argument_ptr.get(), {handle.GetStream(), false}); output_tr_inst.ConvertTo(handle, kernels, conv_tensors); }; }; result.workspace_sz = GetWorkspaceSizeLayoutTransformConv(problem); - +#endif return result; } +template +ConvSolution InitInvokerFactoryNHWC(const ExecutionContext&, + const ProblemDescriptionType& problem, + const std::string& kernel_id) +{ + auto conv_ptrs = DeviceOpType::GetInstances(); + + std::optional split_k = std::nullopt; + std::string id_string = kernel_id; + auto pos = kernel_id.find_last_of('+'); + if(pos != std::string::npos) + { + split_k = std::stoi(kernel_id.substr(pos + 1)); + id_string = kernel_id.substr(0, pos); + } + + auto ptr_iter = FindConvPtrByID(conv_ptrs, id_string); + + if(ptr_iter == conv_ptrs.end()) + { + MIOPEN_LOG_E("PerformanceConfig kernel '" + kernel_id + "' does not exist."); + return {miopenStatusInvalidValue}; + } + + if constexpr(std::is_same_v) + { + ConvSolution result; +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL + miopenAlphaBetaCase_t alpha_beta_case = problem.GetAlphaBetaCase(); + [[maybe_unused]] bool should_allocated_wrw_buffer = + ShouldAllocateWorkSpaceBufferForWRW(problem); + + result.invoker_factory = [split_k = split_k, + ck_args = CKArgsType{problem}, + alpha_beta_case = alpha_beta_case, + should_allocated_wrw_buffer = should_allocated_wrw_buffer, + sh_conv_ptr = std::shared_ptr{std::move(*ptr_iter)}]( + const std::vector&) mutable { + return [split_k = split_k, + ck_args = std::move(ck_args), + alpha_beta_case = alpha_beta_case, + should_allocated_wrw_buffer = should_allocated_wrw_buffer, + sh_conv_ptr = std::move(sh_conv_ptr)]( + const Handle& handle, const AnyInvokeParams& primitive_parameters) { + const auto& data_ctx = primitive_parameters.CastTo(); + std::unique_ptr argument_ptr; + if constexpr(std::is_same_v> || + std::is_same_v> || + std::is_same_v> || + std::is_same_v>) + { + argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, + data_ctx.tensors, + data_ctx.alpha.GetAsFloat(), + data_ctx.beta.GetAsFloat(), + split_k.value()); + } + else + { + std::ignore = split_k; + argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, + data_ctx.tensors, + data_ctx.alpha.GetAsFloat(), + data_ctx.beta.GetAsFloat()); + } + + auto invoker_ptr = sh_conv_ptr->MakeInvokerPointer(); + HipEventProfiler pfr(handle); + + if(alpha_beta_case == DEFAULT) + { + auto zero = 0.0f; + const auto& tensors = data_ctx.tensors; + SetTensor(handle, tensors.dwDesc, tensors.dw, &zero); + } + // use captured value, other wise getting warning + // "lambda capture is not used" since this variable is only used in assert. + (void)should_allocated_wrw_buffer; + assert((should_allocated_wrw_buffer && data_ctx.workSpace != nullptr) || + !(should_allocated_wrw_buffer && data_ctx.workSpace == nullptr)); + if(data_ctx.workSpace) + { + sh_conv_ptr->SetWorkSpacePointer(argument_ptr.get(), data_ctx.workSpace); + } + invoker_ptr->Run(argument_ptr.get(), {handle.GetStream(), false}); + }; + }; + result.workspace_sz = GetWorkspaceSizeLayoutTransformConv(problem); +#endif + return result; + } + else + { + ConvSolution result; + result.invoker_factory = [ck_args = CKArgsType{problem}, + sh_conv_ptr = std::shared_ptr{std::move(*ptr_iter)}]( + const std::vector&) mutable { + return [ck_args = std::move(ck_args), sh_conv_ptr = std::move(sh_conv_ptr)]( + const Handle& handle, const AnyInvokeParams& primitive_parameters) { + const auto& data_ctx = primitive_parameters.CastTo(); + auto argument_ptr = ck_args.MakeArgPtr(sh_conv_ptr, + data_ctx.tensors, + data_ctx.alpha.GetAsFloat(), + data_ctx.beta.GetAsFloat()); + auto invoker_ptr = sh_conv_ptr->MakeInvokerPointer(); + HipEventProfiler pfr(handle); + invoker_ptr->Run(argument_ptr.get(), {handle.GetStream(), false}); + }; + }; + return result; + } +} + template ConvSolution InitInvokerFactoryFwdNCHW(const ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, @@ -722,7 +961,7 @@ MakeSolutionGroupConvImplicitGemmXdlops(const miopen::conv::ProblemDescription& InvokerFactoryMakerNHWC&& invoker_factory_maker_ndhwc) { -#if MIOPEN_USE_COMPOSABLEKERNEL +#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL if(problem.IsLayoutDefault()) { switch(problem.GetInDataType()) @@ -730,8 +969,9 @@ MakeSolutionGroupConvImplicitGemmXdlops(const miopen::conv::ProblemDescription& case miopenInt8: return invoker_factory_maker_ncdhw(int8_t{}); case miopenHalf: return invoker_factory_maker_ncdhw(ck::half_t{}); case miopenFloat: return invoker_factory_maker_ncdhw(float{}); + case miopenBFloat16: return invoker_factory_maker_ncdhw(ck::bhalf_t{}); + case miopenInt64: case miopenInt32: - case miopenBFloat16: case miopenDouble: case miopenFloat8: case miopenBFloat8: @@ -748,8 +988,9 @@ MakeSolutionGroupConvImplicitGemmXdlops(const miopen::conv::ProblemDescription& case miopenInt8: return invoker_factory_maker_ndhwc(int8_t{}); case miopenHalf: return invoker_factory_maker_ndhwc(ck::half_t{}); case miopenFloat: return invoker_factory_maker_ndhwc(float{}); + case miopenBFloat16: return invoker_factory_maker_ndhwc(ck::bhalf_t{}); + case miopenInt64: case miopenInt32: - case miopenBFloat16: case miopenDouble: case miopenFloat8: case miopenBFloat8: diff --git a/src/include/miopen/solver/implicitgemm_util.hpp b/src/include/miopen/solver/implicitgemm_util.hpp index d695ba4c17..24b6d15f9c 100644 --- a/src/include/miopen/solver/implicitgemm_util.hpp +++ b/src/include/miopen/solver/implicitgemm_util.hpp @@ -205,7 +205,7 @@ inline static bool NextFlag(bool& v) static inline bool IsXdlopsSupport(const ExecutionContext& ctx) { - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_XDLOPS_EMULATE))) + if(env::enabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_XDLOPS_EMULATE)) return true; // disable xdlops kernels by default due to possible failures: @@ -214,7 +214,7 @@ static inline bool IsXdlopsSupport(const ExecutionContext& ctx) const bool is_xdlops_supported = StartsWith(ctx.GetStream().GetDeviceName(), "gfx908") || StartsWith(ctx.GetStream().GetDeviceName(), "gfx90a") || StartsWith(ctx.GetStream().GetDeviceName(), "gfx94"); - return is_xdlops_supported && !miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_XDLOPS)); + return is_xdlops_supported && !env::disabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_XDLOPS); } ///\todo remove @@ -443,7 +443,7 @@ static inline bool use_amd_inline_asm(const ExecutionContext& ctx, problem.IsFp16()) return false; - return !miopen::IsDisabled(ENV(MIOPEN_DEBUG_IMPLICIT_GEMM_NON_XDLOPS_INLINE_ASM)); + return !env::disabled(MIOPEN_DEBUG_IMPLICIT_GEMM_NON_XDLOPS_INLINE_ASM); } static inline bool is_use_amd_buffer_load_store(const ExecutionContext& ctx) @@ -552,9 +552,8 @@ static inline auto get_static_ck_common_compiler_flag(const ExecutionContext& ct // LDS sync compiler_flag += std::string(" -DCK_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM=") + - (miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM)) - ? '0' - : '1'); + (env::disabled(MIOPEN_DEBUG_CONV_IMPLICIT_GEMM_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM) ? '0' + : '1'); // workaround compiler_flag += diff --git a/src/include/miopen/solver/problem_description_interpreter.hpp b/src/include/miopen/solver/problem_description_interpreter.hpp index 72a2755867..eb974e5189 100644 --- a/src/include/miopen/solver/problem_description_interpreter.hpp +++ b/src/include/miopen/solver/problem_description_interpreter.hpp @@ -226,6 +226,20 @@ struct ProblemInterpreter return problem.GetPadW(); } + static const Scalar& GetAlpha(const miopen::conv::ProblemDescription& problem) + { + return problem.GetAlpha(); + } + + static const Scalar& GetBeta(const miopen::conv::ProblemDescription& problem) + { + return problem.GetBeta(); + } + static miopenAlphaBetaCase_t GetAlphaBetaCase(const miopen::conv::ProblemDescription& problem) + { + return problem.GetAlphaBetaCase(); + } + // adjust right padding size so that filter will not move out-of-bound static auto GetAdjustedInputRightPadD(const miopen::conv::ProblemDescription& problem) { diff --git a/src/include/miopen/solver_id.hpp b/src/include/miopen/solver_id.hpp index c606de1dcf..0a05df6d4b 100644 --- a/src/include/miopen/solver_id.hpp +++ b/src/include/miopen/solver_id.hpp @@ -27,6 +27,7 @@ #ifndef MIOPEN_GUARD_MLOPEN_SOLVER_ID_HPP #define MIOPEN_GUARD_MLOPEN_SOLVER_ID_HPP +#include #include #include @@ -57,10 +58,12 @@ enum class Primitive Cat, Mha, Softmax, + Adam, + Item, Interpolate }; -struct MIOPEN_EXPORT Id +struct MIOPEN_INTERNALS_EXPORT Id { static constexpr uint64_t invalid_value = 0; @@ -91,7 +94,7 @@ struct MIOPEN_EXPORT Id bool is_valid = false; }; -const std::vector& GetSolversByPrimitive(Primitive primitive); +MIOPEN_INTERNALS_EXPORT const std::vector& GetSolversByPrimitive(Primitive primitive); } // namespace solver } // namespace miopen diff --git a/src/include/miopen/sqlite_db.hpp b/src/include/miopen/sqlite_db.hpp index f3c4d089db..71e3a31736 100644 --- a/src/include/miopen/sqlite_db.hpp +++ b/src/include/miopen/sqlite_db.hpp @@ -25,7 +25,7 @@ *******************************************************************************/ #pragma once -#include +#include #if !MIOPEN_ENABLE_SQLITE #error "MIOPEN_ENABLE_SQLITE = Off" @@ -159,14 +159,14 @@ struct SQLiteSerializable } }; -class SQLite +class MIOPEN_INTERNALS_EXPORT SQLite { class impl; // do we need propagate const std::unique_ptr pImpl; public: - class Statement + class MIOPEN_INTERNALS_EXPORT Statement { class impl; std::unique_ptr pImpl; @@ -186,13 +186,14 @@ class SQLite std::vector ColumnBlob(int idx); int64_t ColumnInt64(int idx); int BindText(int idx, const std::string& txt); + int BindPath(int idx, const fs::path& path); int BindBlob(int idx, const std::vector& blob); int BindInt64(int idx, int64_t); }; using result_type = std::vector>; SQLite(); - SQLite(const std::string& filename_, bool is_system); + SQLite(const fs::path& filename_, bool is_system); ~SQLite(); SQLite(SQLite&&) noexcept; SQLite& operator=(SQLite&&) noexcept; @@ -201,7 +202,7 @@ class SQLite result_type Exec(const std::string& query) const; int Changes() const; int Retry(std::function) const; - static int Retry(std::function f, std::string filename); + static int Retry(std::function f, fs::path filename); std::string ErrorMessage() const; }; @@ -210,7 +211,7 @@ class SQLiteBase { protected: public: - SQLiteBase(DbKinds, const std::string& filename_, bool is_system_) + SQLiteBase(DbKinds, const fs::path& filename_, bool is_system_) : filename(filename_), is_system(is_system_) { if(DisableUserDbFileIO && !is_system) @@ -227,14 +228,13 @@ class SQLiteBase } else if(!is_system) { - auto file = fs::path(filename_); - const auto directory = file.remove_filename(); - if(directory.string().empty()) + if(!filename_.has_parent_path()) { dbInvalid = true; return; } + auto directory = filename_.parent_path(); if(!fs::exists(directory)) { if(!fs::create_directories(directory)) @@ -246,7 +246,7 @@ class SQLiteBase sql = SQLite{filename_, is_system}; if(!sql.Valid()) { - bool isKDB = fs::path(filename).extension() == ".kdb"; + bool isKDB = filename.extension() == ".kdb"; dbInvalid = true; filename = ""; if(!is_system) @@ -281,7 +281,7 @@ class SQLiteBase else { dbInvalid = false; - if(!is_system && !miopen::IsEnabled(ENV(MIOPEN_DEBUG_DISABLE_SQL_WAL))) + if(!is_system && !env::enabled(MIOPEN_DEBUG_DISABLE_SQL_WAL)) { auto res = sql.Exec("PRAGMA journal_mode=WAL;"); if(res.empty() || res[0]["journal_mode"] != "wal") @@ -292,7 +292,7 @@ class SQLiteBase } } - static Derived& GetCached(const std::string& path, bool is_system); + static Derived& GetCached(const fs::path& path, bool is_system); // TODO: Fix this for the overhead of having fields per record inline auto CheckTableColumns(const std::string& tableName, @@ -311,9 +311,7 @@ class SQLiteBase if(std::find(cfg_fds.begin(), cfg_fds.end(), goldenName) == cfg_fds.end()) { AllFound = false; - std::ostringstream ss; - ss << "Field " << goldenName << " not found in table: " << tableName; - MIOPEN_LOG_I2(ss.str()); + MIOPEN_LOG_I2("Field " << goldenName << " not found in table: " << tableName); // break; Not breaking to enable logging of all missing fields. } } @@ -369,21 +367,21 @@ class SQLiteBase return reinterpret_cast(this)->LoadUnsafe(args...); } - std::string filename; + fs::path filename; bool dbInvalid; SQLite sql; bool is_system; }; template -Derived& SQLiteBase::GetCached(const std::string& path, bool is_system) +Derived& SQLiteBase::GetCached(const fs::path& path, bool is_system) { // NOLINTNEXTLINE (cppcoreguidelines-avoid-non-const-global-variables) static std::mutex mutex; const std::lock_guard lock{mutex}; // NOLINTNEXTLINE (cppcoreguidelines-avoid-non-const-global-variables) - static auto instances = std::map{}; + static auto instances = std::map{}; const auto it = instances.find(path); if(it != instances.end()) @@ -397,7 +395,8 @@ class SQLitePerfDb : public SQLiteBase { public: static constexpr char const* MIOPEN_PERFDB_SCHEMA_VER = "1.1.0"; - SQLitePerfDb(DbKinds db_kind, const std::string& filename_, bool is_system); + MIOPEN_INTERNALS_EXPORT + SQLitePerfDb(DbKinds db_kind, const fs::path& filename_, bool is_system); template inline void InsertConfig(const T& prob_desc) @@ -446,7 +445,7 @@ class SQLitePerfDb : public SQLiteBase if(dbInvalid) return boost::none; - const auto& pdb_ovr = miopen::GetStringEnv(ENV(MIOPEN_DEBUG_PERFDB_OVERRIDE)); + const auto& pdb_ovr = env::value(MIOPEN_DEBUG_PERFDB_OVERRIDE); if(!pdb_ovr.empty()) { MIOPEN_LOG_I2("overriding tuning params with: " << pdb_ovr); diff --git a/src/include/miopen/sum.hpp b/src/include/miopen/sum.hpp index 7e7c5f2b8f..b1a76e0cc6 100644 --- a/src/include/miopen/sum.hpp +++ b/src/include/miopen/sum.hpp @@ -33,20 +33,20 @@ namespace miopen { struct Handle; struct TensorDescriptor; -std::size_t GetSumWorkspaceSize(Handle& handle, - const TensorDescriptor& xDesc, - const TensorDescriptor& yDesc, - int32_t dim); +MIOPEN_INTERNALS_EXPORT std::size_t GetSumWorkspaceSize(Handle& handle, + const TensorDescriptor& xDesc, + const TensorDescriptor& yDesc, + int32_t dim); -miopenStatus_t SumForward(Handle& handle, - Data_t workspace, - size_t workspaceSizeInBytes, - const TensorDescriptor& xDesc, - ConstData_t x, - const TensorDescriptor& yDesc, - Data_t y, - miopenSumNanPropagation_t nanPropagation, - int32_t dim); +MIOPEN_INTERNALS_EXPORT miopenStatus_t SumForward(Handle& handle, + Data_t workspace, + size_t workspaceSizeInBytes, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& yDesc, + Data_t y, + miopenSumNanPropagation_t nanPropagation, + int32_t dim); } // namespace miopen #endif // _MIOPEN_SUM_HPP_ diff --git a/src/include/miopen/t5layernorm.hpp b/src/include/miopen/t5layernorm.hpp new file mode 100644 index 0000000000..e9aa7abbb6 --- /dev/null +++ b/src/include/miopen/t5layernorm.hpp @@ -0,0 +1,76 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_T5LAYERNORM_HPP_ +#define MIOPEN_T5LAYERNORM_HPP_ + +#include + +namespace miopen { + +struct Handle; +struct TensorDescriptor; + +MIOPEN_INTERNALS_EXPORT miopenStatus_t T5LayerNormForward(Handle& handle, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& yDesc, + Data_t y, + const TensorDescriptor& rstdDesc, + Data_t rstd, + miopenNormMode_t mode, + float epsilon); + +MIOPEN_INTERNALS_EXPORT std::size_t +GetT5LayerNormBackwardWorkspaceSize(Handle& handle, + const TensorDescriptor& dyDesc, + const TensorDescriptor& xDesc, + const TensorDescriptor& weightDesc, + const TensorDescriptor& rstdDesc, + const TensorDescriptor& dxDesc, + const TensorDescriptor& dwDesc, + miopenNormMode_t mode); + +MIOPEN_INTERNALS_EXPORT miopenStatus_t T5LayerNormBackward(Handle& handle, + Data_t workspace, + size_t workspaceSizeInBytes, + const TensorDescriptor& dyDesc, + ConstData_t dy, + const TensorDescriptor& xDesc, + ConstData_t x, + const TensorDescriptor& weightDesc, + ConstData_t weight, + const TensorDescriptor& rstdDesc, + ConstData_t rstd, + const TensorDescriptor& dxDesc, + Data_t dx, + const TensorDescriptor& dwDesc, + Data_t dw, + miopenNormMode_t mode); + +} // namespace miopen +#endif // MIOPEN_T5LAYERNORM_HPP_ diff --git a/src/include/miopen/target_properties.hpp b/src/include/miopen/target_properties.hpp index 4b502c189b..a66ebc27be 100644 --- a/src/include/miopen/target_properties.hpp +++ b/src/include/miopen/target_properties.hpp @@ -29,6 +29,8 @@ #include #include +#define WORKAROUND_ISSUE_3001 1 + namespace miopen { struct Handle; diff --git a/src/include/miopen/temp_file.hpp b/src/include/miopen/temp_file.hpp index 04855b98a8..8c29a7a094 100644 --- a/src/include/miopen/temp_file.hpp +++ b/src/include/miopen/temp_file.hpp @@ -33,7 +33,7 @@ namespace miopen { -class TempFile +class MIOPEN_INTERNALS_EXPORT TempFile { public: TempFile(const std::string& path_infix); @@ -41,8 +41,8 @@ class TempFile TempFile& operator=(TempFile&& other) noexcept = default; const std::string& GetPathInfix() const { return path_infix; } - std::string Path() const { return (dir.path / "file").string(); } - operator std::string() const { return Path(); } + fs::path Path() const { return dir.path / "file"; } + operator fs::path() const { return Path(); } private: std::string path_infix; diff --git a/src/include/miopen/tensor.hpp b/src/include/miopen/tensor.hpp index 0291617891..f4d2b2dca7 100644 --- a/src/include/miopen/tensor.hpp +++ b/src/include/miopen/tensor.hpp @@ -104,7 +104,8 @@ inline std::size_t GetTypeSize(miopenDataType_t d) case miopenInt8: case miopenFloat8: case miopenBFloat8: return 1; - case miopenDouble: return 8; + case miopenDouble: + case miopenInt64: return 8; } MIOPEN_THROW("Unknown or unsupported data type"); } @@ -123,7 +124,7 @@ std::ptrdiff_t integer_division_ceil(X x, Y y) return (tx + ty - 1) / ty; } -struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor +struct MIOPEN_INTERNALS_EXPORT TensorDescriptor : miopenTensorDescriptor { TensorDescriptor(); @@ -143,6 +144,7 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor TensorDescriptor(miopenDataType_t t, const std::vector& lens_in); TensorDescriptor(miopenDataType_t t, const std::initializer_list& lens_in); TensorDescriptor(miopenDataType_t t, const std::vector& lens_in); + TensorDescriptor(miopenDataType_t t, std::vector&& lens_in); TensorDescriptor(miopenDataType_t t, miopenTensorLayout_t layout_in, @@ -153,6 +155,9 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor TensorDescriptor(miopenDataType_t t, miopenTensorLayout_t layout_in, const std::vector& lens_in); + TensorDescriptor(miopenDataType_t t, + miopenTensorLayout_t layout_in, + std::vector&& lens_in); TensorDescriptor(miopenDataType_t t, const std::vector& lens_in, @@ -163,27 +168,44 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor TensorDescriptor(miopenDataType_t t, const std::vector& lens_in, const std::vector& strides_in); + TensorDescriptor(miopenDataType_t t, + std::vector&& lens_in, + std::vector&& strides_in); TensorDescriptor(miopenDataType_t t, miopenTensorLayout_t layout_in, const std::vector& lens_in, const std::vector& strides_in); + TensorDescriptor(miopenDataType_t t, + miopenTensorLayout_t layout_in, + std::vector&& lens_in, + std::vector&& strides_in); // Use only for external API static TensorDescriptor MakeDescriptor(miopenDataType_t t, const int* plens, int size); + static TensorDescriptor MakeDescriptor(miopenDataType_t t, const std::size_t* plens, int size); static TensorDescriptor MakeDescriptor(miopenDataType_t t, miopenTensorLayout_t layout, const int* plens, int size); + static TensorDescriptor MakeDescriptor(miopenDataType_t t, + miopenTensorLayout_t layout, + const std::size_t* plens, + int size); static TensorDescriptor MakeDescriptor(miopenDataType_t t, const int* plens, const int* pstrides, int size); + static TensorDescriptor MakeDescriptor(miopenDataType_t t, + const std::size_t* plens, + const std::size_t* pstrides, + int size); bool IsVectorized() const; const std::vector& GetLengths() const; const std::vector& GetStrides() const; - int GetSize() const; + unsigned GetNumDims() const; miopenDataType_t GetType() const; miopenTensorLayout_t GetLayout_t() const; + static std::string GetLayoutStr(miopenTensorLayout_t layout); std::string GetLayout_str() const; std::size_t GetVectorLength() const; @@ -205,7 +227,11 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor } bool IsPacked() const; + bool IsContiguous() const; + /// Checks all lengths and strides. bool AllDimsFitIntoInt() const; + /// Checks only lengths. + bool AllLengthsFitIntoInt() const; bool operator==(const TensorDescriptor& rhs) const; bool operator!=(const TensorDescriptor& rhs) const; @@ -259,11 +285,15 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor } } - friend std::ostream& operator<<(std::ostream& stream, const TensorDescriptor& t); + friend MIOPEN_INTERNALS_EXPORT std::ostream& operator<<(std::ostream& stream, + const TensorDescriptor& t); friend void to_json(nlohmann::json& j, const TensorDescriptor& descriptor); friend void from_json(const nlohmann::json& j, TensorDescriptor& descriptor); +protected: + static miopenTensorLayout_t GetDefaultLayout() { return miopenTensorNCHW; }; + private: TensorDescriptor(miopenDataType_t t, miopenTensorLayout_t layout_in, @@ -271,14 +301,20 @@ struct MIOPEN_EXPORT TensorDescriptor : miopenTensorDescriptor const std::vector& strides_in, bool use_strides); + TensorDescriptor(miopenDataType_t t, + miopenTensorLayout_t layout_in, + std::vector&& lens_in, + std::vector&& strides_in, + bool use_strides); + + void CheckArgsAndInit(bool use_strides); + void SetStrideNd(const std::string& layout); void LensReorder(const std::string& layout); void CalculateStrides(); void CalculateVectorLength(); - static miopenTensorLayout_t GetDefaultLayout() { return miopenTensorNCHW; }; - std::vector lens; std::vector strides; diff --git a/src/include/miopen/tensor_layout.hpp b/src/include/miopen/tensor_layout.hpp index 2b0920d798..f5659d7dd3 100644 --- a/src/include/miopen/tensor_layout.hpp +++ b/src/include/miopen/tensor_layout.hpp @@ -62,18 +62,21 @@ void tensor_layout_to_strides(const std::vector& len, } return std::accumulate(layout.begin() + pos + 1, layout.end(), - 1, + static_cast(1), [&dim_to_len](T accumulator, char l) { return accumulator * dim_to_len[l]; }); }); } +/// \brief Version for vectorized layouts. +/// +/// \todo Generalize with non-vectorized version, 90% of code is the same. template void tensor_layout_to_strides(const std::vector& len, const std::string& len_layout, const std::string& layout, - const int vector, + const std::size_t vector_size, std::vector& strides) { const std::string base_layout = layout.substr(0, len.size()); @@ -91,7 +94,7 @@ void tensor_layout_to_strides(const std::vector& len, len_layout.begin(), len_layout.end(), std::back_inserter(strides), - [&base_layout, &vector, &dim_to_len](char cur_layout_char) { + [&base_layout, &vector_size, &dim_to_len](char cur_layout_char) { auto pos = base_layout.find(cur_layout_char); if(pos == std::string::npos) { @@ -100,12 +103,12 @@ void tensor_layout_to_strides(const std::vector& len, return std::accumulate( base_layout.begin() + pos + 1, base_layout.end(), - vector, + vector_size, [&dim_to_len](T accumulator, char l) { return accumulator * dim_to_len[l]; }); }); } -inline std::string tensor_layout_get_default(int size) +inline std::string tensor_layout_get_default(unsigned size) { if(size == 4) return "NCHW"; diff --git a/src/include/miopen/tensor_ops.hpp b/src/include/miopen/tensor_ops.hpp index f3c8348619..25d838598b 100644 --- a/src/include/miopen/tensor_ops.hpp +++ b/src/include/miopen/tensor_ops.hpp @@ -48,7 +48,7 @@ struct f_length_is_not_1_t } }; -TensorDescriptor GetFlattenedTensorDescriptor(const TensorDescriptor& desc); +MIOPEN_INTERNALS_EXPORT TensorDescriptor GetFlattenedTensorDescriptor(const TensorDescriptor& desc); template std::tuple @@ -161,61 +161,61 @@ GetConsistentFlattenedTensorDescriptors(const TDescriptors&... real_descriptor_p }); } -void ScaleTensor(const Handle& handle, - const TensorDescriptor& yDesc, - Data_t y, - const void* alpha, - int offset = 0); - -void SetTensor(const Handle& handle, - const TensorDescriptor& yDesc, - Data_t y, - const void* alpha, - int offset = 0); - -void OpTensor(const Handle& handle, - miopenTensorOp_t tensorOp, - const void* alpha0, - const TensorDescriptor& aTensorDesc, - ConstData_t ATensor, - const void* alpha1, - const TensorDescriptor& bTensorDesc, - ConstData_t BTensor, - const void* beta, - const TensorDescriptor& cTensorDesc, - Data_t CTensor, - size_t Aoffset = 0, - size_t Boffset = 0, - size_t Coffset = 0, - bool nonStandardSquash = false); - -void CopyTensor(const Handle& handle, - const TensorDescriptor& srcDesc, - ConstData_t src, - const TensorDescriptor& dstDesc, - Data_t dst, - int srcOffset = 0, - int dstOffset = 0, - bool forseAsync = false); - -void CastTensor(const Handle& handle, - const void* alpha, - bool clamping, // Set to false for Bwd and WrW convolutions. - const TensorDescriptor& srcDesc, - ConstData_t src, - const TensorDescriptor& dstDesc, - Data_t dst, - int srcOffset = 0, - int dstOffset = 0); - -void TransformTensor(const Handle& handle, - const void* alpha, - const TensorDescriptor& xDesc, - ConstData_t x, - const void* beta, - const TensorDescriptor& yDesc, - Data_t y, - size_t Xoffset = 0, - size_t Yoffset = 0); +MIOPEN_INTERNALS_EXPORT void ScaleTensor(const Handle& handle, + const TensorDescriptor& yDesc, + Data_t y, + const void* alpha, + int offset = 0); + +MIOPEN_INTERNALS_EXPORT void SetTensor(const Handle& handle, + const TensorDescriptor& yDesc, + Data_t y, + const void* alpha, + int offset = 0); + +MIOPEN_INTERNALS_EXPORT void OpTensor(const Handle& handle, + miopenTensorOp_t tensorOp, + const void* alpha0, + const TensorDescriptor& aTensorDesc, + ConstData_t ATensor, + const void* alpha1, + const TensorDescriptor& bTensorDesc, + ConstData_t BTensor, + const void* beta, + const TensorDescriptor& cTensorDesc, + Data_t CTensor, + size_t Aoffset = 0, + size_t Boffset = 0, + size_t Coffset = 0, + bool nonStandardSquash = false); + +MIOPEN_INTERNALS_EXPORT void CopyTensor(const Handle& handle, + const TensorDescriptor& srcDesc, + ConstData_t src, + const TensorDescriptor& dstDesc, + Data_t dst, + int srcOffset = 0, + int dstOffset = 0, + bool forseAsync = false); + +MIOPEN_INTERNALS_EXPORT void CastTensor(const Handle& handle, + const void* alpha, + bool clamping, // Set to false for Bwd and WrW convolutions. + const TensorDescriptor& srcDesc, + ConstData_t src, + const TensorDescriptor& dstDesc, + Data_t dst, + int srcOffset = 0, + int dstOffset = 0); + +MIOPEN_INTERNALS_EXPORT void TransformTensor(const Handle& handle, + const void* alpha, + const TensorDescriptor& xDesc, + ConstData_t x, + const void* beta, + const TensorDescriptor& yDesc, + Data_t y, + size_t Xoffset = 0, + size_t Yoffset = 0); } // namespace miopen #endif // GUARD_MIOPEN_TENSOR_OPPS_HPP_ diff --git a/src/include/miopen/tensor_view_utils.hpp b/src/include/miopen/tensor_view_utils.hpp new file mode 100644 index 0000000000..9f7430ba8a --- /dev/null +++ b/src/include/miopen/tensor_view_utils.hpp @@ -0,0 +1,80 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +#ifndef MIOPEN_TENSOR_VIEW_UTIL_HPP_ +#define MIOPEN_TENSOR_VIEW_UTIL_HPP_ + +#include +#include "../../kernels/tensor_view.hpp" + +namespace miopen { + +template +inline tensor_view_t get_inner_expanded_tv(const TensorDescriptor Desc) +{ + auto dims = Desc.GetLengths(); + auto strides = Desc.GetStrides(); + + tensor_view_t tensor_view; + for(size_t i = 0; i < N; ++i) + { + if(i < dims.size()) + { + tensor_view.stride[i] = strides[i]; + tensor_view.size[i] = dims[i]; + } + else + { + tensor_view.stride[i] = (i == 0 ? 1 : strides[i - 1]); + tensor_view.size[i] = 1; + } + } + return tensor_view; +} + +template +inline void slice_tv(tensor_view_t& tensor_view, int32_t sliceCount, const int32_t* slices) +{ + for(int32_t i = 0; i < sliceCount; i++) + { + int32_t dim = slices[4 * i + 0]; + int32_t start = slices[4 * i + 1]; + int32_t end = slices[4 * i + 2]; + int32_t step = slices[4 * i + 3]; + + if(end > static_cast(tensor_view.size[dim])) + end = tensor_view.size[dim]; + + auto len = end - start; + + tensor_view.size[dim] = (len + step - 1) / step; + tensor_view.stride[dim] *= step; + } +} + +} // namespace miopen + +#endif // MIOPEN_TENSOR_REORDER_UTIL_HPP_ diff --git a/src/include/miopen/tmp_dir.hpp b/src/include/miopen/tmp_dir.hpp index a220b06230..c2583254aa 100644 --- a/src/include/miopen/tmp_dir.hpp +++ b/src/include/miopen/tmp_dir.hpp @@ -3,10 +3,11 @@ #include #include +#include namespace miopen { -struct TmpDir +struct MIOPEN_INTERNALS_EXPORT TmpDir { fs::path path; explicit TmpDir(std::string_view prefix = ""); diff --git a/src/include/miopen/util.hpp b/src/include/miopen/util.hpp index d6aee6c645..ac5503d2b0 100644 --- a/src/include/miopen/util.hpp +++ b/src/include/miopen/util.hpp @@ -35,84 +35,84 @@ namespace miopen { struct Handle; -float Im2ColGPU( - const Handle& handle, - std::size_t spatial_dim, - ConstData_t im, - std::size_t im_offset, - std::size_t in_c, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial, - const std::vector& pad_spatial, - const std::vector& stride_spatial, - const std::vector& dilation_spatial, - Data_t col, - miopenDataType_t type); +MIOPEN_INTERNALS_EXPORT float +Im2ColGPU(const Handle& handle, + std::size_t spatial_dim, + ConstData_t im, + std::size_t im_offset, + std::size_t in_c, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial, + const std::vector& pad_spatial, + const std::vector& stride_spatial, + const std::vector& dilation_spatial, + Data_t col, + miopenDataType_t type); -float Col2ImGPU( - const Handle& handle, - std::size_t spatial_dim, - ConstData_t col, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial, - const std::vector& pad_spatial, - const std::vector& stride_spatial, - const std::vector& dilation_spatial, - std::size_t in_c, - const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial, - Data_t im, - std::size_t im_offset, - miopenDataType_t type); +MIOPEN_INTERNALS_EXPORT float +Col2ImGPU(const Handle& handle, + std::size_t spatial_dim, + ConstData_t col, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial, + const std::vector& pad_spatial, + const std::vector& stride_spatial, + const std::vector& dilation_spatial, + std::size_t in_c, + const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial, + Data_t im, + std::size_t im_offset, + miopenDataType_t type); -float transpose_NCHW2CNHW(const Handle& handle, - int n, - int c, - int h_in, - int w_in, - int h_out, - int w_out, - ConstData_t in, - Data_t out, - std::size_t in_offset, - std::size_t out_offset, - int h_stride, - int w_stride, - miopenDataType_t type); +MIOPEN_INTERNALS_EXPORT float transpose_NCHW2CNHW(const Handle& handle, + int n, + int c, + int h_in, + int w_in, + int h_out, + int w_out, + ConstData_t in, + Data_t out, + std::size_t in_offset, + std::size_t out_offset, + int h_stride, + int w_stride, + miopenDataType_t type); -float transpose_CNHW2NCHW(const Handle& handle, - int n, - int c, - int h_out, - int w_out, - int h_in, - int w_in, - ConstData_t in, - Data_t out, - std::size_t in_offset, - std::size_t out_offset, - int h_stride, - int w_stride, - miopenDataType_t type); +MIOPEN_INTERNALS_EXPORT float transpose_CNHW2NCHW(const Handle& handle, + int n, + int c, + int h_out, + int w_out, + int h_in, + int w_in, + ConstData_t in, + Data_t out, + std::size_t in_offset, + std::size_t out_offset, + int h_stride, + int w_stride, + miopenDataType_t type); -float transpose_NCHW2Vec(const Handle& handle, - const std::vector& lens, - ConstData_t in, - Data_t out, - std::size_t vec_size, - bool trans, - bool forward, - const void* alpha, - const void* beta); +MIOPEN_INTERNALS_EXPORT float transpose_NCHW2Vec(const Handle& handle, + const std::vector& lens, + ConstData_t in, + Data_t out, + std::size_t vec_size, + bool trans, + bool forward, + const void* alpha, + const void* beta); -float transpose_packed_MN2NM(const Handle& handle, - int m, - int n, - std::size_t in_offset, - std::size_t out_offset, - ConstData_t in, - Data_t out, - miopenDataType_t type); +MIOPEN_INTERNALS_EXPORT float transpose_packed_MN2NM(const Handle& handle, + int m, + int n, + std::size_t in_offset, + std::size_t out_offset, + ConstData_t in, + Data_t out, + miopenDataType_t type); } // namespace miopen #endif // _MIOPEN_UTIL_HPP_ diff --git a/src/include/miopen/utility/transposing_solver.hpp b/src/include/miopen/utility/transposing_solver.hpp index 41d7f9f328..99978a43be 100644 --- a/src/include/miopen/utility/transposing_solver.hpp +++ b/src/include/miopen/utility/transposing_solver.hpp @@ -292,7 +292,7 @@ struct ProblemTensorTransposeDescriptor inline TensorDescriptor Transpose(const TensorDescriptor& in) const { - const auto labels = tensor_layout_get_default(in.GetSize()); + const auto labels = tensor_layout_get_default(in.GetNumDims()); auto derived_strides = std::vector{}; tensor_layout_to_strides( in.GetLengths(), labels, SyncLayoutDims(labels.c_str(), to), derived_strides); @@ -441,7 +441,7 @@ struct TransposingSolver : Base for(auto transpose : Derived::GetTransposes()) { decltype(auto) descriptor = (problem.*(transpose.cdescriptor))(); - const auto labels = tensor_layout_get_default(descriptor.GetSize()); + const auto labels = tensor_layout_get_default(descriptor.GetNumDims()); const auto layout = descriptor.GetLayout(labels); const auto to = SyncLayoutDims(layout.c_str(), transpose.to); @@ -488,7 +488,7 @@ struct TransposingSolver : Base for(auto transpose : Derived::GetTransposes()) { const auto& descriptor = (problem.*(transpose.cdescriptor))(); - const auto labels = tensor_layout_get_default(descriptor.GetSize()); + const auto labels = tensor_layout_get_default(descriptor.GetNumDims()); const auto layout = descriptor.GetLayout(labels); const auto to = SyncLayoutDims(labels.c_str(), transpose.to); diff --git a/src/include/miopen/visit_float.hpp b/src/include/miopen/visit_float.hpp index b242988383..da08db2a28 100644 --- a/src/include/miopen/visit_float.hpp +++ b/src/include/miopen/visit_float.hpp @@ -28,11 +28,7 @@ #define GUARD_MLOPEN_VISIT_FLOAT_HPP #include -#if !defined(_WIN32) #include -#else -#include -#endif #include namespace miopen { @@ -91,6 +87,10 @@ void visit_float(miopenDataType_t t, F f) f(as_float{}); break; } + case miopenInt64: { + f(as_float{}); + break; + } } } diff --git a/src/include/miopen/write_file.hpp b/src/include/miopen/write_file.hpp index 8320aed3da..4ed51dfb83 100644 --- a/src/include/miopen/write_file.hpp +++ b/src/include/miopen/write_file.hpp @@ -46,6 +46,13 @@ inline void WriteFile(const std::vector& content, const fs::path& name) MIOPEN_THROW("Failed to write to file"); } +inline void WriteFile(const std::vector& content, const fs::path& name) +{ + std::ofstream f{name, std::ios::binary}; + if(f.write(reinterpret_cast(content.data()), content.size()).fail()) + MIOPEN_THROW("Failed to write to file"); +} + } // namespace miopen #endif diff --git a/src/invoker_cache.cpp b/src/invoker_cache.cpp index 6fd7dab2c3..2c155592be 100644 --- a/src/invoker_cache.cpp +++ b/src/invoker_cache.cpp @@ -29,32 +29,32 @@ namespace miopen { -boost::optional InvokerCache::operator[](const Key& key) const +std::optional InvokerCache::operator[](const Key& key) const { const auto item = invokers.find(key.first); if(item == invokers.end()) - return boost::none; + return std::nullopt; const auto& item_invokers = item->second.invokers; const auto invoker = item_invokers.find(key.second); if(invoker == item_invokers.end()) - return boost::none; + return std::nullopt; return invoker->second; } -boost::optional InvokerCache::GetFound1_0(const std::string& network_config, - const std::string& algorithm) const +std::optional InvokerCache::GetFound1_0(const std::string& network_config, + const std::string& algorithm) const { const auto item = invokers.find(network_config); if(item == invokers.end()) { MIOPEN_LOG_I2("No invokers found for " << network_config); - return boost::none; + return std::nullopt; } if(item->second.found_1_0.empty()) { MIOPEN_LOG_I2("Invokers found for " << network_config << " but there is no find 1.0 result."); - return boost::none; + return std::nullopt; } const auto& item_invokers = item->second.invokers; const auto& found_1_0_ids = item->second.found_1_0; @@ -63,7 +63,7 @@ boost::optional InvokerCache::GetFound1_0(const std::string& net { MIOPEN_LOG_I2("Invokers found for " << network_config << " but there is no one with an algorithm " << algorithm); - return boost::none; + return std::nullopt; } const auto invoker = item_invokers.find(found_1_0_id->second); if(invoker == item_invokers.end()) @@ -74,21 +74,20 @@ boost::optional InvokerCache::GetFound1_0(const std::string& net return invoker->second; } -boost::optional -InvokerCache::GetFound1_0SolverId(const std::string& network_config, - const std::string& algorithm) const +std::optional InvokerCache::GetFound1_0SolverId(const std::string& network_config, + const std::string& algorithm) const { const auto item = invokers.find(network_config); if(item == invokers.end()) { MIOPEN_LOG_I2("No invokers found for " << network_config); - return boost::none; + return std::nullopt; } if(item->second.found_1_0.empty()) { MIOPEN_LOG_I2("Invokers found for " << network_config << " but there is no find 1.0 result."); - return boost::none; + return std::nullopt; } const auto& found_1_0_ids = item->second.found_1_0; const auto found_1_0_id = found_1_0_ids.find(algorithm); @@ -96,7 +95,7 @@ InvokerCache::GetFound1_0SolverId(const std::string& network_config, { MIOPEN_LOG_I2("Invokers found for " << network_config << " but there is no one with an algorithm " << algorithm); - return boost::none; + return std::nullopt; } return found_1_0_id->second; } diff --git a/src/kern_db.cpp b/src/kern_db.cpp index 4fb7aec98f..947784a819 100644 --- a/src/kern_db.cpp +++ b/src/kern_db.cpp @@ -23,17 +23,18 @@ * SOFTWARE. * *******************************************************************************/ +#include "miopen/bz2.hpp" #include namespace miopen { -KernDb::KernDb(DbKinds db_kind, const std::string& filename_, bool is_system_) +KernDb::KernDb(DbKinds db_kind, const fs::path& filename_, bool is_system_) : KernDb(db_kind, filename_, is_system_, compress, decompress) { } KernDb::KernDb( DbKinds db_kind, - const std::string& filename_, + const fs::path& filename_, bool is_system_, std::function(const std::vector&, bool*)> compress_fn_, std::function(const std::vector&, unsigned int)> decompress_fn_) @@ -49,7 +50,7 @@ KernDb::KernDb( if(filename.empty()) MIOPEN_LOG_I("database not present"); else - MIOPEN_LOG_I(filename + " database invalid"); + MIOPEN_LOG_I(filename << " database invalid"); return; } if(!is_system) diff --git a/src/kernel_cache.cpp b/src/kernel_cache.cpp index 1c02c7d802..034bfd62bf 100644 --- a/src/kernel_cache.cpp +++ b/src/kernel_cache.cpp @@ -71,13 +71,13 @@ const std::vector& KernelCache::GetKernels(const std::string& algorithm, return empty; } -bool KernelCache::HasProgram(const std::string& name, const std::string& params) const +bool KernelCache::HasProgram(const fs::path& name, const std::string& params) const { const auto key = std::make_pair(name, params); return program_map.count(key) > 0; } -void KernelCache::ClearProgram(const std::string& name, const std::string& params) +void KernelCache::ClearProgram(const fs::path& name, const std::string& params) { if(HasProgram(name, params)) { @@ -86,7 +86,7 @@ void KernelCache::ClearProgram(const std::string& name, const std::string& param } } -void KernelCache::AddProgram(Program prog, const std::string& program_name, std::string params) +void KernelCache::AddProgram(Program prog, const fs::path& program_name, std::string params) { program_map[std::make_pair(program_name, params)] = prog; } @@ -94,33 +94,50 @@ void KernelCache::AddProgram(Program prog, const std::string& program_name, std: Kernel KernelCache::AddKernel(const Handle& h, const std::string& algorithm, const std::string& network_config, - const std::string& program_name, + const fs::path& program_name, const std::string& kernel_name, const std::vector& vld, const std::vector& vgd, std::string params, std::size_t cache_index, - const std::string& kernel_src) + const std::string& kernel_src, + Program* program_out) { const std::pair key = std::make_pair(algorithm, network_config); if(!network_config.empty() || !algorithm.empty()) // Don't log only _empty_ keys. MIOPEN_LOG_I2("Key: " << key.first << " \"" << key.second << '\"'); - Program program; - - auto program_it = program_map.find(std::make_pair(program_name, params)); - if(program_it != program_map.end()) - { - program = program_it->second; - } - else - { - program = h.LoadProgram(program_name, params, kernel_src); - program_map[std::make_pair(program_name, params)] = program; - } + const auto program = [&] { + auto program_it = program_map.find(std::make_pair(program_name, params)); + if(program_it != program_map.end()) + { + auto& program = program_it->second; + + if(program_out != nullptr && !program.IsCodeObjectInMemory() && + !program.IsCodeObjectInFile()) + { + // We need the binaries attached to the program. + // This may happen if someone calls immediate mode and then find 2.0 with request + // for binaries. + program = h.LoadProgram(program_name, params, kernel_src, true); + } + + return program; + } + else + { + auto program = h.LoadProgram(program_name, params, kernel_src, program_out != nullptr); + + program_map[std::make_pair(program_name, params)] = program; + return program; + } + }(); + + if(program_out != nullptr) + *program_out = program; Kernel kernel{}; - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); if(!arch.empty()) { kernel = Kernel{program, kernel_name}; diff --git a/src/kernels/Conv_Winograd_Fury_v1_1_1_gfx11_fp16_fp16acc_f2x3_stride1.inc b/src/kernels/Conv_Winograd_Fury_v1_1_1_gfx11_fp16_fp16acc_f2x3_stride1.inc deleted file mode 100644 index e1f9a72aac..0000000000 --- a/src/kernels/Conv_Winograd_Fury_v1_1_1_gfx11_fp16_fp16acc_f2x3_stride1.inc +++ /dev/null @@ -1,3250 +0,0 @@ -/******************************************************************************* - * - * MIT License - * - * Copyright (c) 2023 Advanced Micro Devices, Inc. - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - * - *******************************************************************************/ - -.macro _v_mad_u64_u32_gfx11 vdst:req, vsrc0:req, vsrc1:req, vsrc2:req - .long 0xD6FE6A00 + (\vdst << 0) - .long 0x00000000 + ((\vsrc0 + (1 << 8)) << 0) + ((\vsrc1 + (1 << 8)) << 9) + ((\vsrc2 + (1 << 8)) << 18) -.endm - -.macro _s_mov_b32_4_gfx11 vdst:req - .long 0xBE8000FF + (\vdst << 16) - .long 0x00000004 -.endm - -.macro _v_cmp_f16_1_gfx11 vsrc:req - .long 0xD404006A - .long 0x0001FE00 + (\vsrc << 0) - .long 0x00003C00 -.endm - -.macro _v_cmp_u16_vs_gfx11 vsrc:req, src:req, vhl, shl - .long 0xD439006A + (\vhl << 12) + (\shl << 8) - .long 0x00000100 + (\vsrc << 0) + (\src << 9) -.endm - -.macro _v_pk_op_x16_gfx1x op:req vdst:req, vs0:req, src0:req, vs1:req, src1:req, neghi:req, neglo:req - .long 0xCC000000 | (\op << 16) | (\vdst << 0) | (\neghi << 8) - .long 0x18000000 | (\vs0 << (0+8)) | (\src0 << 0) | (\vs1 << (9+8)) | (\src1 << 9) | (\neglo << 29) -.endm - -.macro _v_pk_op_x16_v2_gfx1x op:req vdst:req, vsrc0:req, vsrc1:req - .long 0xCC000000 + (\op << 16) + (\vdst << 0) - .long 0x10020000 + ((\vsrc0 + 128) << 0) + (\vsrc1 << 9) -.endm - -.macro _v_pk_op_x16_v3_gfx1x op:req vdst:req, vsrc0:req, vsrc1:req - .long 0xCC000800 + (\op << 16) + (\vdst << 0) - .long 0x18000100 + (\vsrc0 << 0) + (\vsrc1 << 9) -.endm - -.macro _v_pk_op_x16_v4_gfx1x op:req vdst:req, imm0:req, vsrc1:req - .long 0xCC000000 + (\op << 16) + (\vdst << 0) - .long 0x180200FF + (\vsrc1 << 9) - .long \imm0 -.endm - -.macro _v_pk_add_f16_0_gfx1x vdst:req, vsrc0:req - .long 0xCC0F0000 + (\vdst << 0) - .long 0x00010100 + (\vsrc0 << 0) -.endm - -.macro _v_pk_mul_f16_05_gfx1x vdst:req, vsrc0:req - .long 0xCC100000 + (\vdst << 0) - .long 0x0801E100 + (\vsrc0 << 0) -.endm - -s_version 0x2006 -s_set_inst_prefetch_distance 0x3 -s_mov_b32 s0, 0 -s_load_b512 s[8:23], s[2:3], null -s_load_b512 s[24:39], s[2:3], 0x40 -s_load_b256 s[40:47], s[2:3], 0x80 -s_load_b128 s[48:51], s[2:3], 0xa0 -s_load_b64 s[52:53], s[2:3], 0xb0 -v_and_b32_e32 v9, 0xff, v0 -v_lshrrev_b32_e32 v10, 1, v9 -v_and_b32_e32 v11, 1, v0 -v_add_nc_u32_e64 v6, v10, 32 -v_bfi_b32 v2, 31, v9, v10 -v_bfe_u32 v4, v9, 5, 1 -v_bfi_b32 v2, 0xbf, v2, v6 -v_and_b32_e32 v8, 0x60, v10 -v_lshlrev_b32_e32 v2, 4, v2 -v_add_lshl_u32 v3, v10, v8, 4 -v_mad_u32_u24 v2, v4, 0x800, v2 -v_mad_u32_u16 v3, 0x1040, v11, v3 -v_xor_b32_e32 v1, 0x600, v2 -v_cmp_lt_u32_e64 vcc, v0, 0x100 -s_cbranch_vccz 1624 -v_bfe_u32 v67, v0, 6, 1 -v_and_b32_e32 v65, 63, v0 -v_cmp_eq_u32_e64 vcc, v67, 1 -v_cndmask_b32_e64 v67, 0, 0x400, vcc -v_cndmask_b32_e64 v84, 0, 0x100, vcc -v_lshl_add_u32 v66, v65, 2, v84 -v_lshl_add_u32 v65, v65, 4, v67 -s_mov_b32 s6, 4 -s_mov_b32 s7, 0 -v_mov_b32_e32 v48, -1 -s_mov_b32 s55, 0xbc00c000 -v_readfirstlane_b32 s69, v0 -s_and_b32 null, 64, s69 -s_cmov_b32 s55, 0x3c00c000 -s_waitcnt vmcnt(0) expcnt(0) lgkmcnt(0) -s_bitcmp1_b32 s40, 6 -s_cbranch_scc0 14 -s_load_b64 s[20:21], s[20:21], null -s_load_b64 s[22:23], s[22:23], null -s_load_b64 s[24:25], s[24:25], null -s_cmp_eq_u64 0, s[36:37] -s_cbranch_scc1 2 -s_load_b64 s[36:37], s[36:37], null -s_cmp_eq_u64 0, s[38:39] -s_cbranch_scc1 2 -s_load_b64 s[38:39], s[38:39], null -s_lshl_b32 s68, s14, 1 -s_lshl_b32 s70, s68, 3 -s_and_b32 null, 0x80, s69 -s_cselect_b32 s70, s70, 0 -s_cselect_b32 s5, 8, 0 -s_sub_u32 s5, s28, s5 -s_cmov_b32 s5, 0 -s_min_u32 s5, 8, s5 -s_mov_b32 s54, 0x19014000 -s_waitcnt vmcnt(0) expcnt(0) lgkmcnt(0) -s_bitcmp1_b32 s40, 13 -s_cbranch_scc0 14 -s_add_u32 s20, s20, s44 -s_addc_u32 s21, s21, s45 -s_add_u32 s22, s22, s46 -s_addc_u32 s23, s23, s47 -s_add_u32 s24, s24, s48 -s_addc_u32 s25, s25, s49 -s_cmp_eq_u64 0, s[36:37] -s_cselect_b64 s[50:51], 0, s[50:51] -s_add_u32 s36, s36, s50 -s_addc_u32 s37, s37, s51 -s_cmp_eq_u64 0, s[38:39] -s_cselect_b64 s[52:53], 0, s[52:53] -s_add_u32 s38, s38, s52 -s_addc_u32 s39, s39, s53 -s_and_b32 s21, s21, 0xffff -s_add_u32 s21, s21, 0x20000 -s_add_u32 s20, s20, s70 -s_addc_u32 s21, s21, 0 -s_mov_b64 s[56:57], s[20:21] -s_mov_b32 s58, 0x80000000 -s_mov_b32 s59, 0 -s_getpc_b64 s[66:67] -v_cmp_lt_u32_e64 vcc, v0, 0x80 -s_cmp_gt_u32 vcc_lo, 0 -s_mov_b32 s69, 0x1334 -s_cmov_b32 s69, 0xe48 -s_mov_b32 s70, 0x158c -s_cmov_b32 s70, 0x10a0 -s_add_u32 s64, s66, s69 -s_addc_u32 s65, s67, 0 -s_add_u32 s66, s66, s70 -s_addc_u32 s67, s67, 0 -v_mov_b32_e32 v177, 0x38003800 -v_readfirstlane_b32 s69, v0 -v_bfe_u32 v176, v0, 2, 3 -v_xor_b32_dpp v175, v0, v0 quad_perm:[0,0,3,1] row_mask:0xf bank_mask:0xf -v_lshlrev_b32_e32 v176, 1, v176 -v_mad_i32_i24 v67, v175, s34, s35 -v_mad_u32_u24 v67, s32, v176, v67 -v_lshl_add_u32 v84, s33, 0, v67 -v_lshl_add_u32 v101, s33, 1, v67 -v_cmp_lt_u32_e64 vcc, v176, s28 -v_cndmask_b32_e32 v67, 0x80000000, v67, vcc -v_cndmask_b32_e32 v84, 0x80000000, v84, vcc -v_cndmask_b32_e32 v101, 0x80000000, v101, vcc -v_cmp_lt_u32_e64 vcc, v175, s30 -v_cndmask_b32_e32 v67, 0x80000000, v67, vcc -v_cndmask_b32_e32 v84, 0x80000000, v84, vcc -v_cndmask_b32_e32 v101, 0x80000000, v101, vcc -v_add_nc_u16 v176, v176, 1 -v_cmp_lt_u32_e64 vcc, 1, s29 -v_cndmask_b32_e32 v84, 0x80000000, v84, vcc -v_cmp_lt_u32_e64 vcc, 2, s29 -v_cndmask_b32_e32 v101, 0x80000000, v101, vcc -v_add_nc_u32_e64 v102, v67, s32 -v_add_nc_u32_e64 v103, v84, s32 -v_add_nc_u32_e64 v136, v101, s32 -v_cmp_lt_u32_e64 vcc, v176, s28 -v_cndmask_b32_e32 v102, 0x80000000, v102, vcc -v_cndmask_b32_e32 v103, 0x80000000, v103, vcc -v_cndmask_b32_e32 v136, 0x80000000, v136, vcc -v_xor_b32_dpp v180, v0, v0 quad_perm:[0,0,3,1] row_mask:0xf bank_mask:0xf -v_bfe_u32 v181, v0, 2, 2 -v_mul_u32_u24_e64 v153, v180, 16 -v_bfe_u32 v180, v0, 4, 2 -v_mad_u32_u16 v153, v181, 4, v153 -v_bfe_u32 v175, v0, 1, 3 -v_bfe_u32 v181, v0, 6, 2 -v_mul_u32_u24_e64 v170, v175, 0x60 -v_mad_u32_u16 v153, v180, 48, v153 -v_bfe_u32 v175, v0, 6, 2 -v_mad_u32_u16 v153, v181, 0xc0, v153 -v_mad_u32_u16 v170, v175, 0x300, v170 -s_mov_b64 s[72:73], s[24:25] -s_and_b32 s73, s73, 0xffff -s_add_u32 s73, s73, 0x20000 -s_mov_b32 s74, 0x80000000 -s_mov_b32 s75, 0x11014000 -s_lshl_b32 s76, s31, 1 -s_lshr_b32 s69, s69, 5 -s_mul_i32 s77, s76, s69 -s_mul_i32 s70, s76, 7 -s_mul_i32 s71, s76, 1 -s_sub_u32 s69, s27, s69 -s_sub_u32 s69, s69, 1 -s_cselect_b32 s75, 0, s75 -s_add_u32 s72, s72, s77 -s_addc_u32 s73, s73, 0 -s_mov_b32 exec_hi, 0 -buffer_load_d16_b16 v49, v67, s[72:75], 0 idxen -buffer_load_d16_b16 v50, v84, s[72:75], 0 idxen -buffer_load_d16_b16 v52, v101, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v49, v102, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v50, v103, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v52, v136, s[72:75], 0 idxen -s_sub_u32 s69, s69, 1 -s_cselect_b32 s75, 0, s75 -s_add_u32 s72, s72, s71 -s_addc_u32 s73, s73, 0 -buffer_load_d16_b16 v57, v67, s[72:75], 0 idxen -buffer_load_d16_b16 v58, v84, s[72:75], 0 idxen -buffer_load_d16_b16 v60, v101, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v57, v102, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v58, v103, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v60, v136, s[72:75], 0 idxen -s_sub_u32 s69, s69, 7 -s_cselect_b32 s75, 0, s75 -s_add_u32 s72, s72, s70 -s_addc_u32 s73, s73, 0 -s_mov_b32 exec_hi, -1 -s_mov_b32 exec_lo, 0 -buffer_load_d16_b16 v49, v67, s[72:75], 0 idxen -buffer_load_d16_b16 v50, v84, s[72:75], 0 idxen -buffer_load_d16_b16 v52, v101, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v49, v102, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v50, v103, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v52, v136, s[72:75], 0 idxen -s_sub_u32 s69, s69, 1 -s_cselect_b32 s75, 0, s75 -s_add_u32 s72, s72, s71 -s_addc_u32 s73, s73, 0 -buffer_load_d16_b16 v57, v67, s[72:75], 0 idxen -buffer_load_d16_b16 v58, v84, s[72:75], 0 idxen -buffer_load_d16_b16 v60, v101, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v57, v102, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v58, v103, s[72:75], 0 idxen -buffer_load_d16_hi_b16 v60, v136, s[72:75], 0 idxen -s_sub_u32 s69, s69, 7 -s_cselect_b32 s75, 0, s75 -s_add_u32 s72, s72, s70 -s_addc_u32 s73, s73, 0 -s_mov_b32 exec_lo, -1 -s_waitcnt vmcnt(0) -_v_pk_op_x16_gfx1x 16, 51, 1, 52, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v51, v50, v177, v51 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v51, v49, v177 -_v_pk_op_x16_gfx1x 15, 50, 1, 50, 1, 51, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 59, 1, 60, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v59, v58, v177, v59 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v59, v57, v177 -_v_pk_op_x16_gfx1x 15, 58, 1, 58, 1, 59, 0, 0 // op16_add_f -ds_store_b32 v153, v49 offset:43264 -ds_store_b32 v153, v50 offset:44032 -ds_store_b32 v153, v52 offset:44800 -ds_store_b32 v153, v51 offset:45568 -ds_store_b32 v153, v57 offset:46336 -ds_store_b32 v153, v58 offset:47104 -ds_store_b32 v153, v60 offset:47872 -ds_store_b32 v153, v59 offset:48640 -s_waitcnt lgkmcnt(0) -s_barrier -s_mov_b32 exec_hi, 0 -ds_load_b128 v[49:52], v170 offset:43264 -ds_load_b128 v[53:56], v170 offset:43312 -ds_load_b128 v[68:71], v170 offset:43280 -ds_load_b128 v[72:75], v170 offset:43328 -ds_load_b128 v[104:107], v170 offset:43296 -ds_load_b128 v[108:111], v170 offset:43344 -ds_load_b128 v[57:60], v170 offset:46336 -ds_load_b128 v[61:64], v170 offset:46384 -ds_load_b128 v[76:79], v170 offset:46352 -ds_load_b128 v[80:83], v170 offset:46400 -ds_load_b128 v[112:115], v170 offset:46368 -ds_load_b128 v[116:119], v170 offset:46416 -s_mov_b32 exec_hi, -1 -s_waitcnt lgkmcnt(0) -_v_pk_op_x16_gfx1x 16, 85, 1, 104, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 86, 1, 105, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 87, 1, 106, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 88, 1, 107, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v85, v68, v177, v85 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v86, v69, v177, v86 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v87, v70, v177, v87 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v88, v71, v177, v88 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v85, v49, v177 -v_pk_fmac_f16 v86, v50, v177 -v_pk_fmac_f16 v87, v51, v177 -v_pk_fmac_f16 v88, v52, v177 -_v_pk_op_x16_gfx1x 15, 68, 1, 68, 1, 85, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 69, 1, 86, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 70, 1, 87, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 71, 1, 71, 1, 88, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 89, 1, 108, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 90, 1, 109, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 91, 1, 110, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 92, 1, 111, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v89, v72, v177, v89 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v90, v73, v177, v90 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v91, v74, v177, v91 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v92, v75, v177, v92 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v89, v53, v177 -v_pk_fmac_f16 v90, v54, v177 -v_pk_fmac_f16 v91, v55, v177 -v_pk_fmac_f16 v92, v56, v177 -_v_pk_op_x16_gfx1x 15, 72, 1, 72, 1, 89, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 73, 1, 73, 1, 90, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 74, 1, 74, 1, 91, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 75, 1, 75, 1, 92, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 93, 1, 112, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 94, 1, 113, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 95, 1, 114, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 96, 1, 115, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v93, v76, v177, v93 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v94, v77, v177, v94 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v95, v78, v177, v95 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v96, v79, v177, v96 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v93, v57, v177 -v_pk_fmac_f16 v94, v58, v177 -v_pk_fmac_f16 v95, v59, v177 -v_pk_fmac_f16 v96, v60, v177 -_v_pk_op_x16_gfx1x 15, 76, 1, 76, 1, 93, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 77, 1, 77, 1, 94, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 78, 1, 78, 1, 95, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 79, 1, 79, 1, 96, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 97, 1, 116, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 98, 1, 117, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 99, 1, 118, 1, 177, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 100, 1, 119, 1, 177, 0, 0 // op16_mul_f -v_pk_fma_f16 v97, v80, v177, v97 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v98, v81, v177, v98 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v99, v82, v177, v99 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fma_f16 v100, v83, v177, v100 neg_lo:[0,1,0] neg_hi:[0,1,0] -v_pk_fmac_f16 v97, v61, v177 -v_pk_fmac_f16 v98, v62, v177 -v_pk_fmac_f16 v99, v63, v177 -v_pk_fmac_f16 v100, v64, v177 -_v_pk_op_x16_gfx1x 15, 80, 1, 80, 1, 97, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 81, 1, 81, 1, 98, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 82, 1, 82, 1, 99, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 83, 1, 83, 1, 100, 0, 0 // op16_add_f -_s_mov_b32_4_gfx11 60 -s_setprio 2 -s_waitcnt vmcnt(0) -_v_pk_op_x16_gfx1x 15, 36, 1, 4, 1, 5, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 37, 1, 8, 1, 9, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 38, 1, 12, 1, 13, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 39, 1, 16, 1, 17, 1, 1 // op16_add_f -v_pk_fma_f16 v40, v4, s55, v6 op_sel:[0,1,0] -v_pk_fma_f16 v41, v8, s55, v10 op_sel:[0,1,0] -v_pk_fma_f16 v42, v12, s55, v14 op_sel:[0,1,0] -v_pk_fma_f16 v43, v16, s55, v18 op_sel:[0,1,0] -buffer_load_d16_b16 v5, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v4, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v13, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v12, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v5, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v4, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v13, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v12, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v9, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v8, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v17, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v16, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v9, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v8, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v17, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v16, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[64:65] -s_branch 3701 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -v_pk_fma_f16 v36, v6, s55, v40 op_sel_hi:[1,0,1] -v_pk_fma_f16 v37, v10, s55, v41 op_sel_hi:[1,0,1] -v_pk_fma_f16 v38, v14, s55, v42 op_sel_hi:[1,0,1] -v_pk_fma_f16 v39, v18, s55, v43 op_sel_hi:[1,0,1] -_v_pk_op_x16_gfx1x 15, 40, 1, 6, 1, 7, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 41, 1, 10, 1, 11, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 42, 1, 14, 1, 15, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 43, 1, 18, 1, 19, 1, 1 // op16_add_f -buffer_load_d16_b16 v6, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v7, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v14, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v15, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v6, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v7, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v14, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v15, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v10, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v11, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v18, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v19, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v10, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v11, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v18, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v19, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[66:67] -s_branch 3632 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -s_waitcnt vmcnt(0) -_v_pk_op_x16_gfx1x 15, 36, 1, 23, 1, 22, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 37, 1, 27, 1, 26, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 38, 1, 31, 1, 30, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 39, 1, 35, 1, 34, 1, 1 // op16_add_f -v_pk_fma_f16 v40, v23, s55, v20 op_sel:[0,1,0] -v_pk_fma_f16 v41, v27, s55, v24 op_sel:[0,1,0] -v_pk_fma_f16 v42, v31, s55, v28 op_sel:[0,1,0] -v_pk_fma_f16 v43, v35, s55, v32 op_sel:[0,1,0] -buffer_load_d16_b16 v22, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v23, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v30, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v31, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v22, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v23, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v30, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v31, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v26, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v27, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v34, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v35, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v26, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v27, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v34, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v35, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[64:65] -s_branch 3562 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -v_pk_fma_f16 v36, v20, s55, v40 op_sel_hi:[1,0,1] -v_pk_fma_f16 v37, v24, s55, v41 op_sel_hi:[1,0,1] -v_pk_fma_f16 v38, v28, s55, v42 op_sel_hi:[1,0,1] -v_pk_fma_f16 v39, v32, s55, v43 op_sel_hi:[1,0,1] -_v_pk_op_x16_gfx1x 15, 40, 1, 20, 1, 21, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 41, 1, 24, 1, 25, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 42, 1, 28, 1, 29, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 43, 1, 32, 1, 33, 1, 1 // op16_add_f -buffer_load_d16_b16 v20, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v21, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v28, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v29, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v20, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v21, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v28, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v29, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v24, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v25, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v32, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v33, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v24, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v25, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v32, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v33, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[66:67] -s_branch 3493 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -s_waitcnt vmcnt(0) -_v_pk_op_x16_gfx1x 15, 36, 1, 4, 1, 5, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 37, 1, 8, 1, 9, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 38, 1, 7, 1, 6, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 39, 1, 11, 1, 10, 1, 1 // op16_add_f -v_pk_fma_f16 v40, v4, s55, v13 op_sel:[0,1,0] -v_pk_fma_f16 v41, v8, s55, v17 op_sel:[0,1,0] -v_pk_fma_f16 v42, v7, s55, v14 op_sel:[0,1,0] -v_pk_fma_f16 v43, v11, s55, v18 op_sel:[0,1,0] -buffer_load_d16_b16 v5, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v4, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v6, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v7, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v5, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v4, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v6, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v7, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v9, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v8, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v10, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v11, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v9, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v8, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v10, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v11, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[64:65] -s_branch 3423 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -v_pk_fma_f16 v36, v13, s55, v40 op_sel_hi:[1,0,1] -v_pk_fma_f16 v37, v17, s55, v41 op_sel_hi:[1,0,1] -v_pk_fma_f16 v38, v14, s55, v42 op_sel_hi:[1,0,1] -v_pk_fma_f16 v39, v18, s55, v43 op_sel_hi:[1,0,1] -_v_pk_op_x16_gfx1x 15, 40, 1, 13, 1, 12, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 41, 1, 17, 1, 16, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 42, 1, 14, 1, 15, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 43, 1, 18, 1, 19, 1, 1 // op16_add_f -buffer_load_d16_b16 v13, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v12, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v14, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v15, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v13, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v12, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v14, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v15, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v17, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v16, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v18, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v19, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v17, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v16, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v18, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v19, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[66:67] -s_branch 3354 -_s_mov_b32_4_gfx11 60 -s_setprio 2 -s_waitcnt vmcnt(0) -_v_pk_op_x16_gfx1x 15, 36, 1, 23, 1, 22, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 37, 1, 27, 1, 26, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 38, 1, 21, 1, 20, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 39, 1, 25, 1, 24, 1, 1 // op16_add_f -v_pk_fma_f16 v40, v23, s55, v30 op_sel:[0,1,0] -v_pk_fma_f16 v41, v27, s55, v34 op_sel:[0,1,0] -v_pk_fma_f16 v42, v21, s55, v28 op_sel:[0,1,0] -v_pk_fma_f16 v43, v25, s55, v32 op_sel:[0,1,0] -buffer_load_d16_b16 v22, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v23, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v20, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v21, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v22, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v23, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v20, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v21, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v26, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v27, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v24, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v25, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v26, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v27, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v24, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v25, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[64:65] -s_branch 3284 -s_mov_b32 s60, 0xfffff754 -s_setprio 2 -v_pk_fma_f16 v36, v30, s55, v40 op_sel_hi:[1,0,1] -v_pk_fma_f16 v37, v34, s55, v41 op_sel_hi:[1,0,1] -v_pk_fma_f16 v38, v28, s55, v42 op_sel_hi:[1,0,1] -v_pk_fma_f16 v39, v32, s55, v43 op_sel_hi:[1,0,1] -_v_pk_op_x16_gfx1x 15, 40, 1, 30, 1, 31, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 41, 1, 34, 1, 35, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 42, 1, 28, 1, 29, 1, 1 // op16_add_f -_v_pk_op_x16_gfx1x 15, 43, 1, 32, 1, 33, 1, 1 // op16_add_f -buffer_load_d16_b16 v30, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v31, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v28, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v29, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v30, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v31, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v28, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v29, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_b16 v34, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v35, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v32, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_b16 v33, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -buffer_load_d16_hi_b16 v34, v171, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v35, v173, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v32, v172, s[56:59], 0 idxen glc dlc -buffer_load_d16_hi_b16 v33, v174, s[56:59], 0 idxen glc dlc -s_sub_u32 s1, s1, 1 -s_cselect_b32 s59, 0, s59 -s_add_u32 s56, s56, s68 -s_addc_u32 s57, s57, 0 -s_swappc_b64 s[62:63], s[66:67] -s_branch 3215 -ds_store_b128 v1, v[154:157] offset:4160 -ds_store_b128 v1, v[166:169] offset:16 -s_setprio 1 -s_ashr_i32 s61, s60, 31 -s_add_u32 s62, s62, s60 -s_addc_u32 s63, s63, s61 -s_mov_b32 exec_hi, 0 -s_waitcnt lgkmcnt(0) -s_barrier -_v_pk_op_x16_gfx1x 15, 120, 1, 120, 1, 137, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 121, 1, 138, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 122, 1, 139, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 123, 1, 140, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 124, 1, 141, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 125, 1, 142, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 126, 1, 143, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 127, 1, 144, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 145, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 146, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 147, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 148, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 149, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 150, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 151, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 152, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[154:157], v[49:56], v[120:127], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[154:157], v[57:64], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[166:169], v[104:111], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[166:169], v[112:119], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 120, 1, 128, 1, 137, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 129, 1, 138, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 130, 1, 139, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 131, 1, 140, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 132, 1, 141, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 133, 1, 142, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 134, 1, 143, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 135, 1, 144, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 137, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 138, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 139, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 140, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 141, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 142, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 143, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 144, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[158:161], v[68:75], v[120:127], 0 op_sel_hi:[1,1,0] -s_mov_b32 exec_hi, -1 -v_wmma_f16_16x16x16_f16 v[158:161], v[76:83], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[162:165], v[85:92], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[162:165], v[93:100], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 158, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 159, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 160, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 161, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 158, 1, 166, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 159, 1, 167, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 160, 1, 168, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 161, 1, 169, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 162, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 163, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 164, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 165, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 166, 1, 162, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 167, 1, 163, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 168, 1, 164, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 169, 1, 165, 2, 2 // op16_add_f -s_mov_b32 exec_hi, 0 -ds_load_b128 v[120:123], v1 offset:24768 -ds_load_b128 v[124:127], v1 offset:26816 -ds_load_b128 v[128:131], v1 offset:28928 -ds_load_b128 v[132:135], v1 offset:30976 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[36:39] offset:16512 -s_mov_b32 exec_hi, 0 -ds_load_b128 v[137:140], v1 offset:24784 -ds_load_b128 v[141:144], v1 offset:26832 -ds_load_b128 v[145:148], v1 offset:28944 -ds_load_b128 v[149:152], v1 offset:30992 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[40:43] offset:17024 -s_waitcnt lgkmcnt(10) -s_swappc_b64 s[62:63], s[62:63] -ds_store_b128 v2, v[154:157] offset:12416 -ds_store_b128 v2, v[166:169] offset:8272 -s_setprio 1 -s_ashr_i32 s61, s60, 31 -s_sub_u32 s6, s6, s7 -s_cselect_b64 s[60:61], 0, s[60:61] -s_add_u32 s62, s62, s60 -s_addc_u32 s63, s63, s61 -s_mov_b32 exec_hi, 0 -s_waitcnt lgkmcnt(0) -s_barrier -v_add_co_u32 v48, vcc, v48, s26 -_v_pk_op_x16_gfx1x 15, 120, 1, 120, 1, 137, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 121, 1, 138, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 122, 1, 139, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 123, 1, 140, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 124, 1, 141, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 125, 1, 142, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 126, 1, 143, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 127, 1, 144, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 145, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 146, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 147, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 148, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 149, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 150, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 151, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 152, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[154:157], v[49:56], v[120:127], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[154:157], v[57:64], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[166:169], v[104:111], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[166:169], v[112:119], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 120, 1, 128, 1, 137, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 129, 1, 138, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 130, 1, 139, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 131, 1, 140, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 132, 1, 141, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 133, 1, 142, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 134, 1, 143, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 135, 1, 144, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 137, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 138, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 139, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 140, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 141, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 142, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 143, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 144, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[158:161], v[68:75], v[120:127], 0 op_sel_hi:[1,1,0] -s_mov_b32 exec_hi, -1 -v_wmma_f16_16x16x16_f16 v[158:161], v[76:83], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[162:165], v[85:92], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[162:165], v[93:100], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 158, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 159, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 160, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 161, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 158, 1, 166, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 159, 1, 167, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 160, 1, 168, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 161, 1, 169, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 162, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 163, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 164, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 165, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 166, 1, 162, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 167, 1, 163, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 168, 1, 164, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 169, 1, 165, 2, 2 // op16_add_f -s_bfe_u32 null, vcc_lo, 0x10000 -s_cselect_b32 s54, s54, 0 -s_cselect_b32 s7, s7, 1 -ds_load_b128 v[171:174], v65 offset:33024 -ds_load_b32 v48, v66 offset:35072 -s_sub_u32 s1, s5, 1 -s_cselect_b32 s59, 0, s54 -s_mov_b64 s[56:57], s[20:21] -s_mov_b32 exec_hi, 0 -ds_load_b128 v[120:123], v2 offset:16512 -ds_load_b128 v[124:127], v2 offset:18560 -ds_load_b128 v[128:131], v2 offset:20672 -ds_load_b128 v[132:135], v2 offset:22720 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[36:39] offset:24768 -s_mov_b32 exec_hi, 0 -ds_load_b128 v[137:140], v2 offset:16528 -ds_load_b128 v[141:144], v2 offset:18576 -ds_load_b128 v[145:148], v2 offset:20688 -ds_load_b128 v[149:152], v2 offset:22736 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[40:43] offset:25280 -s_waitcnt lgkmcnt(10) -s_swappc_b64 s[62:63], s[62:63] -ds_store_b128 v1, v[154:157] offset:4160 -ds_store_b128 v1, v[166:169] offset:16 -s_setprio 1 -s_ashr_i32 s61, s60, 31 -s_add_u32 s62, s62, s60 -s_addc_u32 s63, s63, s61 -s_mov_b32 exec_hi, 0 -s_waitcnt lgkmcnt(0) -_v_pk_op_x16_gfx1x 15, 120, 1, 120, 1, 137, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 121, 1, 138, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 122, 1, 139, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 123, 1, 140, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 124, 1, 141, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 125, 1, 142, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 126, 1, 143, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 127, 1, 144, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 145, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 146, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 147, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 148, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 149, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 150, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 151, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 152, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[154:157], v[49:56], v[120:127], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[154:157], v[57:64], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[166:169], v[104:111], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[166:169], v[112:119], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 120, 1, 128, 1, 137, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 129, 1, 138, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 130, 1, 139, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 131, 1, 140, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 132, 1, 141, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 133, 1, 142, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 134, 1, 143, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 135, 1, 144, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 137, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 138, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 139, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 140, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 141, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 142, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 143, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 144, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[158:161], v[68:75], v[120:127], 0 op_sel_hi:[1,1,0] -s_mov_b32 exec_hi, -1 -v_wmma_f16_16x16x16_f16 v[158:161], v[76:83], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[162:165], v[85:92], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[162:165], v[93:100], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 158, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 159, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 160, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 161, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 158, 1, 166, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 159, 1, 167, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 160, 1, 168, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 161, 1, 169, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 162, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 163, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 164, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 165, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 166, 1, 162, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 167, 1, 163, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 168, 1, 164, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 169, 1, 165, 2, 2 // op16_add_f -s_barrier -s_mov_b32 exec_hi, 0 -ds_load_b128 v[120:123], v1 offset:24768 -ds_load_b128 v[124:127], v1 offset:26816 -ds_load_b128 v[128:131], v1 offset:28928 -ds_load_b128 v[132:135], v1 offset:30976 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[36:39] offset:16512 -s_mov_b32 exec_hi, 0 -ds_load_b128 v[137:140], v1 offset:24784 -ds_load_b128 v[141:144], v1 offset:26832 -ds_load_b128 v[145:148], v1 offset:28944 -ds_load_b128 v[149:152], v1 offset:30992 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[40:43] offset:17024 -s_waitcnt lgkmcnt(10) -s_swappc_b64 s[62:63], s[62:63] -ds_store_b128 v2, v[154:157] offset:12416 -ds_store_b128 v2, v[166:169] offset:8272 -s_setprio 1 -s_ashr_i32 s61, s60, 31 -s_sub_u32 s6, s6, s7 -s_cselect_b64 s[60:61], 0, s[60:61] -s_add_u32 s62, s62, s60 -s_addc_u32 s63, s63, s61 -s_mov_b32 exec_hi, 0 -s_waitcnt lgkmcnt(0) -v_add_co_u32 v48, vcc, v48, s26 -_v_pk_op_x16_gfx1x 15, 120, 1, 120, 1, 137, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 121, 1, 138, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 122, 1, 139, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 123, 1, 140, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 124, 1, 141, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 125, 1, 142, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 126, 1, 143, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 127, 1, 144, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 145, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 146, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 147, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 148, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 149, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 150, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 151, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 152, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[154:157], v[49:56], v[120:127], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[154:157], v[57:64], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[166:169], v[104:111], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[166:169], v[112:119], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 120, 1, 128, 1, 137, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 121, 1, 129, 1, 138, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 122, 1, 130, 1, 139, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 123, 1, 131, 1, 140, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 124, 1, 132, 1, 141, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 125, 1, 133, 1, 142, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 126, 1, 134, 1, 143, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 127, 1, 135, 1, 144, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 145, 1, 137, 1, 128, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 146, 1, 138, 1, 129, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 147, 1, 139, 1, 130, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 148, 1, 140, 1, 131, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 149, 1, 141, 1, 132, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 150, 1, 142, 1, 133, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 151, 1, 143, 1, 134, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 152, 1, 144, 1, 135, 2, 2 // op16_add_f -v_wmma_f16_16x16x16_f16 v[158:161], v[68:75], v[120:127], 0 op_sel_hi:[1,1,0] -s_mov_b32 exec_hi, -1 -v_wmma_f16_16x16x16_f16 v[158:161], v[76:83], v[120:127], 0 op_sel:[0,0,1] -v_wmma_f16_16x16x16_f16 v[162:165], v[85:92], v[145:152], 0 op_sel_hi:[1,1,0] -v_wmma_f16_16x16x16_f16 v[162:165], v[93:100], v[145:152], 0 op_sel:[0,0,1] -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 158, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 159, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 160, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 161, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 158, 1, 166, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 159, 1, 167, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 160, 1, 168, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 161, 1, 169, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 154, 1, 154, 1, 162, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 155, 1, 155, 1, 163, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 156, 1, 156, 1, 164, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 157, 1, 157, 1, 165, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 166, 1, 166, 1, 162, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 167, 1, 167, 1, 163, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 168, 1, 168, 1, 164, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 169, 1, 169, 1, 165, 2, 2 // op16_add_f -s_barrier -s_bfe_u32 null, vcc_lo, 0x10000 -s_cselect_b32 s54, s54, 0 -s_cselect_b32 s7, s7, 1 -ds_load_b128 v[171:174], v65 offset:33024 -ds_load_b32 v48, v66 offset:35072 -s_sub_u32 s1, s5, 1 -s_cselect_b32 s59, 0, s54 -s_mov_b64 s[56:57], s[20:21] -s_mov_b32 exec_hi, 0 -ds_load_b128 v[120:123], v2 offset:16512 -ds_load_b128 v[124:127], v2 offset:18560 -ds_load_b128 v[128:131], v2 offset:20672 -ds_load_b128 v[132:135], v2 offset:22720 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[36:39] offset:24768 -s_mov_b32 exec_hi, 0 -ds_load_b128 v[137:140], v2 offset:16528 -ds_load_b128 v[141:144], v2 offset:18576 -ds_load_b128 v[145:148], v2 offset:20688 -ds_load_b128 v[149:152], v2 offset:22736 -s_mov_b32 exec_hi, -1 -ds_store_b128 v3, v[40:43] offset:25280 -s_waitcnt lgkmcnt(10) -s_swappc_b64 s[62:63], s[62:63] -v_bfe_u32 v79, v0, 6, 1 -v_and_b32_e32 v18, 63, v0 -v_cmp_eq_u32_e64 vcc, v79, 1 -v_cndmask_b32_e64 v81, 0, 0xa00, vcc -v_cndmask_b32_e64 v82, 0, 0x500, vcc -v_cndmask_b32_e64 v79, 0, 0x400, vcc -v_cndmask_b32_e64 v80, 0, 0x100, vcc -v_lshl_add_u32 v15, v18, 2, v82 -v_lshl_add_u32 v14, v18, 3, v81 -v_lshl_add_u32 v19, v18, 2, v80 -v_lshl_add_u32 v18, v18, 4, v79 -s_barrier -s_mov_b32 s70, 0xbc00c000 -s_mov_b32 s54, 0x10000 -s_mov_b32 s55, 0x30002 -s_mov_b32 s56, 0x10000 -v_readfirstlane_b32 s79, v0 -s_and_b32 null, 64, s79 -s_cmov_b32 s70, 0x3c00c000 -s_cmov_b32 s54, 0x20003 -s_cmov_b32 s55, 1 -s_cmov_b32 s56, 1 -s_waitcnt vmcnt(0) expcnt(0) lgkmcnt(0) -s_bitcmp1_b32 s40, 6 -s_cbranch_scc0 14 -s_load_b64 s[20:21], s[20:21], null -s_load_b64 s[22:23], s[22:23], null -s_load_b64 s[24:25], s[24:25], null -s_cmp_eq_u64 0, s[36:37] -s_cbranch_scc1 2 -s_load_b64 s[36:37], s[36:37], null -s_cmp_eq_u64 0, s[38:39] -s_cbranch_scc1 2 -s_load_b64 s[38:39], s[38:39], null -s_cmp_eq_u32 s8, 0x60 -s_cbranch_scc0 16 -s_mul_i32 s79, s4, 0xab -s_lshr_b32 s79, s79, 10 -s_mul_i32 s85, s79, 6 -s_sub_u32 s85, s4, s85 -s_bfe_u32 s83, s79, 0x20000 -s_bfe_u32 s84, s79, 0x10002 -s_bfe_u32 s82, s79, 0x10003 -s_mov_b32 s86, s85 -s_lshl1_add_u32 s86, s86, s84 -s_lshl2_add_u32 s86, s86, s83 -s_lshl1_add_u32 s86, s86, s82 -s_mov_b32 s4, s86 -s_waitcnt vmcnt(0) expcnt(0) lgkmcnt(0) -s_bitcmp1_b32 s40, 13 -s_cbranch_scc0 14 -s_add_u32 s20, s20, s44 -s_addc_u32 s21, s21, s45 -s_add_u32 s22, s22, s46 -s_addc_u32 s23, s23, s47 -s_add_u32 s24, s24, s48 -s_addc_u32 s25, s25, s49 -s_cmp_eq_u64 0, s[36:37] -s_cselect_b64 s[50:51], 0, s[50:51] -s_add_u32 s36, s36, s50 -s_addc_u32 s37, s37, s51 -s_cmp_eq_u64 0, s[38:39] -s_cselect_b64 s[52:53], 0, s[52:53] -s_add_u32 s38, s38, s52 -s_addc_u32 s39, s39, s53 -s_cmp_eq_u64 s[38:39], 0 -s_cselect_b32 s47, 0, 0x11014000 -s_and_b32 s39, s39, 0xffff -s_add_u32 s39, s39, 0x20000 -s_mov_b64 s[44:45], s[38:39] -s_mov_b32 s46, 0x80000000 -v_and_b32_e64 v79, v0, 63 -v_lshlrev_b32_e32 v79, 1, v79 -v_cmp_lt_u32_e64 vcc, v79, s27 -v_add_nc_u32_e64 v80, v79, 1 -v_cndmask_b32_e32 v79, 0x80000000, v79, vcc -v_cmp_lt_u32_e64 vcc, v80, s27 -v_cndmask_b32_e32 v80, 0x80000000, v80, vcc -buffer_load_d16_b16 v81, v79, s[44:47], 0 idxen -buffer_load_d16_hi_b16 v81, v80, s[44:47], 0 idxen -s_waitcnt vmcnt(0) -v_readlane_b32 s71, v81, 0 -v_readlane_b32 s72, v81, 1 -v_readlane_b32 s73, v81, 2 -v_readlane_b32 s74, v81, 3 -v_readlane_b32 s75, v81, 4 -v_readlane_b32 s76, v81, 5 -v_readlane_b32 s77, v81, 6 -v_readlane_b32 s78, v81, 7 -s_getpc_b64 s[44:45] -s_and_b32 s41, s41, 0xff -s_cmp_eq_u32 s41, 2 -s_cbranch_scc1 26 -s_cmp_eq_u32 s41, 0 -s_cselect_b32 s42, 1.0, s42 -v_cvt_f16_f32_e32 v79, s42 -v_readfirstlane_b32 s42, v79 -v_cvt_f16_f32_e32 v79, s43 -v_readfirstlane_b32 s43, v79 -_v_cmp_f16_1_gfx11 42 -s_pack_ll_b32_b16 s42, s42, s42 -s_pack_ll_b32_b16 s43, s43, s43 -s_cmp_eq_u32 s41, 3 -s_cbranch_scc1 9 -s_cbranch_vccnz 4 -s_add_u32 s80, s44, 0x3c4 -s_addc_u32 s81, s45, 0 -s_branch 11 -s_add_u32 s80, s44, 0xac4 -s_addc_u32 s81, s45, 0 -s_branch 7 -s_add_u32 s80, s44, 0x1ac4 -s_addc_u32 s81, s45, 0 -s_branch 3 -s_add_u32 s80, s44, 0x11c4 -s_addc_u32 s81, s45, 0 -s_and_b32 s23, s23, 0xffff -s_add_u32 s23, s23, 0x20000 -s_lshl_b32 s63, s19, 1 -s_mov_b64 s[64:65], s[22:23] -s_mov_b32 s66, 0x80000000 -s_mov_b32 s67, 0 -s_mov_b32 s68, 0 -s_mov_b32 s69, 0 -v_lshrrev_b32_e64 v82, 16, s9 -v_bfi_b32 v83, 0xffff, s9, 0 -v_and_b32_e32 v85, 1, v0 -v_bfe_u32 v91, v0, 6, 1 -v_and_b32_e32 v80, 63, v0 -v_mad_u32_u16 v86, 0x7c, s8, 0 -v_mad_u32_u16 v91, 2, s4, v91 -v_mad_u32_u16 v84, v82, v83, 0 -v_cmp_eq_u32_e32 vcc, 0, v85 -v_cndmask_b32_e32 v92, v84, v83, vcc -v_mad_u32_u16 v81, 62, v91, v80 -v_cndmask_b32_e32 v81, v86, v81, vcc -v_clz_i32_u32_e32 v98, v92 -v_lshlrev_b32_e32 v99, v98, v92 -v_and_b32_e32 v97, 0xffffff00, v99 -v_cmp_eq_u32_e32 vcc, 0x80000000, v99 -v_cvt_f32_u32_e32 v97, v97 -v_rcp_f32_e32 v93, v97 -v_sub_co_ci_u32_e32 v94, vcc, 32, v98, vcc -v_cvt_f32_ubyte0_e32 v98, v99 -v_fma_f32 v97, v97, v93, -1.0 -v_fma_f32 v97, v98, v93, v97 -v_fmaak_f32 v97, v97, v93, 0x9f000000 -v_mul_f32_e32 v97, 0x5f800000, v97 -v_mov_b32_e32 v98, 0 -v_cvt_floor_i32_f32_e64 v97, -v97 -v_lshl_add_u32 v93, v93, 9, v97 -_v_mad_u64_u32_gfx11 98, 99, 93, 98 -v_sub_co_ci_u32_e64 v93, vcc, v93, -1, vcc -v_mov_b32_dpp v96, v94 quad_perm:[1,1,1,1] row_mask:0xf bank_mask:0xf -v_mov_b32_dpp v94, v94 quad_perm:[0,0,0,0] row_mask:0xf bank_mask:0xf -v_mov_b32_dpp v95, v93 quad_perm:[1,1,1,1] row_mask:0xf bank_mask:0xf -v_mov_b32_dpp v93, v93 quad_perm:[0,0,0,0] row_mask:0xf bank_mask:0xf -v_mul_hi_u32 v97, v81, v93 -v_add_co_u32 v79, vcc, v97, v81 -v_add_co_ci_u32_e64 v97, vcc, 0, 0, vcc -v_cmp_eq_u32_e32 vcc, 32, v94 -v_cndmask_b32_e32 v79, v79, v97, vcc -v_alignbit_b32 v79, v97, v79, v94 -v_mul_hi_u32 v97, v81, v95 -v_add_co_u32 v4, vcc, v97, v81 -v_add_co_ci_u32_e64 v97, vcc, 0, 0, vcc -v_cmp_eq_u32_e32 vcc, 32, v96 -v_cndmask_b32_e32 v4, v4, v97, vcc -v_alignbit_b32 v4, v97, v4, v96 -v_mad_u32_u16 v90, v79, v83, 0 -v_mad_u32_u16 v89, v4, v82, 0 -v_sub_nc_u32_e32 v90, v81, v90 -v_sub_nc_u32_e32 v89, v79, v89 -v_readlane_b32 s44, v90, 1 -v_sub_nc_u32_e32 v90, v90, v83 -v_readlane_b32 s5, v89, 1 -v_sub_nc_u32_e32 v89, v89, v82 -v_readlane_b32 s1, v4, 1 -v_sub_nc_u32_e64 v4, v4, s26 -s_lshl_b32 s5, s5, 16 -s_and_b32 s44, s44, 0xffff -s_add_u32 s5, s5, s44 -v_mov_b32_dpp v90, v90 quad_perm:[0,0,2,2] row_mask:0xf bank_mask:0xf -v_mov_b32_dpp v89, v89 quad_perm:[0,0,2,2] row_mask:0xf bank_mask:0xf -v_mov_b32_dpp v4, v4 quad_perm:[0,0,2,2] row_mask:0xf bank_mask:0xf -v_add_co_u32 v90, vcc, v90, v85 -v_cndmask_b32_e32 v88, 0, v83, vcc -v_add_co_ci_u32_e64 v89, vcc, v89, 0, vcc -v_cndmask_b32_e32 v87, 0, v82, vcc -v_add_co_ci_u32_e64 v4, vcc, v4, 0, vcc -v_min_u32_e64 v85, v80, 63 -v_sub_nc_u32_e32 v90, v90, v88 -v_sub_nc_u32_e32 v89, v89, v87 -v_cmp_eq_u32_e32 vcc, v80, v85 -v_lshlrev_b32_e32 v5, 16, v89 -v_bfi_b32 v5, 0xffff, v90, v5 -v_add_nc_u32_e64 v100, v4, s26 -v_med3_u32 v85, v80, 1, 62 -v_mul_lo_u32 v6, v100, s12 -v_mul_lo_u32 v11, v100, s17 -s_mul_i32 s6, s1, s12 -s_mul_i32 s7, s1, s17 -v_cndmask_b32_e32 v6, 0x80000000, v6, vcc -v_cmp_eq_u32_e32 vcc, v80, v85 -v_cndmask_b32_e32 v11, 0x80000000, v11, vcc -v_cmp_ge_u32_e64 vcc, v100, s26 -v_cndmask_b32_e64 v6, v6, 0x80000000, vcc -v_cndmask_b32_e64 v11, v11, 0x80000000, vcc -s_mov_b32 s57, 4 -s_lshl_b32 s60, s57, 9 -s_lshl_b32 s61, s57, 8 -v_add_nc_u32_e32 v16, s60, v14 -v_add_nc_u32_e32 v17, s61, v15 -s_setpc_b64 s[80:81] -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v23, v27, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 59, 1, 31, 1, 35, 0, 0 // op16_add_f -v_pk_fma_f16 v24, v28, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 60, 1, 32, 1, 36, 0, 0 // op16_add_f -v_pk_fma_f16 v25, v29, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 61, 1, 33, 1, 37, 0, 0 // op16_add_f -v_pk_fma_f16 v26, v30, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 62, 1, 34, 1, 38, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 59, 1, 59, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 60, 1, 60, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 61, 1, 61, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 62, 1, 62, 0, 74, 0, 0 // op16_add_f -s_barrier -v_add_co_u32 v85, vcc, v5, s5 -v_pk_mad_u16 v87, v5, 0x20001, s11 -v_pk_mad_u16 v88, v5, 0x20001, s16 -_v_pk_op_x16_gfx1x 13, 90, 1, 85, 1, 5, 0, 0 // op16_min_u -v_cndmask_b32_e64 v79, 0, s12, vcc -v_cndmask_b32_e64 v80, 0, s17, vcc -v_mad_u32_u16 v7, v87, 1, v6 -v_mad_u32_u16 v12, v88, 1, v11 -v_add3_u32 v6, v6, s6, v79 -v_add3_u32 v11, v11, s7, v80 -_v_pk_op_x16_gfx1x 11, 82, 1, 5, 1, 90, 0, 0 // op16_sub_u -v_add_co_ci_u32_e64 v4, s[58:59], v4, s1, vcc -v_cndmask_b32_e64 v6, v6, 0x80000000, s[58:59] -v_cndmask_b32_e64 v11, v11, 0x80000000, s[58:59] -_v_cmp_u16_vs_gfx11 87, 10, 0, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -_v_cmp_u16_vs_gfx11 88, 15, 0, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_v2_gfx1x 6, 82, 15, 82 // op16_ashrrev_i -_v_pk_op_x16_v3_gfx1x 10, 81, 87, 55 // op16_add_u -_v_pk_op_x16_v3_gfx1x 10, 84, 87, 54 // op16_add_u -v_mad_u32_u16 v10, v81, s13, v7 op_sel:[1,0,0,0] -v_mad_u32_u16 v8, v84, s13, v7 op_sel:[1,0,0,0] -_v_pk_op_x16_v3_gfx1x 10, 86, 88, 56 // op16_add_u -_v_cmp_u16_vs_gfx11 81, 10, 1, 8 -v_cndmask_b32_e32 v10, 0x80000000, v10, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 8 -v_cndmask_b32_e32 v8, 0x80000000, v8, vcc -v_mad_u32_u16 v13, v86, s18, v12 op_sel:[1,0,0,0] -v_mad_u32_u16 v9, v81, s13, v7 -v_mad_u32_u16 v7, v84, s13, v7 -_v_cmp_u16_vs_gfx11 86, 15, 1, 8 -v_cndmask_b32_e32 v13, 0x80000000, v13, vcc -_v_cmp_u16_vs_gfx11 81, 10, 1, 0 -v_cndmask_b32_e32 v9, 0x80000000, v9, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -v_mad_u32_u16 v12, v86, s18, v12 -v_pk_mad_u16 v5, v82, s9, v85 -_v_cmp_u16_vs_gfx11 86, 15, 1, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 59, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 60, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 61, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 62, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 18, 59, 1, 59, 1, 71, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 60, 1, 60, 1, 73, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 61, 1, 61, 1, 75, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 62, 1, 62, 1, 77, 0, 0 // op16_max_f -s_setprio 0 -ds_load_b128 v[31:34], v3 -ds_store_b128 v18, v[7:10] offset:33024 -ds_load_b128 v[35:38], v3 offset:512 -ds_store_b32 v19, v4 offset:35072 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_add_co_u32 v22, vcc, v22, s26 -v_add_nc_u32_e32 v16, s60, v14 -v_add_nc_u32_e32 v17, s61, v15 -v_pk_fma_f16 v39, v43, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 67, 1, 47, 1, 51, 0, 0 // op16_add_f -v_pk_fma_f16 v40, v44, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 68, 1, 48, 1, 52, 0, 0 // op16_add_f -v_pk_fma_f16 v41, v45, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 69, 1, 49, 1, 53, 0, 0 // op16_add_f -v_pk_fma_f16 v42, v46, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 70, 1, 50, 1, 54, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 67, 1, 67, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 68, 1, 68, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 69, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 70, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 67, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 68, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 69, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 70, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 18, 67, 1, 67, 1, 71, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 68, 1, 68, 1, 73, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 69, 1, 69, 1, 75, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 70, 1, 70, 1, 77, 0, 0 // op16_max_f -buffer_store_b16 v55, v20, s[64:67], 0 idxen -buffer_store_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v55, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v56, v20, s[64:67], 0 idxen -buffer_store_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v56, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v57, v20, s[64:67], 0 idxen -buffer_store_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v57, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v58, v20, s[64:67], 0 idxen -buffer_store_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v58, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v63, v20, s[64:67], 0 idxen -buffer_store_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v63, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v64, v20, s[64:67], 0 idxen -buffer_store_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v64, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v65, v20, s[64:67], 0 idxen -buffer_store_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v65, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v66, v20, s[64:67], 0 idxen -buffer_store_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v66, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -s_wait_idle -s_setprio 0 -ds_load_b64 v[20:21], v16 offset:35584 -ds_store_b64 v16, v[12:13] offset:35584 -ds_load_b32 v22, v17 offset:40704 -ds_store_b32 v17, v4 offset:40704 -ds_load_b128 v[47:50], v3 offset:2048 -ds_load_b128 v[51:54], v3 offset:2560 -s_setprio 3 -s_cmp_eq_u32 s68, 0 -s_cselect_b32 vcc_lo, -1, vcc_lo -s_bfe_u32 null, vcc_lo, 0x10000 -s_cbranch_scc0 1873 -s_sub_u32 s57, s57, 1 -s_cselect_b32 s57, 4, s57 -s_mov_b32 s68, s69 -s_cselect_b32 s69, 0x11014000, s69 -s_lshl_b32 s60, s57, 9 -s_lshl_b32 s61, s57, 8 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v55, v31, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 31, 1, 27, 1, 31, 2, 2 // op16_add_f -v_pk_fma_f16 v56, v32, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 32, 1, 28, 1, 32, 2, 2 // op16_add_f -v_pk_fma_f16 v57, v33, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 33, 1, 29, 1, 33, 2, 2 // op16_add_f -v_pk_fma_f16 v58, v34, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 34, 1, 30, 1, 34, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 55, 1, 55, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 56, 1, 56, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 57, 1, 57, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 58, 1, 58, 0, 74, 0, 0 // op16_add_f -s_barrier -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 55, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 56, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 57, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 58, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 18, 55, 1, 55, 1, 71, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 56, 1, 56, 1, 73, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 57, 1, 57, 1, 75, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 58, 1, 58, 1, 77, 0, 0 // op16_max_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[23:26], v3 offset:8256 -ds_load_b128 v[27:30], v3 offset:8768 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v63, v47, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 47, 1, 43, 1, 47, 2, 2 // op16_add_f -v_pk_fma_f16 v64, v48, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 48, 1, 44, 1, 48, 2, 2 // op16_add_f -v_pk_fma_f16 v65, v49, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 49, 1, 45, 1, 49, 2, 2 // op16_add_f -v_pk_fma_f16 v66, v50, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 50, 1, 46, 1, 50, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 63, 1, 63, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 64, 1, 64, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 65, 1, 65, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 66, 1, 66, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 63, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 64, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 65, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 66, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 18, 63, 1, 63, 1, 71, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 64, 1, 64, 1, 73, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 65, 1, 65, 1, 75, 0, 0 // op16_max_f -_v_pk_op_x16_gfx1x 18, 66, 1, 66, 1, 77, 0, 0 // op16_max_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[39:42], v3 offset:10304 -ds_load_b128 v[43:46], v3 offset:10816 -s_setprio 3 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_branch 65092 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v23, v27, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 59, 1, 31, 1, 35, 0, 0 // op16_add_f -v_pk_fma_f16 v24, v28, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 60, 1, 32, 1, 36, 0, 0 // op16_add_f -v_pk_fma_f16 v25, v29, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 61, 1, 33, 1, 37, 0, 0 // op16_add_f -v_pk_fma_f16 v26, v30, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 62, 1, 34, 1, 38, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 59, 1, 59, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 60, 1, 60, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 61, 1, 61, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 62, 1, 62, 0, 74, 0, 0 // op16_add_f -s_barrier -v_add_co_u32 v85, vcc, v5, s5 -v_pk_mad_u16 v87, v5, 0x20001, s11 -v_pk_mad_u16 v88, v5, 0x20001, s16 -_v_pk_op_x16_gfx1x 13, 90, 1, 85, 1, 5, 0, 0 // op16_min_u -v_cndmask_b32_e64 v79, 0, s12, vcc -v_cndmask_b32_e64 v80, 0, s17, vcc -v_mad_u32_u16 v7, v87, 1, v6 -v_mad_u32_u16 v12, v88, 1, v11 -v_add3_u32 v6, v6, s6, v79 -v_add3_u32 v11, v11, s7, v80 -_v_pk_op_x16_gfx1x 11, 82, 1, 5, 1, 90, 0, 0 // op16_sub_u -v_add_co_ci_u32_e64 v4, s[58:59], v4, s1, vcc -v_cndmask_b32_e64 v6, v6, 0x80000000, s[58:59] -v_cndmask_b32_e64 v11, v11, 0x80000000, s[58:59] -_v_cmp_u16_vs_gfx11 87, 10, 0, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -_v_cmp_u16_vs_gfx11 88, 15, 0, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_v2_gfx1x 6, 82, 15, 82 // op16_ashrrev_i -_v_pk_op_x16_v3_gfx1x 10, 81, 87, 55 // op16_add_u -_v_pk_op_x16_v3_gfx1x 10, 84, 87, 54 // op16_add_u -v_mad_u32_u16 v10, v81, s13, v7 op_sel:[1,0,0,0] -v_mad_u32_u16 v8, v84, s13, v7 op_sel:[1,0,0,0] -_v_pk_op_x16_v3_gfx1x 10, 86, 88, 56 // op16_add_u -_v_cmp_u16_vs_gfx11 81, 10, 1, 8 -v_cndmask_b32_e32 v10, 0x80000000, v10, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 8 -v_cndmask_b32_e32 v8, 0x80000000, v8, vcc -v_mad_u32_u16 v13, v86, s18, v12 op_sel:[1,0,0,0] -v_mad_u32_u16 v9, v81, s13, v7 -v_mad_u32_u16 v7, v84, s13, v7 -_v_cmp_u16_vs_gfx11 86, 15, 1, 8 -v_cndmask_b32_e32 v13, 0x80000000, v13, vcc -_v_cmp_u16_vs_gfx11 81, 10, 1, 0 -v_cndmask_b32_e32 v9, 0x80000000, v9, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -v_mad_u32_u16 v12, v86, s18, v12 -v_pk_mad_u16 v5, v82, s9, v85 -_v_cmp_u16_vs_gfx11 86, 15, 1, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 59, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 60, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 61, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 62, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 17, 59, 1, 59, 1, 71, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 60, 1, 60, 1, 73, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 61, 1, 61, 1, 75, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 62, 1, 62, 1, 77, 0, 0 // op16_min_f -s_setprio 0 -ds_load_b128 v[31:34], v3 -ds_store_b128 v18, v[7:10] offset:33024 -ds_load_b128 v[35:38], v3 offset:512 -ds_store_b32 v19, v4 offset:35072 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_add_co_u32 v22, vcc, v22, s26 -v_add_nc_u32_e32 v16, s60, v14 -v_add_nc_u32_e32 v17, s61, v15 -v_pk_fma_f16 v39, v43, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 67, 1, 47, 1, 51, 0, 0 // op16_add_f -v_pk_fma_f16 v40, v44, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 68, 1, 48, 1, 52, 0, 0 // op16_add_f -v_pk_fma_f16 v41, v45, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 69, 1, 49, 1, 53, 0, 0 // op16_add_f -v_pk_fma_f16 v42, v46, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 70, 1, 50, 1, 54, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 67, 1, 67, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 68, 1, 68, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 69, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 70, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 67, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 68, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 69, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 70, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 17, 67, 1, 67, 1, 71, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 68, 1, 68, 1, 73, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 69, 1, 69, 1, 75, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 70, 1, 70, 1, 77, 0, 0 // op16_min_f -buffer_store_b16 v55, v20, s[64:67], 0 idxen -buffer_store_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v55, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v56, v20, s[64:67], 0 idxen -buffer_store_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v56, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v57, v20, s[64:67], 0 idxen -buffer_store_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v57, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v58, v20, s[64:67], 0 idxen -buffer_store_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v58, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v63, v20, s[64:67], 0 idxen -buffer_store_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v63, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v64, v20, s[64:67], 0 idxen -buffer_store_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v64, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v65, v20, s[64:67], 0 idxen -buffer_store_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v65, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v66, v20, s[64:67], 0 idxen -buffer_store_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v66, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -s_wait_idle -s_setprio 0 -ds_load_b64 v[20:21], v16 offset:35584 -ds_store_b64 v16, v[12:13] offset:35584 -ds_load_b32 v22, v17 offset:40704 -ds_store_b32 v17, v4 offset:40704 -ds_load_b128 v[47:50], v3 offset:2048 -ds_load_b128 v[51:54], v3 offset:2560 -s_setprio 3 -s_cmp_eq_u32 s68, 0 -s_cselect_b32 vcc_lo, -1, vcc_lo -s_bfe_u32 null, vcc_lo, 0x10000 -s_cbranch_scc0 1425 -s_sub_u32 s57, s57, 1 -s_cselect_b32 s57, 4, s57 -s_mov_b32 s68, s69 -s_cselect_b32 s69, 0x11014000, s69 -s_lshl_b32 s60, s57, 9 -s_lshl_b32 s61, s57, 8 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v55, v31, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 31, 1, 27, 1, 31, 2, 2 // op16_add_f -v_pk_fma_f16 v56, v32, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 32, 1, 28, 1, 32, 2, 2 // op16_add_f -v_pk_fma_f16 v57, v33, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 33, 1, 29, 1, 33, 2, 2 // op16_add_f -v_pk_fma_f16 v58, v34, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 34, 1, 30, 1, 34, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 55, 1, 55, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 56, 1, 56, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 57, 1, 57, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 58, 1, 58, 0, 74, 0, 0 // op16_add_f -s_barrier -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 55, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 56, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 57, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 58, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 17, 55, 1, 55, 1, 71, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 56, 1, 56, 1, 73, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 57, 1, 57, 1, 75, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 58, 1, 58, 1, 77, 0, 0 // op16_min_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[23:26], v3 offset:8256 -ds_load_b128 v[27:30], v3 offset:8768 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v63, v47, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 47, 1, 43, 1, 47, 2, 2 // op16_add_f -v_pk_fma_f16 v64, v48, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 48, 1, 44, 1, 48, 2, 2 // op16_add_f -v_pk_fma_f16 v65, v49, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 49, 1, 45, 1, 49, 2, 2 // op16_add_f -v_pk_fma_f16 v66, v50, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 50, 1, 46, 1, 50, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 63, 1, 63, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 64, 1, 64, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 65, 1, 65, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 66, 1, 66, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 71, 0, 42, 1, 63, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 73, 0, 42, 1, 64, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 75, 0, 42, 1, 65, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 77, 0, 42, 1, 66, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 17, 63, 1, 63, 1, 71, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 64, 1, 64, 1, 73, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 65, 1, 65, 1, 75, 0, 0 // op16_min_f -_v_pk_op_x16_gfx1x 17, 66, 1, 66, 1, 77, 0, 0 // op16_min_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[39:42], v3 offset:10304 -ds_load_b128 v[43:46], v3 offset:10816 -s_setprio 3 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_branch 65092 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v23, v27, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 59, 1, 31, 1, 35, 0, 0 // op16_add_f -v_pk_fma_f16 v24, v28, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 60, 1, 32, 1, 36, 0, 0 // op16_add_f -v_pk_fma_f16 v25, v29, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 61, 1, 33, 1, 37, 0, 0 // op16_add_f -v_pk_fma_f16 v26, v30, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 62, 1, 34, 1, 38, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 59, 1, 59, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 60, 1, 60, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 61, 1, 61, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 62, 1, 62, 0, 74, 0, 0 // op16_add_f -s_barrier -v_add_co_u32 v85, vcc, v5, s5 -v_pk_mad_u16 v87, v5, 0x20001, s11 -v_pk_mad_u16 v88, v5, 0x20001, s16 -_v_pk_op_x16_gfx1x 13, 90, 1, 85, 1, 5, 0, 0 // op16_min_u -v_cndmask_b32_e64 v79, 0, s12, vcc -v_cndmask_b32_e64 v80, 0, s17, vcc -v_mad_u32_u16 v7, v87, 1, v6 -v_mad_u32_u16 v12, v88, 1, v11 -v_add3_u32 v6, v6, s6, v79 -v_add3_u32 v11, v11, s7, v80 -_v_pk_op_x16_gfx1x 11, 82, 1, 5, 1, 90, 0, 0 // op16_sub_u -v_add_co_ci_u32_e64 v4, s[58:59], v4, s1, vcc -v_cndmask_b32_e64 v6, v6, 0x80000000, s[58:59] -v_cndmask_b32_e64 v11, v11, 0x80000000, s[58:59] -_v_cmp_u16_vs_gfx11 87, 10, 0, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -_v_cmp_u16_vs_gfx11 88, 15, 0, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_v2_gfx1x 6, 82, 15, 82 // op16_ashrrev_i -_v_pk_op_x16_v3_gfx1x 10, 81, 87, 55 // op16_add_u -_v_pk_op_x16_v3_gfx1x 10, 84, 87, 54 // op16_add_u -v_mad_u32_u16 v10, v81, s13, v7 op_sel:[1,0,0,0] -v_mad_u32_u16 v8, v84, s13, v7 op_sel:[1,0,0,0] -_v_pk_op_x16_v3_gfx1x 10, 86, 88, 56 // op16_add_u -_v_cmp_u16_vs_gfx11 81, 10, 1, 8 -v_cndmask_b32_e32 v10, 0x80000000, v10, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 8 -v_cndmask_b32_e32 v8, 0x80000000, v8, vcc -v_mad_u32_u16 v13, v86, s18, v12 op_sel:[1,0,0,0] -v_mad_u32_u16 v9, v81, s13, v7 -v_mad_u32_u16 v7, v84, s13, v7 -_v_cmp_u16_vs_gfx11 86, 15, 1, 8 -v_cndmask_b32_e32 v13, 0x80000000, v13, vcc -_v_cmp_u16_vs_gfx11 81, 10, 1, 0 -v_cndmask_b32_e32 v9, 0x80000000, v9, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -v_mad_u32_u16 v12, v86, s18, v12 -v_pk_mad_u16 v5, v82, s9, v85 -_v_cmp_u16_vs_gfx11 86, 15, 1, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_v4_gfx1x 16, 59, 0xbdc5bdc5, 59 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 60, 0xbdc5bdc5, 60 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 61, 0xbdc5bdc5, 61 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 62, 0xbdc5bdc5, 62 // op16_mul_f -v_exp_f16_e32 v59, v59 -v_exp_f16_e32 v60, v60 -v_exp_f16_e32 v61, v61 -v_exp_f16_e32 v62, v62 -.long 0x7f76b1bb -.long 0x7f78b1bc -.long 0x7f7ab1bd -.long 0x7f7cb1be -_v_pk_op_x16_v4_gfx1x 15, 59, 0x3c003c00, 59 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 60, 0x3c003c00, 60 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 61, 0x3c003c00, 61 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 62, 0x3c003c00, 62 // op16_add_f -v_rcp_f16_e32 v59, v59 -v_rcp_f16_e32 v60, v60 -v_rcp_f16_e32 v61, v61 -v_rcp_f16_e32 v62, v62 -.long 0x7f76a9bb -.long 0x7f78a9bc -.long 0x7f7aa9bd -.long 0x7f7ca9be -s_setprio 0 -ds_load_b128 v[31:34], v3 -ds_store_b128 v18, v[7:10] offset:33024 -ds_load_b128 v[35:38], v3 offset:512 -ds_store_b32 v19, v4 offset:35072 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_add_co_u32 v22, vcc, v22, s26 -v_add_nc_u32_e32 v16, s60, v14 -v_add_nc_u32_e32 v17, s61, v15 -v_pk_fma_f16 v39, v43, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 67, 1, 47, 1, 51, 0, 0 // op16_add_f -v_pk_fma_f16 v40, v44, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 68, 1, 48, 1, 52, 0, 0 // op16_add_f -v_pk_fma_f16 v41, v45, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 69, 1, 49, 1, 53, 0, 0 // op16_add_f -v_pk_fma_f16 v42, v46, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 70, 1, 50, 1, 54, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 67, 1, 67, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 68, 1, 68, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 69, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 70, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_v4_gfx1x 16, 67, 0xbdc5bdc5, 67 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 68, 0xbdc5bdc5, 68 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 69, 0xbdc5bdc5, 69 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 70, 0xbdc5bdc5, 70 // op16_mul_f -v_exp_f16_e32 v67, v67 -v_exp_f16_e32 v68, v68 -v_exp_f16_e32 v69, v69 -v_exp_f16_e32 v70, v70 -.long 0x7f86b1c3 -.long 0x7f88b1c4 -.long 0x7f8ab1c5 -.long 0x7f8cb1c6 -_v_pk_op_x16_v4_gfx1x 15, 67, 0x3c003c00, 67 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 68, 0x3c003c00, 68 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 69, 0x3c003c00, 69 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 70, 0x3c003c00, 70 // op16_add_f -v_rcp_f16_e32 v67, v67 -v_rcp_f16_e32 v68, v68 -v_rcp_f16_e32 v69, v69 -v_rcp_f16_e32 v70, v70 -.long 0x7f86a9c3 -.long 0x7f88a9c4 -.long 0x7f8aa9c5 -.long 0x7f8ca9c6 -buffer_store_b16 v55, v20, s[64:67], 0 idxen -buffer_store_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v55, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v56, v20, s[64:67], 0 idxen -buffer_store_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v56, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v57, v20, s[64:67], 0 idxen -buffer_store_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v57, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v58, v20, s[64:67], 0 idxen -buffer_store_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v58, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v63, v20, s[64:67], 0 idxen -buffer_store_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v63, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v64, v20, s[64:67], 0 idxen -buffer_store_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v64, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v65, v20, s[64:67], 0 idxen -buffer_store_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v65, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v66, v20, s[64:67], 0 idxen -buffer_store_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v66, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -s_wait_idle -s_setprio 0 -ds_load_b64 v[20:21], v16 offset:35584 -ds_store_b64 v16, v[12:13] offset:35584 -ds_load_b32 v22, v17 offset:40704 -ds_store_b32 v17, v4 offset:40704 -ds_load_b128 v[47:50], v3 offset:2048 -ds_load_b128 v[51:54], v3 offset:2560 -s_setprio 3 -s_cmp_eq_u32 s68, 0 -s_cselect_b32 vcc_lo, -1, vcc_lo -s_bfe_u32 null, vcc_lo, 0x10000 -s_cbranch_scc0 929 -s_sub_u32 s57, s57, 1 -s_cselect_b32 s57, 4, s57 -s_mov_b32 s68, s69 -s_cselect_b32 s69, 0x11014000, s69 -s_lshl_b32 s60, s57, 9 -s_lshl_b32 s61, s57, 8 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v55, v31, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 31, 1, 27, 1, 31, 2, 2 // op16_add_f -v_pk_fma_f16 v56, v32, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 32, 1, 28, 1, 32, 2, 2 // op16_add_f -v_pk_fma_f16 v57, v33, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 33, 1, 29, 1, 33, 2, 2 // op16_add_f -v_pk_fma_f16 v58, v34, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 34, 1, 30, 1, 34, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 55, 1, 55, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 56, 1, 56, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 57, 1, 57, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 58, 1, 58, 0, 74, 0, 0 // op16_add_f -s_barrier -_v_pk_op_x16_v4_gfx1x 16, 55, 0xbdc5bdc5, 55 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 56, 0xbdc5bdc5, 56 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 57, 0xbdc5bdc5, 57 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 58, 0xbdc5bdc5, 58 // op16_mul_f -v_exp_f16_e32 v55, v55 -v_exp_f16_e32 v56, v56 -v_exp_f16_e32 v57, v57 -v_exp_f16_e32 v58, v58 -.long 0x7f6eb1b7 -.long 0x7f70b1b8 -.long 0x7f72b1b9 -.long 0x7f74b1ba -_v_pk_op_x16_v4_gfx1x 15, 55, 0x3c003c00, 55 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 56, 0x3c003c00, 56 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 57, 0x3c003c00, 57 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 58, 0x3c003c00, 58 // op16_add_f -v_rcp_f16_e32 v55, v55 -v_rcp_f16_e32 v56, v56 -v_rcp_f16_e32 v57, v57 -v_rcp_f16_e32 v58, v58 -.long 0x7f6ea9b7 -.long 0x7f70a9b8 -.long 0x7f72a9b9 -.long 0x7f74a9ba -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[23:26], v3 offset:8256 -ds_load_b128 v[27:30], v3 offset:8768 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v63, v47, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 47, 1, 43, 1, 47, 2, 2 // op16_add_f -v_pk_fma_f16 v64, v48, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 48, 1, 44, 1, 48, 2, 2 // op16_add_f -v_pk_fma_f16 v65, v49, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 49, 1, 45, 1, 49, 2, 2 // op16_add_f -v_pk_fma_f16 v66, v50, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 50, 1, 46, 1, 50, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 63, 1, 63, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 64, 1, 64, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 65, 1, 65, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 66, 1, 66, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_v4_gfx1x 16, 63, 0xbdc5bdc5, 63 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 64, 0xbdc5bdc5, 64 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 65, 0xbdc5bdc5, 65 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 66, 0xbdc5bdc5, 66 // op16_mul_f -v_exp_f16_e32 v63, v63 -v_exp_f16_e32 v64, v64 -v_exp_f16_e32 v65, v65 -v_exp_f16_e32 v66, v66 -.long 0x7f7eb1bf -.long 0x7f80b1c0 -.long 0x7f82b1c1 -.long 0x7f84b1c2 -_v_pk_op_x16_v4_gfx1x 15, 63, 0x3c003c00, 63 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 64, 0x3c003c00, 64 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 65, 0x3c003c00, 65 // op16_add_f -_v_pk_op_x16_v4_gfx1x 15, 66, 0x3c003c00, 66 // op16_add_f -v_rcp_f16_e32 v63, v63 -v_rcp_f16_e32 v64, v64 -v_rcp_f16_e32 v65, v65 -v_rcp_f16_e32 v66, v66 -.long 0x7f7ea9bf -.long 0x7f80a9c0 -.long 0x7f82a9c1 -.long 0x7f84a9c2 -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[39:42], v3 offset:10304 -ds_load_b128 v[43:46], v3 offset:10816 -s_setprio 3 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_branch 64996 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_nop 0 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v23, v27, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 59, 1, 31, 1, 35, 0, 0 // op16_add_f -v_pk_fma_f16 v24, v28, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 60, 1, 32, 1, 36, 0, 0 // op16_add_f -v_pk_fma_f16 v25, v29, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 61, 1, 33, 1, 37, 0, 0 // op16_add_f -v_pk_fma_f16 v26, v30, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 62, 1, 34, 1, 38, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 59, 1, 59, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 60, 1, 60, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 61, 1, 61, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 62, 1, 62, 0, 74, 0, 0 // op16_add_f -s_barrier -v_add_co_u32 v85, vcc, v5, s5 -v_pk_mad_u16 v87, v5, 0x20001, s11 -v_pk_mad_u16 v88, v5, 0x20001, s16 -_v_pk_op_x16_gfx1x 13, 90, 1, 85, 1, 5, 0, 0 // op16_min_u -v_cndmask_b32_e64 v79, 0, s12, vcc -v_cndmask_b32_e64 v80, 0, s17, vcc -v_mad_u32_u16 v7, v87, 1, v6 -v_mad_u32_u16 v12, v88, 1, v11 -v_add3_u32 v6, v6, s6, v79 -v_add3_u32 v11, v11, s7, v80 -_v_pk_op_x16_gfx1x 11, 82, 1, 5, 1, 90, 0, 0 // op16_sub_u -v_add_co_ci_u32_e64 v4, s[58:59], v4, s1, vcc -v_cndmask_b32_e64 v6, v6, 0x80000000, s[58:59] -v_cndmask_b32_e64 v11, v11, 0x80000000, s[58:59] -_v_cmp_u16_vs_gfx11 87, 10, 0, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -_v_cmp_u16_vs_gfx11 88, 15, 0, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_v2_gfx1x 6, 82, 15, 82 // op16_ashrrev_i -_v_pk_op_x16_v3_gfx1x 10, 81, 87, 55 // op16_add_u -_v_pk_op_x16_v3_gfx1x 10, 84, 87, 54 // op16_add_u -v_mad_u32_u16 v10, v81, s13, v7 op_sel:[1,0,0,0] -v_mad_u32_u16 v8, v84, s13, v7 op_sel:[1,0,0,0] -_v_pk_op_x16_v3_gfx1x 10, 86, 88, 56 // op16_add_u -_v_cmp_u16_vs_gfx11 81, 10, 1, 8 -v_cndmask_b32_e32 v10, 0x80000000, v10, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 8 -v_cndmask_b32_e32 v8, 0x80000000, v8, vcc -v_mad_u32_u16 v13, v86, s18, v12 op_sel:[1,0,0,0] -v_mad_u32_u16 v9, v81, s13, v7 -v_mad_u32_u16 v7, v84, s13, v7 -_v_cmp_u16_vs_gfx11 86, 15, 1, 8 -v_cndmask_b32_e32 v13, 0x80000000, v13, vcc -_v_cmp_u16_vs_gfx11 81, 10, 1, 0 -v_cndmask_b32_e32 v9, 0x80000000, v9, vcc -_v_cmp_u16_vs_gfx11 84, 10, 1, 0 -v_cndmask_b32_e32 v7, 0x80000000, v7, vcc -v_mad_u32_u16 v12, v86, s18, v12 -v_pk_mad_u16 v5, v82, s9, v85 -_v_cmp_u16_vs_gfx11 86, 15, 1, 0 -v_cndmask_b32_e32 v12, 0x80000000, v12, vcc -_v_pk_op_x16_gfx1x 16, 59, 1, 59, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 60, 1, 60, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 61, 1, 61, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 62, 1, 62, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 71, 0x3dc53dc5, 59 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 73, 0x3dc53dc5, 60 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 75, 0x3dc53dc5, 61 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 77, 0x3dc53dc5, 62 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 72, 0xbdc5bdc5, 59 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 74, 0xbdc5bdc5, 60 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 76, 0xbdc5bdc5, 61 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 78, 0xbdc5bdc5, 62 // op16_mul_f -v_exp_f16_e32 v71, v71 -v_exp_f16_e32 v73, v73 -v_exp_f16_e32 v75, v75 -v_exp_f16_e32 v77, v77 -.long 0x7f8eb1c7 -.long 0x7f92b1c9 -.long 0x7f96b1cb -.long 0x7f9ab1cd -v_exp_f16_e32 v72, v72 -v_exp_f16_e32 v74, v74 -v_exp_f16_e32 v76, v76 -v_exp_f16_e32 v78, v78 -.long 0x7f90b1c8 -.long 0x7f94b1ca -.long 0x7f98b1cc -.long 0x7f9cb1ce -_v_pk_op_x16_gfx1x 15, 59, 1, 71, 1, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 60, 1, 73, 1, 74, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 61, 1, 75, 1, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 62, 1, 77, 1, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 71, 1, 71, 1, 72, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 73, 1, 73, 1, 74, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 75, 1, 75, 1, 76, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 77, 1, 77, 1, 78, 2, 2 // op16_add_f -v_rcp_f16_e32 v59, v59 -v_rcp_f16_e32 v60, v60 -v_rcp_f16_e32 v61, v61 -v_rcp_f16_e32 v62, v62 -.long 0x7f76a9bb -.long 0x7f78a9bc -.long 0x7f7aa9bd -.long 0x7f7ca9be -_v_pk_op_x16_gfx1x 16, 59, 1, 59, 1, 71, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 60, 1, 60, 1, 73, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 61, 1, 61, 1, 75, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 62, 1, 62, 1, 77, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 59, 1, 59, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 60, 1, 60, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 61, 1, 61, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 62, 1, 62, 0, 42, 0, 0 // op16_mul_f -s_setprio 0 -ds_load_b128 v[31:34], v3 -ds_store_b128 v18, v[7:10] offset:33024 -ds_load_b128 v[35:38], v3 offset:512 -ds_store_b32 v19, v4 offset:35072 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_add_co_u32 v22, vcc, v22, s26 -v_add_nc_u32_e32 v16, s60, v14 -v_add_nc_u32_e32 v17, s61, v15 -v_pk_fma_f16 v39, v43, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 67, 1, 47, 1, 51, 0, 0 // op16_add_f -v_pk_fma_f16 v40, v44, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 68, 1, 48, 1, 52, 0, 0 // op16_add_f -v_pk_fma_f16 v41, v45, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 69, 1, 49, 1, 53, 0, 0 // op16_add_f -v_pk_fma_f16 v42, v46, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 70, 1, 50, 1, 54, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 67, 1, 67, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 68, 1, 68, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 69, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 70, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 67, 1, 67, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 68, 1, 68, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 69, 1, 69, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 70, 1, 70, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 71, 0x3dc53dc5, 67 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 73, 0x3dc53dc5, 68 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 75, 0x3dc53dc5, 69 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 77, 0x3dc53dc5, 70 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 72, 0xbdc5bdc5, 67 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 74, 0xbdc5bdc5, 68 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 76, 0xbdc5bdc5, 69 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 78, 0xbdc5bdc5, 70 // op16_mul_f -v_exp_f16_e32 v71, v71 -v_exp_f16_e32 v73, v73 -v_exp_f16_e32 v75, v75 -v_exp_f16_e32 v77, v77 -.long 0x7f8eb1c7 -.long 0x7f92b1c9 -.long 0x7f96b1cb -.long 0x7f9ab1cd -v_exp_f16_e32 v72, v72 -v_exp_f16_e32 v74, v74 -v_exp_f16_e32 v76, v76 -v_exp_f16_e32 v78, v78 -.long 0x7f90b1c8 -.long 0x7f94b1ca -.long 0x7f98b1cc -.long 0x7f9cb1ce -_v_pk_op_x16_gfx1x 15, 67, 1, 71, 1, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 68, 1, 73, 1, 74, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 69, 1, 75, 1, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 70, 1, 77, 1, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 71, 1, 71, 1, 72, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 73, 1, 73, 1, 74, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 75, 1, 75, 1, 76, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 77, 1, 77, 1, 78, 2, 2 // op16_add_f -v_rcp_f16_e32 v67, v67 -v_rcp_f16_e32 v68, v68 -v_rcp_f16_e32 v69, v69 -v_rcp_f16_e32 v70, v70 -.long 0x7f86a9c3 -.long 0x7f88a9c4 -.long 0x7f8aa9c5 -.long 0x7f8ca9c6 -_v_pk_op_x16_gfx1x 16, 67, 1, 67, 1, 71, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 68, 1, 68, 1, 73, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 69, 1, 69, 1, 75, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 70, 1, 70, 1, 77, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 67, 1, 67, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 68, 1, 68, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 69, 1, 69, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 70, 1, 70, 0, 42, 0, 0 // op16_mul_f -buffer_store_b16 v55, v20, s[64:67], 0 idxen -buffer_store_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v55, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v59, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v56, v20, s[64:67], 0 idxen -buffer_store_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v56, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v60, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v57, v20, s[64:67], 0 idxen -buffer_store_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v57, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v61, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v58, v20, s[64:67], 0 idxen -buffer_store_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v58, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v62, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v63, v20, s[64:67], 0 idxen -buffer_store_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v63, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v67, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v64, v20, s[64:67], 0 idxen -buffer_store_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v64, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v68, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v65, v20, s[64:67], 0 idxen -buffer_store_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v65, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v69, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_b16 v66, v20, s[64:67], 0 idxen -buffer_store_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -buffer_store_d16_hi_b16 v66, v20, s[64:67], 0 idxen -buffer_store_d16_hi_b16 v70, v21, s[64:67], 0 idxen -s_sub_u32 s62, s62, 1 -s_cselect_b32 s67, 0, s67 -s_add_u32 s64, s64, s63 -s_addc_u32 s65, s65, 0 -s_wait_idle -s_setprio 0 -ds_load_b64 v[20:21], v16 offset:35584 -ds_store_b64 v16, v[12:13] offset:35584 -ds_load_b32 v22, v17 offset:40704 -ds_store_b32 v17, v4 offset:40704 -ds_load_b128 v[47:50], v3 offset:2048 -ds_load_b128 v[51:54], v3 offset:2560 -s_setprio 3 -s_cmp_eq_u32 s68, 0 -s_cselect_b32 vcc_lo, -1, vcc_lo -s_bfe_u32 null, vcc_lo, 0x10000 -s_cbranch_scc0 257 -s_sub_u32 s57, s57, 1 -s_cselect_b32 s57, 4, s57 -s_mov_b32 s68, s69 -s_cselect_b32 s69, 0x11014000, s69 -s_lshl_b32 s60, s57, 9 -s_lshl_b32 s61, s57, 8 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v55, v31, s70, v23 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 31, 1, 27, 1, 31, 2, 2 // op16_add_f -v_pk_fma_f16 v56, v32, s70, v24 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 32, 1, 28, 1, 32, 2, 2 // op16_add_f -v_pk_fma_f16 v57, v33, s70, v25 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 33, 1, 29, 1, 33, 2, 2 // op16_add_f -v_pk_fma_f16 v58, v34, s70, v26 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 34, 1, 30, 1, 34, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 55, 1, 55, 0, 71, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 56, 1, 56, 0, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 57, 1, 57, 0, 73, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 58, 1, 58, 0, 74, 0, 0 // op16_add_f -s_barrier -_v_pk_op_x16_gfx1x 16, 55, 1, 55, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 56, 1, 56, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 57, 1, 57, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 58, 1, 58, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 71, 0x3dc53dc5, 55 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 73, 0x3dc53dc5, 56 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 75, 0x3dc53dc5, 57 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 77, 0x3dc53dc5, 58 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 72, 0xbdc5bdc5, 55 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 74, 0xbdc5bdc5, 56 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 76, 0xbdc5bdc5, 57 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 78, 0xbdc5bdc5, 58 // op16_mul_f -v_exp_f16_e32 v71, v71 -v_exp_f16_e32 v73, v73 -v_exp_f16_e32 v75, v75 -v_exp_f16_e32 v77, v77 -.long 0x7f8eb1c7 -.long 0x7f92b1c9 -.long 0x7f96b1cb -.long 0x7f9ab1cd -v_exp_f16_e32 v72, v72 -v_exp_f16_e32 v74, v74 -v_exp_f16_e32 v76, v76 -v_exp_f16_e32 v78, v78 -.long 0x7f90b1c8 -.long 0x7f94b1ca -.long 0x7f98b1cc -.long 0x7f9cb1ce -_v_pk_op_x16_gfx1x 15, 55, 1, 71, 1, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 56, 1, 73, 1, 74, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 57, 1, 75, 1, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 58, 1, 77, 1, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 71, 1, 71, 1, 72, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 73, 1, 73, 1, 74, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 75, 1, 75, 1, 76, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 77, 1, 77, 1, 78, 2, 2 // op16_add_f -v_rcp_f16_e32 v55, v55 -v_rcp_f16_e32 v56, v56 -v_rcp_f16_e32 v57, v57 -v_rcp_f16_e32 v58, v58 -.long 0x7f6ea9b7 -.long 0x7f70a9b8 -.long 0x7f72a9b9 -.long 0x7f74a9ba -_v_pk_op_x16_gfx1x 16, 55, 1, 55, 1, 71, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 56, 1, 56, 1, 73, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 57, 1, 57, 1, 75, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 58, 1, 58, 1, 77, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 55, 1, 55, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 56, 1, 56, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 57, 1, 57, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 58, 1, 58, 0, 42, 0, 0 // op16_mul_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[23:26], v3 offset:8256 -ds_load_b128 v[27:30], v3 offset:8768 -s_setprio 3 -s_waitcnt lgkmcnt(0) -v_pk_fma_f16 v63, v47, s70, v39 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 47, 1, 43, 1, 47, 2, 2 // op16_add_f -v_pk_fma_f16 v64, v48, s70, v40 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 48, 1, 44, 1, 48, 2, 2 // op16_add_f -v_pk_fma_f16 v65, v49, s70, v41 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 49, 1, 45, 1, 49, 2, 2 // op16_add_f -v_pk_fma_f16 v66, v50, s70, v42 op_sel:[0,1,0] -_v_pk_op_x16_gfx1x 15, 50, 1, 46, 1, 50, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 63, 1, 63, 0, 75, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 64, 1, 64, 0, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 65, 1, 65, 0, 77, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 66, 1, 66, 0, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 16, 63, 1, 63, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 64, 1, 64, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 65, 1, 65, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 66, 1, 66, 0, 43, 0, 0 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 71, 0x3dc53dc5, 63 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 73, 0x3dc53dc5, 64 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 75, 0x3dc53dc5, 65 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 77, 0x3dc53dc5, 66 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 72, 0xbdc5bdc5, 63 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 74, 0xbdc5bdc5, 64 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 76, 0xbdc5bdc5, 65 // op16_mul_f -_v_pk_op_x16_v4_gfx1x 16, 78, 0xbdc5bdc5, 66 // op16_mul_f -v_exp_f16_e32 v71, v71 -v_exp_f16_e32 v73, v73 -v_exp_f16_e32 v75, v75 -v_exp_f16_e32 v77, v77 -.long 0x7f8eb1c7 -.long 0x7f92b1c9 -.long 0x7f96b1cb -.long 0x7f9ab1cd -v_exp_f16_e32 v72, v72 -v_exp_f16_e32 v74, v74 -v_exp_f16_e32 v76, v76 -v_exp_f16_e32 v78, v78 -.long 0x7f90b1c8 -.long 0x7f94b1ca -.long 0x7f98b1cc -.long 0x7f9cb1ce -_v_pk_op_x16_gfx1x 15, 63, 1, 71, 1, 72, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 64, 1, 73, 1, 74, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 65, 1, 75, 1, 76, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 66, 1, 77, 1, 78, 0, 0 // op16_add_f -_v_pk_op_x16_gfx1x 15, 71, 1, 71, 1, 72, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 73, 1, 73, 1, 74, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 75, 1, 75, 1, 76, 2, 2 // op16_add_f -_v_pk_op_x16_gfx1x 15, 77, 1, 77, 1, 78, 2, 2 // op16_add_f -v_rcp_f16_e32 v63, v63 -v_rcp_f16_e32 v64, v64 -v_rcp_f16_e32 v65, v65 -v_rcp_f16_e32 v66, v66 -.long 0x7f7ea9bf -.long 0x7f80a9c0 -.long 0x7f82a9c1 -.long 0x7f84a9c2 -_v_pk_op_x16_gfx1x 16, 63, 1, 63, 1, 71, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 64, 1, 64, 1, 73, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 65, 1, 65, 1, 75, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 66, 1, 66, 1, 77, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 63, 1, 63, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 64, 1, 64, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 65, 1, 65, 0, 42, 0, 0 // op16_mul_f -_v_pk_op_x16_gfx1x 16, 66, 1, 66, 0, 42, 0, 0 // op16_mul_f -s_setprio 0 -s_nop 15 -s_nop 15 -ds_load_b128 v[39:42], v3 offset:10304 -ds_load_b128 v[43:46], v3 offset:10816 -s_setprio 3 -s_sub_u32 s62, s27, 1 -s_cselect_b32 s67, 0, s68 -s_mov_b64 s[64:65], s[22:23] -s_branch 64804 -s_endpgm -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end -s_code_end 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-/******************************************************************************* - * - * MIT License - * - * Copyright (c) 2023 Advanced Micro Devices, Inc. - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - * - *******************************************************************************/ - -.macro PROLOG_KERNEL_DESCRIPTOR kernel_name -.text -.globl \kernel_name -.p2align 8 -.type \kernel_name,@function -\kernel_name: -.endm - -.macro METADATA sc,wc,wg_x, kernel_name -.amdgpu_metadata ---- -amdhsa.version: [ 1, 0 ] -amdhsa.kernels: - - .name: \kernel_name - .symbol: \kernel_name\().kd - .language: "OpenCL C" - .language_version: [ 1, 2 ] - .sgpr_count: \sc - .vgpr_count: \wc - .group_segment_fixed_size: 65536 - .private_segment_fixed_size: 0 - .kernarg_segment_size: 208 - .kernarg_segment_align: 8 - .reqd_workgroup_size: [ \wg_x, 1, 1 ] - .max_flat_workgroup_size: \wg_x - .wavefront_size: 64 - .args: - - { .size: 4, .offset: 0, .value_kind: by_value, .name: n_groups } - - { .size: 2, .offset: 4, .value_kind: by_value, .name: out_W_e } - - { .size: 2, .offset: 6, .value_kind: by_value, .name: out_H_s } - - { .size: 2, .offset: 8, .value_kind: by_value, .name: d_W_window } - - { .size: 2, .offset: 10, .value_kind: by_value, .name: d_H_window } - - { .size: 2, .offset: 12, .value_kind: by_value, .name: d_W_clip_bot_neg } - - { .size: 2, .offset: 14, .value_kind: by_value, .name: d_H_clip_bot_neg } - - { .size: 4, .offset: 16, .value_kind: by_value, .name: d_N_stride } - - { .size: 4, .offset: 20, .value_kind: by_value, .name: d_H_stride } - - { .size: 4, .offset: 24, .value_kind: by_value, .name: d_C_stride } - - - { .size: 2, .offset: 28, .value_kind: by_value, .name: o_W_window } - - { .size: 2, .offset: 30, .value_kind: by_value, .name: o_H_window } - - { .size: 2, .offset: 32, .value_kind: by_value, .name: o_W_clip_bot_neg } - - { .size: 2, .offset: 34, .value_kind: by_value, .name: o_H_clip_bot_neg } - - { .size: 4, .offset: 36, .value_kind: by_value, .name: o_N_stride } - - { .size: 4, .offset: 40, .value_kind: by_value, .name: o_H_stride } - - { .size: 4, .offset: 44, .value_kind: by_value, .name: o_K_stride } - - - { .size: 8, .offset: 48, .value_kind: global_buffer, .name: data_addr, .address_space: global, .is_const: true } - - { .size: 8, .offset: 56, .value_kind: global_buffer, .name: output_addr, .address_space: global, .is_const: false } - - { .size: 8, .offset: 64, .value_kind: global_buffer, .name: filter_addr, .address_space: global, .is_const: true } - - - { .size: 4, .offset: 72, .value_kind: by_value, .name: BATCH_SIZE } - - { .size: 4, .offset: 76, .value_kind: by_value, .name: K } - - { .size: 4, .offset: 80, .value_kind: by_value, .name: C } - - { .size: 4, .offset: 84, .value_kind: by_value, .name: R } - - { .size: 4, .offset: 88, .value_kind: by_value, .name: S } - - - { .size: 4, .offset: 92, .value_kind: by_value, .name: f_K_stride } - - { .size: 4, .offset: 96, .value_kind: by_value, .name: f_C_stride } - - { .size: 4, .offset: 100, .value_kind: by_value, .name: f_R_stride } - - { .size: 4, .offset: 104, .value_kind: by_value, .name: f_S_stride } - - - { .size: 4, .offset: 108, .value_kind: by_value, .name: f_RS_offset } - - - { .size: 8, .offset: 112, .value_kind: hidden_none } - - { .size: 8, .offset: 120, .value_kind: global_buffer, .name: bias_addr, .address_space: global, .is_const: true } - - { .size: 4, .offset: 128, .value_kind: by_value, .name: flags } - - - { .size: 1, .offset: 132, .value_kind: by_value, .name: activation_mode } - - { .size: 1, .offset: 133, .value_kind: hidden_none } - - { .size: 2, .offset: 134, .value_kind: hidden_none } - - - { .size: 4, .offset: 136, .value_kind: by_value, .name: alpha } - - { .size: 4, .offset: 140, .value_kind: by_value, .name: beta } - - - { .size: 8, .offset: 144, .value_kind: by_value, .name: d_offset } - - { .size: 8, .offset: 152, .value_kind: by_value, .name: o_offset } - - { .size: 8, .offset: 160, .value_kind: by_value, .name: f_offset } - - { .size: 8, .offset: 168, .value_kind: hidden_none } - - { .size: 8, .offset: 176, .value_kind: by_value, .name: b_offset } - - - { .size: 8, .offset: 184, .value_kind: hidden_global_offset_x } - - { .size: 8, .offset: 192, .value_kind: hidden_global_offset_y } - - { .size: 8, .offset: 200, .value_kind: hidden_global_offset_z } -... -.end_amdgpu_metadata -.endm // METADATA - -.altmacro -.macro METADATA_WRAPPER sc,wc,wg_x, kernel_name - METADATA %\sc, %\wc, %\wg_x, \kernel_name -.endm - -.macro kernel_end kernel_name -s_endpgm -.Lfunc_end0: - .size \kernel_name, .Lfunc_end0 - \kernel_name -.endm - -.macro EPILOG_KERNEL_DESCRIPTOR kernel_name - -kernel_end \kernel_name - -.if (.amdgcn.gfx_generation_number == 11) - vgpr_size = 252 - workgroup_size_x = 384 -.endif - -.amdgcn.next_free_sgpr = 97 -.amdgcn.next_free_vgpr = vgpr_size - -//xnack disabled by default for asm kernels -__sgpr_reserve_vcc_default = 1 -__sgpr_reserve_xnack_default = 0 -__sgpr_reserve_flatscr_default = 0 - -__group_segment_fixed_size = 65536 -__sgpr_dispatch_ptr = 1 -__sgpr_kernarg_segment_ptr = 1 -__sgpr_workgroup_id_x = 1 -__sgpr_workgroup_id_y = 0 -__sgpr_workgroup_id_z = 0 -__vgpr_workitem_id = 0 -__ieee_mode = 0 -__dx10_clamp = 0 - -.rodata -.p2align 6 -.if (.amdgcn.gfx_generation_number == 11) -.amdhsa_kernel \kernel_name - .amdhsa_group_segment_fixed_size __group_segment_fixed_size - .amdhsa_user_sgpr_dispatch_ptr __sgpr_dispatch_ptr // s[0:1] - .amdhsa_user_sgpr_kernarg_segment_ptr __sgpr_kernarg_segment_ptr // s[2:3] - .amdhsa_system_sgpr_workgroup_id_x __sgpr_workgroup_id_x - .amdhsa_system_sgpr_workgroup_id_y __sgpr_workgroup_id_y - .amdhsa_system_sgpr_workgroup_id_z __sgpr_workgroup_id_z - .amdhsa_system_vgpr_workitem_id __vgpr_workitem_id - .amdhsa_next_free_vgpr .amdgcn.next_free_vgpr - .amdhsa_next_free_sgpr .amdgcn.next_free_sgpr - .amdhsa_reserve_vcc __sgpr_reserve_vcc_default - .amdhsa_ieee_mode __ieee_mode - .amdhsa_dx10_clamp __dx10_clamp - .amdhsa_wavefront_size32 0 -.end_amdhsa_kernel -.endif - -total_sgpr_count = .amdgcn.next_free_sgpr + 4 // vcc, xnack - -METADATA_WRAPPER total_sgpr_count,.amdgcn.next_free_vgpr,workgroup_size_x, <\kernel_name> - -.endm - -.macro PROLOG_KERNEL_DESCRIPTOR_WRAPPER machine_version, kernel_name_postfix - PROLOG_KERNEL_DESCRIPTOR miopenSp3AsmConv_fury_v1_1_1_gfx\machine_version\()_\kernel_name_postfix -.endm - -.macro EPILOG_KERNEL_DESCRIPTOR_WRAPPER machine_version, kernel_name_postfix - EPILOG_KERNEL_DESCRIPTOR miopenSp3AsmConv_fury_v1_1_1_gfx\machine_version\()_\kernel_name_postfix -.endm - -.macro KERNEL_PROLOG kernel_name_postfix - PROLOG_KERNEL_DESCRIPTOR_WRAPPER %.amdgcn.gfx_generation_number, \kernel_name_postfix -.endm - -.macro KERNEL_EPILOG kernel_name_postfix - EPILOG_KERNEL_DESCRIPTOR_WRAPPER %.amdgcn.gfx_generation_number, \kernel_name_postfix -.endm - -.if (.amdgcn.gfx_generation_number != 11) - .error "Unsupported gfx generation" - .end -.endif diff --git a/src/kernels/MIOpenAdam.cpp b/src/kernels/MIOpenAdam.cpp new file mode 100644 index 0000000000..62011bb88f --- /dev/null +++ b/src/kernels/MIOpenAdam.cpp @@ -0,0 +1,626 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_DONT_USE_HIP_RUNTIME_HEADERS +#include +#include +#endif + +#include "float_types.h" + +template +inline __device__ void AdamInternal(T1* param_in, + T1* param_out, + T1* exp_avg_in, + T1* exp_avg_out, + T1* exp_avg_sq_in, + T1* exp_avg_sq_out, + T1* max_exp_avg_sq_in, + T1* max_exp_avg_sq_out, + T2 grad, + T2 lr, + T2 beta1, + T2 beta2, + T2 weight_decay, + T2 eps, + uint32_t step, + bool amsgrad, + bool maximize, + bool adamw, + size_t gid) +{ + T2 param = static_cast(param_in[gid]); + T2 exp_avg = static_cast(exp_avg_in[gid]); + T2 exp_avg_sq = static_cast(exp_avg_sq_in[gid]); + + __builtin_assume(exp_avg_sq >= 0 && exp_avg_sq <= 1); + __builtin_assume(beta1 >= 0); + __builtin_assume(beta2 >= 0); + + T2 bias_correction1 = 1 - pow(beta1, step); + T2 bias_correction2 = 1 - pow(beta2, step); + + if(maximize) + grad = -grad; + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wfloat-equal" +#endif + + if(weight_decay != 0) + { + if(adamw) + param -= lr * weight_decay * param; + else + grad += param * weight_decay; + } + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + + exp_avg = exp_avg * beta1 + grad * (1 - beta1); + exp_avg_sq = exp_avg_sq * beta2 + grad * grad * (1 - beta2); + + T2 denom; + if(amsgrad) + { + T2 max_exp_avg_sq = static_cast(max_exp_avg_sq_in[gid]); + __builtin_assume(max_exp_avg_sq >= 0 && max_exp_avg_sq <= 1); + max_exp_avg_sq = max(max_exp_avg_sq, exp_avg_sq); + max_exp_avg_sq_out[gid] = static_cast(max_exp_avg_sq); + denom = sqrt(max_exp_avg_sq) / sqrt(bias_correction2) + eps; + } + else + { + denom = sqrt(exp_avg_sq) / sqrt(bias_correction2) + eps; + } + + T2 step_size = lr / bias_correction1; + param = param - step_size * exp_avg / denom; + + param_out[gid] = static_cast(param); + exp_avg_out[gid] = static_cast(exp_avg); + exp_avg_sq_out[gid] = static_cast(exp_avg_sq); +} + +extern "C" __global__ void AdamContiguous(PTYPE* param_in, + PTYPE* param_out, + PTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + PTYPE* max_exp_avg_sq_in, + PTYPE* max_exp_avg_sq_out, + float lr, + float beta1, + float beta2, + float weight_decay, + float eps, + uint32_t step, + bool amsgrad, + bool maximize, + bool adamw, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + size_t gsz = gridDim.x * blockDim.x; + + for(; gid < input_size; gid += gsz) + { + CTYPE grad = static_cast(grad_in[gid]); + + AdamInternal(param_in, + param_out, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + grad, + lr, + beta1, + beta2, + weight_decay, + eps, + step, + amsgrad, + maximize, + adamw, + gid); + } +} + +template +inline __device__ void AmpAdamInternal(T1* param_in, + T1* param_out, + half* param_out_fp16, + T3* grad_in, + T1* exp_avg_in, + T1* exp_avg_out, + T1* exp_avg_sq_in, + T1* exp_avg_sq_out, + T1* max_exp_avg_sq_in, + T1* max_exp_avg_sq_out, + int32_t* grad_scale, + T2 lr, + T2 beta1, + T2 beta2, + T2 weight_decay, + T2 eps, + uint32_t step, + bool amsgrad, + bool maximize, + bool adamw, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + size_t gsz = gridDim.x * blockDim.x; + + CTYPE scale_factor = (grad_scale) ? static_cast(*grad_scale) : 1.0f; + + for(; gid < input_size; gid += gsz) + { + CTYPE grad = static_cast(grad_in[gid]); + if(grad_scale) + grad /= scale_factor; + + AdamInternal(param_in, + param_out, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + grad, + lr, + beta1, + beta2, + weight_decay, + eps, + step, + amsgrad, + maximize, + adamw, + gid); + + if(param_out_fp16) + param_out_fp16[gid] = static_cast(param_out[gid]); + } +} + +template +inline __device__ void AmpAdamSetOutputFromInput(T1* param_in, + T1* param_out, + half* param_out_fp16, + T1* exp_avg_in, + T1* exp_avg_out, + T1* exp_avg_sq_in, + T1* exp_avg_sq_out, + T1* max_exp_avg_sq_in, + T1* max_exp_avg_sq_out, + bool amsgrad, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + size_t gsz = gridDim.x * blockDim.x; + + for(; gid < input_size; gid += gsz) + { + if(param_in != param_out) + param_out[gid] = param_in[gid]; + if(param_out_fp16) + param_out_fp16[gid] = static_cast(param_in[gid]); + if(exp_avg_in != exp_avg_out) + exp_avg_out[gid] = exp_avg_in[gid]; + if(exp_avg_sq_in != exp_avg_sq_out) + exp_avg_sq_out[gid] = exp_avg_sq_in[gid]; + if(amsgrad && max_exp_avg_sq_in != max_exp_avg_sq_out) + max_exp_avg_sq_out[gid] = max_exp_avg_sq_in[gid]; + } +} + +extern "C" __global__ void AmpAdamContiguousWithStep(PTYPE* param_in, + PTYPE* param_out, + half* param_out_fp16, + GTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + PTYPE* max_exp_avg_sq_in, + PTYPE* max_exp_avg_sq_out, + int32_t* grad_scale, + bool* found_inf, + int* step, + float lr, + float beta1, + float beta2, + float weight_decay, + float eps, + bool amsgrad, + bool maximize, + bool adamw, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + + if(gid >= input_size) + return; + + if(found_inf == nullptr || *found_inf == false) + { + uint32_t step_val = static_cast(*step) + 1; + + AmpAdamInternal(param_in, + param_out, + param_out_fp16, + grad_in, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + grad_scale, + lr, + beta1, + beta2, + weight_decay, + eps, + step_val, + amsgrad, + maximize, + adamw, + input_size); + } + else + { + AmpAdamSetOutputFromInput(param_in, + param_out, + param_out_fp16, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + amsgrad, + input_size); + } +} + +extern "C" __global__ void AmpAdamContiguous(PTYPE* param_in, + PTYPE* param_out, + half* param_out_fp16, + GTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + PTYPE* max_exp_avg_sq_in, + PTYPE* max_exp_avg_sq_out, + int32_t* grad_scale, + bool* found_inf, + int step, + float lr, + float beta1, + float beta2, + float weight_decay, + float eps, + bool amsgrad, + bool maximize, + bool adamw, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + + if(gid >= input_size) + return; + + if(found_inf == nullptr || *found_inf == false) + { + AmpAdamInternal(param_in, + param_out, + param_out_fp16, + grad_in, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + grad_scale, + lr, + beta1, + beta2, + weight_decay, + eps, + step, + amsgrad, + maximize, + adamw, + input_size); + } + else + { + AmpAdamSetOutputFromInput(param_in, + param_out, + param_out_fp16, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + max_exp_avg_sq_in, + max_exp_avg_sq_out, + amsgrad, + input_size); + } +} + +extern "C" __global__ void AdamUpdateStep(bool* found_inf, int* step_in, int* step_out) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + + if(gid != 0) + return; + + if(found_inf && *found_inf) + { + if(step_in != step_out) + *step_out = *step_in; + return; + } + + *step_out = *step_in + 1; +} + +template +inline __device__ void TransformersAdamWInternal(T1* param_in, + T1* param_out, + T1* exp_avg_in, + T1* exp_avg_out, + T1* exp_avg_sq_in, + T1* exp_avg_sq_out, + T2 grad, + T2 beta1, + T2 beta2, + T2 eps, + T2 lr_weight_decay, + T2 step_size, + size_t gid) +{ + T2 param = static_cast(param_in[gid]); + T2 exp_avg = static_cast(exp_avg_in[gid]); + T2 exp_avg_sq = static_cast(exp_avg_sq_in[gid]); + + __builtin_assume(exp_avg_sq >= 0 && exp_avg_sq <= 1); + __builtin_assume(beta1 >= 0); + __builtin_assume(beta2 >= 0); + + exp_avg = exp_avg * beta1 + grad * (1 - beta1); + exp_avg_sq = exp_avg_sq * beta2 + grad * grad * (1 - beta2); + + T2 denom = sqrt(exp_avg_sq) + eps; + + param = param - step_size * exp_avg / denom; + param = param - param * lr_weight_decay; + + param_out[gid] = static_cast(param); + exp_avg_out[gid] = static_cast(exp_avg); + exp_avg_sq_out[gid] = static_cast(exp_avg_sq); +} + +extern "C" __global__ void TransformersAdamWContiguous(PTYPE* param_in, + PTYPE* param_out, + PTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + float beta1, + float beta2, + float eps, + float lr_weight_decay, + float step_size, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + size_t gsz = gridDim.x * blockDim.x; + + for(; gid < input_size; gid += gsz) + { + CTYPE grad = static_cast(grad_in[gid]); + + TransformersAdamWInternal(param_in, + param_out, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + grad, + beta1, + beta2, + eps, + lr_weight_decay, + step_size, + gid); + } +} + +extern "C" __global__ void TransformersAmpAdamWContiguous(PTYPE* param_in, + PTYPE* param_out, + half* param_out_fp16, + GTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + int32_t* grad_scale, + bool* found_inf, + float beta1, + float beta2, + float eps, + float lr_weight_decay, + float step_size, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + + if(gid >= input_size) + return; + + if(found_inf == nullptr || *found_inf == false) + { + size_t gsz = gridDim.x * blockDim.x; + CTYPE scale_factor = (grad_scale) ? static_cast(*grad_scale) : 1.0f; + + for(; gid < input_size; gid += gsz) + { + CTYPE grad = static_cast(grad_in[gid]); + if(grad_scale) + grad /= scale_factor; + + TransformersAdamWInternal(param_in, + param_out, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + grad, + beta1, + beta2, + eps, + lr_weight_decay, + step_size, + gid); + + if(param_out_fp16) + param_out_fp16[gid] = static_cast(param_out[gid]); + } + } + else + { + AmpAdamSetOutputFromInput(param_in, + param_out, + param_out_fp16, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + nullptr, + nullptr, + false, + input_size); + } +} + +extern "C" __global__ void TransformersAmpAdamWContiguousWithStep(PTYPE* param_in, + PTYPE* param_out, + half* param_out_fp16, + GTYPE* grad_in, + PTYPE* exp_avg_in, + PTYPE* exp_avg_out, + PTYPE* exp_avg_sq_in, + PTYPE* exp_avg_sq_out, + int32_t* grad_scale, + bool* found_inf, + int* step, + float lr, + float beta1, + float beta2, + float eps, + float lr_weight_decay, + float step_size, + bool correct_bias, + size_t input_size) +{ + size_t gid = blockIdx.x * blockDim.x + threadIdx.x; + + if(gid >= input_size) + return; + + if(found_inf == nullptr || *found_inf == false) + { + size_t gsz = gridDim.x * blockDim.x; + CTYPE scale_factor = (grad_scale) ? static_cast(*grad_scale) : 1.0f; + uint32_t step_val = static_cast(*step) + 1; + + if(step_size < 0) + { + if(correct_bias) + { + CTYPE bias_correction1 = 1 - pow(beta1, step_val); + CTYPE bias_correction2 = 1 - pow(beta2, step_val); + step_size = lr * sqrt(bias_correction2) / bias_correction1; + } + else + { + step_size = lr; + } + } + + for(; gid < input_size; gid += gsz) + { + CTYPE grad = static_cast(grad_in[gid]); + if(grad_scale) + grad /= scale_factor; + + TransformersAdamWInternal(param_in, + param_out, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + grad, + beta1, + beta2, + eps, + lr_weight_decay, + step_size, + gid); + if(param_out_fp16) + param_out_fp16[gid] = static_cast(param_out[gid]); + } + } + else + { + AmpAdamSetOutputFromInput(param_in, + param_out, + param_out_fp16, + exp_avg_in, + exp_avg_out, + exp_avg_sq_in, + exp_avg_sq_out, + nullptr, + nullptr, + false, + input_size); + } +} diff --git a/src/kernels/MIOpenBatchNormActivBwdPerAct.cl b/src/kernels/MIOpenBatchNormActivBwdPerAct.cl index d9f81232bf..75bab9f7db 100644 --- a/src/kernels/MIOpenBatchNormActivBwdPerAct.cl +++ b/src/kernels/MIOpenBatchNormActivBwdPerAct.cl @@ -34,7 +34,7 @@ #endif #define MIOPEN_USE_AMDGCN 0 -#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X) +#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X || MIO_BN_GFX120X) #undef MIOPEN_USE_AMDGCN #define MIOPEN_USE_AMDGCN 1 #endif diff --git a/src/kernels/MIOpenBatchNormActivBwdSpatial.cl b/src/kernels/MIOpenBatchNormActivBwdSpatial.cl index c227ed1587..a83b10ee6f 100644 --- a/src/kernels/MIOpenBatchNormActivBwdSpatial.cl +++ b/src/kernels/MIOpenBatchNormActivBwdSpatial.cl @@ -32,7 +32,7 @@ #endif #define MIOPEN_USE_AMDGCN 0 -#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X) +#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X || MIO_BN_GFX120X) #undef MIOPEN_USE_AMDGCN #define MIOPEN_USE_AMDGCN 1 #endif diff --git a/src/kernels/MIOpenBatchNormActivFwdTrainSpatial.cl b/src/kernels/MIOpenBatchNormActivFwdTrainSpatial.cl index 329a943fd0..49632f93db 100644 --- a/src/kernels/MIOpenBatchNormActivFwdTrainSpatial.cl +++ b/src/kernels/MIOpenBatchNormActivFwdTrainSpatial.cl @@ -33,7 +33,7 @@ #endif #define MIOPEN_USE_AMDGCN 0 -#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X) +#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X || MIO_BN_GFX120X) #undef MIOPEN_USE_AMDGCN #define MIOPEN_USE_AMDGCN 1 #endif diff --git a/src/kernels/MIOpenBatchNormBwdSpatial.cl b/src/kernels/MIOpenBatchNormBwdSpatial.cl index 23103198ae..61618584ae 100644 --- a/src/kernels/MIOpenBatchNormBwdSpatial.cl +++ b/src/kernels/MIOpenBatchNormBwdSpatial.cl @@ -33,7 +33,7 @@ #endif #define MIOPEN_USE_AMDGCN 0 -#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X) +#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X || MIO_BN_GFX120X) #undef MIOPEN_USE_AMDGCN #define MIOPEN_USE_AMDGCN 1 #endif diff --git a/src/kernels/MIOpenBatchNormFwdTrainSpatial.cl b/src/kernels/MIOpenBatchNormFwdTrainSpatial.cl index 9eecb69905..c5e224ed16 100644 --- a/src/kernels/MIOpenBatchNormFwdTrainSpatial.cl +++ b/src/kernels/MIOpenBatchNormFwdTrainSpatial.cl @@ -33,7 +33,7 @@ #endif #define MIOPEN_USE_AMDGCN 0 -#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X) +#if defined(__AMDGCN__) && !(MIO_BN_GFX103X || MIO_BN_GFX110X || MIO_BN_GFX120X) #undef MIOPEN_USE_AMDGCN #define MIOPEN_USE_AMDGCN 1 #endif diff --git a/src/kernels/MIOpenCheckNumerics.cl b/src/kernels/MIOpenCheckNumerics.cl deleted file mode 100644 index 693d50818d..0000000000 --- a/src/kernels/MIOpenCheckNumerics.cl +++ /dev/null @@ -1,171 +0,0 @@ -#include "float_types.h" - -#define DTYPE _FLOAT -#define ACCUMTYPE float - -// Must keep this structure synchronized with one in MIOpenCheckNumerics -struct CheckNumericsResult -{ - float sum; - float absSum; - float min; - float max; - - int hasZero; - int hasNan; - int hasInf; -}; - -union AtomicFloat -{ - unsigned int u32; - float f32; -}; - -void cl_atomic_add_float(volatile __global float* addr, float val) -{ - union AtomicFloat current, expected, next; - - current.f32 = *addr; - do - { - expected.f32 = current.f32; - next.f32 = current.f32 + val; - - current.u32 = atomic_cmpxchg((volatile __global unsigned int*)addr, expected.u32, next.u32); - } while(current.u32 != expected.u32); -} - -void cl_atomic_min_float(volatile __global float* addr, float val) -{ - union AtomicFloat current, expected, next; - - current.f32 = *addr; - do - { - expected.f32 = current.f32; - next.f32 = fmin(current.f32, val); - - current.u32 = atomic_cmpxchg((volatile __global unsigned int*)addr, expected.u32, next.u32); - } while(current.u32 != expected.u32); -} - -void cl_atomic_max_float(volatile __global float* addr, float val) -{ - union AtomicFloat current, expected, next; - - current.f32 = *addr; - do - { - expected.f32 = current.f32; - next.f32 = fmax(current.f32, val); - - current.u32 = atomic_cmpxchg((volatile __global unsigned int*)addr, expected.u32, next.u32); - } while(current.u32 != expected.u32); -} - -#define GROUP_SIZE 256 -#define NUM_STATS 4 - -#define REDUCE_OPS(w) \ - if(lid < w) \ - { \ - stats[NUM_STATS * (lid) + 0] += stats[NUM_STATS * (lid + w) + 0]; \ - stats[NUM_STATS * (lid) + 1] += stats[NUM_STATS * (lid + w) + 1]; \ - stats[NUM_STATS * (lid) + 2] = \ - fmin(stats[NUM_STATS * (lid) + 2], stats[NUM_STATS * (lid + w) + 2]); \ - stats[NUM_STATS * (lid) + 3] = \ - fmax(stats[NUM_STATS * (lid) + 3], stats[NUM_STATS * (lid + w) + 3]); \ - barrier(CLK_LOCAL_MEM_FENCE); \ - } - -// Checks a block of data for abnormal numeric values : -__kernel void MIOpenCheckNumerics(const __global DTYPE* data, - int size, - __global struct CheckNumericsResult* abnormal, - int computeStats) -{ - const int lid = get_local_id(0); - const int gid = get_global_id(0); - const int total_wi_size = get_global_size(0); - - local float stats[4 * GROUP_SIZE]; - - int offset = gid; - ACCUMTYPE sum = 0.0f; - ACCUMTYPE abssum = 0.0f; - DTYPE minV = FLT_MAX; - DTYPE maxV = FLT_MIN; - while(offset < size) - { - DTYPE value = data[offset]; - sum += (ACCUMTYPE)value; - abssum += (ACCUMTYPE)(fabs( -#if MIOPEN_USE_BFP16 == 1 - bfloat16_to_float(value) -#else - value -#endif - )); - minV = min(minV, value); - maxV = max(maxV, value); - - if((ACCUMTYPE)(fabs( -#if MIOPEN_USE_BFP16 == 1 - bfloat16_to_float(value) -#else - value -#endif - )) <= 0.0f) - { // iszero check - abnormal->hasZero = 1; - } - if(isnan( -#if MIOPEN_USE_BFP16 == 1 - bfloat16_to_float(value) -#else - value -#endif - )) - { - abnormal->hasNan = 1; - } - if(isinf( -#if MIOPEN_USE_BFP16 == 1 - bfloat16_to_float(value) -#else - value -#endif - )) - { - abnormal->hasInf = 1; - } - offset += total_wi_size; - } - - if(computeStats) - { - stats[NUM_STATS * lid + 0] = (float)sum; - stats[NUM_STATS * lid + 1] = (float)abssum; - stats[NUM_STATS * lid + 2] = (float)minV; - stats[NUM_STATS * lid + 3] = (float)maxV; - barrier(CLK_LOCAL_MEM_FENCE); - - REDUCE_OPS(128) - REDUCE_OPS(64) - REDUCE_OPS(32) - REDUCE_OPS(16) - REDUCE_OPS(8) - REDUCE_OPS(4) - REDUCE_OPS(2) - REDUCE_OPS(1) - - if(lid == 0) - { - cl_atomic_add_float(&abnormal->sum, stats[0]); - cl_atomic_add_float(&abnormal->absSum, stats[1]); - cl_atomic_min_float(&abnormal->min, stats[2]); - cl_atomic_max_float(&abnormal->max, stats[3]); - } - } -} diff --git a/src/kernels/MIOpenCol2Im2d.cl b/src/kernels/MIOpenCol2Im2d.cl index 255ae283f5..24db79ee66 100644 --- a/src/kernels/MIOpenCol2Im2d.cl +++ b/src/kernels/MIOpenCol2Im2d.cl @@ -33,51 +33,61 @@ #define ACCUMULATOR_NEEDS_CONVERSION 0 #endif -__kernel void Col2Im2d(global _FLOAT* col, - const int col_h, - const int col_w, - const int wei_h, - const int wei_w, - const int pad_h, - const int pad_w, - const int stride_h, - const int stride_w, - const int dilation_h, - const int dilation_w, - const int height, - const int width, - global _FLOAT* im, - const int im_offset) +#ifndef MIOPEN_USE_64BIT_INDEX +#error "MIOPEN_USE_64BIT_INDEX must be defined" +#endif + +#if MIOPEN_USE_64BIT_INDEX +typedef ulong index_t; +#else +typedef uint index_t; +#endif + +__kernel void Col2Im2dU(global _FLOAT* col, + const uint col_h, + const uint col_w, + const uint wei_h, + const uint wei_w, + const uint pad_h, + const uint pad_w, + const uint stride_h, + const uint stride_w, + const uint dilation_h, + const uint dilation_w, + const uint height, + const uint width, + global _FLOAT* im, + const uint im_offset) { global _FLOAT* im_off = im + im_offset; - int gid = (int)get_global_id(0); + uint gid = (uint)get_global_id(0); - int im_ch = gid / (width * height); - int im_pix = gid % (width * height); - int im_h = (im_pix / width) + pad_h; - int im_w = (im_pix % width) + pad_w; + uint im_ch = gid / (width * height); + uint im_pix = gid % (width * height); + uint im_h = (im_pix / width) + pad_h; + uint im_w = (im_pix % width) + pad_w; - int start_h = (im_h < dilation_h * (wei_h - 1) + 1) - ? 0 - : (im_h - (dilation_h * (wei_h - 1) + 1)) / stride_h + 1; - int end_h = min(col_h, im_h / stride_h + 1); - int start_w = (im_w < dilation_w * (wei_w - 1) + 1) - ? 0 - : (im_w - (dilation_w * (wei_w - 1) + 1)) / stride_w + 1; - int end_w = min(col_w, im_w / stride_w + 1); + uint start_h = (im_h < dilation_h * (wei_h - 1) + 1) + ? 0 + : (im_h - (dilation_h * (wei_h - 1) + 1)) / stride_h + 1; + uint end_h = min(col_h, im_h / stride_h + 1); + uint start_w = (im_w < dilation_w * (wei_w - 1) + 1) + ? 0 + : (im_w - (dilation_w * (wei_w - 1) + 1)) / stride_w + 1; + uint end_w = min(col_w, im_w / stride_w + 1); - int ch_offset = im_ch * col_w * col_h * wei_w * wei_h; + index_t ch_offset = (index_t)(im_ch * col_w * col_h) * (index_t)(wei_w * wei_h); col += ch_offset; _FLOAT_ACCUM tmp = (_FLOAT_ACCUM)0; - for(int cy = start_h; cy < end_h; cy++) + for(uint cy = start_h; cy < end_h; cy++) { - for(int cx = start_w; cx < end_w; cx++) + for(uint cx = start_w; cx < end_w; cx++) { if((im_h - cy * stride_h) % dilation_h == 0 && (im_w - cx * stride_w) % dilation_w == 0) { - int col_off_y = cy + (((im_h - cy * stride_h) / dilation_h) * wei_w * col_h); - int col_off_x = cx + (((im_w - cx * stride_w) / dilation_w) * col_w * col_h); + index_t col_off_y = cy + (((im_h - cy * stride_h) / dilation_h) * wei_w * col_h); + index_t col_off_x = cx + (((im_w - cx * stride_w) / dilation_w) * col_w * col_h); tmp += CVT_FLOAT2ACCUM(col[col_off_y * col_w + col_off_x]); } diff --git a/src/kernels/MIOpenCol2Im3d.cl b/src/kernels/MIOpenCol2Im3d.cl index 107e01aac8..f521d02999 100644 --- a/src/kernels/MIOpenCol2Im3d.cl +++ b/src/kernels/MIOpenCol2Im3d.cl @@ -33,88 +33,92 @@ #define ACCUMULATOR_NEEDS_CONVERSION 0 #endif -__kernel void Col2Im3d(global _FLOAT* col, - const int col_d, - const int col_h, - const int col_w, - const int wei_d, - const int wei_h, - const int wei_w, - const int pad_d, - const int pad_h, - const int pad_w, - const int stride_d, - const int stride_h, - const int stride_w, - const int dilation_d, - const int dilation_h, - const int dilation_w, - const int depth, - const int height, - const int width, - global _FLOAT* im, - const unsigned long im_offset) +#ifndef MIOPEN_USE_64BIT_INDEX +#error "MIOPEN_USE_64BIT_INDEX must be defined" +#endif + +__kernel void Col2Im3dU(global _FLOAT* col, + const uint col_d, + const uint col_h, + const uint col_w, + const uint wei_d, + const uint wei_h, + const uint wei_w, + const uint pad_d, + const uint pad_h, + const uint pad_w, + const uint stride_d, + const uint stride_h, + const uint stride_w, + const uint dilation_d, + const uint dilation_h, + const uint dilation_w, + const uint depth, + const uint height, + const uint width, + global _FLOAT* im, + const unsigned long im_offset) { global _FLOAT* im_off = im + im_offset; - int gid = (int)get_global_id(0); + uint gid = (uint)get_global_id(0); - int im_ch = gid / (width * height * depth); - int itmp = gid % (width * height * depth); - int im_d = itmp / (width * height); - itmp = itmp % (width * height); - int im_h = itmp / width; - int im_w = itmp % width; + uint im_ch = gid / (width * height * depth); + uint itmp = gid % (width * height * depth); + uint im_d = itmp / (width * height); + itmp = itmp % (width * height); + uint im_h = itmp / width; + uint im_w = itmp % width; im_d += pad_d; im_h += pad_h; im_w += pad_w; - int start_d = (im_d < dilation_d * (wei_d - 1) + 1) - ? 0 - : (im_d - (dilation_d * (wei_d - 1) + 1)) / stride_d + 1; - int end_d = min(col_d, im_d / stride_d + 1); + uint start_d = (im_d < dilation_d * (wei_d - 1) + 1) + ? 0 + : (im_d - (dilation_d * (wei_d - 1) + 1)) / stride_d + 1; + uint end_d = min(col_d, im_d / stride_d + 1); - int start_h = (im_h < dilation_h * (wei_h - 1) + 1) - ? 0 - : (im_h - (dilation_h * (wei_h - 1) + 1)) / stride_h + 1; - int end_h = min(col_h, im_h / stride_h + 1); + uint start_h = (im_h < dilation_h * (wei_h - 1) + 1) + ? 0 + : (im_h - (dilation_h * (wei_h - 1) + 1)) / stride_h + 1; + uint end_h = min(col_h, im_h / stride_h + 1); - int start_w = (im_w < dilation_w * (wei_w - 1) + 1) - ? 0 - : (im_w - (dilation_w * (wei_w - 1) + 1)) / stride_w + 1; - int end_w = min(col_w, im_w / stride_w + 1); + uint start_w = (im_w < dilation_w * (wei_w - 1) + 1) + ? 0 + : (im_w - (dilation_w * (wei_w - 1) + 1)) / stride_w + 1; + uint end_w = min(col_w, im_w / stride_w + 1); #if MIOPEN_USE_64BIT_INDEX - long ch_offset = (long)im_ch * col_d * col_w * col_h * wei_d * wei_w * wei_h; + ulong ch_offset = (ulong)im_ch * col_d * col_w * col_h * wei_d * wei_w * wei_h; #else - int ch_offset = im_ch * col_d * col_w * col_h * wei_d * wei_w * wei_h; + uint ch_offset = im_ch * col_d * col_w * col_h * wei_d * wei_w * wei_h; #endif col += ch_offset; _FLOAT_ACCUM tmp = (_FLOAT_ACCUM)0; - for(int cz = start_d; cz < end_d; cz++) + for(uint cz = start_d; cz < end_d; cz++) { - for(int cy = start_h; cy < end_h; cy++) + for(uint cy = start_h; cy < end_h; cy++) { - for(int cx = start_w; cx < end_w; cx++) + for(uint cx = start_w; cx < end_w; cx++) { if((im_d - cz * stride_d) % dilation_d == 0 && (im_h - cy * stride_h) % dilation_h == 0 && (im_w - cx * stride_w) % dilation_w == 0) { - int z = (im_d - cz * stride_d) / dilation_d; - int y = (im_h - cy * stride_h) / dilation_h; - int x = (im_w - cx * stride_w) / dilation_w; + uint z = (im_d - cz * stride_d) / dilation_d; + uint y = (im_h - cy * stride_h) / dilation_h; + uint x = (im_w - cx * stride_w) / dilation_w; #if MIOPEN_USE_64BIT_INDEX - long col_off = - ((((((long)z * wei_h) + y) * wei_w + x) * col_d + cz) * col_h + cy) * + ulong col_off = + ((((((ulong)z * wei_h) + y) * wei_w + x) * col_d + cz) * col_h + cy) * col_w + cx; #else - int col_off = + uint col_off = (((((z * wei_h) + y) * wei_w + x) * col_d + cz) * col_h + cy) * col_w + cx; #endif diff --git a/src/kernels/MIOpenGetitem.cpp b/src/kernels/MIOpenGetitem.cpp new file mode 100644 index 0000000000..4daba996c8 --- /dev/null +++ b/src/kernels/MIOpenGetitem.cpp @@ -0,0 +1,158 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_DONT_USE_HIP_RUNTIME_HEADERS +#include +#include +#endif + +#include "hip_atomic.hpp" +#include "miopen_cstdint.hpp" +#include "float_types.h" +#include "tensor_view.hpp" + +template +__device__ void getitembuildindices(const IDX* __restrict__ index, + IDX* __restrict__ element_index, + E* __restrict__ error, + int32_t index_dim, + int32_t indexCount, + int32_t dim_size, + tensor_view_t<5> index_tv, + int32_t dim_offset, + int32_t dim_info_offset) +{ + const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; + + tensor_layout_t<5> ncdhw(index_tv, gid); + + if(ncdhw.layout[0] >= index_tv.size[0]) + return; + + uint64_t idx = index_tv.get_tensor_view_idx(ncdhw); + IDX getitem_index = index[idx]; + + if(getitem_index >= 0 && getitem_index < dim_size) + { + element_index[(gid * indexCount) + dim_offset] = getitem_index; + } + else if(getitem_index >= -dim_size && getitem_index < 0) + { + element_index[(gid * indexCount) + dim_offset] = getitem_index + dim_size; + } + else + { + error[dim_offset] = -1; + } + + if(gid == 0) + { + element_index[dim_info_offset + dim_offset] = index_dim; + } +} + +template +__device__ void getitembwd(const TI* __restrict__ dy, + IDX* __restrict__ element_index, + TO* __restrict__ dx, + int32_t start_dim, + int32_t indexCount, + tensor_view_t<5> dy_tv, + tensor_view_t<5> dx_tv, + int32_t dim_info_offset, + int32_t offset) +{ + const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; + + tensor_layout_t<5> ncdhw(dy_tv, gid); + + if(ncdhw.layout[0] >= dy_tv.size[0]) + return; + + tensor_layout_t<5> idx = ncdhw; + + if(indexCount > 0) + { + int32_t dim_cursor = ncdhw.layout[start_dim]; + int32_t i = start_dim; + int32_t j = 0; + + for(; i < start_dim + indexCount; ++i, ++j) + { + uint64_t dim_idx = static_cast(element_index[dim_info_offset + j]); + idx.layout[dim_idx] = + static_cast(element_index[(dim_cursor * indexCount) + j]); + } + + i = element_index[dim_info_offset + indexCount - 1] + 1; + dim_cursor = start_dim + 1; + for(; i < 5; ++i, ++dim_cursor) + { + idx.layout[i] = ncdhw.layout[dim_cursor]; + } + } + + idx.layout[0] += offset; + ncdhw.layout[0] += offset; + + atomic_add_g(&dx[dx_tv.get_tensor_view_idx(idx)], dy[dy_tv.get_tensor_view_idx(ncdhw)]); +} + +extern "C" __global__ void GetItemBuildIndices(const INDEX_TYPE* __restrict__ index, + INDEX_TYPE* __restrict__ element_index, + ERROR_TYPE* __restrict__ error, + int32_t index_dim, + int32_t indexCount, + int32_t dim_size, + tensor_view_t<5> index_tv, + int32_t dim_offset, + int32_t dim_info_offset) +{ + // instantiate the kernel + getitembuildindices(index, + element_index, + error, + index_dim, + indexCount, + dim_size, + index_tv, + dim_offset, + dim_info_offset); +} + +extern "C" __global__ void GetitemBwd(const INPUT_TYPE* __restrict__ dy, + INDEX_TYPE* __restrict__ element_index, + OUTPUT_TYPE* __restrict__ dx, + int32_t start_dim, + int32_t indexCount, + tensor_view_t<5> dy_tv, + tensor_view_t<5> dx_tv, + int32_t dim_info_offset, + int32_t offset) +{ + // instantiate the kernel + getitembwd( + dy, element_index, dx, start_dim, indexCount, dy_tv, dx_tv, dim_info_offset, offset); +} diff --git a/src/kernels/MIOpenGroupNorm.cpp b/src/kernels/MIOpenGroupNorm.cpp index 54d70d323b..1ddf58d232 100644 --- a/src/kernels/MIOpenGroupNorm.cpp +++ b/src/kernels/MIOpenGroupNorm.cpp @@ -30,17 +30,18 @@ #include "float_types.h" -extern "C" __global__ void GroupNormFwdContiguous(const FLOAT* __restrict__ x, - FLOAT* __restrict__ y, - const FLOAT* __restrict__ weight, - const FLOAT* __restrict__ bias, - FLOAT_ACCUM* __restrict__ mean, - FLOAT_ACCUM* __restrict__ rstd, - float eps, - uint64_t num_groups, - uint64_t num_channels, - uint64_t numel_per_channel, - bool mode) +template +__device__ void groupnormfwdcontiguous(const TI* __restrict__ x, + const TI* __restrict__ weight, + const TI* __restrict__ bias, + TO* __restrict__ y, + TO* __restrict__ mean, + TO* __restrict__ rstd, + float eps, + uint64_t num_groups, + uint64_t num_channels, + uint64_t numel_per_channel, + bool mode) { /* * Each group works on a single channel. @@ -98,9 +99,9 @@ extern "C" __global__ void GroupNormFwdContiguous(const FLOAT* __restrict__ x, if(lid == 0) { if(mean) - mean[gid] = pmean; + mean[gid] = CVT_ACCUM2FLOAT(pmean); if(rstd) - rstd[gid] = prstd; + rstd[gid] = CVT_ACCUM2FLOAT(prstd); } // forward calculation @@ -119,3 +120,20 @@ extern "C" __global__ void GroupNormFwdContiguous(const FLOAT* __restrict__ x, y[idx] = CVT_ACCUM2FLOAT(val); } } + +extern "C" __global__ void GroupNormFwdContiguous(const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ weight, + const INPUT_TYPE* __restrict__ bias, + OUTPUT_TYPE* __restrict__ y, + OUTPUT_TYPE* __restrict__ mean, + OUTPUT_TYPE* __restrict__ rstd, + float eps, + uint64_t num_groups, + uint64_t num_channels, + uint64_t numel_per_channel, + bool mode) +{ + // instantiate the kernel + groupnormfwdcontiguous( + x, weight, bias, y, mean, rstd, eps, num_groups, num_channels, numel_per_channel, mode); +} diff --git a/src/kernels/MIOpenIm2d2Col.cl b/src/kernels/MIOpenIm2d2Col.cl index 852ccff955..52f2f12fe8 100644 --- a/src/kernels/MIOpenIm2d2Col.cl +++ b/src/kernels/MIOpenIm2d2Col.cl @@ -111,27 +111,27 @@ typedef long index_t; typedef int index_t; #endif -kernel void Im2d2Col(const int data_size_off, - global data_t* im, - const int im_offset, - const int h, - const int w, - const int wei_h, - const int wei_w, - const int out_h, - const int out_w, - const int pad_h, - const int pad_w, - const int stride_h, - const int stride_w, - const int dilation_h, - const int dilation_w, - global data_t* col, - const int num_ch_per_wg, - const int num_im_blks_x, - const int num_im_blks, - const int tile_sz_x, - const int tile_sz_y) +kernel void Im2d2Col_v2(const int data_size_off, + global data_t* im, + const ulong im_offset, + const int h, + const int w, + const int wei_h, + const int wei_w, + const int out_h, + const int out_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + global data_t* col, + const int num_ch_per_wg, + const int num_im_blks_x, + const int num_im_blks, + const int tile_sz_x, + const int tile_sz_y) { /// NUM_CH_PER_WG {1;4} /// THREADS_PER_CH {256; 64} diff --git a/src/kernels/MIOpenLayerNorm.cpp b/src/kernels/MIOpenLayerNorm.cpp index b73632b9c5..9a5e736f94 100644 --- a/src/kernels/MIOpenLayerNorm.cpp +++ b/src/kernels/MIOpenLayerNorm.cpp @@ -31,23 +31,16 @@ #include "miopen_cstdint.hpp" #include "float_types.h" -#if MIOPEN_USE_BFP16 == 1 -#define CVT_FLOAT2ACCUM(x) (bfloat16_to_float(x)) -#define CVT_ACCUM2FLOAT(x) (float_to_bfloat16(x)) -#define CVT_INTEGRAL2ACCUM(x) ((_FLOAT_ACCUM)(x)) -#define CVT_FP32_2FLOAT(x) (CVT_ACCUM2FLOAT(x)) -#define CVT_FP32_2ACCUM(x) (x) -#endif - -extern "C" __global__ void LayernormFwdContiguous(const FLOAT* __restrict__ x, - FLOAT* __restrict__ y, - const FLOAT* __restrict__ weight, - const FLOAT* __restrict__ bias, - FLOAT_ACCUM* __restrict__ mean, - FLOAT_ACCUM* __restrict__ rstd, - float eps, - uint64_t inner_size, - bool mode) +template +__device__ void layernormfwdcontiguous(const TI* __restrict__ x, + const TI* __restrict__ weight, + const TI* __restrict__ bias, + TO* __restrict__ y, + TO* __restrict__ mean, + TO* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) { /* * Each group works on a single channel. @@ -103,9 +96,9 @@ extern "C" __global__ void LayernormFwdContiguous(const FLOAT* __restrict__ x, if(lid == 0) { if(mean) - mean[gid] = pmean; + mean[gid] = CVT_ACCUM2FLOAT(pmean); if(rstd) - rstd[gid] = prstd; + rstd[gid] = CVT_ACCUM2FLOAT(prstd); } // forward calculation @@ -116,10 +109,378 @@ extern "C" __global__ void LayernormFwdContiguous(const FLOAT* __restrict__ x, FLOAT_ACCUM pweight; FLOAT_ACCUM pbias; - pweight = mode ? CVT_FLOAT2ACCUM(weight[i]) : CVT_FP32_2ACCUM(1.0f); - pbias = mode ? CVT_FLOAT2ACCUM(bias[i]) : static_cast(0); + pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE) ? CVT_FP32_2ACCUM(1.0f) + : CVT_FLOAT2ACCUM(weight[i]); + pbias = + (mode == MIOPEN_ELEMENTWISE_AFFINE) ? static_cast(0) : CVT_FLOAT2ACCUM(bias[i]); FLOAT_ACCUM val = (CVT_FLOAT2ACCUM(x[idx]) - pmean) * prstd * pweight + pbias; y[idx] = CVT_ACCUM2FLOAT(val); } } + +template +__device__ void addlayernormfwdcontiguous(const TI* __restrict__ x, + const TI* __restrict__ x2, + const TI* __restrict__ weight, + const TI* __restrict__ bias, + TO* __restrict__ y, + TO* __restrict__ mean, + TO* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) +{ + const uint64_t gid = blockIdx.x; + const uint64_t lid = threadIdx.x; + + FLOAT_ACCUM pmean = static_cast(0); + FLOAT_ACCUM pvar = static_cast(0); + __shared__ FLOAT_ACCUM ltmp1[LOCAL_SIZE]; + __shared__ FLOAT_ACCUM ltmp2[LOCAL_SIZE]; + + // reduce sum for mean and var + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t x_idx = gid * inner_size + i; + + FLOAT_ACCUM tmp = CVT_FLOAT2ACCUM(x[x_idx]) + CVT_FLOAT2ACCUM(x2[x_idx]); + pmean += tmp; + pvar += tmp * tmp; + } + + ltmp1[lid] = pmean; + ltmp2[lid] = pvar; + __syncthreads(); + for(uint32_t i = LOCAL_SIZE >> 1; i > 0; i >>= 1) + { + if(lid < i) + { + ltmp1[lid] += ltmp1[lid + i]; + ltmp2[lid] += ltmp2[lid + i]; + } + __syncthreads(); + } + pmean = ltmp1[0] / inner_size; + pvar = ltmp2[0] / inner_size - pmean * pmean; + FLOAT_ACCUM prstd = rsqrt(pvar + FLOAT_ACCUM(eps)); + + if(lid == 0) + { + if(mean) + mean[gid] = CVT_ACCUM2FLOAT(pmean); + if(rstd) + rstd[gid] = CVT_ACCUM2FLOAT(prstd); + } + + // forward calculation + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t idx = gid * inner_size + i; + + FLOAT_ACCUM pweight; + FLOAT_ACCUM pbias; + + pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? CVT_FP32_2ACCUM(1.0f) + : CVT_FLOAT2ACCUM(weight[i]); + pbias = (mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? static_cast(0) + : CVT_FLOAT2ACCUM(bias[i]); + + FLOAT_ACCUM val = + (CVT_FLOAT2ACCUM(x[idx]) + CVT_FLOAT2ACCUM(x2[idx]) - pmean) * prstd * pweight + pbias; + y[idx] = CVT_ACCUM2FLOAT(val); + } +} + +template +__device__ void t5layernormfwdcontiguous(const TI* __restrict__ x, + const TI* __restrict__ weight, + TO* __restrict__ y, + TO* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) +{ + const uint64_t gid = blockIdx.x; + const uint64_t lid = threadIdx.x; + + FLOAT_ACCUM pvar = static_cast(0); + __shared__ FLOAT_ACCUM ltmp[LOCAL_SIZE]; + + // reduce sum + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t x_idx = gid * inner_size + i; + + FLOAT_ACCUM tmp = CVT_FLOAT2ACCUM(x[x_idx]); + pvar += tmp * tmp; + } + + ltmp[lid] = pvar; + __syncthreads(); + for(uint32_t i = LOCAL_SIZE >> 1; i > 0; i >>= 1) + { + if(lid < i) + { + ltmp[lid] += ltmp[lid + i]; + } + __syncthreads(); + } + pvar = ltmp[0] / inner_size; + FLOAT_ACCUM prstd = rsqrt(pvar + FLOAT_ACCUM(eps)); + + if(lid == 0) + { + if(rstd) + rstd[gid] = CVT_ACCUM2FLOAT(prstd); + } + + // forward calculation + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t idx = gid * inner_size + i; + + FLOAT_ACCUM pweight; + + pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) ? CVT_FP32_2ACCUM(1.0f) + : CVT_FLOAT2ACCUM(weight[i]); + + FLOAT_ACCUM val = (CVT_FLOAT2ACCUM(x[idx])) * prstd * pweight; + y[idx] = CVT_ACCUM2FLOAT(val); + } +} + +template +__device__ void t5layernormbwdcontiguous(const TI* __restrict__ dy, + const TI* __restrict__ x, + const TI* __restrict__ weight, + const TI* __restrict__ rstd, + TO* __restrict__ dx, + uint64_t inner_size, + int32_t mode) +{ + const uint64_t gid = blockIdx.x; + const uint64_t lid = threadIdx.x; + + __shared__ FLOAT_ACCUM ltmp[LOCAL_SIZE]; + + // reduce sum + FLOAT_ACCUM sum = 0; + + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t x_idx = gid * inner_size + i; + + FLOAT_ACCUM pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) ? CVT_FP32_2ACCUM(1.0f) + : CVT_FLOAT2ACCUM(weight[i]); + + FLOAT_ACCUM pdy = dy ? CVT_FLOAT2ACCUM(dy[x_idx]) : 0; + sum += pdy * CVT_FLOAT2ACCUM(x[x_idx]) * pweight; + } + + ltmp[lid] = sum; + __syncthreads(); + for(uint32_t i = LOCAL_SIZE >> 1; i > 0; i >>= 1) + { + if(lid < i) + { + ltmp[lid] += ltmp[lid + i]; + } + __syncthreads(); + } + + FLOAT_ACCUM ds = ltmp[0]; + FLOAT_ACCUM s = 1.0f / inner_size; + FLOAT_ACCUM prstd = CVT_FLOAT2ACCUM(rstd[gid]); + FLOAT_ACCUM a = ds * prstd * prstd * prstd * s; + + for(uint64_t i = lid; i < inner_size; i += LOCAL_SIZE) + { + size_t idx = gid * inner_size + i; + + FLOAT_ACCUM pweight = (mode == MIOPEN_ELEMENTWISE_AFFINE_T5) ? CVT_FP32_2ACCUM(1.0f) + : CVT_FLOAT2ACCUM(weight[i]); + FLOAT_ACCUM pdy = dy ? CVT_FLOAT2ACCUM(dy[idx]) : 0; + + FLOAT_ACCUM val = prstd * pdy * pweight - a * CVT_FLOAT2ACCUM(x[idx]); + dx[idx] = CVT_ACCUM2FLOAT(val); + } +} + +template +__device__ void t5layernormbwdweightcontiguous(const TI* __restrict__ dy, + const TI* __restrict__ x, + const TI* __restrict__ rstd, + TO* __restrict__ dw, + uint64_t outer_size, + uint64_t inner_size) +{ + const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; + + FLOAT_ACCUM sum = static_cast(0); + for(uint64_t i = 0; i < outer_size; ++i) + { + uint64_t input_idx = i * inner_size + gid; + + FLOAT_ACCUM prstd = CVT_FLOAT2ACCUM(rstd[i]); + FLOAT_ACCUM pdy = dy ? CVT_FLOAT2ACCUM(dy[input_idx]) : 0; + + sum += pdy * CVT_FLOAT2ACCUM(x[input_idx]) * prstd; + } + + if(dw) + { + dw[gid] = CVT_ACCUM2FLOAT(sum); + } +} + +template +__device__ void t5layernormbwdweightcontiguousparallel(const TI* __restrict__ dy, + const TI* __restrict__ x, + const TI* __restrict__ rstd, + TO* __restrict__ workspace, + uint64_t outer_size, + uint64_t inner_size, + uint64_t parallel_size) +{ + const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; + + if(gid >= inner_size * parallel_size) + return; + + uint64_t pid = gid / inner_size; + + uint64_t input_idx = gid; + + FLOAT_ACCUM sum = static_cast(0); + + if(dy) + { + for(uint64_t i = pid; i < outer_size; i += parallel_size) + { + FLOAT_ACCUM prstd = CVT_FLOAT2ACCUM(rstd[i]); + FLOAT_ACCUM pdy = CVT_FLOAT2ACCUM(dy[input_idx]); + + sum += pdy * CVT_FLOAT2ACCUM(x[input_idx]) * prstd; + input_idx += inner_size * parallel_size; + } + } + + workspace[gid] = CVT_ACCUM2FLOAT(sum); +} + +template +__device__ void t5layernormbwdcontiguousreduceSum(const TI* __restrict__ workspace, + TO* __restrict__ dw, + uint64_t inner_size, + uint64_t parallel_size) +{ + const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; + + if(gid >= inner_size) + return; + + FLOAT_ACCUM sum = static_cast(0); + for(uint64_t i = 0; i < parallel_size; ++i) + { + uint64_t input_idx = i * inner_size + gid; + sum += CVT_FLOAT2ACCUM(workspace[input_idx]); + } + + if(dw) + { + dw[gid] = CVT_ACCUM2FLOAT(sum); + } +} + +extern "C" __global__ void LayernormFwdContiguous(const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ weight, + const INPUT_TYPE* __restrict__ bias, + OUTPUT_TYPE* __restrict__ y, + OUTPUT_TYPE* __restrict__ mean, + OUTPUT_TYPE* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) +{ + // instantiate the kernel + layernormfwdcontiguous( + x, weight, bias, y, mean, rstd, eps, inner_size, mode); +} + +extern "C" __global__ void AddLayernormFwdContiguous(const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ x2, + const INPUT_TYPE* __restrict__ weight, + const INPUT_TYPE* __restrict__ bias, + OUTPUT_TYPE* __restrict__ y, + OUTPUT_TYPE* __restrict__ mean, + OUTPUT_TYPE* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) +{ + // instantiate the kernel + addlayernormfwdcontiguous( + x, x2, weight, bias, y, mean, rstd, eps, inner_size, mode); +} + +extern "C" __global__ void T5LayernormFwdContiguous(const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ weight, + OUTPUT_TYPE* __restrict__ y, + OUTPUT_TYPE* __restrict__ rstd, + float eps, + uint64_t inner_size, + int32_t mode) +{ + // instantiate the kernel + t5layernormfwdcontiguous(x, weight, y, rstd, eps, inner_size, mode); +} + +extern "C" __global__ void T5LayernormBwdContiguous(const INPUT_TYPE* __restrict__ dy, + const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ weight, + const INPUT_TYPE* __restrict__ rstd, + OUTPUT_TYPE* __restrict__ dx, + uint64_t inner_size, + int32_t mode) +{ + // instantiate the kernel + t5layernormbwdcontiguous(dy, x, weight, rstd, dx, inner_size, mode); +} + +extern "C" __global__ void T5LayernormBwdWeightContiguous(const INPUT_TYPE* __restrict__ dy, + const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ rstd, + OUTPUT_TYPE* __restrict__ dw, + uint64_t outer_size, + uint64_t inner_size) +{ + // instantiate the kernel + t5layernormbwdweightcontiguous( + dy, x, rstd, dw, outer_size, inner_size); +} + +extern "C" __global__ void +T5LayernormBwdWeightContiguousParallel(const INPUT_TYPE* __restrict__ dy, + const INPUT_TYPE* __restrict__ x, + const INPUT_TYPE* __restrict__ rstd, + OUTPUT_TYPE* __restrict__ workspace, + uint64_t outer_size, + uint64_t inner_size, + uint64_t parallel_size) +{ + // instantiate the kernel + t5layernormbwdweightcontiguousparallel( + dy, x, rstd, workspace, outer_size, inner_size, parallel_size); +} + +extern "C" __global__ void +T5LayernormBwdContiguousReduceSum(const INPUT_TYPE* __restrict__ workspace, + OUTPUT_TYPE* __restrict__ dw, + uint64_t inner_size, + uint64_t parallel_size) +{ + // instantiate the kernel + t5layernormbwdcontiguousreduceSum( + workspace, dw, inner_size, parallel_size); +} diff --git a/src/kernels/MIOpenArgmax.cpp b/src/kernels/MIOpenReduceExtreme.cpp similarity index 55% rename from src/kernels/MIOpenArgmax.cpp rename to src/kernels/MIOpenReduceExtreme.cpp index aa459ab693..a75e15c2ac 100644 --- a/src/kernels/MIOpenArgmax.cpp +++ b/src/kernels/MIOpenReduceExtreme.cpp @@ -2,7 +2,7 @@ * * MIT License * - * Copyright (c) 2023 Advanced Micro Devices, Inc. + * Copyright (c) 2024 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal @@ -29,13 +29,15 @@ #endif #include "float_types.h" +#include "MIOpenReduceExtreme.hpp" -template -__device__ void argmaxfwdcontiguous(const TI* __restrict__ x, - TO* __restrict__ y, - uint64_t output_numel, - int32_t reduce_size, - uint64_t inner_size) +template +__device__ void extremefwdcontiguous(const TI* __restrict__ x, + TO* __restrict__ y, + int32_t* __restrict__ indice, + uint64_t output_numel, + int32_t reduce_size, + uint64_t inner_size) { const uint64_t gid = threadIdx.x + blockIdx.x * blockDim.x; if(gid >= output_numel) @@ -43,29 +45,28 @@ __device__ void argmaxfwdcontiguous(const TI* __restrict__ x, uint64_t input_idx = (gid / inner_size) * inner_size * reduce_size + gid % inner_size; - int32_t max_idx = 0; - TI max = x[input_idx]; + int32_t extreme_idx = 0; + FLOAT_ACCUM extreme = CVT_FLOAT2ACCUM(x[input_idx]); for(int32_t k = 1; k < reduce_size; ++k) { input_idx += inner_size; - TI val = x[input_idx]; - if(max < val) - { - max = val; - max_idx = k; - } + FLOAT_ACCUM val = CVT_FLOAT2ACCUM(x[input_idx]); + reduce_func{}.calculate(extreme, val, extreme_idx, k); } - - y[gid] = max_idx; + if(y) + y[gid] = CVT_ACCUM2FLOAT(extreme); + indice[gid] = extreme_idx; } -extern "C" __global__ void ArgmaxFwdContiguous(const INPUT_TYPE* __restrict__ x, - OUTPUT_TYPE* __restrict__ y, - uint64_t output_numel, - int32_t reduce_size, - uint64_t inner_size) +extern "C" __global__ void ExtremeFwdContiguous(const INPUT_TYPE* __restrict__ x, + OUTPUT_TYPE* __restrict__ y, + int32_t* __restrict__ indice, + uint64_t output_numel, + int32_t reduce_size, + uint64_t inner_size) { // instantiate the kernel - argmaxfwdcontiguous(x, y, output_numel, reduce_size, inner_size); + extremefwdcontiguous( + x, y, indice, output_numel, reduce_size, inner_size); } diff --git a/src/kernels/MIOpenReduceExtreme.hpp b/src/kernels/MIOpenReduceExtreme.hpp new file mode 100644 index 0000000000..b53e820475 --- /dev/null +++ b/src/kernels/MIOpenReduceExtreme.hpp @@ -0,0 +1,103 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef GUARD_KERNELS_MIOPENREDUCEEXTREME_HPP +#define GUARD_KERNELS_MIOPENREDUCEEXTREME_HPP + +enum class ReduceExtremeOp_t +{ + Argmin = 1, + Argmax, + Min, + Max, + First_ = Argmin, + Last_ = Max, +}; + +#ifndef __HIP_DEVICE_COMPILE__ +static_assert(MIOPEN_REDUCE_EXTREME_ARGMIN == static_cast(ReduceExtremeOp_t::Argmin)); +static_assert(MIOPEN_REDUCE_EXTREME_ARGMAX == static_cast(ReduceExtremeOp_t::Argmax)); +static_assert(MIOPEN_REDUCE_EXTREME_MIN == static_cast(ReduceExtremeOp_t::Min)); +static_assert(MIOPEN_REDUCE_EXTREME_MAX == static_cast(ReduceExtremeOp_t::Max)); +#endif + +template +struct reduce_func +{ + inline constexpr void calculate(T1& a, T1 b, T2& c, T2 d) const; +}; + +template +struct reduce_func +{ + inline constexpr void calculate(T1& a, T1 b, T2& c, T2 d) const + { + if(a < b) + { + a = b; + c = d; + } + } +}; + +template +struct reduce_func +{ + inline constexpr void calculate(T1& a, T1 b, T2& c, T2 d) const + { + if(a > b) + { + a = b; + c = d; + } + } +}; + +template +struct reduce_func +{ + inline constexpr void calculate(T1& a, T1 b, T2& c, T2 d) const + { + if(a < b) + { + a = b; + c = d; + } + } +}; + +template +struct reduce_func +{ + inline constexpr void calculate(T1& a, T1 b, T2& c, T2 d) const + { + if(a > b) + { + a = b; + c = d; + } + } +}; +#endif // GUARD_GUARD_KERNELS_MIOPENREDUCEEXTREME_HPP diff --git a/src/kernels/MIOpenSoftmaxAttn.cpp b/src/kernels/MIOpenSoftmaxAttn.cpp index b880a46bcf..417ee615d8 100644 --- a/src/kernels/MIOpenSoftmaxAttn.cpp +++ b/src/kernels/MIOpenSoftmaxAttn.cpp @@ -23,27 +23,51 @@ * SOFTWARE. * *******************************************************************************/ + +// rocblas operates with non-ieee FP8 +#define MIOPEN_FP8_IEEE_EXPONENT_BIAS 0 + +#ifndef MIOPEN_DONT_USE_HIP_RUNTIME_HEADERS +#include #include -//#include -//#include +#include +#endif -#include "miopen_limits.hpp" #include "miopen_cstdint.hpp" - +#include "miopen_limits.hpp" #include "miopen_rocrand.hpp" +#define MIOPEN_ENABLE_F8_DEVICE_CODE 1 +#include + +// Some versions of amd-clang treats MIOPEN_ENABLE_F8_DEVICE_CODE as unused despite the fact that +// it's used in the subsequent include. Some other versions amd-clang failed to compile everything +// without MIOPEN_ENABLE_F8_DEVICE_CODE explicitely defined. +// Fake-using it. +#ifndef MIOPEN_ENABLE_F8_DEVICE_CODE +#message "MIOPEN_ENABLE_F8_DEVICE_CODE must be defined" +#endif + #ifndef THREADS #define THREADS 64 #endif +#ifndef OUT_TYPE +#define OUT_TYPE float +#endif + +#ifndef dO_TYPE +#define dO_TYPE float +#endif + namespace { -constexpr float max_op(float a, float b) { return a > b ? a : b; }; constexpr float plus_op(float a, float b) { return a + b; }; +constexpr float fmaxf_op(float a, float b) { return fmaxf(a, b); }; -/// atomically calcutates maximum of non-negative ordered values -/// it produces wrong results for negatve values or nans -/// but it is a final amax reducton step and we expect only non-negative ordered values -__device__ float atomicMaxOfAbsolutValues(float* addr, float value) +/// Atomically calculates maximum of non-negative ordered values. +/// Produces wrong results for negatve values or nans, +/// but it is a final amax reducton step and we expect only non-negative ordered values. +__forceinline__ __device__ float atomicMaxOfNonNegative(float* addr, float value) { // ordered non-negatve and even infinity values can be compared as integers // NOLINTBEGIN @@ -53,7 +77,7 @@ __device__ float atomicMaxOfAbsolutValues(float* addr, float value) } template -__device__ float reductionFullWarp(float reduced_val, uint32_t laneId, Op op) +__forceinline__ __device__ float reductionFullWarp(float reduced_val, uint32_t laneId, Op op) { static_assert(WARP_SIZE != 0, "WARP_SIZEmust not be 0"); static_assert((SWIZZLE_SIZE & (SWIZZLE_SIZE - 1)) == 0, @@ -109,9 +133,11 @@ __device__ float reductionFullWarp(float reduced_val, uint32_t laneId, Op op) }; template -__device__ float +__forceinline__ __device__ float reductionBlock(float local_val, Op op, uint32_t lid, uint32_t laneId, uint32_t warpId) { + static_assert(NumWarps <= warpSize); + static_assert((NumWarps & (NumWarps - 1)) == 0, "NumWarps must be a power of 2"); __shared__ float reduction_tmp[NumWarps]; float reduced_val = reductionFullWarp(local_val, laneId, op); @@ -133,14 +159,14 @@ reductionBlock(float local_val, Op op, uint32_t lid, uint32_t laneId, uint32_t w }; template -__device__ float reductionCommon(const float* __restrict__ line, - const float init_value, - const uint32_t seq_len, - ReductionOp&& op, - ElementOp&& eop, - uint32_t lid, - uint32_t laneId, - uint32_t warpId) +__forceinline__ __device__ float reductionCommon(const float* __restrict__ line, + const float init_value, + const uint32_t seq_len, + ReductionOp&& op, + ElementOp&& eop, + uint32_t lid, + uint32_t laneId, + uint32_t warpId) { float reduced_val = (lid < seq_len) ? eop(line[lid]) : init_value; @@ -149,44 +175,56 @@ __device__ float reductionCommon(const float* __restrict__ line, return reductionBlock(reduced_val, op, lid, laneId, warpId); }; + +__forceinline__ __device__ bool doDropout(float dropout, rocrand_device::xorwow_engine* state) +{ + return (dropout > 0.0f && prng::xorwow_uniform(state) < dropout); +} } // namespace -extern "C" __global__ void SoftMaxWarp(const float* in, - float* out, - float* __restrict__ M, - float* __restrict__ Z, - float* __restrict__ Amax, - const float* __restrict__ descale_Q, - const float* __restrict__ descale_K, - const float* __restrict__ scale_S, - miopen::prng::xorwow_state* __restrict__ rng_state, - const float* __restrict__ dropout_P, - uint32_t seq_len, - uint64_t nhs) +extern "C" __global__ void __launch_bounds__(THREADS) + SoftMaxWarp(const float* in, + OUT_TYPE* out, + float* __restrict__ M, + float* __restrict__ Z, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ scale_S, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + uint32_t seq_len, + uint64_t nhs) { + static_assert(THREADS % warpSize == 0); constexpr uint32_t NumWarps = THREADS / warpSize; const uint32_t lid = threadIdx.x; const uint32_t laneId = lid % warpSize; const uint32_t warpId = lid / warpSize; const float descaler = (descale_Q ? *descale_Q : 1.0f) * (descale_K ? *descale_K : 1.0f); - const float dropout = (dropout_P && rng_state) ? (*dropout_P) : 0.0f; - const float scaler = (scale_S ? *scale_S : 1.0f) * (1.0f - dropout); - const bool save_stats = M && Z && laneId == 0; - miopen::prng::xorwow_state rng = dropout > 0.0f - ? rng_state[blockIdx.x * blockDim.x + threadIdx.x] - : miopen::prng::xorwow_state{}; + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler = (scale_S ? *scale_S : 1.0f) / (1.0f - dropout); + const bool save_stats = M && Z && (laneId == 0); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } float r_Amax = 0; for(uint64_t gid = blockIdx.x * NumWarps + warpId; gid < nhs; gid += gridDim.x * NumWarps) { const float* line = in + gid * seq_len + laneId; - float* res = out + gid * seq_len + laneId; + auto res = out + gid * seq_len + laneId; float local_val = (laneId < seq_len) ? (*line) * descaler : std::numeric_limits::lowest(); - float r_max = reductionFullWarp(local_val, laneId, max_op); + float r_max = reductionFullWarp(local_val, laneId, fmaxf_op); local_val = (laneId < seq_len) ? expf(local_val - r_max) : 0; @@ -194,16 +232,14 @@ extern "C" __global__ void SoftMaxWarp(const float* in, local_val *= r_sum; - // it is supposed to be maximum of absolut values - // but after the exponent it is already non-negative - // so just a maximum can be used - r_Amax = max_op(r_Amax, local_val); + // It is supposed to be maximum of absolute values, + // however we do not need abs() because expf() above produces + // non-negative value. Plain max() is enough. + r_Amax = fmaxf_op(r_Amax, local_val); if(laneId < seq_len) { - *res = (dropout > 0.0f && miopen::prng::xorwow_uniform(&rng) < dropout) - ? 0.0f - : local_val * scaler; + *res = static_cast(doDropout(dropout, &rng) ? 0.0f : local_val * scaler); } if(save_stats) @@ -215,54 +251,56 @@ extern "C" __global__ void SoftMaxWarp(const float* in, if(Amax) { - r_Amax = reductionBlock(r_Amax, max_op, lid, laneId, warpId); + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); if(lid == 0) { - atomicMaxOfAbsolutValues(Amax, r_Amax); + atomicMaxOfNonNegative(Amax, r_Amax); } } - - if(dropout > 0.0f) - { - rng_state[blockIdx.x * blockDim.x + threadIdx.x] = rng; - } } -extern "C" __global__ void SoftMaxBlock(const float* in, - float* out, - float* __restrict__ M, - float* __restrict__ Z, - float* __restrict__ Amax, - const float* __restrict__ descale_Q, - const float* __restrict__ descale_K, - const float* __restrict__ scale_S, - miopen::prng::xorwow_state* __restrict__ rng_state, - const float* __restrict__ dropout_P, - uint32_t seq_len, - uint64_t nhs) +extern "C" __global__ void __launch_bounds__(THREADS) + SoftMaxBlock(const float* in, + OUT_TYPE* out, + float* __restrict__ M, + float* __restrict__ Z, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ scale_S, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + uint32_t seq_len, + uint64_t nhs) { + static_assert(THREADS % warpSize == 0); constexpr uint32_t NumWarps = THREADS / warpSize; const uint32_t lid = threadIdx.x; const uint32_t laneId = lid % warpSize; const uint32_t warpId = lid / warpSize; const float descaler = (descale_Q ? *descale_Q : 1.0f) * (descale_K ? *descale_K : 1.0f); - const float dropout = (dropout_P && rng_state) ? (*dropout_P) : 0.0f; - const float scaler = (scale_S ? *scale_S : 1.0f) * (1.0f - dropout); - const bool save_stats = M && Z && lid == 0; - miopen::prng::xorwow_state rng = dropout > 0.0f - ? rng_state[blockIdx.x * blockDim.x + threadIdx.x] - : miopen::prng::xorwow_state{}; + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler = (scale_S ? *scale_S : 1.0f) / (1.0f - dropout); + const bool save_stats = M && Z && (lid == 0); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } float r_Amax = 0; for(uint64_t gid = blockIdx.x; gid < nhs; gid += gridDim.x) { const float* line = in + gid * seq_len + lid; - float* res = out + gid * seq_len + lid; + auto res = out + gid * seq_len + lid; float local_val = (lid < seq_len) ? (*line) * descaler : std::numeric_limits::lowest(); - float r_max = reductionBlock(local_val, max_op, lid, laneId, warpId); + float r_max = reductionBlock(local_val, fmaxf_op, lid, laneId, warpId); local_val = (lid < seq_len) ? expf(local_val - r_max) : 0; @@ -270,16 +308,14 @@ extern "C" __global__ void SoftMaxBlock(const float* in, local_val *= r_sum; - // it is supposed to be maximum of absolut values - // but after the exponent it is already non-negative - // so just a maximum can be used - r_Amax = max_op(r_Amax, local_val); + // It is supposed to be maximum of absolute values, + // however we do not need abs() because expf() above produces + // non-negative value. Plain max() is enough. + r_Amax = fmaxf_op(r_Amax, local_val); if(lid < seq_len) { - *res = (dropout > 0.0f && miopen::prng::xorwow_uniform(&rng) < dropout) - ? 0.0f - : local_val * scaler; + *res = static_cast(doDropout(dropout, &rng) ? 0.0f : local_val * scaler); } if(save_stats) @@ -291,56 +327,58 @@ extern "C" __global__ void SoftMaxBlock(const float* in, if(Amax) { - r_Amax = reductionBlock(r_Amax, max_op, lid, laneId, warpId); + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); if(lid == 0) { - atomicMaxOfAbsolutValues(Amax, r_Amax); + atomicMaxOfNonNegative(Amax, r_Amax); } } - - if(dropout > 0.0f) - { - rng_state[blockIdx.x * blockDim.x + threadIdx.x] = rng; - } } -extern "C" __global__ void SoftMaxCommon(const float* in, - float* out, - float* __restrict__ M, - float* __restrict__ Z, - float* __restrict__ Amax, - const float* __restrict__ descale_Q, - const float* __restrict__ descale_K, - const float* __restrict__ scale_S, - miopen::prng::xorwow_state* __restrict__ rng_state, - const float* __restrict__ dropout_P, - uint32_t seq_len, - uint64_t nhs) +extern "C" __global__ void __launch_bounds__(THREADS) + SoftMaxCommon(const float* in, + OUT_TYPE* out, + float* __restrict__ M, + float* __restrict__ Z, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ scale_S, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + uint32_t seq_len, + uint64_t nhs) { + static_assert(THREADS % warpSize == 0); constexpr uint32_t NumWarps = THREADS / warpSize; const uint32_t lid = threadIdx.x; const uint32_t laneId = lid % warpSize; const uint32_t warpId = lid / warpSize; const float descaler = (descale_Q ? *descale_Q : 1.0f) * (descale_K ? *descale_K : 1.0f); - const float dropout = (dropout_P && rng_state) ? (*dropout_P) : 0.0f; - const float scaler = (scale_S ? *scale_S : 1.0f) * (1.0f - dropout); - const bool save_stats = M && Z && lid == 0; - miopen::prng::xorwow_state rng = dropout > 0.0f - ? rng_state[blockIdx.x * blockDim.x + threadIdx.x] - : miopen::prng::xorwow_state{}; + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler = (scale_S ? *scale_S : 1.0f) / (1.0f - dropout); + const bool save_stats = M && Z && (lid == 0); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } float r_Amax = 0; for(uint64_t gid = blockIdx.x; gid < nhs; gid += gridDim.x) { const float* line = in + gid * seq_len; - float* res = out + gid * seq_len; + auto res = out + gid * seq_len; float r_max = reductionCommon( line, std::numeric_limits::lowest(), seq_len, - max_op, + fmaxf_op, [descaler](float x) { return x * descaler; }, lid, laneId, @@ -360,14 +398,13 @@ extern "C" __global__ void SoftMaxCommon(const float* in, { float local_val = expf(line[loop_lid] * descaler - r_max) * r_sum; - // it is supposed to be maximum of absolut values - // but after the exponent it is already non-negative - // so just a maximum can be used - r_Amax = max_op(r_Amax, local_val); + // It is supposed to be maximum of absolute values, + // however we do not need abs() because expf() above produces + // non-negative value. Plain max() is enough. + r_Amax = fmaxf_op(r_Amax, local_val); - res[loop_lid] = (dropout > 0.0f && miopen::prng::xorwow_uniform(&rng) < dropout) - ? 0.0f - : local_val * scaler; + res[loop_lid] = + static_cast(doDropout(dropout, &rng) ? 0.0f : local_val * scaler); } if(save_stats) @@ -379,26 +416,22 @@ extern "C" __global__ void SoftMaxCommon(const float* in, if(Amax) { - r_Amax = reductionBlock(r_Amax, max_op, lid, laneId, warpId); + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); if(lid == 0) { - atomicMaxOfAbsolutValues(Amax, r_Amax); + atomicMaxOfNonNegative(Amax, r_Amax); } } - - if(dropout > 0.0f) - { - rng_state[blockIdx.x * blockDim.x + threadIdx.x] = rng; - } } -extern "C" __global__ void ScaleReduce(const float* __restrict__ in, - float* __restrict__ out, - float* __restrict__ Amax, - const float* __restrict__ descale_S, - const float* __restrict__ descale_V, - const float* __restrict__ scale_O, - uint64_t nhsd) +extern "C" __global__ void __launch_bounds__(THREADS) + ScaleReduce(const float* __restrict__ in, + OUT_TYPE* __restrict__ out, + float* __restrict__ Amax, + const float* __restrict__ descale_S, + const float* __restrict__ descale_V, + const float* __restrict__ scale_O, + uint64_t nhsd) { const float descaler = (*descale_S) * (*descale_V); const float scaler = (*scale_O); @@ -408,7 +441,7 @@ extern "C" __global__ void ScaleReduce(const float* __restrict__ in, auto in_ptr = in + gid; auto out_ptr = out + gid; - const auto end = in_ptr + nhsd; + const auto end = in + nhsd; float r_Amax = 0; @@ -416,9 +449,9 @@ extern "C" __global__ void ScaleReduce(const float* __restrict__ in, { const auto res = *in_ptr * descaler; - r_Amax = max_op(r_Amax, fabsf(res)); + r_Amax = fmaxf_op(r_Amax, fabsf(res)); - *out_ptr = res * scaler; + *out_ptr = static_cast(res * scaler); in_ptr += step; out_ptr += step; @@ -429,9 +462,352 @@ extern "C" __global__ void ScaleReduce(const float* __restrict__ in, const uint32_t laneId = lid % warpSize; const uint32_t warpId = lid / warpSize; - r_Amax = reductionBlock(r_Amax, max_op, lid, laneId, warpId); + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); + if(lid == 0) + { + atomicMaxOfNonNegative(Amax, r_Amax); + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + ScaleRowReduceWarp(const dO_TYPE* __restrict__ dO, + const OUT_TYPE* __restrict__ O, + float* __restrict__ out, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_O, + const float* __restrict__ dropout_P, + uint32_t d, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + const float scaler = (*descale_dO) * (*descale_O) * (1.0f - (*dropout_P)); + + for(uint64_t gid = blockIdx.x * NumWarps + warpId; gid < nhs; gid += gridDim.x * NumWarps) + { + float local_val = 0.0f; + if(laneId < d) + { + const auto dO_ptr = dO + gid * d + laneId; + const auto O_ptr = O + gid * d + laneId; + + local_val = static_cast(*dO_ptr) * static_cast(*O_ptr) * scaler; + } + + local_val = reductionFullWarp(local_val, laneId, plus_op); + + if(laneId == 0) + { + out[gid] = local_val; + } + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + ScaleRowReduceBlock(const dO_TYPE* __restrict__ dO, + const OUT_TYPE* __restrict__ O, + float* __restrict__ out, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_O, + const float* __restrict__ dropout_P, + uint32_t d, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + const float scaler = (*descale_dO) * (*descale_O) * (1.0f - (*dropout_P)); + + for(uint64_t gid = blockIdx.x; gid < nhs; gid += gridDim.x) + { + const auto dO_ptr = dO + gid * d + lid; + const auto O_ptr = O + gid * d + lid; + + float local_val = 0.0f; + if(lid < d) + { + local_val = static_cast(*dO_ptr) * static_cast(*O_ptr) * scaler; + } + + local_val = reductionBlock(local_val, plus_op, lid, laneId, warpId); + + if(lid == 0) + { + out[gid] = local_val; + } + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + ScaleRowReduceCommon(const dO_TYPE* __restrict__ dO, + const OUT_TYPE* __restrict__ O, + float* __restrict__ out, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_O, + const float* __restrict__ dropout_P, + uint32_t d, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + const float scaler = (*descale_dO) * (*descale_O) * (1.0f - (*dropout_P)); + + for(uint64_t gid = blockIdx.x; gid < nhs; gid += gridDim.x) + { + const auto dO_ptr = dO + gid * d; + const auto O_ptr = O + gid * d; + + float local_val = + (lid < d) ? static_cast(dO_ptr[lid]) * static_cast(O_ptr[lid]) * scaler + : 0.0f; + + for(uint32_t loop_lid = lid + blockDim.x; loop_lid < d; loop_lid += blockDim.x) + local_val += + static_cast(dO_ptr[loop_lid]) * static_cast(O_ptr[loop_lid]) * scaler; + + local_val = reductionBlock(local_val, plus_op, lid, laneId, warpId); + + if(lid == 0) + { + out[gid] = local_val; + } + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + BwdAttentionWarp(float* __restrict__ QxK_S, + const float* dOxV, + OUT_TYPE* dS, // may overlap with dOxV + const float* __restrict__ M, + const float* __restrict__ Zinv, + const float* __restrict__ dOxO, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_V, + const float* __restrict__ scale_S, + const float* __restrict__ scale_dS, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + float scale, + uint32_t seq_len, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler_dropout = 1.0f - dropout; + const float scaler_inv_dropout = 1.0f / scaler_dropout; + + const float descaler_QxK = (*descale_Q) * (*descale_K); + const float descaler_dOxV = (*descale_dO) * (*descale_V) * scaler_dropout; + + const float scaler_S = (*scale_S); + const float scaler_dS = (*scale_dS); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } + + float r_Amax = 0; + + for(uint64_t gid = blockIdx.x * NumWarps + warpId; gid < nhs && laneId < seq_len; + gid += gridDim.x * NumWarps) + { + const float M_val = M[gid]; + const float Zinv_val = Zinv[gid]; + const float dOxO_val = dOxO[gid]; + + const size_t idx = gid * seq_len + laneId; + + const float QxK_val = doDropout(dropout, &rng) ? 0.0f + : expf(QxK_S[idx] * descaler_QxK - M_val) * + Zinv_val * scaler_inv_dropout; + + QxK_S[idx] = QxK_val * scaler_S; + + const float dOxV_val = (dOxV[idx] * descaler_dOxV - dOxO_val) * scale * QxK_val; + + dS[idx] = static_cast(dOxV_val * scaler_dS); + + r_Amax = fmaxf_op(r_Amax, fabsf(dOxV_val)); + } + + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); + if(lid == 0) + { + atomicMaxOfNonNegative(Amax, r_Amax); + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + BwdAttentionBlock(float* __restrict__ QxK_S, + const float* dOxV, + OUT_TYPE* dS, // may overlap with dOxV + const float* __restrict__ M, + const float* __restrict__ Zinv, + const float* __restrict__ dOxO, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_V, + const float* __restrict__ scale_S, + const float* __restrict__ scale_dS, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + float scale, + uint32_t seq_len, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler_dropout = 1.0f - dropout; + const float scaler_inv_dropout = 1.0f / scaler_dropout; + + const float descaler_QxK = (*descale_Q) * (*descale_K); + const float descaler_dOxV = (*descale_dO) * (*descale_V) * scaler_dropout; + + const float scaler_S = (*scale_S); + const float scaler_dS = (*scale_dS); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } + + float r_Amax = 0; + + for(uint64_t gid = blockIdx.x; gid < nhs && lid < seq_len; gid += gridDim.x) + { + const float M_val = M[gid]; + const float Zinv_val = Zinv[gid]; + const float dOxO_val = dOxO[gid]; + + const size_t idx = gid * seq_len + lid; + + const float QxK_val = doDropout(dropout, &rng) ? 0.0f + : expf(QxK_S[idx] * descaler_QxK - M_val) * + Zinv_val * scaler_inv_dropout; + + QxK_S[idx] = QxK_val * scaler_S; + + const float dOxV_val = (dOxV[idx] * descaler_dOxV - dOxO_val) * scale * QxK_val; + + dS[idx] = static_cast(dOxV_val * scaler_dS); + + r_Amax = fmaxf_op(r_Amax, fabsf(dOxV_val)); + } + + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); + if(lid == 0) + { + atomicMaxOfNonNegative(Amax, r_Amax); + } +} + +extern "C" __global__ void __launch_bounds__(THREADS) + BwdAttentionCommon(float* __restrict__ QxK_S, + const float* dOxV, + OUT_TYPE* dS, // may overlap with dOxV + const float* __restrict__ M, + const float* __restrict__ Zinv, + const float* __restrict__ dOxO, + float* __restrict__ Amax, + const float* __restrict__ descale_Q, + const float* __restrict__ descale_K, + const float* __restrict__ descale_dO, + const float* __restrict__ descale_V, + const float* __restrict__ scale_S, + const float* __restrict__ scale_dS, + const uint64_t* __restrict__ seed, + const uint64_t* __restrict__ offset, + const float* __restrict__ dropout_P, + float scale, + uint32_t seq_len, + uint64_t nhs) +{ + static_assert(THREADS % warpSize == 0); + constexpr uint32_t NumWarps = THREADS / warpSize; + const uint32_t lid = threadIdx.x; + const uint32_t laneId = lid % warpSize; + const uint32_t warpId = lid / warpSize; + + const float dropout = (dropout_P && seed && offset) ? (*dropout_P) : 0.0f; + const float scaler_dropout = 1.0f - dropout; + const float scaler_inv_dropout = 1.0f / scaler_dropout; + + const float descaler_QxK = (*descale_Q) * (*descale_K); + const float descaler_dOxV = (*descale_dO) * (*descale_V) * scaler_dropout; + + const float scaler_S = (*scale_S); + const float scaler_dS = (*scale_dS); + + rocrand_state_xorwow rng; + if(dropout > 0.0f) + { + const uint64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + rocrand_init(prng::hash(*seed + idx), 0, *offset, &rng); + } + + float r_Amax = 0; + + for(uint64_t gid = blockIdx.x; gid < nhs; gid += gridDim.x) + { + const float M_val = M[gid]; + const float Zinv_val = Zinv[gid]; + const float dOxO_val = dOxO[gid]; + + float* QxK_S_ptr = QxK_S + gid * seq_len; + const float* dOxV_ptr = dOxV + gid * seq_len; + OUT_TYPE* dS_ptr = dS + gid * seq_len; + + for(uint32_t loop_lid = lid; loop_lid < seq_len; loop_lid += blockDim.x) + { + const float QxK_val = doDropout(dropout, &rng) + ? 0.0f + : expf(QxK_S_ptr[loop_lid] * descaler_QxK - M_val) * + Zinv_val * scaler_inv_dropout; + + QxK_S_ptr[loop_lid] = QxK_val * scaler_S; + + const float dOxV_val = + (dOxV_ptr[loop_lid] * descaler_dOxV - dOxO_val) * scale * QxK_val; + + dS_ptr[loop_lid] = static_cast(dOxV_val * scaler_dS); + + r_Amax = fmaxf_op(r_Amax, fabsf(dOxV_val)); + } + } + + r_Amax = reductionBlock(r_Amax, fmaxf_op, lid, laneId, warpId); if(lid == 0) { - atomicMaxOfAbsolutValues(Amax, r_Amax); + atomicMaxOfNonNegative(Amax, r_Amax); } -} \ No newline at end of file +} diff --git a/src/kernels/MIOpenSum.cpp b/src/kernels/MIOpenSum.cpp index 6e005d60dc..28a0326873 100644 --- a/src/kernels/MIOpenSum.cpp +++ b/src/kernels/MIOpenSum.cpp @@ -30,14 +30,6 @@ #include "float_types.h" -#if MIOPEN_USE_BFP16 == 1 -#define CVT_FLOAT2ACCUM(x) (bfloat16_to_float(x)) -#define CVT_ACCUM2FLOAT(x) (float_to_bfloat16(x)) -#define CVT_INTEGRAL2ACCUM(x) ((_FLOAT_ACCUM)(x)) -#define CVT_FP32_2FLOAT(x) (CVT_ACCUM2FLOAT(x)) -#define CVT_FP32_2ACCUM(x) (x) -#endif - extern "C" __global__ void SumParallelFwdContiguous(const FLOAT* __restrict__ x, FLOAT* __restrict__ y, uint64_t output_numel, diff --git a/src/kernels/MIOpenVecAdd.cpp b/src/kernels/MIOpenVecAdd.cpp new file mode 100644 index 0000000000..2bf684eca8 --- /dev/null +++ b/src/kernels/MIOpenVecAdd.cpp @@ -0,0 +1,41 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#ifndef MIOPEN_DONT_USE_HIP_RUNTIME_HEADERS +#include +#include +#endif + +extern "C" __global__ void vector_add_hip(const float* a, const float* b, float* c, size_t vec_size) +{ + // Get the index of the current element + size_t index = blockIdx.x * blockDim.x + threadIdx.x; + + // Check if the index is within the range of the vector size + if(index < vec_size) + { + c[index] = a[index] + b[index]; // Add the two elements + } +} diff --git a/src/kernels/MIOpenVecAddOCL.cl b/src/kernels/MIOpenVecAddOCL.cl new file mode 100644 index 0000000000..4d0af0f729 --- /dev/null +++ b/src/kernels/MIOpenVecAddOCL.cl @@ -0,0 +1,37 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +__kernel void vector_add_ocl(__global const float* x, + __global const float* y, + __global float* restrict z, + ulong vec_size) +{ + // get index of the work item + int index = get_global_id(0); + + // check if the index is within the vector size + if(index < vec_size) + z[index] = x[index] + y[index]; // add the vector elements +} diff --git a/src/kernels/batchnorm_functions.h b/src/kernels/batchnorm_functions.h index 4764324db5..e267be79c1 100644 --- a/src/kernels/batchnorm_functions.h +++ b/src/kernels/batchnorm_functions.h @@ -132,8 +132,9 @@ // TODO: Spaghetti code!!! // MIOPEN_USE_AMDGCN may be defined before this header. #ifndef MIOPEN_USE_AMDGCN -#if defined(__AMDGCN__) && \ - !((defined(MIO_BN_GFX103X) && MIO_BN_GFX103X) || (defined(MIO_BN_GFX110X) && MIO_BN_GFX110X)) +#if defined(__AMDGCN__) && \ + !((defined(MIO_BN_GFX103X) && MIO_BN_GFX103X) || \ + (defined(MIO_BN_GFX110X) && MIO_BN_GFX110X) || (defined(MIO_BN_GFX120X) && MIO_BN_GFX120X)) #define MIOPEN_USE_AMDGCN 1 #else #define MIOPEN_USE_AMDGCN 0 @@ -165,6 +166,10 @@ #define MIO_BN_GFX110X 0 #endif +#ifndef MIO_BN_GFX120X +#define MIO_BN_GFX120X 0 +#endif + #define UNUSED __attribute__((__unused__)) #if(MIO_BN_VARIANT != 4) diff --git a/src/kernels/gfx908.kdb.bz2 b/src/kernels/gfx908.kdb.bz2 index 59ddf7ce13..5f97b09861 100644 --- a/src/kernels/gfx908.kdb.bz2 +++ b/src/kernels/gfx908.kdb.bz2 @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:231930d1ddb47184c478fb48b8f2dd96eec8c7501bc3886279d974ac8b4a65de -size 248661707 +oid sha256:42491c91c931635a4da570b61f80f99290efcd2cf062057e4a8135bccdcc2965 +size 243216518 diff --git a/src/kernels/gfx908_ConvAsm1x1U_metadata.ktn.model b/src/kernels/gfx908_ConvAsm1x1U_metadata.ktn.model index 8ebbaf9798..d9b90262b9 100644 --- a/src/kernels/gfx908_ConvAsm1x1U_metadata.ktn.model +++ b/src/kernels/gfx908_ConvAsm1x1U_metadata.ktn.model @@ -1,5 +1,9 @@ { - "num_tuning_params": 8, + "predict_type": 0, + "num_tuning_params": { + "fwd": 8, + "bwd": 8 + }, "decodings": { "tunings": { "0": "-1", diff --git a/src/kernels/gfx90a.kdb.bz2 b/src/kernels/gfx90a.kdb.bz2 index a53fb807e3..e54999303f 100644 --- a/src/kernels/gfx90a.kdb.bz2 +++ b/src/kernels/gfx90a.kdb.bz2 @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d3febff109a05c5a9cac5e3db14d052081f8a15a431270613a27fba741a33947 -size 138714110 +oid sha256:f06de59b6e773d6a94a4a802ded71e13ca68bb1b90f0a63e07671abbc85df2fc +size 141791876 diff --git a/src/kernels/gfx90a_ConvHipIgemmGroupFwdXdlops_metadata.ktn.model b/src/kernels/gfx90a_ConvHipIgemmGroupFwdXdlops_metadata.ktn.model index fc95cc39eb..ea201b334f 100644 --- a/src/kernels/gfx90a_ConvHipIgemmGroupFwdXdlops_metadata.ktn.model +++ b/src/kernels/gfx90a_ConvHipIgemmGroupFwdXdlops_metadata.ktn.model @@ -1,5 +1,8 @@ { - "num_tuning_params": 9, + "predict_type": 0, + "num_tuning_params": { + "fwd": 9 + }, "decodings": { "tunings": { "0": "-1", diff --git a/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_decoder.ktn.model b/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_decoder.ktn.model new file mode 100644 index 0000000000..1a1a752e9b --- /dev/null +++ b/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_decoder.ktn.model @@ -0,0 +1 @@ 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","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","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","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","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","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","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","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","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","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","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","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","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","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","joyPvc0arT6S01Y95oeBPf/iqr4G+z49NVRGPtfBir25Px+9JZVoPp3Fy72TvZa+cQm4vXlLEj92hUk+lnUAv2NuIj45U3O+xMWTvhAaFL+ihwg+Udw3PX8lIT9i4ja/O+BsvbVXLD6qTz0+JYO1vkbcpT7Vdk2/HsfHvCFckj4Zv1W+QuLgPova7r6TjA6/xBpdPiS3Er+sg4G+sSAEvy/EQz9yVb29mTTEvVF0or5oEPK9w21/PmsojD3jE5E+RR8EvpQT7z4/vvO+GymIvjRFXT6qnJW+kHRZuok4+74TBhO/HmYQvmRYEL7ONlO/8jCpvsqjyz58dA49WvaUPLXq1L4zER8+VerwvZnkFz4b52m+uzJUPUnnGD8vgxG/QwT3vRfKCT/wMCi/smzoPWRrFT5cGnS+a2EOvtsLKj+dOvA9VPsTvx8CFj95Gw8/+kuDvjYolr4RPxw+oQ2cPR+xUb2Xi5C+eCPcvpOxWr6+wJu9ofs4P+zyGr9zqg6+ZDQ2PyG18D3h4ci+Tk0mvRDBWr47bY+/rw6uvg3zA7+a868+at4oPsoqpz6Nh2I+GkkwvVV+Yr8LaNo+GX3tvh8s3z7dtLY9RvihPjsvtL71mOW+bS+qPoERyT3Lole+ZZcMvwNaMDzhrPG+ueX5PF1GTb0FIC+/xebhPquUFr4RP4q/JEoZPyNonT4TtoA8LfByP3lBhb+GY7+9Hpt0vwuMaT55hK2+f+wIv5pq+b2I1Ey/LQuKPkGnF7/g0eS+z9BGv+oFML+oGwM/g/ccPwOOYb1Zlq4+TahCPaVdUb8htUG9d+wnvZ9M2j5Qbge/0FCBvt7rDr964ug6dCKyv6Vlnr4VfMC+4xaEvoS97b4hDRs/7FGavkC1Pr+G9KC91DCFP7PmDj80QI2+Bs5HvgdKeD9T99C+V9kZvtWKfz5fPNE9NkbWPv691r74uj6+yinIvl1Ezr5+s7W+/DiZv3fPm7745uY+XQfkvYGgK79+8Z+/gbp6vUhhxr3tFFC/","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","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","uTuzPkDocT3dJMk9wkVRvrNpIz5olRc+kKlhPhyqDz8MXOk8zFlIvptmAb/wKm087vu+vsfJzj4yRNq9L2lBPkfZhr4AOJI8RIoeP9FTnL4HLcU9e/5dvRjPxT2WRP29wcn6Po/JXD7mC6I+RDI+vSqWRTxq9A6/liryPsR7Uj5lo9k+RvPNvkXo4r6aBMY9wnvzPrCubj5XwZ8+xP0Iv1RuWT6CzNa9wiw7v+uTdLnOfoe9mrZMPzdNFT42IIM+bacIPyKHFj1QO2Y/z5XrPp8YJ786s1y+5PvXvbbUUT4nzjG9SjUAPqweSj94U0K943ujPSaYjT7CRZu+dd4dvgHEhj5IdKe+4MOKvpnEbb/du1i/QSzvPc25Bj/KCsO+ARoBPvx9L79Ng7G9PzWev7ZfXb9bXwo+WjMKP/QHG79eXB6/p/+Ev929NL5UsmI/d2JkP1rZ7z50iSE9wSK9vkQ2lr5Jxs0+SQLVPSRZnz9Ojnm/umzzvV3Jbz+d6xS/aLe9Ppv9ZT7MjI6/vZ2VPakyAb9L5XK/d8WtvtMPpj6tlTS/9LaBvpTblT2akx69tfS0vlWFQj70ASW//mmkvu80V755enm+mXO7PuQTUz+Ag1E/qnGyvsHhV75E9VW/jd2avp1nmT/X1KQ+UWwZPq4ODD/Zy9Q9PWklv5NzP7+oodg93QlfvkP6nz6CQy++xFkrvoNeKb9h3nm9/UZKv+JM6b7gvp099YHQvWzPpL59Z38+wj4av/fR+b0VgWk8XX9Uvqgq2bx711k9jjutPKk1UT50lqe+fxBlPYIOITzu08Y+OuEwvkbteD5aZ1Q9DJk5vniLOr5z3Vq9uQepvg75Rr1s2vC+qDWEvugJir8wS5o+eGw9PSXCrr4dGUM+74nhPWE3xz31bs0+AmciPfA3K76uVKm+2QXovWMzq76GRo4+q2GePd63Mj+9vvs+go2Hvms3iL6djPo9jr9rvmPEobwKPpU+6ZXYvsGshT3nU8g8Anncvck0Jj1y1qE+","aPSCvnD2GD7IlJY+B2nLvV/eF73E0Ao7hIV9PvTrNb3ZpEq+RlSMvemOi74LHyc+INpGvkmwYz5VfqM+gWBLPH/kC79z230+aQAPv/eq0z7wqMO9OaN+ujmhm73IcNg+pIovv4TFaDuusQi+etNoPrHQQr58EaG+Z5ypvt9AiL7ezs8+QTdUvk9/I76vTCo/wyo7v8FhUT329wC9DDCdvp+rfz7v6WA84vAHPhaRET9Qf6G9q5MTPRW6mr1KQbM+zvBMvtwnCD/KrWa+riDXvgWYIL9rlH6/EOCXvpck+z6CJti7EVGUPuGnIz/KkN8+XGhCPmvoxD7aJUW9UhJPPvGsGr7qIYe+lFnCvXKSEb499bQ8Sl2bPUC6+T3/C8c+10zoPB5Rw7xGBPs7X6Fhvox9vL0MAM49PAsDvtgWkj4aBye+u3DuPsoWM74F8Oo9Cr6qPkb7Hj6cQGe9heeDvixvzj7WQhA+R0kYPlvdqT4xyuM8Fl3WvqrErDzBeIg/jKXVPtw3677+mV4+3V8qveEpmT7WvjE/lqeYPjzNNDxvDOs9IyexvZxYvL6LkSM+NF6xvbjJFz/LYQ++N3avuiWwo76Bh0m+bpjAPq9LLL4VJ8S+94sCv2GIK7/Ta6E8c7tivygyfD7lqDY/29XzvvbMiT3iz9y9z7qcvuNKUb1VPj0+/BTMvjAcNr1Wpdo8jgoBv/7OA7++3yM/7H0PviYJkL6iXPs9VTp3vYwSU79dzCy/zglkvWRC2z3XCJq+QQajvqRmor87ugC/WmtbP5w1W76rzQs+0lzmvQoarD6gIG29Kl7NPWoXMz5cT1M/UOXAvlUapb5EGsk+Meh4vjDkib4Xga4+khefv5AdRT6h75e+pggJvxVwB7+ChTk+0bNuvy9tpT1D+Q0+qn+Dvt2w5b6RN/o9QyZ2vzAiKr/VSYC+cF0lvpM1a739XQY/w3WUPY/S7741/Ee+4gtMvhd2iL7IuEQ/dfZXPrpvsj6+u9u+akXaPucctr5LFvu+","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","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","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","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","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","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","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","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","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","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","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","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","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","jja3PuPYZj5CnB8+bBZqPVf/ED54egK+KqUuPn5vpT2aP4U+RlvQPtrloD5ooWI+RFLWPDQPPbxxrt892/cOvvSTkT1PSys+3HYsPkSPrL3yS8m9VUYoPvie0T7zviK5P7cevve4Hb2vqr29Tk7KvblNmD4v6xC+f/Y7P3JZZz36fgU+uUf2vABWs72+p+Q8vkOXPbDc07w0T24+HA+svZ7QvT69m5U9rnIhPmYIJT7S4RI+oxDpPol8kbxu8Bk9rIHkPSAQEz23mFQ+2ytbPQGRPjxTGmc+4SIkPvxuWL3PGnM9SwXEPRVzfD3Y6788mMIAPqJ0nj42CTI9PUMFPTOGEzyZYZI995ALvXp6ED/3qJU+waBfvD86gz4BXHa+ZsoYPrJtgDzv2qc+PSHtvOFWer00mB4/8ZVZvplCLT9m2v4+uAhMvcE3ED0c1SG+nF9hvvVLTT682JM+kSxCv+rKST1XQLE9LVSMPXj+nTyJsjw+1fQ6vsNpID7Z8kE+44O9Phak0b3mFK09jN4zve6o+DygN0C9zc1AvoAFvD4rP46+xrLXveWEFb41TLG+83DUPmlQwj7z6Fk9d42YPgxwXj9qDYi+7bSlPjzpD79muFI+3XCaPR84JD4DEvk+CFLLvSwJvz6x0Ac/RlhCvoH7Vb4lL94+OMFIPc2Eyz2Bx469I0RSPuXUYD4eF54+pKgNPp0L4j3QvCE9gxwuPhirlDvVA8U9mj+gPUYgQ75lrIE+bKysvSaXKD6xJV0+Pxeyvre8az0FxnI9mXkoPneOnj1sixk9gjVbPkEfQT5tFbg+kKi/vvr/3D5w8Fe+q9g0P7rNID7NO+G7PyWLPo6zAT3OHQU+ElsbPgo6GT1nN1M+E2eOvklWkr2UqOe9ofgSvlbH2z4034a+CIOxPfeIuj5jFVs+hF/XPEbDmj6NVaa7ntrMPSamA758Ock+eGM4OwnzTj0bDGS+H8pIvjZ44r3Twta+xiYZPgmLmD4rlMA9v0GsO4K5zj5bmT69","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","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","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","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","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","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","j4EFvbpuET9pmlI+Y8mCO8SDVD4ALdc+oP/XPgfZST4rQAO+3OEBP03cOT58TZ89Wuk3PcCNET5sVY0+o0kevgxYkD5vXXc+IbaFPR/wzT7ATK49P10GPSWa9T3FTvA+6Hv4PjHgkL7F86U+gfJgPtWtCz5udFm+iSFTPqGbUb7+Lmy8hxfWveglLb0ImPg85aI9P3uSvTtEPUQ+wfk7Pi2MMb5BFeq9R/rrPtya8z4XscY+DzMNPvVhNj6q4pi+1a1gPiGmkT7PaVE+pX6LPmzJ+72ETgc9Gu3lPejJdr2jMrg+e9gAPhfopz5F+f68HFlwPkD/0D4NE4E+IoA6Pi4Bvb3kyIu9HJOMPnWfgj5yY32+iQZjPa6ffb6wM4C+roTRPtpdxjxuqVg9EM76PgavNT7GTHq94bZKPQzHrD51S1k+kdhTvlOqvL32m2i+qhydu8Yo9j1sWYo9VgFWvo3ou71wJq0+erITvheYPr0hOKU8TKyBPclSQr64e6s+iZfGvSyQg72wBaY+ViAJPmiN/zsgJ+c918WbPiCAnj04DXE97WvNPAs1Ob55d8I+b7OsvULLT7yAMiw+BQEYvo3BDT7nXm4+e+yVvnVThL7S++i9IASLPkBMxz4q2Dk9/5vdPZg4Iz696pK9tqrHvLLd8j0I5yg9Ew4ovmeDQTw+Pb+8+LB+PlRyn73B7aa9aiaWvcI2Tz1IQEW9Ks2tPBIeaD67Gmk9rf+1vfKVpr3q7mw+Y7qsvGO3fDxLIDa9X+umvWXV3b6LF88+s4Aevb6BgL2dw4U9yDNeva1aMD40anw+jsOGvqLwQz7WjYW9PDzlPLFJMD6PApi9SJHOPSDU0z3RsrW94dirPsOAED73AII9du76PalqlD5n92o9kUY8Pi7zsrwAKyK+62N1vYD23rZIvxe9+LntvQsv2Tx90Rg9aA/PvaJb+r2hN+49Z7UOvmm7/D0DX54+JlsKP6z/Yj0ymyq+0ccDvgwGRr4PsZy+Og+JPZfXjL420tA7","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","zNopvv3/9bwWcxw+josiPh1t6j5AvxC+/r4uPv2TUz0hato8Ih1evk0AkL2jNCG9hfsdPyo6ET74FuQ+USXCvcfHeL2p7/I85nUrPpS387z3TSw+LteWvDg/0D56ntA+tF6Svsoiej529Io+JKauPUNeBr0IcAe9DZ0BvhoZQ70IAnC9iv6uPk2L7bvTUdQ6Fip4PuuV3L0ztIC+G9SAvbxioL1WLRY+J30Iu+VeAz47tAA/SYcpPtKphL4j5VY+9hrEPhZEIb7f7Cg+/aJHPuPLwL3K3IE+I5OrvlwKiz4hjrw+YtqFvi0dAD99RWi+zUPSPjQBnTw6iQM/csBUvTL/qj7nqQS+N1Rzvm+sQj7Qo1U9f/cdvt3ox71vv1i+fVHEPd6ADD5fsyQ8UwGhPkNKbL6x/i29u6M3PROUcr7h56A+P1JHvp8nID1UhpQ+Tm5OPnJhHj1iIYQ9JRKGvpZZkj3jXHw7njtLPYH4tT1dEOK+jsluvVkXOT1dTWe+ivnPvqzC1r3LosW+P93QPU0l6b3vwwC9UEHrvD58ZL39hWA+QNTLPaReEb98lAU+1HUsvoinw7wjdqo+UQWPPpQX7T6BTqC9cxEUPg90Rb55z+o80XGSvYkcKr3K+By9S2eBvjprLz7wdTa+hUV1PqkBkz2+je68sCNfve+Vobx42J27cdgPPrgO7z0obq4+bIs0vqFb8T1sJqG9k3OFPhbveD3JQT0++poovtkiCj6QvWy+oDW+vcZqBLrVFU8+evPqPk8/0r7aBb89o6r5PKRrdT71Ats9P8OhPbUAUTxznrO+abr9veJCTT3fbqU+sI0vPp2OWD3Gwl09VkD3vnr9p70TSw49N+i9veYgo7zBrMG9Qw+Ivje4Nz3PQo8+Gi13PYR6UT2fcg2/IaFjvaf1kz4ksfq8w5oZvgls/L1mOtS925ABPZoBgL3lYwg9pZBpPv7TNr048EC+sWdwPmzlqb7/Kne+0jpNPUiIR7hJYIs7JlC5vY5ZPL4RN169","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","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","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","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","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","pLuJvhmcFr2HBX+90kTpvd9QTD7/lh8+VZKOPrqd/j5wtau9bs0xPuAm6T1hIks+tGO4vRTvVr71XjW7QtaZvAy0/jsTmNO9lPZAPSihoj0DOZM92ELQPFQ1y768HZu+/B1uvbmsGb5hrI29jCFJvUbE9D25axo+BayXPunaaL1OVKO8frePuw0Nvz2SX+29nZ8MPT/qsjztNcM7koCwPbWNjL20qHi9ZurmPjC6k72/qcE9k9HIvbWYyj0jR889RmPsu0P0KT5MJcK9ClEdPu9RVL31L6k62lqlPRVGbr7WSx49jDlkPZPIKD4k8ou9HPguP901i70pUls+LMA9Pli97L09iEK+i18KPkFOKz3m7qm9RYkAvpYnUL4eFnS+oN9FvqqvU75/5bW+pW3oPOa75bx7ENi81zHmvdLZ8z1PgBS+Y6M4vtshzr5/PQW8+DvIvo7sob2EL5G+CFiRPauQlL7JaQQ+o50Bvn4s/T1w6pY9+TE5viE8RDzuq6O+KhwVvwG7Mj1IDpw9rMoPPbV+Kb5ECAk+rcqMPZDWl77Uspi+pMYuvoUCC7ya1Ky9zbrBvYCEi7qDmEq+mF6jvc8Kqz114NW9y5gPv3iEcTwsJU++K2zfPSgZrT5FaQI+znwiPu8Lxj0aXiW/+vLHPbpELLwieb29XxcDPtUI+L6rwhs906EAu5mA1D0BV2A+vPKfvdi7LD5d/p0+2ou1PuyQTz7XVpY+/Xw2Pq16Kz4WTmK+5LefPUuFJz6767E+7tbPPg/DEj9H2aM+EDcWP8TjsD1e6gA9zUEVP094ir6xgoA+4oqFPpXCDj3SwPQ9MlUKP6kRiT1gdki9k0OnPo8XBj77KQs+lMgqPq8lHD0vi5i8V0tdvjPuqT1xSNM+mvgqPc9Gdr3EKus9tqx2vhazvb5l6lo90faKPkxAKD7sXxG+i5uFPvcdUz2ibbA7SwW7vQbUCj84CZk+yEgqvtQ2gD4TwWs+8NQOvqEhbT7nq+K9JSo8PTlERj7+vto+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","uz30PaJSD77n9LQ+nsaZPlf4vT11sXs+JM1cPD/m/rwIl0A+RxqZvRUiOT6MxNS9VD/aPFr9gzvcfpy97K2JPSGlGT0hXgU9bATSPROVqD211jO+T7KavMstAL32piG84aEVPctvsLzbLka9H+GmPXVKtTxny1c+VB0jPWjlrL02CaW+rpo8PXyGsD7Alyq+SEk+vuka5T1Yjau8zVwJvqi3Vr09cgy7sYA5Pl8PAj6O2ic+vr0zviAAqLzDOAI/emdMu2zZpj3JRwe+mJtLPrqxj70oz7G9vEtwPRNzgT2tNQ++BQnUPKVwFr4mFxY9fpdavPS6Ar6nowa+ooY6PBOiUD05nRY+zAUZPgZ4ID6Td+W9wbW7PSCEVD7jHFe97TcJvk0YuT4CjeO98mG2vcBzNzxW6Ac+wi2mvv62DL2r00Q8TnVHPvBaKj6oUEC+jVA2PgADA75cgQ49qAwKvQ8jEL7dexQ+2JJmPlFjVD3A6cm8POh5PUbxAD6G/bU9VTBAvuTssT1lpBQ+fvebPhnL7j0ZIZi9Z4fvvTqWXj46wN+9J1ObPSZkvLxViiC9l1HlvefK5TzKvaQ9rS5UPjzMyj0SfgA+6v2+vbGhYb1qZYa8zQxYPWXDCT0cBbm81bBdPrxxrjxRVYS9Uf0SP+RL1b1pLc68Uy0ePXTOO72kUr09nr7ZvaNJDj02ezM+BLjYPJNvsD4Sbx0/pcbrPoRvgT54r6E9QQS6Put/QT7RVsG+X+xivZ2eeL6dkvK9Tj1BPpsGdD3AY5I9WMXdPG7+M74Yrn09CrjsPj7pOj3VEVA+f/HMPubmDj0EaIw+u+DSPWPsbDwo5gq+9LuVvvsMYj49Zxs+vFm0vXtN3D1/544+qWSKu9k6Oj42gO89KUNnPubGmjvdIXQ+KRSevfwAnj2HULY++JiGPjf3cD5Dnzc9TCAGPlFukD6JKDa9oHpDPojffT6e+7M+3Qj6ve+bYT5jFRQ8RcYivlvrlL3N18U+jtqsPbsSub3S5SY9","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","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","6RfwvOrBNb6nDXS9iJg0PsNVGj50lT0+26mWPUbgIj5jRAI93aqAvLNSkr1ZB2U+MUrJvQdEiT03+oA+VBi3vQTvor1271O6I2ERPTVdMrwJxlK90nq6PWedi7zm4tm903EJvXcBUD2Gbk6+Obk0PNEcjT7/tIc9iL6ZPC8Fg75ggbi+85yGv6CD5bzG5CE+YLL6PGkENzzVEn6+NLyHPRngxbyQhhq9Ui4PPqbi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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","p1Y9voaOg74IeDI9AaqYPdHThD4/zNQ9N9qgvUFO+b160am9PddjPi6bg76qMw8+dSlkPY3gNz0hZBw9kpW/PaW9aj4zY9E8Rca0PRMeQb9FrE4+D2TpvkftX70JJlA/bKJVPjpLDD4giRK+hsxVvufz4r1XEFY+NatFPapakby/ZVC+hEmtPXDCID7c+xS+U5mQvfDpHL5eZBm8bdXsvaW6FL3fG/M+lISXPVKrRT7Q+hI95GmCPkqxM73iTBu+1C0bPTSlCb1PL9k8ory4PTr95D0itgw+eSVTPuDUA77SxSy/H81uPq6GVb7Qsgy+j/chvVblEr4qiz08vsIAvuSu4zxV4yE9pCbJvciKoj6zd3k8mPW3vrmckr7XCyk+5sanPFknOb6cM6s+NUQwPveftj7E66w+7ACSvLPdZzwVls6+PpRxvkbJLT81hvi9pYCJvg/8xj1RL54/3bJWPmla076ewEo+BXcnP4CUnj7Fltg+vfmmvvHDAb9dEUE+9XsCP0Dq+70TtWy9B9e0PDVr3LyAP44+U45WvU61sT7Tq7o8RySxvUJoLz6ZLXA+M38lPiIDTj5fUlS99S9kvsjWZD4D/Bi+XFeWPCR1LL9J/XG+YKRPPSmZ1L2Mt5k+qj+mPohbHT05bTe+Q7pwvlQaG774t/A9P9dkv1Bd/b6frs496PWQPnqHwj1UoUk+7VBrvWl/Ir124Tu9xcYBP9oegT4dKhO9ks6PPj0PVD6jFFw/p/kJPqg4TT4gNQE+4w2dPhZgZDrNn+Y9sns2PkIo7T3G0+k9VWfXPj5TCT8hYvs8xXsaPrqTdD7AJVQ9kfTVPfRJuTyFPwE/ZGIKPuIUfTwIqVU+nK/PPoEyT744YiA+m9d6PT4qkz2PMTo9TbsPvY0epzypO34+k/YTPuptKT4Xz427+Y5UvvX7sz53HQu+N0fOPkywljyBW3S9MurhvYNxnr01hDU+NOUTvh+wgT2uZWk7sXU7vnXAOT4pHFc+CTHsPi1shj7m9G+9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","3O2QPgqbqbr9pcY+dVwSPz0pYj5tlg0/AcKtvsC0eb6+apU+e7ayPleeIT9FW1e+p+MePjbAjr5hN9o+MvcNP15eJ74TRcQ+k+HPvZGnAD1lGO49p2atPlljnr7jrBQ/h+N+vaFOib0KmsY+6DU+vbQnwr42zvs80hmAPmt9xj2krN2+/p8ivoo0ND7kufw+FR+BPuy8yD5/vtu9DgiKP2L+670so2Y9IDLTPlCNBD4UHEI+IgJOPapx7D6QFpq++4EvPYyu0j0eTfc75KqRPl+dUj7jN8E8Rma3PT+hDL5t2y69RYgBP1NuuL4F07y+NE2rPX3hCr41c5c+gPL3Pmg1Uj6iukA/0IJ5PWq9mr60pYM9hiWUvujxoz7zQRG9XBa8voT5qLxV3KW+rMoSPy1YWTzsnZo7cIOdPV4TzT7OZHk+QeWlPhgHNT7ursk8/AAsPu6G0L3pUwM+FFSQveXG7z232Zi8QGmqPg/XBL6I4mA+21gzPUPSZr69nZ88lkX3O/rrEz1YMaq9DjADPSsjOj7Plzc+OPqcPoY/Iz9j9LY+5RcrPoSjTj10FCs+tFKHPl1vRT4AfaG9DCR2Plf84j2QQCs+GAJTvlSK8z4OuA2+QiwtPlEaCD58rlY+fQSSPyvY9jznHqA+awQwvnfizjyI0xq9pWSNvk9aF76G7Eo97ZykvvQiY72CKY8+mtmBPBDsez76N0Y9VxmLvpfjXj5BhY090cITvvZBRb1raK8+I6ySPb9MX74Iz8g8tsuOPB+Z/r3oZq+9k0lVPpvvcr55WZe+OR67vrZMJT1Ju+C9z/SAvRwO6D4FSMs9MFewvuupUzsy7u07rVtkvH/Bz735CLq9w6SovpXauLyORII+e5TmvIe1vr6h/Ym7x0+ZPcV21TxzWy895hUUPsrRy72ZXFO+mxmFvYluxL0E5x+90YITvQEHpb4/3dO+Qn8/PrLulL6lXjQ9bwSIPfqAkD5Rctk9OGQWPoUpFT4Tcd++xGrgPXDRnD0Two49","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","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","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","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","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","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","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","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","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","3fi+vh3xB79sBE09tea9vnvBOD24DGW9P9QyPaMzDL39CgW/egLNvtIF6bxBwOw9fFO9PiONjL4SXBM+9aajvnJ39r62Fei9oKJrvWvcCz4Egj6+4IJRvt4oSr7VlGA+v0q+PZruaj7tWKs9GI4QPmFlgb4nl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gb+UxIe9Kc24vLBWd7z8++G9BY+MvGNp1T0AMvu7i7IMPztkVz63pFw+h7kAPokswT6CkoY+Y35aPjmFI79sWkU9udBQOZYrrj4uBSi+JoUaPvA6X77Aoak+IxmgPkq1tL4MuLQ9YkOLPCv/9T79ly8+xFhePZGvWjzl6Y4+79YwPs+ySb2LCim+2tn4vOuWb778pBA+Bt6GPC54tj5Bgp89UTIXPAaRoT1G2/s9+Js4PKWArT05wcY9cPY9vNIaNT5zA3E9KXcCPp46bj7bN12+w+7bujM7Tr7UOdW81rtVvalz3z2jfIw+is9Hve8uWD11Z8w7utzIvVWLdb6zesE+","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","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","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","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","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","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","VVRIvuXyFb7kpTc+p01uP8gzvz5P1UM/pGlKv+Yb9j4YUEQ+Swx8PR8GnD+ACaU+7VewPn535L6KOgS+vPvSvuinpD4/MUm+8dBrPkdsF7+VkZQ+Zxp7P7WV+b7oQ9s+eKEgv6x3ez4sCJY98cuyPiEmgz44JUw+11cqP1qjjL7G40e/tiLnPqhbFb0wU209jcSePbXbEz6WV54+Ts3CPoBCBb42roW+TC+yvsMUXD2FBKk//xDvvR4VuD1aF1W+/z+DP3QqAj5j96k+L95VP00f7rsIozC+P+wNPgXYRT4fvGC+A52lvgDxuL7vj5O9Kv4TPzgmRT4k0As+2wi/Pmfpkr1TXcA8HI8yPnqtRL3+4jK+dem+PRTwyT3YmgC/n/fHOtfVPr7UFRi+lY/BvsRtIrz2J8S9MiFQv7vlHT4A+dy9HYbOvbVpPr48vb++paYAvy4HB74x1pu+I344vnhKhbzxpkG+WmXFPCf7ib6IJse9zCSQPhNf2LsD+o6+jr/Svs4gmLu2lt+8eIVPP39oAT2iyJi+ykftvUbnsjuHXLg99akgvXY9CzwFRFm9v8oPvr3IMz08HBa+QD6jvrr0ej3ZLAI+B8ymvljUNL2UMzM920zVPkAN1b0yLcK9X6YnPZcUib4+5zG+c3Y0vhlRQT1zTQs+xMyovcdGp77y9jG9MgiZvgzKW789bkW9UCQ/PfjhRT6tIVq8ANA7vXqUkz2kcZi92QMtvGK1Rj6Ddhq9lcsRvEAKGT2W9SG9Nv4LvlN6pT7vRqk9/kMSPgvUH74/Zo69jrQ6vdojfL5abQ6/k95vvB0Hnb3Zi78+OfvLPfWaq778Als+3SQkPf8Oq7zyYOe+CVvvvZ1iZD7X/pC/a/t2Ps/PhDxugQg/22oHPcEVMr96oSK+lmNZvR6ouj6wun8+SmqSPHsiUT4UuUI9YAY1vKk0Oz5iW2q+cxlmPhzrLT502ay9Lj8Zvap2UL4I5Tk81JwbvdaQ6DyxZ7e8rUHTvr1uczz1u4a9","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","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","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","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","IMk/PUbIyb6NOSK+Ivv7PYABJj+2+4s+qgEVvXdkg75HlAI+KsmVvoix7z3SAmI+E209PYPX1D2jg3e+VLqovaj+c7xoSgY/cKi2vdiXXjzCG4s+wYjovbqptT34MIa9CJIVPlcw+z2X+nq+ExIrPqftiT17LR4+7xiHPKB7Pz55Vb883rhaPhi1Rb5vG5W9alkGvlzGbL64Khq9IAPPPGTB8T3imji+Ta8avmmJ1L5sLTM+qqlCuvv5KT5xM14+ZdmAvpM7Bz57CaE9fTWmvvnXUT6NP/S+BtWSvGay1LyKwi2+ZEWAvZJB8r37Qb++T5JFvsi4iL0q1MK+WFy4vaR97z5Boj8+Kwhnvil4Wr79x0k+cWmAvHX6LT8Ys1O+D3JxvhL12DzQgnW+h1vtPhVjrD57KtE9OpjovkKmZ7y9bhq/fSzYvYwGIz9NtD+9+tVMPvINo72RnpI+50bXOjiD5z4595S+wGlxvSYRfz5dYz0/dLvJvqwZcD4HUCi+O7JAv5p6fr7XvmY8RSwTP2UzAT9UdcU+r+AAP2Xjozy0aso9k3nvvVo1trsvgJ88Ppc/vqn1Oz5UzsA++ctpPt+1gD4mVaI9qorWPq++lj6NIcW+tmRjPkrlVj2z9gc/etA3vQ7hOT42vYI+rl77Pbo5SLy3Eqe+GoKzPjhibj4tW4O9GMarvuKXkj4BHE0+aYQSvd1t8z1XBjq+0wOKPLgClb1goB89OZZ6vvEMkD7DNvo97likvrwZCb/obOQ+aoPVvsdWyj0GybU+PNC8u9L9fb6RnhM+2EjNPSwxfr4+5OC97ZWDvXCdYz1PcrI93kJ/vhxJZz7cqeA9q+27PYyEgT3KJpg9HpV2PQuGGr48vVe9G+XaPJQZjb60NhC/VOr8vRO+q70cGbc+KsANvnZx072j4wI+YjONvlH5jb6KIp29UywBviuQKj4L+Ym+6W6/vdkrg74WzxE+qGsuvzYdaj0K/j8+mgoLvug1F75gXlg9LvuZvDvhlz60qYA9","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","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","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","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","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","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","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","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","GVQov1JWUj1ERnU/oi8NPveF+z2S5wC9OqYwvuHmhj/GLpk9jmSRPvFzyb4Ff12+QfyqvaHPij1EH+W+oeyzPSyDpj0Igny9DFapvo0oPr9e+J++sCoOP2/BDr9yxTW+H3ncPrSdir7zS5a9zsLZPjeL070GKIM+j1onvZf5A74/0/08tGUgP6NDiT2QhHu9gg8hPwsc3D34X8A++ykFv2vm+D3Wpzm+Wrc8PY/oLb5N/Qe/8069vtLO5b6feto9sbohv5J16L0+C9S+k4uRPVdi+r6tJAc+e4/IvXoMEL/HK289djinPbf3Mj7ZeNi9xU0tPtnfuz5M/PA6bCBMvqhq8z0nfSG+i8ogP79sSb7OzhA/GH5PPZR34r3oa6i+1I6nvhDgPT2UroW+dX+IPg5giDtTsbw8r8AJP9gOC7+FBME9POoOu8WygTyp/be9e8kYP3jPk74YYAi/B7jVPr+kEj83/zq+tjUZv+bkEL6fuRs9WqQePzkx0j1IdM09+jc4Pizoyj5IH1o+oHmwPttlDL/J2zs8g5G1vjbKGD5RkM89eW6fvsOnhb5n48u96psyvtJxbT6zgx++nVXhPEu0fT6nggO+1R5Uvvi4B738N76+PQ80vsfFQD31xk4+8twVvrr1uD310NY9SEqpPWXJYL4wrd++QhsdPYQmqzzye3i9iUMNv1SZK75pm0i8NNm5PqJepj4c7V0+UMTCPvTBub56pQe/XZ8oPc6Tx74LrZ8+obshP/O/hj+aoHK+wiiUv4Ks4r2vbWc/6e8OPnHOgb88u7a+Aap3PiskPr0B8QG/uZpXPqgAgj6yVfe9ubpwv+Sc974ti5e+wtNMvgSSMj9E96i+WFqOvhuLEj8U6xu/WrnqvbBKwL2y/Ma+ppXjPYAuqj3N0sA99kcRP0jxMT2Jz3o5++YtvgM9db+YeFe+yRB0vhZlrb6CEB+/RnUsP6IvR769wFw+urRwPUu8+L48bBa+ZkyaPscK6T55u4q7NZOhvRXVjb/4EQe/","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","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","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","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","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","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","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","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","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","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","zjzlvSkLCr57TNU+HkrHvvnIeT9FH408rPqaPduj4b5BeJu8IwXhPRnSfD2Hm5A9odNXPBDGXb3CUDe9rhEIPsI0Ez5jjR0/3YdDPXMU2b5VL0Q+O9PLvmxXrb3Jk0g+lVrPPuBsEL2tdIq96rXkvk6S1z39DSo//hAtPq974T7bKE28aj21Po1oED3la4q9EZXavsXAYz5MQ5k+Q7QbPty+f737v6e+0dSlveeSe70+DYY73FgUveShnL5ZfEo86xnVvcgPuz2hpkY6oqXQPLiQ6zyeBSU9eS6/PdX+PL4M9r++3WI5vjZIJr0/0fW9vbA0vjxf2b7Bvj++QItYviqTn77i5C6+eZDjvcVc5b19R7M+NRKtPtZPyD6bT/C9F14dPj5/iD/CXsW9tS2OPoPxYz6cwpe/12aavYIFe715R5a+x79evP81GT8gN4S+dqXdPuY0VD6J8Hs80POGPg6uwr59fz8+9OWvPjTeGz4GMEY/MKSlvRDVYj/l1pW+KVezPm68zT0wHN89zYCcPhehOL7ANdc9X14nP0HcHz50g4U82qfhO5W3Zr2YrpU9ADBLPvm4Zr52KRu+JQJ8Pi3ckT7JL0W+kJlXPnuYHD61xS8+EK45PZYklj6eEx8+Hc7MvjmHuL4UAnW/22YNPyZI1T7OtYi+8heLPv/z7b3IIZA9hD2OuUbMgD4hzGw+LWYyPu094D1OB4I9bt99PmwXKL4TDSM+LQyuPRhawj2FLhc+r1wIPqMAk7/W1iI+tX7TvV4P0z4k51C+IvYrPqBqdb1MpD29gX5PPrSQqL0V8027SvaZvYxjJb7++I++ycIRPt9A1byUuUg+UBBIPlBnj7umTsw9Cr5luPAyIz4vdYs9up9HO/QKKr7vtdC+sUa6PtNlhD6FXog9zeyTPi4tYD7IUBu9bKopvdbZGbvC6DY98F54Phx/Or5ia3Y+d80tvaANpj3cziM92utEPkmGFz0DZbI+geUtvnWAsL0EMX0+FK8kvXrFQL2Z2T6+","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","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","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","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","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","QbMjO9pLdr76vbC+ls9cvjrLrj7iBiu/fOyzvRgys7zUhwi/7GguPgVzqj5GoTw+iObIPpfBzz7p15c9/yl/vpAONb0jWp+8GA7Xu1D2Gz7Rqm2+Gby1Pv0X472f9+I+v6doPIBz/zswgYO+OzJ0vkjpFL6xBjU+UzVGvuUYET43nFY8va4lPgoQBr2zaJK+Tz8GPTHd17tbhMC95hgIv6PwwzxqnFI+zbnAvFL2sz7qQyY9IMpRPluJ0T1Y1qm+QCEePUM9ZLwsxOA9tw90PQ69SD5YSGS+r4a9PXvJ1b2EQTK9XVCdPvmXVr3qDM6+4qQNvwN2wr1jICA+qTo8Pgrsrb0LSCM+GlClvt77FL40m5u+ijoNPguNZj0nVS++ugzGvVFWGL2cnl++NatGvsPKhL4T1ge+13kWPx/M0j7nL6u9YtO1vJ7PeD47INK8gJU5vv7O9j0TcRQ9qa/Svi0YL74V26y+AwuZvqUWNj60j7i99YnaveIwcD3p3SO8/WRTvQnOcz0be0G8dtrbPUgu+r0OBq++6T5CPzvxK7/OhaC985flPWBAFz0kkk49RIy4PmKuur7G5Qo++wUYPuuMXD4OruE9o3aYvtsIO7xxT6G+KTk5Pq2Huz2NXi0+/M6APh4ajz73mUa+9nk4PnjpB74wUOC9Of3fvQzbFz6gS8w8wITzPjYTHr77O5c9P/ptvT8JgT5C6Qu+pdvePfFpPj3xRQi9QO58vbxc2T7YKke+NUd/vuOurT0gEIk+UgUgvc+hPz2Yg6i+xDE9vsOjND7G4BM++hCEvuB0+z1DKzS9oUgxvdSHH7878h0/sCuMPXzBZr0BSio9vlGOvpVxoDysJqI9/28VvYkg1D4PI2U8EQOJvg59V73wmIU+sWu9vivucb0+9EE+ZBPDPiJ1B7535ce9e/aTvsNARzybr3C8/OgavSPAMD44mHy+je0LPhrx/j6pRZ09UBOQPtfZBr1OMPY9/4IcPaFczj2M3uk+TCRuPT5VAD+YJha+","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","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","h/08Piofdbw1bD2/RSQ9vkbb5j2l65W7Z3idPe81kL7wEpw+h8IFP0VU4D2XeoU+sWTuPffhtj75SpI+PGRIvbzIXz7+8QU/wGpXPcr5UzxiLSg/LW5Qv8O7sT53+Ew+mdcPP9VFj77a4zG+a6HEvsimfD50nbk+ZPhZvaqJgz4QHoS+Y5hsPJfPH72y8Ci9sxTPvSXeTL7K2cK+9vvXvkQKhDzNUw0/2v6fPQwNfj74OEU8z7/kPtD9Zr5JkH0+OZlKPmdYSr6P76A9kTOHPe2d5z0fL6e+x6M/vYC0Zz7+++s99E7JPUUgiD03Kre+fHKqvikvzT6XxZW+L2ubvtHRe72k0SM+mckkPkG1sT71D2e96fYnPm+Nfj7SalU9emNTvo5pLT6JFz4/B6y+vawCTD7Ufdu9AI3/vXh0B7xiDfO91/PXO3kG5z3r2P69mJCsOzSvPr2uw8w+luG7vX0WSrwCN2E+WGuyPl/WEL5Xqc++/0+FPhj/N76IBic8nC4DPvAJc7z+sOI9CzD+vCSioTvvAIq+f48jvDNTOb37u9U9Za7/vkuAo72GGZ6+FxesvZUprT0z9Eq+jg49vTggdL7kT/y9IN1bPeHpcb6Tsvc9dT0JP4mTFb7GKjC/In1tPbMFaD04QBI97Kg6Pl/weD3U9Z+6UtbEPgttTD0lMAW+p5qkPcehbT2y5YS+jO4SP8lq9z21JA2/0t54vNhvC7/mBH++SrX8vqKZ9D12Pee+/9A1PtFxUD8dwpc+v1cnP9yIMD3OYwG/S7jNPhk0ZT5355m+ESqaPSLTE7wzAo++di8Yvh9usb5WpLu+/Duhvip6FD8ZVQA/D018vl2Otz92gRK+srQ9vZx6ab4pK86+5OaWPvHzZb/nju272p6UPeKWWb1f8x0+IgyOvlW4kr5m/o4+KOCevVGK8b3mPbi+RE1CveoxzL4LvsA+K7tlPiS/sz5iLeq+uIz8PuvD6j6FyYM+Kkc3P6UMKL8i2ri95osKObOolr+lXBi/","zH90PvKIcD7ySnm9lNxJPrJ2Jz5YUXc9KGQKve7vYD5tBRg+CcWNPQVemz3iP3o+DtqIPY6Hrr2jXR+/+TOxveYrMj0CFxO8q8qPPVXwgD03eWM+mYN1PmJqmj1bXBa9qEOQvb1NWT4TWCC7vNChPhi6uj7QV7W9e34ePpRQij1hWjm6LSeGPWaBBj75CxY+24YePavtSj6uWEQ933SjvL0SYr19Tea8gOsmPv7JMT44ONY9bDgTPi3JuD27K04+lRAUvXsvzb1/0mg+SJtXPWl40j1tjgI+h5DQPX+AOD5MnaI+ScWQvRlXND6maDE7xnB2PpL9Ij4FnHo+DiKBvhvMQ75QuBO+A05aPhBwuj578/E8UCutPXo7JD57UKc+K3Eyvep71b5vACy+T/z7vfvyDD0nKQ++6PXEvHgPrb267Ro+0Y4Ev2kXNb1kQ2q91/wQvcRV2j1ljL08aUAeuzMwCT+4gg2+9zaHPqX0tr2elQ4+BVh+PklgBb1dYTK9A7r5OygDvTyf+T28MNB3Pdyk3z74Gaa9vdiyPixEIb5o/iM+wIyAPk42+z0fNC89Hp4ZvoNx8j1Wzpm+Cee1PdVuOjxaRTQ+PUVuvpoAyDy7MRy+9WCYvbR2Tz5vpNy9KEpPPilKKz1dmGY9RpRgvi07AT7wB+k+S8PSvDqj973sRtu8406hvZu0Kb//gG+9MtNsPi04/D1shtQ98NbOPPtNQT2ZfmC+jnanvYsHRT27f5g9KV0dPQI1br2JQ/I9GOA3PveH/Dt5i7e9Y713vSTcM7yjl8a+lLgOvPxO2z5hwuU+wWu2PWViJb5eRAw98vdHPfCqmD47inE9kTq6PeO2Fj6NBA49/GzVPZFGDj7shby+YX6/vMFroL3/hJU9H6hmuSiUtL5pqy4+wKndu6iJWD1ghwc/dchGvvF+T7x42lc9WbdGPqzygT0COZc9ebUuPPLpDj/ZQVC9b5LzvIwwmr5zRLi9NZz0vWKedj3BHMI9UWGRvp0rtD3V0LU+","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","7D9bPjLPjD7bmiM+dBOHvmOc+b7N+z69vslCPPWqpD39Iyc+f53cPUGLBL5eMZY90lxvvXCjjb2xzQU+C92AvbaUjb1graS+ZdB9vXiZdD2S2+Y+BzarvHqohz4Br7W+1CCWvdyABL7PMyQ+nWNFvqc4Oj4NB629iT6bvZ9nrT4CGoo+cYUWPjLxi71ZQ2U+HpxXPY6p4D1Bzta+eJvOvAxmQj69FXm+jYZXPqebyD5KmWe+Y5tZPpOljD5CeYw+iwKgPaIEh7zr/vC9cBZYvFN+d71Ap4G+kK8YPRgmPTzrIu4+UpyHvI42wT67JLU9iOuTvpPWsr2dAAE+2jzlvQ4Lz71NA0s+PN9zPqVcX7/MIA6/IlU8vhhazj4KLYa+cCxRv2m0R7y6hjy+QjQjv5Osjb3K/VM9Zd0HP9tP/j75hka/OWK1vuD5E7/qVVs+Xg2fPjXiK78bm2a/rSatPs1ztb79SP4+sv/4vC0tUb0+M1Y+kwZJPlt8IT/Wp5k+7NKZvmmvjj/49cw9IjP7vozTpL5sbv09Cn8oP2ahLT+HQCm+DhcOPuNTrD46uz6+9GBMP6RwlD1muiu/kPt3PWENFT85Qta9HVS+Pi5lrT6ndBa+eXnRvvK2Bb4cNAu/d9zmvsVmn7wlaIQ+Fi8Wv5YeGT/y0Cs/q4OGvfeZ3T38MLA80isAvYvF8z28hcy9XZ5IPiMNrT2K5Sc+tLaYPLiKsb54AiK+fI/ePJdrujzdohS+aadQvn89Vj4MWHQ+ZPqSvS3etr16/XO96nJCvbtygzxBxma+usaRvuyXFD4jMr295Dj1Pv3wJb6nNRC+mQ/YPB2Tuj2bRZU8dCgbvRDqUr1sxOc7vhvlPvVSCL/0Ums9hTjruuaPLT13eKs+UYxLvUCze71Pa5s9+cbnPU2aFb5hhp6+cC+ePbd4W71YkAS+eRfQPNeaCb5UOU29hmMKvWa7Q75dOaa9+sQ+Ps9Lgr2H7EO91bNTPv+Hfj4K2Hs929GaPgamLD1j+Oi+","c3gDPtXWPr1RZSQ+mDz+vZFk071GK748Qv+Wvmd6Tr5o5nE9eeQUvhm0br6uu5a9aKmgPuj3zr2DTNI9z0kjvl3sIL1DhJG+nkCEvnP0BT9mPVY9zJJePaX0ZD4kAZ48QK/Qvk0k375Tvdc+lsblPiydgD1lgVO+Z/ZnvUnxwr5BiK49UVJOPGlvAr7/xqw9jWafPuubmLybYh083TfEPvkFRL4EGIw+v4Nlvsx+pj2pBy09itFfPQ1VA79sorI9XRPgPUyJ1D0SrLi+gNdNvZNhr76aJ6i+Iub4vM6vj76Nofw9Bqp5vTUhIT02dNo+o2bpvTnZ8L4b7R0+joH+PSILTD6QIxq+jTG7PU+NWD6wb2S+A05iu8VShb3A2gM+2V6YPE+Knb23Lbu9HhIFPmFWjL3F/Wg9wrkxPjDegj64plE8axd1vm3mVTycvj863KWGvGibgT4KtxY+KLd6PnLlxr0V//q9ewiXvrV0hL5PPOs8L4Fyvv7T9D08aUC9QTOmPg+wxb24tK69tQjaPdoIaz59wN89ujLTvf5trDzD05E+EjHRPX8CAb2XlTw9UvqrPfYuqr41cIi+BSkGPG8LPr2fAAs+txIyvjgt2z3nBhu+fn4hPt8oNjuiJ1I+WgLTPv0S8TymTBe+f+Y7vHxcOD7RAt89wl1dPhW+nT6DTu+8iCUwvV2t9j2H6G2+IfgIvgA9yr6jirI+qphRPlKgHr9Ygd4+LfblvrrB+r1vzW4+iepLvjgX0r0qpKI95hnzPUvgA761TJI9uqlCPv3Wqz7nFde9LsXMPsFoXz0A6Au/P/U+POGgDz4d1wG/EpL8vS9pgr5WXAm+Ub+tPgy9Tb0AJqi9Aw84vqX+fj4Dws4+9S+LPZ/4XT6RL+O+kGNGPXyFSb0IMW4+Hw1WPRgZkr/J89k9T/FTPvxjuL5mYhm/+/0jPrmziL55xFi/h7kZPg46Vj1AN2u99IUfvk8sWr3v5wg/SMbzPtTdvj0V3c+9mqurvnWodT/Uohi/","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","M0rMvVd4yL3j/k29WGIpPS6+mD7q6mw+fSUPPqme1b62fys+1tYJv4dl3T0QZrY9zWvuPtIOoL3W9Li9xmiTPnfBeb3tUug+PoIpPQ9hxjxBcbW9cOb4vYVMnj04YRK+99uGPHbaqj5XhYe+R2fCvrzrgD090+Y9+XHaPFwQJ77/sAo9BUqTPuBOzb1yGxm9PhkyvlQR77sLP429RfUrPb/Rob0J7Cq9ookkPYZaGL4gShy9zcinPrtJMj62FPI9l8w9vZHLhr5PjTc+rLmNvY5OCT6ooRO9SnzAvGIJaT0sd7u+eIemvqAHCb39TIC9vyVyvlwEjbzR6e89tc5TvbDh5D7jm4g+vCrPvpafyD32G+w+IYuuPmAiwD6qB+e+vuWXvkcCTr2Rkxk/ahw3vUkoZT5bBVU/q58Zv1OHYT17U10/1tAsvtA3Oj5zfSK+tzsSP7gUPz7gB9w+ke3ZvEPXjT60EI28MbMkPhODBj/5IYC+Ka/2vU2Dhz7xwyS+67ozv5QsNT0y3qA+LmSjPZjJmD1oui2+lRl6Pgaoor5de30+drNOPiR0Gr4xEB6+swg+P1AGrr0gBF++/FiuvmFIGD4zZJ2+B94dvTowgz8GwMW9b87HPIK+dT5uK949YsjLPLyHYb5csoC+TTQPPpfG2T4YqdK9RzweP0McND71E7E9rYGjvkosm779m6e/dVMcPi1RDDwAfmq7Bk+Rv9+2Cz69aZw+t8HQPr3rBzwj/7e+HpvvPvtrv721Iay9JfC0vSFpUj4rT8q+ppSBPYkhZLs5UIm+vLFdPaECUz0Efw09Y3RHvR4Wlr51pG69CyU+vmmiq762Gdo80L78PjKjob0KtDi/dUDXvdrPyj1lL3+9pKvvvtw6RD66VAM/wBrmPc5MPz5CyHC9lfdCvi+nuDwPNEE9uR7fvfqF1r2ipSG9P7jmvTMJiz5AnTG+NrBTvjrgC77nXdg925QvPVHTmL5WEWW+jiG7PSHsqj6hJlc+6+S8vjwzRL6lZGg+","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","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","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","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","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","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","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","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","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","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AMu/iyurPwOj2z7HLAhALfQDwF1q6b9ZzIs+lX8JPxbrCD5ayvs+vk+BPtNsIz8tyok/EHHDPqW+Lb5+zAw/emoEPsTgEL8ToAU+0VqYPRX1Qz86bGI/eIAFP0Z66z53IjU/HkOSP6myIr+kiNQ9WHo2P2pdgD8RtAC/V3vIPC73F79KQHs/Io4MP6T4jz/FHQu/hWMcv2VePj7Bvw2/IVSuPuY9ab9lEhW/HITPPujV9j8V8p8+VgY1v5aYDj7S5Qg+P0SIPpYFPD2Qqmc/qMaiP1y7Gz541xe+26KHvwuiMr84sa4/21Q0P6RlBD/tV7w+CuW8v54JjD/FkvM/pU6HPx0Hi7+cH8S//M1cvzEQfj9tdT6/LLV3Pe8L4b5YyMy+CZODvk4A1b6LyS++5HzevjCIg74DAQW/","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","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","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","7OjxviuxZb/D+JW9rbGcPWjcCL8j4Se9MQPavZ1hgD4b+ZO+Kejovp0jS7+5Svq+w4Ncvha9Hb8dmL6+AKqZv1QmDb+PIfu8tdZSv9sDE0DzqwpAVNI8wIfo3j+heTE+dUWjPrIo1j04gn/ATFhTPzqExr4xl3c+Q8+uv+n6Ub8p/6S/R02ev2z0n76y/1K/JRopPsAblL7xSW89ZOWOvomkob27vw6+PyZVvhML/z6AS++9+N7yvguRUL7exNI90Z6ovUtJAr5q2LQ+IkFPP9HEJL8PWGk/iDmhvj9aK77eT08/N0EEPzfacj9AQGm/UJ+fPhv/+D4zVjY+B4yHPhKhuj/IjZK/574LP1U2rz+vHQrA04tQQGKuHj/a4f8+AGWwPsZ1sz5a2gk/gxi6P7FHaj/0yrY/NDSJP9Dxib/LfqC/excNQG3C37+2sLO+uuWovt8pJr80BfC+NzmAvx7SqT4dFPk+vIuzP2ZNkj8hbEo/CSOoPxsO/T9ZCX++joKGvjFKnL62lDQ/dMe9v3bckz4PLME9/TuFP7ZZRb/nNf8+O/+BPdiohr8x2gzA4he3P0H7Ij86xeE/bJEPwPJxa78uqBI/J8Vtv6h6Sj/ZwD0/jJsAP2uIpz+GhLM/i2WBvt0dlD4IDri/V72uv2phO78U2yXA6vo1vWadzb2jHI2/gqSRvxmFDT5v0uA9PXW+PmF3vL5/TqE+E9wMPtkmVz7IjM0+fngYPi/qWD/sNzc/SHWKP/wfnz9nyFa/mEiAvxrror8ZOtK/gsvvvlGbDr8GKDu/fDnQviWfc74x4+y+Z+LUvnAZjL5wPSu/CFE4PSRvJz4jz5g9PS4KP+wBhT8jF/K/dUuwvJJ6pb+LJg+/Oh+Gv+hKoT8L41w/PVMIPxtiwb3yqSE9Ifv7vfExkj7clkQ9lOoCPkRY9z7W5Yi9jc+2PlKvl72g6KA92TCaPgIlyT55/2g/Y9mNP6jYiz/rZgdAyW8uQL4Ln7+FmOK/ESG+v8VLur9OJq++","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","i/MsPy3US755MZg/R/mvv3t6zr5Jcey/r9jcPzeOZD9Enpw/2k4gP5zzej+/RQbAd6zrvp7TtT1ZiYc+1MjnvRFs370Sasw+B1DJvPeDVz6d6Xo+nRmJPpofjj6FmhU7u7twwOee/78dziy/k9RBwHkOtL7GWtE+63VOvrm6Vz0m8D6/jqE3v+yatz/bkLm+ptoovRvqAr+dALg99kK5PrZBvL/NbqO+zDHIvxtyizv3/qu+dOp4vm9Xq77oFU8+YhvJPhFCob/7nXa/NWwDvuSxez6lzTu8yQlwPYQAyj7lgoY+25oLPxiivj7ay50/bLYUwGWdvD9IZoo/q/9zv15efb8pnKK++Kkbv4X3kb49k5q+bKpev8yPUb/M4kO/T8HBvLFH6b6JmMc9pf8Mvyotmr6lTAS/DvjxvuR5Qj70uKm+X2WSPxNY5b5wRbC+meRTPu+x1z8cTY2/91i7Pgcqtz6JHI09DUJivxNML7+db80+IfTQPqzzoD8R64K/G4yFu53bTj7GNxE+xGfKPbs9mj+qkiA9M6hoPoqpZj8fUBNAxig2wIqwWz+SOYE/JADXvW8Prr+FcFu/Zg5zPuj2fL4uBVU/ccPevrU5Hj+6ANs+SUYCP+HH1z6Dg4s+E0w6PzFNxD0EiGW+XuQgv6EMujuPtgzAu4sjv2tckD9XplO/gKKEvi6vmj52fY0/H+6HvosgzT4gfsE9tVN3PrqLGz+H/MW+2XC7PEJunz53r9S+9F6Rv3YcoL7u4QE/dMqiPkC4jD6OWPI9euc0wOniBT37PPM+2mxyPPTaIz4e0MC8JH/tPRExzz6XTPg+L3AZP4wJ7j4Me5w/fTggwO4AtT8lQoI+a1GYvgG7YsDKMkVANbMTP4iGxj437ke/4w12Pyd/ab+DPGq/JLB9vzP6VL8Ombu+yv9KP68C6L7v9YC+20Z+vun3Ab5cPAC/ul8pvgpM8r7WCAW/U3N4vqacB7+iVXW+SVSfvrOMGb04SIO+P9xNvtsN/Lyhe0e/","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","O4VMvy5ALj5XncQ9Eg0mvhi3Ob+GIHI9Egomv1SnST+HgFa/+Mkgvo5dKkBcKYs/QNCAv/I3pbxxZAdAmWHrP6x2sz8E57E/KTkhP9DOGD+816M/RcczP4yzKUDY8P0/NVInv+cjSb+KVqs/vqwKwDnZjL9wZYW+zzqRvzZNo79D3iNAPD4LQF5ezr9gorM+MHHevE9kZT6Nz2g/JPtXPoeHab9qoNw/b9P0vjnOlD/UE5i/gYMdvwZRqT9ckvI/r8DNPv1xCL/HRX++/tRkvivCV79zjFI/wxTlvmQMS772Liw/05hLP/xF1T9LKL8/Z2TKv6ZIur+A7xK+YhRZPw+PrzwEaD6/MAfnPr3Ju7yuyIU+brnwPhB/I0C3tRY//HWjPhuqk74O5c0+wtczPfnI0j1Whpi+tf6Qvcdfwb2Hb2W+Ft96vmp8O78SO6G+o0e0vmTSDb7RLDW93LrXv7OqV793Aza/LauEv+4zij+uFYE/xFHnP4Yhhz+S1N8+ufaWPZ+ihT4nKec+a5WjPh6OED54ji4/BoqqPrs1lT5gfQM//nA3PzDYvj4jbkI/OeOavqScDb2Cr2U/lhRBPqQPgb9R0Lw/gbyIP8cUmr+cRKW9TXIlviZS575K0bg/IoERPndpsD7Kjou/OMg1P29Gar7vyzS/NKK+vt25Db8tYQu9iEotPmRqr779ROO+dgKuPmQGsz5drR2/i8q8vzopwr9fBSK//5kGP2qtCz1UMa+/qcOsPlWD2T+VS60/9PmFv1iHhb6zbYK/myUaP4+S379BzAi93K2iP8MWdL4WQ+4+5kLlPhG1OT+zCrI9/BPEPeEXTT78kF8+bh71PkULFD/guCi/ZUQovnemg74lFTC/0wYTv/b9hT7/pr6/vL0/vVAiZ752Ocu+y/TjvgghSr6GwGq/s5lkvzMP0b7Yup4/auPHPnEPrz9CbEk/+BerPo8iRb3r24K+mcbTvVrBjz5yebw+aSgLPE1hlz4JrEQ/KTwiPz6jWLvCZ9o+","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","QSebvuEMAr8P3Vy+cPITv4tXib56uJm+xLRjv2AkFEA8C8O/HgLJPyg96b9l5xU/BMqtP81pXj82+2HAsUsYP4ncyz4+piU/zSifPhrbvj7iviQ/TkbWPlbb8T4QSi4/YjP7PR4XeD6YrVK/cU4JP30oD79ERCe+bKQIvwKKQL4+q56+Ywk9vxfzhL77bBk/uU4eP5DQ779WqUE/XJCUvwBYk76cyxW/0ghiP2c8VT+9U1M+Zq8Zv1hmkb2NNKM/INLNP29fEcBqNCG+X/nEPlhDFz2wsJo+UzMTv31/wb29AAA+XoENPnbGBL/v688++Hq6Ppc5SD5fIs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\ No newline at end of file diff --git a/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_encoder.ktn.model b/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_encoder.ktn.model new file mode 100644 index 0000000000..398a6e8f14 --- /dev/null +++ b/src/kernels/gfx90a_ConvHipIgemmGroupXdlops_encoder.ktn.model @@ -0,0 +1 @@ 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","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","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","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","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","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","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","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","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","Hi79vaLl2jyg47s8cxLovRw84T3mjPW9mCoOvrlTyr1b8kA+WbedO4NNKzylMcs+4dIxvRy0dr19+cM9/8stvmmRBz5DPQU+f0OHvlAvJT6SWVY+/ieHPIa+9btHJRc+YWtDPpHqFD07GEi+Z0PYvcMhrj3kB4O+EbJSPrqiPT5bWXY+w71HvZhWNL1Fl409EL6yvbFN6T1krjc+GQCRu2Zhsry4b8a9fxw1PtTxsjwo6f28OFU8vhc1H72mst+8gIGRvtxzPT3YrTG+1f2ZvSepqT1/xE4+B7drvX5z7z7WNwg+S+MYvnzahjzmbAu+11Q2Pj6koz5Gp8+8cMs3Poe5V75O302+RfAxPkQKSz3iGjQ7AKtMvrK4iD4BuxI+HeZePqW/sT1Cw7a8eQI0PUAHKzzd+QG+Q1R9vlA4kT38iJ29pqqeva/nVD70kce9ihXdPR/Tyb1VoVe+Oy3bPQptVzmiS9e9b+GCvUQe5T6zcWG+z2yQO1AUVbkRutU97l+JPJlCXb4raoc9cxGBvngA+D2p8KG9W/s5vpBt8bytHXq9fofWvZkb7Dza/Zg+KZgdPmTTyj3snTo+FiBwPdaXnD6HdtM7OAr8PUKNRz1IgzE8yi3FvSD7eT06Nfe9IVCgOPZZJ72QNlw8KsjBPe5VF76h4ZY8JZSmvSwW/b1Ac9u9myIrPT/1qjxfIK4+NSNePuJdQb388io+QlwjPlxrsbvV7Jc6QTJOPN95gz26NZW+G7wVPi56rT7AU/s78zR5PRAeSj6PUaa8rqldPb0hIr2f0BY+NJpEvonl8j0AOxE+brgMvtGmX70uCMy9KRgbPXwdyr0G44k9usYFPg4yYD1dXlg+Bo2LPint8j1lAMu9DA0wPbSCGT7ezby8mX85PuDDhD6onRs+vuPvvE+6EL7xd4U9+8vnPQ9Z4zxnHxe+NLoGPvOBNT4Vlrc71TAZvkAIY7ymMlC8KzoUPVICB730oqc+aNdlPLDGF72xhhe9SfVOPp8R+zz/abS8","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","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","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","NGIcPt02gr1ObkC+LjfkOym7Xj3EhAk+sMqdPeIpwD6pZ/o7XI/XPC7imT4CV9m9MGkIPkZluD61gSm+iKVsPjqvbr12ZYG+iR0UvZ7NiL7afm++Hq2JPbpPP73XsjO+V4o5Pg0PuD0IHBc+lhB4PZSAiL1SHFo+BEw3vpy4hL1iqNi8qRqCu4aN07xS7gq+G9Etugnr5b2GelG+JrecPfyzBj4/Pis98VVuvRb6ST5Qr7S9AG1zvuE8Fr4Mn7c90IU5Pgl7VD3RZvK8tp3vPQ1/KL7aK4a+EvZ/vRqC9b6fQmu9g14fvbht2DwYbLG+oAzAvf9USb7BDCW9Yzuevl7aXD7R5PU+VGWEvij8lz7/xkc9uRL+PJvYWr4zTjA9gOYvvoQ8Pb5Ogti9797ZPfMFlb07Pvs9/MUFv/npFL7k9Te9Dn9bPdYuJT28ojE935QlvMwXsj1HcKW8xUnFvag6kD2aVy++eaQsPYkZRr6r2IA9qo4Qvl75I77z5pe94WvcvQ13GT3x5t48XdaQPo5HYD2wTTU9WmopPr259D2HQIC9vGBrPkQNvLyUNd+9xbydvpO7v7zIG2G9AGIgvpp+mL1GCIy9KI6BPVlkkDspePc93gJCPU7nJ76kIHM8BSRWvP/+e72O9v49AXxOPRg7pz2lulo+rshZPXFYYz4gl2w+F1GfvUASUj67iNO9dw+xvT2Ceb5pvyK+WPJFvau4mL4dV/u9DwkFvvsV871Ftns+1riJPqsG2D1wIhw9hjdqPmsvGzziOLm9gvY2vseGfzkmHwm94qcsvhbCOz6nyz2+j0/WvUYowD0gFJs+/rnsusbKQL06lVA+YDimvVuXU7w64oa+M5Xlvgr4s7008jq+Y18Svk19kj6Ftl6+zKhivvCDmj410cO9wl2Rvb6Y/r363ee8ZMEdvqEbXD4fWUE89Y4tvsZajr7HbcY9mFG8PYsaST0DGBW+M5WPPZ11oj3RFQy+G8JJvrGa/L3wGnm9MZDKvZ3dJz5kF/m9","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","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","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","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","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","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","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","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","vRU1PtxECb7ZeRG+ZcDcvSfGa72ZcYg7lubdvoWyqL2ZUFG8mpimvhjcHT1rtI09Z8ohvd57/L11N6O9ConYvOJLUr78w9i9L4ervc+Iyr4bKsc9ehWivn/dHL2kqxK+UrALvBeUYjzA4zu9O3HNPQgg7j2Gu6i9F7C+vAssv74UZ1A+kcgXPpca371S+JO+rVvKPgURcr4S5vW9tU69uetl1b3uRY+96BKwPZOparpqlYk9MCSEvY8KiL6locS+rFfzuwyiwbwpTKK+AyciPj5CGL6YNq29Ec4yPFYwGL5oyLO+v8IrvUwuDL2maXc9YZRYvhbTtr25w8G9qNbMvHjKljw5xzg9RzwVvbPUOL5qCB69uSvYvDtPy713JLQ+enoSvUU+ob7Ywwg/kCeovZh1or1R1P8+JwQtPm0tOzyXfYG9+AMqvnLder7S+kg93zk6vhhMk7yBGYQ7WI0bPaY5Rz7R6M69NQGOPUWGQjsNEDm+66WPPp7X3Dw0pia+6+U5va3hwDz5a4m+2FTZvKxv0zpwMFG+RwMpvjdv1D51Pju+vExJPoqTN79wT+O9uWs7Pnl+HL7yPlY9ejnjvSZvf7wJv2S9P7oqPf/u3Lx3t2q+/xAqvndiQr1/gJm+HkI1PUZYAL60IUU890XivVN/DT65dQg9LA2mvVqpH75iwvQ9nG+QOwT67L1KQkE91RaVPSdfOz5xiZk8ZG4gPjL8TL3yQlW+NUJ9vukOhj4ZHKw8D4GzvZGCob78U6S8B3ZLvUQiwL7dWgu+W3aKPHFK7z3QrrQ6IqcUPVdHVb1fkbm9p7HkPYlsoDzH7jk9YjBIvc++Qj6KW3O+eY34PZXFJLxT4jm+0DDsPKbNXz1ETVo+r32Svt1FZ7xIW4k930qPO5MzND7GPQ0+61CMOw49ib1DlRu+w+DovVx+wT0cqXk+axdAvQopeT1Pa4W+7C0HPrdRlr0d26y9ewLrPaE4bj5KTZ6+USLHvLSJf77Ev5m9iBTXPtSZlL0Y4qk9","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","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","bsIuPVtBWL7xWAC+PvAHvQPNJj5lg3M9L5aSvnJQTT7d0XU+M0daPE3Ofj53vqQ+HqupPRQ6pr0G/10+gLsNPlnMkDsNnFC9eqNWvIKPmL2DZaY+NMY5vehckbp3Q1G8NuL+PFYFVjtqspM8z+oNPC4x+T0cOC292DzOvUCHzroICQg+xHelvGgshzwsqbW+ZNsWOftkQ7wLGhk9RJ4dPwja7L2qB/Q9D/3DPWNJmT6Pr6Y9Hgd5vlMIa7zZJUs9fy0dPoQmCj64Dta8W8WwPVzGtr7ykC2+qfg1vexZ4rzGB7O9FOztvbSFITilvLw+adqgPRtQ3r1IHpG7jv5WPZYuBr7qnpu9Bi98vAD4gj09O7i78pDGPTnNNT66aBC+QJMPPj9EYD6yycW9pkmXvRqfk7zonL49tyKPO2kPD71i0zg9odHEPXyf5zwLdZk+XyYcvh99ZT1zxQe9YfK3vS9iTr0xeFw+kwC7vUfjxL0eXk4+hn6jvgMcFD7Ueuw7DhXtvMm1azz1z9c9KFJyvtkk/D0D1C0+CNUevnNU9L0e00Y90+2IPuM1Dj0kACA9MlIQvk+0A76UPVS9VRdnvmjc3T0yKRs+CsuMvRIXGzzvDRI9h7rQPbjToj3q9tI7P7hDPMnGjT2s1X686IBdvf6ZaT5O7+Y8Lw0xPv0ror1MeCW9rHduvkdEWz4Gu8A+IIwuvnx57j3T9Tg9ld4xvqX39D3hBbU9BvMFPhHJ9Dz83MW8f4xQvpY8azlIS+U9HOwzPUv0Er5yGli9WyphvU5z+j2f5K+9fKiTvr4BVb3dOJY+nhLTPRA+073nFsc+I8cPPh08Jz1Np7m9DrFRvUAhOb4kTMU9iJ1wPgny8z3G0c66cz0vPeObcr6yYwk+pkduvfYWyL1mGB++t1f1vgexHL7+uOo9Y9J7PsSkQr5kGIM90KVUvaw6Gz5pcJ6+uKynvfsBkT1LK70+vzbEvUfNuL5T98G905iGPGS7zD3vZU0+fiQ5vjuJ0L2MEq88","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","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","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","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","L1gJPrIrBb4xxwE+2g6eu1qlCD40zEg+LMMlvX4AMr4/mYE9fYb1veNeKjyXr/s9iTEFPi/jEb1+org+zBz1PKePRr7G0wY9fjGuPVd6iz274Yg8pX6LvjrR271LCsI9LBPUPVQ0HT2n3oy7TWioPXu3Cz4/ZVo8bt9JPVuDKb7YFcY8yDYvOg44BL0eRle+XbVgPrNAO74GUFQ+ktm2vZoUkD39Br6870BzveUbFr6Y5xg+GGDfu2VhF75GWJ283ljmutBfhr1vICG9ybx6Ptoex71Bftu9MgGkuyyNCb6nBNK9kYvQPhB6jz3M5yu7Cb8UvWa6xzyT/im+pNk4vSPziL74wtQ8Uj+9veDiYr5B4XQ9/LS8vbhkkr31wsy+zDcCvusMOb2uSkY9ch/dPjY8ub0nzzw9AW+AvQ22d74noR69QWcuPqDxUr1uIQE9nG5cvliM0Tw/0pQ6FNQXPcbSTz1UfkY9a/NiPnJ0j77SBH89O2vvvazjmb6kh6a9iprEPupLDz4b/yQ9iHKPPhKEWjzlqFy+IzU/vn2kQT3YVJm9HxSFPmXKnL2/7Ds9klxcvJonyTtSQpY9KOEEPSCtND7NxCI9eXj1ve5dYzwGHSI+UKu3PZ/JmT0HW5s9jsUHPdFhzTwPSFA9Ks+6vIC9Nb5/8TS+byWru90mET4Pvwc9ZxStPZlw1j3q75W9onsjvvLIrrzg7di9rkdMPu19P70Zn589Zc1DPmHwGL4i1ck99CZ8vaMTe76h8Xg9on02PvXNYb6r9F0912oGvlvR4j1ct829zDgrPqv1uL35gxe+jLAQvoSByDuhi909sj4Vvv4Qdb7clb+9RmQZvfKMMT7HeT69PPobvpgQozxlaZK+cKY6vnOlsj0TbzW+3dYIvt5iNr5hDiY+UDunvCH6mT5X8dQ91KVnPaOsnD0UyWg+PFYBvtDgFzxQoNQ8EbysPZS2ir4AWD2+2Ku2vk8Jubzf58K94B+rvV1Xwj3685Q8ohsqPvl+hj1FA5q6","DqbYvrztgT5RUuo97mwmvjI6jr7WQVK82Jr1vurSpLyw6Bw9Jum0vkDM2r0rrdm8kpklPuBtUr4+F1a+FKKAvic0gr5awlU+56GBvrOFgb4O/3W9RDd8viYtZD6jyFG9jl0rvpQDgLw0Ymi+6o3/vQWCwb12U/W9A58kPmAprb5w2BY+XZHsvItfhrzgECK+i8a9vcBAlb347gy+mnntveaQ2Ttz6Ly+axQmPqEfCb3QswC/LXelPcgfH77858++RQHlvVkVbTxoRko93rs3PWmGQL4Ze22+T3HFvsH5ML5iXhm+uXtHPVPvsT1v77++MMiQvrtBtz09bNW+rkAEvoYrFb5qqjG9k01EvWq/TT7Dmig98kabPlY0Uz5moK69CUoOPv++jj5Zvu48wImMvZ30Mj1wex0+AK9Xvt4ZlT5xdm89HgEkPtIBi73Fq9o+8GRtPhr9wj54FsA9BtbsPQUaGr2v77U9hog7Pt+DvT1yyCm+R6x2OyqmuT0gIIg9t+2rvWBpKT3xHHI8b11HPsd4g70q72I+GmSmPWNcjjxZhui9iJk0O1gsnD2+zBS9XGdaPSuQir0XA9Y+oAjGPimPVr01kaC8ukVMvYUx+r0Qbhg+hvOcvb1IWjvGmrS9ImcVvtRf4T5//Au+Rn+UPbdDCb5ASxG8ojAYO/WkKz2xSaU9fXniPRIAYT7lmRy9giUBPtY0Rb3ymQg99aSdvfo/172TNqe8dENZvqG0kD4XBJ88MizEvlUFhr18HXY9ObW8vL6mQzwLh8Y9xTchPg5dwj6rCUo9p40hPV7PCT3F7Uu+Wv1qvTzOjL03+dK8hAwlvinwDD5pcek9ghegPMDJZb553Uw8MZqSPRg/GT5mXEa6Yvn/vDQYj70Uf2K+WQQcPvhtob3dfPo9SVl2PWXncr2U6qy9XFI5vvtkaj2C0nW+WCVZvp6AGz021oU9Yq9dPbgWkj51Ql88+jclP32V5b1+ZOI9JVSYvYakE70bL/I7uMmYO3VhVb0qqzs7","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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8C8SC4zPp3hbj0694y+Yxc2vVdpiLy6zem+ugfaPRmGxL17fdK+0iBAPtN51j1DgiG+otvYvmTKQTuSdBa+ndx3O100l75m19s9h3UXvQUs2j0HNKY9I1IlPkRf873IGMM9V8JqPo3Qrr7VlFq+iaoZvtZ9oL6fCwI+1V45PbFM9L027qY+d1rvPKIPLT5lMkC+oJSkvqIKlLvhQYY+T4HRvpvVdT6QACm9L50ZPdiM5z7xWNY9fkROvggODD1anJG8uDypvVyrnr51kU++N6NjvIcf3L5PDHw+BwkkPvdre77ZaMm9yrrvPVoTsDxjGhY9emOaPph43LxcrzG+eWAWvl0dab2Wuok+jAt8PeCUqr5A2Q6+3WpvPtCWEL60U1e+thGPPh5etT4YD5O8Y8iNPjJoGD2fhoK+h7DpvK7fkL4TvRy9Zvz4PAw++b0uFgg+FsrtvU6g5b2ter89lrvsPTAQQL6FPI471hPQPF6y/DxgaMM9ePFNPkWBTD47PA49EdnQvJc9x7yI7Mu9YCdbPYb4CD5o/RK+AJQ0vgTuTT4PKSg+fOEPP/gimL0xSQG+0sqtvQLRjz2QqS69NLmSvc4vKL6kNQK9nztvPiAii72SR3Q+FeIdPm5SKj2YK7k+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","VjafvTdKVr4Qb4A+TyZBvYi0qL1UHCa+AnirPdnC6r52yjS+oAQ6vi8JQT7ffVu9APG7vkMOPz6snHC+rwQCPzh5SD7lFyM8QxadPmJ9Eb7VhRq+o6sEvUhfbj7Prby+Mc14Ovq/Cj3EapW+ObrEvoBEgzsd05G+QNo1PlAxyT2wMNa98BHBvV9WD7sr9a++vu2VvWVogb7Su0Q6T4q1vn+eAr7LEhA8gzcxvv+bdL7MCVC+dXNdvhBhGzzO8D8+zrJkvTZVCD7zXxi8H8ASvfLHtj1Dd32+zTtjvhJLej0OTF+9OcEGPBsho75ts5W+uMaUvS73Fb5ZmuU9nwQPv1xSIr6Hl1C+AKGyvoVSAb5IG4K9302YPFSqkj3rZSq+C0oXPDCLf77mjBq9LsJgvZt5sjwh4Zo9UVNyvuCHhr6kdd6+McWovuNBSL1dYsy9a39Fvjl3P76bGjy/g7GSOkvd8j2DXya/aZUsvp9PcL5BQmG+ufoAPEwL/L0H31A7vZL0PQCLRL1yD3a+1MvpvZyf5b6MvW++zsK2vR2pTr5ssnm/RRtNvsvpCr4iSzW+9nKDvwXQWL2gVJK+0y+6PJdfhLy+rYW+Hm+2vI4rVrzuwC68Mnu4vfDeqL5dDYu+6sPcvRIZib4m/Fc97LWcuwjTYj3hDC6+Kf34vSCBq74wMAO9xIwGPuDoRr2Ompa9cCVsPVZ3FLtqVTa+4JKDPEBfn71Dm2e8LCOBvRGRiD29pYy+O2+6PCaT/T2I9fg+XRBQPR1ySb6FEeS8nBiZPidieb3QsKg9X9Jkvaswer7lmTA+jdmeO3oYVjznO6u9+QXKPaVJIj1AXrg8tqIEPNJkrbwT2kU+1Fx4PI3Vcj6biYG9roifvXWVET4HucC8HDF/vXgimL6mgLC96YEOPvIzL73IYlO9wrMFPLRe1L024ig7ryzcOxD2tjrzP+O9pHkbPrubyL3acoE8+biZvRQrm72+7BM9G7r/vdcKZLw0I607wofiPTHiqb0820A+","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","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","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","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","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","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","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","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","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","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","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","/31GvVX7CL8GTSc8sGnfvIqkqL1ImU+96ItYPR1D/L5mMZW+g2mQPsbsAju7pcE9A/PEvY/RgD5aUdq9BNLNPplMH76x/GG9ReGTvWGlFj26eHi+/dUOPlzxkD4LVLK98kisvS2EXL5yRIO+LlmivtlMg76sPfi9WcJgvriarD6f6Yg+yGiGvPBl/L6+2uc9XC4XvYSwkLzQZCu+omdOvj/rGz1/pho+2H6Avp7xUD1JwAO94+B0PcadNb7ZLwC/OmSDPW7qET71q6Q9btKWvs5Bmb68DZ09PiC6vu8N6L1X1mM+zf+LvQ2o/L6tqfy9h2AtvuQzJb6/lVc+2+nVvpCp/T3VJW29kwLrvqAilbyyCGm9ji4wvpDHSbtnGRq8Y8SvPjUqAj61EI6+sdMQvue5Fb5AyvK99dKmvXmqWb58Y3q9UvBGvoouTD6mMgC/1HNPPOqtGbsGLY2+TiAVvs2OTr4VYOy+tefjPS0/3j7uwAS+6ZZ8veW8h752gZw+r4OSPmHWDL5WeIu9ipLxvW+Ux76fWk4+gDxnPTe2Tb6SD9++MMBpPZaaurkucF0+WniOviuBCD5WXZa+HtbRvj5YgT3eyfS8tXMLPgRx373dXVW+wi6XvYnSND6Dxva+X89xPbU1qr6T1BG+RtsuPglaKr81WIO+w62UPpER0L4pFAe9HhYJPkfrbr51aKI+0MTTPVrefj4yaUC9G8xkPbTpLD6Ig5y+eW+cvG/hv77XWm0+OmtlvnADiT2KmrS+NGWFvHNqOr4rL1A+qZGaPqTBmD47bpA+COOfvleO+j0N56g+TgzsvLCHUb5NA3G+xgiDPatkhj4Wm7U9pD3UvReeHjunamG+6wsBPkIZvD7X+4C+JiJeuxJbij7lrj4+n5F5vhV62z0U+hs9a2yjvngvbD5jBlA930KTPlVs/T2/TBY+FKI6Pm4hSD2kcLE9pnudPllQLb74gRm+lcBdPYUQnT3/LoY9SOWnPT6BDD4AIp2+LI0tPl/Kwz1rriM+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","uZAgvG6ojz4IQMa9LnakPdjxbb6wdyq9tGsGvlK/lr3bCHK9RcJRPkcAkz5hcqg9Dky9PQ7LaD05l1a+OH/HPnTAHr6YRhS+VUPaPCkMo76yBZ++j9MCvmy8lD49keS9natXvnLygz2z/mu9ihVLvi5IJb5fiJ++Dyf0PUlcPD7hqsy9ZTLkPQyzZ74pI4Q8u63kPrxzzrs/v5e+w1IkPyksEz7SVJQ+LG8jPMSfhz1/0Iu9QaYwPpcQFD3fm0E+QB0XPjqY1j4HciM8CyV8PdL7lD2RY4k9NDyOvgI+q7x+08y+Q/mSPqxZXr6Ht4m+U5SDPSHkAr7Mcig+aZ60vDsVED61sVq9TwKovrFVXD1TRtu9Ehm6vswZEb455Yc9BzxKvK3+TD4QxXc9+r/RPAmeVT2saJY9F7dsvlxgA76xg+W+2VJ6vkAV376yY5k8wReEvhrdBLzmY6y8m9NqvnOgNT4Qade8N+hHvkY6B7zED9m8dkYSPgZYML5DB6O8hmspvpEL572RM1U+twZivJBFFL6ZqJ89/zYoPYSHB74O3pO9lus6vuzNaT6KmNe+4iuPvrobSz7bIRM+XghzvbZSlD65dji8CVjzPPm4775029q9z/MGvlJH4733PhU9Y42JvvfZdT6qmaC+DVkSvs3DHL7e+/W9LZqFvjkKIz0A4nQ+4hmdPORwdr2m5uk7mzmGvm+SEj7xdtc929Ayvku1o727I8C7DzpLvbcB4r2hMTY9HZNIvd5cvbzaRoa9aVmovtfANr63HGM9vwU/PnrMTT6mR5q9bHx6Pa1hVDw6E3M+96trvb8nZj7Zp+a9/b1ePoYzkT5pRfk8YswCPnsNqT5+VT++hN/2PPbbE77XzY++ZFhNPfz5MD4Oa4e+9xgGPvbNKr06a5g+TFdpPhYEkjwZlHs+3HYXPswTfDxfQzg9VrhGvMCH871I8hU/Tg3OPdmigz0Xgn288A6tPT6Q8j17tkG+RhB1vUvQJb41ijS+1NjNuy4WPr7YUA69","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","+0jVPXeeIj1Uqtg9sI/Tu2SeKTvQVck9yd8Cvpf4iD5wqio+guO1PmtRlz2Ku4U9LH3zvaseiz5cfKU9Ot6jvXBnoTwManq+UcKaO9YX8T48Bx87P7EDvZtmCz1F+AG8e6QlPn2t3D3WKaO80hi2vZgTKz66I9C9z2e4PWXWJD01bdc7XDGQPTycDrzSqc49VLCSu+MJiL4DKV49sFrXPAHFpr0JhQY9WZDBPFQvM7ybqdU7mlzaPVrB0j7T3Y69GhFSPvJawz3y64e9MpCovTgDKT6FOO+9sFYxvcQ6HT2fjpq9L4uyvTzbwbw/vTi+MyIdvWxvAD4qoDy9lL4/PVS7ob7b8Ia+gfmJvQ7oj70l0x4+w3LpvcPYQD68+jA+z0HTPU5OVz3Tbi2+sbUOvvgAlr4RXWy81E9PvKi6qz2zAZO+BmkwPjrruz3kG4A9rEEsvm7IVD7Yy5W+in03PuWSDr4D4LS+0guFvWmJV7024IG+/b3ovh6hvr0eFCg+jab1PGeirb0y26e+9OANPsWwRL6bXJc9XdSqPfwkdr7cAeU954LMvTywF74lv3E+L3WFPZtSpr5HSU0+OSC0PkYMg748aY+9n9dtvidaOL4lHL89ZQo+vZjpAb4kj/c9uS4TPjUJ4r2Axgm+qD6VPWNytb78Weg9HGycvZbLn75rTE+/hti7vsBE+L0VoKa9mEoXvr9YTT7crZW9y6IMvpKu3r06Ik89ozPAPZWkLj7kFQw9J58Uvsv1T70xSqG+ysAQvQd6mz1Su569EzsvvhJs9L6per29CZIUPrysYL0Z0JW+f9knvSlEBb4iNQe/qXAmvPMdi77wt1A+O61vvYeSq75ia2++RaL/vmMcn759rBK+PZjBPW8elb4ybYu8na2+Pe2Hg75s1tu9VpSdvvgPtb67L4++BcHpvVYY9LxDpAC+9sViPmebyzyFD7e9D9xxvlcqbj0fxf2+jNzgPHFDiL5M1H6+39zMvrhuxr3zKp694TupvrFrMD5pbA2/","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","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","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","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","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","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","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","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","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","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","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","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","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","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","fvHLvDvTuD5JFFS8D2LSPcCP4D4wQnE+FuHuveldCT4lGye+21dOvhyYI71QqBY+KvmzvAh6mD7H5iA+xrCsvZ//LT2/SIA7iDJ5PSvpALvYimq9DlBpvtAXCT6yIJ86s1lavueHCb7+50s9G345OiShsj2npts+rz0dPn66JL4oa0o9PC5Avnsqdzxhfim+FRPHPUjvjb6Yk6+9qS0WPs9r8b3pV7c9ueiMPrg/M7yViUc9AVlePUdLET21ndM8D7VIvmV1PjvGK7w9HyqlvHgmDb9HLn+9bxhnPj6v9b2zIVu8EaSrvjBVkz2y+nK9eY2uPsfddr6jUDi8F0cAPnFIlD7MV2c/mDCKvovdqr7XUgS86cmGPnbR0T1qNgI/1SCIPfx5NLzRGq699kUdP+J9Wb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","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","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","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","beYmPQwnBr5IkQK7HuBjPrB53D1YC8w9+udKvp8/yr6Vfi4+2+4cPug72b3OYk++EhStPRFb8T0Mi8I9vfVbPn5sQD2WNkq9bqnSPTdVlD30CMy9zwLhvd/hVz39XP+9Ao/MvRnVQL6Zhs69lJtcvU9shb5awps9JjZpPsxPdb0If+G8CaxzPZ4fMb49PTa+fV0Gvl3SfztzyDq9jq9Vvp36s7394x2+MUngPZKCmr1IJcy9eVfQvctYfLypG/W9Z282vpBmGj0DKI29oEmVPikXFb47Gr8+/nCHPoZ/tz1Dwtu9hdpPvC9wGL10gno+G5mYPfG2nb78q0a9QkKevd3rjL4NLY284Ej4vG7ScL5jC6m+RCBdvmkIyr011f49lxwFv0Fksr6ruIU8RvIavupbIT7DipI8IxhzvEjiKr4lTIG7AcJBvjFEIz05J1U97blMvmvLb76W3xe/HlXZvbP0CL6q9/K86Q7cPJVnWL5KWZC+7mYXvjFli7xhrAw9HwLZvHQLgz3tgO684gsXvnsK1z2t5Ge9yv1hvZBTSL6UTcw9DALVvn/qYb4/fjQ9yl/PPdsu/TxElsa+9/wHvbStHr4+GpW+Kt7LvmD9ELz6e5I8S3Qgv9RPM76nNAy+mDTavrzXET36p46+kAfUvC/Dzb6OsxS+iMzRvdRDcb6FzMK+uMYaPSI7vT0JAM89R+xRvuInAb59dHS+YLIEPAscRr1nHcS+mVwJvF2QTLukdqw9OqVaPX4ly7wL86e+tGd/vvpYLb7bGyK/uOHZu2Q0GL5D0gy9L/+2vXBZmr59kQC+A9tKvuqWHr5akUy9bDvDvhSuDb4e1Oy9+P1ePtvAzzyN8nG9fqtUPRjQ7Tw2p+u7Kl91vZDov75CoII+GT3XPYfPLL6azCg9NQ0YPSIlYb5iue+9Xs2zvhsfdr5GQx2+8BmFvGd6ib7rRRG+gj6gvq0qjb7+BQG+FlA4PDmgKL9sFGy7rH1Ivgo+Cb0C+t6+l8a/vVdtJL5ACGC+","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","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","fGQNvnU+773qI8u+IXArvnf30boJh869iVt1vSBQ1b7HNUs+2Z43vcyEYb6CGaA8i/rKvotR1r37JWW+FB97vWo5Fb3zsRe+kI0vvhB1vb2D0hG+7QPUvtk02b36ypW92I4qPSPMO74xqpQ9zwpyvhRFDj1T9Za+m2QyPQoaqL7IY1O+ExIyviph8z0rTIu+unokvmfCcb4+kfK8Du41vmGFp72aBrI9YfhHvXODLb1rdbi9VDwMvkkLo7wi3Ia9SCyWvQYmqTx2H/e8cOUQPvqZor7g1WW9CSxCudjkmzxzCku+l6AHvSPSyD2moh++Ht4TvpyHCLzmb5m+UZg7vYkWf74LIU29oZHvvF8BwryfsCa+XUVgvozK/jtrVpG+4AWWve+Ix721ZQ++87VRvv+rSL7He+69QVG8PDPNHbwJ+ru9Dn40vn3H6L3YSlm+LOgzvd3na77qrj6+uGhgvi/TUb3M9cm9Dy+tvsu5n75yKzK+lOqUvYnPFr7zKMK93BF/vgnlE75vUYy7jq9lvmWNab65Aba9ZKeAvvK+9r0fOqS9dKQZuspugb14C1O+HuimvbAkB74b/RK9TfTcvZx13b0FOoO+A/X0vQH5Tb5mUTe9uB6HvW8vUD3SHha+jos2vjHbEr4SlI698n94vdxq3D3evZS+tjCzPUtukTknIWA+W5mTPSHFgL7tSoW96eOCPilCZ77MJ9y99dcLPndsFD31H1u9Em5Fvo+2U71H3Sg9KzGzvWJHaj7MVDc+Wj9EPs7Qaz18xlc+Tq04vu0tPbsHDoi+Km8FvX8jfD7AHDW9XKunvAc5UD6OKl6+HqtVPqqWaL75cL096OWBvrTuv7r0voS9duoJPuI1pT6ml3E9z1MGPib6JD5lNYm8/gnzveSDfr2HB089WS8cPtmv270ptV4+3aJQPjQhFL2VHH4+sDgavXRYRL5Tpz4+YaUMvudXmL1UKLO9dq0KPt9vDL12aoI+QMe7vbSY9r3jAGY++kCgPuLi9b2IatM9","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","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","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","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","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","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","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","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","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","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","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","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","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","Ind/vPiLBb6T9Fc9SMvtvZdz0T19z12+FqGMvd4zJz6IqEk+IvrePUmik76mR2C9iilaPX5NKL4Ocjm8fQ13vSrOjT0nTRU+EIXvPJq8k74trAE+1kZuvsgfZL4R+Qo+xYurvZ97Mb6gv5w+kGwdvs33BT6hMVa+00d9PlblgD2haYK+xhtPvldkID58564+mfzxPLeGEr7V8oc+KfjwvWES9z3P/yq+IImqPU3FyD2XPy68/9KSPsjTVD6bdMo9FKavPc1NA73aDpy+ciN7PM0ldr76Et090+QBPu3tlj7nFqS9Sr9IPsCuHLzn/PO95KwCPmqnkz6rG6C9DqWovf+uIz2HS0865T8bvbGHbb1Pup69b/kdPu/y372Z144+MKfBvfCJTz0jgKU5Y7WwvMytAb4Y50K+kFYEPi1Z1r39ZFY9H4FPvjq/yzzNUWk9dCgkPVU9HD3zCAU+61aUvC1x470+sxy+HWjMPYA9Wz1unr6+h6MLvWn6Db195gE+vSUpPhUP1b1VbMY9zowHPvypLz6qJQ+9HywbvS0Ki76vNhm9OocCvnXG/LyyEbQ9UwgtvQ59yz09S5Y929kHvakC7L3EKes98xlMPviOSr48tsQ9vUMXvh+RI769MKC9FtEHPosz4D3rFd49XfDTvLDzi7z+GnC990N/vjaVEr4vnU++uF/HvcbOn7124zK+SVoNvXC8ODyNgcE8I4BmPtFOYL7x21S9WvuKveg2DD2HPEG+W4ztPfSqlT15lRq8n783PT94V77s4u49zdgnvi4ERr5ovO29VZTgvT54PztZMZe+yCqxPEV1Y761weW8XvGVPe73XL4OxIK8ZlUJvlf3DL4K0Nw9Nj2EPaVmo7wa4BM9KI8ZPl4EEb4gel6+GKi8PXwZ1LyL9IG9SN8DvqVriDtlOyQ+DKvNvf9thr3kQda9Mn8mPLVK9L0LnwQ+HSUFvqswMb3EJCW+k0eNvbEKJD1KOZ++PvtjPZZJLb5MJbe9fk3DPXtNfT5xbeS9","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","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","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","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","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","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","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","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","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","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","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","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","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","yvwvPDLERz5zFMA8Dw6cPtloqT6Zdvc99T0KPBEStD2K5EQ+HUY1Pk4Dab15R2U9Hg8iPomTqD7CLI8+rJSAPYjl9z1RnoQ+fdlDvVjtQb6eDaA95U1BPrJKyz5/ODI+mGb6PcnDZb3mmWa9a/4APvNpKz4YyIy91JSAPTl/Hr7VByO+6dtBPqsnmDu8zJs98eYDvcd4OD4/pNc9Mv0KP87h/71JxQY+EnHOvfAOJr5YvYC+GbEZvsjljD7l3Vc+0AEWPtd4Oj5Jrfk+VVAoPjwSkj1+fHk9jV+ePQA1dT5nqQw/ISPePMRaK7730VA+PQ+VPltMfb0Nbok+wXYXPn7Fnr28WfQ914KRvTcL3rtF/WE+PPSjPgrGIDydlT2+L41+vXDd9byK+sa+MmwzPld9KrxDXvc9Ypy1PeLWWz4z4lC9RhyvPV/Oqj5eBVG99km4va1mwb2ljhk9ACYUPzZo/D3zByc+kE2oPcjf5z1/bXm+vn51vsTORT678Qq+wlF3vgbCz70hMpE96Ln1vZ5ZJ72d+cS8GDLUvPIj572YEqw9dqqBPXQAwb47w1M+RowHvtjmqT0TW/U+Gwd7vXHX873lZBk9sI/CPu3gkzu+LG+8hwmSvurhj72624Q+V/bGPqEFIb4sziY+sXYEvr3VJT2Fjdg8B9hPPojLmz2iKDO9OHrrPMt6hLx957E9imfxvCR8DD4plUs+r1djvSx/jj01zyS9KpCHvFw+/ztB3a68ensgvQ5zJT1Ra2O+lB42vXtmlTu1IgK+kfEQvfIk772ygjI+hCSYPYT4kj32Vyc+iegovme8Hr3Y7RW+HDULPC9KoL2JQKm+C0gsvU1aBz7T2cg87wSMvVluerxFp6u71NsAPpx6vz1JeQa99SAlvR8vnr2lVTE+PqFKPfYm4D2O2M690kQ8vZ3vpjx3Hms9HLzJvf5mRz5NiLM9PGzCPQoUwz1ASyC9la2NvSijAr2c0ze+EHc7PdgWpj1bv8U9E5FaPgbLsL2pix0+","JKjavXPHU704zkq+a8mWvcafYD60Bow8HHvmvAtDwTqnbVo+Yi9GOi6Xnr2deNE9blC6PIXIxztLQQg9MrK9vu4oPT65FJA9DcvXvY75l7gNwq69oLo8vgf3uT3/HNw8R5oOveXaiT674U2+LLyrvobuKD4k1WO+d8m5u2TiLL6yJQy+0EAZvny7hj6jEya+ORGEvUe8V7yK/Wo97XI5vIYPRL4sRiA+RHPBPgqngL3t7Ba89R6GvHpYRT49Zaq97igEvU/2SD6j3UI9kLkgPVpBnL0HWKi9xI9UPjYiQr6gJTg+6o4WPixtzz1qAa69TIvzPT6xzb6g9gQ+KfMBPpP40jxTEEG+X7TXOz3hYL4CnwC+ZI9rvt48Nr7y64C9PXqAvobIv72uR9W90LTQvfSzf77YLlk9g1GovolRhL54dfm9axrBvcQIFr5JPZg9EleEvQ5tj74LIZG+Pv5LvlkEcL44Br08U+rmvf7dYL136gy+sIa7PNT4Db6Q92G+cQUDvoPr9714ZZS9cXpaOurPRb2q7tm95WDbvR47ob1fZqI7ZyqGPenRLL4jc0K9h5B3vRgqej7Vhrq92uAhvsodmr5ZvGi+qByJvks4xb0Lvci7D5AXPfxBgL4VRfG9NMabvaxlsL5MC0y9Sv6ivbFMwb1ssI49z2/hvW+dO75o5TS+tslxvCzhJz3Up449g4oUvsnUI74EYvG99NuvvJrcTjyyTJu+Kfs/PZggX77wX5+85FsjOUN/CD3bxgO+mnDMPaw7Kb6pJhW+kiOcvEXkAr4S2ly+xKLWvXJyYb4DXlO+dtpwvsYrgbzVB9A74dHjvTBol74XbIi90NZTu4fmV76HtTC++mclvW6ML7222Je9gna1vddgKr4leWg+NjacPaE6br54uxk9rly1PCjq872G7VQ9k49XvfwwZ72sUoC8PTGnvkRgqr3CdQA9kVjsvZi0yT1m8hm+ih+MvdB4Or4a0iK+W/DkvMkWA74veRU8BgRWPUfY+rwcEdS9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","DzucPd2mBL7zgwC+rFtoPsREZT3+69+4BH5OvrGVMz0Hv6e9t9r6PYp7J754AAK8z7nEvbe04D2r5MU9Xc4WvdmiBb2CYrU9TkwdvZ99Eb72Bo89qGoAvuhejL39OPI8po0NvpDR3L3Or0g+VcVZPVbZiLwo0Q6+xwJfveOs1byfyGS9QtSMvZPewjobI44+Ye4NPc8Vij0MnVI+N5hUPXdX2LvZnR88B+ecPLh6Vj6+UhK+WK3wPXsdYz6jm7K9w/d1PllJD76ueXm+iJctPTTgwL0pcoO9JUgEPr0atz15cRe+2T5lPqt3Fb4aTgm9GBPoPa6oZj3WHTm+MiFxvKGTMr2eMzi+zRtYvCS0EL1RDEO+9dmRPWRcjL5LS30+5UxIviBKy70MRYQ8oIgHPaxzWr4Yrw+9FYr6vapfVb0I6Ww9F7F7va+2qr3EYam9nPOrPaDUobsQJOk8oTF4PUB7Tb5rYYe9cdi2PJZvST229OC9c3YPvZcwsb3YyFe9tWx+Ps2cKj0Qrb291votvhjiy7y0zEC+jdUnvTVdab4jXCq+W1gVvp1PBD4LsIO9ltTZPTmjvzu425a9O592vfyXoj0CkDQ9BaDsPVvKQ77nvX29k7K5vQQDYb4wu7q6/833PRfHz73ZrCG+hrYZvmtZWzwc4iI+R5yEvXqlFjy8uEq+CSW2PAE7p724YAS+obpuvUx4kL04VjS986+XviJUfT6+3F2+qOOTvvJvxL5VFiq/6huFPlOkMLyE7sC+AADWvJrAoL6SmWi+kQlHvicMSL3gXmG+Sr63vit8Gj0j2xY9B8VwPhvzcD13BSy+fnepvukuWD0j6BC9uI19PLC3Y76XIGu+oRlDvUotPb1H8U++N1KVvUH/3L1Zcac9W3Y/vkf31b5Bcoq+QgdQvqAAAb6Qt7e86ksKv3p+7LtNHwq+ovbovfPjzz2QPue9QpGjvZHtQT4QIY2+w8whPjgKpL0XtJ6+x21UPQSZGL4tVg2+0cW/vZXTgr6c5FK+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","mYvAvYeMAj4gxJa8JuR+vsQlgr1YKSs9Gtl4PSy8z7w0kSk+2+VyPVqomT1Ss4+924vaPHn3QD6o/ni+YBbiu9CoQj3bFwS+B1b4vAYsnzynwQ6+EJDVPEVfiz2+fBW+nifEOJjdHz5eg4W9MEkCPnrgWT6Sf3c+QqRqvdoILr1NRsI+nAAoPhPEor6Fz328tvLHPcqxBb0a3Ta+W7FNPiPxCb4YLV4+RYSZvgtQpb3pkQm8TUIQvrWJ2L1WtuA6oj3pu+dLwD62ULo9WMknvI/YLb42Jaa+a8DLPLzpkzzxTOW88MAavl+sXz6ZBsg+kpEqPmoqrbxt334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pt7mTgz6ZzMY+WWw5PTCMaD1AVbE9uwiePYLmWb0EFYA+HcQ7PT3bvD7ZINY+yIrQPaOESz4cZIY+X8Z+PieGaDyHEQo+wzxGPtyGvDwwp2o8dzS0Pda8Nj6y4iW+YwGRPiEvQj6m0ao9VuGBPiKnrj4PmCA+kQ/DPvvtgT4I/g29uWSRPTC9DT4hw3o8wAbXPRNutD5D1jc9bkkQvnVNo7zKuow+RuI0vU7X9726hUK93fi3veSu3j0MpRq9fmlgOx4mSz4OfRw+/7gIPkVKIT0zSbg961jgPSICCzw6E3Q9","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","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","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","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","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","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","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","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","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","v66zPjsdhDua2+E9XZRFPqXOhb0zD4U7B+2SPX4v6jtK4+o8oNG6PVfVnj53txc+Xo4ovRL8QD5O4aA+i6efPf60XD5v4PI9EfmTvT5aNj78FNU9buRCPkXyij2PT2W9IrEHPjcZI74Rnos+am3SPe1tBb0Mr5Q+M1o/Pd0jmT1SAY49A6SmPQHfBz6mCQ4+0ex5PkZQEz6ZZPE8Dtclvp/tlj7LDSu9CztKPhPt7jyEQHE+3fEAPlfoPr20cSg+jnNFvYZxmDzdW4W8TF4zvgr1kz2ay/k8w2vOvLc5CD5OWLo9YwEePh3wRz7OtIU9qZhpOy1+JT45wfS8TsrfvI1s2T0Oy389hF66PVCl6L316LE9Vm81PkJEnj1rcT0+xNP4PQ7ztT2xxF29d5ArPeOApjyBFWe9A+9tPgYfyT1kSYK9NaCePeiJ17myuDA+P25MPiqxRT5jEZA93Nd5PW1KKz7p2Ty89OMNPi5/rDxo0p0+nckpPhqNBD2d1OU8k5uOPXFvMr1QMxc+6bKFPggIXT4caUE92y2LPdBgWj6XVqs9TMaRvbq2rz0Tlng9D44JPvAvAD5LcHC8Ljk+PcyjnD6zW0Q9T+uoPXKoSb3WVOo9kXDsPVUKcTtpswW9GBJtPoDthrwjkJc+AcRnPsiZMz0JCZQ+WzgPPo5Utr0TXQG+G85NPoDIMbub11k+qackPQJaZD3JPyY+pTCZPAAlhL25hHg9B0fKvcQEzrpFFwy+ODkGvuJdz7pdZiE+nJvmPTu3UL4hJss8aCfIPFmJ572DUqI9wjGFvI1QeL10w1Y9I+YNvnEKlb3Bcge9o3jkPHqI1DyQfsk9zM5qvWPq27vsGMs7y1AUvokW373Xfku9P71ovQwTgT3NjQq9wQFJvUpCOzwA0r++DCq+vEpKmTxviwc7kS+mPYw84T09CJ+8FWeLPQVwqr3evDi91+fBvIPNhz294k8+HDPZvSpwhT3ulQ08DWwTPhFUAL1oPBe+eSGOvWI7Wj6HR6y7","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","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","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","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","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","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","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","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","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","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","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","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","0BodvGUeQT4YT6W+wZ3rPeLgxbxhL5s8eU6iPSeKT74aF6k+SLqBvFInL72ZZVg9ZO6qPbOEKz4Q8VU+mJGWPSqUWr6deoU9H47hvPzsiLxEF4Y9UrYrvXFYBj6qXBa+38ukvV3PuL0WkIy9twqgO0bYUD06URi+6SiavB9zJb5FGbs9UZKzvd6ELb3zQjI9wEGyPPiZPz6xKJa8o8XiPTLZLj69S+09Hpz0vezFqr2pOAM+xpKSvZbXxb0/Hnk9IxMDvn8QWb5HaJA+T1CPPmZxl72guZY+GgypPtNH3rxjkSC+pqGEvN//072Ol9+9R1sBPtT5zjyBruy8rJS/vf1dpD2X2ns92cBNPt1y1T2YcrM970+JvQBrSj7r9nE+wn6cvLgmDT00oQ0+STgIPtwdkjw6hCI8vhoGPtnWmb1L7wA+jAcTPgUesj2lR60+1VukPvIXsT7OFHY9sPr/vH2xzD1N7SY+O1cSPmFMIz4P5yW9I0JbPvOZkj5T7Sc88P0sPqMG+71Rbkk9/hiFPIiyFz7gfP49VZCLPmwcW70zm6w+rpiwvek2Yj3qqT49KK8GPtkDAz7SoYg9hjtFvi8+4bx0W3y88VFNvG+rQT0dJEQ+nl1kvWYV0T1sQJM+z+bdPWD73j3IkyG9+ijFPaD1zj1iyK0+fCH0PWLyRz7iTji8RHhgPRxv5T3YkpM9LAfAPZ8kqz2/tgs+ZbdlPldqczysmSO9NW48PuhWRD7pCGA+CZ/6PWIS/D0tA9E8f7wjPN7drj2szcU9Zc8dPrO6DT5BGes9S24JvWymKD5TkVI+dNqTvRpOUT58ckM+xfhNPhKJOD6FXQw+f9uMPlOFYD0p8Oo92jrYPaTBhT3Xfmw+HEp3vZcYMD7PJMk+ia18vRSKQ71RiIg+AVglPoWTBjrHYC4+Qd1BPtI+ur3gF8w9qM9KvLJ06zwLAKc9FtPqPPqH2D0Sxjo+4FLPPRwM1T0kCTc9gSL7PSlrxT37V1w9ji1uPfmhp7ynMN09","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","GDAEPWff9LwLbuw8zcTtvZMO6r19tgo9FIvxOiyPRTy21Gi++E4kvqlAir0me+q9RUibvvszSjwZSTA8RRskvheiUr50z0y+w7GCvpJQxby394q+dyXGvpU3rb2NuxO+2ohOvpW4873ill+9OSsvvjFHvb1BGcK9xvsMPU/Mmr6PnLS+hKA9PfwvkL0pwjK+nX/dvQlJhz4gLWa+aw2jPZYOoz6skSy+cxcpvimVpDs9y/U9PT0/vpbHir0hEli++aEmvsbGZ77jalm9e9blveI6Pb4TUIW9E1zOuwBHCr24+us7jdN/vcfpED2l96C+fcbjvbXz1L2mNJG+aSpqPam23DqSyte9cp0Yvmes+T0/bHg83loDvbC/Nb52smw6EXICvunFN70wcOS9UeXwPDVSLj1ajJs8HbJBPd4xmz6GYfE9L4xlPGUKy70/1Q+9lfmIveLTir3dSoY84bBWPZCWn7xk4Le8p6svPp3NFL6S5ic9zzdqPRD0XzyEYvS9uH//vA02SbzbcmI9rxeoPf5Tjzy9VNg9GPxDPjECr7zQCgY+MJCSvHIiwb32o1Y9ODJZPNvhDD5x1Pc9Et0pvRIvJT2M2Uo7TkdavjVFmj0HSb+9rVn7Pb2yEDxKQs+76kIZvlCrsj2ingy+hXraPMflGbyvJuO8vDAmPV2h2D3Ro+E9UShDPl38ND5iswu+6TYZvl1l+b0R+Kk+06IBPos3db7tGQM987hePrcesTxJ/RO91cZQPlNlOzxI8Ya9Ct/kPX/yU74m5/+9BjpEPHC/ML1kKV0+UTx5vTmtpD0HKiA9TbWlPQ7nYr1L7Bc+bYvbvh/aDT7aylK9N4MJPCoakT4L+sc9QUZ+vba5Mj2m7SQ+YsSEvlkAOL4Jpky+x14ePWa1wT3KLQ6+pBn+vPTukr0DLJE+cZ3ovZ1WE75KRzk9tZyWvU3WTz7uUAO+ZjYYvB4U0r3wqKS+0dKXPSLcQ7wREKu9uxg7PrpzmTwCmFu+vo8vvcbKEL4SUIo9","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","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","Smd5Pk5/275l5gi9Y8WMPrRXn7u1H0++xik4PReVDLuJVcG9MsUKPQ1ppj3Ycro77YsAvshHibwP0429h8VQPv3AXL31Klu+HO0hvpvfrT274pi9sEC2PNN4sD7j8g+9DxpUPaAJDL02+2g7snJ8vXwPpzpT8p69/vSkvYH/yD2M5qs+c/OSvZZE0D39Frk9+OUQvL1Alz66cGK8JM7kPFnSoL65f6S98hMsvnrNJDxhzBQ+iMfsO8TklT1Xk3+90ojcvfEGTjt8fww+JyIuPaO1bLrncyo7PyYzO5s3cLwK+608hPEwvb8xm77aBZM988kqPFuP5rxpo4k+QX2TvYUTND4QSQi+RSo0vZlSobxJBt++kQkNPgzZkz7RQ7i+nz+fvc8aDr7VLJe9kl+JPm51WD6kXZk+94Q9vqJsn72U1s09HLRdvY25PD4YaCi9rg3KviVFhb67QeS9yB9vO4z7kz6C3O281KIwPkWyiL6A1lI+HgRqvvewYr0DA4++bUh7PkP+hz7nMma9rC2nvj/xHD0TaDS+WZOGvo/eZb5xqKO9HejpPYTpqL7MIq87PCIWvxNoUb5G+pu+C/o9PAt92r1PfWM9IPjivQIkarz3ZDS+1qsLvoMQ2L3sSHq9zUkRvk5nX77yL7W9S+YKPswiKr5R38Y8X0ExPvOzqL6bRyu+J71Ivnkxtb35JVC8uK4NvVKvUz0NJyQ8jnpBvjYZQb3XmqQ6upAXvs/QYr6Vjy89iYF/vcDjbr603QC+oOaZvoBHTL7u/4E84iWxvl3LOb4xpXK+2U5XvSRJgz2rFXC7HrfKvfHdMj7rCDW+ZU37PMb2zbxdgpm9RmhvviwdLL7LGRm+53N1u9RvVT3GqY++720LvdRArL6h748+wA+AvuhZYT4Pklu+ynIvvfDW2b2nwEa+hSfGvAdpRD43iUu9rr6uvKilLD6+tBS67/Lvu6LRrb1aHKW8saVhvuVCQj0Mi3O9nAeKvW38XL54OVW9ltFdvtLBYr2jbri8","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","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","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","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","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","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","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","mIbEvRJtobxZZW08NLaUvArKNz537zs9dYeSPco2oD6vMtA9p6k9vimz57wTesU+pnpsOmTSqz2TyYM9AQPWvdkLtL1TWyE+Y7zOPLqGnT1tPXs+a+qVPZ01fr6fuZM+OUuZvVBpjr5jH4I+/WjbvQ5fKz/3R9m8m29RuhKR3T0Z1Jm9PuknvXZskzxm4008U8JBvelrMb5sY0I+gGwZPi32oTwYxZ++hF5jvfT2oz0Py7w9otVtPDy2cL6XCUQ+jpcDPXNrlr3l1tu9f34EPipg377/SUM87arWPDYmPT7wEgu/IVAbPsm4P72kdbu+RXJfPRVsqT7j9z89YiIaPajmrj3pr26+yB7nPT6k3bq7mOY9y5FevkBdBj129i49CClxvRUYU74Upc08SG4nvqo07r3whgW/ms/BPaWlpz18jT++2Awvvv+X0r1bSi29D4haPt0CAz1A4cw87RKdPfAm8r0hZ0Y+Z/oHvTraLT6EbLG90fAnvrqT1z0hKio+F7lpvljVfb3gsWC9jnQHvZqBFrzGUQa+thEnPmJM1T2Vhru9fmaAvs1Ibj2ypnc+0cYEPfjg0T3i5jG7S8L8vU3MED84EdO8iN00vhMiGz4rMck9Rc0gvmEFJb7fc5m9qEuKPq+oFD4iOSi9G6iovU7bTj464YI+knY8vNlcsj223fK89gXOvZokoj5nmAO+Qqw9Pm7uO71edfM98fXXvAOAhD13Md679RwCPkGWhD5CSmE9IWA5vofMkz2avDo+fKL/PWAOlz7dyjA+owqQPnqO2j546JU9Mv09PtBrJD57e6Q8y1QVPCpISbsnw3U+J51zvFojaj5ACTE+fAUuvfP/vDxQO4W9EY5zPYgQK7pn0z89Pr+zPZpZQT6B9CO9JTHaPuKhVj4nfZA9ykAQPa1Z3j7gusg96sjYPZmG0j1LmFY+1XRzvVTaUzzlgA68loTgPHYRGD5h9hI+LtObPh25Bj49mwq9QtoOPj86Az7voz+9XQC2POOB1D3mSZc9","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","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","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","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","z26SvXGOaD7EkbY9LukEP1ksRj7RWMc+arMdvjQ96zsfDzc+rOpUPk1Tjr2XeZk8OMOpPr34JD5Hx08+kGcsPb1h1T6R6YI+AFgxPqvLgz4zfuQ99g7MPthROz0Rdzk+9oRUPXCNHz4D4bI+VzuqPrqA0D5Nuuo9xrp5PSgq0z1FMcE90wirvSqmO7zlhkE+Lp/GPP/c2j7KDAI+gtv+PYluID55FBw+PLWHPDzshLsjURc+oF8WPrg90D115d49I4YDP+dULz5L4AS+Mg6sPU3Adz73WJo+Ult0viqocj5Rw/M+qxyEPktBIj70ITQ9o6QjPgPvqD0xuKq90EboPcACaL0i5Gk+HN4hPa7dgD5Wivc8sxG3PA6YGL0xlii95bNbvpU9oj1EO5W9hcwGPbO+iD2mMxS+JTYNvRsXgD5JxOy9m3chPgxncb2xh/A9EX3AvqpzJD3rsKU+/5eru4y7er0mErI9gMcrvuBzartgjyU8QWZJvhVmLD5eTo290QG/PpLvpb3L2sm8gycIPs7/8jwNB4w8AIFmvg7m0TtZZom+COAVPpXqCr2/gOW9AMjCPaqUnb6jM9m8k7vWvYR4ar7TmmK9zn8nO0eoXT2C9LS8QdTmPPaagr2bL1Y+a6XXPTqkiD2YvKI9AYUtPsU+L74ZqoS9y2wHvX7XuDpLtnk+gFz7vkPFBz58SFe+jnfcPVEGiD733MY9m3/Kvsk4oDzb9hM+8iVaPu1dVT7qwPE8XckSvoivwL2qLZI91jF4vdXq3Ty/Ub49p6ixPZamsj4gV3Q+fB2svLeeJ76vYWw91TH2vYCqEb8D0Q6+MHQ0PgzP5r0sxpA+RegCPp/hKD8uBK67PAyRvqWWjL77Eqg9f3B6vkGyqD1fhnY9Djy8vqhXjr5j6n68lwoBvuVAaj0AoB6+JdOlPrV4Sb4LC4u9of0JPoNZH75I/qA9dpr8vbMwiL2zER8+m1FmPtxtvD0dp2Q8RcSSvCHciL6sCIY9LkK5vWyWg7u74/u7","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","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","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","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","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","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","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","vpB6vgQ0i7yUb8+9bxMIvYnaSz7JBZe7ybZwvk2/nb1jSGi+FFNRvnQ6nTzxvHk9hfXEvmQOTr5V3Rk8DGUnvqkyED5A9z++BtqSvmt1Kb7Kqp6+hPAvvgiTYb4CvpW+ul32vX/6jb5AJqi9LNgzvo1XED3YSYC+A/maPkJUj76gufy+J7uJvW3rxD2Sy8O+/97OvTI4Bj6p/lK9NVHdPb7huz1q+Pm9a6c9vptSNL6HsSq+j+ypvVBiYb6P0jq9oBTPvaqQiL4XEmk94XervWoc0r4JVkG+2VI6vdJP1r6wJ4o9eaNdvrd/qj1reKe+9Jy0vY+cEb4ejzS+THGYPb7B2D3mto48azVKvnM2iDuG/9Q7AQgivRNJxD241Em9uDw8vpjhuDx2Tus7QZdFPoKTRr9EqKQ8EsgZvu+qqT4UMe29CoMMvFMBiL0+bry9iqfgPeLBljx58ms7cP6dPXlD+L1QXDa+pyZ0PVWz872PKAc+opfRvfVIj72Qc/+9yp8aPidGQr2H6Rc9r6wePS8W+Dwe0bI9DriXPirP3jxptiU9Sp4Zu7PNYb57tvA9G4u0vf7BKD7oEEs+lowBPFjujL26X+S8gIejvkQlnj77BUK+lDbtPWRQID568Es9L+ESvc6BcDwvaBK89SEwvrY7p71DLGe9ApB0PbR5eT2O3jQ72kdAPtjRGT77Lk2+h1H+vYL5Hz3bVty8HfxZPpQMOr7rcCS8xBwaPlbZyL2z9Zy9OLu4vZy55rsnKDc+eCD2PbrOF716Pe+8YCEkviprf71ILMM+4MdmvQGfiT3or0C9Gj+tvVZiqzx+ZHI9M3ievbICpz2Fd+Y8qPC1PCiZWT5LJjI+PvIau7A5fD1IWdA9/V5IvnoYkL24dqA9eDPYvjtCPL0tDgO+0iXyPcHCEL01Wtw90PZIPg+3HL19KK09vuGHO5crcr2N5jC+wXlhPRVp8r2mmGy+umJIPVTXND5oKNU8i0SWPcqEerx+JSC+qsM1vmXOZz2DRiq9","ny9rPgYel73p2pE9CtKrvo5lGLwsmYy/pM08vZ2ukL4dG1e+NViGvhVL8rze+6u+SJ0UP/8qpb5b8928LKfDPjmCUjv7X6O9RhMQv4XWdr76cec++1QWvkpoHD6JhCq/nGgkvsDrwLzc45G+0TRQPiR+pj2tg+e+eMaGvgvkoj3QwFy+cDPrvlu5A72yjim+zqKJPWNVK7/r5UI+noivvsRG9L73pQY/I8O8Pi87+r2KQ6u9w1aqPqI/Kz6OOEy/mIz0PkmhNz5n+vs9l8UpPm6vybyTmiW+EjKWvdfOuT3tnwq/U4koPax9Rj4yJA6/P4jPPk4nrT22k4u+JR24vm884zx1E/I9A4Q9PhCFCj67nKi8qGVcv1gudTm+mQc86z3Ju6lWRD5Kic6915fjPnQEbD40+uW9D/+WO9BW6L6EpzS+Hw0SP42orb6HZDe+mmSLPgHNZD23ere+3Nw1vivOiL549Ds9m1kTvtMn9D19VLc9Ak/PPb7mG7/nyTM9DaFivEpvGr5vCJy+etgOvy3tBD5BJeG+FhQ6vXXX0j3UsBa+FzO1viY28j0IIQO+gZYFvT9uAT7luQU/Zay+ve8lbD4b+4m9zWDmvbbjdL3ob/y8Ly0jPh9cwb1Cdhq/KcQlvnrW8D1llCq+LX8evi41iz4rcBi+6tNGvotV374DoPm+Cf8xPlQycj3E3Nu94zOTPkpHtL6O+Z89JyNePHU8nT73F3W+QhM5Pu3lNT+7YdK9NvbHvgFAg71IeCK/j32yvtrQlz54je++m0FZPA08S75Caci+BK8OvZI1iD5br2A+pMgXPQPkqT2l9vA9UROVPnwuob3n7dg+9RN0PRQekz7E/v+8gqHJvo6iID7vEWO+ZTkPvkfZeD35KNQ+8Dppvqrl5736tGQ+tiGfPQsn+jwHcqG+d0oDvlQ8V72qtzc8vlgovmr8+T29Vcc9lQM9vR4Tgr59Hig+UUFMvWHQt71h9mK+xz2DPnR9x75ky2w+EdPpPVf5hj6DuSq+","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","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","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","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","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","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","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","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","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","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","GXPOPSqEH74KRcm9RQWePV7Ilb0L/+G9GCKKvDej9b30u468aAOKvYHQ/710/G88Vgz6vdEyCT7Gv2k9lHAovrbLUzu7oJS8VADTPBRqx70O+uo9Seeevc4Nn71HoaI9zNTYvdosRr4hTqq+2xX9vFTTU7714ge+Q1GTvRL2kL4b5ia9V5OVvvhdKb5pAAi9F7SNvbcwU72qVkG+BT77PVP/Ez64zsK+d1b7vQ7EC77NON6+TZg2vlDUIj0yI9+8HUnUvWjXur7rmaa9hdhMukWutL2i1H++qO+3O7Vsib2NwxK++YrGPGFi37wpynE8xFq6vaCRL76JciG+EmamOdqOTr3fxwm+5NfIvXdVGD5tQs89XdO0Pa68X7yUKVQ+U6NBvVy8tLyxyh++fBkhPhKNyr0asva9L5gTPs7ntz34VmI88tixPZ4Llr3wGNg976W5PSNm3LsRTyC+NwCdPd8Uobwhp7++pO7LvfMj+b0BYR4+0Aq8vTLn6D2ax8C9oR0jOsjKoj2vqP88t5PzPf1jmzpu8ee9m/EAPqSxBD5wzZc+GTYHvh5Rxz3mMME9N5WPvpn8ZD4Ezaq9zT/yPEBXij3aDnW9sRWIPYwV5Lzk456+qGEYPgwq+L0vyhM+8kJivR9UJb36Wde9K88EvoRisT3pFqC9bhwPPiADOD6AxjE99s/aPYxtjj4Jh7m9/FA4PhabRL0QU9K8pdt1PSBuKb7ikLu8QfuNPuDxsz0iOEO+WP6ivpLIUbul4Ks9Pz0WvBx82jw8EDG+VNW8vQtCET4XHSK9azhNvd6usT0VpJ69FO00vXuMjbrZRQU+CWZXvh0BkT1aboK9JEcKPvCEJz4U3EE9bcDSO+I1H76uTl8+YxYLPs/1RT7tp6k8qwbuPKLK2L0f/QO+deoMPpWagDxKLww+I6GrO72qBL4sf8U+D204PgdTQL3yN6W8zvv+u8W0VzxaDo6+dekiPisNkT4sPIi8PZUsPqamNrxKXqG972OEPV9uTr4r9uQ8","7u2iPUyAcL1eAlC+qZbsPTeb073MWvY8OVjqvFO5sL33AZc+e5soPg/6yL6sREM9baMKvjot0j45Awa+6drhvUB7I738Pjo9qIfIOmwIjb5SK8m+f5+bPV4Zzr3i+lM+ScgqvulqTT0Kvb09tZeEvjEehr6YRuO8teEYvvB1gb40VQU+snZJPGM/L76ww2a9vo6PvvSriL6aefC9flm+vH6ijL4umXE+idNHvpn3Ab43Qou9wTpkvog3dL3I9Zg+Z1mPvsFVW77P+Pi9tI6Xvo/yDb1dVIi9NxyLPGY+y71AE8i7rvYhPS08hr4wWF89jZqXvfZTHb6lLcE96FD6vdr0vT1F//68Zf43vl56F748vDG9lscmPdMoQz1CVf29DqekvbcElTymQai+Q7eMva49qb5Vnww+CBDivKsp9j3fOXG9/Ypcvnc2wTyKbic7icgjvqMCi77pEIe+eUwgPiDiAL46R8a9ESHNvae1K75guoy+4HuNvsVHIz4xATs9T7uXvZc17T2QsJC9aDuNvt1hAL5VpJa+xDb+O12Uib7beQg+2xmiPR/iVz2v1l6+v+n6vTmNgL1vhKy+vzhrPflaWL2miAQ+7T6uu2Cd/733cUy+Ka2Rvirapjv785S+hasCvmsi5zyF51y+UW0hvmS22D0zrWS9yQ6xPe4xJb3r+lw+3hmwvc5ORL71eQy97a/kveIF3b0YL2G+y7HsvEG8tjyQE8S8Dxa3vUrJs70WvyW+hHG0PS3NJL7GHRG9ZSTHvBM/O70cnlu9jUZuvoALI7kd8zq+PXPuvLILaL23zSu+iH7FPUoLZz76djW+Fo6xvHet4z29xD49MTSEvhFYN71S3D+9F5iUPgROtb03bXU+kLgpPjWDYj0JFiq+B3uDPvR9zzwxBW2+0ugkPtJrUb1xU0M99I8OPhDNib5DDpq92XNbPQltLDzkEJ++ngfoPawozL33o4a9WM9EPCU99b3uaI4+MXK5vnvxU70AzxE9qyHnPR7jd72Y12M9","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","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","J4EBPv8UGj3xd5I8dcRWvDXRLzxqwcU91XrEPJv5LT5p2kk+QGIXPq2DUj4Caps9BmGIPRW2HT4IPwI+RIwBPuLokT0XHVk9eQV8PJxISj2Isn49TcwGPvlSCL48AQA9Oee1PTScqD1g/hs++Tt+PqcsXD2AKgE+25dDPjjlwz52sdM9UMnVPVy2bb1QWxq8k5LWPX1xqT3bZxA+zP5NPcB4Pz2JwL8+75cOPV62TT4/UGs9jGUhPoQbYD09kKs+whXFPQL7cz3biCw950yLvU2DYT40aN69jtC7vJKWET4McBK8xQsIvVfcF72P9hc9HAbAvWTqMj45Ozg+y/KSvvfTfL43bBi9924PvWn4iD3CTv69CaGiPbrm9T17wA69yUGfPOfXBjz4e+o9c2QSvgK5mD2OgxU+eEEBPhqn+j1wEPE9DJsyvZ8LiDwPpQc+Ya8KvvIFrbvh7BC93kgtvSuVTj4lAw0+tH3QvAisDj5juzU8L6ICvQ6vvb2zq8w+KsIJPTgGab138T095ZoiPaPoFr13/gQ+yf+avjz8mjzShRS+YLCfPUx2qz1uL1i+7w36PVaptb0px9K9gcZqvcvAMTvgvQs+tFWiPlVykz6806A+KCxlvljBjL2ZOB6+C875PXcsdr2U2Uq8gWCgPnzVur0gVME6DSygPTcZw7v8XGu+nhwUPizbfb7jz2o+qca/PbsGSr1faV48tRORvu4Vrz7HQMO8EolpvWAA0D0Odng+LeBVPQUdmT0E1Zo9WNpPvQErLD3a1m883nvEvSBkpz2mIwG9SEjSPCOREL473cU9pCQUvRa0N75jRCe+H5KGvCdzUb1j+gQ+fDk+vnqLdr3spuW9jZbsPHAQgDzfoyC+6fRJvAT71zzL7ra9lMG2PcwMFT74t5k9uTfTvYuyor1HRyW7TfIOvdSYMT7fi0q+9tvhPbV7jr1sSgw+TCaavYHuHz5IzxE+GfQtvmL6vL3GdPy9cC29PW09lb0sg2u9BlmWvez1gTyniu69","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","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","jfxyvdIX7z2KC8k9L0VHvtUaE77Keu68dnYUPmedsLt92IQ9VOB9PZfOgz5d6lC89rniPf/XjLwF+7g9bWSjO7rCej6nWgi+UTYyPtgHBD6NKRW9Re2nPIesK70Rjfu9lcnkPVvWhT24ppa8LswmPjYIvb06qqU9x7/cvdTOET6P5B8+QBvuPZdGoD0labu7vZSQvah+B75CpEu+G8onPYOo5rwZ4+E9mkZ/PSymF76gCx8+rpSBvn/Rz739fgi+F0+hveqosj3yoLs93dxOvsBFwj3L0B2+oeaLvD4UN7xowog+37GjvZa9xDt8yg0+CrdTO0X/q7yLcNi9mqomvp4umz1OcH694KLYvOc6IT4T5JO9BXRQPDXgSD7aYBo9BEKEPhRNED08b9a9jrIGPSyOtD0dPXS9ae1JPdQhirzg6wa+qN+ZPIK4Gj7Xma+9aWA6vA0bYb5TxHo+gdHXPC6YD77O6Me9K4/OvOj0qr25JMA+bHuJvU9SlDwsA4G9RkM5vmLhnboyX6U901OIPc4TVL4Bduc+wwwBPj/jUj4tUeG9UDCOvOmEAz7A6jG+0FTwOzP+4DtNmgW+8PVkPPPwpb2HVqE8rTMbvsGgBj6Sk3U9t8OUPcjpgj51Tm896TMhvg88Ej01Xxk9hzDhPfuRMT69ZMs7Twowu7ROED1hMt09zVksvXe0ir58DQu9A226vj1LGT7xA629si7cvZdmTb2TDCe+X27/vcoLt70JDpq+GCzIPf4Hor3i/hC+aH5/vm1uGr7/aOa9evBBvmeJCb7yciM5IaBqvcVTmD2XohI9PI1AvuAhRr3MH4m+uS77PXQ2Cr7QxfC+OwPWvUovob4aNf09mEa1Pbj0+L3lTVk9LsaWvka+S76BDlY+0Mk+vi22Kb7rjig+d9CNuvglu70OxaW9N8+FvXZpxb0Q07I6IGHJvIncxz06dg68g8NdvrIH4j6CzjW+ZHj/vfKs6j1fUNy9NcsoPXSXKL7hwog6GpTWvRyLKT0EecC9","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","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","ir/3PS4QZ72E0UK+4c7RvjVOjL4TZ3g+6AqAPtehc70rfJu+f32jPrx8ID6wAMG9eMcCPm4xDD4+FzI+dLIZPsiRyD1bE/C8snYNPtPfkb347Ne+OOmMPgETpLu+T2Y7lW5+PvCd8Lx3DuU9GTYjv2lcKr6SC5o+vKjLPg7PNb4s0li9Wi1NPmvhrL78hXA+AODCvHFDyL7YR8q+1U58vqIler7Hj88+ElUjPDvGX77yUYa+w0GIPtCVcz4jHE29zYsqPSiovj7hLtq9c58Uvmlbvb7+FRk9OSJWvkH8gj4ty7O9I8mEPitu3T0SLxy8iaCHPcF4mz7pZee8ZO4Jvy6XLb439p69RpkZviJLu75V7m69vwiaPU8mir10Hfy9T+SRPPzBaT1bnxu+KCKGvlSOe74qTjA+g+I4vsBMfL5J56e++zMXvjUOcrwLJyS+tfRevccv/buY5WI8v80vPmG+Er2hQca8ZNKtPRAH+b359DQ9cVOyvUhrkb4Fx7U97A+iveF5FDwGqtA9nC4XvvpUM74hvi++rkQ4vursmjt1JVG+7od/Pqf14L01wMc8br2Zvvv5gr4CfN69jhGXPi0aMr2wblu+hnrfPcmy4L2qThO+C1GWPUCc97xdi8e9vLI7vZ6HnL2QGfG9oHwjvs5fTL6xZHO+k1JCvudFob11mBK+Wiu3vhBU3b2vr0a9lVECvstfPL0HTtG90J1Kvq2jrr3UVx09VGqWPFPmhTwzcGu+eaUevkyaQ74QFI29LggOvm7Dh760pz89uelHviAe1T1M3gS+ZIJIvl5XBjxJtqi+f/ESPRpjNL7Lhce9h1NtviGGqb5N1BU+WgVrvvPXwLxNtWy+M+AKPuM/z76XeHi+pK4+vjxOm72cONG+fXA7vvUR1L3NVnI9hvEkPcDExDySqqy9MjTOvcPd773kMqe+f6M5vU1SJD0v5EC+l5GBvhGMtLxs+G899iRzvpCaY77xJIs9M5ZLvoOhQr5z7G088H8WvqOSbL0eHxi9","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","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","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","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","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","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","zhSivs5aqbxnEIK+bAVOvIcq+DvHC8e8NknyvK5HOb5LS7G9+W3nvbB1Q737ko29TUeCvcddyr7t2Am+7z8PPcMObr48kQG+PUUOvcYaYb7dfkO+PSIIvk4doT0hS5Y9iSq9vBD6Qj61MKy92g0Qvmvnyz3Dgb+9BP5APU4UDb7KJHs7BtfEvdOE/T3JW4i+ehZGvjNnqLzcIWW9h2TkPTRlvb7V9es8X6W7vWVTazudGYy+Rd2NvMKRz7xsOAS9yuEbvky7lb1JkJs9KQHgvOs3NL1LVuC9/EIsPf4GDL5ldv49xiRTvnEbPb41qgO+aMcgvubWkr6VuV09SJw1vjDzO77JgQa9TmMmvhP4pb0MaRG9JTszvc8Y9L1vD4G++dOiO4vQPz22p4m8gqUZvrdgeL1Hk/S9Zc+zvsSyorw+Lma73rgBvr5CJjtRFYK+xioCvu4LOr2cOn8+YffuvURl1L2aRM09648SvlSz+71zOQ2+upaHvm3aPz2+ceG9OuzfveH4Er3nVac+F5s8vZ1sgL5Tm2K97kB6vND1EL6YZW07+rGmvQnCxzwOj3++WF7/vQa9Xr6pQnC+w/vgPS6cRL7EiBW7FXo2Pbta6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PRy2xDs9ekE949NZPYQhhzx+Lri+KQc3O31Tqzsnujy9Y2iUvT5nIL1VpXA+8FgIvi2gVL2wDWy7Br0fvvvm2Dzljgk98UYAPUA2Rj1ZX0A9ev5WvWrOuLynwne8oXUGvPy4+7w6GFi+Humyvs9A7Twcmh68PKKKvHVNS70MVhC9s5H1uqyq9b3JeMC+ViqDPZJrL70CV5m+wyVAvnX6N77j47w9K0CyPbyuAz5rLJw8wN8OPeN4Fz1a+2G+dccUPu3ikT3LXt474IMUviaKGL3DxUs+UfoSPLz6/71gLBa+g+hBPSiCF778CxK+G6MEOi/p7r2M8++8VL4zvTtSvL2cs9y9","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","XjGOvKq2wLuc71y+a9FDuzkbU7v/VIa9z0KCPRcB6Ds4bgs8x1upO5QNBzwvqEA82JNPvs9SyDrDj4C9q2JCu4pIODxF3oI7LDkjOYfRm7x5+Gi8f9yDveH8KTuzcYI88oKeuuuIcbwkjwE8SBXpPb+FxrzHDYS7M3vdvahI8bqRyiq7NS2Xu3mSLLz0KCO85GFIPFQbjznpwMw7tPdmvb9DibzLJ747YfMRvBwbybyhf3y9aujXOyFCbzrUb5a5Leeeu67IALs/xs26eOsfu5Civj1wGTS8geNKORzKnrpkDYq8hW7DPIXQqr14SUk8eW2Nu1GkRT0xE+w7JUsUOxsGbDwL+bw7fvFXPZtXUrzxeCA93XTDvLNCnrwfehC9LiisuqFujzyo76W8850jvS5KvTwsvr89qep0vI5VI72k+Vw9icrSuyjvg73Drca8Q/V+vPV+ajwSoHa9iOvovFmwRj1M1X28qJvbPM1L3Lxo2YA9QtolvKDTKrxEuIo9EN2cvKHpUjwl3o2778YLPQiv6L2D73Q6ZGk6vBEWyz3ufjO85ykdPCMjLLwoj6287QmqvAZARjxGtSm9QZvhvJHMebtRX+W6qDoCPJ/bCz03YBS9Z6JOve+XC73YX4C9y9ohPW60/zzQAvS7aM20u6yFFDsUXLi76i/Ouwvlnr2VUFi9JLpSvXpMPjtsYle8O9YKPdDF3Dz07ZI71LEwO1xPvTyxYYo+drV5vPKn6jxfeiu8rPyvvKeQO72Dnxa75hYjO+cGnj3stAY9gc7kPLR0m7qoTYG9TB5VvSjFXz5uDsw5SqDYvcHh7rzsgws8f4ZdPfCNoL2jFq67qv8SvV936zyaKxG90PpjvSDibLzpAQc+5ROEvFV2xLzdWx49+YC5Ow0aEb3z4le+yK1MvXR3PD3Ywx29QjzMvL3+Dbo0cjG9aNzivXODwLwX3We9bbnGvLkWOT4embI7WLeaPMgLfb08Qc+81UlFvXrPQj3xB469wLqwPAQG6jlOWYc8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","3YYHPaX+lLyMBOQ8DZiQvGOeljzJPy48xsWlPGNM4jyiZNk79IMAvI1WAb3S3WS7DGGNPfDEIzqXcea4YU8CvSDxojvP6hk8asPKPNz9xryIz248ZoyZu439kbpahNU7qysePAOBSTyJvpO+pU9uPDD9iD0s00+64GdmvIl9VLzDSy48V1T4PHzrP7wwKkW97RaVvvQriTxGlzM9PS3PvpIFRLvKrck7K5yEPdJhozpH5yg7Pg3dO6ORLT33eoy8XWulvh3Nobwx3tC7ArVKvSK/pL2znDA92GLyPOzrkz0HHy+9tkFJvEfZuzss+N8706h8vQGajT1UOTg8UtOsvKjI+bxt4Uc9cGxVPWaXijrRV6C+2tnNvI3KtDzvDTm7ETgnPSeRjzs68447HoK1PAJI1DsEvd+8aV6hvLPKZL01tAq+z8UKPePIC735Zaa+W9p3vDSrbr7c4s+8EFimvg6yjDq1r3Q8yfHovIw5IbwC1j+8UxSGPCkuWjvBgW09jihIvSynojzlsRu/LbRDO8rp4zxbT7g9EqvevH5Zcb2mFOE6Z5AkvWcfjLyj7EW+ARQYO+zxlDwkqU28E9OqvFSgeL1esWu9E4uevTRc2LyOA668ex+GvMPTWb0SCvC92WyXPEiDTrwgGXo8jkrSuqjHiLwFyhW9dykbPNE5s7vDGBw7rbTrO3IPCDsaB4W8PjN9PCdlkr0/p927lPJavssDqbwqOMo8BrLKub27bLyLvTO/UUuwvGc4IzwyrIu9HmBkPCPl6Ttuw1e8zVCzvVz7/buIK4C6PkI+O+JNf7uZGfi5m/mTO87egLxzVBs9GghTPJpqxjhKX3m86LQ1PIr1zzxHj9y8/s5mPKWIVbylBB28wDrQO4dE1jvD4es81OXQOz0jLj0ZWR+7fKYYPUh/lL0JIjS68UetvEbWhLwZd548DjvlvGVVejzXFfg8k5FTPu9rLT0m1wg5E6XgvLPkYDymxSm8+9VJO3Q65jvQJtY8jvUQPDmEmL3kF8u9","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","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","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","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","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","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","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","EMpDPASlsD6sOtQ6NVnuvUM0ZT06U5s+V5SMvWMhK74hjyw+nDo8PdO7MT4wkoC9H8FZP1VXab7UgYK9SZW/vDSgfj1/cF2+FW47PnW0Br6MLo29RgTYO8/nHL5utjq9UUEAvdE5lru/9P694MqRPSfHGb11Fwq+GdIePs3/1b2tBeo90gUOvlSjH75okb69HJMcvg0tBD1k+R082W98vIVHnT2yC5098cplPbH2Pz5FxRM+vz+kvaGYGb6VRa29lVvivvpdQr7u4Le8ZXGbu+bjGz7avq29dK8DPqHGU7/mUM88PNX9vVEctDvyvak9v6nxvR9eeL2m1zu+9oSmPHZvRz5JVji+ERsDvUU5Aj4bMXO+f0afvVJTFDvTa1y9b2nNPQp1gb5j2Em67QUgvvwSlDxtSDo9nMPFvYfeNr23dgy9HrL3Prng87xSfq09NfuFvQ6qEz05C8m8zs6Nvs8ikruYRHU9NFM4PW42nz4ISTW9q5V+PoabuLzHIAW+UnSaPn2J/T27zdQ720jhPElKC75Rzkk+6F9tvD2qZr1dbig7/jhPvo7N2T6OKKY9lbyIvberEzyfX5g99+j9vcT+dj0/nug9oQkQPdM21DzbMQE+2ZhJviSQ3z2yieU7NIcYvfoIA74wV/I8kvw0PYwbnr52bYm9rd4fPHZ8PL3DVuU9np47veTERT1jjRs8AclKvksRXTny1pe8+tLLPMO9Ur3JY1e82Hdtu85N/77wrJU7wonLvAs5Rr4RqRK9qpI4vcTmO7t8GJK94RSsvZ7IkjxgZ0c7J520u1XqiLtMdgu+icHxvWI3S734Y8+6OlX4vKiUSjwOFa29D4AZvbEfLDqD6zi+Sz7HvOrfibuDDTq915DUvMWt7rwsEMC8oWLMO1QqIz1CIyW7YeLivMfILb1LVas97ZiKPFmWPLyueik+4Ec3vWyOsDzGPg29M2yIPUhMFr2vUmm+H9AhO5HOhj3p7BS+P14KPF7Kw70FBQY8zks/PsPP5jwBMh09","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","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","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","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","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","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","2yW0OtWwAb4WMt67Ckv1ujURg77mMAc8N7U4OnW+PDmcojY79sgpOyIE2LpnFkQ9+BG9u0b3K70LEp07cclNvauCr703tRa8mIQKvsbRk75Wlsm8WToCvYn/4zyguBk984hhPIDbfDswlyY8d1o+PNhdiDuTiui8xl0cPaRpqr02xl+84lHquK7i5rzq6LK8lK3MOwzfHb3mRkO8jPnSvV5HlDuN626+ckmovDW9c777vFm8gbYru746KT1OP589T5ucvWNYyj33g8m968QHvsh0hryVd7c7oL7auxZJxL0XqSa8g/0EPPpKqbuqRpy7/ZyYPOuUVjz70Dm7tDMuPqrLyLwizZq8FMORPJFaeT22nTg8kAJSvfSVRr6Zzwk8qWeovGNfPj1gODM9VNrFPTFmmT90czQ8A+MnPm54yz44kZ49cc0UvqCTVr69t409emARPeJUoD0Saps+oLC3Pf+a0jwrFay939V2PYUdSL3aqK29euUrPmAQtj0J8rW8Sm0/u7PlIL4DJOS9Ms7MPgQVgT+Z1hU+rOYJvQREmT18PAG9cVTGvbdiYL4fCUY+FyqOvUyJKD3NKIi+IX/Lvcn9zr7Xa4S7DGcpvDHudz59FRY+WdkXPXMtq71foaq/7t2Svnbpl75gezA+pnxSvXBX27143im8fFIVvcWD/bx2ErS++oCXPiBCqT1bqxC9l32dPiwsBD7zPza8Rxd7PW4Efj0BqFO+ekWUvQ/5Fz0n0Y89mWsRvb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","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","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","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","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3dBRu9f+BNvXUMPD0mpeG9PPy7PWHOSbtKlAW+ZswxuztJQL7LLuE8e1DZPXypDz3wdEu7d4oFPq4iGL0h34Q9QtWNPDcpKr0UpwC9GdajPdzgwD2Is2s+7hb5PfIg0LyhiKY8VvilvQ7j/T0LJmO9Szm9PcPCOb3TTcU9h5VJu/CB/71Oszc+W/sfvjDrnD6kA4282ftGPFPMk7xo6Fs87YS+u3eqE71BSvE8qb+MvMNkIL7PJYY9/WuQPbb9RD53MNY6ZDqVvU2NZj2RFwK9cfQRPeIf7j39go498arHO6eEpD2rZoY9ndwhvb5hSz2iMzO8kjPyvem82D19Jl49ULQIPprW4ry8Goq+oX/rPYYSGz13VmG9xv/DvaXvxL1n1L29sEaLvemv+z3MaRQ9iVuIvL25/T3b0iq7MzNgPDT5Xr0UzyI+L3edvdy1Rr2iOA++fW1Fvm4rNbyIt1o9LZhJPs1D0jzc4M09w/9fvqHSjb2em4y8yFrJPdwcrjy3HZg9buK/PPf2qzxYOyo7b0sOPQyyuLuPe8w9X2bjvZMRHj1LMrA8QGVGPVwasD3mTZ89jOEXO9Xa5L112tC8FZXfPddNrr3asAu9U6O+PbSlCj4yLqc9Ud3BPYJ3tTzCzQg+4GZCvJrxRb1ESxE+a9xcvsOxLL1jUHC9Kp6OvdeffL0087E9xsAXvpuMpbxLXeW93373vZGMLT0Rgi++0ZHtPacLvb2WT1I9rTdRvfDrhD6r4r09Ly/LvWxDWr7ULdM++9LKvbtGtrzURB48nTOWPSC+dT2ZBTY87nmLvbxU270sE789zeePvUkkqT0ZddM93wrsPdmNrL06jH69t1Gmvd+llz5KDwC99fzfPUufxL0PUIA9ZJbvPa8+/TxvYaS9Q84Dvhs7Fz4Vd5292CbMPeDNKj2BkKc9KcgUvkAULr3sztU8bu6Ovtubej7oHri9aExevnh/cb40nxw+OJdqva/WI756Z1c+3Z3sPfkwar1cCK69","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","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","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","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","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","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","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","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","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","fqcCPsuwC72nEYO9MgYVvpgT6bzi3Vk772IAvH+SNz0pHB6+Yu8avBF66r0/KWi9/j6xvHa9Dz0IAjO96e0MvSJiITt8PiO+tsqJvDEeCT2GCIS9qAZQPT/rB71kZl08p4gGPZRpmjxf9QK9QKuVud0+qb2/XLi9d8Unvvt2373xfZg96IYJPThtyr3uOSK8MeQSvgi/473tlA48Fz3ivZ7dED1Ev0s8wXuUvsvQOD3+5La9xMSfvcBpl71vQ0a91ciRvfiUP7wSqVs758YbvSENTb4HLDq90G6lPcHFErxrfne9vCm2vYGdibzhysk90uSSvaJLUD0Ox7u8lacGPrT2hj2QwSo90tUCvSTMw71L/a68vUXIva1X+r3Nbva9S3DevDC9D70AHe28iD7hvNUR6b3GIpI93qzMPPEn7zvzB607r5WSPdul7DzFWYk8w0DIPeBXkr0jWba8INWtvVTYpL2FZDc9AIGZvFVztDyQ9di9CxsuvjbNCrw0OTu9bPanPcr2Sz1VN4u9RxImvvgBK72pVIq7H1IDvj1Job2PUR099osovhbyPj1aTna90HkHvXqjBLpaNXK9muoLPEjztLxWdG29DHJrvjlfqb3nWAG+S2x6vcb4DT1yc3a9fTkwvRXNkT3rR1w94o+gPAbd/bzTYnc+T4USvmtLMb3LHME90a4DPX88m7qcnTu8bMQjvl1pljl3sNw8YcLcu539iL0CqAW9Sw4MvvMNgztQx/g8IT4cveEKBj6znQM923qWvD5aHz2Rop28Ib/vvTtgKb1wPx09tNsRPalVH74D46W9aRKYvTZynDz8nou90pK+vIYicr15ISi9eWGLu3lvFD2NmF+8xiTgPJa9oD0Aol48MXPNPSLroTyT7wa+RNoHPuKKHL1UTzk9pC8tuwVkdbwJCWI8WrLUvbbB7Lzm4Si96Em7vPP9jbrKE8U8OMShvQGvJT1RJu08gdVvvazB3zvI42C8SDaAvfeCRLyQAPS8YqaovRR/FL3/kvc9","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","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","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","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","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","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","6NQ4vgn/tT0iYX69g9NBPs512Tr7Avq61LNGOt8J+bv6XWw9LnPwu/GcQb5B3429UC0dvlxbFDyWUxa+yEjovThZ3Lr5ins8eiVwvbdxMD1o+yi90pbJPSKwab6t9809ZZCiPDeL+D0LY4a+pHHIPIHR7T1K8oC+20wGPVAzYrzJsWu90DAnPWVVA75jPkk+3jDavAf6Ob0Y0Q89wq6VPS2BNr784p++XEASvjasQj3lwy68RAw6uz9Sp73Gt+A84S1APuuo671PoIA86kx3vhryW7xsUwO9PJyAPWg7tD2t5Q4+uTgEvkHPxr6PXlE95mcOPtMZar0eNuS9DsPNvZGDtTynuRw+R8i6vBZQfL4Pnwc92XWCvfRKIL4JDPm+aviWvmmugb1KWfq9wPADPLKxMz5W1QO+ju3jOu/V5TwmzuM8cMr4vZTu6Lv/wE87iy8BPZXjtjuCgXc9GsW6vbNHVb4qyvY9bH8NPFHozj1ma1a9ovCyOSK4ET7ypLa9eK/gPInb/T2HhPA8xQraPVDkdT2lWRc+AW45PU80Yj2aSf2+vnOCPRKuwryelRG8pfGZul+xpT2e9ic+EfcdvnORDr3h+jE7WJGrPD6bwD2g4VM+UzSgPbtvv7x96Iq96Ys8vhdtCb3pWv08XXlhvUQwALww/408LSdgvVy6573/lfu8uJRXvUGx972nUQS9BOm6PcivmrwMVMK9H6isPXzMS71nDZW9GQHavR6gp7xcCRO+Q2JIPp4Ju7073Zu9tpvJvVQYtb0gVWO67xPsvPAmLT4xaCu9peexPTSoob38BYS8g2GivAyHmb0dg809PG/1PINsjb4gkDY9dDH8POQOBr6ZZ4i9LFWCvTHUYr76YnA808ZUvV+rDT54abc954bLvKktBD2xOEs9EFYWvFabMb2H/MA8uCyIPALk+b0n97U9HFnGPdNji71TuOm9BZflPaopqLsEwuS8RoYOPgCPFD78GuO941Y8vQZ+5L3fcGu+MXLqvZDr4D3peIM9","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","wptNPLXih73Qbue9uAnzvAXr0j0GPdg80vmfvBndCTu5XIq9oYycOknUqzx/eyW+L+c+PZSSyr3mzqe9JyaGvBS3hzqLDQ49UXrSvWLlLL5Mwc09EsATvI0t372HyUs9WOH3vR3aYD1sQtW8TBCivcNoDb4eNKY9/slGu3TPkD0FGL89AOe4Od80+7xAYeo9OQiBvdGmqjvid6Q8R0uWPQpmtL1aFqC9G2/ePYsXpr1bnik9zEUevhw4n7zMU2y89jXXPfq3LL4YlCy+N2vbvasVmb35/Eq9eNmPPYQvrT3tKlC9SxomPTkJS70z9RE9nxxRveHZELpDllI80YfwPU4B7Tuug5y9vBvlvYE/3D3x/uW9sFJgvdaFgr3YiuO9uYygvfngBr48IcW7tKQ4PWk3gDzSaX29+J0TvU7Pcj2lO6y9WyeDPcx9YT6w0/a8fJ4yvfcaQDxp7e475Elevax6Dz0Qp4G92+kpvmixXb1CNiQ945PqPQObKb6qitG9LyFFvfXj5L16lau9cYq7vQxe9T1UNaY9ej3PPIMtib2r33+95okKPgOBhL20wtm9X7IxvOw2M76apAG9qTmnPNVNTz12hoY9TvRjvvF8TL3amWw+5g2zPG5Bh71kktE6czpMPTb99j2abbg9EvBFPp3RBj5xNaO9ECbyvfRAyb0QWys9Y7mcPZkzij3mU7i8ej+EPbFlpz17GyE96V8bPSvMDT0AAki+bSOrPT1lvD2v5Xm8HS39vZS4Ab6Xues8GkaTPUNn2DwGTc09i07Duy7pGj10AVI959iFvWz3pb1RLwY+WrpyvdcHjDv8Poq8aTibO5KjUj6ISoU9YnIOPQnA17ymjoG9591ZPp/BHD45E38+vuI3Pcwdhb0zuzs8Kga2va1iwrw5GAU+8C6nPYcN7z0RNsc9ZmJTvTImEb4OUwK+2JFlPM+GXz7ir2G84v0XvOBC771sUqU8TCsSPvbyo72BUJ09WJF+PhW2sz22QCK+t3ZSvYFaJb0QhFM5","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","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","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","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","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","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","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","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","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","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","RJ2TPeg/kz2srxU9S3IVPWWi+jyRK509GWRCPqBMebzyiy69m76nPV6RsT0g/dY8u0oQvZz/jrr4ROC8Unx9PQtGhL0k7jK9dx7BvkdU9L0PtVI8So5NPZB6Cz02bt+9dGM9Pcmp8jw5WUi8ygcJPYTgjT0ij0C92eM7PdXCDD5fakw946FUPWKelz3j1wu+LcWUPf0EvD1bH7A9dOc6Pb4gYTts2Ba/WL7DPcb8Nj3B2yC9gRCCPDoturzQ5L+6LdMovuiaCj7x+Xg8v/C1PJ9w/L3w5Ay+ssMTvGKGFz5+L408Wy2GPFcvT705G72+/k/2uX/nx714W6q9yMwWvqn0DT58Su091qYqPRdIgDxU6AO8ky+avV9/5b2Ribs98SZXu1jujj0kjda9aNSjudnTBj0fbo69ChMrPZ+zYr7omY49PaCcPIUwYD03vnK9DeKwuxUv0z3I9bk9x/XlvXc1TLz+rXO9cwicvs39vTthHXC8Rh63uylE6rxjhhy79u3OPXRhEj1oB0Q9ETMeO8xOVz3ESM29x33AufSsqDwbPgi9RNaRvqK0VL7dG8q9+4FIvVFVoTwmJv49ElSRPSPgAj3PjoY9o1EnvLCgBT0Lwes9966xvXejnb21r4y9snhVPDLV/L0etgm/xW2IPNz6pDw7JaE9Jremvq1tET3nf5Q9bm/YO/aK/Lv33Ks9ZYSvPVX5Oj1/PLq9b33Kvoqfvj1CU7U90Q96PtRAoD0BX7q9QO29veH1ND0r7TY9odWquoOPBL3N0xO+/GiaPQQLTT6leHW8IuKVPUoojbyl+Ig9+VBOPa7lHzybFAs98558vaaqyD0niYU9VlGbPFU0dzo3dxq+IZB0vr64MT4T0CS9dyBavC2tM7zUzGg9svmlvizwaLuKQ4m8n/WZvHGTmz3Ivm69FBSBPRn8uL3dM3S91nwmvSZ/qr237Qu8e/YUvsVPeT0DUrg9m1eou7CQdL6jzJQ8g7AzOYuiST1YRoW9qlyGPPj+yz0Vs6U9","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","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","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","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","4nY2PcYutb0EY4k9suwAPf0mmr4boYS+2YgHOzl0ob3SViQ+3qeKPSuq6z1zwbo9yj9Tvq7aVj4QbA++sCn6O7XNr728QVy9NFTEPVv6Az67knY9l41pPfFLfjtORVw+WZAwPMvZPr6Sjj++aLKFPZaD773O4Cs+ov0EvtUmUz0g+x8+BLoyvVv8wr2HYp895ISnvBpGnL2tDCW+ct9gvelhyb3GLcs8/XF3u+TFlL45f4w9Jg6sPc7vmL1VNmm7uE2qPXxmc704pn4+ZmAcvYoflj0+U5C9tSqJvnxJabyyPnO9V7kAPn8TpLxkG1W+P+xPPkIzQT1eTCu+Eo94vStnHL556bK9olQ3vVPtzrufMpC9oBGivYn4/T1oQNO90UExvjrd9z2E8EY9poDAPXJr2T2aQdw9NOmYPmWuA77KbBE+714ZPIndab+0ioU+0wT/u6N4pr5xSzC+2QoLvUlb5j0Y8CY+T++9PTsRGT5kO9e8l2l6PRKr8L3SkCm+OwtwvCfzW73En7Q8VtmhPH9sCj29v+u9kteiPUnpPz5VBtg8y76Fv3XNxr3z2yg+nWcPvrkwqT2qHCQ9UQChvXEXI77fb/U9iLSTPJiT2DxKGgm+pgN7vtACir0A+GS+BsbeOaynRz4MpMI94gQXvw7Lvjzke1Q9d7f/vblkTj4G7zs+TTYIvYtLhr3U6Q0+G4P1vF/ZND3VK1y+ZyHmvZjvAD4KwFG8Q5blvWUiLT3NxEq+n/geu4Hy/730lyC9rkKVPacYA75aol890HWTvZquCj6tl6w9RBzevp/Omj2+otU9lrDLvUpN8b0NZlM+vI7ZPKd3jL5eyXi9oK2pvZ5xHb6sSoq94jhWPpPlgb0rVri+fexFvkoof73sA7U9V+EBvsJw8L7ffb6+Z5xfPM1SRrxSBXO9HuU3PhlZj7x+2JI9RpW6vCORmL0gqH29EersvPp3jb0G/mo9DAIkvPGq1z0EsNC8qQ03v+qCmr1mTlM+A0YAvztxmL/ZAq89","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","O452PJah9b2+18e7F1XbPPHZA70u9Fk9Thu4PNcEpL5BZz8+GSasO60ijz0ZaEE9knIVvL37Nz3TUgI+3hCEveoFy7zQeyE+2wSFvJQz87wnHQg9qvGEvT4+yz3hwRc98yRqPDijxTxaH3U9EuumPa1i6r3wnqC6yzdGPZD6Kb3sEhM+RxAmvYAMdb0cL5Q9ETT0vZeYVz3LsXk8dxEEPrfonj1qAg89oe0DvWNCST4r5kA9NnegPV7sND3azAs+OGzjPPSMx7zG2Bc93hmEvcFAT7wAgYu9JymWPVloBz7zRMo9UppCPgd5pD22IiI+wraFPXqZZz2tW4C9ppGLvS9ckz346QY9scHWPa/++LxWmy69nUIHPfkP6jxXtYE892QzO6NUAD4dlUc9HkaRPeUYzLwgGgo9lq9XvaodULxzu2w9XxuOPahi+z3OsUs9BNmgvfWVoj0o1/a8E/CyPXhF6D0W4YC9V+mFPXFPoz2/jjY+IhkfPtjHFr5JhQg+m01xvK/FqDy5A2I9CRSkPbQmczzTL5w8qTy4PUrmGz1LESy9AW6GvcdbxD0xd4U97SVKOhqhWz1nh8+8dtxxPKDsNT134XK92JWCPZVlaby1CPg86XGPvBMS6TyO8oS9dEcYvt+rJ74dZZc+baEpPWeggz1zf2W9v5C8vTrGGj6qaf07Z+z6OwmCgD2LboI9ZE+lu2Hmkzxoo2M9pei+PlWVoL3eutw9kKsDPZIn1DzXn0c+cjQfvc1fyT3Z3wo+iwvkPVs4kTsyeGy97bGePa6lvz0oTAE70LO9PZ3StT3basm9I+MGPeYLjD3irZ08/a0CPhNco72n2ZS9pVpdvHzSWD1JJCw9u7mDPQ70jj31QTk+toAePrq3Dzv4qJA8e1CgPjH2hjwxWbg7cvf+vSHTEL0wrBS+wuufO2DJED3oO5c9DB+SvUkHVz2UgOg8Z2eTvaWLPz29A7i9Aw0TvkGzrT3C4j49cZukvLHkDz4xTEK9ZS1pPUQ7tj2x0vI8","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","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","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","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","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","Ahl3vXcv9b2WlIg9xncOvjHLEr3RYmq9H1AgPB1Ic71yAQA+KMcdPCa3WDgoMs08CCP2vY2bvzpZN+e8qytyvidFgrt0SUu9BNPMOyyL7DwCToU93ACmvLj75ry5+Ia7rDwRPg3gErzWFEe9OE++PcmZYT1aAeI9Ab5VvM6unT0J5SS8lJC1PB+vIT2zstm91VUtvUV7Wb18h6084w7mvA+JxLxHuSM8BqeXvah3XTwjj4U8CveAPO3bCzwGx+W5kUoivtsEOj3eEYM8DVW0vJ9tyj1rQrC90NOrPPl5pr2sfZm8xSSevX6LqD2274u8fCBDPQcHyz1gJDy9obLQvc0eKL5MjkG96rJ+vR/r37u/8cu9X8WGPd5fub1wsxo91VwYvQ5EDz6PDMA93EWEPejfl75hKLY8vJVavUTbjrxZEI88yuawuyjxFL0bnqQ8YCgLPlbgKbwDcne9x6coPY/AML1+qi69HeysvZDLjb17x3g8L1arvFC5RD3u9UO82vyavQySz7vA3tM8xYDAvQ1v0T1xXUG9VoShvPBRDbuwa0A942FROqEIND2B/NW91e79POwnWT2dbf28BDopPQ9oWD3CIia89g9DvTKlGz77vuM73cANvSZagLtAfcC8ZcTFPCc6BrtR0bc9VECBvbX5Crx+XVS9kjSPPWax0bw/aPO9GOblOinAgD0UGYm9Cw2kPbO1P7usK0I+3s+OPBZ/Qj4zw4K+ndciPQbemT3CBzY9YBOVPd37e72dfow92WlTvYOZqb099WM+X2UXPUBwsj20rQs+AqwRvejtYj0QYuw8PsaVvZSzUL3YPso9JprtPM29+7xAzrg7f1HqPXds571ntYs9HUUoPGbblj0qHPs7tvmRPcIjyL30sjy+C0y6vJW+sb2KQ4i9A0a/PGZnJz5GJaM7gISlvexb2bxeitk8D3yvPDUUHr3Inzk9Yt2aParCBj7PpxO+sMgpvWsgGL2SSDW9TzeLu2c3Hr7GePI9QOK5PN6ypj20OzY+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","cID8vMnjjz1Iz9c8TMQrvQiEl71pZmu9DiSvvbM5Cr7lpbU8+/uDPVYGfzzDySM9LDiYPUzlFr7OkyE9ICmvvKgrRD5VBQw+Yjl1vk7A1zxr9a49LKgVPW1PDD0cwwK8Tkl3vRvRSL1ApS29E15rPELGZL1DWbE9YioSvpgs/T2i8be80j4zPL762bwkPDU+Gc9WPiS7gj2e46y9Axm+PNcvF739n0Q+BuWHPW7h5L0PVGo52CWePYIT3rzxpeG9SI9lvQwVfrw8mK+9VlANPiY9MT7gsrU9GP6EPUa1kj3wDog+XKsjPqWyg74Xf8W9qzECvo0PfT5FGjW+vb+Svcesn769Vj484CbfvE6GPb5H0dW9hdMnPljaej02e5I9t9q8PU1+672f0jw+71O8PeKCGj4jNAs+6n3cPaaS+73+yaW848GFvthnWj2oPyQ+dDvbPaBlI74s2R+9k69lvZyivTvkzH290aq6Pd/ahzxVUYC9a9ZgvbmaEL4p+Lc91YPKvdCDCjsmU9U9EcmzPFWUOz6tlUC9KnQXvrYxmjs51gI+orievUrq/DyIWD692v7GvbB0jTpap/a8hKxMPS9zkbzFpHk8ap5xvKoHi7tI1K28SpyNPjfy9b0xlNa9av2lvPXKmr2WA4u84ahNvgZyeLvCvBU9Ig4xve3OBb3P514+ognbverhCD6mElw9iNj1Pfya6z1CZm88Td4LO9yOcj7AglY9irppPsnkx72quhm+uZOovZvjhT01uey9KI+vuyZM4z3ynIC9hO3vvIejHb4EiYA9u3+SPj3CvzxCEVk9hGWvO+He6z1osUq+a6hGvZZb6r1m5e69B2DrvRmmFL6tRAe9xJ/RPfjYA7w+blQ+023yuxFD2bs8poA9Ik8UuzMHFT11e8g7JGRFPlIt7T2byNc8bd2CvsKRkz0XkoI98FGWu6Z9iD7uG+E9cXk8PQYvib3xOXW+DezQPYDwQTt8dNC99tskvRZ6ET7Aw5g+w96YPbNwmD3Emeq8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","TWSDPB8yOD4Xp1+8YL/sO9d2jb48xa09bKsUPcEdWr3yc4Y8gxOkPdS9Fz5hDze9F0LtPFTAlb1ev0k9LsQCvmDHwLwTQQc+YxmJva29lb4IZxm9/U+9vA+a5T3YUpI9bzXZvQD0/j2W8yC+dcnyO7AiBL43haG80DALvU/Kg7x807M9kWrGvV/YWT2hNTc9TQiqPXVUk7wNgBU9lmgDvoWijT3FqdW85N7IvaUCqzzS6CO98wZXvDbz+b2M5/u7I9kAPkQmIrzRb6i8z4T4vYxoOT4SC7o7UbMovdHMCz0X4iI8XTXXvUymWD5cGre9bSaNPUUNvjwfJpq9XucwvnRVRL7arvY9d9jEva/7F74+8zk9pD6jvbhu7zsdmJa9QPWwveQE/L1+xh4+9qzlPXihtLxznq29XxwEPlw2Pj3NYda9kNDOPTVwBzyFivY8qvZvvYK4wb2jS6E9LdjjvdoSqD3+NSA9QeCIva9Xpb3A7G87VegIvnwouzzH0J29mNIovTaUFD6YYMi9x1zlvcwIqT524CQ9TSd7vK8Tpb1AXUi+Jig7vu1ysD586dS8WPtGPkgeST4qrZe9mfhGvR7k673OkhI98UQMvk10hz1i2hi9J22pvrQfjz2j48U9U2zpvRU/tz1gwxe+HpioPX2YGj3ZiLI9OvdCPe6Kxz0Ec+M9J9KYPpBYg73sP9s97xq9vV75tr0pWiG9YyN+PraQgL7KCYq9GhIHPWiQ5j3t1mK9vDQIvcbOtD3HqDW+NGr0PZ2m4728YOg84heXu8fTe768maw7vfEBu9KDBD6tGr+9pG66vYiHJTt0M+m+gY8VPgZWFz4vbpG8LKoAPRMs970k9iY+J8qgvdi54z3ThEU9RSpqvkDtyT2qkfY73e0VPt2cDb0TUYa8KQmYPR7v0j1dY9Y9NPtLPTyxkDzMEjG9YrsnvmCcxz3jnMi9/0hJvgP/czyRjKk9vNSBvd6Vmrw4CjO9sEPyPMbXsL3ckNs9/3iDvdv/37wmsUm+","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","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","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","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","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","go4DvTs7Hr7xzgw9ugT5PCe7qb3bbBc9/DEdvkAiULyQa9C9+/pqPX20eLywJ8A8WeE4vtC4oLwkg4O9p5UPPmidkz0gP3q9HVtYvd6PLD2rPZ894EcUPgRpXD0/vVC9UwF6vWmsJrzadl09KV/RPXKSt73DG769nicwPWUeB70JcGY9YeovvRMyhbx4Pi09xOsfvdq7YD2cXXS9Fc2HvXM5hrxml1U95iWCvXLYbTxUcf699AyTPV+WPTw0Uci8MB0Avvh0+70SLpq8ksobvYfonj32E6a9UydfPanPDL1/lLS9nwIAPnIYRD3/kQy9zwZnvSm8jb1qYOg8rYjOPWEzCT1a4dI6Ax94vS95gD29rA29x8iSvW2ODT1QE969GVTUPIMKHD0m5qK9Ym37PG5rT76lo7Y9Dc6uvT/aVz3Efs29/yisPICFort8xgm+uUCHPaeIU7t5PAk96rKDvbEuXb3zJJq8mAJgPdmWCDwRLHm8C7rmPVnagb0M8789y6uJPXM0aLykcMe8qUfMPHD7drwCkMA8kdItvdR1ur3E+t697nH2PaTP8ryDkTU85H4Nvkfbjj2Ta8+9SeB0PRJpzjs3fsY8I1sUPvysrLxbKhI97d8DPJWr4TxF5iK+r/UwvX/hpLvZqpS9aYmLveA9BL2oIgS+YpejPaMeGr1CyFe7JXsAPUtDvTxNMvI7Lo9WPU+Q0Lz73Kq8nMAhvuBXPz4D63W9GR+hPbRXrLz5Wwq+cSDTut5xMzwxmRq+RsgOvaUqrT188v+8OWT0vf6lljxYcZq8ba7xPblcYj3Ba1c9u6XnPRTGC71XErG8nisNPsE8rzufbnU9hKEzPg550z3O3Yc9DZUsPPV/0jwviiq+4OmLPBViBj67Pgs8tCXVPOvVjrzCqGi+Q8g6vRXxZL0ccWM9LKPku4hBnjxcLUK9Ji+EPc2OvT1Buta7IbsnPOa9/j3DQ/W9rwkZvCeO5jy+C8k9srSSPLFchT3Htay8TYYcvS4Rij76aSU+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","xmoAvWQFxbwHLwy8+Y8Du7CgFzyhiMC9vfoava8vPjwn0JY8nEZBvYbyAD7ZmzE8yniPPUHQh7sxzNS8CbYgvkd35z2EcPG85ILJvaHHST1bOyY94KYIvXmIsjtxSU+9lePcvVLseb1N36k8BycnPQkXmr1nOBA9DCXcOzgE4z1RseW84Wx7PbkMZD0hB8I9FVEsPqh5r72VOOa9FAxEvlEaN75RfQq+LthpPdv+rrs1li08m4XdvV8Tnr0YRnM8ZbxKPCHlfLw2ojg9WG2wPQ60fD2JXKi90ZlBvIm13b1NupM7VxBAPOf00zz5aTi9vC7svNer0j3CM9y83ljHukAKiL68Hza9McUjvnwmKL5fKpy7f5gbPr5rlT3iQRI+lM9bvZdFET3ceQq9JxuWvcosOj1KCbQ9wTACPp7XtDx398C998WQPTY/fjuilYU9JObMveIO+T3ddie9w/Ozu0/RWr3CCt+9GtRDvjFSjL1uo1Q8zNMPvmfOKT6uv709r0jmOzQPkzxucOg9EWxIPKhSRD3lzz69QMChvd47XjzcHlM9dZMfPHp/ZT0S4oM9vvBnPbVyJTvoHMC9UzWEvSgtID0K8ka+nj8pvb/SoL1N6yk+3vwYvcWLlr0nZQ49vjDnPRFayb1ctRg8igzIvTkPFL3natI8w3NrPGCKJb4fAyk+gXC5ul1rPr7WOUC9QJgVvOkaQL0IRZw97iLPvSgByDuKoos8fgzVPcbrGj5rn6s9qyImPDcGUr3H/vc9tsWjPIX+Xb3hSWG8+iU9vXiRqryyuJW8AzkBPrSGQb52YqW8WteAvMm4ur3wzpA8BNlQvYZsiz6OuQq+lWnLvBcjdb1Hpy09XRaQPdqvhT34miu+hERJvRf5E70uoDC77IXCuyxXDj5RYbo9LYYMPMWhtDykfVW+0fZiPRMTeb3/R1c9Q6C2Pe2zlzya6Tm8vwmJPYhLUr0QovE93XCuPZpBg7wAJPc7dMC0PZc1bT3B9Ay+Jq4tvZqWrD0fBN29","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","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","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","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","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","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","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","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","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","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","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","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","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","UuxsPWk62z3U6ek9E01lvPXIpL5I/mW9RdvMvYUK3z2A/cw9LcwFvFmMAD6Aihk9pEjvPQFm7zwq+qM9YqkBPp7npb0f+hG+kXzbPAGuFT5ohEU+pTMnPCE/Sb6dy2Y9lf4oOyFKQT2YMMI913XkO8GWlr3Szgi8C1yCPTN4Nr6V7yQ8NKYhPRbx1zzHV/C9mcVBvUmqAr0SyWS9pHAfPLrISb1+wAS+sP4mPANey73JkiW9scCcvRSCvTt0j+O8KEjGvPw/ETv1gq49s3P+vMcRx73PGfW9ZZQAPhmd4T2f3pg89qrXvW8WADwOfY09Gr0JPpF35jwf3+y9ilWVvTuDUj3GwPo8XPrwPCDcDr2H30A+Rjb9O1CJH76l/eC8RLIKPRXZzLzzUku9gUznvF371D1ewmy6GpRaPaS/C70vtbo9nr2hvY6qLTs3ADi9+dv7uxqTwbxrtxi9T7ORPaVirr1y0w2+aamFPcOzpbyV1gW8srDQvfg0GL4AaGo927z8vTtCmryD5aW95p0/PGBZXTzoyNq9+FjPvO2YAb4t+YI9CgeuPU4ZD75Y0wq+hVldPU2OML6xyVk9u4lWvTel/L3oqLi9Vc2mvCJynz3PpjQ9ySJQPk+GA77pTHO9GVQEvrMIjz0Tdxq9+aMavsV38L2odIu9T34MPjLX271XOAu+e7nWPTGkwz1+ucK8xTNiPOdfA74QuiK8uFrnPH2fcTqWtJe9Jop3PbqUWT052Jw7hiYqPbmy6Dwgbf89YNfROxMI57300gG9TztyvTW5yL0gLc69WdVaPoFLiDyE/sI8/cPyuxBaBr6I8gq+lxqePOAqOL26KlE9VocTPij18b29zcS82McFvr3x0zw/TqO7wBiOPZcwhz6T9SO+azp6PdXOK74QIM49fywQvo+WVryJPg49xT0dvrhTYD1VtwU+gTqfPczyUTzww6y9ZsRdvHhxrz1DGLo9RLHFvYfk1L7DysQ9uiN8vpLuGbwRQj0+lr5QPQQLiz471i4+","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gbC9lmwLvklK1jzjO5E9za5JvL8867x85aI4tv0XPI6HEz0oMEi9cWkau9eHQr12NL08Qk8wvEF28ryQPwq8TMVBvblFBbzejyA8WqAmvn1F/DtC0vK8EgzlO2ODqrwc2We8HXccPLThibuK35s9ZDy2vMQo1bx3i3y8afHqPQCsDryFoaA9KB3yPNwjcD2Trd48otY6PX/d3jw5f0w83tYtvTwQsbzJXim9y94HvlNuPT2YpmO8H5TIuxkSuzwvMDq9kzCJvTy7iD13BIq8pRyzPVFUCjyU/Ko9VdMKvC+VTT2cdVy+ukkQPZKIiT1bw0e+9JgevsQzJD7Bjuu7oxbsO5csvbx/Dfw9ElUNvqQSkj04gWE+Hcx7OvxfBT5JA44974W4ukkX5rxVujQ+FBvlPQKK2j3ivVk+mjZVvcnT8rwIvDa+IZiAvYuwd702ZL09k2zJvc9x9LxqSjI+z6+BPdwr572S5ge8MMSFvtez5zwCBsi9eLRSPkvmCT7jXN49tpShPR2dnTofydq+EM63vAcGSj7D9Bs72heJPYCH7j18Y4Y8HZY6PQwHoj6hwRo+ZXmTPLSRoj04tcA8o7XZPQjBh70G2z49hTOtPUoAbb1g+x09oUkcvlOf0b2+cvw9","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","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","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","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","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","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","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","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","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","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","eV60u91t5DpHiBS+WOQhPEfWdjyuONE8Ldswvk/rFz3iuqy8VW1vPEm87r1bAlc8muwKvqdIWjxkQgY9O3iAPBMlt72YBlU981yQvO3QN7zkU3O8TTfAPGaVbTw2zgs+kVWQvurrhT38ENs4QNQXvpq9Rj23eCS8sqHqOqgHKr5qphK8KSFWvC8SpjwEKje+dxpTvt+uVzskANU8vymsO2oQ+TqmXAE8wLCNO6a3uTxVBqO6Ac+TO6VObbtqBcI6S2OPO/Ep07wHdX46K1v7O16bMb3/Ypc8sOqwPKwNPj1D45m6ffBuPNW2Z76+N5A7c03aPD3CaryZct+5yix6PSAJbj3wFMa8eAviPJRxF766BAS8NpkTviDMGL6BiIM8bG3AvRCNOD1pTxM9Yqsju+TU2L3KC6W9qLk2vYkIwTzKwgy8N+fHvCkXUry2I5+9FWW9vcE6Gb1pO5S8/n3YPHHZ9r2k7ik9Z8F9PG7yAb4OA/w9llPrO1RWnj1WHkM93IeoPbmFjT1+cZC9KOO9vD0+WD2/Evg9T/Fxu/EGS7w/N2Q9owwfPUfcLz1ABtk8Yc1cveKL1z0s/dq7Dex/vcLNA70bZHG9tvTNvWTRmj3Ainy5FGEHvcNIjj2YF7Y8t8GnO66RTzs8Uig9M9jcPU7fazyWkzY9s3bcO3q2ZL0/gJQ9GEiNum7qEj5OZys9oamZPI5KcTy9ItS98A/UupFisrzupwA9VDwQvqCGQbwJ/IC9FEsyvAp1G74a/VO8nXJIvtqcN73VuQQ9hB/6O4LrI7xqV4Q8FM2QvEFKsL0KtQG+anbQPNzKDr1wmtc9/kkJvZCnTTqX6zQ8k12YvACPyDzrzQQ9RW6XPLyPDbtajbI8mYCvO/MKpLyAU2u89aqKPBZ5rrwx7sU6jn4OOq8F2jz/y8q7koKqvKaMlLvcuDk7eZi6u9BHhTwcWUC8w+8TvlsrlLtrYue9Vym7PFl+bzpkcAU9XooOvgVB7zknWZu9HIqxvZKd0Tl6lIy9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","+CnTPY/vuzyvli88whWWPXyKlr184Wa960BiO77ZfDx1eEM8eJXUvY7znD0+fC+89oU3PZFWr7zYUWI9ZI3hPMZJXL5ak7u+xOt7vRlX4LykPg29qznhvroDLrwawR299dD4vG6P3juRjWQ9//M/PUYZ5TwsHGQ9ZE9XvAxckjyXRzA91XFNPNyZSL4J/+k7+1XMPB2hCD13/Zc80wwSPYZ9Br69cvM8SQKRvuI+Wj3UmgW+TrYavJFGzbxT6rw83HVhvSaL9zxXX1C9W9/KO3qDD72dpIM9luG/vMTa3bzAcOG8/5n0vPivybrsUbi9PfojO655rrxvoKU8cpGFPLq2XzwvoZi+FPrRu6zuMr6SgyO8WZrauy2cwTqmS4S8Wcxxuym6MTwyvbm7i3gLvJpg9jpYrNO+5rydOhiP070qusg7pCVlO8LzSrtaNKO9s8UBu626BrsVDHi+1UokvOXsO7vqHhi718MrO720ETwrvmi7k8TOOoEOz7p2CGs8yeUzvnvUnL6uhdC7OOciOiY/bDvN+Ta8vDsGvEOvQj77gZG7SisquszYE77Cgxs7IP0evONc6b26/YA62YEkvUP8L76rZbq+9SDWvVTkib7GxLi8mZZGO17LcDrOn2a8Hop/vnacTTiEZXM73QfFOpqV/btD+iy980nUuyJ7jDycUi89azC1O2EQKb6z5g+9YixSvYA4J7sKBNw9h2uLu8kAWb3tiho9iUSbOzYTDTzbdc09CDDovDEx1Lygd+O7C3S0PcO5oDuO7Ds85L23PHfQXzvuQwy8wSZzu7QIlr7QfLK7QBUkvJi93TxsEoI92n8ZvfdbKr1z+Ac9T7ARvk7UxruI6oo8jYWfvI06xDyhjA085D+NPFxWcTtS8wQ9n15ZPJLSFr6Cbpq8QQWHO9gmHL4+9Cw8QGbquje1IzySICE8TWMaPYcvx7zu3xy9mfhBPdJU1jzPL8O5CHQxPadooTz1d6I85YwaPKv2Jz2eWls6Dj5APdtYFzw52Sg+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","+sOnvTXM+jvSzsi6E2cfvvbGVbxw9Li9L9XgPbjYqD0LLNk9FfqHvCEFv7uosVS9vEjvPVKulzuq47I94iMOPZGVEj03F009XAIKu6HxyDoJ14q95hkDPW5URTzkqkC9bOIaPVrHab3UFIm9hz/yPcI4BT1sRG49lhIguhmXfj3kK1O8SXSDOgeoiz08b5E9K6rXPVRLWz2W4Pq81uHKvKTN6bzVOCa9rfyxPMz6fjzZ7LU5djPtutvjDzuHbUi8RjUpOzqlBb703Ey9qA1hPYEhhjyT6Tk8tH2nvf3TIT2whOE9oAyDvKLRNzxHzdm8Qc+DPA++9jxsTVw90/jSvfV7rj5wZJY9V2s9vEUdVz7ZjB6+9UeJPrS0Fb7q7KC9LDKqvK9t1r1ahd09MN9CvB/SUj0mJRI+Oao1vUbFKr/iPjy9pGSMPWGsWT1dLU89DzSdPYTQGD7dxWe9NzeJPWVWzLty+iA93vPRvVnW4z0NkQi+SxHIvcDCPT1NH4Y7/1CAvRz6p71HCw2+01WqPSsnP7wA2UC9LxnePTfIEz5KEqU94fuJPb9lYTpFJzq8PB4tvU7U4b5VMfK7bLe6Pdnhgz1uiQE+d8SDPjkXdj09QwK8kA2/PbDILL1GDqE9AalWvg19jrw7CWK8ffqdvY8ghzyLv/y83UeGvE3hNr1rV0Y9m4I+uWX+prwWUkA91dF4vaekBDyLhPy8wOivPb9Ip75CpRU9ToYSvkGsizx/tou7r650u+odC708A5Q8Fvc9PWVCOD0onVW9lBhkPCexZr1NOia9OPahvJ4twr0W1zo8ayYYPBxkEr3177q9+4sbPWdv+DwQr5C961cgPj+fujzrKkU7+uf7O7zzATzCqJo9cnibO7+dijw1QrY8Z3JLvN5YNz0MJiy9quqjO9sZPLx7QNG72nvDOqEki72JFB67AS4GveQfWDzzoOi8PWWmvLmtOb3pl569CYmmvrZigLvrV248ZvE8vVTalT1R+PK7AiBvPbKtObvzD/48","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","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","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","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","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","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","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","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","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","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","BdXevH1ZRzy3z0g84SUMPRH/Az26+cU9o3MsvPsvFLy7IME8U5Dtvev7rTwTbKI8CgOLPScGIbzpLow83cmgvAnQIr2nDZK9rPB8vIm3cT1Ee+o7SuwpvFgABz0C7/A8X0ABPEHCMrzUG/K8nKopvWMbgD2aOQE8ezY+PH7cvjwi/JG9NlmEuYj2ojzeajM8cRguvNktVD0WBwA74oqrPfAlLj3SMDO9dZ4Yvi+OwD2WLDo9UdFePHtr6Ds0hH69EAe5vNUCHr23CIA7QY8UvDtv9Dmovb09zD0Qvb0UjLulIVS+7gNiPaWrPjw8equ9X1VIu2lZcD16BYE9QABZvHKs/70Srvc7qkpNvbhzvr18m/s8HZiXPLkGkD0yKTK9xvjkPOq1JL3HeGI9M0akvEivhz2TE5g8zWAdvHv8fbwgxje75Zw9vLsqsb0qJ0I9f3ZmvYjKor2HX0u8dOcavmRli71YXHa9voqMPXXg3bxmGfo8DZsDvBkceT2QKFa9sDXXvK6O7TzcZB29/OUUujngEj2+Hgc9kewmvV9rsz2vPD+9gi7vvU6hBL6Jfhq91LIKvmHrED0DEzu8K+u2PXuA2TugrCU8v5n0PMybL72onKO7NlLdPNQDoz0lW5E9fRy0PJUtZjwUNJo9QzfWPKArDD1/7MI8UYszPOxhvL1zjG887I26usaRpzxgeI89LjPUOorGwzo40oG8uSlkPZoKqbxwpEq80ENRPIoYgbwvm4s84MUSO8gjp7pZj0m8aXfLPOcUEz1ZUQA7PKsHu0OOAb/VO1U83udcOzSKYj3Xx7i++NMlPL9E0b0Gkvo7zUJQPcA1SLuhu608Zz6CPWcKrTukryI93bVKuaVYALzJdh67p6D2uykp3760D428xZwbPQz/O7z0wZq9pbtMvGIOSLttEw69FrPCO8d5FD1S6+07twWsvGa6oDvYbaE8lOSGPAxYTztO8JM83cGePLR8rry+OAi+UOO1vHsQz7vtsbs5YiKOu0szajvJFtI8","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","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","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","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","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","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","9GpQvXFVa7xi3oG9x76LPXFKpj1IE3k8hJXUPSM7DLyVWrm9hm/NvPR5yTyYL8A8QDyXvEaYqb2eOaI8qyk8Peh3Hz2npuE8zguTOyRAujzWLKy8RcQXvXI327wMVn2968GMu9Zxlry1zL89AeMpvhNtWbtWLmq8SjszvRv0tz3pfd28NzpJvAigjjzvStG8IowYvfv3JT2DM+c9/j8zPfBP770Ti5y7xImivVKqcb3ThjU9AVfSvI/hbbs+LCs8vZdXPH9Zu724S0m+ldghPXM4/bzV+gQ9znWbPFubxrwanmA8JUQ5Pdlng70sW8o8cSJqO3Y7eDwlgCu9UvXIPXrJxbvgS5S90V6AvBk0IL7je429/t4Fvq/Lk7zesKY9LdjkO+D6tj02BRm9XYqSvIeD/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","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","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","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","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","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","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","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","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","xMl0PW2nrb2LTpq9ySNcPkNjPD3QKIk962ioPLgJ3b1/Rpg9x7sDPjmrCLp4bj8+nbH8PQJXQb0K3wg+ar5sviu/Oj4g/Eo+XBoBPrzHeL7j8I09DwyFPUzGFb4t8bk9b9PAu13RXj1DcRq9d/CGPXZVVb7EUbS8T0O3Pbrshj2O+gC+BDx7vQCDA77YD5G91RakPXMH0D2fDGM9UaR0vgC73j35GKw8DkwxPce7mLySxgI+piGhvsI1Oz6awJ+9cRyKvF1WG76TPgy+lbKGvOUYrT2TquQ9HCzkPabws71j3rm+mV1jPZTrrrogWwq9LabsPdV5Wb7sOva96RR6vXQPDr13Dwi+3KYaPuajCT6COQc9QQxIvsPGMD6x7AA9lOHLPXm/mb2/n+c8lEJDPhF+CD641UC+OxHPvPV0hr2p+SQ+QV8GPW3P2z1cSra+ongpvIWDVz4g4pm9i9+DPp3WhT0lJCA+IFUSPneJXLzxzFm+A/EYPpS6nryyqig+2RAkviM0N74GPEO7VJUQPp/+lz1chPs8A4upOz8hD71xBjg9Wi8hPbd5GDw69gY9IseOvADcLr7BepM99E8bvoBSI75dEiO+Z19hvknzUr2zuUU+49soPpIzmT1gonk7Nwe2vdQHLT3aIH08JylAvmPAjD2nBFa+FigYO8CFET0O4ng7fc0GvP0M9Lv1ztg94237PQEz/L3Dzjg+SUNMvafqsb3vgBa9YuNXPQxnoz2L6Ay7K2O6vZbOvr19Sd29DaLfPTr2UD3XWRC+GZWcvF92uTwN+RQ71voGvBwo0z1Fo+s8bJItPSrxm7vZ+7I9SAbZup5tljzOf6a8CJi+vARUIzwEHlS+/QRovaFubT35I0A9gmhEPfBrdL01lPi9ieknPUgIaT0k1vc9cm+rvfPm5Tz12Qq9ELh/PR57yb0I0gC+yCaFvfRPBTxojrU8fuQcPewtMj0MYka8jCXfvSCCIz2kXrq9A0JzPac7pDxJFZi8AVRDPWPJTz3MZJo9","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","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","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","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","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","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","Gwdivfvov70lMOk9I1KMvTKBkL3z7nm+VWXRPAAf1z30+gs+M1ISvb6BwL3Qmzg9N56du148ib2CYN88OrcnvlUMHz6i+Pi9PCQyPa2Ecr4od4I9PWbOPSw2trxABzM+7hzFu/pe4D0X5dU8BFzsvWxPLb1R5wo+cM+SO60rtzzVism9haknvPl3gbz9t649eA0OPawFCj12RDi97Li0vWufjr1xnZe9MGJLvadI3j2Ijgi+98Y3vnPHNDxvUR++pQXhvb/D7r2XSUy+trfmvZbCID5K4L073BC2vJ15rD3tGDy9hD6uvXdy3z2TnAm+gaCDvWe9qbxYpDY8QYyNPed5Cb2LmuE9FFLZuwGkXLz8FCW9qxgTPdglyr2vR8K91skJPon91T2KAgy8eIcavcEMqL2Tc/O8H1SbvAPgAj7AI7K9v8CTPKVfkz3atv89ShfpPWvkYj6Jjhw9KZl3PQSIvLxQZT09828lPaqeGb3W70U9LSKmvBPIKL37iCO+A3yPvnvKuDxsxyq80HWbPDBmKb2e8ms78jiEPshdEL23RL+8kGYeO+ZRR77D38899AJePEpAlr2j1U29/cApvWy2iD2Rfzc94n2JPGIwgD0f9My8xQ+EvlxdjL2WaA+9/JAyPHWUHT4zZR69NSJgPSQw1roZJAC9zW+Av/82zjzjblm9pdugPY7rgL12Y4E9GN8WPdHy4L3JmII87DGIPdnPC70fn4W9KcPEu/9xAb7/lSW9TxqgPJJJBz559Zm9x6PnPRe4Vj2v9oa8SNMwPYc3zr1YNS09hbi+veI8Gr2FHI+9Dq5gvSapLjxZ8S89btGFPanMDr3QGgM+e/GIPEoscTwDCQG+mYQlvpvKG73gn906+AzJvX3Cj76BKE07eRqwPQAMDr1EedM7Zh6nvWK1pz18gk+94PLOPB19k7wVXaE8/PvbvW79mzyQMdS9O/zrveRUQzzvl+s9H5SvPAlQhb0P6V++bn6ZPenVR779KV09UaYavN4c2j2xrpY9","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","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","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","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","tmUoPsXeVD1wBLq997ZbveOmJ758LgQ+cbGru0OLzD1yNm49jjwbPkCPXT3aREo9tIS1vFkKAL7hcn09SC09PGIhG72drmI7KsgJPtpIHT7PaQe9kdyNPSeMK7xYAgq9vegqvn6SJrwpdqA93yHkPYRVIz6u/Jm9wm53PesPN77OmA47edIRPqsZD7w4cDM8E6viPbpvB72adfc9n83xvMaqwj08zR89ypxUPK9Zor2yUIK9por/PWutsb24YSM+PFJoPPWoeT3O3AU+EestPpk36bzJiSa9eFZOPZ/r/z1kbsA9IrkFPmc6Aj3b3989uCi6PK8X0rzP3zY9SmWQvCzeHj6m+4o9q5uyu4ZU07wNh4i+pPA1PgaGxb2zZew89vmWPN/Lyb1VA9E7iSGyPWaF2zxuGt46jIoLvQ3osj3ygpa9anCyvE9tjb2ymQ8+ZKOWPYdj0L2SPAQ8YFwJvADKoLu9IJQ8nY3APW/Jez0Efw0+TncgPN3Kvb3MdxE8jmrRPH5CBj2bygm97s71PJ3XEb1PHZ68GqF2PXkd5jz2sgM+vfqXvCHYvzy89xM+y8hFviYfWj1yga499hK+PVIWhT2Emak9lY/tPVMr8DswtTy9hlwBPhowdbwKQPc8rATxvALV6bxmQVg8YJvbvYYOpb27/zY9TzzuvUojoj3hPF+9DK+bOjRBBL6Azmi9t+MQPrb15D0mYTa+mvo8PTXlET5G0/+8cH+OPZImGL1dRKU8qxAPPWeosT2KevG9TzswvffvOjxEbTG8MqHPPPpLqb3M4Mk8P9L4Oh2qur2QgUY9WP1YvZ3KfD01lsK8HfjdvJkdxz3jGLw9NiECvi+T5T1NEP89/JZCPgW7J70/ZJy8wBywPSnGOz2DOQ2+s7O7PeVuHz11P1W9nGzePBOFJr5n2eM8NSgfvcuX7j2xRL09l7uSPJWqtz0CvpQ9oQyFvbFbgT1H9qw8anUIvexRKL06D529UyVtvMAnID0qZy68UWgLvSX15L3CXUK9","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","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","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","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","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","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","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","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","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","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","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","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","NMQvO9Mzjrw3/y4+8C4jPSc5Ej0Q4A49IYvlPQPzXr1mhSg+gAENvfkXzj0mHaA9V66BPHN0jbwO/pY9xPmJvNsdOj7zRj49ltm9vLuFO73TvbQ9GRkGO1QzkrwP0VQ9a0tRPSfduj0+QJI9BGrePTCanTyBANE9QepjPQbRUj3Cjza+C7qqvdnCLj5NSq89VBBpPRf0AT3NpCM9tPPPvYP3oj0wo0Q9uEm5PQyCqr1CkoK9AC1XvYvIbT267le8TpwJvuUVob3A1NA8PXKHPXlgIz5KCWm7Y5KhvTNksz1jJS88uB4ZPuxdRb3yfbo9car2vHGNuDwB7xO+/E4kvrt/7717os+9yUDAvR4YSj5rDhk+G7eRPaJSmz2bTSM9Jf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EvQKz4L36k9M9KulNvmZz+r3qN5+8iMwHvn1i0Tz/Ngw9XrIrva4zxj0MD389KFmHvSSx+z0/1B0+MYe1uzKfgD1WEd280O1RPSF8EL3r2Y29jjX3PdCy7Tws9z4+y+8Svd4mTr2Caeu99oDDvS0mBj2Xw8E9ae+yvTzD8r22iIG9eFjvPX71kLwSuVc9aSoivdj/nz2Sw7k8xvubvG2hMD1xJh49xw/UvT4Bbb5Td9I85ET/vJrOrr0NJiQ+vLblO5rcEL44t7y8Vyi+vTnMDzvSKFc9675FPTztOz63q5y9O6CcvPuVpb1cK/S9KxlVvuQKZ7yL62M9j/uUPdn1FL69xY09mOUPvolKFL1oimY987YgvZKjYD2VfWg9Q/L1vVFf3L2k3sw8JhmyvVQzTL6OCO28PxwvPDAyo7yOQTm+YkcRPZinfrqTdS69L3wpvs9KjbzVjIw8tXgWPU12A77Ts3w9xNeovDbCjT09zPO9gQxzvTOoM7380vI8RUOQO3LcKr6OE+W9gcnHPVva8D33+8I9RT2ZvMRyo70Efq49BenxvB0rV75KiJ+8P9zsvPLpcr31vBm919oHPWmVST1D2ve9kJLNPPxSHzwQlU89YefuPIb6OjzGk1K9QQOOvcUnAb6jwbY822GpuxbLA75GYQu+8UgCPvjeYb0SBaA97m8qPVsC1buisLs9KWJ9vOXLQr0rMns8EXMuvT6I+z2qse48lbRePJE6zz1+cdY8AIKGPUcU+D0Su9E96rvjvYuffDsQdJC9dTI5Ox6JQL1wOBC+xZa9ufG2Rb4AFLE9","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","/PdJPhXefD1orgm+4yQ7PVEffztfpWI9jNKQu2fBiLyTo7O9Rmt7Pdl9krw5pkU8E41UPIGz8ryrrBc96tpyvY6nw73q0uM9TQ5nuqvIWb0Z3jM9rmiqvfr9q71v2Ya9BkEPvU9oh7190AG+dowKvBhE1L22oiM8ew/PvSrDX72MIYK9Z6NyPTCsrL2AtpU9KsGFPdPWQT3sJ4q8AlbRPGVYsT3HfUq9m9rxvSJz7D1AYRy92kkfvUdBoj17/lG9/uU1ve1jnb2MDqG8lS9Cu4i52L3BLUc95W5XO/o0v7w1Szk9c/JDPlo4Jzyblgs+t2qXvNre3LxtjYU9NlcFvRVI+L37+w+93jgNvukfZz3Wxg49AH8zvPO9Sj1J6Xg+aYYNvY5UxLznqui9U4bKvUwYC76xu+y7XwnYPLYEDDtrraE9utDrvbm5Xb0PepK9Yu+SvcxVn727Ec896ARNvVS/sbyKXqs9uSFhvvomoLxnPA081zk7PEmI170ZFRy+0NWCvNeIbzzp7tO9p5y4PfBNyTyk7NY8vkTBvfypnr3vnhg+5S/TvUntKLxdDas9Z4qGO/H/RL3Ecdw8qXoePUAiRr2CoUq8JbedvHzew733C+a9Dx4BPsNJaL3N3oQ70FDUO94/LLtMemK8+ohWPlslFb7CHp86DIiAPOIq5L2dsuc87e/8vRGP072t2jE9KaLcPb5bp7snnHa7iEl1O0eytz2T/O29aTswvWnipjzhGL29YYxOu+bBhj0O4Pg9hHbvvJHvIb6ySse90yuTvSEzlD1X8O69f3/yu9hAkD10sV09EMQKPXZX+b11Xu+79wjnuyd/mb1msh89tHibPbCBqznuhha9+wdyPUAjn75ZRq09GlcmvZ8LHr14oLE8vp7fPaRSwztOEjA8u3yFvYXonL3j0SW9xMHwvA+wub23rjE9wNMKPez7yDyZZx4+H0haPIAhi7xqfUo98GBDPkR94Dy8Iom9wsaKvZUDYb6/k2G9M/pEPUbFH71Vy+c8","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","mQcaPuM/PL1f7LC8zJo4PMnFujtFou88xyKNvQ7wir24u5Y9+76DPXPvq7xpVFe9O9DJvF09Nbxmfjw9EGQWPnPFEL4wGUY9ag6PveI7lL1fSqC9Gfqjvd1SgDunF2m9I+MmPdLNgj33qdi67sKKvbBxdzsBSos8vjGJvkluaz2/FZs9AX+KvJKgCr3b/069xrMVvkBRGTw5GG28g1D3PY0xxT0MxJy+ljTfvKToCD5zujC87Fobva+jgj2eVuE8FkMTveLc2b2IwTg83VFMvR8EUTnjdio9TRn/u2QdmL12N/k8LXQJPTzstz1mCOk9ba4Evhg6Ur2poOY8N5LDPc5Tvj2vT1W9f2WuPXmsFD5yoPS9F66+PdGFlb09HBw9JzAuPXaQwjxip2g9D9PGO9/6dz3OsCw9GHhuvSjKSbzP7rK8/HPsPbg2HT3bhxe+MlmqvYTAQD5MhtW91FTovXriX72mK7O7pOisvSTSTTxIAds9UL2rPS/Cr73687u8tJeyvUK2ZD0CT+68/CgQPk4IS71O6Eu97NPIvSh9HruibHA9YaGbvd5ceDxU5se79XRNPS7tpr3brwo6jZ+AvQBwmrwE4g88kaygvcycTT0GlUO8irmUvafsmz0hBKu92DZvPZ2427xkjRa8yq+xvm6DsLwsJba8ytOJPbv97D3QIcK72/PBPGfd2L0Vtz6+hrZ/PN3WgTxUZNk8/YUKvC0BTL4AG1g9cGPtvM0jmDmtPl29HGwWvO28mr25yKO9ORy4vV63Pj1y20k+6iGgviLpqb1zQg+8IoqEvmy+xj3OwxK9qJj1PCwuqbsypwc8C65NOryraLyjNRg7FFUrvekFP70YPmM9dafUvcHqLj0691k9g6IivSB3ybxvmoe9bMqkPYZku73Wqn895omPPPmBQ7y2Cg2+zBr7PAJBEr4R8Oq8DoOEPEnnhz3k44u9GBsAvJiUxTwlEeM88kSrvB117LwsQjs9jkUsPTTp8DqsWay9aX4GPZSHfLtoj5a8","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","1OuCPjHyvr0nTq29KpMNPgp9AL7bVPc9gGADvj3hIL4m012+NCGOvZaTG7zLyAa+ueImPts/Er3Z0pi92usZPv5xhD1N+Yo8cpUBPgL6iL0CJhs9npt5PhcXhb0Wgcs92bQXvjNySz4LUTu9CCBNvsjElz62lKe8RuwkvjLUaz2FvCu+odEcPpCC9zuZwuQ8DUl5veDYmr1sFyU+qwxDPqQ/u75HRzQ+dR4LvuCZMjzn2508C0NIvi72Fb1UVry+/3KuPVIHO74qRmA9vf7bPY0X/b3Slps88HLfvfEFsz3TPC48fabQvRkEC762ktm++39/PYZYOz3qOsY+ElB5vS824jyXeym9tPelPDLzdLxPW3y9Ltd2veQsEr4nltU9TJSjPblJIj3iiAO+nxHDvNTwdrxZPkK8pJamvW+GWjxGbVK9B9RtPqG6EL6dqRg9rrknPM/kRD1kFTM9aHbxvPTSQL1gGMu90n05vI0EbT1EMKM8qUJevWvzzbsrpZ69cXMcvoUHU7zRVMw9mKcbvdNNjL3+uii9t1MgvuBKk72G4g89OOCkva40ID4/nPG9yQcZvWOp2Dt64ng8e6bMPbQrDr66cAW+Qr+dvORAHrwCyQK+FzIOuvtDijxJA5w9DjdhvSM6uz0lwtO8U1whPrMGbT0Qjeo8bI2cPWzqgT27L3u9JPeevY/JNz0n5ji+Y7zhvQwWoD0ggGG9KkEJPLRLJr1ypos9qvuZvJ3prL2NwIc9ZZ+QORT95D3uU+89JUSIuymgJD45f4y+02TOvaFs0z0Hysi9JJs3vpbRBD0YQDc+i22mvbJh4z2O5k68ttfNvVBwGT22eYu9YN2AvS/Mzj3MU4C9IgfuPfHF6b3Rg429Hg/XvErFHD3suLe9JrOfvaG2jjw3byq+3P67vWM6Qryb65Q9F1ocvV4LI72v/n88VeYUvSu7oz2e8Cg+A74TPfX9NTxM95G9leAWPve1FL0QCVm9ZvFKvQybWj06Dms9ynP+PJfUzj1k5949","bdp+vGCkNT1foUQ9x1LGvc+DDb1EMti9D4aAPKf1S71CdRI+d3m2vSWlT720gME7CILAvVzpv7wggUM9EIAFPnyfJj1YvgS+O3vMPNeVlD0kWAA9fQtqvaP5Crzn1Xy8ZH/fOsrLCD2H49Y899sWvL5EDT0yHtE5h2OGPQCHmr3qA7O83wGLPeuqjT3niAG+ivrRPMM3ujyF64a9EKDkvEIBLLtkEQ+9oDkLPRG+Fz5czbM8vENSPfqEnztZDeK96gK+PJX8iDyHcq87V7GKu2HeGz4MGwK+NdEwPaZIE73Dru88jOQ5vNHVVb2zWZu9d8IiPteooT0wSRe9Cb+nPTYNYr1aaHs8sGauvclQHjznj4I90bAQvgvZ3D3sLaQ81LEdPuOIbz1hNwY9Y8pyu5UfC7w3Iow9hE8/vD0sgz1QquA8CrtWvmTd+L2vNYg96o4XvaaoojzuIIk88+MVvncTsjytvGi98wEUvA+IQL0rGUm96vMjPd55Ij1/KKa9rdRQvAhGAb6il489+oXGvViDJj3wOeI8cLIiu0BtET6B2Wq9lOInvg/3ODwN/fi8StBYPItsgLw9Wau8LbikPYuT170K9s29HwqRuwaySDoj2988j0+jveLiID0HQyW9pfgJPdIIpr1sgYA9GQhUvbivez0n0Xg75aKKPCWiWz1DbVY9lJYsPF5zI7t61xS+C+U9PWaShT0zWk48+vR2PY+Kyz00s7m98+4OPXUulr1PjwS71D3yvJl3Pz4PoZg8emCSvSQoGL3EIT2+sUklOwVznbzdaBY9Obn2PevotD2iiXi71PuiPVsQ9TzAM7C9DawqPXpZmzyPR/I9ONrSPYmQ173/yUg9A2A5PfIzKjwIT649QXNJuw6Zhb24Xae9NpEdu+JEHz6B2Ym8tOigPS8SSL0e7Ta9b7McvUL6FT6Derk9MMbkvW79Ob3f80w9FgTxvVVLUz2yAvK8iIhkPQ8NOjxhapI9pFMfPe/Ytj0xSxK9CcklvaHBqb4ZsBG+","0mNsvdIKqjs9I7M90NoXvpdOG73IvnM9H1UcPgp6tr3EcDS9aZEBPiaOWT3J3HG9AE+au3VITLzV5Qa8ZaobPKf00b3pWv89Riv8vRxkEb0sRpS9vHrYPWLFHj3jtik9JfRlPVrFuD0qaoC98r6aPMm2KDz5XeS8BPH0vRlxQD1C0mi9cjiYPBrfZrx0+EI+OUgrvCAdxD1AokK9e8nzOwjEor15LXC+ZAlzPNVU4Tyn7Pm7jfRXPLlDOj3+DFg9gxN3PH3JlT0NWTU9fjMKPZHXOD7MjHA7+VopPm2myjt0JQI9FDkfPvbmCz5g9nw+LhORvCtY17wyzRw9D1oYPpUFIT4gI/K9yYJEPjyixb1HyEO90FuGvVvk972qy5q9uzZbvfd7E77EFem6eZXqPQTSYj20L6u8iP7jvWqawj0TjIq+m60UvorYI74D0zs+s3JgPOd2cbzNOMG9186Lu75mXj7wHis9qZsEvfEwGj77pmm8EasvvTax/Twkqpk7BXoxvRg0gT0kNho+09LlvRzB0juw7RO7/ROPPGOdTT0cioC95DM0vnHXEr0kB7s8llMrvFOe6zzXPgo9K4JJvY++jr3gaz499hA+PWyu+L013M884oWOPSXX2D3ZslW9p/OJvTVZljsMCbI9iwqSPjidmj2kBYi7oZJCvCGYYz2yNgA8s988vmv6573oyvm9pAuVPKc8Cz0WlAc7PA9Ovee/Mb7HDcI7GFhTPWI/Mj1XQZ+9aWWfPEnJCD68SNa9CLjevQmwYb6OCYe8puOUvU1BBL6bcRM9QA1oPhAW5byjiwe+8rjzvTvnK71w3Sc9m/wDPc/jDDoF8R++oSymPOW6X72Q+Ie70gaDvCDqGz300+m8UMDbPRUzkr1GNk49vvfHvIIfHL0BW0O8MkXIPOtAebzsp5W6m9srPU8p/7z5hjW+Z/pQvdEo0rxGg8+99+EovCW2+70c/FA7nxT0vEU8ITxSzHa939u/PY0xOT4WYyG+J7a1vedlY77n+na9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","ynaOPWuJg72oKho9codeva5oLL3ovaw9hUJyvKrBhT1QiaS990NdPT9YpD25UaS9uwqBvc8EpT1A+e48ePG9vZSJb72BOPm8bZ/XvFVqoL2FalE8lQ4pPVVvhj0LP4W9kF7ePAfd7jwKCzE8AxSfPBikcLxJWVo9EX95vdEOwb3OBMw9Q/LmvHeXl73Pgb09gHfGvTY2Gr3vypK9CU59PQUmiTvw+5M9847BvfaH7zxSdBU9ShVBuxt9Bb22COs8KvJCPea9v70Ptmy8F60QPTQoMj1oQ4498+kyvPopNLu7QhU9eaTYPHSxTrv96kc9TnM9O5zeOTz9SY497IpOvJqLkDwoRJa8fhsQPD2Omb1FPxE99oEau8294z392rM80QulPM5Yzz208A88hJeHu50dfz0O6LI8hFo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nI2+YfEEPtk6CjymJCc9rXwpPpyt+D0k/ta9zfo7PgRLtT1j6lE+jfDzvQGLpj2ceo89aRHaPaOX1L5+STW+Gl6JvlKZZD7wSG87JUQYvk8caT6PZX2+J9NnPkKnuDtsf8y9ZT0tvhChHz4it4I+xqHkvmAwVD4wbys+aRPtvHpcXT1RC/W9gP7fPXGUzb3WHdI8a9ckPaVTAb6EJ3m+H1drPizdz721Mks+","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","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","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","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","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","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","xGJUvSNX4L0zSD69wq4ZvUWrtL78bgy9gwspvREvAj6D7aU9Tw6vvbLQtL1uYRO+5BEfPSoI5jxNYm48uHGDvi0o+Twg7zS+33cUvuQ7Lj0vzCi+2K/XPJ68Iz1uRAe8C7zUvX/vkb7FHVS7T4D2PBizMj0/SEU5fTGPPU7igr3kmys9kNXRvDvFGD2O6N49Lehavpv7Ur7CesK85nuWvRQtNz0/Xxe9bBL9u3JNqD2HnSq9vXhlvFVQH73VOOK9+8WRvdC0Bz60DbY9askQPuiY6r1GCSm9HIemPTsMoz1e5ly9MXjIvS96h73VWJi9biesPVkEu73//M+9BL+SPE+kFb4ZknS9l+9tvVcS77xe9jS9nzMmvr3TUb3ZOiS++Te9vX/zgr0iFy6+3quUvbnUib3v+Vq9mR+ivArpn72oMgE984oEPR8/Jb6tIJe8V06JPP4xlD0Pv3m9A/+mvYeyXr6ffom+RAWRvjJbTT0oCQc7TexHPXLK9b0BuQ08GT1PPfCYtL2ciUI9xqvDveETmD0MVOk8XbFGPX6qp70iNz29mqcNvu4zIb25i5o9JFCrPLjbczsJOX08HJRWvckNSL4XjlO+Uoj6vN1QET6+4V+9FR+cvnZiEr6T926+uMM9PldFJL1L/x++ogVYPa5mWj3mwWC+Cjq9vUkyy7xWPky+V2UwvSuYc76pWyw9NsuevlE/ET60TwA+x3YPPaqzcL2iy/S7mr/QPXCkCb4O/KK9Am3cO5oAqr2EKyQ9k68bvhn08T3//Aa+5ESlPOngWz0xRj+9EboDvtB/rb1I0pC9zu6TPXi6zjpuLhi+FflkvXGJVTwnu6K+F0IDPshb5Tyb2nY+ijsTvo46kL0SURg+VGAgPq1gUj7+gi49G5vHvIYF3L3FL0i9tJWbPQoMKj3i/bE8TfccvujYF77q/Cy+HU+7vU5IML7FGQW99rMgPAmbzDxVFk++SunLPerbdDw2MAk9exAKPhL/uD2jg3q9rgYBPeWaqj3gI5a9","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","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","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","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","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","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","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","ZwzQPZXr4b0fOYe+i60QPknI/j7JxiY+FRoqPvGpnj45cGK+wvaCvgwGbr34Y+K95aQAPTOu9bxFGHY+/dXsvE2AYr5ygf29U840vmvnQT6HnZi8cUiJvdA0Xj63E+48X/25PTJXtT4qUSy9kYB1POpN2LzWE4+8oEk2PX5Z5T5p6zM+4oP7vshYvj7vHf0932rZvbaHdr5I5Ia86vGlvSu4i75Y6TY+OqopPva0G74d3II9hhQvvmRqnTzL4/G97EKIPvS7I77PMp68Jk2rPY+mizxs4bI8A2muvaqfAD4yS0S+xHOHvYLzIb3G2pq912XHPfikHL7fUQg9WBG6PfO4Xz45Bo2+JKagvdBa2DzHsi09Bkt8PVD+cb5Xksi9A7tJvaqUjb1Wtpk+wMwtPoItj73PLj094Bs4vREpebszyZe9OabDvVwod74Hz4E+OyFlvmHGmj15+e29flJxPdNXuz25YaE8sQ6lveyiaDwLOzw9w78uvp1IgzzfyTe+uYWivsRPDD78ShK8z94ovbkxhD08WIM9FA+uvszeHz3WxPY8SjoyPUfZSL4FC2u90PPrvdcVrb1a4N67N6SivVVKBbzItX27X2YPPpKXMLytaKu9m3SFPvpMFj4Bo/C9gM9KvusLnLw443E9wZoHPjEiob39wsE7Sn2CPvGenjwCJ0U+H1UZvb+Bjb0V1gK+/dUEvw5IPT1PVr2+3UOpPpTFFT7dxM4+Jei7viBNkz3qCJW9NL8Wu8MotT0lvIE7Bcn1vZAHR76ROtu905o8PnJBgT7KzOa+bVeAPubP5L03kBk+KqTZvXgec7sUo56+jcsEPQh0rj2sngo+R8xVvmHp7T3mjrG+cWGKO+C7rz0+sn09YtcwvoRI0b4kNCE+IWY1vXUXsrxovIW8qLSKPdwMID6koYq9D4gUPnC5tD6XCIA+CyakPRrKf75AWhU8HYZvPcjXFz7+6iu6fV6IO4ANR74/Vqq8GICBvkDBhL7m5r67BLbhPLNMd77bvjc9","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","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","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","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","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","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","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","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","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","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","1KCovVYniTxZNbe8kVq9vpVijb32dw290aBAPaoDp73naqI9t8rXPdGslr0LIYK88LUevPKFzzs+Iq49MCgCPrwoSr4vt5S9nibIPfOUW7245oG+OhAPvq5NMD5TBL48v/VGvuCaOj6jaCw+TIu4OyCERT6il5I+QORNPQfP9L1jUdA9A55ZvIxPbr3p+oI9+OBGPlW5Br45w3y941C0PLQNHz4vCwK8a+9JvvAIBT6Ufo+7oUjZOw18T72OBIk8WUOsvTZZxDwzWpK+FkWVveG5Ar7pIFw9lRT2PI0MAj6U99Q8F03JvUcA4jz6jH29FzNCPV9A/D0Yufy9WDYkvILvDb4dKjs+6GmXPSHvDr7iLbG8fKvSvdYXZr1MXek9kipFPUcMCL3imLQ6PmvavGMTEz4wmow9NTrlPVQ8X74LUP884J+rvWLzUr1rkUu9Q1qQPBXvzLzQw7w7C0SwPZ/KnL4iiRe+6HF6vP9TPL4kduO9MrarvE15pjyL0qu8nDT5PJCcuD2Hi8e92WRavilp+L1dyxG9W0UJPmL0Rb00klo9Wn+IvvnHDzzJYpQ9lC3TvUZyFL4XM9K9Wpy8vuoDiT2yMtA8qZyAvKe2bb2M/z09fo5DvC1twrtwUD+7JQQ0viqEHbzX8sa9Zd6OvSXF1D3M+xI8zNm9vXuY2z3mQtE9ZzfDPWww0D1i7Pq948ZPPV7APL09+pm7JPHKPEXNP77A5w4+rJEoPRS5l724fhc+VscCvu+azz3ciAw9zyQ4vs+5dz624fU9hpx+PXJRDz579y6+8kCmvFBPY7039Ke8rWgOPeI+gL1tx568y3MIvkmRPb66lCo9XesPPrZeSb2YOjo985PBvbl2TT4SLaQ9o6FCPYv9mT1PWfG957j2vXYwVb0nTAM8XqzTPdTbpL3XsGW+/iJyPe8ESD4LhpK9qr2FPApBFj3g5f68kl42Pis+sj31KjK9VvwkvekYjL1sqL29ZDCEPSvQ/Tz0Gnu9jbPhPTujnT3ftgw+","SDEqPgs8dDw10zy8f90Zvqa1yD4aFR49BGfdvbeVFb7n/zk+AT/IvbRRUbt3vPW8rs3PPQzem7x7IOQ9JbGIPi/rXD3vBLS9WF5CvUUFOj5Wdwg+H4oLPFapXr7fYpo9voHeuueTurzs/MG9LdJ4PBcvsjxTpN86VNPZPYq+Az6pX6y8vtHEvQsQFr7PcFK8Ph7WPcUNc7tkX128Mav1vLz7+b2fO6K9e5FuPbtzu72p4C++qvy8vaQ6fL4B88m8obohPjfzGb6G0gI+8aL2PfiHOz4El7U9AELZu5gjzz2EN0A+Z5qivcprWDw4Pmu+S8+zvE+trb0R46E9qt6AvfHXIr5iC5y9N7B6vF4asT3lL3e9/SxbPXQVgb3DwMu9DELdvcwpSDzxF2U9weRpvb/4uTv125q9dqu6vHZNLb2Ai0Q8xmuKPRL50L1bbNe8/XanOwrMO71lTbY9sx1Bve2gGD4aBiW9hsMcPWeInb1Z8Du8f/O7vr6dqT3bnu+8aMcZPOD4/z2mLd28CDBJvYzpPj4O3kM+JW4KPfR6grw2LQ4+XdASPTqRhL0JzxW9TJ8CPge2KT1hdsA7AyLFva40CLy8gD29dNoEPqbihLxsYQq+VS0nPhDnlz20lya8Mm4dvSo0Pz7ufQ4+vJGfvTBUwj3+Brq8enfVPZeG/r3z/Ay9y8q2vEWwNb2bcRu+gX1Jvkt/6j0fAzo+rLdCvvHdB74WLCW9B0isPW3tNDuUbZ09ZdRtvSutpz1RnZs9UZZ4PGUZr72BUHE9j6JevUvU0zwd/OW9+74NvZYqFD1dffM9CZWxvLOclD11MYQ9CTfPveGzIz5M7Uc+2ba5vlgu4DwV3cw9xF0GvBFx9TsfJie+X+gqO+HDwT1wwrm9xkh3PQFcVD6sIAO+Q3wCPltZ2Tw19vA9kM8IvrM7RLyT1r69PJ87PfENxr0m9xm6Aa74OqPnzb2cK26+eueGPTp28bwppX091jiNPJ8lSL7xAGW9I1b1PcGwCj5wv5C9","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","/pkevll/Xj7KxAY89d0YPhYmG72PNCe5ZpoGvA1hgz16Jy88CFMZPuuPnjsmRA++mkl7PZ9Iyj1kfHE9r8k2PqP7Ez0BGeA9HPoIPOCey7zJP349d+eSPZlETj0WhIu9Rp8Juu2XPD6g6908Cv8Lvifgtb2fYI+8Z9S8vXzdKb19kog8N7IUu+vgU77kVGE8xzORvsJYO74NlKs9BmONvKsZ/D1flYY9f06EvXboj71owUu+nPYGPui7Hj1FxcU9sM2ivfb7kL0Dj4e9QG3kvGIynT6jVgK+GcMxvW/xET4yCBo+MT+BvUd+uT09eqo9n1hNvc/QPjzDi6k9c6v2vfoek70A0gY9Zka7PUqA6z1ifhI9QT3vPVCMAr3axqc8EOCOvfo+2b0CTMk9uI5PPiexMr1c7WC58TZzPqu5lr3yHAQ+BRZNPu0/Fz4zvhK9h1IsvAqrjj3HN7O9hlSyvBYbrz1HOiE+SRTPPeINzbvzwpy98mecPZS6tLrXqEG+cxCnvCgRNr2aUac+tmsaPKJKGD1ZW2+9lZ9rvWSJqzw7mBK9pQ9QPcnJh70/cAQ+PIaPPtZOYD5v3Ps8zsMnPZ6cULx9KrC9OcKAPdPUZLyHF4u9tgTVPbesN73Pm6e9HWl0PRBQWz2xTqs9OtgbPmHz1z0qS0A+M7UAvitEPj0RGRU+NsRkPVp0Nz4NMQm9t1YYvoTH+T3UVkS8Ma0fvrjjAD0VSgg+sApBPek5yr2oKby8m/rvPY4bJT6uAMq9kn35PXHLxz2Cj+09bp4FvWyTcj4/s1e9q8+OPqHaI74Vn4w9V7QpvrhngbyK9C++R2wdvX0sgT1Z4cO9vEbpvJ+LJT2ax84+Yh+Tvc+iHL4p83094DIlPvxQhT3IC0q9InmIPlx8GT0rov4958MQPYFU5z3NwXU9hQHtPKBmtzvQpRM+jtTbvGgOATwTRpQ9cs3VvYEIqz0Yzy4+mIUKuwQ8IL64kBI8IGYpPAvGDj5hC3u8TzetPXfUmjyhqAA9","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","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","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","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","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","YcqzPffflr0iKBG+flffPW9Kpb76pQS68HK4vTxL1L7Z24G+0OQQPRIOojz5rV+80D/QvGVQDT6PCp692NAKPRQ7HzxPFYW97TXtvUOOoz2gAWU9myi6vCe3Wb7/mtg9QguIPAvP9L1AvMO9LEwvvV1OJb12aNq9KVOoPdAoa76qUwU8NqubvTtEbL5d/uM9OtTnO7MfjD3mjki976klvm5SNL5xnPg9rvyXO2+/LbympIA8+JIIPgQhcb4GAo2+aG0MPTTYq73m5g+8xMmQvAbd670yYrg9gR2pu/jFFL5s5Y09roYyvlGrurw5FT89IpatPYu+2r05NjY95YaVOzuxqD2OLzq+9oMSPQtvuL4jjDY76HAavUDtP7w2E0O9dOTevbGWMjtE63k9GrLWOsrw+jvfKKA9IZT3OxoIFz6gLAa+mDcuPJsvOL7gw5++J19Xvg1C7juu6iI+hoKGvS0FpTxhtVI+WUGrvZsvsT0eM6I9oPUavnbdrT1Fxoa+sxgzPMYE8b1UvQk9v9KDPSAsV74rT6q9WPiCvUNUYz30cYK9+C7VPURcQTz1r2Y9MB69PIMFwTxE1988ksWiPZJv+Dwru7g8Hya9Pbrl4L18hHi9Pa2vvIsD0bwiuAe+3fIRPezrUj5jnQu+gAn/PAq6rTveSoY96EvyvX+m1juohyg+9OsJvev/OT6iuAU8rekkvCcmCD3l9nE9O2f/PDX1P745lx6+i3DBO2hokb0gTWk8XEx/PdCEZD2zX5Q9/1xFPVhl+D2xAkG9MRz3vc9NPj07ABi8RBiNvFtOFz3zcIs9UobAvOZZ3L18WBw+YiFAuoJzj70CiCS+9ivLO6+R+7xh1ui9n/rdvX5NcD3wDBk+ItaivRxzG77wams9ilzKPUIQ3Lw+5ag9g9BbPVbz9DzgUnm+HTUPPqC99T3rHmY7D2fbPUAGDL6yJMg94nzVPKvIEL7fYy6+7LYIPgenPDyyisa68eFevtKzsL16dEe9pzLCu368Lb7qVSW+","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","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","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","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","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","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","LjYFvqtspj3WzrC7nW4HP/Amo75LG688ospGPpeEiDzzWOC9tNT7vAfIxzwR3Qw99/jUvFLrib6yAdW9vfodvSUx4D3/nBM+/xeXOx6sXz5pEJm99H0BvoAtIT60M0M95ayRvSlwMb5qmge9WV2nvXtrkD0SMk++idv6PBvHAL6uRi8+eHLiPH4/Dz1ddgS+nx6dPHyngz5AndQ8KekBPkHeAL43pbI9TaQxvrigID1McOa8Z4VEPi87q7t7XXC+jTObvXBjrr08/Zu9cxCNPskPzj2/4XY+ZoLJPWVrgz3SuOg98VkAPawU7Tuq76Y9SIojPingbL5ltgm9TJeHvi1eAr65tIs9pMyLvnGCB70xyNQ+64envTkg7bw10em9JJtYvu+plL4oTvK98ekXvFpDfT2SGxC9e77MO5z3C76oYBW+xc1IvTtMLT1k3v09MEOMPYBkFj5yIMa945SiPcfkkr2cJ9c9wP1gvjjRkb17zBS+G7kXPVhvZj1pvqS9HKNbPfS13DwhXE89DQxtPvueuTmeAVi+gcW7OmejnL3mdAO9ITVxPp6MIj3CtG29cmYuPRlBjDtOY6O+mMKvPaXNXb64zzy+d/oHPmwnnz1YPk0+vsuLviirgr4hZtw9F7bBvUeBHT3+tBk+qdFNvoggr73gXuY7NC6oPXLYoz1l6sI92D2NPYY7Db4PIDK9tQ6NvU62YL3X8gY+hJvAvSyy0r2Dy9C9ZSYAvv/+Xb6/04I98/41vbohSDw+Q8u8x3MYPsUKYL5UnjI9ILCnvAKfBr4zpuS9Fjd6vWFsxz2vigY9pkEWvjDGjbx9zDa9VIq1veuCZjwetXE9KhdyPkXDsD35l4o9ygGBPAyZsrvuMji9ViGtPPVq2D34Nsy9xPQlPdpXSr5ii2k9Ox3WPU5HcT7iVB6+9H7SvcU+HD7xAyw95jXwPPNaATwZuJa9D3/MPOixrTuS52s+j5sivoNVEj6Nw9s6jg4xPrVFpj2+s3S9DjA8PdCkmD2VfxK+","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","WsO3vry7mz0ZK4w+fTfMvXSqR7xhRqW9Wd+hPNnB+T3AN6Q9Hv00vNMrMD5RRAM+mj5sO58cvr1mTy29+HdBPdWQwr1hoXq9c+TpPKEEODvfYKa8WXyJPO+BkDyPhC++4FZvvpiZIr3RoeK9+xwhPceK1DwxvKq9Fmxtvanfl77bNXm89JnrPQJfV77k3zg+et6QPXJLBj463oA9sjC8PJvzHr09xEW+Xzb+PN1sSL1ajYa+BMLZvVxlBr6k7mM8/RpgOteUR76PbmI9q6xNviKNMr1b/3++Q0fzvP6pDD4M7BG8gr/8PdHKE7yVGyE+6jlYPsMulb1kTVW+3AmGPjbtTj6sZTu9X4EtvbHwIb7Qeow94mlivtWvOr5JdCg+6SKnOySxkrwYiIE8bCxzu2IEPT1MZQQ9fm1bvsuRtD0rthu+TorMvSqgvz2WKsC9Li+IPr+H0D1goBo9jahQvSxuRTxjNEK9q2zUPf0NOrt1UyG+0fdMvZQErr2j8T49uECHPmRYBL5qfPs8+TBhvjPSCr2cMOG8qfCIvaSxwL0FzVY+aYCdPSvF173KWM49OJPRvDgZl70pcYM9FDE3PbLhDr4fL8w71y2RvrQrd76M6s+90u4JvRJ9rj3CmyA+EUIWvvomkz3utES9XIF4vIdVBT5TnbE9m3TavCSXKby7nzS+dl1CPfMhDr3hY7s+Lx/Lvf6hxr12/xO+/9H7O1qr9jy1Gzw9g19nPl3RBr6NX2Q9KvrDvOlB7j1tU6i905bDvD4Xsb1V4eg8aFAYvi1x7j2fPyk9EiprvbdyFr4DF2y9XbZFvn0JYb6O1Da9BC4vPWcwQbxnHig9Cn1VPBEQ073SygK9qfbxvMvHjr1xlhI+r1IRvrWioj2U1MC9v7cQvn6w6T3NOOq7YXJdPZN8pr5cSY+9bpc6PaSg1ruJygS+sWJ5veAnGD6cToW934hrvnNa2Dxi3oA9FPXSPQA/Tj79vYC+9ZRUPDum9bwWwq28ZBJ0PbeQY72v1Ua+","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","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","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","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","KeSBvn7Ti7xc5DC+YybVvdJSzj0hayg9UuSYvDfN3LxV0Eo+AByZvajAlD5Gbme+KoURvdfUHz5lVbo9DNP3PcK4Fj4/m7i9+Py0vMgRYj3e4g499DWOvMfWkz2n0c09o8ARvl7nBD5J3/+9fuvfvVGyUD16A7a9ZKcpvsw3gT3N1229ghMCPbSIcLxjkAU926KKvqHSRr4VrQu9OJVBvgxfuDxLco0+7+6SvGfxMj5DwaE9yl1iPrCjT75zb0m9FXt6veYNkr1EAl4+R2KgO2hpiT3SvzE91R1gPgQU0TwqKHk9W+UDvV5vKL1vZA+9OV+VPXO22j2O9pA+tfvpvUBtjryj5xQ9O3idPb8K7Ly9moq9KnXPvfLCMT1Hyw89e5nXPDDL5b1ne3K9GrrOvFDA0jwSQJA8J1jBPTgBTr0StbQ9FzDePU5K0Dz+zse7Etp1PXsXGr3g8969C5XWvGQz3LsMW6o9aDCuPfBw3LzFrrw95vYKPpD2iLy7Sue9GdZ8PbMg8b1XVaY9gf4yPaz5GL5POsK9BWbqPaJ3uz0VzA6+nEAWvj4BMj7HP1I9dgyZPDH1rr3fTYQ9LcIlPqvIOD0lMjC94h1iPMXoqLuEHNu9Lnc/vbkGt70xfQu93k7YPIgK+T2SgnY9wkyrPWCZvj09ouy9x/+oPhPD1726MOw94/ZWPZptjT5WQQi+sQ6Tvnzxfr2CWgW+z6FwPghEK756OQO+WLjWvf2fTz0XiBw9P9/FPYl4wT1/4JK5UfinOx9nMjzA+hu9Z94XPllz570xMva9l0bsvdWM6jtYbrW9qHz4Pc2fwj3IU4M9BNBZvKyONTxt3+Q9JOVuPoxiqD1xcZK9cglOPOA/nz0np5i9WmjKvbHinT1nNuW9d2XfvSOsgL3IaMC9zEa/vZZnFzwKDge+EnARPZtrNz4N36s85suWvV7Vm72S3w6+LOhsvepBrr1jldc9i27VPfiWZT3cpMW9z1liPSnkKr46Bpc9Jtb8PRvz9js49eS8","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","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","kd7qPrjBBr3d0dk9WxsDPWHhAT5gUAg7UPStvY56LLw3nce9zMa1POt7u71ezFO8kNQJvgVaMjyfnA2+FiPCPUUL5L65Tac8PQ6mPW++urthjVC+RStcPleJsb09IdG9nZtQvlYy+7yJQb29pInPvbumez5QJDQ+N86zvT6OdT5YBGo9JFxsverlID5k8PU90YmTvt3UDT2g/K09w+9bvQ9QnD3ngoI+sAkEvilEfrzSW8m9iSM7PdRELL6WAnG+udwbvfrgsTwCFKi9W5pyvSe5tb4pGDA9dnRqPb8Mvj1o0468c1FVvCObDz6eWeC9OYOYPX6pUr6Hx/i+9q4ovvAilj2C6qs8Nm0pvXsTDj3EtKA9w/lPPgZAWL0+K4u+NdMBPjDQ6T1/GRY+RlnNPbgVWr19NiU9eU4pvk+DLT7DeuU7jdBkvF5BNj2h3SS+SdY9PbyExb0PAHY+hmsDvm71Lr2DDdS9/qMYvkcuej2/OSU+c6ZJO3EAET4Ghi0+6iPJvEcpFT3v+AA+nW2RPVo2mj1RRYY8e8djPHct1r30yAs++sxnvW01+b0fhZo9Ir04PiHLq71Mxy+97fFFPTe6+D1xoeC9jLmjPdFfoT5k3hY+f79zPQhe/jxQFJC9RlWZPq+pkL0b+Z68U+iqPRv8mjw0CT8+MXnFPC43Wj0uyPC9RaufPPsiSz29mBW/Fy3dvXJ8OL7WuJU9opEWvhzigz1qOPw9Luy6vaQy+T3gzxU+e1eAvpPC6L033/S9G8HnvHPY3r1ZUB++q9/mvfRlFb6tTWA+UBmbvCDhXL4RDRw96GB0u06Znz3oAy2+JZ02vGRMwjzWpSW8CDtWPQ7g8j3ywz++bqsqvaP9gD0RUZo8c2ZMvuYJC73d2268pLoRPsjdGj6rrUm+6lw0PfiOgL16qBI9odFyPGKvA73OqQA+EDOxvWPHR771LyW8VjaMPlBrzT2WbdU9jOKtvY7+9rz2sGs+qSQOvgSmTz3pzuo9Eh7FvbLagT0XeWY+","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","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","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","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","dI/AvEKQD774gx08BuImPu8xsD6Uzm+9s5qNPpvlFL2vn589JfDgPYbZ570XSv08AzZuPacc+z3b8Vu9nt1YPn3dmrykIAa+CuOUvA5JIz2+dqy8A5k8vQSWAbxreCC9lkAMPpacqz3qWBE+Vt3pvYzHJL3Fj5G9gfxNPqRUO70frzE+2nVzvV7CGL5Z4Qc+skAJPjcgkj3Zsbi+EKMlvn5ehr1yXJO9NJs1O9+5Jj5pX/8969KVPWEKqj0RZ0A+ZuVhPoVp0DstWv69EV8QvZwujj0wa9Y9ClwnPMbjpb2bjka8Ed9aPXYlDj7idFW9ho9Hvm/9gz1I/Ve9J1MFvcGtGb5Pvtm8vY3zOAlzzT22uMI8LWWHPfG9Fb1sDrC9uPFcPWm71LzSW7g8TsdYPXmqF74Ntjq9CcuqvUSLB75BqQo+xonivdF3Xr1/PcQ9A2GnPDGZyj05S7q9Qa3zvTif3zzCvro9iDNdPOvpQjzQYgO+qOPUvELkBD24z00+oGe4PHzSvz02L6K8gd67vNaT1j1B9Ke9qE08Pv/lQD0AH4C87rMgPiD+bb0hvQK8Fdq3vBx+/j34PsS9guqwvuL49T2l9xG+Sx8mPeIqpz2fL0C98p2jPhH+Wj5GYUa9it4EvUXByjwViXu9XbYZPbPa9zwvRTc8P4rdPueWLD6GyBG+ancDvv9mr71i+Qq9a4c5Pq2uFz4yasU9zgqoveK3drqCeBo+BtTUvb3fUL2wXhQ96wXmvPciI75jmR8+35gXvCjBDz7+Mxa+kV3WvrhEqz0SvB8+H10CPUmIEr4DiO49Z0MWPY5kqz2UH1i9vw5UPSCiMzxD33+93fNKva8Kxbxu2qU9sfv0O3K6CLx1BI++s/vFuvNfyT0tpzS9Bd8vvRH/rD2MXdy9pqA0vjUsOb0yAaw9vbv0vW75Ob57/V49Y70/PpuPaL6ObbW+9vYqvRxa+LwkCBI98Mc1u14O9z3a0N09ylQDPk0fCr45uIW9Jeg2vRSR1rxhMgQ9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","op0fuzafJb64abm+WrI/PmPIwbtlALQ9PRE9vvMaFDurkca9slTyPAvaTb7CRJQ+gAOMvQSX4T3vHo+9v2deupQdAL0FE8o9tdDQu84hGL4SsAS+rrQUvkYjxL1HNTM+wHGaOkEGmz6Uj069h2HYPLhnCD8zHVi+m/h6PS9Eqb6gpvE8W1yKPtfOH70XRqi88ItXPYuEAr59Gme9KXQHvirDrT55lde9+m0jPYF7VruK0Iw9RTnLvTjpqT0QQSE9y4W0vWcMkr6afPu80fRDPROl1D3elJG9faG5PJt3kzx/hq0+sX9GPPVdfr6sGFm+qLrSvo1J2Dxo7gY9LRIAv1mc+r1aw4q7h3koPHwyhj7vcoc9fs1kvh8HAj+aI/c9wffLvTibBr5u5QG9+qaMvMzhr73dSb67AymYPo3zmr2JgMU8C79OvWqIn75Fpu87/QT+vuWTgT3+b9k+I89KPoIkDD2m2F2+CgU6uTxCHL1TLNM9B3hLPhBR1Tw4X+C+c3ajvScBQ794lWQ+l96UPs1jKT7dQXE+0+KnvdNSHT68g6o7X2QuPuz/+LwbwuC8D8XKPuDZDr+ip+m8ytoAPpXj3r2dBkI9m93UvYTzyzwZJJs9GHxYvYzqvr27sL++VwNpPSN3ZDl/xhO+BvAZPrsWjT0Lgys9vFyFPR0DVz2CWJ0+GS6JPUAmir6fANK8T4WaPPE6CL49+ga+EXHNPUS8irxLuEo9vHuXvmPjTz6Bd+y8uTUZvTuJVb7mbnS98UGAPdn7Wb5XOrY9zWUzPj3quT14lLy6ma1uPjTWHj7gG+q8UW6Yvq28ID63m069+JogvnASlr04L4a+RxFVPlrwID5ssSC9HkS4vbgYiL3cjZa8hS4mPWpEAjyrBd+9SJNwvIOYjz1qt5a+3d9OvVUr3D3G1/U9PX2APf5NZz2PZdm8RLfqPIuHOr7zcBI++NwVvocKAb7cLTO9vNwtPFSfnL4xmsW+0zpNPSUyp72zIlE9V8PPvS2aQ7yXbRC+","nXfNPUYHvr6Nn6y8yfywu0Abgj5M+hE+Vnt5vvvO9j56Bj29nnyTPqSR/r6ig189w8ZrPRfFp71s5jM+Nstmvfavar6ostA9ikFsPQFN1b3sSpG92BWpvTw/mT4z1/q9XKQLPo4VFrs6dBm9G5HePvQeuDzqKiM+jUQqPo9LVb1py629IE3XvvCo076y5Yg+unZjvJcvuLzhx1K+zmrtPUMGqr0LyWW+jQNqvW2GRr6Ilza+0PPhvZyPTj3URRs+GUrvPbyaFz+nlYC+wkoePgQzpLxr0m28q6eoPTJKgL3Ij8s9jgWOPQ9/QT7QB/q9rAffPYMrkL0Q5tW9CRfvPbytGj6Z7Jm9WuVaPUbkt7zEc7A9zpWRvZaW4z1RwM+9UmFsvX/hC71il5W9hn7KPY52ir2qi8w7rzChO9Om/DxEejY+ADSpu1wXV71cTYK960B4vcRV77zvFju94JdFva6Rdby0sgQ8Yrk6vcM4zr3/rE6+cgHtvauyAr0rGHg++yfBvNRJ7r3TWE2+LSGpvajUsr1PmA++ilFaPpl9njuHSQS9v9hevFprCr0zfEW9MnOHPXFQ/Tsm1fe9pQFbvlVSkbwKHAo8k0Q6vUHnhD0amN090ac+PreLQT377fg9M5FHvQ9PnLy4n0e9LzUoPbdjUr2zLyW+K5Z7vjZkyr01eTG+JtfAu19ky7x0QRm9rEDYvaCthD0WHIc8kvCxvFOnDD2W1Sm+CpzIPG7efr3ERRS9GgSdvGHYvzyR6IU7NN/1vfJpIb7Wzk68w3OaPZPDB73AA0m9pCmHvIfjaL400Lu7BCQfPa34PL1xH1E8aHH8PaANwDs3ZD88dWlOvpwNOTruNYK97V+HvWjYMz17sxu+xpc7PaTpfb0aPWK7JjbyvbdLFL7o8y49xbFqPao9nLwlay2+tLUOvRWJer2PsJG9kq6tPK0gbT1PDA6+wKGku9fGWT0rFlK8SaluO4s3Kr6OP7U9GhGXPYy1db3w7PS9cENzPGtyhr1FGZs9","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","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","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","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","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","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","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","ERm5PMfQ6j0laA09HusmPtt01rzC71y9CNzkvF0aOD1wX/m93OqyvWSiZ73hRqI8KKNSu5Y2Jj6Sbgg+XxetvRHJNzuW8nC9vAUevh/k372fIDi97NHpPbbyCD2Oi2i9kMu2vAdf1r2+b049eMGgvRamjL2sIF0+z4D0PRmMMb4aIic+889YPQJTqb13s0K+PoWkvB6vDj5TTh6+5h2KPVS8x72o6cE9lgn7vcJlHj1D1ei837C3PbNWv708Lds8KnX7vZ9JLr3cCRA+vLfqvMBIsDxgUeI9hTUIvu/YJDzhhOS98XvhvJd/4r1oTDm+tv8DPu/EYrsNBUQ+CNF/PQYEMrwa+IE96e28PXVMwj3oSWm9aiGXvQ5Orr2Tp0k9ygrAPdQ5Qr0pxzA+7K3UvcNbRj3SPMK90HvjPewtITxzSgm+3c4BvvCMbjwR5cM8ly/BvcOJPb574nI+bwuRvcQsCr4PVc09T4DPPXyz1bt8oLo9gEzLPQMcLL1eih69xrU2uqDd2L1oFV6+tfDRPaUGlDtMwTS7QHfkPSr8jzxdrii9HpWhu1am1L2RrkQ8GzSGvcX/vT2W6ou9FyWiPbIdJr0abR49G4CZvZJFhT0/TlC7jGiBPrDVMT0Ci/S8bZXEPEP+zr09Ugy92t8xPhIDaj7cat49mOzSvQoH2b1N4Rq9z2zFPEMECD7RUxy+eq5Dvnbk5T31E6M9HOmKvfozlD69miy90LqbvfryZbyZypI8KKTWvAWsVz1ft+G9RZENPVj6T7ymrxg+rMaaPIj0Fb4Mdv+8fr6svCGCP75MT6a8tYPAPW7KrzxDVxa+7htzvmFoxj3KqCm++/RrPmnpxz0KmCk+wcC5vZJ+RT0T2mI9gukXPYrmLrxYyvK9mrhfug2HCL6QSci9sORtPb7aDL36wCy+kgxGPspYKjsqrMO7hvC8vMkLTD3KiaA+jjCMvZVRlb1kOoA94JX9vKl28j3LG/69jQUsPbsAzj36gQ49HJRcPf+dc7wTT1W9","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","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","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","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","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","aVgcPgWyzz04S9+99K2TPeyxN73o9ta7LJiqPb7VX7vraiW+R2a8PE06jrz4bq89QoA8vi6FPrx65wy9amxBvoFfpjx3ngq++KlYPVlKlr7A3IC+TsaYvQcZCr1/GlI+OI1pvHHknr2SIdQ9kYccvX2a070kPka9t4ZMPgMJzz0oYwg9Lj2Hvo15G73E57+9QoskPj73Oj6r3bm7gMUNvgqcLb4pNse7z06TPupjUrpHL0e9O2oAvhQOSr4VYN29VvNePSUeVL21I9K93vZVvkh6nr1wVR2+MVfMPRp6gr3dJSS+UUAoPWYOxD3XrIO7+NVSPaqrgL7Rlg++WlJTPZT7tjpe6/Y9mCVRvguimT7tUuK9O6YqPP1UOT2P5z88GslgPiDwBz79Fow9lX0LvSPZDb7NAdO8i5hMPZqTnr1N45E77QfmvSkSE7woTzi+4UgBPo9uU71yoIS+m3c1vlCTwT1SyU2+HzvHvRFcKb7RNem9ZuMkPuzUI74B84W8E1CvvZ2FnD1qtTg9sbKKvnTXDrxIzZm9yUFfvc0yQb5sZS48og7zvBNSFb3+9fW9p3ljPNw9tL3VuWY9gLYIPfPcJr12OJ892YHBvkF5aL7l7s2+dtwKvMgOnb7RaDO9MeQvvWyXyj37QOo97CH4uxXjk75fMVu9h+UjPlvFVTxuMyQ9P59zviV6KT2Nhz0+z3Z8PUe0K777nVO+/WwLvmMkUT5wugc+V72EvVgQKz1nZkq9QRbzvRN4/r0J/fO8xP1EPX7ARr4CeAO+ddsPvvIRAz1mgB+9xeIPvZWzwrzYgNm8P6aGvjuqMr1uw4Q9fS49PJy8mz3mENq9H0uuPFjIMD1wloC+pbrUPbHkkr2coiK+UhWTPXZYk72lkh09WSVhPcW3wL1lFJs8BnJjvWq5uj2ek7y+UqBKvVjQZDzsUx+9xe5+vP+9hDxkD5M9MGEkvsAKwL1tGPe9gOQqvqtPGb7UaAq9QIhnveDQpT24SoK9bMvVPFd25L2uWwo+","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","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","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","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","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","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","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","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","k5gdvYREBL2o4D2+IY6mPOQl+D2NpHS7P4zvvG8h8Dwjsd881JaOPAkln7u9vLC9kfAAPeagaTxYq869u0m1PJYBFz4yQmi+OusfvmKMCDxgD+y9Pw4tPkDwiD6WEEk9OJfFPdRyBj3Rsra92i59vQcUmj2cfU89Ft97uQeeArxHPmU97idAvTlMZL0yrFy992sMvtjvWT1nNwS8JchfPVVMk7x2gxu+6t7qvQIpHD3toN87T5TAvDHOGb37dN+9gR7DPETmAb4cL609B8VzPR53/D2GDRY89+JjvRBXmTwp2q09ELxNvfIP6r06in09GTIRvi15mL6Qg16+CcMVPJSxU71vM3I7pP40vZZgMb6rIFk8Xn7pvXjSDb6CrWO+vmPHvd+Kur2DQOE8qC5TvsHNq73BDng8+iSOPSkxBz0q5P+61W0IPA2Vzr2MGDi+ftH3vQpAKj1GQIE8rE2PvEz+JL42l0a8bLqPvv6AUj3xEx47gpaUvaZLVr1mv7G9KZ06PUi3W75WXn8+orgfPjea2zwuhbi88JbQvW9+oLyu5oe+gBVyPHx1aj6LoJE8vX1uvdXTZ73U7Bq9nZ4RPYLzwT0RwCU+i7H2PLraCz5n7gm9Kc4hvaxCU7vocya+oxbjvEysNbtAac+9XggTPv+Oqz0Amw++ezsXvT3FoTycnJi92HoDPbKOEL6aNPo7S6SVvJ3S6L3jcaM9LxghvupRQL0Liqe72SYkPQ2byr3BUEy+TOdCvRkLMLyXJAE9HNOOvRQsO72ittM8mivyvFXWb72g/mc84X6rvbmjLT6AbEe+Q33BvSugqb18QOK78UrJPXTFXLyjnXe9TI/PPO780j231iU78tQbu+t/yL3kV9Y62Icwvcbevz3XSUi9KiJ+vRwv4zxZ4W2+/dqJPHTvoT3EH2u9w8Y8N7xq/D3CpyI+jcHnvWBQub2DkB274fuLPYuMB7zASya9dBW5vQKpE769iaS9w/j+veFk5b1wORO+KjknvC+fNr5AVGy9","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","bco4PJkn2D11CBQ+DOIkP/jpeT1CvBM+e6ODPQru/T3E+YU8bHu9vSCycDwOjo+9coVUvYeBQbscppg9UGeCPVjewz1QbOU9+WK6Pb4niD476iM+sdIbPYjDzT2RZpW9vzt7Pbj3ILwifIw8/YgWvpnOWzuDd809F5+3PbuydT4aVOq9HlWWurpCBrxkuMW8Kp+7vbO3jz4/obW9vEChPdjvm70PAyc80sqOu6XSWjw4Szc9BwQoPcatrzzrGqM8IC1WvsKgsj1j/Bs+3P6IvbSxPD2DXcW9wRYWPUhlKTzn9Rg+wJHrvLEm5T1EGMA9yiMaPFhZBL6cDca9/Vq6OxHDDj7LVAk+mwH8O92ACL7SZhs/VbGcvdrqiL0NIgu71DoaOw6xAT3ofIW9ubx4vWaCXT1qyd49CQOEvZsScj0clM695rDePb8sHT1Xchm+QsVKvpi29Dxf4bk9uMz9O1SQ6737/IA9VHDYvCElQbuRo5k902WlPbIlED4KDAU+l8AgPhsN9L0t+D+9VlYovbJ6I73auFI+TNcFPmiHGjxDZ/a9tAsRveLaar1AgII91bOrPfd8TT5qS/08DfGkvQS2z7zFvUC+vwBSPg/O5jxbbTa95frKO2mAAD6rY1u9O88CPh/Tbz2Fr6w9UclUPCL7vj2LvNi9ozYhPDa3VL1ZzMK9qisbPJANbb2E02q+F5ZtPZUPBD3f1Ic9KVlivVWWqL2jjxK+DVGEPg1IkzzVdGO9mXSGvMSJ7L1K3wy+bEvdvMr8WT4CVmi+lm0gPloeBr1kDAK+W+MPvlinBz1O1Be++MyxPXwUBL1DADg+1/0bPuppoT22/4Y9aRHBvbUbujyFIxQ+1teSvOyDjL18dzU+alN9PqyJ9rsTgK89L6MaPQ82m70hWEa+3iIFvdvvUDpe6II5cXSJvJHy9712rfs7ZQcpvlLmk702MrO9XybDvWTvar1sCBs9PIoJPgwcID4EDwi+ei4Vvvfq5bwK9A0+8sC9vl2Cub39Mc8+","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","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","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","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","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","ugfNPcuigD1wq7U9aYSFvUTzQz1ufSa9DnJ7PAHZgL399hs9TdovvSe/cz1oVs08vrUaPVSwIj14TRu+IuH4vZ/or71G9gE+PWumPeeYTr0+FzG++bUKPsbqNj6fMks9cVC7vXO4v7x8mi89cmF/vRgsvb3tmQ89YiRVvMl41zw6HmI93727ve77Gb75Evc72bNpvXQsNz0IVHm95a6ZPagYnz1PTAO+Yf8pvU2eAD0p7mA+EMGbvQ6jLr3gN/E8rviNPVkKOz7D/z09J86GvTC3kjzlHLK78nabPfqZjzzA7hA9rNMcvS09XL3GiPC7LdoGPvcUpjyPKXs8B4SIvQ2tprxH4hK9c3P9O6H6SDyCtA69y645PhhxNT1ug9691WBWPmnCBT4N9lm9ukGYOymJqT3D5F48D8U0Piwje73FyGo9r5CEPDrwHD23+Ta+9nmnvBVp6j1O05+96wa8Ork+pT1fGau9ZWpaPVYvu71iXBq+Jv8xPY8UPL7EKHI9HAkgPUNZ1T0FOqE8uEioO57SyL027go+EDvmPTGWPb1vBAC+VRGDPRXlpr1yCEw9XrmRPNJaqL1SJvg9zmHzPQZG1T0JrZi9qc1VPi11y70jahs9m6EgvLS/QT2kRhI+dasSPrdm4TxLJII9E0U2vThbqr0nt2M8w+sOPa8+3TwcIDQ9U9g8vQFcAz4pjLO+4Rr4vT72Hj5XLdQ9sUOcvNhm3TyOMqQ9gH4APqogXj0/PMs85DT0vJPuor2K4Jy9jVOYvJ/QFD5yVHq7bl5CvXdavz2euWQ9YIYKvgzI7b2WbyA+ybK5PU4Egr0Gs3W9L484vRTv+D0Y4mC8mAWUvSIlWz2zbIS8v2WqPYWxRT1HvoI8GhjfPWyZxTyNqPi8+70su5/ruT3bKzo8fgAtvbolpD0zA6i7OdgkvTGiLj3ibge+rzoDvLUsMD4+NQU++2dpu/QQC774Aw8+flTVvSUGDT56GTE9wjmEvYsKjD0+DTW8Jd6lPQhqRD1BJWw9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","HsX8PXfumzykyNE9Y+9evemoDz6XsRy+5k+tPI6lC73iQNA8yTiCu8bdQD1L5IU8nqOWvS7yKD7OoHc8c4mjPNvGlT3t8yk8enU8vc1XXTvkzG69JLbivRm4hr3uiaC6IM9QvgXKqb3cq048PZMbvSDsAT3x2TK+n4e7vT1PwrhL7OW9f1N9vcCspT4n7ba7tHWgvsXCiz1np+m7pQKZPKWFvT3Lxec9boKWvPNYKj17F+C8OzkivRUSHj0Y6iU9KGt+Plbs8j39QMs9+BdwPCH4JL1bMl8+eIOKOzVp+7xPAPA8Mq6gPBjZEz4WM0G9To2WPELymj0E9LU9NUkHO2XLZj5voh49PPoCPj/2ob5o86K8PO2gvJHnxjzFE6u9V0TgvVCugzrDMdc9bTPzPc1muT1KUhG+XukHvh85rT1nyy69vE8JO/mXbb1Jq1M9czgaPaUAAj4iGmq9uWMPPkVfiL3ncYk9EHX6vaMuWD0LS8+8SqKkvBnoPr33lM4+1j9/PZyMqL5rsqy+N1FrPh8azL2WPNA9d+5pvgqw8D2gySQ+U0gRPpfWyj1UhyU+MmdDPfDnxz3R5Tg9PM23vZyJpb3zHhs9zqLnvHgDI7w1Y5C+YossPGxTsD0eoJq8vz5APAaanLwcTr681Dumvd9hjD2VGx8+d0cEv1I4GD6Ih7K9PjB3PcQQ5bw8k6k7ap+fPv3AVz7Pq4G6ifWcvstuAj9gLYu7CYCYPPUL3DyCkPy8y0WYPdBCwbwDGkW+9YL8vAQOhDshxBc78f1gPfD6oz4IPB2+66DovesGZb2zZoG9VaMFvs+lOb6LSTa+MgHovVIS6z2pDiC+PzM0PXKop71L/eW8B6+7vcKxLz0nRCo+PnWivY/ozz1GDji+hgIcPlxVmL1f5Hi+iNsOvj6FPrw3rzW9o9S3PAYd3T1NCPQ9G0g2vd2JGr3Y+E8+1iKVPabmPz08xsu9YH0Uvg0ZUb20zL+9BqI1vQsElry4YjU9P0gTPdMz4rzY/4m9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","cmyLvVqRKD56Vtg8Z+MQPnyTnz7S4mI9tMo0PmUx4D30LEG7+3DIvD/wSD5eJP48NKT2vJXC5D1/cJu9M/yQvTPhnD3Fl009HawMvkI4lb1uh6Y82pqFvdiclj292jg8lMOrPF7cBb4LY+W8PHlCvSI4WbvgQkA9UCMgvkDPWz7V75E9WawDPeC6Cr4naIW9gpXSvSQ6Cz7KuoG97xexvdCgWb2S7NS98oJdvd/3gDwPPIC7VmsnvliVGj6qwjo9w6OJvF2KDT7AJSm+GvjZPdeOCz7jOAo+nNvwvYXCDT7S4+09CWOHPBMjLj0XM7W8MbU8PuL4mz0gqb+9uMBvvcNo4LwDF7W9q+88vtuEIL5PeAe+CzjKvME1Kb5LHhK9Kqp1vS3/xT1vTm67E70ZvnQdsT3Ih469d9mUPYuL1TsWQ9Y9qR7QPQw3d7y1kiU6RNSkvWWA+7tR3hY+T1K4uoSvWr1af3s6jKOJPWcklr1jm7E9FDDGvIbEqD2PfRi9AAE/PRJUqr1WCH69kUXYvazDlrxBS1q950SMPrhxKj40lus7DLqxvMNkcr02Wga+uNywPMqe1D1vT4U92xCYPXqCh72l8LQ9m/CmPVayUL4pbBa++aMrvu40OD6sEnC9A6jLPdGuH7wMtlU9RjuGPYtenL0xk3e+4LLhvq7amb0a0SM+wz9evRMu+jyh3yG9i0CRPQ+AD72C/Xs99vSZvRP6jj1TTgK+sLBDPadBvr1JMhy9qIBYPVtLKr3TGRA+D2VzPQnhvL277RW6JyJ7vrikn7xIXGW9+tg6PY9uu7wXwJ87o3xjPqWByz3ffA6++eUuPVOG5DyrVxM+41c5PsCLrb11Jdy8DLsnvp0jFjtWH3i7pe1xva4ZdjueGK09+vjcPWmHB7zw6LO8d2HivSx97jyN9ZC9W2EePftrxDxiTHQ+A/SGvSwEtrtCpZK9S0LVu9NZBz7y0aU9OmSwPemQUb3ZTig9tc3pPJJI4DxCwpe9w9KGPXkTyTwjDGk8","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","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","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","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","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","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","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","9vg6vlY9m72BhV29p/RLPGIgg77E74e9revNvVEvwj35idO8axWIvQHQtL3/ntE9iHaeu5veWD359eE9HEoNPRPu+D2c6s29MGOVPJHd9rpKuqu9Ni/mPRlC2jydPlI8+alCPQzLvjzcw+q8SXz7vZk2kz1nfsy9zP35PdheQ759L0e+Xk2FPvOXrj08ekA+LN9EPeiPOj5EmYe9PGmcPJwSyrw/le29+fYFPaulkr08E0e8vqw0vZF+F75z5D89DZVvvo/Bhrq6HZo+DbUBPjcv0b0lGDa+dglqvmNaGj4GSx+8uLyNvRfpnr3Jl/a89mnvPdvQBT5FTyu+qPoEPJCPiLuG9a29iftvvVR6qbxJ96s7s38dvX6OBr3ACZE7o9BGPaJwN74a5H69i6pJvqQ5d73u8DA8YBHbvdJQZz2I3jA+dHZcPeesLT2eQPG7Uya7PWD22z265xS+Oz4WvlHGgr2464m9w82OPaEjaz0XcA0+W+PqvXgwVjt6FvI5FbuEvF7JLT2bUFC+vTMoPojYBT7wIQy9vFNAPbD4s72CdK4868FBPj/Okj0TG8c9qIwxvTdpSz3Et808HnUyvXOCmL1Lrv+9aTZNPXAn6L3EyEG8YQaKPltqnT2m8Ga9hd6+vRanUrz9SYO9PR5YvULHmj0cbCo+FcSFvs6vkD0w0+G9GuWJupYGiD6Da1a9o1nKvYQUq72HE1K+t4lBvuoGfT4dJzw+VQ9gvcPVjLzMvGW8mCFAvvuORL4zQ8K9r22mvWEhTL0KNWa9FIO2vdsRGz7mLkO+gUarPQw5kj1ABwM+5gLcveYWH750Ohi+VuVTPK+kgr2yMXW+7kiavY1gDz3f2Qi9sScAvr+zV7xC3Ao+Kmj2PKmpJT0owvy8uA5wPYe3jT1//PW95CMXvr4pBb5c/H+9VC4qPSVkBj78ZhA+yv5cvgIpVT6XpNA9SBPcvEHupb3qmCW9bs5DPgFMN71MthI91wZ1PdzWRD0bBQI+DtCjPOquDTyegnQ9","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","j+7Guwhip705BqQ8nxTsvTCPkD7joKq9HFh2vaprnbuDyCK+PR9BPKgrjrwWizu9R+a0PWuQXL1+rd47nv2ovXyciD1jVx8+Yzm8vCCAtL3TAgk7+4TWPVVQYj2HOY29V0a9vRULfT36FU68jLpTPTLH4L11f469tKuNvVwiYb25w9w9yQi7vA0scT0WWfy7lzNMvpvq9T3QUJQ9ghGvPfmQl71G1pI9Yr2PvdMRVz0oqvi9GZOau5ih3r1QGFa9hVdgPacZXDxVr5W7tRD8Pfz9GTx5UI67CDlUvKfstT3h/Zg9yaDwvf/yDD7iJf+9y2aePTTqI72RFK69biyVvU7SDT4p7ha+TLwSPQgy+DwQy4g93fJDPaiU971vHrI9AJbFvK69gz0w2gs+rggRvlcKhT028fa8V2iSPDY0rr3uAOi88oerPYqczb0+wCU+RIeBu/wCvT04WaA98FlDPmFRGT4XVmU+72sePuHwCj5+5fE98iNIvjlSBz0dhnc7gCXfPFb4s7yjuN67YDpMPlfujD3tWls9JN1uvZIKmzyTx0S+RIfIvOKLHj2HP/I8ReycvZUm0L3bpfO9wFjhPJnsND40MiY+5FoAPvnWVj0soPQ9pl2HvbSCE70fYRo+x0fAPca0Ur0BABA+LucfvuFDjD15DvY8Pr9kPUG1Dz4qQN87EDY1PvOJIT2wSY+7igohvaK3mLzOwgy9/GfDvf8hVj4LYkU8ZJhlPft/Aj3nkX29N1utva6p5b00pCG+EH6ivWwQp71J8Re+9mx2PQQ91TwnMO89ZvjhvVfvxjzpHok9yJdWPeL4oz0RA5A9GMQCPbkhOj7uZb49M/clvm0pEro8dsq9BlpcPEzh3b3hICq+22uMvpPO6L3+r5G8/7NcOu7vBb4KTUk9/A3ePULerr3ZLyo++2O8PeG2lzxOEwe+QeIavXWiIL6BJgW+iBLsPHCuCD6Mwfo9kUNCvTFsXb3v5Gu92EWxu0BI3T1zbuq9M0mYvI1thz5DbfE9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","0RzyvLPLBzxMnVY9F2+ZuvrlrDznKEg6LVIWvH0oV7wdb4e9L7SGvdDs87wTEiK+uNxMu9PkW7zOxVC68GCuuyO83DwTxh69HfRLPOwYojtLB5E86tO+PRj5rbwq8po831NHPURALTxY6wi9pS2lvFt2RDwElfu7X8KoOzEp+zxibHQ83qY9vKmnmbyA2kq9eqQgvrnfAz1YrpC9Yo6zvWBlTbwV/f08VPkJu9y0Ij38+iK9x+4PvP9wUzvOWkY9gGWuvVeHljw/1Le71UdAvRYw/LwyF649EYwWPRJVt70M5l28+ROcu5ZhBb1Sqwo97tdRvCnE4zpDBJg9Z9lWvHQoUby0+Y+9O8tYveQgSjw7Nc84ZbGdPNyf4rsAiJg5wgzcvGAvNr2dZss78qQGvc4vD72jt3k7+THXPRO2JD0ByqI8JYT9PNw5lDuExoQ8ky7BPANfQ70qCa682EGKOysz+T2p3Hs9ac+hOytSBrwLAR+8i1bfPFDNcjq92Ys8p/fwPKzSyrvXNxa9FE5dvPmE7DuCAD08450oPSvGHr01Mkg80MryO1vyOz3m8oQ8GEpjvc2tnLy+CwW9hckZO7BLzDz56M47uVysvLPxiTxoFyM9XDt+PJDU07qH0IC8YgiBPK3MXr2HbsO8x3MAve63DT0Hg6I8NzSYPSDv3rzA2ca8GlypPU/zO7rPg3k7kkcBPTZ8arwgHpc7BU1Bvnz73bx4ZGC8HASIvW1pbT1gANs8++q+OhTh9b1veE2+po2nvf3z0boL6R87FriZuy81Kb0nZ9Y8zdGKOjHURzvaJ8g9PF0TvQwDzL2Pt9a8mztoPT05Ar0y1Rw8FbPTvavZ9Lufkwo7kHb5ux04FLxN2hk9UZNWPNEgUz1d4Ai8smAKPXdNRz12pFs6TiByvSqJ2TyQJ7e8YdMSPVP/8LuaGPq8kH9uvLj1BbxCHYq8uVp7vH/8+Dxz27k8G21RPDnHFr3jQhK94ocMPHdazzyucli8uyNgvX8frb0g15Q9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","sYs/PFvDcjx9yd87cV0MPAgYMj31gMq8XACOPHThZb15s8s70O2JPFeu77uYRp+9Fs8/O05tvDv+9HM8DAMFPexXzzyH00a9mLoPvP1ly7wyW0K836YOvdf2Iz3X4rU7jKSHPKzoFz0KvMw86BdsvcscOr30trw8rUHxu1ac6jz8yAU9D7/wu6rIRzuG2RG8DGDVujBCLb49xsg8ahxtvNgajrrz5P07R97/Oe4ucTwpkwC9s+WxPFtVI7v1c6w8iMiUPPTF07uISB68NxXwPBevSzzTJgk9JcMRPZGbljxXjyE9rW4kvGyX3TyMBs28pqnqPM6pBjtOJ349Yvy2PK9tp7uAPdE8mNaGvBueFT7aQJE7nWaQuReDpLt30au6e1wUvGi4Vjx5FqY7y+cxPA61obvLkxI8oojsOrawoDyijXS8qOeUvEfOcrtN8TG6O+zCvAuPpbve2om8AbfEvD+RHTzwwtY7/srru/5977l+UfE7biEAvM6ibbwv3vU6Cqk4PCQxQDyTm2c8NnDSuxdR0jxtLrO+5crDPAMGHr3ifBg8gr3GO7bF3LsvgJ8790QqvRFiEjth7ou7EN4MPTvRCL1cDuS8LKzzuiOTtbzmNzC84yYLvamXh7zzyR682v/RuyWe5Ly+++S7QCTWOxrDHrvSty07eo9WvJan+TrPlbC76iD6vDqFK726kBU7J3PXvX9oq73Dkle6nXTYu7iZ0DwRxk++BZyTugydgb3gEMW7lopjvKf+C73P2bw8cBK5PIpCA75aCdM883ikvXQwDT29K3M8AF/PvZ6IWDx1GPM8uDOwPNNMhzxFnVU8ZBWnOkDwQTxVdty8Kej2OyvMRjvfIws86dO0Ot1znLzc8oO8ABuQPEVfQb06Tpu70Ig1PLrqnbo/m/Y7yBXRu/sV3LwUBzQ8lHD1uiTViTxSHuW9KRebuw/xBb0+KhI8B4hQPPlFhbuQqNE84a6VvYFIyDs+CZy8sMupPNi+PDy/5KC6ZRWzvI/5ALyUw2c8","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","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","qgtePEsUjzxDnLI95A8Mvc1Nurz1t7k8mzJyPHryrzy0Mv468oUaPZJZb7vqqZ68cS2LPG2tVL0xYWc9DKW6PcvCyrzR8oi8dcSWuljf2LwsoYE7xFLaPOjuNT1R+RQ9tY2fPGMCCT7s+K28ZD4rvTSsG72repw8LLcSva5MxzvaSNs8aMo4PVCRlTxspoG7aKMiPRsO3Dy6qw69hCm4utTLCrtF1LQ869JJPfedoz2rNN+8A1m3PHB/EL29WSc9qJG7vcE6iT1/WUg79j6lPe1PizwGWYk96m8rPCACZj0lsRu9CDdoPM9/ujt/NRy8TkIWPVhb5bpdDMC8FksnvcxkAj0GYnG8J14KvftZxT09WAc9ENKauwu/Q7xOiPC7Rj8gPCUlvbylkiK8jucqvbZyqr2XALc8JhTCvJLQWLxLM5s6grYqvMMq1DyqCVC86+SkvYgADrzZDqI9GHTwPBxdNL2hUT27HKw3vcTMa73S3Sy+Tk40PX7GkTxgqbU740/xvL1UgL1FHoY8mKkiO18hcb3hoYS9VV6yPIN4nzzrNxi8BJ1Vu943b72u3hA8q5oZveXYtL0gZfK9ev2SvVeJnTvfLrm9TwTvu6te4T26ckY9LAPOPW1sADxkGg28W3uHvTB0Qb7wB5y8LwVWvnj3oT1AnMG9rCQGPe4h0rwAMKe6lwsQvgdytTxyeTO9uoUovItziDwaBO87640ePTB/nbpFsN88eWobvCTw1Lw231M89MA0Or529DtyaDC8EHX2O1OgJD3CmEa6boXwu+adFDzpnrW7JDCWPM6+RzzqOGE95NG5u63o8jmF15W9UGC4vcjxsLwjAC87IYkdPUEqu7yTvAy898KrPAPyqjvpwPo8x704PBDfobwKVx096dCZvC3VEr7vMYG7cVhlPFrpJLzb5yc8EAWOvU/uUL4Hgyi8kG6cvS1zg7c7n0E7lZRVvO9kfT3rURu8HxmovPVEf70HL8m8iYAqPP2Pnr2Vq468DZUYvbKnDb2xOd86","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","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","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","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","8DGCPL98UjyQJoq8oCkXugJ/Mrx2I8m78R1AvBbQ4LwVxqU8jeozPSRFIDujdcW6Yt4LvVL+Pj1kQWU7KEClvNVrArsY6ry9bDkMPLFR+LyxXku920FvPSmHzzwN2FI9vdBAvMafyDsSTB+9bfWivbwxFz3ldog9p3iWvC7alTw2Ge48lxjCPC8A6rx8BSA9H5mevdTUKbyd/NS8TS8zPX6KcDx5jD49PYFHPAKNmLx/Gl69Tr1FvdT8n7xOMjM9KP0MvLqug7wdXiE8ZTHEPaQTET1rvJ870YYEPCbFIz3fZAS9aAuZvcxcHb3orhq9U0biO9Mj4Txi9wk84a5IPIsCs7y4nYm73rTiPMHIaTxmLzk8OliLukrsNr3P8jW96T1KPRQmB70EFyG9VI9XvcFXcDzHhtU8qjhXO38dbjw0wpa8DLSZPP12HDytePu8jj47vS3PqD29UlI8G8obPVA2DLzMEmw8g8izPduzIrw7cY27jfSIvEg5M70tX5k8LQ4YPThfHDybBTm9NZtBPOswL73nbmS98bVAPKK607hzYBi8ZlULPsbHNbsiSgi9+4mIPXs+Nj0g0nQ9CAMsvY4WYr0tZbi8RoLgun01rDz4vMi8MLTEPUDLBz2WACq9rnykPQDkKT5EsCM8YRXJvYJ/JTypUeK86/3fvVxDjL14TyK61OJhvcdVNT0a03a+WDsZu+MqB7sInhG8Vq2wO9K57Dz/nF+8y7F9PI9UWTwbMcQ9Tl6dPOCYZzzepVc8BFZRvU0ELj0DM3w8hZd8O6FuEj3Pm/w7TtFUu9iNezxj6iw8NYUSPEM3FD1mMv+7mDduvbVVQj1qaVY7r2XUPXh0VL2Vg4i92Q4bPTiQl7xQ6688molHvAf9vjzjHx88dZ7lPFyksb3rZEs8vFUou847Br1KHXa8S0AQOwb9UL502be8dBUzvhvEzLn12UC98t/mu6sKvb2rYNC6xYAKPWmEEz1Lmry98aGNPc1SY73pmg29S4aNvNCJ5b3beD+8","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","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","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","qGCovN8OKDzP0gO+TvEWOzAVIr6UqxC+JU5IPFwbFLxP1xW+nuBcviiJXbxXrb29KLRLuZCXk70Vy/I8evW3O1mVwjz6/Le9Q0tUO6QkKr5PdJU9++ZJvvK7tr1Cm847oSqGPA9lJ72/Qli6IGC8u4Men7x+Xo47VXU+PNc7mTwY7CE8EMGbuogfq7yNKh6+aOYXvF5NGTtDA+q8dQ4EPTKmDLvuxiG7Hnj1vAQNPbxfm4486LkivLVrt7uZLZE8yWQpvckAHr24JEe99D6SPInrbz1l8SI9MDBJPNwRC77vKgk6Qzk7O/n1l7yH4gG7ox1KvPlGcbw58+A7seDFu5jlijkguFG8yk8LvtyHF77BZSI9BsElPZRNTzwwJhw77eLoPMXSxD0dR5q75RMHvr/yrDwU5Yi9I3GaO46HiTqo/bG8KzkAvtZIQ7s8Mzg9ZM2bPHkcHzzmTh+9DOa3PHk3xbqzsIU9BhopPCW/MLzHJes8vVN5u2gvg72iATe8fRwUuyD0crtbuhs8rmAXPZXRk70iNpe7tgBiu3WWAb5ESZI7wc2aPLZ2Dr0m5Bq9TBzTO8AR9brDDQm6ljFIPIJ6zDwOt5a8f7ZXPX5sJL53StM8fHO1POEFCr20nwq93Dpuu6tp8zyq30W9M/hQvM42XDvX9gI8DfCcvGnnYLuw1RO8vgQ0PbREmTup5cc5dtkovENTNzzgtQY5BcqWvLiRlLwPHek7uxKjO0fRxj2I0NC5R+f6O2dtlr2ByVW+d+SFvQWHZzzCsXS+rkfYPGMNlb1Nj4w6sik9PNl+jjthZxk98wB7POeBjTq/G3y8W+3kO5vfKr4aKMG8Ob+vvBkWarvoVts6mMstPN54RL2DhBS7zraJOxYvhz22ckI8LMnzOiuDgTztlfc7WXyrOzwSqjyj1OU7PrW8u0AGCzyjsQ471YeRPMrth7yFtOs8H/YOvi294ztjSn++Zt2tvGdc27t+t5i8O9+mu9FiDTwGWn29QXBzPLJxkb4KzNS8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","gXlnPX4pMTz2k9K7fNDYvKE7nryfvqY7A8livBfnEL1J4oC8zI2APGXuET2j2f49INBavcWkmDzhMYU8gTS9vPhxSDvAXyC8mJNOvULXmrw3GAW97sYdvSfYBjyhiH469TqOPrx5jb5GgQ2+xgkFvLDVDzyuT0k8MnAjPfyQWL1XP1u9oL/bPLA9Nz3wJu+89qbqOlVc3LtNTIM7A68qO63+Ozy5UFU98xHRPHt6eDtzpcm9OalxOtPiC76jJow7UknjO+WRvzqFKMI8OWo4vDhnRjtQfQY9k8mBuzh2qDzjYcu97MffvX8gWTxCSiG9bAqYPRMV2TzDV0O9MwTdPRj5Bj3vy929UGRSPapqxjutygE9SWQ+PSxMETzYXV68LYGNPfv7rLshmIS8F0mKvEeuiDtDvMO4MFiavNPMcTqwLs287CbFvRjnFT1c8bE7GPv3u/y8RT7FoAc7lScPPIVhYL5wtD89NZzyPOrNH70aHka9BxEuPZR6MTv5kMG65T5UPPLjfTxEVYu9VlyMPBk1g71cBAi+J55KvRdJPLv/R5Y8P/AqPolFdjutfAe9sSt5Pft587u0M7g9oR6XvU9CPL3RhIU9kuOsPIoEhr0jWLG9kyEbPl2uiLyV+ao8gQ5VPRNAq7oGyRa8gPjWPDKApzxMOLS8YlOUPQLwN7zFjCC9X6ZHPRf11zyhxaY6Rg2RPBR/xjvL7qY7ylXVvZVfgz3VPN08YVi1usCShboMTBI72PPKOxT4sj2D7NS9hOsJPIhhmby5eoM8XrbFPKlT4DyQUQg9miq8vDRcNLzEgcg9wnSivFZBIL0olWM8MYCCPOazN73pEQS9ItCYvY7qmbwU8+E6XKrCu5Ge2DzXg0w9Slt2PI1mF7wr9rA6YRPjvCnAtTzC84K8lWGju1GVmrzj0AQ97WOuuqDsjbzYgQY8qSxuPECUITyELpI80MUwPuN3Y7x24Km9ZwbPvOXoM70qfdy8FjBGvNAc8Lz0WwO9x815PYTVjT6iTSk9","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","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","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","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","QzSWPGtFmTwhAZ67ANicvEvIyjw+hR09fgwUvEnSP711Yew8EiE8PTby5Dyjxdo8INaWvE8ihTq2zsK8r+eyu1iCCT2ZM/Y8OTZ4PMezCbveylw8Ih1tPLJI5bzArH87T0iEvi579zxsiFs80w//PLoliT2jMGA9BjVxO3oTfLxDtIC9oI2yPMcj7btj5/w7CcV1vE48rzzlT988Mx/jPCFIJDpphXi800oHvZMbCLyz0gc8L7RyPPrCKLz2vp27Bpi4PH/8j7yRbyo7a9HDPKgn6zyW4hy7+mw1vbnvSD2njsk86kGCvDyPODz2YHS8+uk1PFNlHzyoNNc9qV+mvXS1jzwQxjA62gNdveB5hLz4Cwe8rDzwvdYKKDtGOQw80Scpu6AqyL2xENk8UY/tvWoqHDyr2Yu9rvwdveQLZ7yDTQg94brCPfLg0zshptw8XNyVPBu9lT0J7fK8Ev+tOzrvWbwu6fe8WMowPMMXEL1lnba8wekWO6Q1ib6PeEo8B89kOn4Oo7yKTyY7A0qsvGIE07vJghO810sLvc7ljr34swE8E0bUvBxXhzzOD4c84F+NPFduJzx7nMK7lOTgO7iWQ766Y807p4WPPKAlx76zpiE7ukBauq/oprvSw2k+UPlGu7y7wLxVv287Obt4u/yxx7vCNGW8SQZ9vmU9C7w+Bsq8POYrPs7+Ej38O7Q6EP6TPKHhVzuUcDe9P6SdPDI0QD0LpAi9xgY1vDR1MD302jS9O1iOu0uaX7zXkmI9jQuzvAEojzxNTzy8xtVNPMqaHT2Nv9U9yYJWvKZ1+zv7Q3M9/m4APvTW07zhBho8HSYhO+QXjDxZx7g9dXC9u5zjn728D7m8hn48Pdlq1LyX76c9TgTkvbBZGj1MDva3ZmH5u88aqL1UoA488FsYPaP1TD0GbNO8FACwu0+jv70FPFO6HrjHvXIMEz2EH0S92qlPPd3W9jv8RxA9i9jxvJ+BD7wVEMi90LNEuniEkLwG/UA9SBIZvVpPHL1MvA+8","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","LbwcPbc1hz3AUUg9WYYXu3yPRLycqAQ7qnJnPOvDGD2k+iw97vAAvjuVlDx24mU+EKgmPCU7/byKos87Xti9vMZsT70tzK45qh+Uvfj2W72cKOe8VOQ9OylNGr6Xw8G95Ji5PbXirDx43Xs7HIPYO/Eu3rvV6le+kY+5PVXkajwwofW7WvsYu0SClzySJ6y7tS5JvFHprzwM7Sy9EWwGPfBxLbn2BR89QNsVvVKqFzzPbi29BSEjvBWFEj1OpuW7MNpqu85PHbx0Aqm85a9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QKVvd8zjzxrpYW9XAe5vd5l+jxOYMy4bv5XOx584j30Dta9WqgePXgI97xtPOe9EbyPOzyBj75rtA4+QtzgvZdgHb5PofA9Qmv/vYU3oz0VCoa8lpQUPrJTGT6NWfo9vHFgvVHiH771H/e9P4zOvNjkC73DyeS9OTyAPeuO2D1z37G960ukvdJDoj3eDa89R6MuvuBxjT1uuws+Y9uOvRXFDr4wypU8k7TvOyRnKz1UQ448TnEKvj3gIz2JGLW9rpoluzhHfjzOHjQ+Tg9CvoojP7xzP/U8+kh/PZ3zmr2Qqo09c5W1vYg3zLwUxF89gcd0vdW61z19Puq6m48vPcVv1zxbc4Y9kk1JvVdg/z2VCTW91FMRPhNOGT0HXQI+70y6vKxA+T1XfNK9lzlKvRXH5rwEnL49ba/jO6Pf4Tz86809hWxhvVJ0A75hJBS86NOePEJtPr1ka349azINvcGxYb6ozPs9UFPxvKeTijwY5iI+X/gNvey/Frz7Tkc84IyHPsDQ/D3U0369W5UlvYNVzD2nQeC93YAyPqHR/Dy9mzK9eZoYPt6vMb3ARK67o5QxvhEUAj3s+KM85NEJPh4XMT63+ta9/lFOPkDfgjsHsEu9A5u1PZiClT04Da294dO9vP7Vgjyu8Y29iWmQvIfuMz6Y7YU8CX6Kvpn/xj2N+LE9R79uPcDtqT14WsC87WuJPb0lzjttlde8","0JzTPaAdhz0nsOg76svYvJQ5wL22Kkm+hYydPd2A/LvcGZy9H6kHvRy5tz22l9E91XnUPfJtR75VACa9RygYPLW3BrvBsgu+uk7fPcO7Jb7rhyq8oi+avSBDV71dI6e9si5kvWQ6eD39zyU+yroAPiaz8LtyNMa98Rf4OyzsMj5aO9q8kUe3PIv5uj3721e+59M+vQZl871jaZQ967cIPmtVer1OtZY9wc+CvPGzHbzHMGo9IFWUPYhqXz2bbUs9lVhhvXt6/jzprSW9mB5sPVG+YL3FaxQ8hqqCPHe9Vbz59JM9EDHavQddi72nyTM9Zjj3vMcwCT43g6m9asgxPWdNjj2RaQG8SdoHvnp5Wz1tv929guWevbzRbTxnoM49SJFFvX5pjL3bMPI90bbtvflQGD0h3DU9TkSdu/11Lj0fDRE6FccGPb6MEr7e2/q92DG6vbaFD71gJXW9cBUQPkG5Jr49vMU8XQvIPbZUxD2kAGs+p5x5vTXGzD181dg9cvtZPusCzD11v4s93tG8PAK+sT0xMiQ+N3ZOvOrYjD3ffVo+RYSrvZsOE7z3x8U9zEIBPtTTPLygqBQ+kuZ4vCZGq73boFo9dZtVvSz9gD2mlCG9wmRpvbmVuDwD7Cg9QiklvQV8Az4+Q/A9CEaiPeDmAr2Q8Cs8Jfq2u3cKiT2tdb49+9C4vaMrZDwdXPQ934UPve2fdb2LjZg9CFG+O55EuL1j4Ia9Uc/TPLC6970rKDy9XKZ7vGFHsLwGOLs88dE/PeEcCzu8bWe9kgKlvaXAeT3tpVq99OCZPVQ+oj3yyiw+4ODove9GXL5woZk8n0pLvLLCSL5YmuE9Z1nsPcZH7bsDHkk9DtdzvfOMmjy1biq7qlGZuyWjFD0gW+k9g4gBPVZbfr2OoZI9ulb5PY8GDD1WRDm9pBDMPSvchDx0yTA89hErPn7Mhr3mGMA9+b0avszaoD0RsQc9y2k2vefBFT3nsMs9RVzwPYud+T1Gbks8KgYYvtdhqz2KHFg8","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","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","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","WESTPc9JFbzhIaG9MhN3uqAGsr3n9Ju9wc5wPUp+zz1OWCA9hbO8vf6euDt5R5C9A5PdPfAFrDwnfOW8WXzJvI+drLzSiYO9v+Z5vuRYsL11Zqq9lkvAvm+ol73gDfG9JZbQPMPIAj3T0f09M6vEu0LUoTw13T489xNBvN+fvD0WANw9yJIJvdHctD2xTMG6eS3jvQzNzbyVAJs8Pk8gvZn62D3V+bi9b9wTvuaLQLuLjAC6QjBCPecSkrudrVq9Gam6vFoUPL1/e3G9qCskPNXHAb72QQS+bVpHPbHGRr48rQ491jXovEpJDz3RULw72DDtvN/QCz2RlSy8kmmeOspuJT4uGTO8sz9BvhEfFb5ipd69PsjavGFvvDo7xLK9kRKkPK9R+b2vqD89lNYlvghnMj6OLxe9pGvsvU+zor1jwwa+StAAPmFLS77Nmqy9tSLAvflgs70r5JM8+IWlvcaXrj04Yc297+IrvQYa0jwIi/e8vlp6vWrvBr7D/yC99kIZPeNoFzywJQk9BfCKva+amz1ngT09X7wAPqjUFj4vN4M7eyTNPC8zCr5zoH28ALLCPSndRjy3vKS9iii1PMlYPr3z0Y89n9iqvNECDT1Ktes9wItOPMZVM74+K249/ZgTPXO/eDz5N5+9F7uWPBPJhLzCSIU9LxDHvUcHoj2244E6eq8WPTHVUr1ovlO9ovozupJlhr3B3oU9edCWvJVZC71TLPw8TXfhPOWivL0jADC9GcOPvQrLl70Uj7y95/m6vTc4FbyXl5G9O0hdvd96vr3LNbG8jsjRPf9nrDyjc1e+bJ7uPBtTFD2naYg8PUkQPen3oD36TCK9mS75POe5l7zVo/89L3bxO47LqL0FHmw93kiVvUp1sz2o5gc+IdokPlu6mT1KDtm9cyVcO/xJi7tpjjo98aCFPZvy47yw5Pa9DT71PabQ/LxShlU+ibPgu2CJkjuM5o29ox8Jvhz+8TmprbI9XJJzPL6GID1MDBO9y71APKqeU72zxIY8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","iuhAPbFCv73dwBU+zfEKvZqFHD76y9w9g8z8vJQZJT7yy7E9Ook2Pr+/4TwP2re87AOqvOEoCT117kA8+kT/PRN8tL2GK+Y9LsdXPfmOKj6FqTU+gEZwPStU37zseXC9SBGzvZ8DoT3MTTw6HHFcvfuce7ubNiU+nA7FPYIX9b3QrR++kuQDvhL+s73cxDW7dB1TvPJzFb0w7Ss+hl06vG0YPj3RkxC+xrQeu3sPE72M9Bg9DzEzPELKBb3mBJm9QRbXPWw0Cb4WShA+JmdGvtWupr2Ktl+8SIlXPnyqCT4z2T29eXuPvL+I7zzIRxa+n+u6vZco6b31IWy9VRDZuzcZAD12hZq9EA9kvYBf0TwkS6S93hIAvipDWD14Iqe8f8RRvQIi3rziBCw9LxPlvSQ4FTxPhpy8VVQmvllI1Ty0lr48kr6ePQNwtjwL6Xy9UeDoPBVXI74XMvu8RTlLvWUwL77+dbU9khgSPshrVj1E5G49BRoHPTN3Tr2Jfdq8gPU4PU4f7DxEVdY91uHGvE5asj3SETO9fsQTPh5Dzr185YK848d8PZLt6b1kLMW8TrOHPW2fNr1wv309up6vvRaUtL2cTWU9qpp9PA7TOL2u8wQ+8JysvSsBkjwDGc+9lnXJvL37wT0toYK91ON8PcJ58D31kIK7lGN9vVE5NLzd9b88ljU1PYko9TyDrUE9cCeAPbgSk7yP6j+8rqtUvrtStT3LCBE9XLAcvT47nTyaUHq+cuU3PXprr70iQku9IzBCvUaiEb0NaPu9qafoPeYl/ztHbRU+g9OyPeob2T2MRhO+bkb9PB+cz712Tfk8G2ekPZQVxT3AIcE904UKvqPnnD15hYA9qvWIvSyw4z1E5++723VKvZPoUT0B39C8HwIAPtI/070KHd08X2Y1vfnI8T3QK/I7CfjBvfLI5TxSg84960utO7akJT6bi5490oBcPWshp737nxU8t45hPXQJe70z9488Zhtjvd+78j1q8l2+znKWvaLjcL3PfOI9","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","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","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","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","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","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","STbmPa4AhzsuMQC9j/O3PeMCG76Z/8g9iqLAPdYFjL2mW6C9sVYkPG9z1r1Xsp69MZHNPWMFxLo21zu+emMUvvK9mr0V/5y9ECsAvsTHALsAvg++RDj7va25PD4DWOU8qLb4PJk+FzwfZma9mByEvGHINTzYoA2+0BEnvTndTbwkbt88n24APtMs7b2EZPw9V6uCPFE8+j3dAw8+0xSbPWSE3D28hCc99Rp/vDrpDb3YuKY9DUMZvTKQrj2XL3g9vlfYvPDJ6T0/Fka8SurcPaX/BL1yC7k9Q0kkviD9Ar5oN748+scBOoKrvjzFdjs+NyT8vRjrDD2+Gia+hOu4PDgbA70RQWC9PNdpPsLxQT2oRSQ+pLUNPgxWGT1K9hs9qN/FPN96Hz6qwx49fVRjPWrv672MuQU+tMwIPtUMt7wBXOs9FF/rO88aEL2oZfY9C0GJPUHKCr6zCDa80GoDvnVJiD1ZLE69zQsYvrapf7w+NlU7306RPdWT4j0vunm+EH2HvI6Iwb3Zx9C99ku4PWI+Mb4s7d286NuVPU9EXb7oAFi9r3Z2vjfVErt3yCk95wyovRxvSj2dcF6+mpjAvM0qID7hUso8RwepPTG36r15xvE9SZnbvTlcQTwAVKw79gabvSQPp70hyzQ9i2UIvgvnHL3FTYC9397WPX3nDT2Y5eC9z4kVPl1gcbzdyFS7q8zGvYIjjTy878y9r8UdvtsPjTyBrNi8h0IuPX0WRz7Gt4693v6gvekc87299tc9c+k5PAaWNTyaXyY9fLyJvdx5Sz3aMum9PVTvO6jSMr3w4pS9jXXPPSlQ+bzWK867HoamPf+iMr1Wc0U9faY2vPq1Ebz63ly8tHFXvWN/1L0ZKvk9pBVYve5NWr00WhC+yDiwvKvB0r2CNZY92nZ7POOUbr3lE0m+9AWPvcD1rby4rY67gmJePdlMsbswrKy9JDNkPe4WHzzdEoW9BOdauVpAMr5/A6a+NZgwPaTtKD3+IAu9MtsvPUoEbr17ZKS8","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","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","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","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","fMC5vB9CSL1oP9w9h3a6PQOzQz5FvTU9NJLGPf5aMDygFGi9asQTPj2WGz0OJ3c9bgbDvHpkIz5R52490YK6PWrVxz3jorc90LO5PQUFxz2bI8w83D3zvZDEgD0hAS++a3sVPfXGq7wRjuO9mcA5PVeodbywqSY+1OW+PfBoDr5VEM89n+9FvhgpOb2tEQ678FAfvhmt0r3htBM98LisO4klmTwJBp69PBwsPXYqGL2+S/O8KMhlPAWfsL0g34S93sOOPcqL8713gYw9GwEBvX98K72sE/08w9M9PWSJ3D29bJe7y7BVvmqB1buDp/A7cxEPvli3571V4zM9SMk5PVXwrjwoGU8++HDGvLVaIruw2gC8edmLvWp2NrzNthQ9YrO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","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","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","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","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","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","w+wsPZqwazwFzxm+sTjDPfwYSD058/u8cXRoO0Cs67wom3G9SUaPPU0Tub1uOYI9M5ymvZNco7118iW9yO+NPGuEFj4PUoy9G02CPUJP3D21Wzs8QgiOvc/HuD3H/+88Jf3UPLrigb1Aa4U93Mv3PBAj9b1hd2c9XhmDO6C1d73D/508Hl6BPUzClrwSYZk9CZW/PTnZDb37Hli888XIvHxPIj5+r0k+zVh1PVFe67xfHq28dp0JvbdSob0mFK09IITWPAZbZjwvF4Y+9RNUPhzMrDwXVr48CqRfvfGTfL07tQ08gVexvDfoTTwOogu+7kmlPRVD6r2k+ei7ROsrPaCXQz59RUu9qd16Pcfbhz16K2U+2+rNvdo6uz5gV749FVA0v0O/hr1BYxG9WZWpPUfAmr0v81w9BneavS1tKT4Kzz08c6G6PTCMKb1TgVq9aQ6hPRNxOz0DLpQ9BPnSva1N27zAlWw8KrEdPjInqzykiIa/RUMBvRyderzDsJ6+LYCEPdUosr1vofi8ZyBnPZkTDj4hzms941e4vPj8KL5QU/A9qz0BPud9HD5tOK68VBOZPRJr272gk1U9IIB4voDh5r2h6ny8ceHNvbREtD5/8lw9/0Fjvr+UuT1oIbc+ubqVu6mjqD05Bt295zFSvb4sHj1c6607Mt6vPeLaX76R+xC7qRlsPTWM0z2fV7k7LX+TPX7CXT3tykM9gccsPJHy5z3iuoc7mnUJvCVYOzzRV7O9iDQCPCud0j3N0Tq90KuHvfV7jL1dXCK87XpZPXX24rwOqOC857/7PGIuPz3cDkS9WlZXPW4QcL0yZHM8+joLPC/SQDxlIZ09dJqrPT1pKTyEDPK86/afPY8FrD05BAe9CDXkOshG5LtGHcM7u7ucvWOXrbo3uz89jHwnuwoe3j3ovL09YSkPvt8q3zwtrfw6Cir4PK3eR77W5C48LOdyOgyFuD1O+Gi9EZWoPWiSrz3dHqu9bagSPSr6ib29n7s8myj1PE7wn7mK7dC8","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","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","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EvuJPBT+pWES9","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","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","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","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","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","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","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","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","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","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","QX3IPLirUL6hPl8/8JoxPtShRz2TshC/6b2Vv4vXnbzMi8k867KrvUiHVz36z4c+bkeWvckua775YJE9Lhq2vaV/4z6x5Te9MjZqvhbRCj/SKzI+DWWivCwvSL5K9Cy+sElpPls1o74slPG+F+GQPnep4Tx/65w+1Ir6u9Zsfr72RV4+pNPmPchG3z6wy7w8p/a0voKdmD0WGD2+dVVsPLNuBr2tEym/tYVjPjvw7j1qTCo/VGoiPhly473cAbE+7JxMvn1Dhr1fU5U9KhmIPv8vsD76kSe8tfjrvmHEhz3n6Hc+Mc5MPkR4kb0zXCw+tTn0vfaYFz3Gu+2+Fj0/vewgl73/D5Q+VVPdPpPIZL8KSHA+m1qVv4Ci4D1w0Ke9O1hKvTcMAb1i51895vLLPm/6Hr5NMAq+UflXPQzhor1YTSq+3xciPq2TFz/7oWo/bj77PD2vEz2/j9y7m9CevHePoL6Sz+k9KGqEvusEmL4xea49qT6gvu9BYbxKIIa9IeBcPRL5rbvOS74+8fwkvTTVSb8VDjy+SDlGviKka75ZrRE9vbHfPaU6aL4OV6w8+qDTvVCvhL6+Z5Q+9nsbPhXA1z4FMIq8yfPfPcTOOb5bWHO+Xj1GvlOZoz4Z3MM8KA1qPq5GIr50Gyy9orzlvFzD5L2SxZE+ZX0yv5p0Db0FaTc/ufB6PqvpxD4rJkw/vJIMviga9r5kcls+M8iXP7b/zz3Jmbm+8e61Pu0cqD7rOYO+1u/Avi+uyb5Rq9A+W43GP3IFSb6rBBy/ehwKvVx0mL6QsYw+3KIRP/83aD+N/gK/W48Uvlk/5j41fwc/e2BoPWXqnD86Uqs+8bgIv9nPPD95qHo+N1A/v8a7Vz/sEZM+u6CbvQGAmD0eKGs+QQAoPxmLOz9Pm2C/EB9pvajsxL5OJUw/bdwMP0E7Uj9hWcI9QoiDv3R3gj70ztK+OS6Wv/Q2Tj58WT29u8ANv6iC4b5Jtjc/Q2sYvx5Ymb4DYoA98n6aPcmvWr/CjRK/","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","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","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","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","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","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","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","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","8bWyvAk4n74+4os+AH09PirsDL77x46+wG63Pt/fkz6TQWQ9YeGVPvCY4T7MBkO+7KY7Pqi1zDxWaHS+wjLZPSVZPz5miTI+gaKaPaJf8T4wes4+DMaaPfozw77WlVG9YDckP9jjAT6WKM49EgkgPvONej8gG+k9lXndPfpKUT6DQRW+CRgrvQ+hK70F/TQ+kK7WPlmeFj2UaUM+2QgnPmRKgj7aRJO+N2qCPiF+DL6Exlk/UTCJvWnn4zwOnIo+PSPyPDl1fD3KdiE+yrBQvew6Kb8za7Y8CAeUPk/vwz2q74S+PdTUPjK7Jj9z1KY937wkPiAxir5MQig+vq6SPXvETr7hL5M9vAA+P3JLij3cRiC8kRvyPkGZ775t9589oRlBPSFyEj6VqhA/ep/NPirEGr04T669MS+Jvf61qL2FpQ09UDsqPjU+iDlM2Yo+WYm0vsPAYr39pl++xRgRP0T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P8vRQlsz20tXs8smCSvXvMxT58TAW+","QxLvvkmnXL5q4Es9AH6RuzsS6r1d5Le8iF9xvuQgGr9snja9vnjHPsUut70VxB88qOAfvnC/SL78fw2+HT7gvTYvvL0pq+k9XYENvsmxO769ra89SAhTvtW8oDxnDJe/j51QvecRD75+Fte8sdyUvFAyw76ER6i++J2SvtklCDzyu7C8gZKWvkEpsDwIpuu95OWLvihnqL6qCc288n2KvshLyD2LhAQ+hIw1vlC63b12MIy9/0L3vlDWjj63ZWy+g1o+vpHve76K0rm9YZQvPnCKKb6ahsI+J8V1vs6SJr0jgXO8Em2MvV08hL6LhTg/mFmIvrSYEL7qGWO9pnouPQNnDLlLCeC8k+pZvhS6ID0un5O9agjmPRzJcL6sPf09RaNZPOQcrL5SS6a9tAyGuhiyCz38/ZM9GN4tPlZUXD1GqAi+uCPSvElXlb6ZBZU+aTnXvVKrpLyLXQg8N570Ou7Uo76xgBa8CFwzPPBnmr5qkb+9AmRiPXVJx747bgy/40YcvuHPyLz6nV++FItbvdbJHb6kwn4+VCEWvcH/DLs+W2y+U0bOvgxD9D2XHU6+WuPqvfv6q74jsn6+xyykPYm0Pr07eg2+u64tvdvXdj7F+0y+23MNv3RyfDpJn5A+A0CavuuqEb67OhA9tdwNv9Ne7j0fiAO+ppAfPe8OfD2V3YC+cX+APtBIuT3FLVo+oTNYvusFUT7AVRW9qRDmPUB7sjzI1ja+C3NEPW5BB76e2xO9gJTmO/xgxb34S+I9xLgKvdJWJz7sMEO8Zny5vOUQsb3LUSC9hyPGPi/so7xanVy85frYvL/gvD0EBHY+eq3avtdhqz7iidw+4KAWPwiCFL1Lcca9fl6HvtZYtz242s+9Rj7TvEzYnL5qjZA9IG8ovgZ1/z2ThMc9pnAdvRTw3rwHHdy9DkbCvE0vAr5IbAK+EiKQPbQlQj7SfKA8BbJhveLTiT4QHdK9fo2IvYUY676WqoQ+9vvNPPpEU70u4pM+qDtLvTmQyzvpfZ69","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","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","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","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","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","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","q2ApPdWLML6o3O89Im0KvpA6urzz1vC9fumtvSLLmL23VrW9kfjSPY4zAT2ffH69WwMSvlheAT7uLWW9v/8uva/ZFb3n4zA88xWEvGHBHzs9+ZW9RIcIvcZX4bsJ7Rk99NGhvfq0uzxlqz495fufPZiQM72XV8a88Y8fvv+Guz0MSFW+fIPLvcNDfr1XnZQ9kqUdve/KK714c0+9bNPtvZFms73AO2c7gmOCvUtSir1rCdm9FqTKPEt/gL3inzQ9thwAPsmup70kQIm9FlXxPI7bBr0IyLk9QMQvvS18+j0nOgA8cWY9vJJ6M762ySK+9XmhvTEMkz0kCPq9N/aYvIAvZr3xWoQ7c7WSvX85AT6nZCG9dqFJvren+DwWGkM9wHy3PQ8XEz6Tnc+8dvXOvXaVeLvlzhK95mgSPlfi/T1acie7RuCcPda3Ir0mfEq99ZCePBXlRjyeuAE9y/AsPLwrHj7eWYy9uhrCvA05Mbw5mWi88xT4PHXwEz1Q6CY8M4bXPJ9mXTyNRiA9Cin6u67QLb1OGp69KDG0vcxhxrw+gxS9qraBPPOm1z3kBqK9Ks6iPaPTx7xGE+o6j1GvPWrQuj0RFie+iBxtPTczzjwxiI09HHVKPHrZPzxXXkA9h44Fvq4BJD7zV2W9VL9GvPo64DyPcsU85OX1vI3Tsr19rVa+JUFDPS7Bzb7CC4G9uBeTvYeHLr2BeOW+l82sPG894D2DUsY91NYKvnDIcr3+N5u910xSvSyqkr4pq8a9xNcCPkE6Ab4wxQa9tv0/vvOeMr6Gb+u9mJbQvQCx9TxmAkU8U+JgvdpWG77Ipja++nMJvVyRBr6rnam8gqbVvfp0P72leDo+Masbvk3Eqj38d5G8KdGlvbrY0zw1Y5e+BvfqvCclG75H9Ve+5qkKvv+7N73D/AG9+IN5vZRJir2pi3y+7pugveLCFb4bF0a+q5wpvpARcr5i1iu+Oe8gvljyGr7sOjs9kr/8PB4ZEj1rEgS8s3+XveL4hr7dcUs8","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","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","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","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","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","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","yewaPdO+kT0BAtS9I0cwvQulyzxJJZy7JIk8vcOzlz1WZXy9+sKjvX5uLj1WZ3y7dsTGPYHsqT54EkW9Hl/xvYisxT0c6u+9+W85PVwz1b23C5Q9yZtbvoC39r06LBw+/1UiPGdDNj7DydS8M2uCPOQKiL14LJY9htuvPOQYYbz9JbG9FiiUvQkLkj09gQ++ZSWQvULCrL1+aJ49R5KDvSu+27yXqIs8qrvdvJqD+TxmDZy8jcwRvZAL3z3iF1k9VeyIPf0jib0CC/k9KyqRO9WsmL1Iy1e8tWaLPPhEwr3cI5y5uTUbvs+4ITuAUY28RDkRvQhLJr2CWvI82CBKPYRpPj6tqAy9kxamPaVNoD7A+Qs8By1HPgnGuD4Dzy2973itu3a0yb33zxc+5tDkPGW557vI2su9NJQQP7bbTD1vqwK+iiAEPvO//z3475Q+Hge2PRlS4rwUfWE9JRjavISuUD3X1+88rOePPsOWAj6jd9s9Quc1vdJFJD3GdhO9xDOYvcLID76hwQk9pQzFvR4fVbwLJJ48GSP6PQsDgD6U2SY+FlS5PehvibwZoQ2+HZ2hvbr48zyJl3q8Num5PMWqQj6PDrS9IMYYvKNKnT489Ns9kl7ZvH7U9TwhRKo9HZhrvRC9yL1wXFm9YdsDPpa+Wj7hZ+89ejQvPEOB5zxCdAO+EOHPPfjXFb3PKF29mJO8Pe6bob1wA0i6Eyq8PfNoIb71ZFI/mDeVvcGq0L1I+K29bi2SPhQOgr4NJrm9i6PJvUEFJLrXSbw+ZMCRPmajcr4GrQo9CEZrPuQsE79K27M+u7DBPiNUlL5LbkG96UBGPq9fEr421g0+1tcrPmMEBz7RKAo+fPIePZ/J+z1H4RC+BiiTvpxurT2c7Cw9ybdSPuVA2j6MP849Yrp9PYwoL74qlQq+/fJOPoKS/73atuu97lCrPV2eQT6zWHs+2NXrPUdwsz5rIN+9fwsmvpYiZb09g3Q+YzkEPjmgqz90GGy+nwRQvsIdhT4NVJ89","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","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","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","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","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","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","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","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","i/VovV5AlL03u5C8YL5TuqXR+DxJ7tg9ZGtfvC2tWr0430A94d8vvjgMwLvKxP49LP2iPZwU3b2d0gI8HNolPbjIWz0YEwm+CNYEPpHAhj23dII9NXR5PUpZrj7vtB0+U5GJPd+qo7y+lo49EopDPFHoOL3Q2RO+e6q5vYIumD3OuZk9ycKiPWeGTLxYD3k+8GgRuxU+wjwPQ9q8uwVkPFhSHT0teN29MOAevZ4jQjz9TQ499HdXPqK0iD4wwPY8s/EVvRu/SD6y7Mg9SJUyPS++Br3skou9OjS0PF5hEr36mwm9d1dEPTLvJD3tfOk9S+RlPWrZN7uFMQM+z2MMPnHExz3pwsw8t9GAPTUCKr3x5hg98lg1Pcw7L7x7xIm73yQ4voNULD2/G5m98aV8Pn6YGD79TO29F4oIPY14aL1GMyY8boiGPl868bwzelg9NpS1vO3Aqb2PDKS7yfBPPVG6aT3FQnI9SPiOPYxhtz13FN68JSgHPfeKGTutbva9mxo2vEB/F72kAtg9HYR9vp9TRr3jkXw8zg/4vFi5h7ybH7m9PoT/PSxVi70f0uk90w0qvmeldb0uhU+9biC1vY8F1LwK1R4+RlqCvdErED6wiw6+8lzEvR3NiTz21tG6fTyEPUOasLwuGBA+w0cpPnduvT22CJ69eIraPKU0JLwg/jS8AkbzvAnyPT5zTSM8w5S4PYMEUD6TNoA+bg7TvWVeLL6z/129vxlzPpu8YjyKSog908j6PZkoKr15UA09BMXjuytgzL2TYL080ilJPmodp7uvNPc9j3hTPUlZNjlT+y0+IcqzPOeZLz2ODZ49v09UPputFT660Po9XauxvEvC9jtZ5i4+0DF8PjW7gb3Cql883v8SPUm1ezugWrE+TELjPPA3WjZXDXA+sJfTvLDpHLwoFoC97GM2PjaVnzyXBAA+LfEAvs8FUjwCxxY+3tG6PEr6Nz03gSg9AnVPPlG/a70dEFs+tooHvd+LQz1gPJg+ozy2Pqk6nT59QPQ7","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","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","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","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","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","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","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","+byavt1jGr5HpPo93EikPtwmPD7UYgI+OyRMvvqMIj6mbbY+kfxPvtjiRzvd01G+tQOWveFbobzKEnI+7r1MvY7rUz7/eBO9+6/lvboPcTou2VQ+IDpjPTgUjr6lIME+ju2qOSR/Db40IV29RqnoPQWpUr44dwu7Wpg+vMp/Mj6FxSY+8YRUPsPkhDxkKNY+T1dBPmVL9T35NBy+gh/qO90Rq746rP+9WrZVPkPMST6lwgE+vMPnPRwKHL7yNWU8D89tvEp3vT369T0+5X8TviWwir0Aer69RJUBPjg+gT7p1Js+TfJ7vZaElbpbzcy7zu9oPcvCCD5v9ZW84blnPQlDoj5QMHg9Krbnvd+ckL3jyVq+UU74u1lKQD4LP2I9tGEnvY60qb2Iha29lEUlPtQVpr2AHbk+gVw/PrB1rj3r0qE+M2VTvh/WqzstRtU9xEdDPSErUT7UcWE+f9uqPj9OvTynw10+LV1kPVMMMj/3JjC+vWurPPSAtz3EUTU+5cL/Pr3biT3EZl2+4rdlvh5kzrxOb+g9st/IO35Fh70PO2m+DVgfvkM66b070VU+W+nBvT5Vpr65X7I+AgRrPs+3Tr4yHtA9hPn+PS5KnLzPnhK+Vn0IPeootj0Utsq9GOm7PI+FoT1oUiI9S+uvvjOCEz7mhmK7kCEEPa9D07ulGSC9rxo7vjXZJj3Z5SU+gc+WPvNMZz4n20I8y5ovPnjflr36EPO+ujn9PpE0AD5BQae92c6rvVAOcT5cgAs9baNyvPKpCj2mDro8+QxvPtD/Bj9o3oK+eT5uPrnLb70AspY9PLtPPA+9obzJkGq+wg87vgPPsD3qAAi9Hq9bvqgQdr1uOxa8ooJPu3I6ij2uOX+9OYXrPHThaD6KZN89JqR6P/Bmyj7SCNE92PKaPPj37D1KDTS+mRkDPa0For3wgWe+UWvovbsMAr5D58486BiSvsA+fr6zsuY+n61kvg0WPL5ieIm9oLxgPqYtcr5Cwhm+shsrvvkIqD15d8S8","FjdzvgyMHz6G6po+RBNrP3GYFj5FlOE92JETP8bF0D2EAnm8amnSvhIOED+ET5A+voIgPqNon76wDA8+FzavPJZvcL1SmIw9ncw6PSMnij74lJy8P/wjPvPMgD2egHK9OxeZPsUr2T1oguY9RfqUvbzIfj5KGTs+IdopPsPV8D35Rvw7ERD4PDXyxrwIZIK+WVctPaVZFT9uXE88C5/NPiL54T3jIiI/bogbvshXpL1xj1o+Oy3ovgMYgr6m8747tX4KP9Dm7D6qv+29dZKxvKx4cb0veCm+QTpluvHmmL3vroM7jdHhPYvMRj4EAq89Ll+bPmuzzj3xLKk+Tyv9PJaCebxvNkk9blqVvj5pZb4ap/s9cFizPf/2AjnhCjc+J5XQPuEKEL/nNwU+b+7qvcOpDD4ojTA+WuyBPWE1pD2pg94+IqxAvaVHjj1xu7U9le1/uzgrLb5Fjgw+akC7PWaUDz4GZK49lZdbvTwrPz5YKrw+1L/SPWTdtD18aM89pwiqPY5MIj6gY7s8E5vjvS4ctL0c2vW9vV0Avtnuij3LuIO8RlQ1viGeHT4qAXc+8xiBPrj6J756eYe9j7iBPnIHrD2kGVm9vCKYPWqo9D1NwyE+KHkmvjzxgD14TZU99tIMP9+kJr4htAc98VCEPWBgKL65Z5M+UcEGPlhNGL1QATG+LV9Su6v4Mb4vuLI8O7mpvf3tejv6GLS9c8yZvnJbsb39EIm+jEpIPSbM67x4DBK9bLpLPrJnhLy1v6G9tnzzPahcO72sv9K9Q6drvWaNK77uk5A9BH3qvbJcLT0JVsC9ZE0PvoZAiT2cj4E+NX4dPwXsxT09zAi+8CcLvDm/5z2fTUy+jfeBveHIQzz06v69+3E5vXl4Mz14axu943HCvbYSn77iC0k+enHNu5vXxr11gic9xhk8PmMcaD1ZTUW+XIpNvhqU0b3QdpA9Vj+9PQVyfT2G4GE97oCxPnbjpL34M6A+vc0avWa6EL4xrBa+S5QDOfktzj1nTEI9","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","HNqcuj3YpL1FvyY9X/fJvEdQvDt39sI8dqK7vONPmzzBj6i+rDHRvJpDKj1Dexk+/Qe8us7iZL3nHr88p/9dvXcThb0FM7W9+aUWvasYjbzNb4S9whYCPRxk7j3eUpS9S4vsvaUi/bwuCUg9R5D5vTGa1T5I9ee9iz9avTDl6jychzi9iEmNPaTgj719eRG+ddxkPb34sr1ea4c9oWK5vfK4lzyNEbY9YBGePQuSoT0/ekO8Qqr1PRr1Cb0y2LU+12bVPd+/pz3zO4c9vHrjO+NuqDv0ZdK9c0skvdwcu71yWl49yjJVPUDPozyLrcU7W90hvqaSOTxF5xm8FwMVvQfy0bueFSG9J9Q0Pk1EqDykH/s9G6H9PZYpij7W+1K9rWh6vN0roD2luu+7Oog5PRYiZD6/lN29FMTVPT9jc7zCvg09zgvpvazfmD5+Wx0+B1Zgvd1njj2HeNg9hMsCvhULPj4CZyQ+bYT6PFsGmD7G6SW9Tfz4u2LN0bxyeug9l6QNPjM9H767ZYk9THk+vfKLcj0Nkhw+GJKsPqYDKD648EA9g0J3PUYj/z3QrpE8yKqZPSBkeT4GVxk+R4i8PY+6Mj4J/Eg+xVyvPVIcUz4YMos+tQNMPm7mUj1+t0s+b2sMPtuXqLwmyhs8RCgzPokgFj1lZxo94oVTPqyzMr2XPog+/7tqvmyHhD3opLi+bfWivinbXr6zLeu+Py2Kvq1+tr6gDYi+e1UPv2415bvOfIi+xReQPfYrkD7h2HI+AwkIPOHwL75if6s+vdcSPZuXZjs7VBg+39DCvYK/ZT1hOzy/axgcPqD+W75Ryg2+If6mvuRSIb7Bhl++ufDiPXTt5Dyc8Bs+TplZvk1unL5hU+09PPCwPQ+K4L46qy2/A48lPkAY5L5KQ8G+UxmQOW2jB76elYq+SGQmvoJ2y77liRe+1wVRvWB4Cr1VRHi9jSLtvlZxbb2n2rq9af5fPDW2gz4/qQs+2ou+PT12xr74nMK9C5dFOWLZqb46kAy8","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","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","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","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","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","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","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","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","7XxBPmUWKL3B0Y+9u9O9PWlxtrzZX6w9LWK0PJuQfL3Ncoy9BQPWvRpzb73YU5a9penlvate0j08WvY88CS1vXsAajs4d+G89LnBvOMrKD1NGDy+bucKvaXpqz1jNoW9lMqHvdd2o7zrEUs8hbBFPsZRsz2AKQA8rKXAvKMNwz1Ye/i9WzirvT3sFLw358c8T+QhvWqF+L3cV869g5vpuj0SQb32Nbm9hVMTvi3/h7uzxpi8MmzivN4MVr2yXBO+9g0/PVJeE70+FuK9/YMFPYhC9b1G8Uu9QgtIveUZ1DpUuno98kI9vimLuL1cJsm919EZPQhje7weoui89EpBvl+rfbzCMc28HkOevKN2wrtHUry8uFwuvVaaY72soIg9mHfEvUR5Ez51uT88WVngPTj3br01zhA+SJmfvR73cDzHxtM8NVnwPXGx9j2L5529waOtvGWqHj3Ab9o9QyYPPoB2F70YH8K7+zlZvepA8bw8uNW8EOH7vGtJcrrzqAg93IWcvJBsxDxAp5Y6glBbPmeCNDyzNC++3xLIPFMRwzv2OJ49OwI/vQqptj2TfDK9U+6VvPzu+j1Snz899CrBPQdgyL3toWS9SrmMveNXaT04krc9FZaZPfjcjTyANMO9mMjkO5xE9j0dxt27CNz4vL/jpDzkSkm9o5wpvJ2RtTw9VzO9LCy9OsoS1L5u02e+NwF1vhUkKL4cNOi+A1ywPOxDczv0PS48GvM4vn9H2bwo5u69UWDpvZX30TwVwYS+oemZPeOcEL7R2Xu8DlZhvvU8pb20lyM8yqQ5vkadBb5SBn6+v/MJvqqqGL6aBa69Meaevaj3S73zQRa9C+CHvoBbmLwXW7w9fptAvlCeibzE2S69538cvsedNr4Pp6a+6IMsvQwgTrxogwu+wiiou60sCj1T0ac8djHnPfiICL5KCqW90hPEPFyFYr6lBN+8ktwCvfXfQb35IG49LeSYvdRV9L1W6PO9lYj6vXmzpL1EG4S93RbOvZh3kb5Fcgg+","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","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","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","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","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","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","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","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","apinvlzrCL780Ge9pzSVvY/XmjzHCog9cKzFvuumh74oOgE9cwsAvVOnAb2w2J+8Max0vPeUXTx/uji+6cR8vvD6WL4pXIm8aoN7vZg+VL6Kn6a9dBywPfuH7L5o/zC+GCxVvkHyzr7HXwC+LqUhPbrKAr61UNI9Yv4eOkmmHL4wWHm+tUEEvmeg+L3QUyg9bV08PawMib7mDVo+J5HIvvf0SL2mFxu/FMlwvoTZkD0kWcq9e31JPk+1YT6Kb8q9G0TMvqDfC79D5ZC7GCGLvn7IDz6BTYm+hdFFvrGGj77PpnO+PKZ5vtZXZLyJS1+9rJWSPclBg710cwa+idMjvUX5ZD0ALEe+W48yPYOUoT3gsgW9AtQivRBaWr0Hk6g9mfF6vkl7FL0jLFa9IanoPF1GOb48sDa+XIllviS4Sz4UmMS9i/wGPmm6Jz759p69ROqCvWaPjj4Imbe9BHyhu/M/xLx5eeo9pxvEviELFb2EyRm+ZJAfPbRqKj252by+3sUpvDzKqjzY+gi92OfBvrX89L0c6P68gm0EPpsl/b1Qwow+qFnDPJIoSr5KDJa7xZoSvU2Pjb44UKO+4PKQPuDR6r2PGhS+Dr5vvulnIr71F3q9wToXP5U/t7xLpUi/pM3jPKZYeD2vtCG+ejqNvpBAtD2/ZC8+HwfSvaZ3dj0XYp++K4vSPRqRAj7G3Ci8ihjmPRktEjzIKW29d6h7PnDLKD4yKqU8E0VIva92YjzZmlQ9DWLzPQ+hzj1zsbS+K+WYveREUbx9yjQ9MhoiPanPUT3RZW++6HrNO3jPNT2has49QCWHvrqPuT3LQOK+vd0avpBYp76GFo09ng0yvs2Gzbwlj4U8MaIbPKFajzyBlPO9Hgb3Paw7/TyzjRq9iW5kPvBFSr0N+mk+7aM0vYp0jzxrIHA92NGmvrbqYz4fHxI8ct8iPKKu6D28ORe+aOsEPalXRbzFAGa9kuIZPpYYEj5dVEw+tk7Luzb0Vj2Poug7C0wnu3PiKL38CI69","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","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","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","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","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","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","nfrOu6rAwj2Pt4G9tqqVPX4gAb4M6O88S02KPUTptzzf2G8+Mq70PU97/70UUew84L1wvV6Poz4m4I09QVf/PPmOw74IhW29WY9uPIK0KDxOIQo9fUYgPA6Oub1wLgo+25YbPbAyhz2Dl5u7+hXzvYTiAT+uETS98PCuO0PJRTt6a1Q+AQPnPc7VIb03+hk9gZeMug8lvb14C4C92ViKugBz2D3nkJE9GSIyvXfDUzyhjNk9+pWgvcSYkb1NFN89vQ9Wvp1/GD7k6CI9/bdQPdbmiz26wSk+u3TBPBXsxD5HHDq+dahvvMLqoznJ71M+JFC1PR0foL1G0nG9KFS2PatTdryuriC9wku4vfE3GT0c5Uw+OJSIvQInrL3d44Y8QCSTvrtgObxBWNq8hLW3PfZVAL5RSmw+85DNvewieT0X5x0+3xpoPOaigT1//tU9iPrQvY3yI7xsgwK91GO/OwQmCD1lBQm+KwdyvWbDqz1DyiY/1AkUPm5DHb1D1ls8usoWPkK+vz3EvyM+9VtwPRk3Pr4I4QC9inY/PTG0bz36kwk9+puJvVmJDL2DsbG9PdmLPHUmgT1KK1s9Z3aWPhDWoT0ziZA85Yfsu2L+RT6Dpuc8QNWDPRtVWL0qcJu+sy5iPN5zxb3nRqE9GtWHvHxoTbykqi++UrKCvYF6bDy3yQY+jIqoPR7iPj6s1R09UQ2OOlFC4T0EII8+FMh6vQYbVb2fTrC9slS2Pa0lkz0THRI+q2ZzPQ37Lj4Q8qU+IX8KPo2UxL3+aUo+1oEKPXPL8rwfWQw7z4ksPs83KD0R8pQ+QMUOPSJVRz6d1GM8RgxfvZH6DT4cVOm91eibPcxUKj6APIG99AY7PTVKgr3qlLg+2JRKPL812T5xC6A+4FYVvmvpHj6kP0s+x7KQPcn2BT56tlw+2L8CPUrCIz1+Ge497PMKPvVkAL1B220+GpNDPvt9uj1VhI0+9uGZPR9tAz68LIY9MloGPq3cRT5noii8s5oyPRmVBT9Jdye9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","pcwkvkWhtD66pxO9mTLZveikPT6ughS/QsTfvfgmk77wYgq+kMLuvAjRrL7xeS++Tqm1vqRShj0dwjU+Bt3JPToENT2ZJ4U98d8BP0l6rb704e69UKqMPoqpp740his+HSu6vugqiD3WfUe+WmlLvnSjGb6N6za+0be2PCEgBL66mUG+3KzcvZwA1r6ffCu+oHyBvkbcZD2eu4K/Ln27vrbPyT3H8cw9qSA4vsx8Zr7Fohk/CF+ZvThjWb5snlI+fUvhOlOBFb7BWW283yzWvqyw6DzwMPy95JSIvfjQwr7U0bm98J14viKwk7312Um+FE3svsIzqbyxOg6/KMGzPd+hub1xhOS+FHe+vr2RFD4B/Cw/hNgmPoEdmj5E2C4/pHn0vQCUAj5B4qY+cy6tvsUSdz6DCou8uNIdvgx2i7/dlag+ceeNvhAXI7+lv84+gJOOvhaHGr6NK6W98tmevi7U5z5Mi+G+aDzqvG0IxD6SUcK+5YiNvleoQT1Thuw833mZvlZCKj7xSpk9RArePEecDr5HWry9XVNEP6IkKr0nZZS8uSW0vV5x5j1vWvA9bPQwvoLM8j7Kv729KvFTvqWlCD/o9i893PBWvKIgBL//qR49QoACvs+LAz6frde+EiVVPpWCKr++Hi4/sfBPvRN2Xb7bHdi+9MLxPP5/BT0a9J29dEBsPoS06j7W3d69RHPkPX+AxT48Ccq8H3mPPtnw4T76oK29KugDPt8vxj2olIi9WptIPRCBuL6utwE+r3FIPeiISb0/JJ49zYd4Po83tz5iykO+vpqNPT6Fiz423uA+k9p5vfJPXT49C0g974l6vmzQ771TqGA9XbE7vcR6IL4l4O88b34OP1QpRb26pZo8d6d5PRT1Pzp1d5699AWkvXuYUzsXWjK+ZCw4Pj/dEr7W36u7gJQkviX4yr2dtc0+xHwcPruaq70EI2q94uUFvtn4QL6R9Qu+PUbYvifJ6T16sey+SvMKP+Ua7T4CL8Y6u81Cvg6o/b0mmBM+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","oUoAvb8sLb5KBGa+owUuPcstrL00gCS+w8eavtve+70HUSC+miFyvoK/1bzbg00+kfbyvWwvir52UJC9m3GHvn33IL7kwJM9swFivmwsDj20BYS7UkvKvcYPxTwJWuu97m/hvXudKb6arGs8m4rDvuHhLL3+Hk29GukEvqngYb53n6G+rpIzvi1MaL4iCVq+1QuJvpWhUr4pD0m+/DGUvyrioD3TzmC9B5dvvkztVj4SUg8/FPnzvc8+Dr1Dp4i9R1GMvj5Pr74ztWy+fCe+vm3SEb7A3ro8CaDzvniWe74FUhK+U3RNvXy7er582Ja+3aEGvvZlQb2gn1m9wNEEvpKvrj21qwG+JYJiPumGJb60lpg9Ny3zvWRt4bt0Nuw440iMPXGrab6718s93jQIvvHZvD37+o89tGGKvjoP4L2lrva776ZpPQHEyT3DbUA+fDezvjV17b1s9gu9ivsDvoymjz32ErA9IrVBvppmmb3dgp6+Bcrlvf3ae7zv2AW+q2SpvSW4ub2Zfea9/HEGvh0/9r3HQBu+bGm8vQKXEL5Ahoq95IJ0Pmv10L36FUo9YniKPM2dlT7BAMc8ZPpvPluayD1Um3M+93wdvaT4Qb1+Bhi+/5pDPWYTzz3IXvM9u4F+PonEdz5Y0xM+s72rPg1dUz1/Qn898ASsPEG3Gb0S3aE91hTXPeurPL6CVJo8Dy6bPVV2Gb5y9Jc9gIg0Pn4sJbuirJ29iP+nPUvsN71wecq8CvWjvZYxGL0ityE+pZ41vUkqQ73BhR4+tEN2PubL2T03mhA+XM/ivb5Lcz2owCk+BYsdPsNSej3a6GU7rWO/vM41AT5ol5i9XLINvglHg7507Cs7B0bbPCAZ4z2Xngk/+kRcPNrWmL1GVgm+CBSmPSqcoD349L09IrvHPbjBAD1qlwK+m3sePYz1kj3WbQK8CeJsvdBUHT7RKh0+z7bovb+Vkj30zW464vr7Pfu3fD1UboM+XNWNPYtE4r2Wnii+p8BvPcGFmz1HdGI9","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","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","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","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","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","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","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","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","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","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","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","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","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","sSHJvKQaKz6P9QQ9uqRVPfvRvT0wmRW8ykLxvMW1jr0iBo08WkV9vdEmvL1M8Hw92zXZPbVYob2kKMA9fyjEPByiVj3eaBK+1qCdvVJ8cj6SfAw+jlNfPb8Gk70rfaq96FDFPXl07jzEGAk+K/a8vXxHQ73kDFa9TkMyvudoCj3AMge+A0j6vNmr+j2szki+2iTvPfeaXb56chu9MOs/PftIrD05XKc98vkDvB1+ar1Egxo+KAHSvW7mvr2C/nS9QHDzvRWpBj6q7Oi9JXu8vWV857zle/O9St3TvR9sYz1b4Y49DsEevM36Nz5rMKi9JT+IO6P7Qz0Qp5U65dPMvWdL670JNKO+M6CJvp6RSj2/yq6+XISovtD0rT1HAYK9Migpvg/ms70t412+nOBLvq35LL5UpcE97jXTvT3hC77baIc9hJT0vQlFKr7YwH6+RdX7PGjadL4J1p++cFCWvLJclby+zNa9NzrdPReQkL5RtNy9rrMBvOkaYr22rBc+JGQ9vgfL+r1aSU4+nRiFvnN1Cb7kFQC+rNRyvtMmW74yw4E9IRFwvt7WBL55VZA9ozKVvrY9vL6345I9noxgvk8d7Tt64oe9tWEevpHrK75Wbkg+0YUfPl+Tg74U6Rq+/pOBvaF4Tr4ReII94uscvrJ3jb0F/oQ9LQb1vvSAY75TtE8+ycG2Phj6QT7ftee6FWdPPpkmtj6Zcmk+WRHCPoyTLr3vc3s+nUGPPRD1kD6pnJ89mVkhPRExdD7Nhkw+pyq6POhiRj5+7WY8c7UWPRtFRz6CmXE+9V8zPlxOLT6Kmu093H6YPg1Gwj3BxxI+BmenPRZrTT539rm9UyedPpz/Tz6xnUM9MzcGPg5zqj32v6w+ZjDBPUww1z2U5pC9cL7evH2gYbxm5Xc+6aSNPgZAYT73fwU+NszJPTyrXb3yQ18+HTO6viIGsDxjGXQ+q9sMvfQcMrvkDJy9xCYSPYF/mD4u5bg8+ZKSPh4KJLyJ+6M+a4IXPhFvHT6RNEQ9","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","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","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","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","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","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","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","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","aQiPPSbzKj1RA5u9AKe1PP4a8LwOWYE9NMRFPW3BB72yq5Y+rY+jvaofPD4psLa9K3KzvR1RozwR0h6+v/7dvRuV8T1Jhbu918sAvStw9j1/dHW9rm6tuiOIUzwbpQY+9bkJPpRLHbydMdY7axqgPWP8pb0d01a+gBTiPDgrXL7J6o29gL/KvdrNR75nQIQ9qzWfvG0bDz5Kngo+BrrGPbtsR7wXa3y+bf3dPhDB0D0qmi2+JCCePLTZPb7VREo9qvkHPkGcKb5yzgA+yAvQPTecQr31r4C9lWjuvUHWOD0x2hO8xtTLPYBa1z2Dkoe9J7YAPWWGSr6sVRI+Y8uBPRp55z1LaYI9+TWbPl80g7025x699zj5PDrKnr7uuaE8ds3lPapsKj765wM+ueSAvq4RGz5faZ+9euGFvkIQeb7EyAU9akMoPrGt5z27d0G+kJggPesTR76jDJQ95EJrvhDEMT1eKTg9jOG9PRYUnr2tUwE9MIaIvinJej56BYW+gTm3vXQ9MT3z80O+YudAPl1jFb21OQM8pIHiPfHewj2QJqM9CW9RvmYM8TzR6XU99FkWvhwV5j3oTig+/1+LvfZFij2QlQK+FsraPcgCIz4TsVM946+evXJdG77km9u9jsKjvqt6T77seRy9miSZPn2bcD3kEFw+LXvfPZHmyLzS1/k5PEWOvKB1Tz4rDgU+kC+cvEvm2z1nzWo+/vIGPsvRRr2Tkms+KUMtPnhULD5CI40+ofcqvWiYcz4aAFK9DEpsvhO+pb3RajC+KldzPhxW3z6kdIY++EqFvkIxZL0Yk0Q91ZG9vXmzYj5pPLU+EKjCuj1F6z3Ahoc9EusMPmIBGT6lt6A+VuX2vYrSkzzSdFQ9584pvkPRujwLg3I+8HdTvgJAaz5fr4c9oMJqPnL93z59CIu9qXTsPGiMK7yfJ3Y+4RAYP/ebKT5B/og+ty4Rvva1u73gPo+83rZ1PhQm0z1pyig+ZsDLPSdZuD5MDiq9LoJNPV0lUT5aBho+","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","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","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","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","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","qzaSvgzdXTyUxYi+c0EDv//Ckb0IyQ6+sWAQvl72xb0+7g6+wF+uvsb5A7/9rrC96538PJfZlr4n0DG+PPamvltjmb5P8g2++NK8PcIo+Lxin66++JXivuI44z6+S/++jrVWvR9LA72hvly+pKYBv6Qt8rzBKLY9GpAFvtWkC702Uyg/k16yuU1v8D49BpI9qxsqvr7q1b4yYRa+hNSaPg34FD9W6X6+oGOPvrMXkb0n8mi+pmo6v9PrjL6uPbi+cAjWPRardj0btEI/tOGEvnY9PD3M5zc+GUK4vvOP8jyLGCa++udov9SHXj4oE1u+Cv95vjJnAz52P6G+v0vIvvu9Mj4Uqa4+YLONPgawmj2ZVas+uVAPPcvT5Dw8sGc85ACpPeAIhT5FNYS+0FvsvPujPD4PLbU96pXpPhDcWT65ZrI+WAJ9PijiWD5sJ28+6aYhPTgXxD7d5Zw+OjysPg/4RT5pULg+zGSqPtDqlz7NwKM+/uxGvQjA4z0NNlY+Su6WvUyeUT2N8Jc9kR0DP5glcz46XZE91jHOPCgC9L1V8cQ8l3taPiIYgz7xZQY+usZbvV4sSz5hluC8LAl/PZCDUj5OP6g+wDOKu4pxsT0EUxw+3QJEPpDSVj51PFw+dUmzPZOw/T3DusI9EietPUJbcT60fs0+qXUFvre8zD0Etnw9uuP6vHwbwr4/MyO+lakePkAOGLrB1AQ9w6wpPmxH0TzaSnM+uYuQvRLEJr6IfIs+1nV7PcBq2z1gbQC9nWobPdWjcb4LUt08MZ2OPt0S4r6PoME88uAxvqUk/bwjB5u86rQlvsgPzz1wJC88Efk/vv1ETr189Mo9688Jvv+8uL1NjwO9xI/qvUyADjsZ+Vq+Y8cZPj5iajx+CsK88LAsPR7x2L2szgs+ggxVvNswvjz9HII+lLqEPVj1n70smki9Llg8vo/MDz3rPM88iw4LvXrkhbzkf+A9qMvIvShoPTy4LGY9/WX7PVNs8L0kLD0+jIjuvcm86D0duFg9","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","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","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","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","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","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","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","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","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","W/B6O7RCsLsDtLk9twUzPk+AFb6dVtK9zYFsvQd5BT0JRkA9DMvDvTT6YD0GTg47i2PdvdTH0DtvCQU9RwyoPXrvcb1DUQk+TaDFvVBq6z1xczG+Xdi4PTBh3Dw4Dey8oiAtPYYeGz2Rm4Y8g1r4uwcwvb08XxO+60YQPEtY8r11mDS8RVXIPRk1Ib3y1IK9RJo3vVooGDyxrNy9I9YXPd33Jb7kXQE9vJlGPS7VEr59kpy9TXnwvFal9Dz3uDO+0ccKPVawVD7McuK96qXuvfOpPr6W1jO+jY7APW5WUb0/GK49SIB+PZFRyj1hxWc9W/+tPRTvFr3gUh29zXPHPJ4wVbxTJio+NCqCPH/DFbyQQoW+bcUivt5M0Lz+TO+9vIaFvFNwvD1EJN46Rb4ePe1PuL1LYmo9Ik6cPMBTA7rcaKs9nFFIvvkTg729Bww+3NJTuxK/aLywTdW9AybhvTtFOz0UAuq6UdWpvCU8gDxoEKo8I3KTvTxr/r3VMZW8RmfWOto+J722oqs9UR67PTltSb0axbS8tcG9vTeiYz5625E9L71HOyudSD1Mkp+99oOcPQE2RD2cc8S8czcPPpOOC70AYgo8nFSZvdBLC7yzM+g99jN4uwDMALwzHO8906RWPeh97Dwcb1k9/73GPfkYkj327jS9erMMPaORwb2U3IW+u57ivRfvXb55MKa93xhcvjaysD3oOV49MzoDvU4l4736cby+m8hKPcbKf764HhO9ElW4vbd5eb4apby9ZUgkPghjhjxnUVG+InMKvgx6PL7rusm+wMrxvSErEL0u8Re+nnobvldSvb1+ZYk96rtCPjAmPb5Ft0C+FtVLvQ/E9b3bLoK+T+oKvQodP75j8mC+1ooXvkn4Gr7zRzO9rlmTPT+mNb7/You9cL4tvc9vi77R1Ju9tebsvbB6mr0bYuW9nXwkvrNYgj1Fe5i93Ah9vjNb87x4l869VL/FvfkGDb6c+qu+GUEdvuWot71VK0a+6CPFvYdqcr0sJg2+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","3dylPauIHDzE5i0+eMC6viBqVT6MUcI+NM1zPg2pHz3MLSE+MFVlvcdR6L2i6oE98HAQPu/A0z6x7k89E7JMvahIGD0dOeQ9qa6uPVfTRr1TOHK9DokaPm4zpD1nGI09ldRPPOoNUTz3kU49zKeRPrEvbz1a2Lg+J32mPRdix77XoW29flOFPdIulr5NqDm9RCkZv3sJzb3aWR29FAx8vsHXcD0/mAY/JKyevSF4K77S2cs+VqnZvVZsET/63hq+akvGPRwHyj19lpC+gxIzPrL54z012Og9A4x5vjAV2r6UL1I/wU5lvuowBT6B3ys+rsOQPm5Yozw5eHa+Kz7gPa3tWT6h1BU+mAHOPiA4I76Zc5E9yxtzPp1M0D276+U+LYvzPDOuqj3+7829iyllvsZTp7yPp6+9bizWPRD4ijwQtEK9AJkuvTvq/b3eQKs9Kkl7PTsJ1rxYJvw9UDD9vJUNl71IW0W+N23QvCKrEr/1p9k92KKyvSbOjzye6Zw9p9CfPjpk4L6S9IK9VJVGvEC7LT4NSt+6E927PaUdQj4ESpy8RHudvZ7MzLxTNAQ8m/PmPfKcfT4UhdG91tISPk2tJz2mLlU9BX8VvSJaWr0IQbS9yELNPVEY6DxBK3S+SbTlPi49mr5lu3e9JRaqPWPYX77oX9s9CXkdvg25xr2NLbE9PSc5vtNg6z6DAJY+fgSSvsA8E77AwQ4+4zfuPnPekT43Re8+REr8u5Aetj7yunC+Q29YvnnRRT3y5q69Oq76vhPkFb5OfBe+447vviIDCj+izHc+W0tbPaYgsL7jGK89B887vhkrcD/CnF8/eXwpvhg5+T6eEBG+YgjzPhIWkD5m/aY+L3afvj4KAj9K0tO+c/n5vI0ZuD76B+88VZ9xvXG6DT4ffAU/lc76PXYVjT3O/Y6+3fhaPYtuMj73ZrU+LYn0Pf3k0z2VmoE9gme5vb7orb6sods9GVlpPBHI8z0is/Y9BHgCv7BBoz0H7zI+NyRaP2d4vr1nJXg9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","VdjxPpGYRD5ffAg/S0x3PrkUqr5a7wQ/T3+tvu0fLD2psLM9aANSvs/mCD61ID297JyxvmDzdT6MlnA9usHevThyL738S869OOfjvAkH871fqso7XH2EPq4Smb64VFo+8hFEPZjRfb4kXqs9py6yviytiT495oE+pOchvlZpfT7kBrI9rg6EPdLnW76FpgO+F4GdvuAtKT3biRc+MomjvjMubj7qm38+yaGCvdB2+z1WFIo+cXYXPioO3jpJHw0/6Gplvj0jB73A0wy9dFKWPs7X7TyPIRa/OPsNv75BOb4KsFi+oGEevjIjGzzr51a9KYmNPkX1hr6CWRQ/MvkzPyGkjzypaXw8fBPjPWiGpj0XNvU76rD4PWG6g75cB8U94A8RvvdawL2clXG9RuI3PFjsJ73Ucok97OUaPuFtFL7llWs+0f77PvFfRj7EUdQ8apkivuAQhb0BJLE+3XzJOuC1HL11ROS98zBXvG3Egb2g0M+8uUn3Pd+Wkb1rKl07M8M8vhqmN77u8lm+cxrtvSV1v72RlZS9fJqyPLNfKL6CBpK94jh2vdGmK74ksIS+0epFvRbDIr2E61C94XPYvaidKb3QfFG+OLcpvgDNS73wYYa8Gj97vBdFPb6mRHu9ZflrvS/tLT4+NT6+SC+nvbG5nT3KRpW8Xw3YvRfZlLs9mJI9qU29O067qL55ihA+2P1TPZZ4hb7OERq9iMaFvZzEIr2yyR+9+ICgvUm5vr0Klg084PvmvvB9dj0zOLI8LFoEPpVvaT6GscW873nqPWh6yj04qyO+yJpyPhjIIb5AbFC+eGLRPSViJT1DWd49H0EcPSvELz5N3Q2+MNMivt31XL0yjM89DTNwvehYGT7AKYu90KJkPR9sij2qBi6/lYlduw6mgD00Eeg9KmCAPV+ddLzJzRy+OOncPYj7T750FYe9PR0gvjOwk70SjIk9Yki+vfAN0Lp1oum9TGdFvdl6f74XroS92IvhPc0cTr7ceKm+4E7hvd+U9z1burK+","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","nyfCvREL0b0kQ5C8ZBTBvf0FDb4a/O68WtVBPDPrAT1MzLk9+IznvoG9lTo04gC+4rhQvFARDT04F447ns3kvQsUfL7S/Lq+YLcPvscn8LqQWkA+XsI7PYJE+z3lRYo95KIdPtGEm72N/jy+p3fAvY0fjT4206C8ftrYvdCvOr02VqI9Wp+tvAgR6buWBfu9I+R4PgYINDwSwHO9g7KrPp79Bj2OA5c9GLA4PuRbi76D9Jc9Fv6RvmP+DD2wjRu+9ReavYzagD5OEsc9yGuKPUP2ir3/jYu9yhApPZtpiD1pxtu9Za19vr7eNr3zvBq9EHTaPA0Cbb3lYPq98VIevXrONT5DjrE90BUYPt64LT250va+PTUCPHO2dD4oRmg78ftMPiBZnr1Ujko9GFPBPafwKTu3njW+Yr0VPMyJgT3GoDy8mjGGPQOFhLykJlE94idxPuBUyj301lu++lSmvQp0xb3R/Bs8+lFaPY401z25RGm9FIuUvKTGVTr64Ic82X4BPtmo+z2WHOm9Pg/fuxGvnT2R1BC9Fm26Pi2TLr09Xgi+1TvKvLiXHb7kupc8C4pQvahftz0l6IU7PYuRPTBAKT7e8E89Ef0pu6d+oL2V2CO8AMRnPcpO5D3i3Am+L2TZPLtD5DzS3Hy9a82WvDbZdzwALnK9g8uOPcrKkb3eS469YsyevFJr3b5YwTs892EIvuBEzToFwje+5zUZPln0ML7HZx69auYiPVG2x72sZUg+9WQyvlWIa73hH4y8qR2LPr1ySr3uCP09+9wDPmfHO75RUs++V2cyPuTMyDw7E5O+LD5DvE/lMr2gu3m+ipWTPdDWJL+Jrki+Kx+kvqRhKT5eX5C+Q066vaB1AL0zaAa/2BJyPWkKer72DDq9qjdDPKVvCb4K1aC7kt8avZh9Yr7qCAi+IjEPPfdvE75SEzQ+BItnvjivtT1sQ+W9V2NQPPvbzb4WxS++H+OYPWvZOb2hgxK+4H5DvZPMZb7tvKO++qcxvR8Fmr2qgYm+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","bFIAPs37ab19fPq7mwflPHMKrDxrdUS9rlwjvq9nKr3lNTi+JX/8vRsnHr6d8uq8GjQ9vQ1/ATszowi+Qa0uvoTt6LsN3PS8h9UPPolmhT1yT2a+/lmzvUrRxb7W2KS8d4RdvrfCQL6y9wW9W24zvlmHT74633W9QEwvvRSiXTx2Qpa+lbGXOukIRz7jfsm9wBkXvlTVN74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","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","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","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","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","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","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","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","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","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","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","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","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","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","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\ No newline at end of file diff --git a/src/kernels/gfx942_ConvHipIgemmGroupFwdXdlops_encoder.ktn.model b/src/kernels/gfx942_ConvHipIgemmGroupFwdXdlops_encoder.ktn.model new file mode 100644 index 0000000000..fd14f1bc16 --- /dev/null +++ b/src/kernels/gfx942_ConvHipIgemmGroupFwdXdlops_encoder.ktn.model @@ -0,0 +1 @@ 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9CoHHPdjAzr3o7uU8XMbevVmmEr0M9Yg9w5/EO5+e7T1Fmyu8ncwTPFScez62vjU+Y5upPaXF/zxUPJy8fvlFPCW+Zb0hpSy9JZARvt4oHbz6n8w9fhy2PfZZiT22od+8cecpPWlUyL3DxpI8TP4BPF3sID0wm1A8IFYMvn+hC76oOc+9K5jsvfxj/r1q35O8oLrKPYT0rz250Aq9hKvOPc8wxz0i+oA8k9ryvdLCvj12zJi9NmiovSb7HT2axrK9y8ebvUmdCL6GhFq9+r0fPcUnKr5GVak85vGzvT86r73+l1m972/dPN9erD1/rZS9WxKQvUbeMD3FBLi9QglMPk5rZD3e6sQ97urYPPcNBL2wurI9wzmHvSM2H73/KZ29UuEOvoNqCz7lV508","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","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","K0SLvf748j3xHJY81tRPvsG0xD3zKI++k3ekuwgvKb342GG+7EP/PfLYnr33Cqk83frSPePgm71JfzK85XzLuujs2jyGwnk9lPpoPeWkeLwPiSu9MToGvqltlztJsFk71YbjPb0YVDxIuSW9IkAqPuTOIb4U2ju+C7KRPWV0ED6eL3w9PhAvPKSN2bxsWAY9SiGqPn+eF7w2lAq9uBSaPTcmSb0yuQc9OiuAvCwS/T1Z8Mu9Nn7PPVkMY72SNeO9DTdivnVbez0sUxg+TkW8vdPGJL6BayI75XEevo0CGbtqvNS9V4eHPbje4DxdTTG90hebvZcgkL2cw1492CSdvYAwDL2FOw2+qwKjvQdWJz4ovia+GIXuPb1qIj5D7ay9BVsMvTBHTL0CXEK9LlInPuiGu7wY+TM+31eaPeRb2z2oktS9rNz9PWOPML32OUG9c83Rva83bb1YTmU+t+j2PWSsD7wRECw8GnGcPHmq4zx80sI9OZ0CPjryRD1Opeu87jU6vclgs71Pz6a9dT0fPko9vD3VDp+9rAviPYzmDb3VRqS9uCz2PXZcTTw7gXe9UDawPdtJtr0SPUQ9pjCMO0QDID4kMs89oqSCvb6VxD3jeSs+Hk2fvDdi270Vl4m8VvQlPkJHjztFHrW7kmytPf1pND5uf/g7saXyvLWRtbsveIg9iD6oPVQvzD5JDF2+So0gPZ5bDz4MVxm+1QyrvWwNDj5Rve+89Lc7vTu54rx/KAA90k+MvEQkcr4CyxI+uQtXvDSfDbzqzRw+d+LTvSomyj2gUrY7fXeMPQiWIz7B8mS9SMN/vQOnwT7hrTe95S/2PIL5s70AfR+8QGZavcoACj5mDAa9Tq6hvXJ3Rj3dmRc+aDtpvJh8UD7XSQi+zlqYvXUYfTzOCQ0+Yjv4PWu8zT2lhRc97BIEPk06yrzwBaa9CMHkPVdTkbti0mK9d2ilvckq4j39z4G9Q1UXPr3rFD46F6Y9hDWVvdnlWj0gaci9v3fAPd68yz0m2Ao9","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","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","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","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","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","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","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","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","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","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","ypnoPLjicz2fa+C92y1RPZ3yzD0w0Y+9CR5JvHDCZjsYrLw9flwYPaIcI7vzcri8PGRAPmRilr0p9229fyYAPsEZiT1BKz0+PTzcvW/YQj7qsgA8nv2mvCi/hT3Yuxo+jMtMvReSq7wOA9a9WN2VPXecar1CH8O8FoedvMMLGL7L69M94dKaPJHVlL2RjWc9pEsdvtkWm72zJEo9ji19PQGYxrwE5wm+hKgLPbZ9yD1mUUw+0k0AvipSXj2oyJw9qyrFvL0hjD3fsJm8ue6aPWx0Xb7ADYu8i1qfPTtj372yNkk97G42vY+R6rzGMqc9dXCmvb4VEb7WYQy+PxXlPaaxoT1lA8y9yLeJvjEdNb6UfQq+AwI8vg71wr03MPs9du70vAKUkT1X94o8EkMZPZsQbr4hQkA+5J2ZPJGLq70C+y+8dx+VPc9QUD576oS9YAogvhs+6T31+ve8yQPQvPlazDxYZvq7PfjEvjsvlb3vhDy9JzpEvCIU+L1KNFs9p7d1PAwiAz3SngU+pJy8PQIeyD2kF5g8rcAuPa0/DbvuVUI9FfZhvU+mobx11We+eRAcvRRwRD7QALc9+BxtvW7IC71NAYs957q1PfnGTD2JdTc96WS1vSicpj1QtAI9oZ9XPSf/ij0Txfa8VNt0vArn+734dc69LmadvdzBgzxWO649iUykvRi0Sj2ZRoI+DF0PvbqfPTyReX26Xld9vIRKijwDYfC886lAvvqF5r19Ugq9rneyPAmGRz6eWB68Qk3APWH0F73C/bw8+VfqOwphVb4wr3i9OD70PZM3YL2mV4W9V/BRPc3rTD568nK9PpgJvooYPb4Mrfk8dSpEvdyBi73zri09beq7vbtHBL6JbLy8bUS/PBGezTtwSrG9SHWiPfO2073LVdk9VaT2PcOczr34nDs9cTEzvBcHGz7xi+29pBrmvVuegr2St5G90qdZvVSdsr3hmpc8V+FqvQXHrr1VcSA+TY5LPqoxVL2/y4k9jm8MPj9RyL1mxyI+","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","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","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","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","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","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","rruSPGucrjwmgs8++ZZ6vUwQSjx1h00+SDBIPQOcL74phgg+Dl9JPhMBwr0V8089fsi0PafXCj1bnJm+/PTzvIiKCj1+HIu9q0E5PjOxUj74iIu9r24lvv7J2bp+RTQ+YkBavQXIqzygtec9Y7KCPaG/Qj2ne4a9l4kxvcPoGb7Wswg+WwZMPDB4o7wCHpa9Bv1fvLhnij1jM44+tM8YvtdbQj2vwrW9pUGAvU1/U73GAxq8sWGevbGAFDyCiVW983WSPOZYFD7hLz89qKFNvGNyhT22FbA9BoGkPFhlgLxa+Rw+nQH+vWaDKz6xGgy9LKbjPXwPjDuxKRA9zQ+/vc337z2DQVO+xAkxPYlhUb4kWWg9ZQzRvSU8CD4P18M8SjSvPW+/HL5ECt68NvEFvgeWy76AsTW9caKfPLvSxL3m3aA9umLQvcDAhL2aSoC9H5nXvanF270O6B2+Vd5IPE5ff72YBfu92y62uwx/tTy6unk+ZmqwPpqRpL1O0jw+uJJ+PbZ5XD0St4W7SXS0vUZfozye60q+BfPnPahxJj6nHsO9G0IuPkH+O7y1oc88SGKlPCWfyT1GzqC8WHUAPlTTtDzSvC29OsDqPOQIBbzosp89AZaKveFc5z3wI6Q+CdKOPLvYDb4A2RQ95vGtPEymBr5uFgi9UhGMvVMQ1L2jPb49hdlIPiGG6D2ILZs9r6R/PQrcSD4AyAc+c2WfPt9kjL3dieG9pixfvRgkJz3Pb4c9xU7rPY6sRT7VcxM+fGwrvluALb5AWmi+EI5BPcNocD3no0Y7+wIfPoaDwD2x0Hw9xZDPu+Qkcj6DzZ89B96zPbjKMz6MlHw9aRThPYPwGj4uW2O971trPeSbxT09yq69+s+oPEWlqDzVtiw9BdgdPk2nQzt8SBY+kgq1PR9nNboqxP49cXhHvSbsjT4Xbq49Tta7vVW1Yb1ebx0+P5EGPmtvCT25LIe93M0/PU8Fib1ogxs+cISlvaXgmL0bI+K9xRuxvZCvZrx+UBo8","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","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","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","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","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","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","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","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","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","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","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","bNGivbvusjw8YVQ9J1/0vPr3Xb5GtSI9KTbFPSswj75I5wA8N8IBPvQ2yD03BLU74PA7PI41j71ekk89L1PGvYXk0jyaP6o7owMkPlhI5L2643K+E8sYvcqBIL33axA9qIyKPlV3x72nGcW9MobOPVf4mDy1nLE71PaOPVQCjb3xM/+7KYIEve4KgD2J7Qo9dcf/vbRYBb4tKUe9GzWYvdbO573kbIK9gzrtPLV45r1+rUq8dcqjvqw1NbzL3169jZ9WPZKhAT2RO0C+0+LwvZHrFb4wyLA9PQ0Jvog4Tj2dipy9DHgevmdYUb76Oui+LfGMvfz//r2K9dO9IivnPQRrhjtXjQY+MFzduuyWhz2S5Qw+y5ivvaM3FL5U3B0+z1MBPqH8kj4SepK9A8PxvaQGtT0JGB89j3vGO+/SRTvb2hk+zpnvvfx8cD04l5i8JDiXvqzEzzy1tiM+PAX8vVgISb7H0VI99t6DPXYSC73Kooy8BkODPcGyMj17as29K22SvBl7W752gR49dv+FPkUYRT170gK+/gVePajrAbqFwoU76vCkvccdMz0kORi9CxgTPVWRczsNN929B8EPPvzfAz3C1y+9F6BDPpQjLT1VLeg7XPmsvfvd9TyLCqI9/w2VPRleqz2QUo69cq15vYZzmL3Uf/S8HZhJvi+mrz03yKG9vI7xvSnrbb4C10c88XKevbTCyT248vE9KfRbPtbM+L0dlao92S5zvsXq7L0zN2I9Nb/yve6MnL1Bcei8srclPko/tj1YqhY99rFFvVp4hr5I6Qy+/6sTvgRp7DxmiBK8Ium+PdA5Mr4rxiS9Kth9vcceQL1AF1+9qVoYPeq9WT1vlqK9E8wAPigGvj06ATo+FbmJvTH8Eb64n/O9moidPaw70r0Tlk85oBpJPt+Q7z3O2Vw9f3X3vXyOSzy94Z6+u23KvSENLj0XrdE9CvwYvqaeybsMnQi8rFeGvdaa6rzCa7o9ku/DvcAWIb7TqJE9yGmPPbfCKb4zrgO9","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","CNJFvOeDCb7Tml89rSX0PTP+XL4mi+G8RApKvqAPFr5smhg9+uJkvnJ2HLx27ZG9o6lrvHmRF74pcTW9Th+1vOLJwz29vs29XTH5PZ8IQj2LEW8+b/YRPiEkaD3zf4i7/XvGuwfwpL1u2T8+TxlwPU258zxbcgS+ldl5vqZ6tL0N41q9r1AhvVcuRb7XBsc9g6kWvs6jEr4K5US80a3VOwEgtb3/IPk9g2G6u7P8Dz1tDCu++j2DOrA21jvCqh89aXxnPbfDND1uLYi9vdjNPQueY7zp+wi+1wHgPKWJkD2VGLm8/wgPvfaZJT7kMyW+6AcivVnaXz1V3ec9Dc6hPaQcgD7oepC9FnnvvKLWGT1uWWa9ja6kvcJu+z1P4XC93DzNPdt7UD4qBYe9PaaVPUP1b72Mzam9AYUHu+5i273tRcq8CNtevTG+8T1YljK+RsaJPOXkxL2Dqe2840wzvj/M6j0dwC6+ijMAvkdfJ77qgg2+xmQbPlHDxT0pvS4+cooFvsFOWLxKOLs7qL1tPW6PDz7JEO88B5CvvBMBqz2AU5e7Aqv8vDFBUbw9OJk9DLRZvfBR271+dam8nB2ivWDCwj3OO2g9K2RTPjReTT3NJS49CceBve5D3j1WrtC961ehPSH/Gj5eSxk8IHdhvXAKjz2LH4C8TIj4vQlYXbwHD6w9Kzw0u/xVfj7X/Yo+nkmAPWvoIj7qbx69IAO0veekLT2WMAy+76TWvCNi7z2LEJK9PX4APnGDpT6rQ7U9Vr8JvT/dUD3v5tW9VQw9PukAML0VB2g+3aiIPZS787zlUHm+bn0lPiKR171HOca8bsePvYigE7o/vwk93R8UPizNGD0K/FA+M2WXPXW8KL3tfHO69F05vbTs5r2/tuC9N9e+PYSpfDymQoW9czSjPQsO+L14WUi+u/F7veUEVDw1VqC9x9f2vKpgHD4H4TY85AHHPQExirwT+849IVVQvUXCdzxTIbS9t4vyPbP9E742IsC9ybn1PLUf070IvLm9","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","rXX8PYRU/jum2Vw+QowHPqAxjbwA9Bs9jzJxPTlAwD12brm9lKWVvZW8QL7akYo9m6ABvW9inT29CYg+WXrbPRcfJb055JK9Z5cLvnJUNj39Cpi9srl+PW8opr0aIhy98N2uPYfKzjtOoBs+skNHvleJQr5YcCi9+ODuPUUjDr5fGJm991zTvRS36L3EXWE9GIVmPLyMhr0iBMq7vdF2PcyoS70jwfe9Mq+bvYhTpr0xW+89szugvJduo7xHDzM9DmKYvVYevjw330M9v+L0vKo1Dz3H4N+9EKErvpNzrz042ug61ioZPaE6Kb5fJCW+5blIvScmGL4AE+C8a1UAviqQE76/9yC9c57hvCgFWT4W7uc9SkMePIqmc76Jz0E+al8GvbP4VLwLKHM9TGgGPrPu5b0fDqm9COh1PjnrDr7bPJC+1hI7vCSEs71bdzQ+hP+VvhW5lD0g/ng8ygsRvcFrHb47DG49RhdZO1uRBj7/Zni9l9zNPXEJk71YTTE+vdKRPZ6WHLpMaDK+khs6vn1hIb3Nxxg+g4wQPrh8sTytG9o819ujvdnByT1ygTe+NFZLPOcRCD6IqZi8jbYVvsVmxDycxSc+ICLtPYibCT2MDSG9ffBRPTm6yz3ps70+SeGsPUeFNL2u82s93Yp8vAeeODrdsw4+He2EPc7gZr2bKYW9CLQOvucKzrxT66O9utE4ve6fCD7jq4G+AxGIvhUVp72Uc3g946TFOz+/nL6YN0g8PZwVvpEBFDt57Do+0ck9Pb/IU73Bc1a9RZ8mvsl/rr5WE4K+t1jHveU4B76tvwc+Ow6+PntAJj2bfmK9Pf4yvnt7lLxRtIW+k8vgvab4Tr5Z/da+X5k8vnUHSr5aBlY+yMPivDoaTL5i2qq9H/+4vFgAm74TB8c9sJFnvuNp4jymCpM9rV7PPP8iMD6peBE8saVovLF5cDvpIeU9Qo4LPlLyQr1rwpK9ENLTvdj51j1vMXq+eRC0PYOHb72QgiW+Cc4lvflHDb2R7Ay+","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","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","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","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","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","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","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","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","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","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","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","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","OdQ1Pnwglzzb4Ss+8Y0mveDUAL4nung9NbAaPnJG5T1M6PY8JfPwPKckB72KYgW8uzesvSnt8L1MvvE8jX4FvQwQJj3tVsa9k8o1Pf2uqz1DHAY9todZvdv01Tw/u969tHuiPBJdnj5xoec9+gOIORWg/b1S5fq9qzfCvEH3pz3t9A4+Hs+MPUX30j2FYiO+Pa1KPT+vEj4EVlQ9GRsVvgwDVz2z8vC9KEn5Pfc6lj0TRRe9K1oCvlG8gD2A2ES91JOXvTOyXLyDthI+jsMMPgNYkj3qSry8OwUWvncecb02wZ494rrNPVro9j3WaxI95T4IPT3sgrw89Ps8CSiWvbzZmz18siu8XgSXPabUA755ySI+Uf8XvmfPCL5I+Yq+NkjoPQ6K5j0Lrle8NLlQPj+9rz2EzmA+pHfmvT3/0r1IXMG9kBygu84hKT4aLZk9UJKUPXAHVz6NEPS6nv0HPvA9Rr5aoAI70rHFPJec/b1OaSO+JKIku98hM74Far68kybOPdq9crx3j5S9T5KdPDZ6K73N66U9B7+DvTzGbr5vYtq9pLM5vQMn+7ztDXS8kq2aPdN5D77F1yo9/ViNPW9n071V+Ps9tl5gvSH04T3mv0C+zzV9vdbcQz1vww8+1zfZPQYH97xx6qW9XSkjPU6VIL6/Shw+IpwUvGPokjxQR7w9UpCJPVa9kj03EHC+R3vcPM+1ULnp6bg9bXshPnRgPD04GSg9iANmvPIxO778I/e7s51mPQOoh70vYru9iJw6PgtT0j0oQ8G9kSWMPhl4t7tI9vY9Ngo6PW4aEDyD7To+RgqyPWGShr3DAQS+SZ48PtlXIT7rEYs9uYtKPbreKzwVXiK+CbRkPBianj0Mpyw+EJaDPby1FrwbDJU9vO9jPr8UDD045nY9+Gf7u142WL3Qvia+lRCUu8f9hr1iyIQ7GVkfPuXcgr5hCPA98biTvUR7Kz2gLbq9M4YWvinbFr6gURS+6QYBvE38570z4BQ+nIg7PXfGXz4ln7e9","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","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","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","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","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","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","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","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","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","hECzvXMADr4qSmS9LesiPp0IsL0yKwM+VJ41vQIXEj4fTAc77UO3vT7J5jsP9Xa9uQcvvo8mKr76rzG9SX9YvtYCA75i+QW9iIRpPQABGT72KNI8aKv0vTK0yT1Rsg0+/grhPdt6r72n13u9lw/vvAuvfz0gyxk+YvRsvDtxnr7XVxw9190TvuQCFD01s3I9fwE1PQKwjD1MFsQ9ffaMvHqXJj7EKsu93lmLu1AD87zPAJ88I0uXPVA+9L0mhzA993tCvDstMD125wy+UZE1vfHWPD4UhBC+WEmTPYjpHj0SKlw96czqvarZiL3ky3o8p3lEvkNUzj2mqAq+PL0BPg0yIb0er649MR8vvIlRHr2LHTI9gznsve3ftL3mxSY+6gH0vIzGcD37VVI9zF4Kvp0Om70yE3I990r0vMm2CD4WCRS+24hmvS2rE75xdeC9w7ywvH1ajb5zChE9q5GvPPqW0r3hgYY8viDUO1kOED0Aydu89XK1vB3JAjxpwI29hQ29vZiO6z2Z3l89iBu0vQhr+LyoZ6c9DPsbvkjZvj06EmY9sh2/vrVLkz0Hhq28cI6qvUyp4Lzgyh46HfDYPOMOJL4r3ic+r/R0Pd5uub1dalE+QdXbvD9qTb0ZP+I8vpqHPaT8ID5GixW+ssvPPX5Mqz2VTrm99Ts8PHtliT62pzC9L5l7vTemuT0Lgv29N8OZvT5oqbt9qYE9HI6vO+f1Cj5pDEw8WAkTPq3ItT3lF1+9Tqu+vYMHvL3Zdc88oSGDvUnzrjyffNk98zgKPTEBHT7N5io+7YxFPb0yFz6CQxi8IR0evRjvHj3sloE9/pVUPVx3ZL3fjZk9nurYvHckKT4FJ709aAGhPDpJhD39adI9YaiPvRYeMT7s5Lw9LdE0vkrJ1Tz+u6k9ObEVPnr857wqRPs8OsuoPTV4JrxdAaA91iKtvVid5bt8uUe9WL3PvNpSjD02E8y8ZcK/PaSDB716HoG9BZCoPdCbAT2zobY96SmhvT7n873k0Bi9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","+p58PPJtGz1/mpG+eFGGvCM5Rz0i0+29LAitvXtIqb1oV3I+nVXBPOKpcT2PZ1A9MEj8PFeGbT7QUn4+llkovdmdFj79Bw28icI2vqEAPj5+CR893V+FvDD9nLx1lcU9OiVXPe/eg71sB2K9d3EiO5NtQD1WgjS+AjuQPeBLAj0mxmk9i7/BPZCIHz46E9k9Dnl/vab/Bj4H8Oq9a6+DvrxWHL5acGK9zd8wPYUYaL18t589k3WoPSZ3tjx3jQ+9NfsTvgznH70uxJM9o+OEvKsMDD2K2js8/EFJPj3ON7xFDZI9jrXlPZPK3T1vRlc9VbsNvqnPCb6M54E9pOL6PLNj7LzedTg+3XKPPTiqH74bZ9W9PrGpvbKmx7u0I568DKC4vGJ6B74Xt3u9nx+HvhUsUD7GQfg9oFOMPk7bOD7MaoM+/weAvf7Vo7xvuQW+5rqNPiVpAb0yiki+h3NcPRAwpT0xlA6+EwznvYk4Ar4JlsU9kLvKvauTI70p4So9QJsavg6g5L1k6Qu9e1TUPaatwD2uXaQ9GGDCvbXNoD0gZfW9Z1nNvZvRpL2k/Kq9PWKVvf4pZb0ooSU9xuA9PsxhQzyugCq9mwe9vSmdlb3PXg29FA32vSvCBb3eowG+hg1OvWeXk7xkDqA9zmLvOlKi2z2ksO09esouPipC1Dyhd5c9rRE5PdMRbL6PHNu9sTB3vv5p4b20oA29M0XNvEiSxz21YNQ9cHAMPgaFDL40zUo+f28aPcAiEz2dDmw9crNOPafsDL2MqVc9oGYdPrA1cj4aTS0++5c0PrTfWr1Zgeo9riGHPeTFFr7+RT69WVaKvZVZxz36N5w9ez91OxsS1L15Zug912NlPd3cDb6WvxC+Dn42vWT0PT4H2dE9PmMPPZwoID2J3N+86HtyPISW0D3fVh49TCpjvcwgUb7gbeS9gunePTfefT1rD0Y+A8s2vNDUOj5t3fo8yeubvbJEBD2pqJs9gAWVPr2vYr1K0Hm9a9hbPeC6DD67MaA9","j5kCuwEVkT1MdHo+LS7JPdOOoj1SUUo+thIlPb1AeD18VL28h8qXvMdTwDqt4Ke8gcETPmtISz2RaNO84Oi9PEgSuDyrugM+VgkfvbmP6rxYJ7a9S7AnPgPvd72Kefg9V+1RPAtP2z0cmLy9JH/NvbWVsj04QAC+dciqPJRnvL03s7+8KTQ5PXbxFz6cGB++sJUNvSxX0ryWvAU+9VxMvLRnMbz6iMm9VSz8vSBIwruRD6g9EogDvnyYT71AcPs9I5gaPktWBr47oA29YAQYPpMlDz1GdJc8txv4O/POf72Oj6G94auwPQx1wrqoVFE+n7fVPSzjxzwPACq+8wO5PYA0jjyjMog9WEAMPvc4bzouFWQ9AO1xvBSMD74xlk4+pzL4PQYIprw0XII9I7EQPrdkdTxql9s9zEr6PS7LbD1HyyO+GFT3PR6uEj7APSm+Fu7PPVkJqD1qb5c9z3eqPvCaHL6sLIM9t4N6PVWotr120c+7lmTjvFxY8rtnngE7LwMkP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5W+ePhXvuJtP71tcqg9hxCUvacior7ETW6+ek8CvmrpzL5l5J68wbyQvgVieL2YBma+YfDOPYa9FL43qQI+1UqFPVsZsz3vm5m981mLvn48yb0WMFC9guhkPr+ivr1UTMU94w+Yvotdm76GS9q7cEBRvjj3r72wbgG+0UWBvjLLGL6zJL690Vo7vrJZC71B+Li9y4mmvnziAr1hXse9n5T8PSzQ1b3+AYO9k9JfvOyRxL5bVSK+L8dRve8iDr4HVZi+7cIevWO29r38ga09VYfPvRdy/r2kuR4+kTXMvBcqTL561Hi9AnMyvc2ZXDxi3A88DH+RvnDTebzPGoW98fe7POV5Er0KNcU9aeXLvOza4r2hM6C7OmyPvVeu/Tzu21G9qlfnvUkJDzyOPqW+m3CnvVyYYb7QZ6W+Ajo2vrnELD5tskw9fUIzvl1Xg73Giwe+1pojvVw/+L3H1ss9Mh0EOQGkG77SiZm+t09IPLIfTj0bnBC907KAvX96Sb2eGim+90XEvd0xq71Y8h89wHwTvKCTaTyCmba97Oi7PQsPuL4WQfe9A/+AvtE4RL5M33m775q8vcP34b0fp429kPgLvrVMTb2hN7C8OskGvzWRsr31bQK9TplsvVOd6jyXJTG+PEYnvtrPW72fWom7VxEgPahxRz2bG+i8gmP+PZeR8z0hAJC+B+s4PWovL73wap68+HuuvRqC6zxIIwE9bcIxvfiByzwyNCg+snonvY3yHT2GWRo+akG5PKsZjT1li6s89bqmvRErWD1qIYY9189wvPG1gLxcdxk9R95bvEKNAj7Pu189yeRrvuZ7EL7Kxhe8yp0RPnEHrr2Hkrg9ugYHvblGiz0QQiK8khY8vonx0r11jDa8o6o5PHJqrD2vY66947wrPY8pUD1t9Ay9BB+HPW+zLb0wpiC9rBeBvYoDqT6Jh0C+XOVvu+7Tpj3RptM9IYG6vTO+Cj3HSxq+FC+3vXQ6yb3WKKg9XZoRvUNfQbyZsYq9","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","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","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","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","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","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","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","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","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","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","xIydvabh4L0O71C9LGZTPDFV1r24FhS9+pzSvMT9Hj0Aztu7QGkGvrCm0L3ESwW92l8cvgjdVb2jb+Q8njSAvtylhj0vWam9NgdfPRh8nL1KjAa+jitTvsjtTL0RTNC9FmcWO6dk1b07JAS+AdiBvVrNcb4SPoa9yVm/PXzzPD3Yk0k9zLioPTMoib2c8bq8n+xovUiDU7xatIW9gyW5vGhYFb4BeU692wqfvmpTc74SXCW+01gSPD4Efr1Yvr49KImHOfmVbL7vBOa8IJOcPEZZnj2Db/6+8EqevMEfUb3G7By+N6qKvtzheL36Jhy81lLpvQoqCzyLPIE9Ic9gv/EYWz2RgQM+Ttw1PWxRTr3uIdU8apehvfGrNr0Csyu8W/+ePi5ye7wAHtK8WKqpPdZGQz7NwUY8oybjvOUoC77PeAk9XeTLPQFEqz2kZ0A86W30vTE+gzsynbC9f7slPqkWcLz7Nfu77AogvZjDgT3cC1u9FHvmPfD0HT090LO8F8e9vTLd0jxG7mY5+dC+PHNVoLyfepC96D7hvbJMC71kYzy+EDDdPAz2Bb4ljJq852H6PbfY2z1bbRE90rQvPnK99bwSC5q9bK8zvTBTALuDo5o9hgEBPuj4ZD5GvAw+BcanO62MmD0G1Em9+NSnvDAcmbkFzfI881QKvlXZNb+nFoe9rjE3PLt4C75dRuc7RkeTvc4vNzm5AS6+I0Envq0rxL21KOa8fayIO4rCMTw0rtg783s+vne01b1/GDG+YYlhvuJFYD1jdSC9PWS2PQdcd71Dlc6772cIvkDuzb0ai+O9XeKAvoB0F724+0i99yetPSi+SL2izgu+Le9Evd7rHj1wXvA7V0zTO2qeG73O99+8W/hCvh61NL312xi+TxQ0Pd8/9r0Av6W+1w6zvQOgFb76yWu+eY7XvcWIrD2d5ja9kK7QvXcyKL5k6YK99i+vvq5W0r7Z6l093i3HvPFtBr+DaPc8DY40PaobHb5tzh++ThGNvcMYYr2+Efa9","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","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","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","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","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","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","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","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","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","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","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","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","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","XkESvqXHeDzKimy+Pbs9vl7VnbralSS9c6c9vr8IfD0FwEg8ZXELvT6Cvz0zq4S8mC0OvjxLG76EaQ69m5upvkU1q76pKic+NzPFPCjEYL2ePG690KxbvULER771Iwy+eBGRvm884r0MmMq9gF8avivwND6rF7u955QmvafaZL4/V267irQhvTMtsb3NKkI9ryw5vprFfT2qanS8fussvpMw4byRscW9Oio0vr8qSDxmnJS9W2E3vsWBT70jC1++L5aVPE6mBb61mGK905yjvcapdL2J8ne+R91zu5yCrrnlRkq+Q9DlvDm0Qz1Rnia9a7aKvtqSIb3EvMK9IvUHPXOXUT6piGk9Fz8nPqddmz4VcQI+wX/7PAHmjzuqhqE+5d8DPgDaqD7Xt48+LvHlPasIGz4wIcQ9No53vLQNGr7v2mM+m9WpvfT2ebt3RxA+WQVmPmenOj67iFE+uPfovbwYfbtIsr+7sFSfvQe7Xj3J+Vg+OQIXPmYCJj06/F8+yTdkPVuEeT5q2ZQ+JS3yPfO6Pz6GZP08hduaPO+dKj7LdbY8IXHmPbNn6T1Mn7U930jUPTijDT6USy0+MR8qPnY0/j2/ovg8hARmPXXHAz4AjzY9NZTjPuvgqj3mgrE+CAD6vct0aj6hEkk+ZegIPV6E6ryzLN89zOVWPhNevj6BaoC9BE8gPmNHCj3sj+O9u9YNPsarij7Ns1w9AToZPjoLRrraaCw9IXPRvDkxRj1ijJE9QRyYPgZzXTw0Zx4+NxCQPpV8UD6erSC9P6WdvFfr3T1iR4O92sCUvZ7Hlj7fJBs8K5tMvZhPxTw0ctc9ENmUPWa07z3md3e9dNfDvJkrVD0wOHk+0FUpPsFRsz4FBQw+BQqcuu7GAzweTao8MKyMPBje5L1aaHw+GRtLOk/5Uj6xzRs+1x8oPvGtrTvoSjs9wKuRPZzbwL3pfAU9B4viPf9ZRbz3xPs9IZwDvuRPiD2R94I94WGOPbM0gD03Vfg8ItGzPXw2Pb3rAUA+","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","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","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","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","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","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","O5QbvvymfD1YGwS9JkOHvmsPkjzGhQW+OdtkvQa/QT34jri2EL55vmM4AL+riFY9aZAFvQ3fjL6YhL29AAoiviYl0L4i0Y69DrICPtAWyzu/S2O+EhZ2vW3vjr1nQYa9kvCyvkEQOr0KUZk9Nhr0vqwEBL8NFjw9686PviqoejxWruc9OwSCvh96WL17JHG9WutdPXzRhbwTHHK++3lRPhCYj7ymbTC9O5e5vTbAl72pBPU8jXB4vlO5jr4o8Wa+KtjeOwDJkr0r9C2+q+mtvTB58L1lTD++OGYlvhKwlb6R832+xgz9vdcCGr7YJD89f2r8PNzkcb4T/hA9oh02v33jGT0hcpG9JdiIvr4ZiT4YzpU+Vq1OvRPBB78ARD2+dBbuvdzANb6qrBs9iBqGPYwMWj4gKES+oZl/PYCba7uqmxg8iSWSvWbXUb2ZERg+su14vd5jNL5xvF89id+YOzVb3b0NlCG+Mj+5vXIFOT3QnUG+YnzmO6rDgj13fY+9rjF6vn6NLL3uv7Q9FBczPcQZlr0qiJq89RDVPTda9T1VYd89e7+/vocFkr6VyIC+Jrtxvk3Bjb3KvIc90/KQvlQtOD5tB2++70W4vUIgv7qq7ci8VNgDv8n/3r3eHae9OMPpvVV8Dr6iWAW+Wz0Kvuq/Mb7HDga9uKr6O4mCJL4b1UM+OcHlPeA47737l7G92yTlPKD+3LzQksq+SuQRPYxX7D2yLiU+RHBYPl/E3D205wm9HwmfPOTNDr5WAI89J8G4vPS/iz6+R1o82oorvEbYkD3dqAI9xHVdvZektLyszEE8fRF4PvprQjxH/Du9CawPvWRMRT4hbIQ+pUM+PXtI2r7FhV0+k8EoPWk43btNT+29W9YJvgx7lrzuo/29DcH+vJ3b8r6SyC68vFYtvsJmw71QELA9MH6fPTz/gb1mQy2+WB8QPuen6D3/wWu+9nEQvVkeE7vF6hI+roM2vf6LHTyO+WO9r2PZPeANxj2OPAu+tauSPArXy7171ZC+","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","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","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","NjVOvuy2Ar+X00i+kD5pvkJT3r4d0Gm986FOPpPnvT1XwN69pma0vdO5nT0V/mS9A2KAvRXtEL9QGIS84P2AvRKLdzsTNCM8e886vbbyW714BBy+qbSqvmqW/r1AoMc9Yk4avtRqJr4QQps+doQbvQkRTDvfw1++Is5EvkSVLL7ThB+9Vs9NvtFxFL9/zCu+dhGqvRsbCT1Z8la92fqCvvvJrb0OUWS/hCvFPBNr0b29g9U7yqIBvhQUAb7+gSI/YjbMPRxcnL3KB9k7iFtdvVgRjr5blBS9iKt9vcPSv74SvnG9jaiFviMy5L0fY6W9e3PyPJOAFr7+5oI+OGmjvuCDiz5jXLq+YgwDPitZ/DwtGtq9Mz9ZvpKFOr7H4+S8ydqJvZ7BCb21Jr89ARuGvfWrxbzYYYq++q0+PpKqf72qoKG9tNT3vXJcUr1gGB4+0batvLxuML0L91q9CJwzvlI5Rz3ZH3c608sKvVYBKL2WHYM+7+rSvEqUTr6pbfu8YKyAvogWRb23dTc+6lYMvmyK7D3Gk4O9JtOPve2iMz7OaYy9W/KmvJ5Zsr5NkBS+H0BGvm3jVLz1Nn29Qi2QPk81wL0s5Z29OyaaPWh2Nb5+M7w93R+yvvFzA746ZTi+A0WEvfoJyb7cL4q+8+ezvXoUq7wP6Mm8eQhgPnHFYr5uqdG9ho79PbFoyDsNvE8+4abRPQaeO731Jx++5cPqPD12pL3ABB89wfx/vsAqIT5Rc1e6U2RkPXt3lb3DSbO9Smu9vWzJjD3yaXi9d1P/PT80zj0hSRU9WMGBvf+hDT5EYoY9B+pUPtUbjz7mjI89Y9a9u2u6Kb7FNkA+Wn+kvcYB+Ty2rBM8hLiTvAsqnzzjEku+ve31vQGDpr7PUW6+gokVvc2QOT3vsIm9SAgtvr0S5D10c2u9wAasvH9rFb7pMhm+dogOPe2N/rxt0zw6lXFnPT6nqzzPwQU+m+huPZJ1nz0ZKZO7JnfdvWAGJj14fZa+hCKivfqNAD41Qwe+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","kbbvPaff1j18vpw9W//+PqKWiDuJSss85oMbvU5fnb2OQwE+nGByPu4Ftz4CrIu9VdAbPmtjtj5pAak80SWLPd3IPb0HxBc98+2bPWvjMT4iCAg+bvKgPuC+gTw7Apm9TQTLvKxI87y+7TG9gaDuPcY1ID5lkQk9cSbLPWXloT1KdDk+kIeCPn8BAT49u08+i85VOiwJ6bxYUks9A05lPhufkz11Cvc9NfTLvc5oMjuNXuy8hXxgPUjw0D2TwWk+xtkhPvN7KT46N489ky4vPq2uxD2pWrA9uigdPo90sD5/Gya990LCPhJzo7t4rFk9uxo2vLZgojuhVIk9ve2xPl3qcz0XQgw+D4SUPmITwb0y1do7kgWJPR4A5D18qpM9ua2MPTSgVz1rTAc++7OIPXih3z0OwE09NDSJPhH4EDzf0jc9uK8sPuA5ET5wKjs9XhA6PULa3z0hzE08YIu+PXy8JD5ZQlk+olgVPZQkJb3fqNU9JgMZvnIhBz7OEgo9Z39sPeqyXz1d6NY9NnqjPmxkKD5OUgA+MCQvPUFz27wdeUI+JjC0PSMBSD5HlZA9HEyXPmfFOT3ajZc8/Q4MvNqgdLpOC5I9KAqRPebS7T3kox68v97GPfgsVD6T4D0+Br45PqwFDD7bw787Qx9+PsWHpz1iL5Y9T+RRvIErDT5cHyy9JZdXvsn4VbxyEwk+2nM5vhnNlbz8dYI9H/movMWt4zuvrO+8D4rTvKRulL1TwRS+sp0DPZmVEL031JY6nzBHPIpm8Lzns1+9R0TgPZHldrunntA8gtuAvTzJCL0pd629Z6AAvCliqbyUj0k9gwOkvb7Llb3IMCS8zWrHO8nstr0jAoK792fWPfQuhLyh71c9hhl+PdDbIj0Kz/W9PZ+/O/PzCz0Uk7A9Te9pvRUAGr2juRK9OzpDPYdgqLvBbcy9oB2AvaSpujwyZB8+TnVqPdPWxb3OFmC9oq7RPJGLer2bFLI97uP8PXHFMjy1tWG9w7GqPR9hTj5Vqgo+","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","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","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","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","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","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","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","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","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k7JCkovuiTGzxFS3y+f8eAve6OZb4C2dy9cGA6vhGQbL1WB8a94AQcPo4KI75FBXu+L28vvoNxAb4LGDk9pr5QvhEfvb1MI1m+KQ2svLJfs707VWM9x/pavYjjqL4BF6E6uxzsvdaf573dgla+zbEPvlmKqz25ETG+1TyDvG2GZTxVmfm9Y0oGvq8RH7x2iBW+","luaKvb/Vhb3UoOu9rmRLPrYf2Tswkyc9JK99vbmtwL1eg8+9jA8/Pt7dP74lrSe+1j0lvlJ4nz1Ta2O9ZfbZvZ0uTr3g2v6871M2PVqrkb3L/RC7idOYvW/fBD4ZUyw+OORGPKZAVr3QUZS9dRxMvFlZNby/hiG9yUoEvlP2Pr4Cshe+d1D4vfIN0L2Miag9oZQqvX+BCz7dK3k9UKwnvJe3Ez5JQI693zoHvtexBr0AvBq+jrxrPf63MT5aPeG7whQUvkgA173PmA++FrgiPggxxL2nxSu9JEWVvUEeDLzX6os9hif4PTA4JT2xspS8oaAXvE4D6bxcKdG9iDIBvqwgJr3pcX88PeUYvkLJD73yYLK9rC5+vg67iL38TNu9KgAZPYA1Sb53VBi+DojRPWnnO77Rxfm9PiO+PVf/tD0ymp+90uFDvSYOb76YzxC+YivsvAw2TL6QUmY9WWB1vTjIJL6lYQK+pFgMvGidez3qs369c1PzvR7YcL2xqlK+EPUwPayjCL79St09weKNO0Z+3j1KKRK+b2SOPBiq571KRYs9MslyvQsx7L0ZthG+he0MPR76truzL6y9gNYqvqPxnj0vJPS9DAFAvhcsXr5KQQ2+S5hRPePl7732/vi9Y30lvWsU1T19Rdm9QqATPj/5vLwxowu+HUQVvQPpbL5AlxO+G2IoPogsUz2enBS+nUN7Pnx3N76hpHi9dLeNPf1hOT0MspM8SunRPHIJkTt+aBi+RpY2vgblV7yfcxm/RpqePoZbfL7ZOoa9bI4Zvhoyt73cRsW+6+BIvkzJrryJykQ+JJLLPTiA+L2bQPA9+lfLPUL51LzGxj0+fj/4PQ/oSr1vjlC+cHg0Pomj/r2lH0Q9ocShvro9+D22Npw7zmwhvn9CiL3+oQy+WG6Avps54r0bXIe9ChkYPY9Ei7463C2+r8HSvo9LN700fo282uomPj11lL2oz7U+gAyavS/hXr564ke+7EEEvj8/dT2Ih4C9PnUQPqm9O70hueO9","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","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","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","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","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","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","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","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","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","2Tosva7fJL6tiAS+6MXuveh/rL3kVRK+qUhgvWLWY75DIii+QTQOOyroCL5HoeK9jW5gvnpL571EiEw57XigvdlCAr7l57m9QDGzuyDwSb4hQqU901Mvvo/GhjsIgGM8s2SOvYYovLwtN+09BLVivgzbpLyd1CW+8AJcvhqqar2UTQi+Z9wZvjQSnLwJaHe9Pd5Gu4vAt7lDFAE+82eave7/Fr66b0S8q5cMvmgbkr39CMq9K7Q4vtPBHr6T2Yy9VieAvsKerL1v9Q2/0x1fvixK07wRR/O9yBbgvaPyZb5s73e+JzOivpNVQL2Rvzm+g5FsPDYgh75vbQK+vssxOaDAgb2ycdy9iBtCvhRgXb6q9JK9e+kdvoxCwj1+/zW+yoZuvM2pnb2Yp6y94UyzvQDkhb71an2+X9Xfu+9HDL5bG2a91K6hvVkGwL1noa68tfRnvgwIhz12wDc9ibKaO44/m73BXlm9jNn0PBhSAT3GTpG+yTYMvrX8W77QOXS9zhpAvvblwr1I8jm8AswVvW3f4bypf+G9aFONvZ3mZb7PZhi+xYyJvsk9F77R6g6+gf+/vUV3s70qjlu+uM5OvUW9/LzbaAO95JVevQhsOb7kABW++4pBvnlUS74eEW08CNQYPW6U7711YIa+K/BePShokL3IARm+d815vAqJTL2LtyE+3gijPVzcWr27slk9P2c2PUQbHL4FjB09xSMHvnsSjb3Aclm9gRxMPGKbhb3eQPk9khTNPYQxob3ClgO+jyk4PniQ0j2SNRy9nDgJPueYK7xwynW9UB5CPTkenT1vzxA9pPE/vaQNt70Bf8i9rf0NPgEmAT7PVfE9hLbCPfJuzb0K+/I8vJqOPc+dIz0fDco9pHM/PVw9rL1HCci9W8xTPE5KjD3INdA9o/xrPZ7/nD2SnWI9BVRKvfMwCD3yeB49sn45PplFuz3F/Ku9ywkWPjg8Dz76WmW9JDRjO6Ydob2uMxS7GAOJvZUiwz3yTPo9XQzYPfVYPD0ILCS+","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","HJkEvAz33jyVjfc8pF8Bv1639jw3cKI+Z2qUPbkzDTtlzQW+/Y30PUnZ8j2UNoM+g9JGPmDur71HOo8+dDugOwy2nLz7fiG+xHENvlBgRrwvkJc9wbacPTtdBb6ft0m+QncgufqdmT2R0CQ+lictvkZR+zy7vGu+8ymAPQyiF75KKuk8kDO3PU20xz2JGAG+rvzgPSlG7j1TQEu8m6OwvVS5uryq5A++sx7gPSBWfD2F77+9EjWoPQQM9r3mWi292ffMO9jT1L1X2mS+zFeovGTAJ74/+M08F+i/PRXLpL5k5AQ+Is88vY9v8z1mhfK98vAJvlZfTr7YhCw+GeHIPPvwoL3oH4c+KhcWPjHdeLw56609gk4XPLNOYj6p0/A9ti8NvUvkNz7yEM09oxZ3vUPUaT5PjZe92moCvkecrr0Pcl0+LWDpvRH52b10Ly09JEblPfgbND208gk+aV4zvqPCCj1MX8U9mkU/PrWNhr1UHqA9GCAZPa/WuT0i9pk+YFOLvNdxWj2PW808cOHlPT3f0z3JlBU8MEyTu7oNfbx77jO9OjSQvYYCA777yte7Y/vQvMHKO74jHZS9La5RPf4Bxj0mw3u9APTSPHtaBrzpbMq93rQgPZ4oSj4lZQY+pRxPvu9WVT1iEoI+JokBPvrciLxtm668W+xHPI7V77zvfPw92p2OPTdqLj5jsGG8MBJGPnUqczyG5bM9AlwVPncKc720X809B+mqPSLnQj6CwzI+FbikvXmDvr0PjGk8k6zDPemLKD4sF7Q9awJSPscRJr5YY728lkE4PlJ/6T3DELg92D/TvFehbr0AthQ96fbPPV69BT0RD2g7/agHPtxTPj4FHS89QoYYPvmj4bvhcRg8Hr2XPQLn9T2jFto9ybIcu7/qET7MX809Cq69PXnR9T3pIXk+it9FvWrhVr3+ggg+IcThvQuWJj4rwM88TFGYPRgipT2Aq2s9pn/OPtB9jT3eKiM+08wwPoG/xTy5FJC9AiqnOy6BhD0oB1M9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","Yb6mvlwljz1bLFa+J5wDPuaBKj2Tk5Q9zhCYPnoCED34fWU8RZYavvUoZ74GrHU8z+mLvQQFob1J8bu+zSPuvEZ8Cr4Zkgs+bFdRvpumu747EKs9w1eVvdUNGr0B1w4+b0R/OgKIer6FlTA9zmQ4vsje6j3UEkc+LsUjvojRi76a3vc+7brLvW9for3gNla+1lwnPmhSSL5WD509zJlyvrKRob0qzOa9FI2Evg2Dubzcdp+9cD41Prj1iL1+ej4+D9rwPQBStjyGdIm+mH+XvpoAcD1D3IM9LntFvoJOWL142ue9qP6NPP1/SLxece69RRaEPWZobr2Iv0C9Q4S9PbMf7rydvdo8TzVbPhkU/j1cBfU9U5zwPbvsnT3sSqS9XZnZPDbaZD4Y5Xc+pnxHPjsX3TwPn1g9PgOXPHsRXT40nHs7N3gYvauEcL2Dzpo9QTbTvNUtGb1drIE+2FLzPV02Dz53LVU+x4ZTPQOHIT5Fhic9qpBGPatSDLvUTCY+4KNCPg5SHD1EMx29xfigPrw2Vbsvf569CwgNvIPolD1Eh2Y8RLvAvCjQGD609Cs+6buKPW5msj3aDhc+7I5jPrF2tj2YiDa9qakmPmg4sD0riYO9ZOH+PP2wKTzia0k9+0w/Poho0j70oJa+QlZhPoGWKD79DFs+zeUdva6fwT081dG8LJiMPUMOLj5/d8Q91sJDPmqSgT028O47h2WEPoSVAD3RdEM+VW6MPqTG0D0jt5M9qHsMvX4+MD61u+48pKs/PmACCz5FDdQ9uOPFvfaXVz4HFyY+j96yPJ58ELoFMyM+MvdHPo3ds73x/bk82SPuPfxahL2kass9rU3PPBpxED01lhE+L3HiPaHjgr2FoIE9gx8OPilktrzlirs9k7/lO0p7Yz64PgU+/VPjPJFF8LwB7sQ9QXFvPqVJmr1EdWU+kyqdPdeQJj6aTts8f08xPvRxfb0YWku7Wgs0PJYhwz0vkOw708nGPHqsET6rYlM8MR81vL1XsD35BOU8","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","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","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","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","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","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","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","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","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","b9C0PIh/hr2scQy9HRNJPaK4GTvWEvi9+LGXPRNFWb4eEQy9J1lXvpG1SL6GrsM8KzrcPG/jc7tcVhI9WzlWvEEitj01Q8a89GZCvvfHKj2L1yI99n7CPI0HMr5KYMM9YtiQve/B9b3xl+88C68Dvc74c74tFKe946FsvmE5r72+g9m8TujXPLo9DzzjpdC8xcRtPa4lmb34LnC+CbqAvuFUm717dSm+FlRaPWyOqzzQdim+WmQvvq/0Xb36In2+uoc+vuWY+D3/BVo9gUFhviH8Sbu/HE2+aas7vEYYkb1wcxm+oxMUvTHu2bx5ZQK+CNY4vjHd1jzyoDK+GDhOvWf3LrxhpsQ9WrJoPTIa9b0s3BI9AfEEPn6YNzzJgwU+A5CWPRwzJr0hHvc9GdkYPcI+sT1/Sde9WqArPn2u47zZuvQ9RrUUPinlobtGC/49iQ9GvDDgLby3N6s9lsLyPAhYST1+INw9ELYtvlokQD5hxOY97SxIva0DqLwRqCc+boSWPXvwtT18MgE+EHbRvfmGXTzCtBE+cGoJPtNsub0HwoA9k1MEvXmRI76aKXM9thWoPZzhaT2rues9IN1vvMQDi738VZA9jq0HPnW1Sz1StTa80S+5PULPjr3wuM09VNoEvtTCCL4aZ7K9NSbiPZbKL72NGI49hvatu3jnyLxQFAI+IBiePcyIqjyAg1k+m15DPoLyPb6JZDS+DTh6PZJksj3xHvG93l0mvmF7Sjt0Mda80fosveXwuD3dZU29a8yCvllQYj1QU6u+8qcuvle2HT4FQaC9n7uYvWJtKLtTaci9CzUhvcChjr3f8Rw8qJJtO0E+4T1Genu9LS4kvn2Agz2jIYi9Ac4Avlc/R77fPLI9rzQvPVXzeb7mDk6+PD4Rvq0/Gj5I32O9FSUmvgrRbL0GzM49211hPSvoNr4QWyM+TTuKPT2tpL41Y7k7Ojg2vhSt+D1OpTy+1FL9vc5YNLwNC7g9+CndvSoIPD5EsBE+AIKovPJfUjw8vb29","VLAFPSz9ED7A6K48r69ePshnKD4QFlE+jB55Ph3HFz40Jgg+H6LRPXPNEj5R+4U+DH5mPlWSXT68aic++qeaPHGZ+bw5vuM9oxDZPUWxFD2Qh1U+MZdAvIngCT6Vo1Q+MZHYPdDKBj7ZK5i8E52VPuUfsT7Otsk9yAeOPEquYz4N3os9Ia1APVZC+TwgcIW9IyWQPBDKML4NoVc84gXuPaajWD0h4iY93OYDPcffkj2WsIo+CpH3PQnfBD3Zh6I9tqp5Paq3Mj7nyTU+A6lHPkDFXL0CU8q8TXUcPiQGiz5AQh0+OclMPnQAMr1uN807exprPd3DKz69eVs8xY43PsbFGD41y5M+0mALPph+rz0Kijk+zdxLvAJ2sz4LBSc+i+O4PtBPhT6LXNM96TOqPYLDBT7G5Uu9QBEnvZZVAbtd3Z8+caaPvdDCnT6HPQw+aC3xvE2aHT5jdnI9MehJPvxfBL700N+97Az8vTpdCT2uAp48kN2HPfMwdb0gGRK9Mr4iPpZwrr3ECes7ikZMPlwLLD57xgK+706HPhMNvD3a3z67zzmgPtdpIj6qajA9GFeSPkLCGD4YuGY+2cd8PcYvZz7nVJY+yiMzPuiAazzXGjU9fiMAPi2o8bxfmZM+qtoSvYJ16j3qVDI+HDz1vHlsP71Irz890uLnPXm5lT7787Y9rTsovsA3sj1XL6i9StHZveZA8D0TqsM9hZgXPnpSTz1ifq+9eGqbvYqIw72XCF47cSYVvp/0F77b4TI+qM3sPc2Iv72/oj8+kcoWPihy/z2Vyb+89jj6PKZPHr7fAKa9C8y9vDb/kL08FK099eDRu1r7Nr5ChjQ9/oqMvZqDtL0Ulli7ng6KvdCiGz7I78K9hmOuPfupg7yYmxy+G4LjvWcgRL2/r6E9ZCLdvQxanj07NNG94BqovYXquD2Mbqo91PsVPb566711w2g9jseOvdTIY75TE6E9lTT3vdk5Ob1SuC48IhiVvb+cVz7oIzq+k4FMvC9bLj5CfNg9","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","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","+rBTu1trgT0u1SC9fzrXPVvKMj07ARU+vdLAPbRlSj2BZVC6VGu+PZ/DZT5aoh4+HiNJPUk+CD40LXo9+GPwPUuMFL7jWYc9cwnAPXwHCz4ZKMG8HBk9u68VKj5c8kc+jQ8JPkw+rbxUTgQ+TT8KPrzAcz0GMrk8eQZ2PXtfIT5WK+A9f97avcnLwr2kZqw9jZB0uw7KKj1oPEe8TNpMPtR/A76I3U0+7qa+OzANnr1vEeo93isaPjFWd733B149RA9yPq2W9ryOBYI+gIi+vJd37jsWUY29tDi0PcEOjD1EI1Y++h6ZvfW3LT69mIs9vv3uuuhlWT4795E+H6k4vZl6FT0VGVO82pqyu9BO/L0Eydm9xG18va2y/z2yihq+AOTZvQ0Ogz15pPs8LFqoPc2RWb0pRe+94CfTPdPOiT0bvze+RBhpPWQOyr33qYI9LI/SvUBDpTyWzM49+TZlPWed+73+jYk9REhPPqAtnTwJX/+9LNZFvZJ49L0LBuw9PyAsPtsDzr0UpvE8NKlLPTzLKj0nSSQ9m2gSvghwzT2mkWA7o6PtO072Db7h//k9uhcHvfoW+D0o8xM+IiyOvZQ/er3KCVw9OHHKPUSHpD1cpeO9CF/QPftsS73QrqG9yoINvhln6ztNzes9MD0kPW0puD1yc0A7WW/4u+lruz0hXRS+9Ee0Pa0MJz4GfDW+vmXYvY9vxb2TCpO7OWe3PJ14WD6olqM+rXZuPnM6HL1sShg9jfYdPhKJJz3YC9m82UjRvUeVzrwma0k+wHoXPh6PCr1LiUu9oJkPPoNhEL5L33C9v5gAPdC7a73iyWs9Y7uWvWMjc77tazw+BPUbPkjYCb47aJE8IxYwPgYcZT6ZYCk9vqSzPTv8Vj57pZU9B7nlvSUAKj2ZJ569hTSZPeUiEL6PzQO+PWiCvdEQSz00Vra9FcICvRl37TzD4UI9qh0evpnQNj6kkLC9nhK9PQUceL3L2MU9WWWSPtO6VL3PArA9Cw80PRe6pD3/Ujo+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","ZkAJPeLB570KGqi9faMlPdIZGr6hORq+pbYNPjkCab5oYqY+nKuqvfF47jzIao69/bpzPpS9Jb02w/G8RgwMPrHl8z1QYo485yVBPU6HXb4iSRq+H/IdPvK7ED5CQwW9h8A6vnG8Ij6/iIm9Pv+rvpc+S74OYA4+kmncvav0D7yq6RI9bioWvWJ7nr2Tg9a7kvb8vuBYnT1V0SE8f8afvggs4LyZCgO+B21Cu/EHLr7ZSqk9HRp6vnfmE75kdw++eqGGu/xzIr48zpa9EnrSuwBXmD0dkKA9YKpmvrvlUD784Bo9IZJ2PkscXr25n/C700wqvnoODD5zwui9vSXrvRRma72nvAC+z5Mwvnc6ED7AjWI+iI1/vtl5Br6T7YQ97hzKPRPKDT6fHDe+CCT3vYEn4b3SqTA8jaGwPLwGQz6syb0953SwPV2ugz0nnU68rY5JPJuDPL6VQoK9iBSQPf4SQb5lkbA8MsVqvasxfb1swQy+P80xPlwLqL1IOmo8ae+zvUYpgr5qkWq+LHmGPlKuMj3nOAa+Hc2RPgotez32KRA+fIKLPTGc3D3VCX6+fbZ/vSesOT6uXIk87JFXPS/hpLvrg729doU/vWnWFT7/6lw+barDPKpBfL4X2fk9vw9JPq/WZb1Lnsc8KXu9vdv9yD23YME9bTaIvMz6F77KPr29BtxsvbwaEb3ryoK9hD13vYiBMr76/U2+1DhGPm/QmD3jTSy+5WaDvC6+SD0nBk4+DzvXvl4qvr16i3Q+syIuu7wgUD1DXT++ENEAvQ8E377+r7i8hzlJvQquf70X3oQ9kkEbvqsukD26pN09Og6VvViwjT6rUKK+0QrnvVcwZL1+hA6+uAGBPvhSob0yZwg//uQ+vizFmb1f+5y9/Zg+vkjwpT7VGng+1BCCvdWCEz8UWRe+n9rXvXF3zL3/YwU+2g+wPUdfyTzSv0+9x3cvPs46ET5yVhw+L9UcvijHaD5k7k4+tayCvTBloT53t3e+A6NFPaUc0L0PUwk9","7Qf5vYOuC72sH5Y8FMC1PDsiX77osgc9EnRrvQKgqL1tiWq+q2TEvSSQ+r2QKZG9nehQPM35sj0SSIY8lUQHvW5Zhbw1Scw9nJovvXYOpr29BuW9pVgnvALBqr0jIxi9xxapvUhgZL04NA28znvwPHwYBD3bEZ691+RfvnBQG72Nz/a9Dr73PAvHMrxo1y6+1aoRvoL8TrzQICu+pKi3u4X3nj07mMY9deOdvXkzz73wLkk9FNsAvg7aEr5Z1sG7xsRqvQl2az1v76Q9gb/BPTlNqL3uedm+jbeCvnwI5j3XRfW8467QvcOOUz07EYU9aDmUPLRvub3Czd2986PqvZB3mj3Awyg947Trvev1Ob25m/C8JyeYvQEmwb2cQyc9l86VPUGKhD3rQ9o7RDHjvXAZD71YXbe95U0evlLcib2f3+A9u30bvtrpKL1+WW+97P7XuX0L472Kg1q9PlTwPVv23TydsKQ9IAa6vcnIAb7jZuC9nbSRPWqLvz2u7cA93/fvOhoNnb2DBCc9GMenvYC0F73d5iy9W7kIvqBom71cQ1w9VEyPPY9CTL2KIAa+Y586Pa1EJ768vdC9EpfTPdm55b1Wzic+hp5uPs82/j01JqA9gpkcvcCtPb1v9uw9lRsBvhOLujzlmcq9J2rpPLYMGL2ycl46IQDivWFwZT1pAKu9n3SlvVUPb7xgjP08wVzTPQQAH764BBQ9980Dvgr4P70gmDc+Yblcvj+CP70FXdk7i3k7Pv9yfL4Lmey8jvqJvc+GMT4xTwu+nscCvnbzV7652iG8u0ODPa7StD1I7e+9lYaivb70QL0kNHM+o0ihPYgY1T0Cyw8++0CivW92/b2jIMq9KghHvuNSCj528W29borkvSDc3j0URbw906oEvkwUi7uulxQ+jawZPnbIFT6ovgW94CkJvcEzE70BflC+bia7vSjb2by6MT69ukSgvDKkrrricfs7zBCTPWKduL1zbIk9T757vfRJXT7s1s89E98EPul0iL2CW3k9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","7hnrvVFWAj3ECwW+mRX4PCGBHD4tRka9jxCSvVNpPr6GsjQ9aq26Pdf3Sr5pJLK9xROXPWXFAD5x2rQ9GIoPPM266T2Zjbw8EYQ4vQqaA74oOha9Lm2FvLqkPzyNGJY8hkrFOQ/Yfb2slQq8TbLVveKaMrwvilI+uKkZvRQvc745rpy9HPOKvSdJBL6AEEg947iaPThGEDkVVTs9bHhhPfBEbz0kKr89Oy+TPa7SIrwOwUS9s/ouPsUgvT1SiFw9oLJivg623z0Vuvu8b9eBPgxsDb6b3KU95h0JvniSi73sIGY9Q4KnPaVUyrzhkIA9KAIZPhhN0D3O/AO+525PPOD1rL3DntM8tzbVPXSetb0IaG4999oPvlLHMztAdRM9Sgl3vUNwHz3JvUQ9nmgxPa+mAz4iwRC+4+dDPbCRF7xyVu69tqZMPn2bhb69XVW85Dh8PWi7Mr6LCpU9Og6GPTAd0z2l/Ja9y0lbvfmUcz0y35e9L+3lvDP9A74ffim9U0N9PRLITj1Fc2q9b2oyPREoELvu9qy9o5c8vn46Hr3ZwXG9QOsLvUjYT7zlYKg9Fk0JPuRPvT2Rm5G8KZ1oPBdtg7wwld482vrovdfh7j2XvWu8fZ0NvfQizLyyqo69QRgNPvXNyT1RkSa+AzsjvpOaH75rDMQ8CqupPT3/cb0x0Zk9IK26PVcYFj2GIy4+rDcOPt3IfT3m1CA+SiXMPf9VLj1pVO8801/vPSDVDj6e7QM+Cu1jPoOFvz0D0os+jQT4vLlW5z1a4Ms9iCxmPol/Sj6pL9k9wwGAPoMM8j2N4sU8UNhIPBLHlT1McTY9OufhPJfeJz2v1Pe8iNHSPQjwCr1J3y4+6QkFPv5ClD11XgI+cgKcPVqpNT6z55Q9zKjJPVrXbz0u9AU+qEKEPogwOD1XoDQ+AIIzPsIDOL1MRom8zltRPHKUnTy20ie884pIPnok+bzIG0E+SDPivSiIaT57m5A9r2dtvSzKgD2MC909x6d0PQjUgj4RtXc+","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","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","X2/0PR5XGz4aRMQ9wzXKOsgtEj1nA7G9rBkDPfv7gz09H4A9828oPkzy3rwpl8E9LrzxPaWSqz18Y9W7bHL7vHEmxT3FrR6+SNWpPb0rPz3wize+uUxtPSug171H+Ra+6H8DPrbycr3b5xc+BkgbvXr7+D1Rczq8Uq8YPrGIUj4GEtk898HrO0iZ3z23sTM+3mwHPZ/Azr0a4Bc+QkCbPTF9uzwgRJi8LMqsPYy8wz1rDY48Q4QBvgDcEj4Q/j8+23ntPK2C/j0jmbo9k/IoPRhpyz1ePXo8LTewvel6LD7kKY28PM0gvagc/D1QpAQ+1dGRPUMKnDzuFAu+WLchPh4lRb1tfZ+97kWKvntbWr5fTwe+190wviOPzL2MWle+t4AhvHBROr4FWJ47vZcGvIt+ZL7IwUi+sLE6vuQqn745URE+IJmEvAyngj2CyrK8KNksvhdcAL7CsEy+xt+LvYUlKr2FCn08czCgvbaJM71OIX09TpgcvlB31L0Y61S+pTafvTiPab6P/dm9WRj8up9VJb6L32c9Yk40vv04Ir16PWU9RXyTPDygr71Enwa+jJ1uvpI6Wr2x7YG+II/ZPbzhaDxEiZo9AX2IvpDCjz2F0bm9n7lgPSHRB76tyDo9iig+vnOEbL4Edrw9Gucrvg3/GL7PPyg9XLiGvkfBM73YoFO9ENrYvFx9R73t1Cq91qpNvnx43L2fAwi9p7zrvULcI71aRxK+xnCxvd3qBr7eKJ+9/TujvVwFJb1V6Y28u3E7vUtjsjva0eu9AVIyveft971jdqm8lbYCvn/14j01ZgE9ga1yvYTcubxmbI08sPoYvmqcWD0AyFg8WXXRvYLarr1NRxK9LzFYvU2imz3pu6E8LyHHvWwBxb1aSDO9aQpPPQlfEr5i5jS9c3EbvgpPnzycVlO+V961vVnBBb3wKcO9eZhjPWBjsL0fAwG9NAAOPNhHBb4lr7q9f6/wvWbBtLyyB9a9Dz8jvk/BMT1P+y89rPEEPVnxsL0Jhoy8","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","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","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","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","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","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","f+wyPhj0Iz0pABg+PhrDPa8vLz4HhfM9GgzAPdXLszxN7Ik96PQ2PhqQjD4XI5E+8mVFPbiUhT0uZxc+CKZLPm93bjxKgxQ9irtPPV6WO7yffvY8Rf0YPhyJ3T3w3OU9iuE4PsWUuD15gT08fyioPawkpjxTfiw+mHxTvSargz00ht88aIyFPe/LwT0iD8s9dwX5PcTqRb3VkVg8sc10PWuNAj4VQd68CPwYPke6ND6g70U9CvoQPpqqhT19wyE9e2wJvX2Sqrw2tfw9GG0hvQCyLr3WnK280DIcPj42Lzy6K5Q9LEMMPuygcz2K5jo9lI++PaggkDsCljo+8agnPt8KRD36I2u9hMSPPeApET6ugOc98KNUPSIKUD0f+ec8NYq1vfnWBT5CLeI89KiGPKCo6D13Y20+JQAsPd3npj1S5EE9T3w8Pe4exD12vS0+WPULPpnlSLwTbR4+pS+KPW8Evz13Vhs+8kyePbbJaz1Kq9w9Mty7vCgao70ZYtA8rbibPZjT6j1DnV69pO2FPTwi0T3Z3xQ+Vo4WPnZcNj6DS789IwY4Pry59DwUseE8oXYbPL6SVz0BshA+c3trPQMtMj3uPDS9UyCeuq9L/T26wEO9GWnLPVLzJz3lCYg9V7I2PgP2vTuORzE+oUMWPh9UQj1sEoc9QMC3PUgOEz0n7oE90u2bvYA/PDtHama9lHokvWV6ED0Glrg9+DVFPrQNkr3tuKq9uVovPahX5T2qDrK9CeO9PKgWCT6v//O8cGMAvQoD2TzjiJe8viPePd8iAz7z5+g8VI22vFhOrjx8AMy8kwfZvX3WjD1wo7u9rf2zvf2rpL3W8OI9acYrPXeoY71HZ4o9H0OGvFLJq705Kco9VD+RPUAj6zwvEWC9yENyPf0Rn7xzHtQ7EmmBPF3NJL66WRa++faNvQ/CsTzQL/I9UcYMPShFcLtj/r28u8mvvB2Is7uiVxi+TMuHPR+hbr18Nbk9MgmPO7cqX70aFAO+VZO6vZH4lDyueTW9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","YlIJPk0rPj3EpYa9sRsCvZlDFz0jL0W+BX4IvDlpKT2X+0o8Fr+tu/IVCz6m5Do9J4QuPXXIhr4xQjo8lPGAvbOzwz3hPJo8pslevifq8L0ejTU9n4oFvljeDrx5/PW6HvkYvdUOjr1tqPi8YvsCPfWPPbgVF7g99Gs6PG6gD75g9SS9Be/Quxd1hj2KaJu9abqavf/0qr0KRo2+m1FjPWMkDT1bLfI8B7EevqHdfr3LFAE+/fx4Ph/uzL2UJGQ9LTW1PfiVKDxndJq9vcSxO8niWL1Wj6m9AOoDvsoMXLw8tCu9HC08PpbxXLzFTtc9vNp+vKA9pbuxx409o8u4vXZcUD3Hxd89N6JiPsuNQj7nSX650eMzPiMOPT5bvcI9+xYMPkq+AL3kbYo8E8NJvZ4Xjz5uszg+LtwyPohBlj5TFI68PXqqvE4kmD323889n85FPqBn3D2Y3GU+0YyqvRbPJj1aCRQ9SZS2vLSZ7T11PxI+VTiZPZSYkL3qQgE7OJcnPoeQQT5WjNk9DHcCPv0IOj5ncnA9ggARPrzScT1wrR89fYOrPVP+Uz1j1cU9CmdhPsxarzsf6aS8CpuHPCZocD1YEUM9DdsuPgX/QLyTgKE+R8iYPIcTBT4oPi69wGrsPTSzTz4e46C88Z97u6ql0j3UJOw84lK2Psx49T31euK7PWEoPU55groDi5A9zMIGPOpdoj0+5co9JqNYPM3G4TyWewo+nfknvJtqqDx4O2e99ldPu/wetzwlKk08oz+XPfew0z1toVk+aUqGPfUEYz1tuUA9ZlTPPdhpRD5kuc090DbbPSoX4TxW+Yo9juc7PST+kz0/tRI+a8EmPNhS9z0r5xe8qH9XPiSEnLwUdaK98My/PBWiIT5rCQc+UY3BPTE4YjwKdC889pcxPjnMuj2F/14+Mb3UPdRpED3MSHG881C3vZfdab1iSru8w0VtPPOW4DyGiN49+v1hu+VKgD0GY4E91+dGPbNRiL1cNx693eq8vcn1vD1au309","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","p+EsPYliaLzltX2+0lQnPv4UdT2Z7kM9cgczvnswjzvTk368YbtXPTSUMr3kFC6+XUMtPTAyPjt1fg48n4v8PI9WGj0u/08++mXiPd2vhT1Xx0e9IGJMPZ6ABTtbXpq8nu8oPpyPU7xqWMG9BUtVPRyrfj3rvJs9jXn/vMFBuz37DJy9V0iOPSTddL2+w+486IK5vH2OB765qSc+RUcDPTBhrLwxvIi9z61mvYYXFL6gRYM9GmcCPqZ0g7wlE508RiqLvdv60z0SwQC+5KFMPqtNGjzlvn6998C8vUcIHz72Moq4zH8VvoNm6r18bMw9GLbtPabudz3J+Ou8x/pYvWBAtDtvO6w9nQs5vcFbOj2VDBI+5thgvlj/hj2ZJRA9HEMEvoAQij3d+OU9oFKGvRVwCb57EPG9PhJCPe00JD3Li1m9i3MqvTXinjyEUlY8p107vnQyqr0Zhiq65J2HvIU1A72FO4u90tkyvcxErD1q66y8NZ6Avf5+qL0azja+pvqkvaaliD2phgw8UbYbvuzIlb2Xcx6+DDfJvIf+mzvjwCC9hMA+PQrVAD0sme+8JawBPW15Xj5JlUa9A6WWvT6Rpz26ITm938jPvJLBNT2KhjW+I9KEO++Ufr3DYm48T2lFPj5/2LtbNLG9+GScPKgzn73PK5G9V5ooPAUTF75yoW+6tgAFPWnQGb50jiI+3kguvqQ/q70DEQ++pEb+PF+MSr77xuq9rVAXveQvM75Pr4M9nyEyvp2ybj2dTxY+cG+oPcUesb3tq4Y8d8i2vfWn87xgay2+Z9DpPVAkjb3ZawU8GH/3vf/Ioj4jxJK9z7GhPUNn/bwOpFK+A56+vR5iZr2HFIO9b4QEvuO2Gb0fNtg9y65rvtj8SL6XsZa9VHIMPj6WGr6Ks0q+uYg1vq8a0T0aE+28dic3venilD5L1G09QkunvG+d1T1/a/S8ZhnOPGMPDj3da2m9X7F4vmMPIL6lZuI9qQnkvP4VSb1ghWc9ZykSPcHI1L3gxx+9","SCCrvcVp5j2ELrK9W0TePdegnb2sOME8qJEcvpV8tzwkZR+9QLUUvuQWT72fmS++vk26PUVeRL3hW649vYVSvv4RxLw/xqK92vTOO8uWpzycxw494E9HvTCrQ75YIpO8NVzcvSpB87spFCU+4O2TvRY7rbtbXeS9GNw4vIb4JL4Ye567NdP0vXeflL0fGXw9+5B4vfs9iL4uMW89jEWLvKOt+Dv7T8K9780YvlKzGr7/knQ9MwXbvTBGhT0gGze+D18CvtJncrzAcIE9Cp/8vcM+cr1ZsPy9A4oXvh18xL3DiAe+MafIPI25QT1+rHm914DPPY/z5L1N67+9w+YevkJk7r3d1hq9rJSEu1Gxuj3PDCk+PZV8PC7kG7yHScM7bCfkPYLlDz3VJ4m9lJMkOxglMb26Oes9op2xvKGvIr3bLOE9rztGPsw5ITwdjgS+jJCkPdcZOb58/RO+0NWpuwNvZz3AVya+8ubnPVN/Ir4UWee9i9SzPTwrTL0EOn6+oaupvE2NK720PIu93JLdu6UTOD0XC2Q+hyNBPl67OT5ixK89I4RnveASbz4eVvC9B0qlvVvlOD6izK887fKAvKuBT7v3qGu9DlhNvsT7Zj3O/z29vFYZPDXCgL5TEGo+JoBGvcmzLj1g0C+7czekPoXgBD2rX5M+3O/Bvby6J75u3RW9nSNpPNUZwL2MHBK+QTGzvLL8Ur60f2494ybhvSXETL3s2Iw9AK2svUypXrsTp2w9e8LivKGE4T3Cu668bfZDPGGlez0dYdI71aaNvCDhhT0XWdC9/Gy5PSdq3b0j7A68IaElvtzJuL0u0Wi9QDeNvU4RUr4x95i+yhabvSr2pj2IbE89D++svQ1sNb26Ue69ligZvYmUxb2Uc4g8ypu6vaCsOT2PczO+or1wPEmA9r0Hsn09ql0OvYLDGb0eQ1s9JZjCvbU96ry440y+dmDzPRb+OD6+JbI9sKstvnNXj75lCtC8YIuFvQH1Yb3zkVg8z8HPvZ6YiLvQx6y9","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","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","S7PSvXnIyDxjmxq+TWwIveH3PT4Wdui9BEXbvXl2bb0m2aS9zQAhPnmhrry1FtI9Aw60u6kKorytgsY9Enw/PmGOLj4ad3+9zkslPr3zL74hEMq8fDqwvvoVMT2os289srP1vs2MRb0+ltO83wDWPQg2Or51Sqw9jHB6vnaafb3vCjU9KcouvS/ks71uBQG+T1UCvlqLHL560gy+tNJ0vVK3Nb0p95M9flcPvbkPtruY11A7BxPjPVpyvj0/N0k+7dsevl6uoD2y9CU9jUEQPaPkdb6cE3s/i8swPUZEij1Mt5y+7Ql5PdfsVz0N3oc9oTfNPVjLpTtHRQS+GmIqvSV/cbzZHcM9uGJJPWzGHr5Jovy9kLX2PGpXvT4nLCy9UbD5vpn22766sYI8/AuZPXVYBj6fUco9bekNPm/rCr6w3i68lXEwvzLAqb2c0o8+7qxJvfeJML35IuE+oASYvtBdzb6p4po+nBqRvbMNnLydFYU+QAklvuAE5z03SpW9sJ2OvV5T6T2FNAE+cHvVPYtcUb7Wfa0+XLasvTRDvb6MMhi+44Y3vT7va700VZ699W9JvVHSQrxHGJa8bikAvWB5c74A5po9yeozvCBPeb4XyoE+dRD+PZe8gz5MYA4+hHqTvgZ8rryi9Z28BeeyvgjLZb4hZfm8lzw/vU394zyNj0k9AGYxPduVoL11FBO+KOQwPhMEjL0FpYK+SjXlvGPVHL5AUG2739QxvffX/71LSTC+xzIkvjvBJL7BMKK+0SouPn/D972axVQ+eGGrvX7tXr5rPU6+0CsdvpKBm7yG3a69IsTnveYgYD4yGie+dO0wvvE1Qb58eAU9Sc3mvaP2H71qBuG94VQ8vvf+Er5uon+7wBNrvQpmG71U1J+9Yg0yvaaEvD334q290/dzvgt7P773vq+9K/zgvZScs76API+60LjFvdy7Ir66rOK9waYNvsob2r0V9Ba+l2ZlvbfU2rsBdoe+uz8ivrFD57112fm9kIkBvT9Brr24cJW8","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","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","a40HPMDzQzwYGG08hSxuPRYb9D3QBp88tmIFvRUT6z1ihWM72hRevR6GyL2WKEQ+LoCKuwDO8bxNkhA9bQRDPX6X5Ds/3zk9WKPHvSJKgTpXaWy9Mp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","3E6Dv/ZLJr9yS4m+zAe2PeRg+z5UCAm+n5sRv5o+8b5089W+J2jePipRi7/6Zyq86oqBPyC0Gb+Sn3e+ZNaKP2E5sz771pu90xFrP0ywkb+rfoE+n6aSvmyHvD52WDy/wHUPv+gVCz/3eTa/Hkshv5KfQr8JS4k+DZssvuWxyD66Lsc+smBSPxXO9D6U+jk/kXldPkJC/r2tdpe/eMIdP1+xcz6woAC9vRV8v8DAbr4ZZsQ+hb36PoKBer7kdFO/kdezPvcoBr/AhW6/5HgtP3IuFz8NnY2+t85hP0GGA7/bNDe/pLYPvfUQ5T0ULba+cyrbvsXAaT+kdma/dDWHv9D2bz4bQJG9M1CmvhNnY75Y3Jy+6cW/vvYBuz5aSfc92f8CPz4c6T6v90++YBrxPZrCVD6tfCC+cbqxvr+SvT6K5QK/XwiYvjLPpT6zCnE+IqDzPrFw8T3ZQEu72M2bPoAyY77dsSU+FHaIvph6yD1wbRK/jtmpvYeHvj2n0/A9Uc4OPrF+mT2NUaQ+K4/HPpil9b6E6zS86tDdPqXiHr5kJJc+/5+YPJHV/LxOqf49deQDP36cWzz3DL++65oBv4sJKb2lEY89Kd8TPUTrJz9ZWNo+dErqPQBybj36EXi9iHcGPqnZKr/VAie8Mgp6PqDyXL7IGjc8VheDPbJsOL6zT9Q9T6puPtvo/b7J96I+hPiePh4uLD4sugu/uL6LviMIBj8BH8O+afRgPpsqwricmIC+Pj8MvoaHl71+ZKk9NiyqvVqPMjzfcSI/kmu4Pgzjez0PQZc+w3pGP4ofxz5hfV68xqbVPUwj/L2nFGc9lC8wvsnlL75GY6c9AgWWPiAShT2SziC9E73wvOvnzT3VRZ4+glKdPTjGEL5utE678T6jOmxPmT5ZloE+cK6iPS2PBL/fw1u+JW36Pu+iCD3pkWU+VoHmPo2h6r0//3C+Cha6vluCAD5VJww+2TF4vQ77J75O55I+ol8GvmLXTb7ZMHU+qctMPa8W0T5681K+","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","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","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","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","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","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","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","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","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","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","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","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","ABwQPg4uzTzEKjA9gjfQvN9Ojz638Ve+KYabvu//rb671oG9RK0Dv/RFSb5Vgh6+vUsfv6wTET8g/4c+ALWVPh0nn74aciE/I/JOvhjIs70UJYK+H5CmPpZNgL58A1U+ASDZvd8DpT3jH1q/QIk/vrG+5D3Grg4/SYiqvVyiET75OQ2+KW8evk3Rwr6wPH2+sfTBvtNJLD40FIs9SYoLvy+yeT6P2/M+H+yLvmS4qb7WcOe+QfbfPVWNRz7yimo+TSkCP79Koj2AHky93JuxPkjpPb5UpE+9wuK1PnBLBjzpeHS+rcsnvki4ur2P0yu+wwetPq0+JL752pY9xBsVvxJXiL9iDuY8MJCrvpYL474v7Ss7ayqEvbRtVD+yVYK+ORvzvpUuYbypYZa/GysovyM9mT9SCTy/CArOvVCUoj/2rMS+XTIJPM+WfD58dZu/7xC2PibMC79NNpa+yCapv3sohL/0Fsc+CYo8vsWTdb7m076/NBN5vGXy675hX2c/WZ0CvzkfWT9dC4g/MJw9v2J0iD5vzle/Fpjkvs112L4+Jh0+zSQzv26udb/8rJK+JKvmvlkTd7+KXgm/tcN6vyhwND7CZhS+AcaYv2ZYTT4sqdO+NA4evTYoKb5j4MI8jnWUvmwcq79+sIO9kZ73PSJQq79gDC4/zyCav8csd79+aIO+bH8cvs90Oj83uQo+IVW6PrUYjz4oNh+/7mwRO96mJL8ujV6+hR0mvpCOOb4mUlq+CLs3vq6lhT7BSAC/mamyPjrywj6ZNY2+65jEPZiRbj5qt9q8m9hZPvECMD5CowW+0AYsvdgKqj4SrGe9km8LP/JTrb4L8mu+PUGzvhM0qr5Y2uM8hSaWvoUx3L6eYMM+6lOaPXd85r4UeRy+E0hUPiW8rDw+Wzs+AvkTPep+Cb+sQ+G++kGsPo3gxL6BGG2+YfD6vc3Zwb0vXPm9gjIQv+9XXL00eV89KmhGvH+FHb1ouvs+V143vrYZ8r7rMG29gqlBPS0bAr7cN6M+","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","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","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","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","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1oIw+Atv+uzPEO76sQNK9QAaavX5PFD7+l2q95fQCvaAmDT4ZNY0+MOI9PckVhD0KAXe+L9anPlu9BryqpAq9SBKQvBosE77H+GE+OqIyPvxPkz7ibla+PvgWvsjacr7DjJS9","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","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","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","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","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","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","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","JEGKPs53Eb6COPa9ZBzvvT2Vbj1fMOS9vD3WvYeSvr2J6wg8y5J3Ph55Fj+uHWE9WMr9Pf5JpD6gjJI9riFaPvc1kz5+6S++Wdk2vvD8Tr6kqVS9LCYOPinHBb7bNRe++ikFvTUIc71dVzu+a3lxvfU3tjvrcqO8jdorvBgAujyo/FQ+0VdaPegyqb0FB/U9boCrPoWdo701DiK9wNRHvbxK+b2zzm6+Mn6LvfEMcr2Blpo9Y8VOvpfTzrxOBBQ9UrbIvYm90D71iUe+F9GpvXd0xD32CSy+tk21vKNdeL0fIMa9oO6JPTJInT1tNHO+TqfBu0ExID554Jc+OrX9vS6jDr4+e4w9JWBzPQiVPL7X/ZY9MwqUPtJWEz4Ye3e9uM2NvVGmvT3iIiW+6/wrPiFQ9j0u1IG95rgyPkdOU74pHKS9cIdpPJJEDL04XgU+2WJuvcr2PT3WfO89P+GjPi9UBr6ZKvW92qo2vlEX6DxlXN89ZGY5O3sWu7t6hKE+exRdvtzZlL6vycu9JY85vXu3zT2efXs9Q7UqPhssBb59yey9yZAIPsALCT15vgM+A0i1u80GQD3wQAg+0MhsPna1672lsHu+yq/lPZbrgb1muEi+E9GAuWEC6L07tP69v6OyPVdqIL5L26s9nyXGvaMZSD5tkAo9aybbPSCoyDwdKni8mdG9PKVvDD+OknA+F/wtPrt1Uz4dzGw8LvXGPfDHlj6OYeu914WOuy/Wh7yJyTi80CMNPWLCpL3RKAy+nOs/vmP/nT3ISSQ94+JdvpImybs+tKe8wt6buV3xQb1rkPy92f66Pr/Fqry+tGe8aHufvYZDFz6jAMs9T8hKvU7AxD53pXI9pfANvrfdpL2rnD49K0bSPYFTib4MWUg+pKZQPl7+gr3+YZe+NviMvJPkdL3WZua6WNnTvTmWWD7NvYQ+q4NyPfQxtr2NqTK+1sAVP6o8x72Cs4A9Z2o8PkX+fL5ArvO90N0vPicCk71Cxy6+MekYvjQ+tb0oOVS+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","KEXOvUs1oLz6lIK+tET9vMZZ/bqoyKk9PFk3vWWirz0Xnu09M+qPvMH9tb2hCAy9VOcmPpjKGT5/Xaq9SBfHPey8yb1CfSo+5gI+vW/vDT123f89WvvjvCCJMj5r/+w9SdDxPL3eHj4S93y9YaSGO2ASoL59Q+I9uG4vvnvT9rzEqx8+nccXPQp7sr1Z8cg9C564vfvIu7zLau08QGVzPuTTnT2vBsq8PZoJPRUuq71PikU+Kqoiv3E/EL4XKf29zZsbvU4o/r3FrRG+GaQGPjxHVrp53qu8Nk6wvbbzlz0tLda94yDxPVYonD5mYcw8tuklPfnPxz1x8r69T7g+PT64yrzVU1C+9yIgvk+xjr2JyfO80O1FPalByD3cqY88xUSSvoZodb38z0Y+fMV6PYw6MLyOSxY+hY5cPq8WFD13fAA9ggVsuylDNL32M+4+aCAevj5jTr7xG6+96zQdvezqfz0Wy5u98dJKvo4sV70XkqU+ZZ9Uvm9BED6EORw+htoPvq4Wmr3JRG09ipPXPaUo0j0z9gi+nEROvWGuTL5deQI+k+MLPvZ2qD3MgzS9GW+ZPenjJ74dbiK+D2+/voLnjz1Ttpq9I12KvcIUe7zQmgq+ow9wPr7zYj3c4IK+8NKavQLIWL5cTpG+HKm0vgaEcT9UrAG+L1abPVaCrT67sQO+COJ/vUwTY745UPW96xiyvR9/m76SWsu7GnhmPgiVGL4+BK+9j/7LvNvVP75C/jC++1CfvjnEqz0Zdg++o+R+vbdMAr/MbWG9BYnsvVlUJjxtYWW9+pSAvpefpL01y5i9CqLVvBUYSr53rKE81OJevrwzeL6Ke4a97orpPCv47r3hQZq981Gmvf6nAL+fHfw9fr6Rvis8074ws+W9eKqsvNLxZL6o5gu+4KYVvjQtzj3dnpq+SbXrO1pCWL4pHu69o+EFvddiir0LmWu+siUwvt7lfbyWSFa+614Pvv5X3b41Ltw9lWgrvlz3rL6UnRw+vShqvjpKAL7RDjI8","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","SzWhOwFYHb4bN56967JFPQYsPj1AoA4+H2eavZjUvDwKSea9Z1tyu1mJi71Labk9WYsfPpgI2T3Fyrg9Y5zUvHqJ5T33o7w8vQ6YPrSqFj5+U8Y8ppwoPKYsX72+iVO9XQGQPcaZ1b19v2K+T2eAPVfRrTw8nqC92HyHverMwD3GvZA9Ew/PPHE0MT6dWNg93evOvBwCDj5b3BE+V/EpPoxdcT6TsCc9PMisPG3AlT3CJI69N21xPVvQgr6DmMK9qs/HvetW2j0/pqq+Ng0pPsmcHjsaCio+1ZebPbthdr6RJ7s9GMPAvpjwh73KYcY9G3TPPVl0Cj4Nngc+klLUPW+QCz2UVDc+YItiPkPUU70Ghy28HhF/PUUSsj14GxQ+y0kTPjo3dT3sagQ+9YTqPS1n4j1OC9W9y+OTPbnnjj7s5dk9rvt0PN4R6r26WQc87j7gva87dj1K+GI929iRPONXj7zZLoE92dcCPYPsQD346IY9h1IQPZzjhjwhHQI+q4QCvbCAdb2d50I9Rs76PYleGT2e7dY86hZnPRj10z2jIee7kUpvvPE9BD3TXW09E3sIvgog7L2YL3Y+TtDcPa+8aT4LPnY9FNkPPlbg/j2rSBg+YyUbvfGDF73Gkns+RltyPAcp2D0Fe/I91EWIvXdp0r0EkrM83EyUPdxGn71FaKy9zMo1vhww3T2KULi9YG4QvhSDJ72NNpu9hJUIPs6TqD0lwaW9mgE3vXnfTz3Nvek8yCwlOpB2UD0d1vs7Dqf7PWUMob3iLpS7cv7dvQL1lj3uhUQ8vq02PTNAXrxV+C270aiNPXRbBD55umM+ZtHQvDUQmr45cas9+NAwvfx0kD2gLgC9hJp1PYGT6j2H1iA9dz8fPXAvPj2Y7MU8yDiePdRsd73ydq4691hEPMu9ej3YssG9RCj1vhYoDb4Xpo8+WeXcPaLtMb5b8eC9uhsmPqDB2L01fJ+8gYRsPrBIjD0t1Bo+RcsAPjtd8r2z/ea9uqm+PFME1b2A6BI+","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","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","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","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","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","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","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","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","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","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","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","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","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","JQtjvjUP0bw5wpc7jX6ivX1OqL20qlw9vIwFPrKw5z3my668nj3TPVuV0713mR0+aNlfvRatjD3+SeM9EG3eOKiJcbrUIpY8xUDbO/7gcj1MShu+jDCtvVYOOz1Sxqo99SdivbN3U7ylFG69OIpcvAnTgD3ezgM8pRiJvXGatL2Rg2g9Hbt0vZ7ZlLuuX/I8sSkPPeIHCj7Zwqi9hhgEPSrloD3k7/a9kw8kvY/mKT4Vz0i9OTANvK/kjb1gmrK9L+ExPWLX5T0eMHu7av7cPcPDDL0/3BK9VcWePVaZDr22M5w8AcouPdLGlbsqxY28289VvQlaCb4BPy8+agwxu/+mBr6bP7u+LO5Wvm+uir441JK+laQfvjbrDr70D8O+l67JvQ/DZ74MALK9Y5Fhvd95Q75IQhq+JsMcvsWAI768fEC+5wqVvY+oKD3Ap0m+6XeOPRRGjL7EhsO9Ky6ovaozpb3X+sW9sU8Hvp6J1TshWyq+R7sbv1W8wzxfwku+LpBxvnO5Ub5030e9h040vmSaNb4coRG+dycSvvPy/b0Os6y9fzlpvvtqBL3RPPI8t1pFvs/hTr63NxK+NBs/vq9MT766d3i9VIhkPL5Ylb7tIxO+ceabveh8sr27ApW95PWSPZlrcL5yV1y9DSDGvtKHM77DrCA9KHHLPFkHX76idnS9M3xYvQE2UL4iEYa8IF31vf5gJ75ti1s9w9OXvQxuQ74Smva76CGNPb2yaL3P4aS9uswNvqZJ673d2529/bWVvZAX776Zb2u+txILvht3Cr7ZIqm8MH2gvpfphb6sMW++6Ef3vLwG4b2eKyi+mqVlvipEEL4cm/49+e4/vii8lL0pBW+9GY0xvVGUDr6LDzm+FiA2vsdc4r1f/w29q4TWvRzOP77IqbK9/0wePHq52r5KKx68VzXIvYqJEb2bwqC+XgdBvpoaX733N8q9gPXgvaR5Q77vLV2+xbeZvIrwojyLK2e9fIJiPhd6o77LhV2+1JShvT/SpbwGeQM9","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","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","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","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","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","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","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","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","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","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","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","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","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","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ysTwdIso9oTZGPxzRLb+JpFM+5LHtPowOLT7/b7M+HlBEP+6q6L7Ltzw+9FdQO3+0uj/iVsY+","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","haACvkxaGr1SYU8+1DUOv6M5n70VdNc94RAKvfIoq73m1e4+DI8qvpoo2z2owfO9cPDmOwm2B7+D+KC8x+4GPsjv7z3hlIQ8HotJPzIbeb62hxw+3aN/Pk+T3r3S+tI84cGCP18hGj44Me68FQs0Pr6qIj7fKAI+KLJTvlxqcj6nA8c9Z6DmPt6x3736WCu9M4gyvgr68L0oNDC9I6e8vuIzrT0EfDs/Tn17vV/9j75K0yu9IM7jPtzFBT87Jag7dQkoPrLJlD3PA0i9UCixPmhmKz3g3ea8tEBdvm/WVz5mV4Y+jR0aPrHOCb7yeWy+FfToPG8af7wwkkq9KsU1Pmgxpz7P38M9zNZAPrRzgD0mKXA8jfGYPjL4vD0qSps9+a+mvnEsCr59F1C+kWqhPVkj0D2Q+ku+czkWPfqXAb6uwCY/bcCxPmCfkb6qZ7c9QakNPjcOkj1kM6y9E0O7vYYSSDzPXEk9f+BVv8zo1b2jaLO+DOicPgxoV74qiI29ceo1vtVTrr3hRHg9nLFivhAi5b2auZy90qKKvcqSDr44QS691XXjvUNhzzvzGSE+RZf6vQ2Vor3ZNWA+0mMJPa8H0L1GYEw96hHDPYQ4Hr8ScQE/eJDyPgkLG701pZ8+pTaHvkA/9z0jHpm+BPIBPgGMUT5Upyw/C36APvIuhD6GuFo+tCxGvZc16j3fHoO9fqoVPR6Bo75n8Dk+ztn4PS5arL68MB0+bsWdvmvnqz6YhbI9+90Yv99M1T3tMKW7Y74Pv8mUXL5Gkgu/Gr7qPZBmXT5mlIC+/BnSvk/D7b2VPNg+TrVXPcl98D2Q7Zu8mrrIPLRgAL43SeQ9q7Ilvvz0iz3UKXM+FhaTvonwDD9q8nO9s+OSvkaV7ryr/qc9Qb4rPm2sWr46ZIk9QKOYPqoiQr7U/Ec/0pciPS9FgT4NGi29oKqTPU+60j6ktq+9EXE7vylPV703626+a/jpvfK9VD7N47A+ny2IPbK5Gb5Qp3y+GmEIP5t6kb7emhm+","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","+A2eOndiZT1/60c+hnucvflaSb4DQZM+rBv0vPEAmj7HkdW9yyVwPhDstD6d+ny9L6o7vV0pNz7Rv4o9ChaUPQxRSL4eIbi97hhVvhC87bxgKh2+APmRvhEsYr5GqiQ9EulUvJwwbj4P96O9kEbavbEDnT1KTSy+DN6gvDhc2L15m4U+x1ZRvsgKub0dfM69ns+TPBBefj6U9k2+ZswEPE9KCr4VRmc9O9FqPq91jb1pbFA8I6jjvTFmST2HA6q+7E3VvVZZYT3TpI69knhGvna7cT5+Kmm+BMZdPuJQW77bKRS9M/dwvp+r8T07IE4+AObdu5Ii7r35BR4/17CNvjrlkT1nF/y9MA09PmePhDzO4Pq8RQURPi5X6j0QPDE9rydSvV6LXr4puYA9igb5vBPupb1k6dy9uwfLPR5U2byo2xu/MlnNvaUSfj5u4Ci9co+iPoIwb71/jLE9J/HEvYCrcr6D35M8J4pZPfu0yTsA4zO+3DZIPiyRtLvBMek9wYhaPa0tub64NoA+fMhsPi5tnz3k8dy8yDZZPNODTL5uQ1G+uaiiPdsOgz2g1rU9VIWZvQ+GIb1WS9k9jM4xvk6vvz1FBK69OlQYuWw7gD3FfqA9Jc/0vZY3E73EUCG9hl0mPKr/LT6yoTC+0xniPrKHh71J6Bo+0csrvdxWhj2IVnI9R+gDv2qjQz2Cpiw+hpNLvh/Acr2UYwy+mKt+uzvZBD+VVwq+X00jPWnDjrwpAjq+OQFEP2DeTz7HtOo+FluWPdZy4z1MMzC+gztxvhotJT0rKB0+0Y4dvkzTGz4fQcQ+rrsKvg3SkL4vUoI9vpB/PyVZVT6DScM91xS9PdwJCb+ozVs+8quvvXkfbz7yyy2+6TYgvlVdiL6V+T6+krgWuyo3Cj+EE/I+AeERvcuNhz7L4ha+QgS0Pg5Kk75WCJ89Md9CPu6FL75msS0//QiMPQFQET4y+hy9pPmIPnl8oD9RpU09vxgHvpflJb6JKpM+YI4Fvd4Mw7zbMnY/","f/prPuOvgz2w+xE+q+iZvSoJx70ItNu9TlWDPSjOOD6ayMs9VCsXPdIhgD5mLcA+vtu4vYojPj6MjRc/rVTbPTOthD4VGY+9/NQAPifMBzyJUFc+zke4PiRjoz0F3pk9eznFPQSv3zwaew2/wI9gP40EIz69i2w+qV7BPb5GKD3x+QQ9sJevPcjyxT0819c9KPvpvRhHpT4hSty9J0aYvLvTHL6JDVA+EYDZOypzJD5O94Y9IB0NPrPrAD6k82q9koY3PpK8/T1I4/09xBJ8vWBF0j36GkM+UpXvPmVEqz4buo88dNefPd5V5T3Hg8E+ja0WPoGrAT7zuk49v4hOPj5+nL6mInC8NdopPlzDcr3DqCu+rVEKPx4Ow70hXia7MApmPFrnLD5sOvU+lY0ivhdn8D3Yu5W9vxSWvCyaS7yskxK+59EnPxjLcr45pwO9+0uavMn1Zr16QoO99URNvPu10D0actO8caU0vr3WLb4sl/c9EtV+vguYmb2Hz3a9t+XUPWYh9T23w5u93Dtqvtjvgz3cgy0+YQYRveOJDz4GPco92sOQOrHdNL4PzBQ+/AeNPiQ6mbzer5A+iJlMvtzIID4KE169Mr6NPqJ2Qr7xtgy+O4GsvZk/2D4Jqss9HsfKvdSfsLwPMAI+53QTvgCTvTuh+JA+3htrP/0bEz5CwPa6zQhYPmhHgLwT63i+HHbSPQPzIL5/RkK99JhhPgA6Hr24x7C+fUAMPtpvAr1BZxc+uxaSvq/LBL/gnTM99/K3vsUBnj0+ETi96XpJvXXGf7sA0Z09m301O1PmG75DnN89biGyPKE0/T3+R0A+tPlMPealbj6eiyy8AzZoPT/BpD7LZ4C+Lq/2veHSbj4/4QW+kTZQvQC14DzwQMG9adkpPIdJkD7dbhU+mdwUvjcALD5mWc69AeUQPv5eBz5M3Ug+lzwNPXbkoDxx6Vg+xUP4PdzayzyeiqU9edRBPuR8cD3SrSE9vxSIvVSHzD3zkhe+ZTtKPswaXDw6vQs+","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","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","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","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","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","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","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","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","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","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","6cyOvpnDIT6Adfe9cDKuPU5oHb+wVBM+8/YNvFy1rbzWnhA+Wt+fPRPrPr7aVa07ec/avQn0Qj6iEDa+U5tFPaFPDj+e3De/K4JxPfyXOz7kI648dMNBPcihNz4SoxS+wpSoPKMa771eMb663z0Dvs72uz10D9O9Z706PXYKSb5rC3S9brWEPGXfWr2dVl09n15LPp66lr63TUw+UjoVu8SATT175bG96EUEvTcVCDzQKF2+YtsqvjjLkr4+Fc6608b+PGxRHr6RZZe7cpPTvZ7DBL7gJp08cbSHvT7Roj5yrRq+EtsIO0VQFb37J+w9B/SRvdpXBLyaWHK+CHgDv5j7Qr6a1EO+dYKMvTTNHj6VQdq9T9YZvWjpAj3psPa9UEfPvUaF5b5GKx6+PqzwPXQLy7w0+ja9Zv68vQdJmzvYy8A+7Hl9Pmy+iD7Z5aE81fmJvARcKz46SPe95il/vVOd0b0gdhk+aUrOvfH2Bj58lu+80nl4vIUghL53ngK+MXJnvRKyQL4jSiK96z8jvuPMBj6J0lO9zk3Gu0Xhwr2MrCK9m3kLPCIFXL52Bz0+HTRhvuxhHz5WhSG+NVlwvnQJ07x8wlq+kDskvk4SGL7Qp6o9XRQqPvnkorxMFwq+xiiZvTEBDb6vxRk+hjPfvKCmtz1OU4G+QCEbvmceYD6v3Zo967nIvhZx6j1g4Fg+ah0SvwmuRz6mq4k9jZ6jvqCQd75MvEG+4+9kvj7cbL3Qxx8+jxGkvvNurb6KPpS8G2IWvomKWL7GR8w9+VWDPYa+qT3FMiO+QtaTvvm38r3W55m+aEeAPj1uFL/vsg2/aKHwvjiQlr6mHR2+5hQFPglI0r60lJS+ryIKupxhCL+Vhy6+SFBCvk6orj7/eJi+9/q0OlT5Rj5P2tq+S1GOvoS/Mb4bA6S+sY7ZvUXR9T5DFvq+kx7hvn2gEj7HWIy+syxYvdeVvz3hf7Y+sk4Bvu+KIb8nj34+0roxvpeX3T1Oc4++fbA5vsQIM75iwDu+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","tAJ1vsnzR77HXEA/c8dyPRxzLj8FABq+yXKSPmPE5D7f+pi+3V14PhQknD7Mj4A+59fnPmfgHL5mUfa8NS5/vuVG+T4pxRo/VDgqvAoj/z6on9y8BMAgPUqrEz/ztvQ+mGAYvZEZHb5Kx7e99CujPqHNGD0usha9KMAJv4rnmL63t4Y9mbAIvs7Joz6L/Y4+q2dQPje8nD1T/4m+DtfNvu5j4D0GqZK9vWE+vA8jJb9mT6s8jo+IvpCv971z2ZQ+XiPQPmDSE79iMZa9b05evUMVsj3dqhE+oJILv3pKbj0KAg09iokpv6623T2IbIA/eeZYvbPhCT/8Sza/AMZdu1muorxO9RA+DxyJvcSIq719jQU/521DPguVWT6OsSY9Tf2lPkN9vT4RLMA7J+QhPlEdjz5zJNg9TkNMvTlPsT1+WG8+ec+PPnXaEz4bU6o+R6SiPrUq1T3N5VI+3/+dPihcED20/yg9j6OUvRizhj3XGKe8WkYAP73xlj6DZwe8bItkvWUWuz0a8LY+SuHWvQZwQj9l9xg+/L9iPjlt3bwBpti9EXutPtBjqT7dU6a9KzbWvdCcmj10eUI+xRqsPkVBQT4obp4+Aln1PsnavT2dL04+MJ0jPn+v7z4MTxQ9HemRPjWK3z5WDp09t/A/Ps3GJT4m2bY6vdbPvXSepD6pajy9tpVgPZT5E7vHWO49NlC1PgDpMz5dAQQ9WiWFueV4/7ojE5C9kDv+vaS/474ADmM8N3h0PeHO4T297GU80BAEPit6Or6mFNU9OZKrvH6dtD7IY1m+PQGKvqJNhL4JLS0+61xivX2OKT0P/ci9BZHCvbzqPT0k68g+4giyvLZeET7OeOo9UVwQvplZuj0uVZ0+YdmHPSNpqTwTLu+9KS/YvV2OMbx9x1E+jePUvZfsND4leoa9Z4ccO0A1KD6vGVg+M85bPiWRDL6TKX49101tPnJ7Y75lFva8jDbBvfoxAr6xHp2+fBdGPn7jCj8ZpFC+lvdqPaHlsr4+JGI+","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","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","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","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","PXg7vVC6oT14D+W8l/TovWaL0zzp16e9py7Ivb2OFT70/Vq+hqBdPaBCRT2GALk8ql6ZvV+NIruDlx289kx5vOeXkT3BYx88nwx0PspeNT6ipx07JMfJvXDvrT028Wu9aPlGPYC3gj1GfSE+VNH0PFyOqL1F+xM+rw3uPLGFIz1nMpw9yAYHvp8xcT2Fm/+9aQrWvX7Tf7yaM2W+F7HDuQj8Zj4d7a28OTAxPKiU1723Z9M9NjJUPkMkRr5dZd67H8CPPS5FGT0XvM48EdXJvQ5Kij0YtNm8pdEdPYr8Br4ATlk+R+iePQt1S746Ibi9dacGPYvqD73T+a+8CZWePAt6a70aAZY+W0XhvYn2Pj7EpwC+ZK9dPkMQBD5sigs+iNMYPW8O0D3osTS93/UxPjx4Tr5w54+8+7AlvvWzm71aGSQ+dSGZPsNOfD6oQAY+SyI1Pj82YT6GCbM9nIK3Oxhf0D5DP5M+xoWnPl9qCr4oNR0+MZ1RPu8qsz0T8Lk8g4fePsDrNT5OY8K9EGAKvNAlqj6vRK49NZWRPgdwGD7yIII+Dx02PlU3sr15KfA8UHenvcZiB70QF44+bUAHvi4Nnz2M8I89nQybPq0qWT6275Y93g+xPdtNCD7N5rI+cpeLvSux1D2tFbi8iydnPe8SsD289Gi7Ii3iPAm7K75enY6+//2GvjwI3L4kCry+BD1+PiCNFb7oeCE9WOmhvrpY4TtnFR++y17Uvg/d6b3Q+Vg9/GEvvh8GWbwrbMo88VMzvYwB+L24ugC9qu3WvEKQmr6E0We8eC1svifGkz1pFge/ausmvpQvGL/A2zI9mMpUPTSlTr0n8sS+LRHDvsNgjz1xg349ps9Hvu0xG77W0GQ8nHDIPF41iL52vea9cFEwvhUADT6eJbI+3nN7vva4O75AKnC94naOvtmyz77XklK9h6iAvil3yb1oNLU95wr3PoYsnDxPdDU9YsCIPWjbszxeLoG+fbO+vcUFp7z52aC+3IoWP1Xrjb5gti69","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","QmXOvjqjbD7pghm++VQoPYGh9b0CFYA+eTRivNmPez7wxU49JUYeP5ONDz6iq449IztjvnBjtD2gOqm+TyfEvfO2tzzyc28+WAbcPM3zFT1CkwO9Npc6v9+2w77WSlS/Rk4IvsR2/L1qOR4+4gt3vdAhnL53FIS+YDnivRH18D3/g+69Y/ssvcxWDj5fimi+wLWDvJ5ptz2iKX6+8+ZbPu60iz2N0iS+O862Pq/eiL2nCwo+pW8Svsinr70aNxQ+yjQYvnTfPr0hdQO+2zwcPVKKvD4A02I+MspiPdNt/74wdsm8cuODvirb+706JEo9hNmOvh0Zn76coWG9YLNYuypWSb0yQSS+QWamPVjz9LvP4Pm9GcJJvhMQ4bxXK0G+zglHvSjwz74aBJk9JLh0vsJitL1KQd099WIwvezZkb7Huv29CX0ovu0iI70iBjo9E5qjPEw+Bb6S8Cc9LEbAvpBLHb3GLEK95wcevohjgT1G7ig9v5+KPRGXFryPn5M8WS2OPQ+uWb014DA+b/PcPYbNFT7nX1874HE4vVnuEj6b840+JWlXPh0ohr0Ebom9zheIvG9W/7uqBie+YTGlPTj6q7pUHf4928qxvQEokT1ZBsQ+m4XdvbWnFj3COa2+/3XFvupRhL68pms+SBqvPvTFzrqlJJM9h6xXPLuO3ryfUTK/EL4mP/WH/T7XZ5y+RLoOvxgCID1oM+Q+/LIvv9VxPj6V5De/Wc0hv2TBFb5yRQq8UM6+PjA3mb5V7iK/62isvqezRb6QkRu+IatAv01D1D6nQZs9zx5Nv1XEO763Hvm9qnRuvQSF2L3fWds9igpVvqbgGL4JZy0/v3/yvXLqBj8cxiW+HLAwv5wmtL0Sf+y9Dmcxvrg3jr6ITAc+aLIzPRvq3T0WM7s9SRfgvsXs6L4rQ1A/2AIcP3hS0b6MwgC+RHgAP+lgrb7taiC9wJ8WPjD6Hz8ymKE+CBS+POrYxz1mGgu9OIB6virhI7+pPQ2+Lrk6PT7EM79iz36+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","bUIMvRRcRr4WLPY9g+fJPtwzib4Ad4u+W8fMvqNYg76PeQ2+/t0PvyJJhb0CTbm+tGqiPiQ3q77i3nI9Ymu1vRZ2/r1zaag8kWS+voQlQ7tqYZM+ipXyPYmtDD47Qhm+vra7veOHML/O1+A9iMsFPDd9ojtYXOW+gmDiPtVvwLzwF2a+HkqVPmLgvz4DkJw+Zm2CvhvLyr44tvi+zqiFvBE7Kr8ZZeC+D6XEvrtDsz4EMnI+8Mumvsnojr1NBRe/8r6DPqdZHb77jaG9+OGPvdu7mL7UHdG8ecnevlntfL2qI4O+yxcHvsgSuL4gOM6+jmVnvsqUNz5bCmY8RWo7Psl9Ab7ANNa9iyrdvdpFmz2vTek8sYtIPoK5Nz4ycLw9Nj2JPcSVE74FigC9REmqPN568T1Kc6E9lMd6PRXJ3z2qA9o+TErWPc7Eg76l/Gs+SiZXPixMlD2xspY+qADlPRZP8LvhCpo+yzdgPj3K+DwS1ys+scGKPuQC5T1mIOS9W88DPS2wEz6xVIk+h+3UvLW3Rj6dFQ0+CQqnvd+G9T3thbs9AKn+PQqsRz5G9pW9SRTCPT3qOj0D8Jy93lbIvSWX/z2m49M97G2pPXSysz6EITg+7kOJPro/Bj5FwgG+9JGYvoMnUT7GYPw9Go91PoRCqTzjVwA+AoITPnOPcz5Z1yA+Rj6TPoyvk7xzaso9r76dPnIQAz4UNoK9mNntPcPuID4Uhzu8PO+QPmbM6D2mtSI+jwvUPSkEcT6ZLPU9ZmLDvlZ/wT5KwtQ9wKLkPi1sXr2bOlk+M96VvlrJnb3QYUs+ayyqu0rrJL1hgta9GCmOPN+VRT7Qk8a8PEubPup5uD1Iku498azcvFIrmTz6l7g9IpVmvveRjz550/M91h6rPXVzEL2SKOe8aPACPdoZwDoEPTs/QL4QP9w9pT5rwY68KK/HPpmnCj5yJRM+r7WPvum72j0b2SU+3LFVPVFsyr27+p88cCAavoFAI70WCgE+dRIvvLLtQ7vNc6E8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","4vATPjXqVTxmavO9akyNPb27GD1mike9M3YJPgRG+7wY+Gy9POdCPmV2/j32vkQ+gD9mPbTPJb7JKjc+TJLbu6+nJj7K1cI9+eCKPprKtT2QP4u7XpUdPOopRjuKHbY99b2HvcyOoj3rl9k+ccXfPXai+z6/Og49HeoTPtwaMr1+FZQ8e+8ePpvTFL1tJzQ9RM0QvrUpXT7feIw9Yt4hPrFuNT4KmhS+zlCAvM15Wrvls/09ESyRvR4bk7w3ttu7MSINPvEy1z2WpK89MwPVPjlWIr2D0Ai+MpzUPX2JYjxfWn0+gbL3PRP55j3ObyE8x64ZveILH71JwYY9v+uKPEyoqz5mLLQ+enicPftIqD3A6Mu9+T6FvZcfiD1qx4u94STTvO1XFj5ip4E++WWmPYeG4z1XIao9CruZPITHxr3gGRO9XK6UPgS2NT5N500+KM+tPT5TDD8y+qu8De2ZvBZuUj6YcVs+YcSIvZjSCL2eIBs+mViFvn1aPT2CyNo+BAYUPQUfVb64HZs97ejXPfL/oLw9Ut89XcpRPqPduz1GgcU9ythFOjEU4jyUD2+8k6aMvLXESj4YIqY+MtngPicrqT29sUg+Q/sPvvSbNj7nd8+9qfH/PSIvFT7+PJQ+gq8PPgOVCj5UNWe9NttsvYtcQr3BWIq8qTWQPrrfbjtC7Mk9mTwlvmSLJDzObCC9ri+Mu12RqLuTBla9KmMnvrJGsD2roSQ8c43KvFUfMz4QrmM735qhvXU0xL0Quze+2CazPXHnur58DqW8z0VmPfp0uDv987S+vxMovbNY5z1NA6i9Ty5cvRGo7Dw/XI8+CYJHuZ39frzkJqk9h8YHvtLuwr0Op5Q9urCfPQnVlL4HPKm8LbykPayRsb3aD6I95kybPWmBxjspUIg+ljUsvt6iRr6q7FG+F7kmPmzG6zwySaU9OCEnPlPaAjsCP5W9n2EbPLIhKj068f486qWWPpcGib7/hsE9xcI8PZjQRD7v0qi+hQiVPYte9z3olhe+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","ZmvpPJ62RD6z59A9rX9NPYfNFL2uWxi92hwcvrF62jqlnCq+YLq2vYFApbxqWF0+x7DBPLj3372MS6M8MsSDPWCgiL1I0uy9ZZNHvjMEC76F59q9JH8QvfOjsb3Dc7k9I+0EvlzWsr2SjA6+1zoBPBIbCj0cK9y9GPhwvW6KhLyWJx++B+euPXXFgbwoy529JkuFvPcpCb4DjiY+HsQ8PI/+2rxnysw8wl2dO+l3Ar5XngY+pOddPeYAmb44UkG+AC+3vUUBsj1avJI9XwyFvodpOz1NIPm91zQhPQvN2D4ZZIO+YQZEvmLDjb73FRu+Sm7NvUB5hb0cqRO8IDmFPbjUQz63LUK+i4u9vd81t713Khk/EccTvRVNhL4pzSK+nHmlvZNP6TtZWEQ+PfE7PYwiQb0NrZe8l0fEPhp9JL/Um5+96NFrPuX4CD49WZU+1KxpPLaXZL3xj3C+GQSsPb+ibb4xBik7uHf4veUoGr690Y29Qn2HvnT1rr0yVSC+P93rvVH8OT2LWK8+Y9dNvjIcgD6BkMe+ez3Uve6P5TqniXy+tf+UvfXCqjz5TEo+Yu8IP199rr7BBOO+64/dPni5rL5QUB68aqWnPmHk/z2d1RU+fsmbPrQHe73hyhu9awGFPH+5f74xbX2+GieyPUO/xL6IYMK95cuQvsVx3b7Xtmk9FcgfPZXtqT7PDZ4+myJDvWyVzD7xQYS9uyyaPTmZMj6LcF8+8SsFvoi5mD7Sqn08AP2YvXCaa7zwzbE+YHsDP1Va/j5SRVU+xxTNPGcZIr4lcnY+leAkPWV6ZT5km6+8tekMPjTIyT4ntUa+rWo0Pj1CEz7yQM883lMhvSWu2jwwo8a99qAnPou/Tj7BXDO+tkLoPrDkWT4BDFM+opMcPppFTT5vxCs+YmOMvVqglzyMSBo+2um9PquzBz672G+8AwcDPigcozytCKg+7Fa4Pdnxr7ybL7I8SfJFvjj+tT2OQNA9ubK9PQApjT2wOpI+5uTLPr+UgT7r9C8+","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","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","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","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","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","iL+DPVRaiz4XX4w+fqcbPvA2jr31pBo+RYbCvMWDzzyyKRG9NRigPSQkhDxrah8+5dJ7PVG6Tb643fW+NjjSPR8/fT3HcOY9QHzO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","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","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","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","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","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","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","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","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","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","D1MPv8fSnL5DpsK930VbvTQVuz7kKaG+8jFEvtoonz2aHTW8HStTvkxRrL3h5Ve+Ww6DPwHN7r67f6y+5z6nvs/6R758zy2/akAAPxw6LD8IQwi+674tP2L9GD7ID3w+4ZuVPmPZRT5XI6s/fVFmP0deYT+TNNo98+Jwv1c7LD+NqFe/S5ylv+r8/L4hLNe9HSAEv35Y4r97OhnASvbJPqqz3D5xgpY+tdn1Pus3Rj6o4qO7iBvxvPO5r78OMeY+K4EMP+4BmT7kr4C/vKoBwG2ocsAGxEnAXwgmPzxVPT6nxXfAWPdYwJMGZz+azgu+o5lNvluXej/hBrQ/oNGmuyhV8L9s7RvAwiuiv4rJSMBNsRA+MbiZv4me/7/wXqs/aQZZv9LQfL4BBXy/0yfYvzXwPcASYZI/r94Zv6fuVb9ckivAw/k0wE7pP8BhAkPAEt2gPXvIkz4KPho/lLqGPkTz/T6gI0c+9xXqPaWcpT4c0GM/306tvpP3pb8HOp+//X2HvorFZL+rC5A+amLfvXSQJL/hsBfADVvGvY5a5L0JpYq8DQwAPimCKj7AVB+9/HKmPafIhL5kfwo/Ryy8Pvm4Yb/e3RE/9SQsP2V03D+nqPm/jvJTP9e0GL8fSSw/nL02v2wn2D8+Dua93cp3v/hdxb5w6B4+pQ9kP234AMBJ0Ao/T+KqP2pZnj6FuydAymQjwIDDfr8ay2C/r97Mvh5XUr/u6J6/41Ekv7owCEAp3RM993C9PwUwCEDKkpk/gZiOP5yg2j/GYP6+DXDHvtliqD4Q8Lc+/pp6PjyO6j8j5Ke/AruVPxxd6T90Xl1AdXtxQLQkBEA1iKW/zwPnvnyW8r40Lvi+hK0fPp4Jbb5MJDO+JyGNvqVIur7DNY++ZNsUP4DlFb+/fTW/PtzJvpJ2Yz5DvNA/D3YHQIRIfj80WrW/FZ33Pnj4rz47bOc+wd7HPo3SKz7WN6I+YUkbPj5grj4lJWU/2STHvosAnT+/cka/wBVXvxxLF79VxUS/","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","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","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","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\ No newline at end of file diff --git a/src/kernels/gfx942_ConvHipIgemmGroupWrwXdlops_encoder.ktn.model b/src/kernels/gfx942_ConvHipIgemmGroupWrwXdlops_encoder.ktn.model new file mode 100644 index 0000000000..a2fcc35165 --- /dev/null +++ b/src/kernels/gfx942_ConvHipIgemmGroupWrwXdlops_encoder.ktn.model @@ -0,0 +1 @@ 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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","QAUVvAeU8rx2XHW+BSuevHALJr450QS+8OiTvQbwKj6JiCi9MpsGPioRgz2RqFa+0YzvPYjGDj6M4E27I6ohviAvKb3Udki953VAPghTFD5L/5M9vviTPV5yMr1+DsG7Dbf4PpcBbj4aBum9MGvTvOiOgj4uEJu9CxcCvptGvz0QKIA8mZ6ouzPBHT7hDNa8lMewvXyQPD6cJgQ+oPaqvEaccj0dn808KEDGPe6VFb4LAti95JPSvfXyKL0YY6i6hFT8PZx+/T0lgIY9PdkhPZKafzzIFQs9yKqTvYrCyT02TxW+c7NCvcModr3EBTS+4l3xvFamJT2dvSw+7cFQPgllIb6lhOe8RL2XPSBpqz24hSa8IbUCPi+rJr59Kqa9lkNIPWFxB75xHJc8kGdlvciR3b0KD5q9JcO3vcWPlr4lsue9HGkfPjE7hz3X6Jg9Um0PvqhK470XvuE8wjwDPt0Xa72FN7s8X4QmPiBaJj7bFjw+5ldZO6v9vz0z/pE9GzSUPQ6knj0OeKe9rUnnvIyB7D2EP+09yRfLvAHZBT6mlQ+8WB+/vde8jLs7f5w9QwQnvvy3Zj2/hya8myzPvUsK1T0oW8y9kl6rPbeJCb4HB8c8BzOHPW7yL71ovIw9wBkGPmg4Br5SZJs9wzMFvRrTaj6VunK9SgOtvUl1iD0+bcS8eAL0vJKACz5VRWC9zSzsPRTWLD4A1dy9GFWjPnefcT2HLOS9Ns7GOyn+Orw29fm8iycTPV5O0L3/uru9Q2CZPqfIK7w4ZCI+UlcLPiOrJ7z8X4k9PxmCPb1DvT0bpmI9zmHZvUYmFbxf8l2+uF7hvV0qpLu6BCu9d4ENPRqOxrwQiNu95hxSu/GrB75oqDA96EqLPnIwHD2pIRy9h/rOvSyXxjwCQY69+F3UvVL5Fz3m23w9dukFPWTXNz5O8IE9qbaEPa945btCN5a96bWgvfqdkT7fgwK9/mkyvn+fED7O7dq7G6G1PTTtajwo5d68D5hVPBqwGbx+nsw9","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","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","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","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","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","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","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","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","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","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","+MlbPECExD0cnPo7kamqPRc8ur1IQxG+GaltvtvR1r7tvjQ+axV1vnTiXj3S7KG9hZH/vaQZg73QMgG+2+HMvdwLd71gWgI+GhYePiaEFT6CSUa9jt6oveDnGr33VBu9WWDVPeWrXj1hXz69IzaxvY3pbb3r/Dm8u2Xcvc17jT1mzNq9kH0pPpWq6r2niY+81JnNvRIxvbz1FLm9fAlVvYjtKb5rYva8HjEUPjn8Aj4RICQ8tTsEvJhpwD11ovI94RuSPU/kAj65XKo9m1UcPGSdWr2MqxI+CSnVvfPsRT5DELE9W3yYvTfDJb4mMd68y9O/vYH83T2AvlM9DuyUvdBz3r1MhVq9HOjpvIzo+bziC7w9YXn5vY3BiLzzmFe7QCjLvfvmTz0i+7q7tZSePauPx71fy9K8gQ/5PaUGM75Ov3Y+inTZvXPh0z0pDK29zGbrPNwpZr1nQf28c+g+PWLn/bwH7tq94ns/vVnyID4sKRG+pvz2PULHML53qTI++kKivfOCvb0L0729sMMZO2E1pT6ZJRU9c2ytvNBiiT3KAiG+m6UHOwJcFL0mAxK+VT3BvT/FlzzDIfW+hIajPAxK97xJSQO+5p0kPiW+ZzzWllo9kL61PU9pA75IzZs9GeAPvqKSr7wSuJM9ULBDvoLXeL3bByW96FKzPWYueT58/Ji9GMAfvpxBBD6DLpa9d1aDvo9b1b1/v7Y9U2Sqvb6hUb7sWmi9uOidvaYhMb1pZyo9XYlevnq4073xFrg91+0pvo+BwT1a4BA+YBIpvoJU373Hq248CdKAvccHdL0SbFw+S3TiPXrXYboKYWy+4FsVvvHWiT0iFau9KOSIvYj3CL56awC+Xx4fPuDvxb0tjzi9EvBLO7+KFj1TqO+7kdDTPW8wwbxSWYK97RQcPrXMlz7kdTY9RgNruxtrHD5euBI+g6FVvDe5BT5hK1Q9VjlnPgBjBL15uhQ+WeyfPNTlaL576xI+g49Fvh/ao73PokA9trkNvhOs/jx2OrK9","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","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","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","/P74vLCxSDwEBOo9ic6gPXUTTb2+2mw9NKcgPpOjlb1BeB89IVnFvUzm+z0duU29zYorPgjID77Eb4E7zUujvq7wlLxYKNc9mXbvvceKjD0KXtY8biMAvl12wbz03Uc+F5odPt24ML5QfI29o2HdOvprAb35dKu9UhvKPX2N5b0+lwI+B9QuPrPXgT0fwES+lq4CvnUmYj1obl499jLTPdKBlz3UjYG9oLM9vf7haT1zt1e9G9eevXiK27x+iho9LeESPl70nD0y3588xcLxPYVXrL1XUaY9A7D6Pai+vr0a9p09+aijvSALjT3H4oY8d28XPfkv8r1ARs67jobovMrFvL2wO1a98VELPsyxFL60S2G9wBxNPe8I7L1wMYg9pAMOPqvFW7mjgew9D0/XPf1sC71zocq7u83dvTG1mTz4lEY9WySsPUmq5j3kkis+cNE0vfAWaLz59oO9aNXiPDqMe70Ma0w8SGFBPQ+ubT1Z16C9Yp/MvQNI7r1GROC9Y3DsPe386T2DxWy+kZ6kPU2xx70vn5U+9PX6vchE+70dXpO9oa62vaz/5T0knkC86QOCvej2az693+48Hlg8vqq64j3gVuU7HElMvcvN0z1raZA9vGUrPmjnK75m0S49L1R5PUVsFj5+2jO+NCItPR7qkL2H1a29Oae0PXHJvj3Mds67yx21vl/YoDz4vKY9rnflPd/cB7752oW985vJvbFUAD0U+Ka8eVeZPfyLm71m7sO9UUc4vTRfWz6roAI8kmU3PpZFjz5hQBy+b1qJveYKCT6l8jo+twmMPRC9Tr2dA1+9XVcRvjvytL1lB429ADtlPaSqMD1p7Wg9KBTVPDqKzL3q0O+9rq4LPqC6qL0SoIs+ZZvSvWLmIL6SwYC98qkPPq62Mb2Ytku9B8TqPbyWYD4a9yu9JWCZOp8/1jwxzTC+uBZBPbcqTj1VQni84KTnvnqBRT4QTWO9imkevtDFyT5KJiI9+cOhPc7xzL1vBBG+sfCfvXEjbj6mSOM+","3csWPZeZgz2FqYI8uaOmvYEqpb1uKuI9nk//PaaZrb5QRNc9Ro+OvvF0NrzeGqm7H+jivfh8Lb5sYkC+5adXvSVupr0xhLq9O88MvjTeT777gPu8/O7yvAIbWT5lBDq83SsMvvtavL3NtCe9FqzLPQO6PL3tB+C7N5ILvsgu2T1W2Me9TVHsO6T2C72w6JW9v6yQPc9MX72JjyA76bRIPfI0GT6pH5C91eLTPVC2dr2opJG+DczfvQj2Vbwg0KC8Y9M3vg2mGDyRMmG8/cd8vQo/Xz3v98o8nvsBPbd/l73vvaa91LPDvelWJz4b7oA+GrSbPSL7Cb0Reiq92xHjvcKqljx2k0g9QJYdPqJB8zzihNk9ZwhCvXK7Wrz9EBk+DL61vacr5b2Oude9oozSvOpXTT04OII9GHP0vbLREL5E6my+7D18vVNPFj5MwiQ+qYimPFKCWj3f0fS9BmlOPofNa71uaAC+tanAPAA6bT48g5A9L60+PhQtUz1S9gE+PnlZPhBAfL1O+ng+X7f6vdb6Brz5Ei49PefWO9IvBz7YmEG+j9EgPiUY6D3PBP29ZP0zPTceb74XwhK+sahAPj9PqL1iXna8e50NPu6dwDzCb3y9YnRAPh8rMbyz6gM8IjWZvSL6Gz3CERs+RUvLPa3tFT7OnBG9N++GPl/D9j1m9xu+Peppu7fKhjxKQgS9urMOvXNeB708BpG9V8QVPu0hN72TaQI8QNPhPaophb2gFSK+iCCTvK37gLy1jck9hL2aPgA1CDwVmva9lNLJPERozb0Qn6y9sFU0vY2jKj6j/PE8i1TDPXaROLzjxpu9ry7mvct7Jb4/OEE9wLdBPXuLyj1ySVy8XPEFvlUW9r1BJyO+PLsrPtGv7Lugt1A91xdSvRKPxbuSHDu959/mvOXTYL3PNRO7WZg0vdyBAb7Yvyw9VbQKPk1pF77PHr89cMYWPm6HyTyMWYe8+cTkvJTLHbx7xsC9ZGIRPgCg2b7i1Um9Pgq6vXLo2L2eHYw9","LsitPWpCTb76Y948wkjGvWm8sz3gz088td7TPQBRTj3Nhgy9g/HkPPaYcT3B/Ca+XIJTPoOyBb8/3nM9oCz0vB92iT1KwNq9ATB0vVMxHb5Vd6+87raVPIAb5z3RGES+tJyjvpJ5Xj0NcxC8pxnWPWHmzjzYSkk9D7lSPotNOj6/mjW9RZ2ZPZFMcz78Vg090eG7PAcqwT039ty9Ek4jvQNOaT7RDWs+JEBmPRXcpb3+/Yi99STgvQ/uKT7NK1Q9Nd78vIG69D1eAFe+ol/ePZBonj1fW0E+6ddPvSYcND4xzho+8KaLPQHvPL3O2xE9apQ7PSNcPj0UYhg+LLXUPjnCYr0Zhjw+em8cPjMJKD7ND08+M7EjPrGFYj7ct3e+4SvmPanUlL6Es6U9OZkqPg3arL1Hh7Q8EHwwPV5pnT4/k5Q9wFnrO+/fRT4Nr0W+R2UUPolILD445fQ8zR8CvgkAkb4WpTC+5JcrPqcmrT4jAPG9O/6gPRLblj0wF289/MdjPfMp6T0GRwS+FpLbvQHeOb2IojM+q3MSPgfmmj4x5CC7a73hPDqul70ubOq947WAPlRr67xskRc+ZdaAvZFwMb48sjQ+MDDNPZpEEj5KFaE+GoxHPuTUdT4WH526Jtw4Phesnj67glc8y1EVvTi9Nj3YgcI+NIMrvipLGr5jQ5k8kxMlPaOxfT1xGys9ZKkoPaeKjT2rqNE+eE3NPWGXEj7v6Ss9eUSsPdxW4zxHZu49903rPVD4mz2+5gq+SrtOvW4jhz6AJMg9kzHMuqm4773T630947SmPssBQD7bgN29wAySPaU1o7zmDzI+Kh1pPrOPab2ileE9u5QCPG/0ojweeGe9t6QnPta/D77xyIQ9Yd9BPiw4Rr2gR/69QkobPlVdZL2xuwo+61JlvAom972Lhw++p9QWPvphsT1F4vQ8c1v6PCzW5T2phgy8lOhVPkMWaT4LlBC+tKlQvGZ3Ub1AmuI8ceUPvJLCFD7V6PM8YShEPPjFB70dO049","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4TM+2+w5PqoHRT0I1W87v8UUvYw4Pb1zCje+8wjyvC5CSj5/W4U9LxW/vfa4HD5+F5w9ik1hvpRpMD2e8P+7tqSNPe6xVz0N7lg8UWPevPjuSD22M+Y9aMAjPlonTj40Mcc+cjsUPtO0tj23CBo9Z8EdPSN5Pz7KIIu+ksDgvNOINj58DeA83SwZPsHkHr51M4a8GKhgPcPQ5T3J0eg9fe06PvfjbLwMh2i+PelRPBebKj7sZDW9O7wmPdJT0byHxaA8N7kfvVPlCT5dQdI8KoRiPut8JD7m+6S6aP9VvdGG2bvvooU8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","2AeWvcc7KL6owzA90GksPSQ4WT1YBzy+vL8Jvym5AT01/Uu89WQHPgSHhr66O+47iSrQO4Vmb72/DgG83xcAv55l0j3ifK89eLYbOyhE076+IHY+QXqJPBRvoD5cV7a9Ke7rPEOETDrZUpI+rytWvOYrqb77j5m+2zWPveg5gb5+YqG9KLYXvp6ykz3Ni1M86iZjPd8dFz1yJNi9fqJSPDizVb2q3Lq9mcszPfQ3Oz37ogs+xbLPPHhlnL5QNYa9+O19PTfU0b2sLPu+5iQXvjOBir1xqHy9oTMBvrIYjT1X+/q9wG66PVemhb682YG+cUiMvcPhzT00cRq+oA8MPgOuQ76k3II+aYj3vc1vOr58uhM+PQmJvH6Phb7t1jE9GQocPhBHID0Rtx09+59DPWNEUDtrXDS+y3jAPZIb9T4M5cU9u/yKPttdjbtEKuS9/tWdPSojwr0NeDW+EUygPn9Sa7x0pw2+7B85vX6Apz3sqo89Uo54vj9QOD55OFg+REZ0PUJw8D0O5g09SHNXvfb6KT7zAnW8IgwLvpzJMD7QlPe9DGixvEnDL75OLAE+qzE8vo8ugj2czjQ+t4Ibvlp5LL119Nu9/c1DPuPIVL3htOu9WwqDvaPI3z3QUca9TumPvWhWKb1LPz8+jVWEvWSpWL3fHai+2OXMPYmEqb1TG/e8/niUPBQjBL6oy2E98MPWO2vxhL1OT74+Ay/UvpIbvD0M2AM/Y2kTu9n4qrwhHiW+QZstPoQXHb4o0we+q3imPBF2Lj6lRjM9Ew+4vteeHz7ST2M+YLWjOy9psTzwZZu9pBqBPhmiKL4LQIu+afKMvnmlFL7F1cU9YhmPPSkyXj398yM+rgchu/qgNT1Lv1m8E3GbPR9dLD4UkJA92v12PWW2uj0ECCY+HZBhvVKLj7qeRK69RwWLvcZqe73mmkm/rS8avgHxxj0Bgjy/TLeiuxcycb6JcAO+yPgkv/ryVD2Xm8G+LUSkvunLDL5Fe2C+V2WsPUrHsT5NMDu/","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","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","Vs2KPYeEB75APk89rp2WvWZcLD3Vqom8sCA9PlRj5bzLo7M9+l1Mvll3R73zqCU9SKsnvW8PSD05YKk8c4S6PcYHYj4fEZY9j47fvcqt1b0SWxy97J7PvfKv+L3fCg88O+OsvZxAJL2sGL49mC8kPQNA/7wEcoS9Es89vX1Vyb18W+U940NoPR2Cbr0sqD0+qhmLvdtNVL3gCJo9519QPSHlubxDiwm73P2DvY8jsr26ECq+VFS4Peyjuj22FXs94lKzPU7bqTxxOdQ9gbiMvYBBqbsY+rg8EkusvVKZUDsloT29M7ITvnsL+zxPLqQ96YQJvckTIT72Nwq+PbWVPfHE/73ezRu9EsTRvfrNozohERG+qwFWvnMOUj03sYW+bS8GvfYSs76QVwe+/qA2PXlew7w6BCW8Eu45PoQVob2mH+i9aDSivaPeOL5gXrg++Tt/u7Hb+rzaQpO92jc/vnyfyrw7hyg9/gKRPRDXYr72KyI9NV8JveCkG7tyqby+jVi5vHhkZ70zKpe+WRoNvnJVNb1n2OG9yn8YvcVOnz0dPSq8eWhevTK5/r0a8A++cNMKvr39zzw7Vji+cJziPV1GxD1Tare9e7cLvksuG77v6lo91GX8vSbXAr4MxhK+Vo4hvX93tL15apg9HPCaviIn7j3a/rg8/RuVvdFQu77ocWQ+HB8ovo4Ft71w3S09F9+mvYfnDz7AaKg9l9uXvr/glz4w/YK8hVPRPSYNpL479Sw9sh0avOy9BD01SkW+yLdwvoSBL745ZY48N5sNu3/hdz+21QU+aEmqvaq1fjwcP9m9/jMkuo0sQT2EDxs+kbjwvbLUYb6oGoS+ANFVPV2AOj5VyVy9OY4LvgW+wL3SEy69SwFJvuDTAz6bxi6+AIr7PUPvqL08fPs+grBivj8Zdj7gk++80EQhvo8fTT65jQY+BQ+nPJugob7teoy+e4ylviLFkjyEEoi+AhiCvoG+wL4uiiM+Q601PvoY176614+902O+PnjUfr1QZ2m+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","kId3vrSLLL0xldm9kBU4PtIOkD3mH6s9/LpzvqlxCL5K1fe90X5FPo76U72E9O69UIgyvvvpw7ywQCA8eYPYvkoTwr1Ixq49NPbcvZWViT3WGfe95kR2vZzcmj5gVwQ+VQ8gPS9yNb4kOIK+h/7JvH4QoD0BymW+013DPiTuCT1JLgq+vN+gPe3Mfz1Mgz097WkyvfGFCj7NW5Y+rWvpvRoiTD7jKdI9rT80vgDuLT4bicO9fTLEPbKR1D0GeBQ9cacePdSiP738fCa9FqtLPGAjbz1JBFK9le2wPPIPA76Cw5C9+tIpvetIyb0mYYw9YEInPn8gnr1nu6E+UeAVvZaHsDvdYwW+uDdAvH3gHb3Wgtw8E2uvvQBvET6kYQa/ehQLPvcUAr67Dka9NNzgPF3ce71iuVI9ENQQvugvi74FDxG+mujwvdPrND0WgOG+8DgfOv55bTug9429aGEsvPHJAL6HG9i9iI25vSAenb6nAEG+CBHivLoXIL6IPp2+79DDPVdRaT5RuFq8AWiTvleU2b4qcDG9vKMfPiUaIT2yMbi8POBkvajQzTy+h4O/wtZ8vgaU6r6gIoK9glvxvjHPrL6qP4e9yyyGOwYjFL+GQp+9pocqvhDB2L1nv66/RerZPWN6zrxcDFO+VIsWvawGDL2/op+9A9eoPsURIL+nZ+U91XUkvoQU2D1wPIS9hYqgvaj5u70dxIa9kSJ6PntljD0BRmW9EIY3PQykVL4hP+O9hGraPb46HT6oYAi+i+snvmoR772PrMy83I/RvQHgm71Sft68Nc//Pdlyjr3mgEM+8usuPS3gPT5HK+m9k14WPjKKyz2hiZ29ggl+vBtxLLxo12K9wMDhPsH1Xb27K9Y9/jajPI12mT5N7u490lHxvftDsDrBVKo+mPBOPvfNAz6BnJ69bdalvfx7yju/z4U9OX1wvl8amz1sWoU9AkJLPRc1Tj7TXpC+cBqIPPzr4j6ZeOg9xP7kvRRZL7719pe7IFFlvtWmmT7GnKo9","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","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","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","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","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","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","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","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","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","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","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","znu0PUGd+b0j62u9Jyc2PoLvmTxQU7Y8VpFBvom5Vzw5ELQ72RTmvXxkgr2VMkM8wVLBvQ0gs71ufCk+Y6RkvmfVjDrmdvg63RzuPY1jiTzM4xI8dSOoulWnQz4MNzI9GxsgvZ4XgL2Mw7A9xbeaPcs4Wr2v1ZK9gPmGvbrVfr3kBZc8Qe/NPL/fYT0xF409H7BhPWmXor2OZlM+AJCjvZKe7D34a6I9FCSUvQThDDy3r4u91cywPfyErz4CamQ9V9IUPPN4Kz33Jcu9Wckmvf4pNrze4eQ7njULPp9eqL1+p5W9O3alPeL/yDtNE129Cna1ur7sXj0j/CM+DcX5u5egQT0PtYS+1grevDXfnryXKSI8wrkwvip7Aj1vRK6+NLfpO/b2Hr4osGy9tkONPVNk8TybVgW9kRxkvvp2sLvk8MK9P3TLvV9ysb0HWYS+zzStPC2O5DzhvKi9l+RovWhHh74F87O9V48SPkgF3b7RFn285zS1vFu4Ar7nW/G9RUvlvf6d0D1aXKC+j3gcvghZib1Jpuc91jwcve/mmj1dd7y9WZIYvaYCCT68hCG+DtdWPVyxRryyCYe9JJR2vYpwW734pzW+h/drPY1F9714z489BYgaPv50AL7bHf6+kFaSvRmkTT0qqzq90bcevvxYTzzOI5A9mbGDvZPjSb65iFA+Va26vWPkvbz29lS+RNLZvBnTpD1yzym+qouyPYfA+z2YT6C85jWrPU1ZjT1b84A9uiQuvssK/z1pQL+9EuSHPtYPL7sHRDq9hCZGvoKkCT5Nzl68QilIPfMrk73mCTU+lXjePulahj4Ppbw8ja0CPrYLmj4hfmC+WqkdPdvAhb3BC1o9kiPwu9wDkzz1bMo++pulPQ1Xhz4HV+U9YyYGvsVW+jx3Qqu+d9+ZPvdpd76vFfc9j6luvrArjL13J289pciDvmCX2r0UILw9oa1GPlo2AD0RMFO+hFSgvIJz4D2VynS8/DWBvkqkjj77nZy9vcLpvQ5DHb4LCT0+","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","k7wrPcw0hT4Gh4w9RIDuPURbjT0Esy09PV5dPnDOvLzSMuY9wolOPofcpz3Gz749rBlyPR8JVj7C/D8+LZ3LPSJWID6MZd49y0knPlI3Hz/rMp+9HwETPm1pp73FuG099DFMPrxIbT6PxKI9Hz1gPqPKB76WKjq9UixXPiIqgDwNjBQ+id3sPVQnHr7UlZE97/7xPf2/FT7jasc+Bj4ePpdD0z2X+Ek+kLenPu8jWz6q1fQ9QJ/rPJE89T0IVeU9Pt5cPfxxr712Cf+8V2QcPoMWZT5U3Yg9pxcavPLpjj5mw+g+1VHRPTCxjj3ju5q8cZuzPdyoOj70EbA9L8zdPJJwT7zN/by9n9nfPGWK7z06hsU95olSvDg1LzvPZvs9RFy4PbMw0D1IryM+MaiRPfZ8tb2Vuq89kRB0PVynxbzlfyY9W1YFPNy5AD4d0uI+vAF7vIxfBz5+nio9ctysvH0MEr2qLB0+sugnPggfSz5PreS8nNKTPYWKMj5fjA8+QpUTPm7sej50NvS7dXfZvDXL873oMTc+QKPgPd/yxz07pYw9/jBNPlHQgj6z/o68zqEwPj4FKj1J5a+9RaKiPSzhD73yI5A9etHyPWAmLD67eto9aosLPmv4Yj6Wiss9x+ZuPmjh1z0kwzE+/8hHvY+ljjwLhYc901HqPQzMgryX87U9e4d9O8cmSDx+nVu9wBmRPZ/isb3I0FK9PsqAurhtPD3QUgy+OxKZPTuizDwfBjI89P1wvXuFtTzIoUU9OBMzvVSs9Dx1LHe838MQPcRkOb5UFgU+MGVxvS1KC70E2nA96lkZPGsL97xbvTu+x1HDvFuSsz3GwHy9ffyOPIhKvr1JNck8IBJVvY1wGr3/RCa9Fi1ePVIdlzys/QY83JsHvbLP7j3Fpjw+23EQvq77fz0jTLA9Zci3vUERkLwO+JC90QYqvNjBTTytfz0+qY86PvKroD2G+/S9XROgPXXiab2Qc0W9mFHMvbt71r3vL7U8nVLZO3SodzyuUYU8","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","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","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","K6pqPuLVjzwJlPy95H/bPNwdOr5nYMy8jEBiPaFD0D3L0288LLRkvlC7HD1XlQk+hiaOPiWtXj28rFW+7G1wPShAqT1kvZg957TBvCPyrb6hXZ+9gZ6tvF5XAb5Wfo6+lZQsvlr28T37sjk9AJuEvtJiKL5Xdx8976X7vclwHD7f+JY+z6SWvilSD70Yn5g8gFQQvZniyzuCoeu9ggP5vdJSJr4gQtg83FWZPhpeOzxoB5o9f/ifPROFxL71OD49Vp0JPli1272PYG4+WQ16PUqZYb7aCqi9ihh3vquf3D3F0Fk96lshvhjXxL2fEvE9KL3qPUyA1z2blya9CefGPk0YHr2jZcA9wPYMvKnhHr1A+xS+sVh8Pin64j0ZtaQ91ZPQvXf0Ab65bse+LMdnvT6fYT6Dkrm9OOduvaCIOj4Jris9t6hyvgapKr7MvCO+vGznPA6eoz2YPpu+YbyEvNmpCr5cWsA8SDmVPRXlwbthNQc9XNWjvGweiDwmWig+PztsPKRWU73k7oO+Xk6BvuPdWz5i4Lg9aUPBPAZJEL4h4FY+reqWvR/i4D2b/vo7oJ6oPXnSmb3Odwq+j4H1PQBdaT0ddoG+vqhLvVgCBD6E1PU9Lo4XvnhoWD6W8pe+LXe2PeXvqLvKDca9eFwgvp0iXj0JZxs+JbqkPWqU5bwk7MQ9y3zbPZKu6j1Pug0+nFM7vtaWJT4bzUO+GiiIPmFxUT3DXaK9axI3vvzDyb08L8q8zo/pPgJ3qL14kN08OIujvRgDXb3PuI88BYzsPXI1pjz2kqa9Fkv5PF9BRzsV7Og8JrQSPprpNLvGLMA9ruPIvWzU3r0tDXC+3Ir2vaorK70NfwY8Df+DvRLrQj6Blym9Ii9xPV/7lb0e+KI9kNVyvS4bvD3w6RG9aomUvs4CA70L9gc+JmgTPkWzfr302Nq8zDLvvCg33T1On5s9Z3DkvTK/Hr5PhUo+AflzvooOzr1UkGK+Ca+mvW26s7waMAM9HoEYPplvQL1gg9A9","G9Xiu1A+tL5xMwa9iNWpvZ0TUz3mxyO6IrrLvTQk/zwpfmA+UCNiPCl6zj1Jf8M9JZWVPp3bJr6NSlm98qIlPj6IGL4OmuM88glqvmV8Kr5mSe49iEEnPmGa172eDGI9h1W+vWGloL13RDE9g+QwvQrNCLydXUg+mZIOOv6t1T0E4OW9bdEZvl0N2T1gcua8uI/rvZqMiT34b6c8UXzqvRlxL70mlb48rq0pPqzKEz6YVzI86yiDvHo5Br1G3aa+Oo+ivsY9W7yL1UQ9cGpYvmhcBj3cFrk8zN6wPR+ixr0fUtw9r/QdPmokxTzmddO9pPODPXA1X7ywwqa+jzBwvrv/grwMY7C+QUO2vYe5IL1fKAC+KhnYvSBBLz65sLo97C7yPXAxvr37fpy9kWP9vRoW2z1CmXK+DTI0vZD0Ib55JQS9jQYVvlWx/72YnW6++cG2vI0wgr1CP6C84Vb1vf7CH73y3gw+adbQvR/ESzwS/5y9E1GbvYZ0yL4kEAm+dn2IO9sHgr1kQiu+hqEYvuOlHrxAa7G99AYPvleUB74nW3i+/gEovfvDAr8j6FK+Sv7WPR8RWz0sCRq/BVm3vXVlfb28I2w96SinvuqkOL545WK+CFcMuubW6jrvENK+XQTmvr64zr1dAp29jPiPPee9WL1Upku+YCzvvbEl/r2zXJG9JQARPFi1472LPaU9erCMvVgjhr1pQkm9kMYIu0iGgbsTyq68pXJbvv3WFj0tB/A7jmSjvc2bk707iY291LU9vZRUGL6RJe29T3AAvorsC70llqM8GNY8veXCkj3gWaY9tEEgvFDoXb1y1re8HOYQvZxMIb24AJa+tCUYvbzmEL7w5RS+SWjFvVZNSzwtkxg9Xp6/vKx5r77lYdq97OK1vQQz172hpVu9AyaMvaIXv7vWI5u9CXgXvdpPp71g6vO7sKuHvFNyCr44j+S9gj9cvW5n6jzLJ6G+sqZwvqZxjb1PHZC9GZVpvn1fEL3PDGW9dBHKvNW4pr1duFG9","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","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","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","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","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","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","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","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","VgjsPdUBTL2dnSU7jePevHdihj133Gm9upeRPbjD2j1JaAM+yTffO0hBHD1PTpQ9oZeePelK5r33wQg+a56XPWb6I744zIe8ok/yvPR36r0NFAA9AWY1PcbI2byorPG9eWTtvXGGMzwI98C8TuULvnP+zz3OJv890bqrvdkDmb1FpMO8XGnevFYvij11cJs9k96sPX19Aj5g67A9Aqw6Pc6o4z04YRc+e04CPsXLgD1F+6U9n+rPu3XOcj2Mppu8ly21vWpxyL3DiSQ9z59XPjBtWD1yn627IRh2vDTc6T0iryw8LB8rPbeWgb0mA7c9lQ3NPLGxjD1oV9W87B8cvUa5g71ejmc+3w/qPVHjCj7p80E8Kl72PaVLVT34W+o9eH84vaOlpj0ULjY+zaKtvYdWhj1pHoy8w405PpBcST7iWBY+Hy3xPSw9Az6bP6M+aJG8PSplpjzFZjA8JQfSPTB7gj4Ctyu90PTKPI44Gj78MSs+zeE5Pen0az1YfSs+IfSaPdr8Az0pjMU+dYeLPhWfgz1Ebng92gEpPTHrujuRt0k9z7DkPTClJb0qpxM+SZ/NPQ/WQ7x2M0s+DOXYPORQJ7xzreg9OzQMvSdU6T1Uqiq+SM0fvcCSkz2W8nY+rDqKPbUmdzwT9rE8IeudPoVUDr6qXIi7ZkJmOwW0eD7Y4KK7c5MHPQ680T0jS8S86IngO5mPQr4Rr2o+ha4vvT8ovz3nN8E90RgmPqnxej3g+hO+kXacPlymXz6TgX0+B8EXPiTiSb7F0eu9VBQlv74l2D0Xyhs9t+1tvQYkirwGmws+rqxiPkD2kj1agQ0+hNZOPlM+Yj6ayJi+1093O6JYwj1fJXY+T9ebvrsnp7yjjgY+3toUPU6+FT4d2DA9sOQKvopXJD5oxJW+637OPWkL5r1IDTY+d5PuvvlnWTyAaKQ9RLCOvCijI76/m3Q+TFGVPSuHLD6vIJq+LsASvmXGcD43HYu9qWlevbY2oD52aEY9YY8wPf7zDT6QDhY+","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","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","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","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","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","73P6vOkIDj0BBDi9BRncvUW+o731hp6+mfh1vuhHqrxr4WC+/NXRPUbtWz0fr8i9PHz+PKaugL0mwR492jQMvl7B4ry5cxs+HZl6PrBaDj84ZFg+L4iWvbaxgbwLlEg+ECa7PnVs9r1WuK09D9lyvaV73z3xbXm+1qISPiYUwDyJeCy+fq7qvFatMr4QlZ+9GX80PnaWoj2rOiw+i/GRPol1ezwXwq29bGOIPp38AL52dSS+x4i0vpFLar6l6nG8RdyyPS7rjr7Epgg+4qJjPRrXNj62VZW9oFOBPlBtKD6DVGg+3njFPTfstz7kN5O+fMhCPNrlBj4GnRC9xXd+viCWSr3XjhI9aV66vFaN5L0wFU49RQymvRZ1KjygEi2+M7GNvX59gjx8qIY+X4abvSHA072rxg6+N7S2OtBQSD6d4pG69Ol8vbatiTyZ9cg+5eaevTB0Z72bseG85zhePeyQwD3zsx++9ZUivaKuGL1hrUk+R1KsPbPrKz7/R628NtrfvYFrwz1TUwo+qmq5u+VGyr16FiM9XgTRPbHz573sagG80GaJPPL97D0OU5Q+KWrnPCdlzT4FWpI9+67pPOLY7Txl10C+UZAePkzeN7yhRdW9w+pMPGgunD6/UsA9ZOiMPdt17L3Kun89CpJ6PSFkID1SPKu9i3kUvb7pBr4d/969aIQWPun9zT6WiiI9DEtyPJxJbL6XHZw9sE6cvgEH471sI5Y+aGGkvuOJDT67DgI+9nAZPmsfKz2maiQ/bXzPPeKSA74zkoi9uYI9vnzRuTxlmGA9ad6cPRPKhjxRxaQ98HEAPgv/kT0C2929EbgfPRZG4b2bv6m8hTeQPGXTKj5XBV09l8o7PtM9e73LuBA+hPIZPguUc72DfxW9sunOvHC6Ab65YZ09Yp+OPk+lgj2AuQC+sxyHvtL0AD75BsA8blscPSBuEz7fBZQ+5D7WPKTUqLxD6oO8xcOxvWI4/z4mjpQ+XrUNPjtlOLx+3Ag+Cl1dvTWm6j0Bij0+","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","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","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h6KvrDywr2ZAYI9RFdmPTHZ6b1dTkq+XYgjPl3bob1TSHG+3jR7PfDGjj71ZtY8v4P1vTo2ZD6nHLq8txq4PHr+R7120Jk9LJeqPdFETb7QeO48iWDKvSoh+j16IWq+J7Q1vaKvFzz+86+6PFmBPb8pXb2dc5Y7A9ttPTXbmzxjIAe7uIB2PQW+Bj6ycYU9e7ffPUQUBr6p7a29PZgZvisXaD62BEk+utTGPgyUtD6gCz+9BXvWvfER872Sd1Q9qROPvZSxCL7zuAE9NrUBP7qSVr4fkUU+Dvw4Pj8Kf7yoz5W8LqZtvSqKvLzkxpu8dxCRPssuNL5JtYS+fuD6PWbfuj1cZvk9yEEBvZMrur78WTM99bSlvFw3bb6HC7k9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","MkgTvop96Ls61Hy+U3hlPBbNtr0MomG/haaXveyv7r2yY729WGzcvWDqK734B/G9v3iwvFm1eT6ccTS9NwwEvpLhmr0XLr69V50wvirbwD3SSVy+oW+jvTqzKr3LZjq+3EgCvS+dlb0g/gu+TE5OvqrSar4uZJI9xsZJvgdV0j3UUG29timQvB1Aab1BwGS+b330vjrrSj7hMq698Bm9PWcmoT1w4Xe8qw3QPaIACr5bvIs+9HDsPOa6Zz3HyPs8+aWovWg0vL4OK8M8MOqSPibGiD0MZEW+BmUkvaxFv74gmBG8smhhvqvzR7xPVCY9b5XLvHYEnD3JmeM9MH8Ou7Wuf71RpaM9ecmpPUer+D21VWm97ilPvmVnMj61Qtk9hr3bPZO3ab4UJQW+3ASdPn2vrL7RNJa+3yEXPqO877wpuBS+kbqavCdr7D3JTsu90lOxvfKEub2OQZk+uRcNvjljmT1ZS7G+UITsPeb1GL4HfKq+8jkFvYaNMz2B+469YD8zvrehnL0xsHA+rlB9vbH7WD4MAaC+qZOMPQLu7r1kVVm+UjDAPtU+Fz6vr0c+AqIAvlSnND6tJho9Vcy7PWqahj1r+xs+LtvdOw6yS75RRyu9UeYvPm7YQb7hPKG9NGRyvq69Kj3v2TG+aJmCveJQEb4ahwW8ACUUvoB80LtsdcC+TyQjPInNoL3P2ya+BkqvvoK/lDwCG/a+fU8nPUtTiD7eSFC+uPs8vrn/mLsNPmu9JxI6v5Qd87301I6+xQ+8vjXiwDxR/6M9cX5svrHpIL7z/9E9Ipz4vk6uwb1seB2/q7rTvp8NDDwRaSK/o9jnvS9stL7XVV++/+riPLmOwz3Sszu+qVzRvo/zIr0lMVq/iADgvmrFrr20N6u+340MvhC5l774tOW9H9GZvT8ibz6NjUG+729uuxWCDL6jTkm+8chVvydfs77jLzM+J+0Lvxwhsb6Bxl++U9ACvvtUf76bm/a+2dtBvxTMbb7p75C8j/u7vvRM2b6NRxm/","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","4lVePntxGT6pzM69PildPrvtzL0ekKM9Ua8QPvCMmzz/ldI957lHPgo/Mj5USq89sLDHuxqjsj0GkI+9Qxm1PlPjtL3jrNc+BsXnPT17NT4Hi4+8LMXcPRPRbT2Or0E9u+ZRvfzzCT40zxU9dgFaPfbN273hQw0+6mFnvaCLrb2COL28yH20PXL/w71GVcA9Eh2zPaD6/T2VSS4+mfrVPR7PgD0gi2u7VNHGPXrSMD6X7fA7QsmLPn7s173qlGI99pH1vCkcHT+yR4g94eI+PqmY1j0Jt0k+GZ6PPWkaJT53JJy79GdnPr3nIT4ljkY+GmUNvcMCx718kLo+L6B7PgpvDztIWgQ+5PuqPH9W6z1kokY+AJaIO4W/9zyArcc9x8u0PQXIsT716Kk9Uhy5uu1Saz6zwGS9THTsvKJjlj3u/wU9TpyoPgd0Cz6x/B8+KZ8dPn266z31dju8WI2EPdScnD3uO9482vraPYm3Lr13Rbg9Q5mdPaJA8D0YSQo+HrhIPf6wQj0d/AA+UFSNPib3fT3TLaM9Of6qPIwFPzx7YRi+LT6ovEZo6D3vt5S84moLvSTkTL0lh6A9r178PRFSRj6QpY69CeUEPaL6UT3Ao4q81RRYusr6Vb0zIOs9QIqAPhnCwz05x9k9z/TIPfbJcz7/hYo8C/rbPeKl5724Mu28fI35vIMFV72m2x6+WBO5vVbFvL08EGK9FTuFuyMkK72HRxw+afnAPsAoHb5XHXi9vwuRvX59kr1VVrs9h1O0vfXYWztH3gS6PQo5vsDfy71y4ao9T0AoPZz0vj3Wot09zEZNPnR98rzVBhK+4+KJvS9cnD0HD5a782LDvbKniD3HMkI8or+rvI2IGL40LC8+kRUwvedkGr7bDhe+GgCZPUy5F76MVdC98D+avZBkrjwPQXQ97SS+PfoUnD0JGoy93BtsPkjQdbyXNHW9CxAqvRU2pTv4zHy9duC+vfitQz4MDVY8h8F7PX6EGT4SwfI9R2EKvjXDEr4+Zii+","KApNPR+J3byMy5S8HnPpvaQRdj5OfLC9BP/GvbrfN7ut4nE9ufXNvRGPbz051E49wFSVPa/eiT3OTtQ9SZEhvkDa7r08KMA9UDDAvQpG9TzuvQu+/pgOPY9pubwsw8M7XsolvWvZMb6ntNw9KMONvQL5jL1gQcy9AJXCvWAVh72WTBq9jma9vbXvHL7uJ9q8zcbKvDcLgL12FLW9stQJvkx8jT11Ymm9q8ADvIMfkT3W0Qo+i0vBva2yyD3jgZK8f7OuvW8wB73hM2I9xo0EPgKzyD2gJS2+CJyFPcE7BT4iWbk9SejiPdBOEr73xeA9z4hrvWLSRr6IyDW97WG/vYWktb1EcEC96kGxvm7uT72lvxQ9CdD+vnlfprwunWK+o7levpA3HjrA9XG+5lcIvouWSz2T9WK9kYk/PmyZYL4wYQO+6uluvniA273dmJi+/r0ovf9jPD260bc9aqP4vbaPLL0O0Km+UZoQvquVnL3/vp6+g7k4vkCCQr3tzdu9X+suvmMqQr2InRi+VxHRvdiABr/hh68+ud/Lvd1rMb6JOTA+d2ehvi+1Jr0q0VC8pcmlvsQmiL6b35g9v4xIvmhe5bzYzEm+nCU5PXDEe74XbBK+Jd6zvXpHd74AWCi+JvASvjHIHb58oRu+lxQSvgUWhj34WkW+8Rm/vhBH971GwOU9W4cCPrpAh75SW0a9JrtYvT+aq77YQJo9xV5Gvh3vW77TZxu+5v83PlT5iD2cz6W9IStlPtjz1Ly5OSO+zl8Av2NCKL6A9XS6rmvjPdoAH75KaR89TouOPMPOnr5EuGU9FB9YvmzFMr2taQ8+sumEPct8V77UwDw+y2UFvVgHzz08ITC+Q9ErvfQhbr6d+KA9gOerPnOvfj4NU7Y95+YJPckjfL5G8U4+tiH2PS4rqzwkL4C8GM0vvfW7RT5hiyY8b0qnvoKGqD7A0n4+rFUCvuew1r7spw29sNobPUBfHD2cMBA+YfvVPQZVwLxAQiS+4UEPPg6Vm77TAMI8","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","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","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","quQIPozLc70uewC+LOWVPcFkuL7zyZE9qog0Pbmk5j1onDI+jDupvKOuSz2WAhU+udPQvRd4SD0zNva9EvZ9vuJpsD0M5X6+1VKaPHUa+72yitM9f50DvRtspr1FkTu9trhqPD/EBT4gtCA+OOB/vdKoV7zFAwc+gjxcvbEE0bruHJs+ffOfvIQXCb1tYP+86yvvvYFNQj7stjs+LYbQvLam/L13+gm9b8x0PsEEiL4OrOO9coAIPmjaCj6xJhc+r1qxvRaLDLwVN++9sf1KPkbzhjlN1wG9cXWnuvbJujwsIEI+P5CSvUT8QDzdXnE9YHYSPW4XAj1F2f09J2E3vRgELz2qYSM+HlURvq1tDT1RWy++JQuUvpWwsT3a5xG+dpt1PQl9Wr6v7KM8POUqvqScfz2iJP69JUK6vZ3YN776wyG9OpeXvlTB5r0Tbky+QrHqvRWKR76Zccy995uKvo8mQT1dwgS++tQdvooxw7wy+Eu+sTyWvmOdEr6gkeW8ff1Yvg4KCD4bdLi8g4rivQ48Jr8NW7o+ZVPlPE3ePr49Bpw+o828vQwzRL20+R2+D8eWvj0Ng75zNbE9vMEUvsNWBL69e0y+zZMfvZ1Tib46ai2+ortsvhN+Mb6Q+kW+rfgHv3uwgL7+ZCg8VGm9vaNru7xDIVS945eqvQN0Nb4K4SS+LvhkPFDlPL2BX7A9bWE4vuWMJ75pLHE+mdwwvmnx+71nt2o+teALPvTAwj72KlU9EO0WPX259buqFO48GkISPr4m+D0Ub1W+4Ee1PeHn8b1xXh8+YG26vjOMQr7A5hc+mT7FPuYI6T2v7qi9vnGHPZgzSb3hSMW9aJ8mPjoLCr6zMzU+FZPFvTcp4rxRRRG+2k0HvgWpP76zaiy+bSswvBwC9z52Y4Y9kQMYvcTKs77vyks9bvSbPvbs5LwtR6A9yFu/vaUJm73emR++v+clPg09FT4gCog/Xvv5vI9zrT0MRVI9J1q2Pv/hTz1A6wg9mP4cvkREc7sOB8I+","BIkLvhuDhzy1ULQ9xj4wPnhulDwTLOy+QnLEvShRMz6DXLq+sA9fPuqBvL0eIpE9W+DvPcJ2gD4HYQK+hc0kPJZ7oT5WuwU+0KX0PU8I8ju7fB889IEGvkKV2TwAu+++/68sPgg/l70xUzg9d33cPtuEhr07yLg+MsCWPgDHiz6qDH08u1YfvtH6gr0Phxw85YjaPb64jjsQea890pXUvd3Rer47YyY+JlmBPAx62D16duS9Z6gIPhwmtD3GbS6+iUHhPTphML7AiIW9z4CePsWhjj5j58s9FRrCvVgwcj4Ygg2+GHYivlXzRj2f4a49vpAuvTGV5TzX2dW9v1Ucvrn4ML7GVyE/0XALPSazHz3K7Eg9osG/vkFJrr5Newm9p4CcPkWrhD5TzMo8xtvMvkWrnT39ObS9oTe0vQ1COz54i5K+jAKOvo6VAL/1tWu/tvMAv0mfI75jEQu/CHZ6v0FKFL5xGFW+RAyBPn2S4r7VNaI+G+yZviPAMb+5+yM/iigAvfDqwb19dQo+R2bevSPQCL4xiLS+RlsnvSoGCL2KG8+9uqFUvZWzaL4c0f4+JZVBvnDOOj7U9yM+ycwJv0gze7//PYU/PcQZv8GNeb/yeNe9pcwyvStRW76OmXY+Y3YLPu4yIL9x1S4+EN48PiO7pD4cmge/guyPPD5g6L48oQK+QhwsPtXDBD3CLBK+/VCGPJP2Cj5oh249qoOFvWRM/76n0Z89KSKJvkbrRz62nji8+RfdveHskj3XHHC+22/Yvtdr4LwsQw2+T6zNvmWeDb9lGte+41CbvuYylL4LywO+N2hmPLTR9LxK3jq+DF5WvkiJVb6UXvi9clSevdAoZT2Asn2+B4Vrvub0JT6XMZW+1JSdvouJ5D1CX3O8TRCgvZmFL76TBm2+Y0ZQvQOYU73iJRM9K7oSvYoIk73eanM9lRNOPh0R770mrAS+TpNxvuOl/DzOSQs9voZ1vmzMl76qwAu+f3MQPfCe7Lz6c5S+04AevgNDwL0wLW0+","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","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","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","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","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","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","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","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","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","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","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","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","VG/EvqcVUr7iXY++ffusPB79m71g1iU/v79LO0t5CL0cHaq+e50+voy5h76CeoG+cMivPIe+br4R5R++W6uUO1vSXz9n9b89RqLDvdrHaz0Ptmm+DiEcvSwiSb6+Zse8/uZMvjkJir6dAA++kgG/vM0cJr7rmkW+17/+ve7EBb+cryE+58UOvp6qQL5jQwO6Kjh+vtpP+76G3bG97pOWvtY22b298Xk+WZWePfypAD+aJoi+6KgEvWRB8r2jCNA8N9wnvQhTWb46sz6+XszzvVEac717Rq6+FO/7vv07SL7e6VW+VNEUvguvNr2IerC9c+TuPE4VUb43ZK0+SdupvCsdIj6HIFk+IM5VPir0zz1Vc/A9MvcUvbVZ7z1wORc+Vf5evr1ZA74fUqO9snJAPi9o9r3fy+C87piLPHWuFL6o+ou+6rO8PYfKUj1Zg5c9A6KYvZdlxr2sf749G1w2vY5bPj1HRDK+sJzwu9yMcj7MV4W99U2pPuihSj4W/vo+Hn29O+F2gb0AYUa9QhSYvX5qXD5aKbu+ycsLPpneKr2X/dM+aZqMPdCCBD190aK+GYvOvUX06T0T7869EPMAvpYg4LwScwC+gpZ9PSzZAL4eXXg90ZwXP1aGIz6aVj0+DfhQvnqVJT6wfSG+zsrXvU9Ck709L3i8Iu2ivUlPnz0Gt5G+AsRPPrZSJL6XI+m+4taqvl64GD3iOdG+ensOPkQQfb6MpYW/yxKOPr9WGD+JyuC9ldwJPdGplb7+eYa9rDCgvXN0xb6U/wM+6NGnPPnroz1twBU96rO8vsq5Zr4zuta+tc0xvpzGzr00k6K99DP5PuLv2T39h8y+YtmhPR08Oj4A2am8tMhTv0NGY76F2ga+A4qAPpFvG77Ikcg8XAkMv4mh+j0242a+FFw9vdLFe73eUV6+gmAxPmu2xT6FHCW+O+2xvpf1qb4ihG4+IbPxvoYmP79wMPe+W7ODvjJQY74LGGw+sA0svxsfGr/3t8e8A6Oivs6Skb02QU67","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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nOBQ79OLXO49b1L3ntQk+EgH8vAWLKL0f6RO9LOh6vI8DCD3PTyq9oe8vPqQ1Sj2MZLo82g+ZvQagCzxRgju9ES5iPjOFIj26tPA9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","B4ctOtaeRjxgBPk7/7R4PagEVT4vLBi+TMXVPI8KbL1ikw++j3PwPf0PJL5mlca9pTYXvY2/FL4Tkde9DV9NvIepoj67Zrc9uaVKPh81nD0YMty8Q8govUncVD7aui8+afuhvQCDhb6ysi8+TOuuvR5ktzuOj4y9DxeXu0if+LwYFoQ9Li9NvLfEIr6rGY89BYv7O458qr3a+z496b9Hvcn8fz3lKJa+wxAAvBmSDD5HQSw+ZucBOT881zwJ8WG+FM2zvV2HOL1feRi+fuhzvq2dVb4nEv89UqtjPSWio71xsii+O1JgPZ1BWb29Ji49bpydvXtpQT5hf0K+9cZIvjzAH76v69a9QZ89PJrikr3ONY6+EocWvgxED733KK+9T2YxPhvSNT7Vohy+zlEevmmqxD1uNvw9JtcCv++N6rxMDBS+p6OcPhs/Mr2GLoi+VLkmPsA7E7220Jm+peDwPiwslDzOaau9FPAKPLl2rTs1KRg9FueDPTDulD0efx49tlkpPqmBAz79JEG9Xe4kvmi0B79YBlO+2juSvvN2w75XcRo+jkrMvHdkej1fQL48WUaWPZVvj7wifMI9QzhUvbDBqT2yShA+YTDMvRk9J71yO4I+Gl41veyG7j0/CUs+YFkDvn//bL4HMKS9zI6KvSIzPb3avNU8jU2xPvJfZT4kISU8Z8TkPi8fuz0yYNY9wdQdvdvN+71/cI+9++mIPXqcZrz5Un4+B4pevm007b1nuiu99s+pPP9pLT1x1+C9Eh0gvl9s77xtgpu+JhYCPjsIDL5tknO+C9SvvXU26DyZWfM9MqCVu3A/p7viGs29HUTKPWBltT25/js+b1oRvrsZXT7X6gi/krmVPVtsPr7Qkm2+38ewPSUpP75+P5I87C/TPKtPGDxiymA94+eoPvgULD45EqK+HMfzO9RYsbzST3c8IYoEPi1wCj5lNVO+yBFNvpl4Er3p8VU+z/Rdvlg3A768PRK+Mp03vrZ/Dj0lUp++klG/O0qcX73kBLe9","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","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","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","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","TekIPhOkZz48GXw9fDeqPS7A7z1pLo4+AXxEPKrdB7vrJAk+s8uivUNB7T11C0E925ScvSEDzj2Ghtw9AGENPuX3Kr2UuvI7YfYkPe3HkjqO5YI9gn5Vuu6DOj5WcNc9512FPXY3Hr3iH4w9JDLNPTvC4b2X7Po9dJJSPUcKqT3MWrq7PQizvFGTjL3G56497x84PqTZjL29rh4+ccoKPoKudT3/po69Q6uuvZR3pT1NDT6+09EBvq9fBTyMj9y8zZndvWfPej7xxho+wQAUPio+LDyEvYE93AaHOYxXpjxfLwg+rLZxPup0nL0IuZA+QUoUPe834z1QNGA+R4SKPQdqOD7mes28sMMjPVox9b2OtTq9JKw6PdQ3Pzz32MO83uWTPvleaD3/4Dy9HcV+Pt+Suz1sKgI+AmUbPnixoT6Z2iM+u49LPHcUXT1Jjua9H16RPH+wBD1Z3cQ97TguPn8kvzppS408POS5vYKdI72WtYw9fmu3PH7AwL01rnk+RmQhvrQoz72KffI9huBjvdoQOj4ea1O8JBWUPjE5ID46pvG8WfuovCKl4jyUX4O9iJA+vSfiRD3sYQq8U0DTPVnbKb4GZSI+6FdCPjCXVz2yAJC9LWMXPgbuZbwqn06+MGedPSnp1D0t1Nk9o+tBPtBUiDsunuk8/pE0PRMjbT3dRbO8AohNvYD97D1UtLU9zxpTvMN0dL4F1w89ZQnmPHP8kbw48+o8pATlPcTY4Lvz3509gWEYvlj+pbvcJOm9UesnvojJEr20tko962g8vSmMkz2cuT29ov/PurCvib1Hurw9lh4Hvd9OKj23MBQ94xU7PYFmdTxReIC6WvJ9PFI1g7xWPN2945SDPUxB8D3dtrg9QyhFPuVFE75adQc+y0k1PRqdEL6bBOE9ywGhvcXvgLyTBwk9WSA6vWnth73wDs49JhhjvlEUgb1SNRQ+tdDjvf6m6z2Oqsa9GOwhPdMLsD3YfB0+OBHiu1sx2juZdQc9My4RvhSSAL1RthK+","Bvt0vRmYSr1ubzI7VVQrPX0ltj2c9kE+d+phvkn1lr5Nyma+ocUqPf0WHT6xwde9zWsGvva2F77ykQ0++klJvitKSz0ITJM+3u2jPdXgmD0p7sc8t1D9PUqDQ7t47so80K8WveohbTy00Py94CIlPiRYkr7nOPS99NQrPefakryZ8OY7BWsuvsnYbz6XTa48wbfwPOKZnb134Ok8FsPGvU4Rzb0h/iy+tdvVvdUQYj05xM09qTsZPvFYYL0vp6+9KXzbvLwDxb1NaGU9RV/uPfJ6Zb1ogKK9Lro3PgCrPL0Ei7+9GehCvNS0az4hqNE91pRnvu8G7T0Taw6+gRdSPWZBK75N8B2+MrcDvXYYd7pLdWg9lRN4vteKnTvyxum9pWQivtVyR72IY3i+ToggvmJB97sqepu94DEwPV3T1L0YhI293weRvK3+Db7hhZq9qKINvuP9iL18WCa95WRCvlzQZT1AKl096gWbvfEKAT2Slsa8K6rpvQkXIL0Of5E93FNtPcixfL3S1ZQ9/dM1vqoWor0/BU09U4D6vUsMGL0u5FW9LNebvIuOGr0FRZe99WEIvsbCK7xvdx69jy7IvTYy77zhUn2+EBTIvRgWKr4dD7i9ejs5vvPAjb4JbsC9eOj6vaLkW74FAQO+gvtlvlWGJb3lehM+PGqAvte5lD00ske+EvY4vi2x2T3kbE0+t82Qvdsr7L0OL6K9IsUdvqkaCb5Pwwi+QdupvU1EiL3Dzye+DAbEuyHxOL6gRpG9GtAuPcyCbb4wWRm9b6VXvS6bLL79hSQ9t3dUvi3Re77yr++9X6sSPVG1Sr3SEhi+Lb68vY3gtL39A0+9GEOEvehKkT1YCAo+aEMRvkU6Xr5/CJm7wSWdvgc2E757ZDG+6C7HvcTyvbyM8de99j1fPfc7L75a7tq9atNkvXBgfLzP5bk9d5BYvVsxmz0Mlee9MNyBvIh7lb581dS9864+vWqWU76INxm9Z7aRPfGym76uomK9QVtFPb2IC77ldiY7","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","wvkSvu9Tzb29idC99iK0vR+2/zumaFK+70SrvUJ+T76Bi/M9UmmLvCfRVr5OAZy9xKYpPsnSRLyVpIa9RfkQvtEZgr3tIBg+q3OZvcGCp73/kBy+xXubvfR7gL7HSg6+mqglPjjbQrp3Nuq8YfuAvIn06zycl1+9sKFtvSGCMDwThIm9O/EgvoezGT7Db7K+mYwQPWfxjL1oIFu+32I1vUl1qD2IYfq8YzDxvObjy707x/o7J8diPTQiCr776ZS9mFuNPXA6Fr3+PEm+oz4rvpsIrLwnDjO+NpE6vv/uCDyvYuW9rC8cvkMFDL5jjsy9/QKnvZPWiTzuK66+UTyuvf7PUr5nBXK9aZq/veIVBj56Pfy9paNLvmg7xrzwFd29aOpyPRC37r30rKW9n3SRvawwTL1ILn49yI0jvq4O573OUmG+QDyYO9hUib76bsa8ceBlvhVhrr0WhX6+jbxyvrcTgL39ooG9FD8kOimjY72RCLu9yc/OvDYPN70oA1G9rHOOPR1IjD3XtPa93ZVfvpn5CT4xHc+9oxlGvnk93L0Sr7G7wfiAPcmYnbw+FSY9bhMbvv6mir3vHAG9NZPrvAZMzr0GeIC9l10PPZADRL76BMA8T6aAvoPJqj2AgkE8n3j2vWxwvDymFmG8v6zxvUY4i739AqK95FmhPUZJGb39GN29C9FBu1QWRr0T3vu8/8qSPByMWb5Fbgy8uV4fPKzcAD5MKTc9Rt1Iu4UsAz5KjQW+myDdPU9LJb1F9Re+lWRHvVeO4Lu4O3Q+F67jPXk0QD2Hx7W9ypNuPosH0TwxZ449QB/NvTXgAT47jbE+g6cJPmw0jr3SF+09RFEBvX76j7zlX5a9a1igPZWtrL3QAbW9FeE8voF08jw1+408pXMkPseorT1Ss4Y9L7N/PQaWk70GgL49strVPHKjVL65HGK9KlYsvoCMrr1daZA+jgScPRUW5z1WVFM9ReiQPY0sK7uxswQ+U0igPP7SJr60DAu+3IsWPdrxOz3T5FY9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","Z5VgvgiXBD67GMq9baKVvSXDwr0Lr46+S5KGvAfF875TUxs+m97yvZmmSr774a29F7kdvm7CkL7804i+fqulvssUrT6zRto9BxSRvhtDlr3woi2+9rqRvQeG0T2Ax2g8lj6vviduHb2TOZA7bFyQPGf2N74b3xG+YPQovr28xz0srh6/pwWPvv14Mr4hYt+9CpE2vK0ELb6HJ9G85PAYv5JvQz0kDok9jnH9PXmifr2V0bq+AFgxvpu9Kr5+7Ai/p13SvsEYSz1hgdc89YMvPhTwuzwZef694lcaPg77Mr6LQwo+ndOfvX2HHb20aYE+aYllvqemTL24C0o9RYcWPgPsZL6T1iA+vQyNPL/OmT4MXS8+oZA0PGSV0z7c0+k+xVOdvfiV4T386o2992UEv37mGT5mfYk8yBOjPcR3Tj7vk5I+ChuYPncXrjwUeSs+QFSBPlZEHj8LWoE9P5hXPsmDjz4kaxU/t9LVvkQ4uT2sCbk98zfLvVzljD41NKu+xpmOvgERmT63V6e+EGCrPQiqGD6J3AY+LVg/PqFtqT7WrpU+3dK6PW+BJz+m4+u9V+c2P2rdAT6THsc+DnruvkF5+zwqXc8+3W1cPujnJb4RYjc+okGRvhTHKj/EnHS+q/E2vMcZnT7u0MS+YvFvvO4CZztUIwU95qTRPYvfbj4nQGU+O6X/vSCgtj3Glyo+hI0jP3LwFj4Lo4q84cfRPmM1qT5WnrY8PPVxvgJ7jT5auJA+++8rPrGFrT3CpkI+0IuivswMc73M0x8+R88QPDMA3L3eRBE/ukMkPpJjmb3+Ftk+YWsKvapi2D6HkY29+3eJOgSFaD4VKPY8Qk/KPRzysj7Cfw++W7uZPtb0Qb6C4yg+IyLHvdGyij4HRz89YgddPm5U4z75sDm9I7auPeHGejyynya99ANCPsRFjD5CTQ29AY4YPn/Lp73onQ4/C/h7PTnIX76cNmI+K+7XvmI0AD+tfcQ+RQpyvIQI5TwOUE6+ecTKPnGtRj1R1/m9","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","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","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","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","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","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","hb1xPgpr4T3pwbQ9Ye4DPvAeqj6Qs2k92AECPkJW4j0wdII+PcY1vmjjmD7zaDY/cYo5PWUZuD65k569ZZVDPrijuj5b2GU8NCqgPVtk8j6X6ES++wiiPvmFdL4/zLM9z4tJPjcE1b1FcTQ+bLGoPlA55DyXbb49ggh0Pr0JzT4D6UQ/l6JGPoZRTD7jJD6+/9FQPZsmCz4KE5A+4yo3PstCDD+lYFE+lyrmvLmw2j7Jd+09O1wGvdKKor3XIGA8TIhEPsRg0r1ep6Y+CeAGPgmFdL4tCwk+3ZkWP+rsH74CJpU+xWlaPRY1TL2PRYm+iM/TPPDC1z1WENg+zO++PTF/AD7cGMe8d8MdvsQ9ED6GraW9uaGAu3/rej1mK7a9Va4HPqUKLL3vfju+2khpvdQSsD39sLU8OPscPm4lIL58QtM+ybGZPrK8lr2LLAq+QxeAPtZNzT24zyu8zhkpvOLt9r3ry+U9FzQJPZKFuDzE+Eg/FYm8PCGiaz0cqiw9IOmYuf46EDz36ge+rv8GPhI22D2FsWs+vRF5Pc71uD3O9Dc+ibKKPhZb3r2fp6k9rmuFvY/m6T3mcf09ixIcvtB5RL2Ph4q+7bgjvkngbT7Y+vo9R6aGPdS+Xr1CzSM+KMHEPXE6hT3H9S+9f0ZFPpBQzDsHx2K9ySrAvGqSkD7WUAs9ZTz5PZTDsb2QjLY+0vCRu2soeLzQnGy+a4ccO6aXZr6cyGY+MO6QvaCFlL3lfum8NGYyvj/YPbwJwjy+BnN9Pv7fKj6BqLk82ogXvIw7eDtQhGc8gZwNPHBQN77mKUe+mtlWPv2s6T3bhMa8yA4avb9J+T1yizO+RqsHvh892jzTMie8F4NWPq7IpT3jmjg+63pyPMJJ1j0+yH+7EMtOPg3tAb6S1iG9Lw0lPQqMwT2cR3++MPwQPXq/Dr6bfte9oWpHPLsCw71Vw2q+RGI7Pb22nD00KJ88SLscvcMmHD6eb6i9ItQ4vr21+r3r/eo97OovvrX1dzw9kcI7","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","1ppqvpQnSD6niCA+ZmWvvhv7CD31dfw+12uEPr83ej4N75S9vuYCvm2i2j7KNQM/C1+OPoLNwD5JZi4/NNiePtesLD7EWGU+ruqbvAJuQb74j1g90XjHvrrvPD8Z0ps+CNIaPRuXqT4vNjk+CTckvp7wj7xGudI+BNE8PRnuMT8dSMM9X2gCvv+9yz56/Kg+c+G0Ph+RmT3k49q8c35zPM/OHr35DiA+yZuBva8/wD7rrWq98QKAvnSwSb46ReU87XkuPjvh+D7WccE+S/SgPkhnfz7W+JY8O0F8vkN4fD3mWNm9hMEWPp7fBj/NiIE+qw6rvURz4T6URg29oK+LvsGibj6pNCQ/6Vg1vCVMuz6oljS8gx7IPYb52D6uQWI9WTzsPcUngj5bIsw+D+O1vSpoAT7hQJs+5psnPp2SJT7nU6g9hd+jPfZZBT7ieQU8ltxePh8vCT4A24e9Y6t4PJRfcz6iwnG9cJfWvTEpID8zU6w9/hqFPHmYBz8p6o4+3hmxPgT8QL4b2lA+r+jvPk/1lj4Af3q9xtGSvcqxAj9pNKc+DhI3vtN8Kz5o/xQ9Al6XPSy2mz51XT8+MC6vPsIxaz78mj88fVDMPrwU3j3sPau9RBJHPpUI+j6emJA+aBkPPnEwgT2AuAc+c2StO7sKHL2jhYA+PrTxPbF1qD226Sa9cgHsPH3fDT6TA5c+cLp7vnXKWr3GTRc+D7trvfZP+zt+xRY+rCHsPZ8WfL66EIk+hsAqPfDsFD3oTZ+8JgwavbQqIL53Res9GJo9vhsAGz7jxS89Ik+DPunjNr7Vp0Y7nl3LPQX7Dr7UGtc9uPYlPhsuAD5Z9tO8ntMivkXDMD7DQZw9MUbGukBef72z0NQ6wYCDPRw7rj1jR5e9OkFePgM0Uzz5uVY8cjDAvbf06b2sOCQ9QsO7PSBcsD139um9zlIjvUonLb1Ptxs+yK60PKZ3KD7Fuxq+Unu4vSEwIz5BLuY90tI9PesshD3VzKK9O9dNvdgvkj3UVng9","/lHfvTGqVD1eaAG+K41nPhH8ED4qk3+6Jwm0PXBnzT33YqU8wusJPoIDhz3QNeS8+W2wPdW4/rymH4A9cZu8vRbywrxiB1G+mV3LPdntmbvQ5Im9SMgnPpFr9r2uaZi9gsG2vVXnlzsiqh2+P58IPhxbm72adh27sIukvT7hub0avtS93XxEvSUqyD3+yCQ7oVXdvcIdgL2ckas9uYJhvfQMLD04ohm+PcTEvJJSM71iASo9VIgpvmR9prwhLTG7U9EMPoK/6TzXUBw9kaCGvfTUHT5ehF4+4sQ0PMCWEb01Kha+W4IaPbZll73UJFo9rpY/Ph3/Bb4YGCC+8r8DPUnb3D6GYoA+IalEPrTJKL5fsIU+zms2PmaQfz0za+I+hTj2vUP5yT30KVI9RZOiPsFpCD1oVHY+38UNPhEHMz7SpDo91wDMPOXGhj5vhSm+grsAvOwAOj1yO4E8hJPcPDKmgj4Gh1E7Wtm6vWoAQL6eZv08ZjINPiM9vT3ZAQW+uaj0Pk3HSj76T2M+kT8jPs/42z0gsdK9CZR1PnmJdj6+HDy+L4+AvYi2sb0AWZC9ULMaPiOQBL0LMVc+cQM/PlSXcz7gxPc9026qvWAF/b0gjTO+d/wgPrLd9j27MKc+LJL4vSkLCT5YF1I99e07vmPJPz2UNP+9q65qvbFIQD547Dy/HwEuvwo5IT4yNB89vQuVvq6Mub3ksTu+nLdLvx0yZ77Y/pY+DXv2PXL3IL+dmki+sLpvvjqIwTwAkfe8YhRnvsBYMT0vJIE9bjfovfEL1z7pepO9gXlQvgtcRT1JIVa+qE6Hvhrxfr7vgRa+kSGPvjzlDbxORbQ98dt3PqlQ2D2j35k+xFkrPgjKxr4m0H6+IkJAPqlA1b7O8ZS+1pxiPkvVoj1O4uW+7APhPqLsnj6OYoC97bmLvnQg5b4EYkW+7fQXO1IwhD4CnCS9mQkyv96Vcj5SOBw/reM6PnxOjT1ImX29UGNovYnfnD0AVXo+WQWSO0usMb7MWc+8","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","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","LypvvN3vtr56zhU+2ue3vfL68r2RD7u+LzLePQ5WPz2ZeeQ9OVK6veVBWL6iEU29ZMX9vA2HC7/kcbm9T3s/PgPtpb5nVxw98pWcvoQUML6AGrQ+aReDvrIflb3AHme9ECA1PdVgwT0eBsU+2ioTPuzwhL7Vo5w9FfXlvrv9HL8vocO8UripvrBYzr3KYoI+50GRPYszCL+e1+u+BNKvvi2ELr6hPr2+2jrdPcFEcz0n39G+nLzPPZi0KL7kHBw86z1aPUge4j6sc5m+OH8tPPBfITxd8m2+pf54Pd69ir6HcGG9rrUmviACoz7W29I9H9s5PRhmMr5ILQI+u5Kpvq/49j4uj7i9CaU5PsvsSD1L5WM9cyUEvpgB+T6N9hm+YU4uPiYh6DzufZe+dSVQPpoZw706tVI+rv43vkfefL7wsFg+WSCLvt0kw700uSa+Lah8PeQ4aD4Xxcu92Utbvh9nOL8wBc+9QMduvoOfpD52flc+v1dHviHroL57a5m+TjeVPj1toD1dAs2+EOvLPhsHBD9erJi+ojaKvobUzDwISAy+v1mHvgRZyD67JsG+6TJ6POEjg75SJgc/m6kuv4wqWD5QFy4+BtqPPOS/Lb/ehCk+He+Avuc2ErweksQ+TlHcO79EHT7rmwK/yuICviLAFj6sWo8+lKkkPSW91L7c4Jo9jRcDv/KUBros7Qu+xqUAvlKtEz2/S8+9y6syPCSYfb13h0U+wjTtPbX3mD7GkWY+TRhevUQaeD5tBKm9DyKLPjP13T1qOmI99PDVPtJr9b6yd6G+UvEUvqirGb7ZRsU+JIgIPP5EzL38HLa+26U3vqocED7PmM68D1XaPUh1tD2v7869TL2iPawyWL6GexE+sAcDvp0Qsz49Qpy++J+KPSTnHzxbOgS89zQ7vlM/gb7Mq7K+/tz6vljTab4hOq4+8dwjPim5lL7CeJ8+oB/KvaenpDv0t54+e7djvneftj7zyjy+bwgRvuaSnb6Rq6I+JYmlvt+zuj2/Ey+9","FxsjPmV/Hj4Jiwc+sfOdvjGuoLxaJky+zC+wPXDqcj5Ncni9ZViDPX6laT1BC+m8MuTjvfk9jz6EzS2+2g2XPQ3+Xz3lo+a+p7y+vh7uMj4QBkY+g8gyvqbQ+L2GSeo99GzwPoBpHL22gr++Yv3VPukXcj2ZnWw8CY7uPcxbyr4PDYM9FqAuvmvPGLzwWAM+HA9wPnaLp72v7ty9pvD5PQjTET9AGIS+/IsgP37MWD5Td8W9YA9BPnOJAr2CeZA+PJbFvljav76VSAO+ORK6vYbVND31ceU9FQBqvjK7ij0xgkC+aEkKv/9ddb1Qg70+icABvyo7BL6TwqS8M5mWPmn4pT6HgIu+7RRrvgHalr5Jnfq9nvSkvnKzZD4P55M9dP+2vcEXTD64xxu+FnSOvqjhpL7cEhY9ZKy2vZg/4r2qbIg+UCKHvdXrzr3+YWW++M5GP9gJLz3SH1g/GKZMPjfSwr2Pn3M9exWrvZ4Mpb3CKRe9IbPXPUNySD77Xh29tl3GvRuc670YsAK+hlXiPegeEz/EWe2+3+eZvalA7b50XpE+lh4vPM1z2D44ZI8+g8hEv5t/QL1pdau+Jye0vhtSWL5JQxS8UufCPuYugj5oOF0+03AkvlW44r5nbya+7nePPUp45Dxho68+WfA+Pr9PBb4eXDQ9SRndPhoGS75abSw+2k3gvkl7gT7VFxM+HbmDPYcOPzyqZwI+b3SCvqj1Yz45Xwo9iBkFPX5yJz64KSk/c3q9Pt4TBD7fjD6+OZVbvtmzbT8tVzI9Dng1PoBYpDwFPtQ++miMvQxXQj4yJbW+8GWuPXh4kz6AsgO9UhfLPi6qQr3AXaa7xuiJPsgJLr7RxuI+kxz9PUlqDD8HphM+VH9IvryiBz9fgou+UFrePsIVGD80ZvU+F99rPfElxT7PK5y+a9TRPgjzFb+xfJu+wSBfPReIqD2+PBc9NEaaPmcgWb50uKy6u0HYvbNeLL4fwIq7EouBPY2NB73aHEG+MauLvuXpMj59RYu9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","XaGkPVTNED75ne29Je75PaYE+j1TYBu9op1rvSYKBD6HGiq9TF/SPaXVlL2J6VW9XgYqPjmIDD6YKMy+q2TevfZ6zj7xOEe+HCKmPgtQ4L3lFTO+HSpxvtYXjL0Tzym+a51+Pe6mmT0TwM894pmaugwStL1E9QE/gYpJvTkviD7J2XW+zbldPku3xj1MPdC8+qKfPTiamz0yO9c8VfPtu6kgU75+zvU9qvOovg4T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","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","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","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","ugfgPbKYlr8/Y4E+r+wmvk3Om74wntY9uGlqPmITjD+hZ8896kTmPevsaz2Lb5888aEYvj4cU76GwQk+x3w2v6O6tL72A9w9h2C/PGKoHj7d3sa8Nbz+veJl+r3NBsm+y6PLvTUlsTykql8+FZhGPkZ1vb6yZgO+pBIYvbOxtTze6Qg/o00qPW1cSbx4KBa+tYySPm5LTT9kPns/agGfvgrIhL5UUYS+9Di8PRxtHr3RJK67/Baqv1AjWr6bjas+z1UUP61hrb0y3tO+OfH2vREUB76V/DI/ecDmvgrGHD4pP+0+tkdyvbBS4jxZEoK+oGx3Pt7uG75Jm0M+BCRGvg7bOj6R8SM+7p3LPsimjj1PH4Y+0kXKPXgFDT94oSi+eEPuPjlFiD41uKg+driBPUrSjD0aqLo+OSKfPcNZmD4a9yE96OaTvqyEub5XojE+02ihPiiEjT7zEgM/+pnpvX6EHDxHbKg+NzFuvsXfXj3rRjM9wOn7PQ78nz3OIYo8yuh/vrJvXL6Z9Lc+4zdtP9hP07yEWMS+Y+UePqLsjD5vAv4+fYxuvsD5YT4nXJQ+uPMQvSZdjjo3jKC9/9jdPWbDlj1sUU4+jzWxPlWJST8So3Y+28zAvgMGdT7eBqI+C+q9Pn3eVj4Zy3Y+38vLPjOrs76vR20+lDrmvin1hD7zpqM7aZGZvTzXgD1r7Qo+SC9DPPCFQj4tdBA/HuL3PYdYYT6COgq+TNWTvvpBtT0idaw+/ntTvhUn8z1vMDU+N0EWvrKSZD2sHgI+GbYzvrHvAj6s0Cq8/TzvPdiyoD61qlO7ImA0vxOPmj1Z4sa+18WpPsa7DD230+E6ayssPX6z2j1sVEg+qFa2vVzvv756+vk9DvPTPhyeUr6wNG8+blWxPtvQJT+XfkC9mMVDPv9Gs72ByFM9CmxYPXQBEr4Jdaa+5g4qvqN2aL7UEEk+q2xAvs0CUr4PcNs9FhhWPkfI7DyJAUe8v01UvkkoUL5CGG893OkGvbkEMD2oT3G+","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","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","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","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","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","J03EPNKf6z0dLMA93osPPptOUT62gJY8lAbhuysMpb2/49U9/t3OvQNYob2XzI++H8SnPVtYLj7m7IS7uTq1vLXrJz9kTOa9YNAVPqJqJD0Savk8b9dAPsswqL0qDZ6+q7BkPYmM1z361qy9KmRSve+tDb6ib5U+4Jx4vhdTkr7YXVu+govavQQOG70qlAW+rBD1PSQ+wDzD4z4+TTR5vin9mL42OAO/+mrpPebxhDz6CxA9sdFQvook9ryebqW9qtDDvuLPtjwwuaK9x1YJvnLgbL1mMtc8+mMhPQN+0z2Kfkm9VZ6ZvXDM0zyz2xm+l+eRPc+ZGDvtjOy9ISj/vR5aUDyxm5U+4Op6vXZFDLzZbkI+FQhKvTtlYb6K0J89V2xSvGjmpz4Vx7K+EK6SvZLr/r2e7rA9PJcIPRC4Tr3gtY0+2ZIDvXCA5rugE0e9N3ubvGiQGj9VspI+oDSrPZLIW703h1s8ZcetPfIitDtoIvg81A6gPblLTL0yCQM9Aa8gvjjkq7366aQ+WLvBPRLpcD6DkXm+wvg9PaMuXT0CE08+bYn5vdu0/T27/RS9FlSHvVA9yr6n7CS+uH+VvngaYL7H3bQ9jC/fPoAN+z2tUeY9dVtmvbPVqr3aq3c9qLRlvtHnBD5aha4+kmT5PbnLQr7SvD09SsS7PYx7yT4acEY/P8WpvszPGb5FETE/ByQYvrhhBD9VCUi8IOefPjAOH78W18W+w27YPQE3Eb/lTpY8seGcvgrRHr95llm9bdTIvGzFKD7nvVu+EGADvycqEr/GHbS80Z/avlG1MrsfFS4+/h3VvlMkhL43j9O+GphHvt0z675HiNK+fMNrPS6FET9tnPo9nJBIv+u++r4fJUq9FX05PiA1iz2HK7Q+P+cGv3+hvz4PF8y89kNYv/2vFj7120u/VB0EPk7ynj4Bkf++YNrSPU6FwDxVsFy7y8yJPh11jD5Px5S+QhjYvomZSr1eQZQ+xBtlPTTpDD+TYcO+xtjsPbIBzb6QLUi+","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","+NUYvkVnR7+sLiw/oL6Lu8rwhz587jK+U2ymPhwtlT5c1+C9YOXSvXvAAT8ME7o87z8nvxButz4nn++8B0INvyEPCj4e5Qc9xxXpvpxb1T4rnAE/+3TAPE6+5r4Nl7w86TR5Psef37q/Gns+Os+yvSepSj07ezY+w/XIvdVUxj27ocg+4qnmPtR1Qzw9zfw9NAPHPA2+YT6uOCU+iTY6PlTJFT3WIiA+yx6CvYn8nrx97Zc+PhwLv32HJr7dt4y+n7K4vQGSLz5eH/49tyGfPdwdm76T2Ik+dZi3vlp0TD6JMEg/T0PYPOkWhD7RpeA8fWEQPsE7Qz4DtVQ8y2b1PQW4J78WNuK+KRaKO36gdz1E8Ia9yGQvvnpxgL6nh0W9eF5nvTOZXz7bPEo9FwNjPmK1Vz1J+LE+7BQFvjdwCj5bIes8gP0YvRAAK75vUyu+V7CAPGrYcb4OuaK996h0vR1hgL51HzW+NnY2PXKzOL2JrYu+jbKhvQSaED9GFeu+PQlCvcAFvzxyPpQ+VByGvpMhUr7iApW6+3Isvd9V3Lw2eOE+oybFvZDOp73Ic7U9tdh1Pqd4CD7LmaI8HeBavhOGGL1Xnko9kL0NvhbI+r14OkK+pUkYv5+yeT4exEm/oCYfPeXCg72xsku7gz6SvgZHaD4oK4W+k70EPsGtlDqls6Y9OwkFPzgPFb1fqJI71MKDvjLIQjyTj4O+WEOQO6ZGmL0TZx6/5nZRPq7wCz7/ASO9vQwCvuOMXD1THWO9GDY5PnNSNL50d3K+UvKzPQqjRbs/Mwq+7ykKvjqfsr3VoT+9kyDOvsetsb05Ayu+hsqMPlROnTwqXN8+fArEvl/tgL6oHh09wbICP96vpL2vnSQ/esB5PMtovT69kCG9KP78Plo5Qr5604m9jIhNvt5wuD0Q41i+9hVoPvSJ+D38wSs+BFxkPXqxET6xfGC+n5TCvUeDGT4A5IK+/yS+Phl7yD28O+W+QerePR3Rzb4qnyM/m9ibvo7w+b2cczQ/","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","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","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","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","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","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","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","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","XXybPS/sSL7s5VO99uFDPSAYob6bIr+745WdvZDXcbzFhIu+fdi1vfcbxT4VygO+OxcdO8dx1L28/6y98GJCvp0lWT0nOPM9ZyTUPuMJH73u4hC900klPEcX2L2k+WO+1/fkupKwB7t7CkY9VKIsvZZ/nb2+qjw9kxFYvbtDvz36MbU+DWrqPiCtwr0s/WG+lrMTPsj/iT7pFsC+2Ow4vWgXNDwjbCQ+ufwpPXTSFb2jv/A9MFkCvEcdIj334Aw9XzfZvrqJIb6BewQ+z5URPmM1rj1+E5y9kyYbPWNEFT5gj5s9joLLPuo/Cj73WzM9AgrWvQdcBr07su0701PXPWM8LT0ecqc+RRWbPhpz/r4ZD509u464PmGMWT40NGa+kiOxPrKasb0CO/29rpXRPmT6C79UJWm+JIZFP7Gx2z7nKYa+qAiTvrq4Qb5TIyy+sG3vPd4SC74edUU+vtzHPvtumT76gQw+2jwuvXg6jr4ileU+X8QsvnQSjD4woOI9oZt3vlVd0b3nTts9YhQNP+fPiT19y7E+w1LHvFr6Iz9j4Qo/FvSxPv1tfr4aMlM8SmCBvtn3MT6ACqC+y8kHP2KZer5mwy8+twhKPmzKtb7kBN8+c+qJPqjlcD7oVqu9NiAZP6KNyb6lb04+plQJP5qQVj4igps+k1pzPgr+4L4IPFs+mMTlvSjMmb1fV9Y894ZavuAQT77cERi/Pskhvu8+Wr5feeK8f0zuPb2nqL6rqDK+fJRMvhwhPb5Od189yefovv1RBb7Z/BC+3o4+vUfzSb7FLEw71T/5vOY2Jj7nhjC+Pqm7vOPnKD459bu9VeSavTLmkb0qKNi+9caVvlX5Kb5kxqi+u4xevhLZUr6KKNC7rrBFvnD9+r7cFSy+cBTqvfq4gz3mrHC917whvnhV4r7UzqO+V8WtvuVJCr8Jfcm+D4BRvW2+a77npru+PtF+vh+Tv71cYEQ+T2yuvnUipr4VZrC+VGimvtv3fL5otIW+PUMSvyxu1j79CWq+","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","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","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","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","w6QLPjiYXb42TxS8O9bNPfjVlz0q+jy904UfvhVodj0Fugy+pZKPPbC4Mb7IDEu+tilYvt8DlLtYhdI7Tp4DvuXLVz6aEX8+kKUrvgH5FD6/+2w9KhLXPbrMoD1EUAs+CEjsvBX+B75T/cg9GAaHvUYlhDqr5I693o0ZPRP0Uz2lwfC9LeZJvoABgb66WVc87sh9vYji/b33lfU8W+Otvfn8ez027b8+Ao/rvL0nR7yZdve9BUFTvL+/tb2uZ/S9AXSyvQKhY7xT+6I9oNgRvvzZHz41Gbi98BOZPBdhOD2gwk8+XP1mvcK28T3f2WQ90CMNvoc7Gr549+g9B/zOvP2TAL261wo+aZ91u4fhRz1gXpQ9qmhTvTqTmTxac2A9F3AMvRuHBL1LYNG+CAzbPE10Rz3FjPw8fuOSPZNOrj1nta6961/WvGWiUL7grb07xkIovFCrF72tQw08D+ywPR7ttDy3WrY9Uezuve3yU74R2f07OpciPhqMN7ykSAA+H35+vI7HHr5fRc49GL5KPTWpo7zR2yu+4TLUvcQWHr1Tfmy9mvspvuu1Vz3obyk9OZKHPu6qM74Bkhq8Pv1YO/Fpir0h40o7i8ZePUV1wz0bwSo+RTNmPZ3nrz0yeVy+UGbZPGwZUb7ZyJM9ex5XvXkOsLzVDss8l4IlvsxXKb0kbsc95/GkvbZVJ75ukYi+zgkbvKpprL5oglq+1uN+vqcylL01LxS/9zJPvc2ydz7H7VI99EWtvraT+75Znh6+wc2ZvXzOCj478bm+hsCavTcze70OBAu/pZF4vi1Jy733BSk9qkCEvTMoxL3Gv8e+krlpPS9KM7mIKTK/A7+UvXs5bb23KoC+uq0Jv3+D4LvFTQG+LQKDvYZuh71/ek6+22z4PaaCEzzt1+08873IvoVXNT5sjQC+j9zkvRdJ+DyEW7y+cNLyvQ+TmL4YzYC+FpBYPaqmab2jG1C+LPHHvha1zL73r56+xtuivnw3jr3MHA2+q7DHPevlCz5c+mI8","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","ufdsP2NMjL+1Z5C+NKRDP8ngFj8quRs/uIuYvsLEgL1GGN0+gLILPqedOL8u5py/wZMoPwm4LD5hlaK+bpbIPnolMz+hDZY/46kIP9sdkz6L+3+9iztMPwRyGL/KMRU+HBZ0Plm5uD56/s08F9KCPRqH2j4AYY0/HhPcvnLUEz2IvAY/v2BQP7Cq8z2YX4G9z0RSP5Z/Qr4B/6G8taLIPuNAlT57Qc6+TOddvpa2CL+/PRg/3uFtvth72T4WVT2+5gL0PK+KFD7ko7A+zeCgP4Gb/r6NGvI+NMsUP70/rT8ESAW+qHr8vhgU+T7zS1+9GA4pP571qD46Y+S+YUC5vXhjyj1FfRY+FiZUPvQ3Hb6sh6Y9wH7APn7J7D6mJ0S9cZ7YPcnSYz5Scls+mBnbPZDZGj6My6w9vgO5Pd3yBD7BtZs+LtZHP9iKa727pE4+hoQqPeKJv7wRLty8y9xVPc/6QT6LJuM8ImEyPv+Qdb4eFi0+ibToPoWVvT4w+8A+ClPJPv+KyjuGdVi9MHoNPz82XL4biBU90B+hPjIoaj7qwqU9UTQsPjbZcz7+T1g+NdvWPRsChj5rj5K+uyGFvv98kL0nAGw+YsqNPez0mLsqXrg+qnAYPk94qT5bkCA+c9f/PSvCjT6c7/E+3E+RPgazjz3qIQo/I9zmvXnpgj4vnNy97JRlvT2hDj0TCBE+VASIvc3aTLzpr2A+Lf8lPgBg273oFwy9diuwPY78uz3UdCs+S8LHPVnpMD7GqoS8AWqkPSZy+TyYys89bdYJPhyl5r0WOys+zI/hvEflqD0ghZy9hMzePbj4O7z9ikg9IInhPVggXD2LNPM9TRNRPnFMpz0TSvi9bE9mvhlCij1mgzS+/uwKPjhD9r3bbZw9mJWQPbkl+rxFqvQ8oTjYvaZLIz5If5E9Nj8WveIQJLxqLxQ/HYgfPuXsmT4qsoO9JUKJPaw5nb1Ne8K9wfb0PS2rXD7kkes9/7YdPrrcr71R0Ga8sNa2Pa//mT7bm8s9","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","9HS6PvQFCT4NxQo8kaaTPqlRLL7LcP4+cTkaPtHDqb1KVvW9+7njPYv1f74jH40+CQZbvvT62j7ha6M9VyTgPXV0dD7bija8mMixvf1WHj3nrdw9Gra2PmuPmTzfotw+/2EKvqAAVz67a0I+3aMJPnOlez4uw3w+9O5rvkFjCj/zjD+8XFKzvTVA9j4brKW+6ngwPm4zyD2sRLs9ZBjDPvtlaj9GfpU+18pIPneekj0MW1O+yS1wPhXl3L6D0hU/ymeGPkLBZz686Xg+awsivgR8gT5bOVy9WOcsPBYDpj7MqYw+5tuTPlbpgz6DqF4+awupvZNQRz7zRZU+5f6RPn5Nn75mVqq+P1GgvTVlv716mwa+CTw/vofj8bzuVgY+5Sl5Pg3Hvb4WRmu/7NMfPmtV+Lu158G+TOP1vfiIUr64hdS+ql3JvcipVr6iuza+ZLGpvXr+ADunXXM9jBSxPs1sq72oYG++/L3BPbOihL7Zl7a+UjjOvTtt+zz1zYK+usdXvUARrr4e6za+8JH5vVkg8L6eWba+OGkOvn2I2L5wchY/cdwwvtfTgT3WlZC85pASvnEgyr3FJHO+cwvGvm2uxT7rcom+c95Gv+AZoLxru7+9gFxVvm+ZX71N/4C9Y4e2vj7BP76pj7W+DNmUvoEXdL6CbVe+DuMLvpPfcb8f/Uu9pCN+PXIuiL6lCt+9bamHPkb6Wr7fgrg+dFNQvPbCDz+WDlM7cTlmPmj30DyyJom+LkChvoAS9bzgsge+FpLlvUN92rwmBYG+hFbOvsmZAb1L7GW+gVg4PXlS2D2b6bq6Mk3vvIuSKT2BbgI+7O/Tvc5Sdj2htjY9Eo5SPhZlTL8nPhA8+h2eviBkkj6FDEg9cdlOvrI7Eb3Hb7i+JYshPo9UNj2aFCo9bP3GPPykab0m1Io93YSXvID1ij4jwp88g005PKRaQb+N4x4/uCVQPtm/JD84fzq/jzwHvLieKr09k889ck4aPdQZlT2LSpI+0ugzPrwBTz2bEAm9","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","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","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","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","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","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","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","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","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","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","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","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","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","eBmiPvia6LxpZCE87EmJvY3Lnz3G140+HiyYvmcyorzToBK/OFs7PqjN076O9Mm9RqKQPr0pk76qWS689wb+vPY9Jj4ww669VACSPlNabD7g3os8oBljO5IQob2GBa4+OWQAPqTU077KE4g+PlTUveVF0L1TJrs+AGhzPSZbh763A+M+ByUfvXZKGz9ySfS9KvOsPRXKGD6chwy+a8UsPsu2gDsJxnK9vrMCPb2e374+aNe997wbviJHVj05yhm+/p+WvQAIUD4rHqM+zfDiPNBj1rzKgGm+ZsOqPdcph7wFEHE+V/yuvbFedD4zjSy9aIGJPlJi0j1HND4+8CX7uv/W/T2Ja0E9QzP8vhwHiL3yyhu9EYJ6Pjywbr0WOWu9e4qGvtm0570v0Eq/rosIvgbLKL2GnXU9VCLrvIXEXj1FEtg+las0PUt+gj79IKA+/gNWvj84Sb39fzO+yZMOvq4ZNL6RDr8+1ugQv8whg7sfH3S+yAa6PZeKhD5Pan89sJ+QNwBwIb6ZBu8+sWmlveNCEj8zl98+CSLFvWmvGL2N3dq+qTuGvs7QQL4Fw606Xlfrvclbo72Q3BW/DnSIvnckyb5YQkc++5GNPse5lz3JfI4+YVybvedVej5Zfxq+0UkHPuzEfjvZzY69w+XDPW1+sD5uqks+6wNDvrLqML6U3OO9LoNQvc04OT0ARj4+9s5QPrEQBbw5hBs/qZN6vgITKL9VdiC/4yE6v2CeAj3pfws/cRrAPqAFvr79lCE/FYvVvV04Pr9JxyM/xwpfPi3oab483I69EdpcPj46nb6iE4Q9Mzs8P7NvMT385Fe+ntqHPqeITz6S8Xa/5ImgvqbRxD4aakC+VtHHvqqow76Seke/fVlGvxR59T7+Ja4/HdYGPyH/9rwZWpy+A/iTv5zXsj05JQc/tjMVvzxBAj/TxEw9UJQCPqd6ij4Bhre+yyvmPhg+ib1NaCQ/0CszvwLulb7+ilc+/Dc6vtxZRj0RyDO+3wvaPqlk1T0hv1g/","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","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","r2gvPmwuvr5RUi0+ePNiPjpYXz2U9q++apVDPJIK6LyWt7q+jGHovVLoLTyD4Km99IogPtw1jLzebpI9iB+GvvcmKT526oe9xNWlvWR+nb3+PG++WkpJPe2us71p5m2+yYwXvmYupTwKsyg+xeEKvt8YBrwPv4Y+n3BLvTMxr705IyA+Q3ZvPoOP5j0aBEk+wSsePpqdND227ge+DzALvorwjD5Yg7M8iuYIPjUXKT6TZTY+nwKSPWtr771jO4Q+OBZTvrpNqj6N/kG+5is1P/fPm73eQoO9hXGKvsoxFD23pmq9Xyp9vuVhvT0dnTA9YKbzPW9OOL3Gruk9VTG/PWbepj1G3l0/echzPv2feT6gXTw9pfkNPk9cyb4pYJo9clIRvvAWkL4zvnu+jtjAPu80nz7ZKLg+bXfZPUXwTbyD4no+iy8SPkVKt722yzo97qr/voo9FD2bCiW+cdWRPhVWcz73b0M+rdAVP8vmGL7n30w+H1rePakQ9j3O7ks+Mm9Nvyw+Lj5uDMM+IUZevjXumz5JBqc9UlzfPl4f6D4uaqE9yRdMvoggWr7qtOO918oTP4WRUz9dPUi9wY+GPuO+TrsMcJE8k1GBPK6NXr2Gf9Q+CRrUvsxpXj1e4WQ8AM+APne0fz0iWIU+1xCbPnzugT6FHJY+Wa7ru5r2dDzN5Vc9nvhxPtaTxD3ldPW9ZndGPASYaT74Klg+15dLPKw6yz5rlIo+/fb+PRU8ob178T69nUWZPHApHj5V108+3uQkPZmUJz73dRe+Kxmqu0FNEz63n8q8NSaPvbrxQD6xBis+7HfoPpcKQ7w1GyU+XWiHPBGtXj6Qo9s9J7PePcmxJb6Mza09soDnvfTF7T7O8t4+rBKbPlmEjj1Io30+jF+6PkyhGD1sfRI+VZx2PvNrAD+9r7o++LuAPZpc+j48gRo+0a2EPnH++j6T04Q+1ZTpPmaIrz6oPbS9jygAPgWHGD4H1ic+ka6IPde4Cj3HL5Q+vkyQPQCJbjwGzXE+","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","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","m6A9vlAxKb6RyxG+/2nCvssGeL2ojy2+oxOrvgh2jL7i7ua+UmwZv1/VtzwcRKS9PQpLvrtscr0HUoQ9pv48PXJFNr5tFH6+ZOJPvi3YP77iG6i9zgluvfLIkLxlTuO8Qx2PPOiogb7tijy+ITEAvurdp76phdO9vymZvuxCJL7mq9y+t1tOvszCur62x829oOI5vtFgjr1T4ba+aWo2vrFA270nkDW9+zhNvozT4r54fns+esxOPYeni71bvNY8/nsrvviI0b1b2FW+8GDZvaNOEL3Jk8K92BAnvTCCzb0LWWu+RNYOvo46M77HWzs9E78SvjUmlr0WxN89zXkYPbZekj54pwA+MVLAPZZe2b2j2oQ+CmUHPNRpOj4yO1S9HZFhO/W/pj22HKa9U9mivqThmr7NaPk9+maKvLkhVL0lobw+APguvUOhBj3l4FG9D+wgPk8G5T1qAFI+Zf9aPt3EALxdZzc+4YXLPVk5hL0cP6o8SeS4PfKqCz7QNMc+nZAPPiyI9z4z0lU+2tcgPwJvp71Sk6c+2wgoPkzQeD638a8+CFgtPo/j4z1nd4c+43/ZvDcr9j1r/PM9V5ZOvFzCUj4A5Pw9aCzyvYh09j3AEwo9HmO2Pl8vhT4PDaE+65kOPumnLT6ZAKY90e+kPeltrD4xOps+I1sYvvHCiz0xUGK+6pXpPCrwWb52Zcq95awlvv3Ep77CIoQ+CPbrPctqXz71fnM+6ZWMveIYBb7rKag+Kq8evH108jtmdLO9PDwLvsw3370WYQi9CMVGvUqgtjzomGW+R5+MPmdEXj7Ut0G9B2yXPaZ5Vr5MN7y8J9/ZvVhZZL0GD067XVkPvkejgr32Com+SgGsPZoOHz33aUy9VkAMPgE0j70Ml5i9O/UMvl6fW75R/Wc+Qq9avkN9Fb5Wr749NwPkPepJsb352g++JhzKOs5J6L3RBic+X8g4Puwke75j4AA+dXryvZoUfL756g8+QGW8PTKtBLvHvDu+BaAvPl0m/Dyfa9q9","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","pGO0PW9RjD3T+yO+chg9uypwer54Rve5nlwtPpEbFr1c0409dveqPf17Hz3cHRQ+dgeJPsCjujw2Fx0+2bdBPpyBvr2MleC82knlvqLJKL5sdFw9tTu3vQZ+OD5C5Hw9ov09vRN/OL2BBjG+DPWtvY/2BD91Kpu9MT/MvUZdFjy5c8Y9QB8Ruz2LmL6nHS6/xRF5vG8U2z7OPIy8xtQuPgWO2j0BJCg/1Xkjvs3BGL4IRsI9hCVOvtiTsb10dES+woSIvWlGIT5VrPY9Z2KQPeFA9rx/pbQ9WsF4PjFsJr7z2QQ+4KqevpA9xb1spBI98FehvRTk7T2ap1K+VjCjPTROmz5pMyw+6GcpvGhBtT2Ya5W+eto5PloVab50fSM+KaLuPefKCj0RbBk8X+DPvcIIqb5Yfcw9UX6NvDogOr10e1I8AdV3PZHYB77CiXa8blq/Pc7nJj3NP/S+wdGGPcWclL0U39g9PcNwPTjSlr1xtVG9iOBgvnxGwT3ccz8+IWjePbFacr2Dqpe+F3EMPoKiVL1+7XC9gQ1QvoiOmLsKuMu9+51LP0mnjb6FKg++dJdoPGXmpz7kTVc+SWgBv8DJMb94A0W+pZnFPaJb8DzKXky8T5ivPakbPT4Ac9w9QUi+Pa1XJb1x2y8//iYUu6b/3j2goh++5PVKP5xRyrzNDrQ+0gfRvXEvqbvY/Y+9RRsZvlb1770/yos9waImvhRCcT5AV6c9PNNjPm9CkL67zLQ9CQk1vjPOeb49SkM98aeOP9JR5j69pYg+7inNPgSlzr5SFre+m9XFvaev0b70KTs+r2huPqXkOD6WE/4+2e8GP5oxnz2ORKS9V4uSPjaGBT8vhw++L/bLPs2R/j4nN8k+YUvrvRew4T6J5lw6iXysPnxGVj5nKUU9KM9SvcJ9xLyqKQc+9KQQvtpmzL0baWS9YHv9PhUvOj4g6Gw8X4SGvV3Lsj742Ro9Yfl3PvQEAD9hoEm+3aGfPr5i/T2A0A2/woIMP6UzDT/gfZc8","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","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","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","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","fmdJvs2bmD2OvvC9weLgPWlDTD0n//c+H6vlvVoTGz73Go8+Zf48PtRpSb7lbkE+nibSvEJePD0Qtcg7dynJPZ66Az6CAh4+A1QIvsuJvD0W1aI+mjN/PlQ7xb1sdB4+dtAVvE/gMD/h9CI76dnNPoX6iz4slxc+udsYvpBsxT6Yg5w+7hFXPQDy9j2nvjg+RrLavRBuljvLVCY+bu6IPktKzD66KYC9CK1dvarSa77vbjY+bZKxvbwu1T1/bqc+7QfTvDNOCz7iK9K9cW1VPifYij4hVzM9srdRPis7cT45th4/8JnOPkfcML6nB48+/sAXPquXBj95+Dg9UvsNPgKqBj5wMuM9jB43PjqoAj42Qyk7uNJ/PGmdFr48ZoO8+voWPZYkxjz3kd69YzbQutGr1T6J+AC9e5sBvR2s6T3Ckpg+Fi6xPTpplb7Pqao8PLjYPeBcnT1OqN8+GRUoPp2BVj3RQj2/AL3ZOtKZgb0SgGi82kmmPiBx9b3kBBe+gE8DPeVtVT0VrD4+5dAQPv+L3ru22sK91FL2Pb1Ilz1lLKa+hBhKPVwBWD6YljU9OidlvN9Tq72yGE4+S+/bvDHWu7z19jI+uL6nPnlURr3ohKO9OP6RPtNS3Lxm7CM+rRiZvkIz2r0MpcQ83gCIvJbuIL6rGBU+1XEnPh+HMz4BgJE9s6HLPY1slb7zv8A9lMmGvYGcJT51doc9JXH8vQvBL74KYli82qz3PeGVNby/ueM8SDo+PhdYoj3hfI+9xPNSPuewJb3bD4G+76aRvW6bLr3Yywy9YQhEvlqHkr4+0Ci+FdCLPjNZpbued589aYGKPXw6lr2eFXS+l/xBvr/Slz6DhrS9RSXOvR0/wz7Q30A911cFvunzVz2U1V2+uZeJvS3tA76Vhzu+pzLJvUcfIDwnwGg+SiLqvS5TMT1VF6O8IEcQPpO15z7jOik+QtE5viZj3L3/5wC+qeiBvUBStzozsPC7ziK/PUkoZD5ohSq/OHgIvlD7iT5dHBI+","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","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","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","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","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","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","vnTXPZtNKrxxAIA+QL5hPctuYj0Hxy+9to8NPi/wODw9718+/LqLvhyt2z2yK4W9PBYAvcfyXr0rf5e7LZHCPTiza775q1I+ks45PVmU3D0c4og9+XDDPm18F7ylfpA8QOH2PYGXGr4ryy4+dxHCPcnN6TtJL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6z760E29P2GCvrgYBL1eMaK+wnwCv7LyqT3Cf5q8XgAfPq8vGr7eHBg+A6sPvk1daz5VoXe+FDwJPjxJ2L0f+Kc91BBbvhFBUj5b/8K+uT/jPRtslj0HrAE+n58BPpu25b1tZTA+fYOJvgP46j0hsog+6YECvukNiD5xUha9fhuYvawKLL9sBuI9WzHqPt9Epr5xr6o+EJmJPl+07zz1GF09DfQlvZEVYz4vqoQ+5kD8PNBUYb0n6Wa+Nbk7PgNJkL4PUbC8y5YoPno5/j0KBtY9+zaoPfC0M73Avcq8CNiSvu3Lkr55hwm+c+fiPV6CfTqp9Fk9bG4WvRk1Kj47Ulk8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","gByDvoMyir1B8x+8sW4mPMRvYb5bsQQ+fV5CvmEwR70QR6w8jABwPiI1T72uDO08s02HvHi9Ar1aqlw94CRVPrJRSj42kws7MQA3PQHGDrygzYE9q5cDvgwFDr7s7QC+vm6KPUfefjwNEBc+ut2KvtVfvD1skD8+TYM1Pt33q70+JZu+/CghPq5ZHD6yMG2+AxDAPlng+T0IDim/NmEMPc8mBr80+A69RS5bPc69Yr5tBZA9ZWSDvkrC3jwRX/28jO2vvvoOBj5FKGO+ZYIPvVx/kDzjLpS+AwlCvZzMaTzf1FG8bIkFvkPkhb74Bcw9tyOrPlL8BT20q7G81yLPvBWHo74U5tQ+0+ccPbYzMz3HaqU9XAb8vfkkHL5WuCG+oXWTPXe4Nj7jLW2+4Ap0vUtbajyUrkO+IKZoPTePvLy5Wy895V6MvVVfeb0okrg9XTRzPVz94L2dNZE+Tml5PSv+8L2sQmo+UsriOsb+Br4YOQm8rEoJvmRaHb7JD/e9nNcyvRWOsb7pbAy9SItLPdPovbxySdM8EPmYPq1zNj2MVPW+EQjlvWVdRzxeneC+0NmmPT/YnbzvV5o7W+ewvbf9qT6bTZ09S8ZMvmo+3z3goUs9twsRPiIMnTtj5AW+7+LTvfpIqj61YL+9HLKgPJ3Eez1B9ZA9ZJZYvYXSAr54dJk+vIYGvno/mT2rUEW+YorVvtjymb7Wc/E903/hvn2Xkj09ewS+n3CQvpoeHb8TKPy+7xlKPnevAr6x3wu/5QFavkxIGL/9Qo69iFoCv5Pl3r52Shk9vx01PvG2rLwmqzy9aQIhv57eOL7IfD6++5WivdOT9r7uJF++sQgXvnYyez0E5DG+aR2uvq2YE7/eBJC89z3LPvakej6IvPy+PaEAPcah2T6yHbq9K9GnPcKPB78OciC+A8sNv9VWND9W5H09D4gzPjBgJ74Dvbm+vh2FvVigA7+V3a0+7UiCvZWFJ770MEc+niYgvg4tRj5BGr898aQAPTI2rr0VCAu+","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","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","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","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","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","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","OI8BPU/Uh73eMas+0RPfPX8R/L7dXpq+Nm/HPS7Enr5g5ui9rurKPmLrB7/I3be9SBQHvlEQyr1KDaO+K+EuPaluCz7hU5Y8d4ssvavL5j03zmK+siI9v051mD1zyTW8g7TFvTmhKL4oves+Zw1mPi8Hhr2i39y9pf6QvlovOD2pj/47m2V0PcmKM77cTFw+2ORrvXOctj0eIZM8NPIvPucVLz30J0W9eDPhvVZJCb0c9Ly9uX0rPrAm/z3v82K+XUEsvS28bT4ifbq9MI1RPrCBM72hvyQ9FfU/OmwPA713urE+1JSAvmzepT6oJ7E9J25uvQ/AOb65TVI9ectGvqA6kT/ZpQ++eZk2v0f8TD4YnzA9oTwDPjywL7+z5he/4V8VPzSoTz78fr6+9Sp8v6Al6762OiI/54gQP/ActD0MVYo+uJUsvw11wr4rA0K/LjiPPtaUoj6R53S/gg8KvqWfHT8IBYg+Sx6gvlp6hr2V9We+zsnXvqaiED9xHVa9anviPeG1vj7jbes+uKu3PtPXgD9V1Aa91B0BPzfhhL34TiK9lHrvPrViTT6kffw+yW77PiKvPL7gXTy+4PDDPgU07L65nYO79kuqu7KHAT+dCMM+9e+oPvzV7T4hYA6/GdWsvhn4lz4ABHG89/OkPsSTV77Ju9i9wsJBvdwb3z5K5QU+5puEvaj+nrzbnTy/StqKPd/GBb54P8a+0siJPkkHcj46U8G9npOEvkgeBL60keI8DcNpvZkVwj2/8UG+55GavdfBXb5WFDu8OKR7Pe7aO75HDW2+3cAuvXvnKb27xyq+8kg5veaPSb5Pfmw8EXJcPISZJb7meoa+sLJvPKeqsj0zDt2+PbbevRMmaj5Iyg8/zoiqvVVmCDzdkQa9TYtfPaE3er7zXYm9+WmoO8kSzz6MnoK90cJoPeDNEj6V/fc+MjAru1X4H79lvuy9nE+XvloMw76CGK69UXD0Pb6cnz1oBvg8Hw5wviYDf76k3ug7lF8nvvWO7T3lB42+","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","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","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","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","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","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","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","NioNvqhjUT6jaXC8i7n/PQZ5Ez+z0a09wWMwO4j6mT6LdMc9NHc1v640Kz5qlqI9TNlTvnywnb3Djpw+3Xw4PjdSBj4SKja9UheLPbVcz71/SeK75MSpPV8L0L14BY4+J5i+vQtiAD5Kvb++uEdhvi311jy8hYU+p00OvvBVJj8wcx++tFghvtR01z0HBYu+7HskPr3yqL1d2ws9RW+DPce+rb5/yVw9kYMzPRzJTL504k496asnvh8Lm70Rp6U9jj++vaVnKT4D/QQ9UE4HPzAkED6xVag+KJbMPbHOir1kNdi+IYapPlNB3b5B0Ey8yLr4vGEJAD4JWRe+enwTPhdEn75WQgQ/oUUrPisWw76AYoO9r7vIvf4PZ73tlgi99xKbv1fwtb5ZLSI+wMcWPR+nLD93xhm+Ev58PkzAAj9XgL49DrVwPiywPbyth+i+Bq6NPqI9Fr4PBwe/lXwYvbPJVD7TKoW+qIPlvmZNiT7cRwU+2BjsPbQMBr9iwoA9E/YCvmO7f76jlhk9NovIvR8Wur67yHq+B4SjPsXoPj0fDxA+jwBhPZ87/L1srSK/YZ2YvWSqFT8almi9qKx5PQ0QrD6WRg+9PkeIPuJYJr5JR40+0fHyvndMKjzykh0+gN6bPhV3FD9vSos+WZM6v3GM0jx3Zzk+vY5cvbUW3b5Maj8+zomIvqJZPr3TIFC+Sh2vvsjSED4DNqO9opx7uwx+4TwUFMm9smS2Nyu9RL1TeCy+J+f2PZv+Yb3nrs69saO1PPG3aDwwR9G9QvnkPWEILD2y9KY9IthTPTyT17wbq2i7rckgPcZwZjwEMRs9dLOLvh7eBL5Ai7a+CtafPXBtrj4jbie+qwL7vrqh+b0AglI/SbBzPixPDz7lGJ09rlBCvdbV9z3BODw8GlWNPphX9D6QQ6C+NcWrPo8FnL12v7W9PQIPPtRkDD1kTB0+RdwfPau1Yb3Xme488H5gvphvqTuamvy9WfRKPdlLez3Nboo92UvePQs3Ez7q2k6+","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","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","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","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","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","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","ZnXlPtyUVL4Tu2W9rFcVPy3Ngr36aFQ/lKS/PukYE7+xqIk/wQkcPn5/Fb7BW2i/Sls0vpXy4z6SXRO+LKXcvnpJCb99Dqk+Z1pOPwjxGr9HpBk/ay6qPmMovr6lvUI8J6VEvhZbGTw50RC9cKrBPIiJqT40lNm+wXt0PgJTDj7DQgA+TUcovhTqoz068AO7tgEiPnVPvz6VMQ69jd8yPU0WDT6roQq/nNK8vRT74z5y0f49xxVXvjLswb5otYc+W88TPawC3D29ywo+2nsTPZwKpD678hC/coRTvtfGmbzKnqo+KV1FPwEz7z2l+IM++e+nvj3qDL9JRWE+pRe9PXAykr3TjTI+Q4Vavp6BLL9zK509zcsKvkh8nT2T+a4+pU0hvoJKl74xgNK9oLWnvUHLsDww9wi+ED1zPepwUb5kEjG8TW6Dvg3VKT5SSqe9HxRJvOLwRD0KEB0+Jt7kvtFwKj70V389hIRzvIj0vj63bHY+YBoCvsBBxj4PoGI7YTdMP8xbAr6O7i4/CyETPvpRxb5JnGm+LZ2tPZO07Dx5J4I+sslpPoTwvb1GFlG9llBKvq1uSb3ffo89P/5cPovXHT7/1zQ6XcPmPo+z6b0jD/S9GhdKvXFv2T040Wk+ro0FPs7adT66dEE+GeuNPeKUj75GrA8+4y8HPpoCzLwIIwe+qVs1vok3lT4Tzrc9ILbCvUTqmzssXKm8zcEIv9ywRD3c36q9aGo1vs9rQr193a27WeMSPiF+774TvP0+KppSvsgKAj6mCX09cVODPA3QlzzjE5U7sb1EvYUPnD6WaII98rBBvl2Wlj7VeKG++YUIvT7PBT7suTA/TO/EO37GPL2scpI+qZEFPhfZ0D6A1A4/wRc2PJ17CL4+UxK+tA+SvhpUvb0ay9w9XAUzvnAPlT3UNqs+j9vZPak/cz6613i+MLl2vYlqMr030kI+MyGaPVldRz6I45e8t70LvduhTb55xpu+wSuWvuQX1r5d8Bg/Im0KPhyvD75XYBa+","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","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","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","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","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","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","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","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","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","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","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","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","014UvgYWfr1FgBs/vWFLvm7kUz0gnAG+2zvbPCYxAT4YTlq+PTupvWJa7L2nS0u8FuT5PU4VBT7jzHQ+mAY5vqT6gr6s/M09ePwtvjH0j726rxQ+0X6RvcSznD1/CkA8C17UPaso5LrraA0+OG07Phw2Qz1s0Sm9fGkdvsxgwrtAyE4+oRVSPq4aAD1UZZo+J1/PvUeeuDwW5eO9YZgTPUb3YjsyZh8+yaVsvipxEj6lp3w9NjWcOtwXET438We+5PvavbfiSz6AnYu93ejwvNVqZL4Opiy+dyqivpSZjTtrc428QhfQPUPreD2n0Qg+iWc7vkKEub4McTA+UKcNvVlZJr6DpsQ+e+ClPdJ41D5SROM+VWD4vv9OkL4iBxi+o/Rxv0MEnj4gp9m+a+LQPpu2tL41fHA+XhAXPzH2jT1ebT+9KNFcvjwvCb9R1/i+UZdNvzNeaD51DZc+03qyvNgNNz+zeCq/A3iBPV2XJL7m/C++UQ8vvsBUrz3bjhU+W1lwvixNsj5Lu2m+ZMYXv1qW4T1VqjC/bHZuvo4IPT8GBrS+AbgCPlyTzjxQhPu+4ZRDvX0zwj5EsS0/FlyTPjmGD73FAkg+/Hy4vr28Fj89XVm+WEizvjhYkL4Pa7e9J5LbviUQ7b1R0F+8icO0vvIOUT4skAk/nC2tvQO0174X+Ec+VQ6Avmxfoj4wNle+qhuTPjZeBT4mbQm9as4iPHg6gT7zAoc+f/v3PHMvDL7wosM9O9QsPoueCj4Kwvg+4cOtPnLTi70jcj2+Yew2vc9eUT5iV/U9YjpxPX90xj4c8249NIWjPmyUGr3mAjs97oqIPRClqb6yieQ+nL0ovsu1nb5NzGE+LDpDPw7wJb4Wxae8lryHvnrTnD7lLMe9bSwyvcNkLD7DtSG+SYtBPuiYUL4SWH4++CIVPgEKUz66qaA+dxi6PujJwr3hxPA8et5oPgYyOL5xPN484fCgPcPbnT4mcpI+YO7+PhMyAj694nq8QPipPkwAiT5Vp4k+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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w2lAXSCIvQchdUBX7pBATYILQCU7OL/b/2tADb5AQBRbj0BXb5tA4EbWPYhqDT+6Q8Y+T02AvX+MD7/TFaK+MWuOP2lrML4JOM69CMibQP151D9qbpy+JoMwPnlaG7/CcDu+JZQLPyGp0D8CH6s/E5lxPzUiD7/KEmo+tE6cPn29wz+stke/6b+HvlZ2b7+ZKq8+8C8OPypV/r6n3ie+dnIQPXRHBb+oAp6+y9OAP5EUir76VYA/UHgwQDpGv7zhbjO+NuOfPnIgnL3lxx2/1OlgvpFM9D6Lw2y/40HzvrVVrL64fh4/wpQiv7LnQj+U+3m+L28xQN8A1j8KO4K/Zed5QA01IUBOFBo/X4IWPzjfqr9mHc0/16gxPh/AKj+31lo/7xDeP5sBG0C+920+yaHnP8+ZE7+nxPK+","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","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","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","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","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","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","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","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","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","iECZv6tLIr+0EW2+qcm+vztQhr/qifs/+3C7v/MBgj/yjc6/Bm1UvXxvmr/4W3q+LhO4PV21+Tx+NM6+Au9MPvtXBr6H2JQ+tgU8P2KDcj9x88q/vqSmPo5VEMBzEXk/SpPrvq+EQT4PRxg+1nnGPu471j2jp1E9wTi7Pr2cub1O8JM9P4ppPSC5kj5tTw4+gTAGPRiRyb06Qge+HWhRvg0Fuz5PcSU+jZ08Ps1b6D6jTGY/AfOwP87yMT/IIXs/lKgZv4/UAcD3bC+/D+O2v3jKer8fX/y/vTw7v/yw9b7p2Ti/2Sl6v5jfgb9xcw+/Rr6CvrB7hr30Fg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\ No newline at end of file diff --git a/src/kernels/gfx942_ConvHipIgemmGroupXdlops_encoder.ktn.model b/src/kernels/gfx942_ConvHipIgemmGroupXdlops_encoder.ktn.model new file mode 100644 index 0000000000..77c2ef1f93 --- /dev/null +++ b/src/kernels/gfx942_ConvHipIgemmGroupXdlops_encoder.ktn.model @@ -0,0 +1 @@ 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","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","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","jYl5vbAiyz36rgC+9184vuI6yb2YIu++NHpJvjLEWr5TXJY97zuAvo7gY73q1e68b82Nvt1zsb5XNgy+sgvfvbbHKb4cgNq89x1vvvrOgr0LDTa+GTfNvRqkBr60A3u+M0U6vsTuWL4nYSa+XpUkv4DsibxHij8+nXfiu9Upkb5R2oa+2gcAPvsfKD7N0e6+xgGgvc5eJj6dT0O+JNcGvnqssTxsT3u9Y9wcvRzUVz7eV7I+V2PwvkhVq76FChO/TbCaPmw4VL7jVvC9ggc1vkLLA77QZgK+1p+6vm4Q+70pDQs9NfAXvgtv2L4fhRq+pEYavvuBIL4YLK+8Wxmovly2W74yn5K97baDvoA/rT1PSAi+06FvvgXH4r4b0p29KmdrvgMoTL5yJP68ULYzO1H0D7670Ym+ZjiXPKMe5b4cgkS+NyJEvo2n+L0oMBU+D7ewPHtdLT1X+qe+KAFovQ+9OL74C6i9PTOvPW4umL3066y9Scr2PYd0BTkQaLK9Hhz6vcFBQr7Wjiu+jwJuPZY8BD4ex0g8cTIyvmdMpT3jhbu8tqtevBvIhr5IdSC+hX6PvVcCdb5rrt49mgjgvKuxfb6uqX++VflGva9af750Rfy9BUbAvdBNVD4HHeG9j8wgPrvpEry2dqQ82ypMvr7ljr7Z7Nm9s8IavhAFN767+D2+5K92vX/lDb2GEjK+eu+jPGRZNr7uMD2+rPErPky0gr16Klo8XnAMPajGF73qw+a8oruGvny+5j3mbya9v3wgvpf6sL2TgkA+EDxluHeLu70cbjY9UJ4ePqjukD1l4a88E07WvDodkzzTD7y+tBSmvaFJlTxVY5E94g+1PU2Kmz2T6Fm+OXiEvk5N571E0Xc97ffIPgGCGL53YOi9KK95vs6cqb1yLmC+4A/9vSSVMrtQ+jq+sNUrvSp44T17uKy+CePMPYnY7bqV0h49/1X7vbTcBzobVja+68uGPCeLoD7nzrM7CX6ZPOYDir4HXAQ9eKCQvFCjAL7H3Aq+","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","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","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","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","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","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","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","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","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","4LvavX7nrL1H2G++Vae/vZeT9T03BNU91nXmPUvZlTxmcwE+KBa7Pd85mbt9iJi9mo0fPpO7UD5TI22++CaEPEZxrb32fQu+0roKvrsFpT3LQBI+Jt8APuSZNL2eXke+EG/WPS0r9L1nrKI9wBqzvQrvCr6e5Ku9lm4oPdihML4vkX09DcC2PViXA76rphS8BvYGPl/xj71mf+W9vj9WPFBGw73xxIu95Dm0vW8Oij2ehdM9WRodPI4cj71QRiq+8A61PfoYDLx52Im+JpwBOzstw7383Za9cVgDPz8s+zyLORg+WAnWPXkz1TyIaAo+B1WCPqtRpb1Qh3A+USSVPFHB6rzllRU+9F8SvTvRBz1cTB4+V98WPhP1yD6kr+Y9TD3AvYonEb5+33s+pBbPPEzUA740V38+8SE/PhzlLz0kowi9Er7bPW8KcL4dnai+FQflvAiHhr7ziBi+lFaCvjvmab3RHYG+lSMlPbxbn77e1aU9EqzXvbCLET51SMo9oVAPvdRiJ76sV8M9fhPrvRwpp7xalNA9wuxmPaAMHL4RRe87oN82PapOOL5P6ko9LbudvED8V74pbWw8nrstO49sz70QDQw+cMV5vnQCAj7SSKC8HNqvPXR0WL7VdAg+odqWvbHaEL7g16g8TlEkvjRD8b3AvDy8xF+mPS5rhb3w4BG+AbjePWp8uDyDzc6+H8XIvayhXT15pxs9Ax8APa98GL4DYy2+6K7WPLD0nD4KQs27xQYKvrgAKD5/iIs9l91evnGEgb5LPwa9Co1CPUgSKbzgSHS8znDDPRUrHr6rkAc+iXBMvHFGBT4fOiC8AuPJPa1nL753Gca9EtX5u9dYGb57ZQu+WhaEPYZQAz49dFS+NZODu5K1Jz4Ohrs95xEovhADd71/8Iy+rVLzPVX9Pb43Y3o7e9VgviCgBb58RvM9oG1BvuQkbD2Kwr89+tElvsH6Vb6V58O+6FoGPisHXjwGVCi+zUYwvmao1D3kNdO8Zf1Nvm7Lwb1US7y8","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","tuaBvQ/aW71xc4m9siNZvmF+Dz6vpHi+VeawvREtgz1piWO+p+lKPt3muDt5v6M+azWevlE5FT4L/vG9CAIVPpJEtj1TsJY9S020Pf6buT2qSji8MQmuPXzJOr76q5q+H0mcPhkF+j28iHs+frMsPYAwYT5de+2+X2HbPSrxX73QDbS99GEevkk8wL0g0988mkQ2PoVdgr3syoW98DcfPo8AhzwHojy95N1xvYR6tj67ou28CEWpPVGijz483TS8rEQ/vqcA0D3pF7c9dkkXvS/9wTzWFfo7KguEPQuCDL4EL3e+pSvPvJO5DD53UqI+BKYLvS9Fj75tekA83+5kPjYF/r5VsZA92+wFvduZ070qJ3u+IPmkvrmOJb7V6Fc9m3bJvXl6z74Rzwq9vs9RvTbYyr5OSUy+cUSnvWCYXj1KIEO9ORCYvpIdcz1Pmou7HAC/vlSwRj31p5K+/5x2vg4Bpr12ZYK+kxGeva7lib7OKHu976CQPTOIB73BF389KgIFPeM0Db52oF29ZzQqvYoZmL12xwO9NuVHvYO2cL022KO9B9o+vJbvOr5mfb+97XPvPP/O4L2VyaA816e7vX5dCb7/OXi+EMyPvc6XQr68Fkq+Y0sLPeWAb77w/SQ9Eb34veigS73miGe+cw10vU/FdD3MRUU+j+KtuuUTBzzKmvm9SrY4PgpmAT47jFO+jSMPvkJRUjw2kDq+uDUFvZEMoz2Yj9s9nikAvcVqHT2SN6W9yxCpvJo8sD6hONe98AmGvKtxHr1oaZm9sbvnvcjUgz65QkG99kQ6PkHMUr6w3u68aAIoPnUahT5SZQY9V0qpPDEXWTxxYFQ92digPZaOg77JKM+7AnnGPIlsHj28s4S9H0i/vR8mZr75bLU7mxzyvfzYrT0XTho+llo8vtzAi7766L08NpTRvVw0y71N2oy+IuKOPX3ohj7My9g9Mu1+PXa39D2tnJW+JdKRO0sxGD633gy+PP8xPqVBFL0V2VG+QgpPPp4GDr6FNju+","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","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","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","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","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","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","iS+KveSNnD2/sD49Zz7EvvJEBT76f7+7EbS7PTWUzrz6dCw+fcSUPE6+ljxYdEu8IIm5PTn3SD03nPE9dV1IvVhDib0EOYW914DwuzEsP74z9kK9tyRKPAxMRr0I37C9i+alvR9Oir1M5nE+FzUQPm8QYz34Xam9ure5ve7qpDxQmOQ9CuczPSgT+DytBD8+nrcQPfQIsL0kMjK7v0SjPRVqrT1sjv29Bzg5PtkvBD6pgDS+WwnIu184szxIuTm9d3oGPneqCTyv4Q49kfSnPJeIjD3dWpS8rQ7UvY3G8D2zsO+9i7saPlO5PL0bv/I9trWAPharAz7djoU9Bh/HPPC6ej5OmT++clYrvgLHub1X6d69SEKMPf0qLb3KrDU8AUofvtUILb0iydM9mGFtPCqz7L1ctVM+2yqHPqOJKD3wyKw8ZsuEPeHvRL1IJ3O8iO2Qvd28DD5woDc+8zXXvIvWHTwztJs9SaA2vEXFkjzWr+c9Vfz8vT55/L2YujI+NzD9vMru+z3kn8U9KTiZPdvuRz22Hrg9fAcdPWA7Yzt2t5s9MCSRvftmUD77Sbw9kGsBvuRmyz39Jv89+k7mvHfhST4ZZl2+EK5PPgNNvjulqWy9y8x2vt/1Gb7rJia+t3WVPbNLnr0dpV29fa+gvSBqa74LP3i+jzgSvQ6sfD5xih89lvdpvXv0q75wPfG9TmKqPrNiC764s6S+opYAPqw0YT1Yt4s6EtE7PhLBBj4ArCc+js6RPdsPD765TUu7m+7aPZBQ5j2KapK+ZzRAPfwsSz1yBiI+KoO6PQ8cWzx3zc295wtzvXYw8z2DSKo9G+nHPQxsOr7Mp6o98Uo2vv5spj0NLTc+E1lsvaHkrL2nxYs9QGkHPhjAsT1EryM8AWIDvrZ0/DxHBy2+p7OtvbrFCL0dWkC+GiJXPfBqJb5JBYq+/AcMPpp6Az6enbg9IktkvTdicjzrAQm+FYOEPgX9CL1fvAi+3MLqPPQyPT5InPs9ERsVProOez7BzIG7","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","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","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","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","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","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","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","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","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","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","Pg8OPjVfCD5BAqA+vhl9PasYBD2mg04+xVIDvgCiM77rLFW8QFxkvZvPmL0LNxc9oNIBPofBrr6BnSi9HMFyPlNDlL1goDE+uPpUvUAezzxeqCI9KBO+u7xMOb5LSKw8+du7PQbrLD7GYAM+BxoaPU0LxDunLYo+OT8svLYHI77oUqq86LcGva4oCD6YR649CkjDPZXvbj5epI68/9zvvKEHlT3ZkO09Ds23vCSUPb4vU1Q9jemQPe3FqbyoAgM92ogBvUIeGr4lXwK+mhtzvcMdRT2lH6096dMlPtXWi7zY9ES+X2jIPSK2Wr2esae6PiKjPW4G8j1y/qw8jFwPvTulxL6KHDE9fM4jPj1Bpj2RzIy9S/gtPTDGqD47/hS8bBVlvcIO073R8Pm+YY/7PeeLK718y3y9i9UrPlnQjL1cIro9nRcJvgRsXj2Hd1S+FK4CvpOZqD4Q6AQ+tP5CvXdBjrwu6CA+6oTivUodkb3OxQ69fzpzPeeClL2dCXC9seCPPXONyL14WwK+tvB3Pa6VVb6/mdo9fxRHvo/Ejr2/ogU+WLfaO8DdOj1Oaco8H8cKvVQUuz12PhI+L2wGPuMd0Twp9sa9OsbPvWRtiL0wNbE+qck0u2Xs7T3CLK2+MFeSugvPPbxTyNQ9LkihviYjfj3orSW+6oyCvk60RT7JWxe+N+GmPXFE5Lz5SXA+nBIRvvFoML3MPkC+S1OZvLGtMj4gi5Q9GC+rvdVnWr2f3CA++80ivYUYyT2FOum9nk8uvkBa7D6GaRM+a/GuvIulvz3ePFs+3kvGvR7VWD5MXia+VGApPgrJezwh9Dq+zGktPm6aDT7axwi932rZPNdSb722Gqo+8yXLPWffLb4x4Tm9LJ9wvST8mr5VYig9YTOHPa8QAj5Ini0+ZWGWO/ouojmXZcI96b8VPlOEBTx50I28XuNdPThthD55Fhg9th1JPRd5VT5/CUo+VCYlPJeftzzxne++LAZ8Pr+dlj6Sctu7A5fdPebWFLyLf5c9","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","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","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","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","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","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","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","YP6hvQ0kvrz9pzE+78EPPuH9hz1fJxK++6GLvRjZo705nAw9p2VCvXz32r1Iqls9KgaaPfF/jr7JBvA9rILEPb+aqbys0Fq9e0pcPfipY70Roh++iV91PaQ2Pz5/kMe7C6ehvUBQ7j1pUEq9SQyJvUPWh72W2yK+w9rmPViTOb36V/a997u2vPTAeD1oCuk9pznVPKGziD751oi82He1vSDhnT2uU+o9bHtlvvkWFb1+qZE+ggKbPEZ9N72bMoc+iqauvnzBFzwoiDy8KPmKvRhCQL3V5lC+xPUkPiphIL68tbo9Q7PgPcmAMT4P8Ai+QPWdPkym8736Kxo+yt4hvI/MXj6Fkzo+IN5yPpMejD3uTmC+Dnn6PZB6zT7+Fla+047kPdKpHb61MdC+P5N5Plk2jj5CNeO95tCcvWK12j05yNa9721fPrm5DD7zT8u9jAq5PbbFcr2sKqq+BcRivqIhAr4vWiU+pMBDu6w3u7yySwy9RpZVvkZy8r1L9Jo9957kvQC87L2iWqg9HFQHvsfsYL08ZW89rOynvqCdgT0sFNa8tyrZPEMk4b0HnvW907YkPpZPRL4OTnC9QY4gu4MaZT5//gE9ShBJvvL5sz0BqO28uJgAPb6lZTwAZp6+KO0xviblKr5ScLu9jRDKveaTK73E1h+++s4VPhoWvz38q/288gLIvaxt+r2/i5Q9OtIAvmfgT70YFYw8eczmu5oBj71YV4A9YL7ovUQzcbw4VUI9DkqNPei/qr3oIzi90gHePddkmr5hhmG+IzqvvQXwgz13MgK+fwuCvgBfbL1h62M9ZTOFPMYVVr2COca8pVMhvW0Zlr1GfyG+CKs/vXh2Ur4Gwok8W4UiPTYIXz4wpyS+kb9qPaBWmb6ka5K8batfPWdg+70DVua9I2xKPoVwDz4g3jK9eoibPmGBZj7xJhK9IuKFvsN2CT0PtI69Df+CvLPffD3awqc9/7RCvpDqZz0DV4I+C5RRvUBjgT6K8Ew+CC/nPiSQMr3bPFa9","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","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","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","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","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","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","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","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","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","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","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","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","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","xWSiPsLL472DznA+nS/nvCokKz5jrEA+v5e5O/fanT44ljS+5RxfvEyqJD7Vy6M+i+71vRaTZj4eneK93zqFPmZ08D5OJGu+94PiPgPEN77QJBa9bP+Au7Tudz2M2G099ltePgu98j5qvX8+xF9mPmTQ6rwXcCY8lmFnPldryj6Cd3Q+7SMDPobecD7/Fgo/Ub5vPc1Ukz5JggM9kqKKvV87CzrJWos+xq+WPt0n4T4+Jb69r9xxPg9xEj1ot3s+zzPuPjZfaT4DLNo9CUnHvdGJFD5gAJG9b//APiJncLz3wKS+WfXqPh+GWL7i0MI+XLnTPj4MCz/6gCa9igF7Pr/QY702r389Rr4SvjPjfj4hnRQ+qp/hPSI1tj1c4cW9mSxFPVLI670QLh8+X8PDvGCjFDwPvpY8a2YmPrpXUjyTAcg93FpQPoLyjb2ra8S8ubQaPaqCqzy3qy++VzSBPaq6Q7v//nq+mEZavA5bJT5HWLo95sq2vbRB0D0UXbo9NvRVPorVAL6GFfs9WqU6PujHaz0L/ow9qrc4POqjeD2e2Ic9TWgzPXqEhj6afgw9y39evkUaaD2FeW+8cbcRviFIMz54Dlo9WwMuvrmnGbyyd5Q9Wf+fvbz227z9bFg+WrMDvop9bD3MyAy+JaeKPLpxhz5f0uo9ElzGvS9N87wpfmg9fnKKPVj2HL4evLU8+Hs9O2ekjj1Lp3u9YW8kPseWIDzHtgs9+MYAvoS/gTwDgcq9mFvYPWcwYj5Lhgm+bGLBvd6IWT245yK93L5IPajPBT5Om4e9IE42PbLKur2dM6c9IGXnPTntLT2Az+k82D+zPeDnqz0JwSM83XisPtSeBT2Mbhi+rL0FvEyHVL2X6gG+XSajvn7IzL2X3gQ+veNqPYynFL7eGSw9Zi/PvFO0Tb6+Id48PawiPPmQUL1RXo8+2wqiPfllnz4V+pc91XOhPBTrIz3co4S+FRz+vMhIC73FyGc9MnQVO+21y71Kx6S+MprxvTw1GL5ErO69","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","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","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","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","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","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","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","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","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","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","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","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","Au4OvrJMXb7rKoE9j7mHPunLfL5n58e90H0dvtVqgb2Ite69GDKyPMNwdD4l5Ve+ShjXPTOYUr4C4FG++XqQvj58W71fj9C9IaZ/vK2RTj6PkgK+MHOXPmxvUDyxN1k9//EfPp+/Xz32lpw9rLIrPXGBybr1K7I9gyTDvQ8pXL5FaCo9fLpFPiyqoj2Q5EE+oRmLPnP3or5edgW+QsP0PUXUvD3riBa+CUbJPWBf4b0/JTI+JpwCvohJxD391DG9c81Wvm6Qkb39+6c+kBMLv7VnNj00NKu8ibbOPTej9T3IFBy+tl32vQgp3b1cmJU8InoNvgRhnz4bev48ZCUEuzFfSz4QIc89kTOLPiKYGjyBy4c+aIhvPpsyyT2NiW8+ExNXPIvHX74zkZo94v40vXe9gj47OCQ+DLAGvggXND6Wxns+u06uvDtVIz7jcB2+8t0SvQQ0WD6tUK+9B6r0va8Xlz3tm48+1GnAPnxvzrxNqGs+If53vdtH5T4NRk09P+62PmXjFz7Z7nw+DMURPf/xFb1tgq49VJr6PRitmL0vxOq9bkQDPeH/or0FUCA+gTDfPZQijTyQvxS9KQgYvt4O5j2O9zs9HEPFvVv0HD4qRJY9Z6a2PRcB1z26EYc+Zq6IvWqhIL46DPC+Z0NCvfbE+TxtmLO+/9dqPkvhCz5o1tQ9ob2TukR5w76k1YS+9KBPPYdhj7wPfAM+3LChvQVhDb4lTr+8cMufvUu0eb0ncG6+cvmIvaaXDL7sAIE+BJsKvaX37rxx29w9mesXvubBIb5Ej+06M7bgPHY0gb4Mz9296X04Pu6tJj1x+QO+V97GPDF/wLyctQE8fz8CvVnnCr43KEC9/e9Qva6rvr0mL+u9kyDtvfv9FL0wo1q+C2HbPPTvED7eTYE8tVcivjuVq73d4Cg+sZAzPTRM5L3w4GG9Rdz5vdM2tD2IPqU7TgKIvWdtXj1Yy7a+V4kuPnJ9YTwrqj8+R5NjvZKQRr3LTYQ8kg3GPbrfFT74sS09","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","fl76Ptv3Uj4e1vM9OquQPbNV9T18SGg9sxfLvsCeCD1tnUs+vC8PPCVknj4C6Ig96C8rPlYI5T7uJYu+fywLPg83Eb6A4vI9aB2DvQEHpT5rn+S9wJESPbCTar6z2mW8+DjSPdgb1r0kz4q+2QvIPZesRL77DjQ9Z462PMopMz4ywKk+xVa1u+Oj0r0E5EC+hx9xPnMy57vcZI6+BYr2PTTybz46ymU8/wUBvlW2Eb4wr2o+LZnHvgS8DL5uNWI9Q7XovlEFzbzvrDo+qJG6vtlDYz5drLU9Kahrvvshsr6i5G6+6m2RPvfX9jusA+E+1LL3vBf73j5CXq6+qT6kPqiqwT7TdB8/I0P9PjtI8zsRFt8+LSu6vqqzC777Xqs+W68Rva3UHT5kbFk+YyXLPvs0+L3BoMc+AvKdvSnQaT4qQHY99DmqPVLBEL3Fbh4/TmupvrC3jzy8XZI+cWuNvgfnC75PSeo9p53qPFPHjr4NjSW+KQF0vmOIyD0NKUO/MfdIviq/Lr30bmC+cfDCPlLFdz4xagO+Tlgsu2V4Uz6Pk+y+rAYVvpgcgT7tdR4/jfSaPfDr5r441wG/na9FPpsrJz4f+D69CfiKPCTLXb7f9Em+O4LaPmiZ/74NXrI+MXhnvbptCL+lgm2+qZvIPSy8m7yrgaW9u7aVPpkcAj5W3yc9Zx0Kvm3N0rz2vYo9T/Pzvm7JlL0mJ5+8cm4UvZSEZb9KCfo9EJysvTWjVj7OPbu788MKvbqVFb7cI7Q9buYmvnpuWr8mF1O/fJsCvjr7Tj7RpCy+wp6dPhuBS75erd4+DWxZvcmg4L4yxLi9sV/BPXvwHb/OycO+CBkKv5u4yr7+PVq+tf9PPp4Baz5WZZk++PBVvvvIGL//yja+eF97PMfXAr+ozIW9/ejDvtgJST4oQLc+hVtXvbskJ7/ZLJQ+5IzFvQOfnD3j7qC92NrOPph7CL5QShm8R3ZxPzC9D75gCDO9+/zvvpTehL4Y9I6+8KzJvtuYND4bRG68","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","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","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","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","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","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","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","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","lUJ9vQRTPT7mKAA9YbJQvaEZTL5zzx09yKbtvTliIbw3zpm9x+sCvx6UQD1bF/093gJQO4JgZz2AxPU83SpfvfWzAL9UlD29FQnQPGi8jr4HXfW9xEYgPUnQmj74DOu9njoJPpNjab6SU2u95hSLvtJ1hL6M5XO+C5YBvBmTTL2z9N0+UtTcvVOYET4oY5u+w4gXvhGi6b1KTiq+M4uJPu16Kr61tC0+cILrPIB3vb3lwhu+JqvBvU5LUj5p2ZG+gmbMvEn3f71M0O2+8sMIvn2ACL5w/Au/RLoUPkGxWr1KdWy+ScnJvqkASryOE6O9KHknvrR3HL7V1Ta+r/ABPbt5Qr38+Cy8376kvDuGlrzl14E+7CsEvbqKlT6wGms8AQjLPXTAfL4clpC9cITyvDRedL6SL8i9qmDYPdSQlz2aETe+r2fvPrxReb6mqcU9KkEAPDWeUj5uM4y9XtqRPs9vDL6aB8C9KJibvTKDAL6omuK9ABqPvXARjD1Qlzs+mU7OvVGEcD2Ybqm73OA7Pj2wDz4gID8+eDqaPvMpCT4xvIa9dEWCvRhTCL6GJci9RKbEPYai+zxha9292S86utp+Db27ArG9kYFHvtc52z1LQIW9X/31PgSlbz2EUEk9EJqJPjsBkr1WxHe96BQLPZUpBL1DvC6+T8jcvtIJDz4q6sA9DZaVvlLQSb4q/2C9gY+WPQR5kj5Zdom9sTLzPf0ImLys4sI+GsZvvtxmNb3U1kQ9TDm9PopbCL7fMb484imsPifjoL0/NY49yYaQvgzWPDxTexO+aWl2PrOISj5dgtG99c4OPj652T336JE+cq+cvTlVvL0kjcm9OZRcPUoJgj5vTIm9ugjNvXvdnj7R6iY8NeRmvbUhab4mwsO+y4aivoKDmL4eyCw98zdDvgADW7765L49tKGmvmyao7xquy2+IlHvvN8KAD6qTjC9gNa+vY1JPr7hPoE+q/2Wvz1SRz0+5CK+q3NTvuESkz6VQla+id4fvFNjjb6NZTW+","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","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","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","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","OKsMvk/ItD2WD00+D10TP8xASD9rPpw9KZEKPxY+lr3rNIE+pzTzPJUHcj0DPZw9+uKQPhnWNT4A1Bc+UydaPgdArT3MqRe/CD6tvqFBFL4DLNu+BnoUvovGqj62sbI9EVeMu/z8Yz5E2Tq+60iHPppMm7xmmg4+ea0Fvm3GBz8btSW+VKgjPqhMDb5o8Ci90GZZPYUehz7Fz7g8dX3BPmtRkb2X3YO8VQ3nPoz2HT6FAfU9vpo5PspgJLyxA2A+o7NgPiKpyb2XECc+G65gPvLIhr4pfEu+FamqPm1F/j1sSuU9GztyPoywTL6yInc+aX0pvoxU7TudLT4+5WgJPr63pb7eLPI+SlZ8PnlAnD7my4c9WnptPTImkj6aEHm9j4rkvbirXz69CO685GnEPVjD9T2IXQM91RDPPsmcBjrq1KU9a+i/PjRXdj6HyIQ+FCS0Pofz+jxvRTQ+cbfruuNpx751bJa9UyWBvpHhmD2bUy8/HWt+vT7VhT1FwqQ+Zqehu3LHcb5BLRO+ks9HPfjaiLzfUw0+hlYmvrWuLD7uuwC+xFelPqJyNj4Gu5298s5SPok/jz5nFcc+mqBRPo7mrj4iCiU+FO6wPh33fj5Bl2g+tK9gvhUgB77P9Am+knkGP58mFz+wxAg+sruqvWYDAT6zIsY9FgnHPVIoQb7Xx4G7bC9GvqN8Jj4SfBM+bKmnPSsQmD3t4EC+ERTLPVlUNb4u4IC90l5IPhEjCT2nBck87faZvB+FUr7L4eq9nqvjPMP3fr4HRII+sJFbPfEqOr492qg+4kxsviCSEL6bK+C9wp7pvo5EiDyAfq08CICbvB+/zr3twYU9+xoaPnU6vrxWUcC87fJ7vTwXjr53+2M815GSvRQkzj1/ixK9+YF2PhTdHj7Dgts9TW0RvkOWEr7jQPu+ryEXvXX8LLzZjjY9PiZ0PeWFej7MPLK+O+aPPrO3gL51ZZu9U6mivNU3rb5FiJ8+CLSZvPHIIj3/HyC9UYyaPZg4hL2mZnA9","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","YoyqPfBrHD6exBC9UmFSPR+BH77dBSK92jxsvorPxjzmAXO9Ix1BvrKxpT0a3wU9Y5hqvtp4/LwzEKM7XSQ0vkHv9b0w8Vq+Cy2oPGOfnL3rtpA+JVjjvbY5Ez+5RB0+oNniPFLIQL0OG9M9z7iCvZ8qSD3HsGC8v/syPuSN172p/tu8gKdbvj+2VT46fey8wz9WveiuYL3jLAy9BniNPZR5vb22VjA91ni4PIHSMD2Jgb09HKmwPbHgWbyCQ/i8XHwUvcnepL6qAUE+b1kvu5UC172jADc9yiTjPOfLb70nDGe8byYJPuIwyj0pb64902nivft9Kr196Is9+DBuu8DOp7xw1Ky+11W7vZ6vSz5Aj9y934+ZPjl1az5Gm1W+y/rhPX/Waz6IERi+3UFxvteG7D7CPPo88rVfvUP4Gj5F8He9zg44vSvEpL7ryKu+oG5PPm/uB77ujqo8+lN7Pgudvb0Xv6M+IY3NvWeXTL0Dxey93fe2vfNx971PYj29Kll0vQj9hr0xaOy8zy2yvUtnwL3ZhJU9An74vqPSjr0rv3Q8tQKBvuP2iLxumE6+hj8evbVghD5UwA++ipPAvMU8rDx/s4s9G+8mPjR32j1AtD89fjNgvj1GZT4w3pi+WtIbPqOSlL02Lhy+6ov6PX2uuz3ZbuQ9d5WovRFBlr5luq+9FVwPv3Qowb1tmA+/e1TdvY0Tpb5NbJi+H6t1vVz7Sr7MLtq9Biz/PSX/XT7MeDW/zbKpvd5Hv76kWD0+02VpvtmU2b6vMyS/o413Pd2QXT0eAKy+oGGOPRhYk76fv5A84YX7vS8Uqr1VdvG9q1wSvAkQO75woZ2+/yANv2USuj12wUC+aoVnvGSdYD1SK1A+PPQrv26JC79GY7Q8GFY9Pge6iL5hfw0+Yi33vYh5zj5aO4q+nttFvmBt377el1k+ZrHiuzBs47yJST8+wlp0vhoTeb6sr6W9ErkTPo5Enz09ivo9Evz8Po5Pd75nwas9YaMGvic6IL/K0gy+","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","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","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","TnYXPmKcjz1QJhC+ceQEPndbdT5yLzy8QYgZvo97Cj1rsau9HlGBvWd+Ez5atgM+DJqlPnBhVbzTpX2+G9IzPr96hb6H/L47CSRoPSvRpD01uAu+7+FNObFSEr2cQze+/d+0POu3QD681LU9dPwBvcrDtb1mW1k7w8C4PBf5JL45wDU+21Peu/p5Bb4NqUA+wgGSvT7cJb0XHsS85ObFvatJEr4T70E+YZo0u8+zK71FHOM8BLywvsx/TL2/orW85F1bvVrSLj5BhXA+cz+cvvxb3720yUs9/x4UvbiCJj3XTI2+wh8NO91zAr38AvO73fYQvGJF2DzWQn4+QgsyvQimgD1fPME+Ne9qPnrZ1z2mIj2+sURFvh8hU76CYwg+bbpQvUH7272ivLg+kIYcPl7vqr5YAt49NW2MvRqLnj2EUSW+iz91PRLOd7yZwZA+4zd+vpLTAD7UfBk+fu/BvcXrALzwJ4g99PkqPl7/2zzn154+jnr1vUmToz0QGEo+03SwvaVK8b1Q4be9BxdZvC6k/j6Ogpo+fXk4Po/Khz6awzU+FeyWPl2C9j2p700+UH8AP8LopL3YOja+FSvSvQCdvzzTFSC90LJIvlmU6DzOmtU9Q+yxvD1U0ru68gc+wCWaPkpOgj6Upbc9IRndPDjNB75bzNa9blZMvX49kz78b4K+SGKvvq5SoD5l6L09riz2vk/KIr7HeMU9pRg8vrFujr7q9yq+AqPTvZvEGD+U7ZG+zQ+0vgqaOb65bgS/96a4vsbJwL4n/E+/oEewu0c+K7+KQJ8+stqoPsCBWTxj9yw+3L98vhPDhz4Gj4k8Vk6eOxB4PL2UUle/QQ0Qv0WriL6YjpM88QG1PKvaYL76uak9QDphvzAkTT4FH2+/2ue7Ppvp3D2fss++B4wPvwe6br1/l1k9fMdGP/vIeL66Uxu+6J9mPdLQKb6FRg06Pyz4vro2C77Cao4+F+/DvtmMAL/xGIk9K5Aav4tRB79bNJC+uyyGPVUhPr6FFrS9","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","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","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","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","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","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","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","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","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","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","CK6GO3YtFj5qjlQ+IgwRPqP+1D7p/6i+8YUovnkzDT7VS5c9QXijvRIxmD3FSwo+oYI4vpKgxr1Y8io9cgkmvlVga75H6YM+jWiJPflGCT9frAa+Y7bOPVL9Bz5ucY6+vXdcPR2oK76itqK8IwmKPIFJkD76IAK8S8mYPfUmgj4Nvwg+8x7tPbku2z2W2EG+hJX5Pa/sE7w4rNI+MGOGPoE0Dj3YYwE+piorPrQhqT0Up24+yfiDvlMlzD31X4S9zsJ2PhEdGL5w6lS4cbMLPk91YD79ues8/APjvR75oT6rVVc+e4JFPiJI9D2gM5q+ozA4vuTJoT3zsDk+jatlPl/Vorx3O6U9+iu9PIGRi7583GG+esP9vWCJar09Y5m7bfE4vsAhUTxG/BK+fdcmPjJs5D0EFqS79coIvm6Qib24RfG+Vqw+vv4Xv7yYlt29ABDJvlQ0U76yQVU9B6Hcvppx+D1GY5m+oX4OvkafMr4wu4q+PrayPYC63r6CvJo9OSjquVqyKD1nyWE+2991PQCrZb1W1RK+JbTdPaZRor2cvEs+IDXnPeRwpr4+Uoi+GcnwPX1oa74s24K91RaAvvo22b12V5a9gYTsvZiWB74DL6C+0YIGvhmKWT43zmC9GmBFvVDzOr0dO/O9N7covkfWlL2lCSi9+BwEPrXZnjuGhxG+0fXuvVyWcL0oXZy9imV7vaVdib1KKii+B1AAPUMbYb4NwQC9VeeYviivYb7zibq9wZ8hvoEtV76By1m8LVu4vqBJAD32RU4+DaUvvegpsTzNG8u+aBqZvU8gp73NrDo+/VxrvpI3SL3QD8O+GBWsvt7ifL5L8UY+56G3O6y0QjzZEza9C0jGPC+dWj5HfBW81qLQvdONZjvhcOG9ZIgOvnxbF7yqrLC+A0u4vU/JrTyjn9u757bWvfD6Wz65G5I9u0M8vithez3Lvi2+ArOxvpUXtz21rm09NPLfO0/3r77fWd4969mcvZYOJb0v3Iy+os6eOyliWL5TfG6+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","8ij/vNfWlD17Y3M9RtK4viOvDrx4bIA+3wqyvgTEh7282nU+FYZjPiHwFrypLYg+BlqyvvWNIz1D8U4++yysvc5zgL1R1ze+ixcnPp7ApT5k946+NRsDP9KrCT8B2Ei9ovOzvCKolb4r86Q9vDmKPhyYwLmXaII+/0kcPhXQYz2s+aw+MqFWu1flCz1zXco9CFIZvcLO4TtG7xo+n3KMvULSMz4omES9/gSYvtrb1T6UV30+qrAEvSZhXj3rzcA+vxWFvt95qz4YblU+DsvLvXsJXb59xNY9OWc5PfGeT7y+OO0+RQoEP487wT45QO09Ux6TvmXP0jzE9XG+fbkyPvoBOr7FrKq9o//Jvc4/ab4Ib6k95IeIvU4SD76Woy0+ZCWvvfThOT6lcqI9N4u0OQqDmr50Vce4HywgPj5jzD08LDE+oX3kviIbK75tpVS7kCKKvoEPzbz0WHQ9ll/+PWBGpzxaVDs+8OxcvnM4Lj5rtpA9BqceOwz3Sr4jZw++B1+BO1hIlTzEDqS88bRLPcScK7o6+Y49iy+mvEO2h75HcMk9SPShvlsr+T4+Ohs97YH2PTv1q76VA8o8Dqa/PZ7ycj1v7Ek+B85xPjdUgD6iuhw9vpQSPptL873oVvy+0QS5va9MzDzNrWE9x9EBvk5dar03tSc+zROfPdlAVbxeKIq+VusTPnjNkj5zVDS+eDI7PqS02719SNK9qVmqPdwc9L0zKNu+ZE6QPrCAiDzw3Ua9Jp+1vKackj5Alz6+VPMcPXbYFb4K6Z4+deaEPmQaP75qnzk+0t0XvBm5gT1fnUG8nh/PvCUGer48NV29ziE3vUeOzj1P4fU+JPulvlUGxT5gyCq+tkSsvYG1fD4oSJ8+gFzQPvVkKb6IF8u90LJkPub8er5ljKG+ptqHvvKaAz8Uc7i+z7TUvn4gUb60Mwq+xDjzvTuK5j1OF1O+jzknvux7tL5fdRc+7hlzP7hGRj4YbBI9ZrDaPZaELD4sBzC88PWfvoW8sL7pNH2+","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","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","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","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","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","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","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","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","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","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","+0m/vcqpDD7bSGU+6r3mvGZ0eD6SVw6++MUzviXzf7zXH5S9CfnUPbZRvb3MCwQ+816NPnL3A74ta6m8Oof+PR/buD30xac9fmrfvfxnJz0pcII9QcjXu18IIT5YAaA+Sg/4PbJifD3vKxW9Pn/JveX3PLuoChc9bGOnPaYZfL0sdEm9yxAMveZ+zTySFEm9KHFCu2eHVj4y2+o9X2T5vVDwxb0S6oi+ZtECvuxIwD0/ZtQ80KhRPuuxAD6sttg9CqcCvtd8rj087jg+ubI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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","KrlxPpOAoLzlqkm+KnmPvt7cV71V/DC+wJ6QvdLrBr1ysri9qSnFvscYWT1iuMQ8sD4VvRxjKT74Fke+Tzkrvsakh73TCpQ9udImvS4akb5K1aC9Ygr2PRV89r67s6C+HEGpvToB9r3RnjW9L34Avo1VsD3bKDC+paOVvSIrxr1B7fk813amviST471zPee9wHUsvqHnKb7xMP68NVduvcfoaL5DwE6+Gg9dvj+5Ur012V29NuvlPOhUDr677Sy6w7I2O8vxmb4a/+g9kJaivhciQb4I8RW+ovNCPR1pI72Rioa+LtQevoAMxLvBZgG9oCTcvRDJzL1OnzO9f3KSvp5S/z1u0gq9pEiuvVENaL52mx0+RFp+vt6xuz3///G9r417vTeVKDyCAS8+XLWMvIaLF76NMCG+T5m8vP7kU74iIEQ9N60hPp6WfT1QpDk+WxiHvSHubb1Fw189MvaBvjN6kr1mm4m+EWB1vjvgXb6FME2/6Jo0vqI9gj2qW5g+Co8wPttkIr24iQC+42Y2vlXyTL75hZU9gD9UPnW2RzyRUPS8z4iIvXtJNL3lYwe+RSsGPR1XsLzxy9M9rOQfvo3v+z1diw69Cpc5PkOWSr7MPGS9RB4uvh6cmTven+C98SYfvj1aRz1YZko+9fQpvj5NMr2YDSo+ww0OvadmV7wJkmO+IutBPfVtfD5AX089VeNTvdldQz14pFk+QfzRPUWc6b24ZDU90oWePtyozT2mhec9h0P3vYfRw72dBAK9eyH3PVR+Aj4aEcu77HUgvnMEjr0EYKa9Dgbcvdj34L1Rnm07pLVxvnTzBz6/ShW9QbfvPNNyDL4NxYi9i0T1vElaCL6Vlpy9EcrRvSmP1r2zuqQ9kQ//PIWXBT3IDLg9JINvPC3prD7UZ2g9rA2VvYZ3YjyRzPM9AcytPV1l2zt2zIy+P67IvbRXLj4DdfK+bSWcPZ+ERL6qivy9qiLFPVLMAr5MMMs9V8IKPe8LFT4wgbc8UTccPu8Qir24pIg9","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","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","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","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","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","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","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","dbPfPJnvGD7SqHw+oZiFPghTzD3NAxw+Kdm3PiY79LtxzxM8HSDDvfsDPD7JcCO+LWplPFCQEr6yzRc/DpsUPQDUjDzvE8W9GPGBPthdED5Wzj8+XOIevgYdE77fyKU+kSEOPoy4Zz6LoBm9ncduPu5yJb6OCEq90vDBPcOWkjsXez2+IXkaPvHcND1AawY9V06SPZlO8jxzTBG9ozl6vaf/370/ZCE+yJwXPrIXmD0S1cY9AYVmvGl7Ez6o9sc+RPMrPh1l1z1O4bQ9l6bsPSaBiT4n+II9HD/kPJJMKr2DAMk+BbwjPX12ej4fRZW9KYcovS6m8L3w6Is+qolfPu9yVz6RUL4+OsKPPqVfoT1KoYQ+dJqGvcyqozwtwAA+VKuAvfRVh774tJU+0MEAvv1xhj3wVDe9EteBPQbTQD2pMbS9wLXjvRqagz2DZPi8i07dPf23vjsqXqk9y6i7OiH0Jz68bUI+nNgxPv4vSL6mqbi9LB5pPXTFDb57XC69oCInvnC0PT1Eh1E+tbXVPqZQirx0fFs+rcNlvZ4rAr5/kMO7b/lEviNk3r3C2gM9i+FKvVfi5zsjtc691Mf9Pd8ZmTxevBQ+iRYVPqvwVjxASIc+ZywkvQUGMD1MQ+898hlEvs3zrj0eMgo+AqddPmRGNb6ddI88RLPGO9wiOj7C33+8HU6uPklxUb657ge+JaanvBPDCD1L6B2+HaZVvr69Kj7gM8U8AIuVvsVc4jyxqQC9idGwvS6bJTqKIAU+vBdNPGIoWj4z/X8+GR5WvaBkqr0+CQw+xBpxPhJ3AL7+Dxq+TgCBPpvfyTyDwhC8JrUYvQMJOz6e+t891aogPbiqaDquOps8ytohPmKMsT4+zgA8ZxGnPD2ARL5wKiu+jinVPW/hyb3RBbs9+9kTvhh0kz0GIVq+cj2OPU/el71kJOI76tj0PcUwRL47sTi+ZxmfPZEzrDxNN5o9CKkLvirZlL7RGAe+wCFEPkmEEL5pgFQ+T3ccPqvr0j2YtcO+","ty/yPFDl3zz03P2+EMlQvNNYnrzvf0Y8fhULPCBSTL7Ku8O+bkfKvRM75b3nIog+d4nIPVYjzj4JVYS9zR/Lu7O8HT7O+kw8OK4QPlW8ib46XR0+3WuZvbW5Sb6NKFa+4iOFPRQ+lr38ZHq+as2Evt48Ej0VpkS+JnBKPer/+73JXrg9xtItvkbYU70FgWc+pHfWvXAZgL4O3Ak8aPafPjikUL6bPOs9/oXOvV1g1r1VPAs+ATpRvlFgVb6gHmy9uWFdvj6vGr5qn0q+gTACPpIkub1l3mm9Bv0CP38jRj3+CAU++BqOPfYpZ745sJm98/5oPhAcVD6ffdc9NPhXvl1SrL5cAwE+21AnPkf3vb0aYvi9pp5YvcvYb75UKKu+FjJ4Plorfj035LC9ndthvUBvHL4pf8a9P91nPYMpD78iZjy/Ra04vn/JEz66toe9dV1jvb05VL7HCha/Z9TmvRzaob1PFlc9/JQZvqNZ3T1GVTa+fbQYPdIDur0ioms+ga/1vXoFgj0cUIE9JIOrvdpotDwhBo29rHAGPpOq3D2T+E6+zAkOvaV/yb2KlqE9A7fWPZLN8b5dXqC+7i06viGPxT1mnCS8tBErPp4uZz26Zyw9NzLcvZ+0iT3niZ+9+RZ8PTOkRr4ajFK+3qMovvmqO77oKhw9y0mRvQMxqrwpOyq+Kygwvk3/lT1DzUK+siAuvkAK47z234u9UgmtvLqXpr18MoW9t06gOxEhbb526gK+YxY9vWiAcL0Z+9q9AgAYvgN1bj2GWY0+INMNvtqwTr4GyWa9/R7mvRBSor1NtHi+emTFvn1oFL5IG1U9yJHBvZf6TLsQlzq9cDrqPfs2ZL7oCzi9F5hvvuk4ub0Hcqk8fcAVvBbb/DzJNxa++zKPvB/dCT6DQXg9mQE+vUY1AL2n37u+rlcRvrtbRTx74XC9BgIZOxhNJL415GG+WvclvlMUl73ht5o9ioyevhD4qD0x4Ii+fx3MvfetfLyvZom+4fvJPRqDZL6Hvki+","AIHTu6ffdb2S4py9JY4EvsVIWb7FRiW+5cu/vS8rVzzp9Ii+RC0cPjBRfT08fw4+iDjhvevm/DyDtts9coYWvtj/sj2GkCG+p/13viKKPz79UBA+Hk6avb3SIj6yBly+MqtbPqEzgr5AdpW8Y4f1vS2h/T3igkS8FFxYvMAILz79vYY+O32FPc6Sgj3vvzI+bg0+vBhhKj2BvW8+K6RyvqTztj7o8IM9xm06vi3GnTxMAD493C4RPinaaj3vdD++3Se2vRZr2L2bfji+prWePfw2cr5t9aK9c+BNPMwKDb4vgVk+JudRPpP5Cj9Bgtw8P8WfvrnBET5B82I+svV/PYunVL7sSAW/HY1MPYgjQT6MtsS+u9JQvaNwx75TRmC+B5NSvv/qd7yzpgm+EiaJvt8uKT5zaf6+mvlavtl77L2haLu8LlIgvS5iAr6lwt8+VUUdPgiMtz3DS8o9q4wlPYMdlb4WBma+Vi3CPqS7Rb0vNK492xfRPi5Woj6dRom9BhFmvlsfrL2cgE4+gqgVveJCgL4ZwSU+BikXPDiAQT18uCK+pSWKPiIOZ77cdTC+opqJvqjR2D58YsI+R6hIvr98+r0f3Ci9ykyuvQrMSD3ZIcK+SfYMvZrlJ70N3o8+4D2AvvzQRr0/qSo8QaAoPocUOb49WLY8hu5LPXCZmL0RYV88Bu20vZGCij3i7WS9eWWBvZim172s/kc+C1d9vinCcr2CMiK+ExeJvpjUr7rxiVq+NsC4vZ7qdL29iIc+r1Odvp7omryvVr89OORSPAwkF75p+he+tGzLPvJyEj0tqIu+sKQPvfbKer7CmDS9upR0vrrsAD6w1p2+vg3nvTkuJr6ksWa9Zku0vEqNKr7m6Ba9Fk/avgPxJ74CT0w9c8Rgvs+1FD17nk2+6QxDvoJ/B76GD6i+nVc9vviPNj5Mx5499GCau1qTLL7EE5+7wUZpvS0bRb5j+Ps9X6Ugvs2KiDvS6Da+UCs5vTvAmb7A5O29xcoIvkZfmT5X2nC9","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","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","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","TTpJvi9xFz6bRtM8CasjvgzId73L7Jk9Fdz2veiIlL3HjRu9B+UZvJXN7LzfUxE+uJX9vYo8kj07fT49ttIbPpD0LL4arYY95FCeveTxJD5DVk+8X6QOvRkgoDx0rUY86/j0vHL49z0q6qy9RyMZvUFO4D0Hgbw+424avdl4vr2a2YE9qVcjPf4qF75rKo69XaZLPsu2mD2Wl909J9rzvTU9EbyJ5wg+2HoOPrynPT18hmQ+sFo9vli9Gb2O4vc8QCUrPYqGAb0MXEa+FfU2Pl8CxT0KySY+JUsbPukQH758iLm9oI86viUNMb5Sj068mi35PL0mED5N4yM+YCi+vbIBKL6ysj2+fLqIvZhy9TwB4om+SnxQvoW/Irwpkf69oNddPfwWTz13P0y+uU7WvYfJPD45kZo9pmkpPtk/470cmt880ApRPI9ZEj5Rhs+9DU2jvZt+C70WBYg9HyqiPVrlm70RwcS9WBcBPkabj70A0Ry9C5mLvADFsz1h7YY83EglvopMAT5wWxY9bcTLPebrfD5jd8G9cgHOPVeExDtW/re+ashHPduNUT1BVgI9+SElPIEjjD0iORy9lLxRPs2EgD2tXWi9MUxJvjfQnz179ju9S2W/vWZdg71dVxm+1zw/PNqGQT6TdXW8pSWTPfu5GL5gTcI9w4tePoRUhL4/SJU9oHyIvV1kJr0eEyu+ho88vqzmn7wQ4Vm90a3JvvcrAL4pWDm9YSWRvAu8Mz5cfSW+8LNhvmwD4b3nFYK+oZE5v4OxCL9Xwby9w8GtvRHmtDxTEmO8tfioPVhs3r2NUqm9+lsvvkvwor7AazC+P5IZvhNsAb60/ze+A8UQvnd2qL07HKC9yQOBPZvjOb+Ny2i+lAvhvRVO0j0RWwu9kh/avoHDtb1124++gJYovtCsar7+Tdm+ORWLvdA5vL0LOV69QLfLu0ym+7sZ8EA7xQ6gO4Yzu75lfVs9UdKgvV6Agr000BI+dk44vl7qiz1C9Bu99Q7KvlDf2L0HFKO+","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","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","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","8ByPvmgBir7GMuS7wh6YO7qMNb4BOn49LEn1PImp5rzWviK+7HNmPq1hAb/wzR4+H6V2PrjF7b5FViC+PAMEPXGF272RNRC+0701PvSL/zxMEZY9aPKHvVYG4T7oMVK8ltunvUaNWb5Tc1A9GiqXPt2/bb6efW690RghvQ3w1z5jqSg+PRDGvWgkuD0Jojm9t+tCPltYVz7SjKM9lVkZvmsyI74TUIO++7ioPq60/D0nxze9N3Zdvr5v9L0UQsC+SnMSvoH18zyj7FS96PRRvMy7nz3o8Ga9fBORvqjjbz6iM76+dqELPg2iPj62zGI+ti1DPjMR2D27l6M+8DpdvY/mHT55QjA+60kTvj1ZFT4NW/K+1x2uPioQb74cJrc+qZgvvg9fXL+/h3k/2tC/vIsYZz7Mbao+E5IZP6ZtuL6KbhK/pHy5PpixqL6TQ7a+45bnPmZEaj0072G/sSV4vwQO6j5XDT0/6kAGvlFG3L6pUg6/NcIQvvFpJr7CXp4+aahfPLYX3DxvqEK/UVyaPuPj8D3sag6+FronPvlsK76w5oU++5H3vvgtoT2v8aE9GhkxPp+DGj5DjIa+wfihvfXrkjvLWDG/HzewPrs2uD6rROa+MMghvn95BD/fxgy/IUocP8h2BD+wp6c64wzevUebsD7KioS+yY/+vllkpr7EdcQ+jj43vkX2kT5a7Iy9XrM9vemLXz6/VQC/7xBmPQmalj11RrU97TzaPn6qET63Jd69/+z4vEhKPj0PMZQ95TtfuY1PqLxuHH6+ZbTYvREmOj60YIE+IFBcPpRvCT6CMFg+hBHFvTtNY700Vj29Z4oSPtwnd75zYkA+XyjgvS4UkbzoY+k9keqdvcthi77DJLg7FOjOvF5LIbxOwAA9lFx7uvlB3DwAqVQ8dOm7vZH3uz3F20q+DXe5vQRBkL7MdiI9y2bRPSE3Gb57ZIo9cHYyPvqJ9T1R0tQ8jhA8PvBsP70LmJs7RQP7PYcwsD6//w4+G74kPiialbv7LI87","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","9ps9vLDRBD4ckPM+rRZ2PXlS2T7BecW9oQxQPuUsTz3ku4++T++avvdEYj7J03s8LDP4Pdb69D2Br3O+gatfvmxbgb5Bsai+NZpavucceD4QlXI9GeHvPaNns77Y/Ay7I6taPjsR6b5EFbu6CkTLuy05yz2bj8+84NMxPaoovjzoGMk+vza3vusc4r0H0mm9Y8ahPUcMnb1qEaE+ChwzPmolNDxHV0W+2nsrvhHIBz8uhJe+uqsXvndFw77W+HW+4I5mvlDufr10xJs+vVQhPcw7Yj8tpx29iIzYvTJQFz7LA3O+Y9SaPsdZmr53BYI+3tw0Po2lHD49JFG9iEtpPi1nTb4ytHy91SNEPVYCvbzxYr29Z7DsvbjqQL20gD6+TmkEPkDvHL5shGG+LBF7vmh4nj7fX+a9vO7IvgBJqjx46Qe+rkmsvX7frD643Du8CXu4PSDXQj600Cc+4vhDvmWR072D6uE9/IjqvUxs4T4osf49/FjCvPGweD2Zah49ohdbPp0QHb/m54g8oi4APXafmr10b5I9H1PuPeS5ET/TgoI9SruJvkU9kT2iSla9zpn/vbSNnj1DiBO+eEUCPgpSSDwAiLE9BAgWvngFUL3atSI/TxqXPsC8VT0GIDc+06CnvuBI0r2+vkE+k/dQPFUyQT5RfDm+jqmBvXtcd775twC+Bd3VPQwJuLwD95W+6p3PPQnW0L1TgLQ+8rjHPflZyTsJXAY/AuKTv4md0z7QVAK/Y4hePvS9E7+Q0Uc+PpELPwwTkbysJ769s0zBvjYF875Z5T+8H+StvLDBBD9YM4274fSmPRrVpr4vt3k+Y/qAvtkUZb4ADse9R/26vuTUtT4AKhk+ZQo0Pkqt0T2T4sA9AYOpPrLzgL49dFq+0v63PhDe/r2cmqA+Ke72PoFaOj5ZHJA+Fzb8vuSAMz7vFlA+JB6mvoC+lL7cz4o8pZqjPll35D5l1P+9K9nzPTzjVz50A0e+sI5HvfBIIz5V59S9KaEyv6Ntgz7DC34+","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","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","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","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","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","TTZXPuT7rD79wQC8WiA7vawbhz7FMy8+ZfA5vVwGNj5bpSg+YhCePvyPvz73vdg9mpFUvunMPT0oIiW9htf1PRxHtb15k9o6PMn0O3K9wrpOZfg9RPqvPRXRx72+7uU9BojAPcu207yn+3s90FqWPLvGojzkDNQ8fhbJvWoc+j3Kwgc+6HxlPnEjoT2UzDW9s84Ivpr1BD7JfZM9iNolvo/bsj5XzJU++oTSPRlprj7QWVC890o0vuN8Qj09dVy+oM3gPd41oD0m4vU91ZfiPDmGPz4q13Q+AF+qvi13BL2Io4U8qEsyvjSnpz7MHSE+2huVPi9GYr5N9Uq+m+d1PgFUujv4Cb0+7ZsnvbThTztXKqQ9dvwmPEXjcr43eYY+4jg1vlKwdr3RdFi+pwo5Pp2bCb5zjJU+8c99vv9G8TzIb4k98N0Svop5Fr/Sh4I+FKCSvZIdmz5VxTI9K1sjPsNJ7T3cgwI+ZRzJPU+hBb1v/6w+hiikPs9cKT8Ajwo+oQ3VPnfsY726jam9DjwQvU2EtD4LR8m+sDU+Phh+fL4xXoA+4dZYPjf11z1fmwm+kplHPvcB2j4YWk+9BpocvqZFTbzOJQM+BwD1PspRpbynZq++ilrRPXdikj4Rc7M+6VjkPavo5jxoPde8UlqKvjCHvT2w514+ecqZvl1/sjxyYDQ+1df6PQAnIT2Rrpq9mIQ2vgrsRj4e46S+U3KQPY8AiD4Prgi+5YIiPkD+mL5VlqO+01SiPbVrn7oLhoa+ADS+PWsRJb60FI49HuKHPrCfRz5rG6k+65WqPlqSVj6iBna9x0L+PjNCU7y4hu69BBg6PoNsoT5Zgu09RUEwPo9kML7VzZ89WFZMPltAqr5gFIE+d/agvFoXLD03OYC9J5lfPl0DYj7I7n++v3lvvs8c5T7W96s9U/RTvk95pDw9n6U+T3NGvMPAjD4PN9o86lGYvtvwIT2ok5s9/xZqPTwfhzygRkG+eFW2Pvin8b0QMjE+ThmLPqXzWz73tMi8","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","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","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","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","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","jFEVPh35Jz4FIS4+fkEdPhEtDb3qrAQ9zLX1vAaGuDyPPyI+pn1LPrceAL7sUv69tw3fPWgkLT4+hwm9ErLtvECNUrz+mw6+oY17PlBZMz6Ympw9WHklPq146L1MqxU+FrZ2PgVS9rzfg8I9oCRfveut3zwzqnE9e4XXvaJmUTxgtp+9EoxuvaNwVz4U4NI9jCzwPF2YRDufdU8+gTCaPUvOSz4e8Ry+BkkePOwUxj34MdI9sgCRPpzz8L2lyPs9ie8XvSqf9T1lo7A9iG8TPqwHHjyWJPo9wfcnPhoeoT6MBMu7rZI/vKERvjxY61c+cR0APm/Ka73zDMc98VrLPQRmGL57lFM+/WKLvDf4BL1Bw6E8MavSPV9SD76Js26918PQvW1fXz0f+8u8uuwGPrODXr7ggtC9k6c9vbEaMj7JaHm9b/oiPUgykr1zOXA+5176vet8Ab5EQKo80FZKvWnJmz0ELKG9zQsyvju35L3Tg5u9H26ZPhXElL3bdje9/OsmPTakKT7qxto7Zpw8PYTzBL2Gqfe8tX5Guv42gz3/x109ZgHkvUMNHT53B8m934OgPMDgGr6eQYQ7h/MhvEQymT1a5sU7TC6jvQKj/jyIwXY8/PmBPZGgJb3B9389t73OvWpkLz0ajzm+rI3xvWh1xz3v3+U+qwk/vROuGL6NBJ69eeQBvm2w4L3XL8I9rCQyvl/aRr4WiYM8dEEVvbl0Nj69ZVS+hCwEvqkqljz88AM+bu4+PfgRuT3OeZG+k5LPvY7Exb1WlE0+tbjcvdNICj4RGQ2+t08UPbXPzLzcPh++T+nMPcpp0D3LmgK+ljr+vbrXr73813g+c9zwvRDbVr3OejK97w9lPeNBDL7V8Bc+MHmAvivssj1cnzA+Ebb+vvq+Oj4fnSq9nT6kvUo/N7pua5u9A74jvEKgHb2CYNI8HufLPXB9q77EGA4+IC2DvTAHQL6hp7Q+AhNEvdLBkr0qerU9YN1WvZY5yz05Oba9zqoFvpO1hD5TngK+","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","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","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","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","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","uG4pvttgzr5hbf87FH/RPR3Sir77L9O9xdY5vffzqD32rAK+bF7BPnctJD6+7Ji+GLgVPou0wj4R2Ky7OSjeviKBHD4bQk2+GHiyPXFMI76opao+bBI9PY5yRj1T5om+utd7vv8VSz4r3Ag/aZmZPiWZZT59qzO+x6m4PRg42L61Tfa+l3rPPgL9Bz6GvsG9A7h0PcdnX74+qto9TkBrvjjpVD7Ue3O9aYK9vEqJJj7Cu1Y9DDtzPepcJr7ZgpM+6JlCPvMeAD8jJtK9XmlbPe2H5btmx6k9LV6EPlkLdrxPGJc7TM86PvhzRj6nZb29wfqgPmIPsr1iVJU+Y9gSPpsKyT0ntPI9bL0sPoXzSj5jlwA+LpDHPYzrgDylQgk+KDLsvLUMkD0ngls+X/92uukJED6Mdl0+v2uyPeia9zu7/JA+SoH2Pb6mbz4eTw4+FFlcPgKEX70+Zcu9cjsZPbDp/D03Gi0+VgoLPlAKKz6sUEw+TcWSPqcNwz00Hls+0xUDPrs+Wz6bMyY+vXiWPk1rqTvrJ9u9vnuhPu87+z34crg9gPoDP4c9dz67bXE+HmQnvonJKjx59/o7QQZ5Prisiz5uz3Y+jYdTvaLIoj35BGI+1rWGPpohKz5HfyK9rwmxPoOmcL3n+Dc+elKnPYn5Sz7GPyO8YdhevggMkj7BCV89yfqXPZprn70NSS0+VE5PvRHGWz6pdww8w40uPjTFPr4Zhns+/HAYPh6kH76Hmwc+lzpkPk2HDj7Kt1k+PaYAvvBQsz0Ul3g+PVvoPaaAOz7VUaE+IVcjPrDDjT33mOY8nzGtPtA9qLxL7lY++BkwvqeYsD4BQGO+/GOhPSheyT3KuDe+bPPbPT1kHb5pR4E9MiaovdbmPD5GoTY+oXQ6Ppv8Cj912FW+LH/TPt/6nj4W26e6J/02vdVC6j4afDA+KXWPPgytXL0F0jo+o1Jdvru2xD3ZGOO80ww9PvFPqD2IcQ8+0MwAvh4yg7wtXUY+e62pvePNHj7pGrA9","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","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","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","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","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","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","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","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","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","Ab8qvUs8zLx5NAQ9iLpGPhtzKzzgqis+gi96PoJVMD2k+zw+qnlZvZMFjT7GygQ+nCJDPvMsnj2Qesw9cTXWPTqEET5iDwA+eVPMvf6cHbzfWng+J3j4PQGqW7yMBaU8Q+VRPuymZz2Qznc+q71UPpZjjL0qx4g+tGsYvq6Bir3tjLI8a2raPUV1DT5ECY4+ik8MPgMZ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qve4zMT4wARa+A6+yO6lINb0wkWi9/4WWvvbTiL10SrS9vlKLvgXYx72gXlm+NZr7vQTr/7xdjQe6W51DPOc+Ob0dic467rzbOhTM773d6Cm+IExPvSteSj3GYo49qA1JvfLsXb1yLvu9Dl0WvmTS/Dymya69bk+xvE1LjbxHTo89DSEnvsLMrD0jMRY+5MpTvRJLi74Sv4a9mN4Ivu1hCL3Sg8C9YnfmvHakLTy+6VG9IwE/vr+UjT2yY9K9G//6vCs0qr1bIw2+EyB9vfLB/j03HtS9+EwFuyuEBD6bR4++","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","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","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","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","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","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","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","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","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","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","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","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","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","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","ok6gvUdPtb1jxVM9RcC3PB6NB714xAQ+r7U3PULM4T2NVMs99kGWPX1hgT0o0689+m20vStNmb5fM0m9KXgtPuiXsDyrzrY9FUe+vm32Rz6IBYa8TJ/GPQtN1T0NsWc9sFcfvbWIyb3cCRk9FKO0vLuADb5/ic47xqqQvpxcRb2xPia8b1dTu79WrD2Mt/e8M7thvTyKuz2X8826JE02PjSPjD37/zY91+FhvQsukD3+HHU+tD+SPYRDDj6AdfI8tUJ/OzcpAj4JKaW8mhrcPC/Luz1gHi89FG2XveGzrj1uyeG9/eEZPvl3oj1DBu08OVjrvddbjjxceEC+H5SMO6UIg70emlw+428pPr9toD0BH/o9DANzPTnpgr0qIe68eY7AvH2AAT7A67489i/APSYIsjyN1P89o5rmPc8YKz7Ry5Q+h4PUPTT+MD2rjgE+OAQQufAXsjvBy0m+96hNPASvrj1Hv8a8avT/PU9OiT6kk7w9/KTjPTML3b2QtZE+DE1Avb03hD3R/Os9toRFPjL0Gr2z+jK9stu3vTjTAD2TkpU9tfTjvpXHjb24Vh4+GYksvagDPj4aXRc9eptcvHvJgj7u54E9zs4UPoRofj5jhmA+QD8Iu8FBNLyvaUU8gJ8EPqxSgT6PY2g+XDbqPX9sCT4rjKw9GddZPodiiL7gIgO+iAlDPlz5LDwblIw8N4CiPQ3dIjtMkhe+hF4CPtQm3T3Xc/E9cQp7vo0xLD54GhQ+SWDOPXrOQT7zhCk+0VsBPqnCqb3H6h09K04uPgiinbxhfcs+J4qNvtat5D2FGGM9qs4wPl/x4z2qKQ49TevPPFecmjuulwo96OZ7PUlANT2SpOC9wOEAPhIDlj66BNE9Q18APk65VL5QdCy+TZI8Pv9ogb6V9zk+ssCOPhMBGL7QCo07LviwPblckj2ckvW8/xpgvEkwMT/Ls/g9sutPPaR9Bz4x0fc9f3v1Om1DNLvuqq0+7nNwPSluFj5XgtS9S6kYPiTCc75sKoG9","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","RDCvvWcLjj18j+a9GSPhvXL5Db7+xZC+NszBvaDutr1f0sG+HLp/vvOlOL76zVU94BLvPTipjL0dIkS+hnuRvQuLF76i+Zq+cZv+vYksSb5/boi+D7KRvMpvKr6jS7m9GO1GvnFnhL3cLia+M/WGu1s/+72o+Sa9wD8AvcWch7ySi/C9JQ+8vd48pb1aWru95knPvchL1b0Jbvs8turVvaWekTwM92q+qhFgvlX2lL237pC+TvHDvrth+71tvCo+KkE4vjW+HL4L82G8boEPvj7qeD308LS9T6pwvutcgb47d/K8NE27vXi1rr0ypSS+NKK1vX7DiTsi2qg9HLEWvrr7gj0a5rS9jqkGPhyobr0OYCa+/bDTPdlWrjweIwI7wIFpvrrw2j4jVpU+nJ6oveUUhD0aBYQ9EFZivV3hc73jHwQ+cqMsPlAP8Lx8KBC+MgyevCkCj7wgszo+LD7FvWYHkjlFkZ89+oJOPsgQJb64CRe+UtzXvCDiij3lcFc8xRdSPoimlT0HILs+nm0nvVL2kL1OQeC9scv3vbCOKr72aQ4+thFKvDJ1cL0iWCw9RznlvTwokD4acMQ7ZwdhvY18Eb5Az9895EngvaKUFr0CtBI9zG7MPd3tqb1NJI8+snCHPo508j190748KAEuPeg0eb3INH++XH19PT5PYT4m+io8+PeZOhbOQD7Lcx6+3aaJvQ3Egr7rPJM9FFOOvYm/7D2YwuI+pDEvvUBoWL0WL/y9cbddvqIStL735xq9q+lOPamhF70/hBG+SZHMPgycCL3Ys6Q95K2YPbTGwT37/d69XKtBvVdEuj0Oipc9D13PvFRJFz5QAw6+rTqUO69k7r3mTq+4kHRdPoFTaj6NSDy+7IpHPudvvz3Kcda94servWEEOr22GT490apGPUDG0D2NK9c9BAWBPob3Rj1Id3k+/yUkPqhhP77HSw6+AMSlPqvFBrvLF529WKxCvtZUArsPNWA6J18tvcHXzD19kp67XY+SPS51pzxepog8","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","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","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","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","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","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","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","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","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","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","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","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","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","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","5lIfPv4kIrype728BLsFvliPlr0Kz1M9RCqwPWlqLr2nQeQ8/UHovUeHFT1bWRm8V+nBvUevnzxur3I8KSTSPLGSYL5EuVi+D7FePbw8F73RrA49fRAGPaESA769v569b+eDvcR9tj523yK+OnSMvLmVZjw0vGA+0hNgO5pYqT5NOPk9XsXivb+N/r2WbuQ9xpzZvG8FkD4gmSc9y6kEvSRrfL2hUcK9jFjWPTCKrz3E4jS9zAV1vrP+Bb1KG2e86XxEPnDgtT1PAGS9ANSVO+7DrT2crHM9SWEqvZFlmz3WuQU93nyQvS6fpru4wJW9aCWmPbHGI7zV7ik9kOu8vQ1zbT22nBC9C2s6vnIevLzHs1m+PvH1vLA0aD5BApc9s0ESPcWVvr4WHJs+Smk9vjNWRzx2I8y9R2ScPsY5Ib65XqC9VaQBvqp0bT6EWUe+f09sO61LkT6advk83ZqBvomxpD688hQ/BeKGvcK+Rj3ttao8e033vBS5zz3juc+96I0RvuJeuD14OcO9iX6XPgIvCD0NxI69o3tjvTvbwDz8xL+9FWjhvf2RrL0xpoA9IAAwvc05wb2PS6e9SYQlPncsBj5EGuq9Ke21PakxvTzY3du9yI+SvR/cvz4JU84994SfPkpp/jlrcms+piWkvZZdZT24wFM9C0jcPdJo5b1GvBc+7KzhvVQyDL62ila+GUXWvfbJxL2i1EU+L/QMvRtCYrzOAGK+4dMVvTs6g76klVS+I/A6vttUEr1WbCs+gDY7vtIepTy2rJ+94Y5yvgSWIb5APZ+9q//oPep55b2YelQ6/1luvg9jlr0duy2+pl1ovjiuB77wiGm+LoM5vnYIVb4gPZa+cY+XvjiSHr7ja8e9ZQymPQ1qQ756u447QI4ovgmS+D1Z8U2+I/dqvc/ihTy7zf+9s8CZvR1QYr4a7ok8e53pvRXA8r0JDGi+jAY9vlyCZL4RsMm9aGYmvs7Ryr0i/LO+tFkEvjI6VjxwKd47oJHzPOZe0L1UA28+","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","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","jGGcPRCykz2tar89wMTaPHrRz72ccza+XLd2PcNuGz70QCC96SWOPTNSVz3LPbU+KhshPN/Jvzx1j6e7lUIXvqXRBL3W3Fq9Sz8avqKZ1b0XLyo83mZDvkEIx70fAFM9hUVDPvADPb58Wu499IcqvdZXhL2fJzq+vJU3O9tAcz6w3sQ8kzEfPhIdcT4WnDW9wwVWvgijB7tW0DC+8QStvVKXQL0nZOS+6RwxvBezID7f+yI86ojePfOz7jwms329KmoHvvBrfr1ouiu90hYpPiuzib5ZaA6+sXJOPXwEsTyZh0s+KgAevSCPc708lX092HsAPAerUL0dBYs9ojUcPdr9k73skWI+DD9ePk32Wj5dFc49TBMavYT0L71MO2m+8jLlvGrPIL3algw8ageMvB4NET7fss08PbcwvS69n70yN3q9JeprPKUf/r0HLtc9v06JPlCEUD5Pboa+YNlWvkbwFb7lVn6+SSKpPYbhpT1jBDs+uKx2vavYm7thm44+0PwxPVoNlb7RprM9TunlPEiXQ77BbiS9/VMEvkcKrr03OD6+2coxPrAeDb5UyJa+tx6PvcVrJLxZAy0+SayIPT+0Kz0FeuY9wYIJviSAtz2j0Dc9s6YfPd1BZ709BAM/E1gxvY0/GD0rMwC+Yf6CPQQNvr4w0bk+vUDBPTR/7L2VmP29zUolPfE2BT6JP/s9Q7qoPZWJij2LXLY9GEUVvgrqUL0abMm9xtTsPap9p739huQ7P2EJPtP2zr1XU208dNWVPbM/ej3K0Wc+rMJ0PXIK8rxOQt690b4LPu8KRj3bu1S6+p2+PTNNxT2seg8+bbddPoGBgD2Wnoo9j8QEviryYj5M6z49GvmNPR0v4z3Q1TI9JqijPEnBIT6Vwco9/g6HO9zhrLyBF2G9gnSoPdN8cj03KZg+IsCmPXibcL308Gc+MWi0PQ7Gg72K2Ck+9hrnPJz15L0ReIw9UwuaPWufhbqo/8c9NGsQPuZkkz2Pe048cMuDPO8mOz4qPYO9","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","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","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","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","AFYRvWM6nD2O31U+3E2fPZ187z0anCs+lpirPR3IHj7FCzk9GfnuPWETN70fkpG89SB4vLhWszzRCj0+wAsPPtR0HD54hJ293lRBPgCmRz6m2UU+liRPPG+dPD3i3Fw+K7spPuqHtz3QR/o9nenwOwHN/T2huTA+rqcLPIXd4T0c5JA9TwfNPAJ+gz0wZ4s+ucfZPQ59Bz6xkBE+Vt9WPRxjaj0LkAo9F7++PVpd0z0uQXI+eTcVPgPuhT148gq9zVTKPW/owT1qkoO9/w+TPeZZwj3K3Fs+FKvIPb9wrD7xNyG9meyEvWKymz1vH2Q9k2gyPjA9Kj3zdxu+90DwPYZymjwV4S29VgYwvmWxST3g6qs9hPXiPV0xLryJVDi80xVEvUlPgT19liC6LC4+vh+zjz1cVWq7i7MfvaBmKD4/nwu9ZWf4vRzKWz2R/6a9Has+vgqhhT5xXEe9m4uZPedOM75eV529lKivvTBVrD13P+W8uTAIPrtylL1GghU+03IdvtDCGL4hAQ87lZG1PcY7PD59c1Q+egywPD6BarxjHgq+n/X/PYJGGT4yM/W+XpyDPRwZsr5RW/C954wIvmaDBz5PdQC866OYvU6h0z0+Usw8O2QbPTCUgb3BHSG+fR5svtXuVb61N7+8CZUIvlVRiz1hwa68qrb0PMRvB74fvly9ghPqPZaqCr4dEaM82K9/OwGqxT0Y/lk+sM0CvlXua70TSpw9rjlVPe0hGj1OoOS89ItKPtZPyz3hv1S9XVcvvFtiiT6UAhQ+rKQivfH0Fj6G2R0+MmBmvpEx2D2l2lc8f5sKvsE5T75UgHC9TFqzvfbRI77QJrW9GgKsvcq5c70kDFG+9zEAPi+5OL0LVt49JytzO/3s4r2+ZgY9ScZDvc7EiL56vcg6tXZYPcQ4h70I4QG8QGFAvCsGAr2R9p49ScOqvfVZtbt0G+899POavWTDVr1wO4w+jZkTPmTobr0wmlm95bgbvT8lyT33vbI9bFGuvQEEGj40RO+9","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","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","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","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","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","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","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","qK+Fve5dCb4JFBm+5FxJvQCc2r3C4eK991d2vgsGLL4QK4g9JmrLPFuSIz2spTC+FM80Pc4wk77TJ6I8ag/zPfCzFL6ETbo9eUn1ugezvDz0cbu9m/6WvhiZbD0fmk6+0RTXu0I7KL7UzSG9JjhxPlRuVrwPAgS9Jgv/PAlLHb42dQM+OW7SveK0Oj5tLN49+NyrvUK3hb6GZxQ+fCIjvpdbqL3jmWY9L6opvbtVHL6mR6K+2G1JvaQ9xj3EJMA+5USGvooxpLxTAoc8xVLMvcWl5j2L/bu9joMbO3RfE74oEZE+ZNs3vq6/K75yoIK9JR5lvTb9Kz7U0nW+1V4BPqs69LuhGBe+Bmi4PeahqrwluRG9aU4HvnaqiL1tbka7FkyavZNrxz2Fgne84uwZPs0yMj1dZjk9cowTPrJmd70ROQ8+6FdXPtMTy72GPdM8bn/zPXW1arotxCw9NV3HvaT0Tr28e/S9cRYTPrWQtb0UTKm+cgehO57du73gNFI95RsoPWvO1z0HWUa8fQEzu804brweQ86+mE8ovu7aG74ayPM9sOi7vdUElr4upOQ9eIDyvXmeqTwJleW8pQSUPbrlHbyvkwe+d28nvV7W1T1YucI9hQkpvmWxej0+7bY9+iNFPjB9FjykvuM92M93PRBFNb7Ec609lswpvkWyTT1vsa29CCi6Pk72ND5qA60+o/q/viVoAD9bu+C+H310PgQ/UD6V/I4+ssL4vmc4VT4Mnhc+sTeAPXFqOD4CvpS+HVAVPhqy5z1SM4u9qenIPaeWoz5YJhW+naeAPhBR2z2ZxuC+yUsVv3Uhsj5HRTI+OfNMvqcDJD4zzlu8aBaevvi6Hb8pdqQ++pxavUpJMb4C1ps+UwwSvZIS7z3Ze5w9IkObP0tmBj1YPrO+SFvPPiQcgr4e5xQ/fdobPqFb371zQz+/zJumPk4XRL5ORww//3nKvlJBPj828pQ8tywaPlHHBr0BA40+TUsqvqnjyL3tA9I+jh0mvzqSpr5Aom0/","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","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","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","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","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","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","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","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","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","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","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","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","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\ No newline at end of file diff --git a/src/kernels/gfx942_ConvHipIgemmGroupXdlops_metadata.ktn.model b/src/kernels/gfx942_ConvHipIgemmGroupXdlops_metadata.ktn.model new file mode 100644 index 0000000000..b74bc954e2 --- /dev/null +++ b/src/kernels/gfx942_ConvHipIgemmGroupXdlops_metadata.ktn.model @@ -0,0 +1,72 @@ +{ + "predict_type": 0, + "num_tuning_params": { + "fwd": 13, + "bwd": 14, + "wrw": 14 + }, + "decodings": { + "tunings": { + "0": "0", + "1": "256", + "2": "64", + "3": "128", + "4": "64", + "5": "128", + "6": "32", + "7": "256", + "8": "128", + "9": "64", + "10": "32", + "11": "256", + "12": "32", + "13": "4", + "14": "16", + "15": "8", + "16": "Default", + "17": "Filter1x1Stride1Pad0", + "18": "OddC", + "19": "Filter1x1Pad0", + "20": "8", + "21": "4", + "22": "2", + "23": "32", + "24": "Default", + "25": "2", + "26": "Filter1x1Stride1Pad0", + "27": "1", + "28": "4", + "29": "32", + "30": "32", + "31": "1", + "32": "2", + "33": "4", + "34": "32", + "35": "8", + "36": "4", + "37": "1", + "38": "2", + "39": "1", + "40": "2", + "41": "4", + "42": "8", + "43": "2", + "44": "1", + "45": "8", + "46": "4", + "47": "4", + "48": "2", + "49": "1", + "50": "8", + "51": "4", + "52": "1", + "53": "8", + "54": "1", + "55": "8", + "56": "4", + "57": "2", + "58": "-1", + "59": "-1" + } + } +} \ No newline at end of file diff --git a/src/kernels/gfx942_metadata.tn.model b/src/kernels/gfx942_metadata.tn.model index 63f5f25b0c..71c76582da 100644 --- a/src/kernels/gfx942_metadata.tn.model +++ b/src/kernels/gfx942_metadata.tn.model @@ -1,5 +1,5 @@ { - "generated_on": "14 Mar 2024, 23:38:55", + "generated_on": "14 May 2024, 22:13:51", "database": "tuna_net2", "gpu": { "arch": "gfx942", @@ -7,9 +7,9 @@ }, "golden_v": null, "kernels": "hip", - "num_inputs": 18, - "num_solvers": 20, - "num_outputs": 20, + "num_inputs": 19, + "num_outputs": 17, + "num_solvers": 17, "encodings": { "Direction": { "B": 0, @@ -22,419 +22,387 @@ "BF16": 2 }, "Layout": { - "NCHW": 0 + "NCHW": 0, + 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"t3": 165.85752868652344, + "t4": 0.0, + "t5": 0.0, + "t6": 1.2906914949417114, + "t7": 0.740736722946167, + "t8": 53.14275360107422, + "t9": 121.94573974609375, + "t10": 0.2878236174583435, + "t11": 1.8997714519500732, + "t12": 0.43335336446762085, + "t13": 0.30722543597221375, + "t14": 3.996527910232544, + "t15": 25.22776222229004, + "t16": 179.25038146972656 } } } @@ -455,6 +423,7 @@ "Dil_1", "Dil_2", "BatchSize", + "Layout", "Precision", "Direction", "GroupSize" @@ -467,7 +436,6 @@ "Pad_0": 0.0, "Str_0": 1.0, "Dil_0": 1.0, - "BiasFlag": 0.0, - "Layout": 0.0 + "BiasFlag": 0.0 } } \ No newline at end of file diff --git a/src/kernels/gpu_reference_kernel/naive_conv.cpp b/src/kernels/gpu_reference_kernel/naive_conv.cpp index 2bdb8b3f62..2e8aea697e 100644 --- a/src/kernels/gpu_reference_kernel/naive_conv.cpp +++ b/src/kernels/gpu_reference_kernel/naive_conv.cpp @@ -102,6 +102,29 @@ inline __device__ __host__ int8_t cast_to(const int32_t& val) return static_cast(val & 0xff); } +inline __device__ __host__ bool IsZero(double val) { return val == 0.0; } + +inline __device__ __host__ bool IsOne(double val) { return val == 1.0; } + +template +inline __device__ void applyalphaBetaUpdate(dst_data_t* __restrict__ p_array, + const acc_data_t value, + double alpha, + double beta, + size_t index) +{ + if(IsOne(alpha) && IsZero(beta)) + { + p_array[index] = cast_to(value); + return; + } + // cast_to + acc_data_t val_alpha_beta = + cast_to(alpha) * value + + cast_to(p_array[index]) * cast_to(beta); + p_array[index] = cast_to(val_alpha_beta); +} + /// \todo remove template parameter 'bool ASSUME_PACKED' in a follow up PR /// --amberhassaan /// Notes (Amber): @@ -116,9 +139,13 @@ inline __device__ __host__ int8_t cast_to(const int32_t& val) /// at index 3 while stride for C is at index 0. This is different from how the /// tensor descriptors store strides, which is always NCHW order, left-to-right. +/// alpha and beta are double to ensure high precision. + template inline __device__ void naive_conv_fwd_nchw(const src_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, dst_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -233,15 +260,13 @@ inline __device__ void naive_conv_fwd_nchw(const src_data_t* __restrict__ p_in, if constexpr(ASSUME_PACKED) { size_t o_idx = static_cast(iho) * wo + static_cast(iwo); - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } else { size_t o_idx = static_cast(iho) * out_strides[1] + static_cast(iwo) * out_strides[0]; - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } } } @@ -249,6 +274,8 @@ inline __device__ void naive_conv_fwd_nchw(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_bwd_nchw(dst_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -369,15 +396,13 @@ inline __device__ void naive_conv_bwd_nchw(dst_data_t* __restrict__ p_in, if constexpr(ASSUME_PACKED) { size_t i_idx = static_cast(ihi) * wi + static_cast(iwi); - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } else { size_t i_idx = static_cast(ihi) * in_strides[1] + static_cast(iwi) * in_strides[0]; - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } } } @@ -385,6 +410,8 @@ inline __device__ void naive_conv_bwd_nchw(dst_data_t* __restrict__ p_in, template inline __device__ void naive_conv_wrw_nchw(const src_data_t* __restrict__ p_in, dst_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -502,16 +529,14 @@ inline __device__ void naive_conv_wrw_nchw(const src_data_t* __restrict__ p_in, { size_t f_idx = static_cast(ic) * fy * fx + static_cast(iy) * fx + static_cast(ix); - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } else { size_t f_idx = static_cast(ic) * wei_strides[2] + static_cast(iy) * wei_strides[1] + static_cast(ix) * wei_strides[0]; - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } } } @@ -520,6 +545,8 @@ inline __device__ void naive_conv_wrw_nchw(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_fwd_ncdhw(const src_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, dst_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -657,16 +684,14 @@ inline __device__ void naive_conv_fwd_ncdhw(const src_data_t* __restrict__ p_in, { size_t o_idx = static_cast(ido) * ho * wo + static_cast(iho) * wo + static_cast(iwo); - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } else { size_t o_idx = static_cast(ido) * out_strides[2] + static_cast(iho) * out_strides[1] + static_cast(iwo) * out_strides[0]; - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } } } @@ -674,6 +699,8 @@ inline __device__ void naive_conv_fwd_ncdhw(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_bwd_ncdhw(dst_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -820,16 +847,14 @@ inline __device__ void naive_conv_bwd_ncdhw(dst_data_t* __restrict__ p_in, { size_t i_idx = static_cast(idi) * hi * wi + static_cast(ihi) * wi + static_cast(iwi); - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } else { size_t i_idx = static_cast(idi) * in_strides[2] + static_cast(ihi) * in_strides[1] + static_cast(iwi) * in_strides[0]; - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } } } @@ -837,6 +862,8 @@ inline __device__ void naive_conv_bwd_ncdhw(dst_data_t* __restrict__ p_in, template inline __device__ void naive_conv_wrw_ncdhw(const src_data_t* __restrict__ p_in, dst_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -974,8 +1001,7 @@ inline __device__ void naive_conv_wrw_ncdhw(const src_data_t* __restrict__ p_in, size_t f_idx = static_cast(ic) * fz * fy * fx + static_cast(iz) * fy * fx + static_cast(iy) * fx + static_cast(ix); - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } else { @@ -983,8 +1009,7 @@ inline __device__ void naive_conv_wrw_ncdhw(const src_data_t* __restrict__ p_in, static_cast(iz) * wei_strides[2] + static_cast(iy) * wei_strides[1] + static_cast(ix) * wei_strides[0]; - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } } } @@ -994,6 +1019,8 @@ inline __device__ void naive_conv_wrw_ncdhw(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_fwd_nhwc(const src_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, dst_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -1108,15 +1135,13 @@ inline __device__ void naive_conv_fwd_nhwc(const src_data_t* __restrict__ p_in, if constexpr(ASSUME_PACKED) { size_t o_idx = static_cast(iwo) * k + static_cast(ik); - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } else { size_t o_idx = static_cast(iwo) * out_strides[2] + static_cast(ik) * out_strides[0]; - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } } } @@ -1124,6 +1149,8 @@ inline __device__ void naive_conv_fwd_nhwc(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_bwd_nhwc(dst_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -1245,14 +1272,13 @@ inline __device__ void naive_conv_bwd_nhwc(dst_data_t* __restrict__ p_in, if constexpr(ASSUME_PACKED) { size_t i_idx = static_cast(iwi) * c + static_cast(ic); - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } else { size_t i_idx = static_cast(iwi) * in_strides[2] + static_cast(ic) * in_strides[0]; - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } } } @@ -1260,6 +1286,8 @@ inline __device__ void naive_conv_bwd_nhwc(dst_data_t* __restrict__ p_in, template inline __device__ void naive_conv_wrw_nhwc(const src_data_t* __restrict__ p_in, dst_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides5D in_strides, Strides5D wei_strides, @@ -1377,16 +1405,14 @@ inline __device__ void naive_conv_wrw_nhwc(const src_data_t* __restrict__ p_in, { size_t f_idx = static_cast(iy) * fx * c_per_group + static_cast(ix) * c_per_group + static_cast(ic); - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } else { size_t f_idx = static_cast(iy) * wei_strides[2] + static_cast(ix) * wei_strides[1] + static_cast(ic) * wei_strides[0]; - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } } } @@ -1395,6 +1421,8 @@ inline __device__ void naive_conv_wrw_nhwc(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_fwd_ndhwc(const src_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, dst_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -1532,16 +1560,14 @@ inline __device__ void naive_conv_fwd_ndhwc(const src_data_t* __restrict__ p_in, { size_t o_idx = static_cast(iho) * wo * k + static_cast(iwo) * k + static_cast(ik); - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } else { size_t o_idx = static_cast(iho) * out_strides[3] + static_cast(iwo) * out_strides[2] + static_cast(ik) * out_strides[0]; - - p_out[o_idx] = cast_to(value); + applyalphaBetaUpdate(p_out, value, alpha, beta, o_idx); } } } @@ -1549,6 +1575,8 @@ inline __device__ void naive_conv_fwd_ndhwc(const src_data_t* __restrict__ p_in, template inline __device__ void naive_conv_bwd_ndhwc(dst_data_t* __restrict__ p_in, const src_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -1694,16 +1722,14 @@ inline __device__ void naive_conv_bwd_ndhwc(dst_data_t* __restrict__ p_in, { size_t i_idx = static_cast(ihi) * wi * c + static_cast(iwi) * c + static_cast(ic); - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } else { size_t i_idx = static_cast(ihi) * in_strides[3] + static_cast(iwi) * in_strides[2] + static_cast(ic) * in_strides[0]; - - p_in[i_idx] = cast_to(value); + applyalphaBetaUpdate(p_in, value, alpha, beta, i_idx); } } } @@ -1711,6 +1737,8 @@ inline __device__ void naive_conv_bwd_ndhwc(dst_data_t* __restrict__ p_in, template inline __device__ void naive_conv_wrw_ndhwc(const src_data_t* __restrict__ p_in, dst_data_t* __restrict__ p_wei, + const double alpha, + const double beta, const src_data_t* __restrict__ p_out, Strides6D in_strides, Strides6D wei_strides, @@ -1849,8 +1877,7 @@ inline __device__ void naive_conv_wrw_ndhwc(const src_data_t* __restrict__ p_in, size_t f_idx = static_cast(iz) * fy * fx * c_per_group + static_cast(iy) * fx * c_per_group + static_cast(ix) * c_per_group + static_cast(ic); - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } else { @@ -1858,234 +1885,249 @@ inline __device__ void naive_conv_wrw_ndhwc(const src_data_t* __restrict__ p_in, static_cast(iy) * wei_strides[2] + static_cast(ix) * wei_strides[1] + static_cast(ic) * wei_strides[0]; - - p_wei[f_idx] = cast_to(value); + applyalphaBetaUpdate(p_wei, value, alpha, beta, f_idx); } } } -#define DEFINE_2D_NAIVE_CONV_KERNEL(direction, tensor_layout, src_data_t, acc_data_t, dst_data_t) \ - extern "C" __global__ void \ - naive_conv_packed_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ - src_data_t* __restrict__ p_in, \ - src_data_t* __restrict__ p_wei, \ - dst_data_t* __restrict__ p_out, \ - Strides5D in_strides, \ - Strides5D wei_strides, \ - Strides5D out_strides, \ - int hi, \ - int wi, \ - int n, \ - int k_per_group, \ - int c_per_group, \ - int ho, \ - int wo, \ - int sy, \ - int sx, \ - int dy, \ - int dx, \ - int py, \ - int px, \ - int fy, \ - int fx, \ - int group) \ - { \ - naive_conv_##direction##_##tensor_layout( \ - p_in, \ - p_wei, \ - p_out, \ - in_strides, \ - wei_strides, \ - out_strides, \ - hi, \ - wi, \ - n, \ - k_per_group, \ - c_per_group, \ - ho, \ - wo, \ - sy, \ - sx, \ - dy, \ - dx, \ - py, \ - px, \ - fy, \ - fx, \ - group); \ - } \ - extern "C" __global__ void \ - naive_conv_nonpacked_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ - src_data_t* __restrict__ p_in, \ - src_data_t* __restrict__ p_wei, \ - dst_data_t* __restrict__ p_out, \ - Strides5D in_strides, \ - Strides5D wei_strides, \ - Strides5D out_strides, \ - int hi, \ - int wi, \ - int n, \ - int k_per_group, \ - int c_per_group, \ - int ho, \ - int wo, \ - int sy, \ - int sx, \ - int dy, \ - int dx, \ - int py, \ - int px, \ - int fy, \ - int fx, \ - int group) \ - { \ - naive_conv_##direction##_##tensor_layout( \ - p_in, \ - p_wei, \ - p_out, \ - in_strides, \ - wei_strides, \ - out_strides, \ - hi, \ - wi, \ - n, \ - k_per_group, \ - c_per_group, \ - ho, \ - wo, \ - sy, \ - sx, \ - dy, \ - dx, \ - py, \ - px, \ - fy, \ - fx, \ - group); \ +#define DEFINE_2D_NAIVE_CONV_KERNEL(direction, tensor_layout, src_data_t, acc_data_t, dst_data_t) \ + extern "C" __global__ void \ + naive_conv_ab_packed_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ + src_data_t* __restrict__ p_in, \ + src_data_t* __restrict__ p_wei, \ + double alpha, \ + double beta, \ + dst_data_t* __restrict__ p_out, \ + Strides5D in_strides, \ + Strides5D wei_strides, \ + Strides5D out_strides, \ + int hi, \ + int wi, \ + int n, \ + int k_per_group, \ + int c_per_group, \ + int ho, \ + int wo, \ + int sy, \ + int sx, \ + int dy, \ + int dx, \ + int py, \ + int px, \ + int fy, \ + int fx, \ + int group) \ + { \ + naive_conv_##direction##_##tensor_layout( \ + p_in, \ + p_wei, \ + alpha, \ + beta, \ + p_out, \ + in_strides, \ + wei_strides, \ + out_strides, \ + hi, \ + wi, \ + n, \ + k_per_group, \ + c_per_group, \ + ho, \ + wo, \ + sy, \ + sx, \ + dy, \ + dx, \ + py, \ + px, \ + fy, \ + fx, \ + group); \ + } \ + extern "C" __global__ void \ + naive_conv_ab_nonpacked_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ + src_data_t* __restrict__ p_in, \ + src_data_t* __restrict__ p_wei, \ + double alpha, \ + double beta, \ + dst_data_t* __restrict__ p_out, \ + Strides5D in_strides, \ + Strides5D wei_strides, \ + Strides5D out_strides, \ + int hi, \ + int wi, \ + int n, \ + int k_per_group, \ + int c_per_group, \ + int ho, \ + int wo, \ + int sy, \ + int sx, \ + int dy, \ + int dx, \ + int py, \ + int px, \ + int fy, \ + int fx, \ + int group) \ + { \ + naive_conv_##direction##_##tensor_layout( \ + p_in, \ + p_wei, \ + alpha, \ + beta, \ + p_out, \ + in_strides, \ + wei_strides, \ + out_strides, \ + hi, \ + wi, \ + n, \ + k_per_group, \ + c_per_group, \ + ho, \ + wo, \ + sy, \ + sx, \ + dy, \ + dx, \ + py, \ + px, \ + fy, \ + fx, \ + group); \ } -#define DEFINE_3D_NAIVE_CONV_KERNEL(direction, tensor_layout, src_data_t, acc_data_t, dst_data_t) \ - extern "C" __global__ void \ - naive_conv_packed_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ - src_data_t* __restrict__ p_in, \ - src_data_t* __restrict__ p_wei, \ - dst_data_t* __restrict__ p_out, \ - Strides6D in_strides, \ - Strides6D wei_strides, \ - Strides6D out_strides, \ - int di, \ - int hi, \ - int wi, \ - int n, \ - int k_per_group, \ - int c_per_group, \ - int do_, \ - int ho, \ - int wo, \ - int sz, \ - int sy, \ - int sx, \ - int dz, \ - int dy, \ - int dx, \ - int pz, \ - int py, \ - int px, \ - int fz, \ - int fy, \ - int fx, \ - int group) \ - { \ - naive_conv_##direction##_##tensor_layout( \ - p_in, \ - p_wei, \ - p_out, \ - in_strides, \ - wei_strides, \ - out_strides, \ - di, \ - hi, \ - wi, \ - n, \ - k_per_group, \ - c_per_group, \ - do_, \ - ho, \ - wo, \ - sz, \ - sy, \ - sx, \ - dz, \ - dy, \ - dx, \ - pz, \ - py, \ - px, \ - fz, \ - fy, \ - fx, \ - group); \ - } \ - extern "C" __global__ void \ - naive_conv_nonpacked_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ - src_data_t* __restrict__ p_in, \ - src_data_t* __restrict__ p_wei, \ - dst_data_t* __restrict__ p_out, \ - Strides6D in_strides, \ - Strides6D wei_strides, \ - Strides6D out_strides, \ - int di, \ - int hi, \ - int wi, \ - int n, \ - int k_per_group, \ - int c_per_group, \ - int do_, \ - int ho, \ - int wo, \ - int sz, \ - int sy, \ - int sx, \ - int dz, \ - int dy, \ - int dx, \ - int pz, \ - int py, \ - int px, \ - int fz, \ - int fy, \ - int fx, \ - int group) \ - { \ - naive_conv_##direction##_##tensor_layout( \ - p_in, \ - p_wei, \ - p_out, \ - in_strides, \ - wei_strides, \ - out_strides, \ - di, \ - hi, \ - wi, \ - n, \ - k_per_group, \ - c_per_group, \ - do_, \ - ho, \ - wo, \ - sz, \ - sy, \ - sx, \ - dz, \ - dy, \ - dx, \ - pz, \ - py, \ - px, \ - fz, \ - fy, \ - fx, \ - group); \ +#define DEFINE_3D_NAIVE_CONV_KERNEL(direction, tensor_layout, src_data_t, acc_data_t, dst_data_t) \ + extern "C" __global__ void \ + naive_conv_ab_packed_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ + src_data_t* __restrict__ p_in, \ + src_data_t* __restrict__ p_wei, \ + double alpha, \ + double beta, \ + dst_data_t* __restrict__ p_out, \ + Strides6D in_strides, \ + Strides6D wei_strides, \ + Strides6D out_strides, \ + int di, \ + int hi, \ + int wi, \ + int n, \ + int k_per_group, \ + int c_per_group, \ + int do_, \ + int ho, \ + int wo, \ + int sz, \ + int sy, \ + int sx, \ + int dz, \ + int dy, \ + int dx, \ + int pz, \ + int py, \ + int px, \ + int fz, \ + int fy, \ + int fx, \ + int group) \ + { \ + naive_conv_##direction##_##tensor_layout( \ + p_in, \ + p_wei, \ + alpha, \ + beta, \ + p_out, \ + in_strides, \ + wei_strides, \ + out_strides, \ + di, \ + hi, \ + wi, \ + n, \ + k_per_group, \ + c_per_group, \ + do_, \ + ho, \ + wo, \ + sz, \ + sy, \ + sx, \ + dz, \ + dy, \ + dx, \ + pz, \ + py, \ + px, \ + fz, \ + fy, \ + fx, \ + group); \ + } \ + extern "C" __global__ void \ + naive_conv_ab_nonpacked_##direction##_##tensor_layout##_##src_data_t##_##acc_data_t##_##dst_data_t( \ + src_data_t* __restrict__ p_in, \ + src_data_t* __restrict__ p_wei, \ + double alpha, \ + double beta, \ + dst_data_t* __restrict__ p_out, \ + Strides6D in_strides, \ + Strides6D wei_strides, \ + Strides6D out_strides, \ + int di, \ + int hi, \ + int wi, \ + int n, \ + int k_per_group, \ + int c_per_group, \ + int do_, \ + int ho, \ + int wo, \ + int sz, \ + int sy, \ + int sx, \ + int dz, \ + int dy, \ + int dx, \ + int pz, \ + int py, \ + int px, \ + int fz, \ + int fy, \ + int fx, \ + int group) \ + { \ + naive_conv_##direction##_##tensor_layout( \ + p_in, \ + p_wei, \ + alpha, \ + beta, \ + p_out, \ + in_strides, \ + wei_strides, \ + out_strides, \ + di, \ + hi, \ + wi, \ + n, \ + k_per_group, \ + c_per_group, \ + do_, \ + ho, \ + wo, \ + sz, \ + sy, \ + sx, \ + dz, \ + dy, \ + dx, \ + pz, \ + py, \ + px, \ + fz, \ + fy, \ + fx, \ + group); \ } DEFINE_2D_NAIVE_CONV_KERNEL(fwd, nchw, float, double, float) diff --git a/src/kernels/hip_atomic.hpp b/src/kernels/hip_atomic.hpp new file mode 100644 index 0000000000..aad6b0a63e --- /dev/null +++ b/src/kernels/hip_atomic.hpp @@ -0,0 +1,95 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + *all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +#pragma once + +__device__ static inline __half __ushort_as___half(ushort x) +{ + static_assert(sizeof(ushort) == sizeof(__half), ""); + + __half tmp; + __builtin_memcpy(&tmp, &x, sizeof(tmp)); + + return tmp; +} + +__device__ static inline ushort ____half_as_ushort(__half x) +{ + static_assert(sizeof(ushort) == sizeof(__half), ""); + + ushort tmp; + __builtin_memcpy(&tmp, &x, sizeof(tmp)); + + return tmp; +} + +__device__ inline void atomic_add_g(ushort* addr, const float val) +{ + size_t offset = reinterpret_cast(addr) & 0x2; + bool is_32_align = offset; + uint32_t* addr_as_uint32_t = + reinterpret_cast(reinterpret_cast(addr) - offset); + uint32_t current = *addr_as_uint32_t; + + uint32_t expected; + + do + { + expected = current; + ushort current_ushort = is_32_align ? current >> 16 : current & 0xffff; + + float next_float = __uint_as_float(static_cast(current_ushort) << 16) + val; + ushort next_ushort = static_cast(__float_as_uint(next_float) >> 16); + uint32_t next = is_32_align ? (current & 0xffff) | (next_ushort << 16) + : (current & 0xffff0000) | next_ushort; + + current = atomicCAS(addr_as_uint32_t, expected, next); + } while(current != expected); +} + +__device__ inline void atomic_add_g(__half* addr, const __half val) +{ + size_t offset = reinterpret_cast(addr) & 0x2; + bool is_32_align = offset; + uint32_t* addr_as_uint32_t = + reinterpret_cast(reinterpret_cast(addr) - offset); + uint32_t current = *addr_as_uint32_t; + + uint32_t expected; + + do + { + expected = current; + ushort current_ushort = is_32_align ? current >> 16 : current & 0xffff; + + ushort next_ushort = ____half_as_ushort(__ushort_as___half(current_ushort) + val); + uint32_t next = is_32_align ? (current & 0xffff) | (next_ushort << 16) + : (current & 0xffff0000) | next_ushort; + + current = atomicCAS(addr_as_uint32_t, expected, next); + } while(current != expected); +} + +__device__ inline void atomic_add_g(float* addr, const float val) { atomicAdd(addr, val); } diff --git a/src/kernels/kernel.cpp.in b/src/kernels/kernel.cpp.in index 57a25a6eb6..6af132b342 100644 --- a/src/kernels/kernel.cpp.in +++ b/src/kernels/kernel.cpp.in @@ -24,9 +24,10 @@ * *******************************************************************************/ #include -#include +#include +#include +#include #include -#include #ifndef MIOPEN_USE_CLANG_TIDY // Huge generated source // clang-format off @@ -36,9 +37,9 @@ ${KERNELS_DECLS} namespace miopen { -const std::map& kernels() +const std::unordered_map& kernels() { - static const std::map data{ + static const std::unordered_map data{ #ifndef MIOPEN_USE_CLANG_TIDY // Huge generated source ${INIT_KERNELS} #endif @@ -46,20 +47,12 @@ const std::map& kernels() return data; } -std::string GetKernelSrc(std::string name) +std::string_view GetKernelSrc(const fs::path& name) { // Use the base name of the string - int start = 0; - auto slash = static_cast(name.find_last_of("/\\")); - if(slash != std::string::npos) - { - start = slash + 1; - } - auto key = name.substr(start); - - auto it = kernels().find(key); + auto it = kernels().find(name.filename()); if(it == kernels().end()) - MIOPEN_THROW("Failed to load kernel source: " + key); + MIOPEN_THROW("Failed to load kernel source: " + name.filename()); return it->second; } diff --git a/src/kernels/kernel_includes.cpp.in b/src/kernels/kernel_includes.cpp.in index 79a9fa8b97..6ee45ad58d 100644 --- a/src/kernels/kernel_includes.cpp.in +++ b/src/kernels/kernel_includes.cpp.in @@ -24,9 +24,10 @@ * *******************************************************************************/ #include -#include +#include +#include +#include #include -#include #ifndef MIOPEN_USE_CLANG_TIDY // Huge generated source // clang-format off @@ -36,9 +37,9 @@ ${KERNELS_DECLS} namespace miopen { -const std::map& kernel_includes() +const std::unordered_map& kernel_includes() { - static const std::map data{ + static const std::unordered_map data{ #ifndef MIOPEN_USE_CLANG_TIDY // Huge generated source ${INIT_KERNELS} #endif @@ -46,52 +47,26 @@ const std::map& kernel_includes() return data; } -std::string GetKernelInc(std::string key) +std::string_view GetKernelInc(const fs::path& name) { - auto it = kernel_includes().find(key); + auto it = kernel_includes().find(name.filename()); if(it == kernel_includes().end()) - MIOPEN_THROW("Failed to load kernel source: " + key); + MIOPEN_THROW("Failed to load kernel source: " + name.filename()); return it->second; } -/// \todo [Optimization] Kernels are stored as strings in the static onstant object. -/// Let's eliminate unnecessary copying: return pointer to the string. -/// Get rid of GetKernelInc(). - -const std::string* GetKernelIncPtr(std::string key) -{ - auto it = kernel_includes().find(key); - if(it == kernel_includes().end()) - MIOPEN_THROW("Failed to load kernel source: " + key); - - return &(it->second); -} - -/// \todo [Optimization] Kernel storage is constant. -/// Collect lists in the static object during the first -/// invocation and return that object on subsequent calls. - -std::vector GetKernelIncList() -{ - std::vector keys; - auto m = kernel_includes(); - std::transform(m.begin(), - m.end(), - std::back_inserter(keys), - [](decltype(m)::value_type const& pair) { return pair.first; }); - return keys; -} - -std::vector GetHipKernelIncList() +const std::vector>& GetKernelIncList() { - auto keys = GetKernelIncList(); - keys.erase(std::remove_if(keys.begin(), - keys.end(), - [&](const auto& key) { - return !(EndsWith(key, ".hpp") || EndsWith(key, ".h")); - }), - keys.end()); + static const std::vector> keys{[]() { + std::vector> ref_keys; + for(const auto& m : kernel_includes()) + { + if(m.first.extension() == ".hpp" || m.first.extension() == ".h") + ref_keys.emplace_back(std::cref(m.first)); + } + return ref_keys; + }()}; return keys; } diff --git a/src/kernels/miopen_rocrand.hpp b/src/kernels/miopen_rocrand.hpp index 5a81c444a6..eeffbfabca 100644 --- a/src/kernels/miopen_rocrand.hpp +++ b/src/kernels/miopen_rocrand.hpp @@ -25,39 +25,53 @@ *******************************************************************************/ #pragma once -namespace miopen::prng { -// borrowed from rocrand, since hiprtc/comgr has some problems to compile kernels with rocrand -struct xorwow_state -{ - // Weyl sequence value - unsigned int d; - // Xorshift values (160 bits) - unsigned int x[5]; -}; +#include "miopen_limits.hpp" +#include "miopen_cstdint.hpp" -inline constexpr unsigned int xorwow_next(xorwow_state* state) -{ - const unsigned int t = state->x[0] ^ (state->x[0] >> 2); - state->x[0] = state->x[1]; - state->x[1] = state->x[2]; - state->x[2] = state->x[3]; - state->x[3] = state->x[4]; - state->x[4] = (state->x[4] ^ (state->x[4] << 4)) ^ (t ^ (t << 1)); +#ifdef __HIPCC_RTC__ +#define WORKAROUND_IGNORE_ROCRAND_INCLUDES 1 +#else +#define WORKAROUND_IGNORE_ROCRAND_INCLUDES 0 +#endif - state->d += 362437; +#if WORKAROUND_IGNORE_ROCRAND_INCLUDES == 1 +// disable math.h from rocrand (it conflicts with hiptrc) +// NOLINTNEXTLINE +#define _GLIBCXX_MATH_H 1 +#endif - return state->d + state->x[4]; -} +// disable normal-distribution shortcuts (it bloats prng state) +#define ROCRAND_DETAIL_XORWOW_BM_NOT_IN_STATE +#include + +namespace prng { +// based on splitmix64 +inline constexpr uint64_t hash(uint64_t x) +{ + x = (x ^ (x >> 30)) * 0xbf58476d1ce4e5b9ull; + x = (x ^ (x >> 27)) * 0x94d049bb133111ebull; + x = x ^ (x >> 31); + return x; +}; + +// borrowed from +// rocrand_uniform.h has too many dependencies and device code inline constexpr float uniform_distribution(unsigned int v) { - constexpr float ROCRAND_2POW32_INV = 2.3283064e-10f; - return ROCRAND_2POW32_INV + (static_cast(v) * ROCRAND_2POW32_INV); + constexpr float rocrand_2pow32_inv = +#ifdef ROCRAND_2POW32_INV + ROCRAND_2POW32_INV; +#else + 2.3283064e-10f; +#endif + + return rocrand_2pow32_inv + (static_cast(v) * rocrand_2pow32_inv); } -inline constexpr float xorwow_uniform(xorwow_state* state) +inline constexpr float xorwow_uniform(rocrand_device::xorwow_engine* state) { - return uniform_distribution(xorwow_next(state)); + return uniform_distribution(state->next()); } -} // namespace miopen::prng +} // namespace prng diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c16_stride1.s b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c16_stride1.s new file mode 100644 index 0000000000..239931f6d5 --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c16_stride1.s @@ -0,0 +1,42 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +.include "Conv_Winograd_Fury_v2_4_1_metadata.inc" + +.if (.amdgcn.gfx_generation_number == 11) + .if ((.amdgcn.gfx_generation_minor == 0 && (.amdgcn.gfx_generation_stepping == 0 || .amdgcn.gfx_generation_stepping == 1)) || (.amdgcn.gfx_generation_minor == 5 && .amdgcn.gfx_generation_stepping == 1)) + // gfx1100, gfx1101, gfx1151 + KERNEL_PROLOG _1536vgprs_fp16_fp16acc_f2x3_c16_stride1 + .include "Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c16_stride1.inc" + KERNEL_EPILOG _1536vgprs_fp16_fp16acc_f2x3_c16_stride1 + .else + // gfx1102, gfx1103, gfx1150 + KERNEL_PROLOG _1024vgprs_fp16_fp16acc_f2x3_c16_stride1 + .include "Conv_Winograd_Fury_v2_4_1_gfx11_1024vgprs_fp16_fp16acc_f2x3_c16_stride1.inc" + KERNEL_EPILOG _1024vgprs_fp16_fp16acc_f2x3_c16_stride1 + .endif +.else + .error "Unsupported gfx generation" +.endif diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c32_stride1.s b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c32_stride1.s new file mode 100644 index 0000000000..0995c95da7 --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_fp16_fp16acc_f2x3_c32_stride1.s @@ -0,0 +1,40 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ +.include "Conv_Winograd_Fury_v2_4_1_metadata.inc" + +.if (.amdgcn.gfx_generation_number == 11) + .if ((.amdgcn.gfx_generation_minor == 0 && (.amdgcn.gfx_generation_stepping == 0 || .amdgcn.gfx_generation_stepping == 1)) || (.amdgcn.gfx_generation_minor == 5 && .amdgcn.gfx_generation_stepping == 1)) + // gfx1100, gfx1101, gfx1151 + KERNEL_PROLOG _1536vgprs_fp16_fp16acc_f2x3_c32_stride1 + .include "Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c32_stride1.inc" + KERNEL_EPILOG _1536vgprs_fp16_fp16acc_f2x3_c32_stride1 + .else + // gfx1102, gfx1103, gfx1150 + .error "Unsupported gpu" + .endif +.else + .error "Unsupported gfx generation" +.endif diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1024vgprs_fp16_fp16acc_f2x3_c16_stride1.inc b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1024vgprs_fp16_fp16acc_f2x3_c16_stride1.inc new file mode 100644 index 0000000000..724d25137d --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1024vgprs_fp16_fp16acc_f2x3_c16_stride1.inc @@ -0,0 +1,4346 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +.macro _sop1_lit op:req, sdst:req, lit:req + .long (0b101111101 << 23) | (\sdst << 16) | (\op << 8) | 255 + .long \lit +.endm + +.macro _s_mov_b32__sop1_lit sdst:req, lit:req + _sop1_lit 0, \sdst, \lit +.endm + +.macro _vop1 op:req, vdst:req, src:req + .long (0b0111111 << 25) | (\vdst << 17) | (\op << 9) | \src +.endm + +.macro _v_cvt_f16_i16__vop1 vdst:req, vsrc:req + _vop1 81, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_rcp_f16__vop1 vdst:req, vsrc:req + _vop1 84, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_exp_f16__vop1 vdst:req, vsrc:req + _vop1 88, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _vop3 op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_cvt_f16_i16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 465, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_rcp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 468, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_exp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 472, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_cndmask_b16__vop3 vdst:req, vsrc0:req, vsrc1:req, src2:req, opsel:req + _vop3 605, \vdst, (\vsrc0 + /*VGPR*/ 256), (\vsrc1 + /*VGPR*/ 256), \src2, \opsel, 0, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_s_lit sdst:req, ssrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, \ssrc0, 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_v_lit sdst:req, vsrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, (\vsrc0 + /*VGPR*/ 256), 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_lt_u16__vop3 sdst:req, vsrc0:req, ssrc1:req, opsel:req + _vop3 57, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, \opsel, 0, 0 +.endm + +.macro _v_cmpx_lt_u32__vop3 sdst:req, vsrc0:req, ssrc1:req + _vop3 201, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, 0, 0, 0 +.endm + +.macro _vop3p op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3p_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_pk_ashrrev_i16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 6, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 10, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_sub_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 11, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_min_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 13, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 15, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 15, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_mul_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 16, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_mul_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 16, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_min_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 17, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_max_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 18, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +s_version 0x2006 +s_set_inst_prefetch_distance 0x3 +s_mov_b32 s0, 0 +v_lshlrev_b32 v1, 7, v0 +s_getpc_b64 s[8:9] +s_mov_b32 s10, 0x5ccc +s_mov_b32 s11, 0x31014000 +buffer_load_b32 v2, v1, s[8:11], 0 offen +s_waitcnt vmcnt(0) +s_getpc_b64 s[6:7] +s_load_b512 s[8:23], s[2:3], null +s_load_b512 s[24:39], s[2:3], 0x40 +s_load_b512 s[40:55], s[2:3], 0x80 +s_load_b256 s[56:63], s[2:3], 0xc0 +s_load_b64 s[64:65], s[2:3], 0xe0 +v_and_b32 v8, 0xff, v0 +v_lshrrev_b32 v9, 1, v8 +v_and_b32 v10, 1, v0 +v_add_nc_u32 v5, v9, 32 +v_bfi_b32 v6, 31, v8, v9 +v_bfe_u32 v4, v8, 5, 1 +v_bfi_b32 v6, 0xbf, v6, v5 +v_and_b32 v2, 31, v8 +v_lshrrev_b32 v6, 5, v6 +v_lshrrev_b32 v7, 6, v8 +v_lshlrev_b32 v2, 4, v2 +v_and_b32 v3, 31, v9 +v_mad_u32_u24 v2, v4, 0x900, v2 +v_lshlrev_b32 v3, 4, v3 +v_xor_b32 v5, 3, v6 +v_mad_u32_u16 v3, 0x480, v7, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v1, v5, 0x240, v2 +v_mad_u32_u16 v3, 0x1240, v10, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v2, v6, 0x240, v2 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 6 +s_cbranch_scc0 14 +s_load_b64 s[16:17], s[16:17], null +s_load_b64 s[20:21], s[20:21], null +s_load_b64 s[18:19], s[18:19], null +s_cmp_eq_u64 0, s[60:61] +s_cbranch_scc1 2 +s_load_b64 s[60:61], s[60:61], null +s_cmp_eq_u64 0, s[30:31] +s_cbranch_scc1 2 +s_load_b64 s[30:31], s[30:31], null +s_bitcmp1_b32 s14, 3 +s_cbranch_scc0 2 +s_setreg_imm32_b32 hwreg(HW_REG_MODE, 0, 8), 0xf0 +s_cmp_eq_u32 s13, 0x60 +s_cbranch_scc0 16 +s_mul_i32 s1, s4, 0xab +s_lshr_b32 s1, s1, 10 +s_mul_i32 s23, s1, 6 +s_sub_u32 s23, s4, s23 +s_bfe_u32 s15, s1, 0x20000 +s_bfe_u32 s22, s1, 0x10002 +s_bfe_u32 s5, s1, 0x10003 +s_mov_b32 s45, s23 +s_lshl1_add_u32 s45, s45, s22 +s_lshl2_add_u32 s45, s45, s15 +s_lshl1_add_u32 s45, s45, s5 +s_mov_b32 s4, s45 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 13 +s_cbranch_scc0 10 +s_add_u32 s16, s16, s34 +s_addc_u32 s17, s17, s35 +s_add_u32 s20, s20, s38 +s_addc_u32 s21, s21, s39 +s_add_u32 s18, s18, s36 +s_addc_u32 s19, s19, s37 +s_cmp_eq_u64 0, s[30:31] +s_cselect_b64 s[40:41], 0, s[40:41] +s_add_u32 s30, s30, s40 +s_addc_u32 s31, s31, s41 +s_add_u32 s15, s12, 15 +s_lshr_b32 s15, s15, 4 +v_cvt_f32_u32 v4, s15 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v5, v4, s13, s13 +v_lshrrev_b32 v5, 16, v5 +v_cvt_f32_u32 v4, v5 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v6, v4, s4, s4 +v_lshrrev_b32 v6, 16, v6 +v_readfirstlane_b32 s1, v5 +v_readfirstlane_b32 s22, v6 +s_mul_i32 s5, s22, s1 +s_sub_u32 s5, s4, s5 +s_cmp_ge_u32 s22, s15 +s_cbranch_scc1 5809 +s_mul_i32 s13, s1, s15 +s_mul_i32 s23, s22, 16 +s_sub_u32 s12, s12, s23 +s_min_u32 s12, s12, 16 +s_mul_i32 s34, s23, s46 +s_mul_hi_u32 s35, s23, s46 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s18, s34, s18 +s_addc_u32 s19, s35, s19 +s_lshr_b32 s35, s23, 0 +s_mul_i32 s34, s35, s51 +s_mul_hi_u32 s35, s35, s51 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s20, s34, s20 +s_addc_u32 s21, s35, s21 +s_lshl_b32 s34, s23, 1 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s34, 0, s34 +s_add_u32 s30, s30, s34 +s_addc_u32 s31, s31, 0 +v_cmp_lt_u32 vcc, v0, 0x100 +s_cbranch_vccz 2749 +v_and_b32 v20, 0xff, v0 +v_lshrrev_b32 v21, 1, v20 +v_bfe_u32 v17, v20, 3, 1 +v_bfe_u32 v16, v20, 2, 1 +v_mad_u32_u16 v17, v17, 16, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v14, v16, 0x1240, v17 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 0, 2 +v_mad_u32_u16 v14, v16, 0x90, v14 op_sel:[0,0,0,0] +v_bfe_u32 v17, v20, 4, 2 +v_mad_u32_u16 v14, v17, 32, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 6, 1 +v_mad_u32_u16 v14, v16, 0x480, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 7, 1 +v_mad_u32_u16 v14, v16, 0x900, v14 op_sel:[0,0,0,0] +v_bfe_u32 v18, v20, 1, 2 +v_mad_u32_u16 v13, v18, 32, 0 op_sel:[0,0,0,0] +v_bfe_u32 v19, v20, 3, 1 +v_mad_u32_u16 v13, v19, 0x480, v13 op_sel:[0,0,0,0] +v_add_nc_u32 v18, v21, 32 +v_bfi_b32 v18, 0xbf, v20, v18 +v_bfe_u32 v18, v18, 6, 2 +v_mad_u32_u16 v13, v18, 0x90, v13 op_sel:[0,0,0,0] +v_xor_b32 v16, v0, v0 quad_perm:[2,3,2,1] +v_xor_b32 v17, v0, v0 quad_perm:[0,0,3,3] +v_sub_nc_u16 v16, v16, v17 op_sel:[0,0,0] +v_cvt_f16_i16 v15, v16 +_v_cvt_f16_i16__vop1 (15 | /*op_sel*/ 0x80), 17 +_v_pk_mul_f16__vop3p 15, 271, 240, 0x0, 0x1, 0x0, 0x0 +v_bfe_u32 v16, v0, 6, 1 +v_and_b32 v5, 63, v0 +v_cmp_eq_u32 vcc, v16, 1 +v_cndmask_b32 v16, 0, 0x400, vcc +v_cndmask_b32 v17, 0, 0x100, vcc +v_lshl_add_u32 v6, v5, 2, 0 +v_lshl_add_u32 v5, v5, 4, v16 +s_mov_b32 s23, 4 +s_mov_b32 s34, 0 +s_mov_b32 s40, 0xbc00c000 +v_readfirstlane_b32 s74, v0 +s_and_b32 null, 64, s74 +s_cmov_b32 s40, 0x3c00c000 +s_lshl_b32 s49, s43, 1 +s_lshl_b32 s53, s47, 1 +s_lshl_b32 s75, s49, 3 +s_lshl_b32 s76, s53, 3 +s_and_b32 null, 0x80, s74 +s_cselect_b32 s75, s75, 0 +s_cselect_b32 s76, s76, 0 +s_cselect_b32 s22, 8, 0 +s_sub_u32 s22, s9, s22 +s_cmov_b32 s22, 0 +s_mov_b32 s35, 0x11014000 +s_bitcmp1_b32 s14, 4 +s_cselect_b32 s77, 0, 0x8000000 +s_and_b32 s35, 0xf7ffffff, s35 +s_or_b32 s35, s35, s77 +s_and_b32 s17, s17, 0xffff +s_add_u32 s17, s17, 0x20000 +s_and_b32 s19, s19, 0xffff +s_add_u32 s19, s19, 0x20000 +s_add_u32 s16, s16, s75 +s_addc_u32 s17, s17, 0 +s_add_u32 s18, s18, s76 +s_addc_u32 s19, s19, 0 +s_mov_b64 s[36:37], s[16:17] +s_mov_b32 s38, 0x80000000 +s_mov_b32 s39, 0 +s_getpc_b64 s[64:65] +v_cmp_lt_u32 vcc, v0, 0x80 +s_cmp_gt_u32 vcc_lo, 0 +s_mov_b32 s74, 0x23d8 +s_mov_b32 s76, 0x1a58 +s_cmov_b32 s74, 0x1e98 +s_cmov_b32 s76, 0x1618 +s_mov_b32 s75, 0x2654 +s_mov_b32 s77, 0x1c54 +s_cmov_b32 s75, 0x2114 +s_cmov_b32 s77, 0x1814 +s_add_u32 s66, s64, s74 +s_addc_u32 s67, s65, 0 +s_add_u32 s70, s64, s76 +s_addc_u32 s71, s65, 0 +s_add_u32 s68, s64, s75 +s_addc_u32 s69, s65, 0 +s_add_u32 s72, s64, s77 +s_addc_u32 s73, s65, 0 +s_mov_b32 s45, 0 +v_mov_b32 v4, 0 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 308, 341, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 360, 377, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 396, 397, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v16, s40, v34 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v52, s40, v86 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v104, s40, v122 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v140, s40, v142 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5561 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 341, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 377, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 397, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v160 quad_perm:[1,0,3,2] +v_mov_b32 v85, v161 quad_perm:[1,0,3,2] +v_mov_b32 v121, v162 quad_perm:[1,0,3,2] +v_mov_b32 v141, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v121, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v141, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v160 quad_perm:[2,3,0,1] +v_mov_b32 v85, v161 quad_perm:[2,3,0,1] +v_mov_b32 v121, v162 quad_perm:[2,3,0,1] +v_mov_b32 v141, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v121, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v141, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5462 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v34, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v86, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v122, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v142, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 290, 291, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 342, 343, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 378, 379, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 398, 399, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5388 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 342, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 378, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 398, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v34, v160 quad_perm:[1,0,3,2] +v_mov_b32 v86, v161 quad_perm:[1,0,3,2] +v_mov_b32 v122, v162 quad_perm:[1,0,3,2] +v_mov_b32 v142, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v34, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v86, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v34, v160 quad_perm:[2,3,0,1] +v_mov_b32 v86, v161 quad_perm:[2,3,0,1] +v_mov_b32 v122, v162 quad_perm:[2,3,0,1] +v_mov_b32 v142, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v34, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v86, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5290 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 403, 402, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 407, 406, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 411, 410, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 415, 414, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v147, s40, v144 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v151, s40, v148 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v155, s40, v152 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v159, s40, v156 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5215 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 402, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 406, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 410, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 414, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v146, v160 quad_perm:[1,0,3,2] +v_mov_b32 v150, v161 quad_perm:[1,0,3,2] +v_mov_b32 v154, v162 quad_perm:[1,0,3,2] +v_mov_b32 v158, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v154, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v158, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v146, v160 quad_perm:[2,3,0,1] +v_mov_b32 v150, v161 quad_perm:[2,3,0,1] +v_mov_b32 v154, v162 quad_perm:[2,3,0,1] +v_mov_b32 v158, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v154, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v158, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5116 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v144, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v148, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v152, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v156, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 400, 401, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 404, 405, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 408, 409, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 412, 413, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5042 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 400, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 404, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 408, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 412, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v144, v160 quad_perm:[1,0,3,2] +v_mov_b32 v148, v161 quad_perm:[1,0,3,2] +v_mov_b32 v152, v162 quad_perm:[1,0,3,2] +v_mov_b32 v156, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v144, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v148, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v144, v160 quad_perm:[2,3,0,1] +v_mov_b32 v148, v161 quad_perm:[2,3,0,1] +v_mov_b32 v152, v162 quad_perm:[2,3,0,1] +v_mov_b32 v156, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v144, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v148, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4944 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 308, 341, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 291, 290, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 343, 342, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v16, s40, v121 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v52, s40, v141 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v35, s40, v122 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v87, s40, v142 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4869 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 341, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 342, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v160 quad_perm:[1,0,3,2] +v_mov_b32 v85, v161 quad_perm:[1,0,3,2] +v_mov_b32 v34, v162 quad_perm:[1,0,3,2] +v_mov_b32 v86, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v34, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v86, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v160 quad_perm:[2,3,0,1] +v_mov_b32 v85, v161 quad_perm:[2,3,0,1] +v_mov_b32 v34, v162 quad_perm:[2,3,0,1] +v_mov_b32 v86, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v34, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v86, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4770 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v121, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v141, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v122, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v142, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 377, 360, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 397, 396, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 378, 379, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 398, 399, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4696 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 377, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 397, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 378, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 398, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v121, v160 quad_perm:[1,0,3,2] +v_mov_b32 v141, v161 quad_perm:[1,0,3,2] +v_mov_b32 v122, v162 quad_perm:[1,0,3,2] +v_mov_b32 v142, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v121, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v141, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v121, v160 quad_perm:[2,3,0,1] +v_mov_b32 v141, v161 quad_perm:[2,3,0,1] +v_mov_b32 v122, v162 quad_perm:[2,3,0,1] +v_mov_b32 v142, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v121, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v141, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4598 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 403, 402, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 407, 406, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 401, 400, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 405, 404, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v147, s40, v154 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v151, s40, v158 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v145, s40, v152 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v149, s40, v156 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4523 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 402, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 406, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 400, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 404, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v146, v160 quad_perm:[1,0,3,2] +v_mov_b32 v150, v161 quad_perm:[1,0,3,2] +v_mov_b32 v144, v162 quad_perm:[1,0,3,2] +v_mov_b32 v148, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v144, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v148, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v146, v160 quad_perm:[2,3,0,1] +v_mov_b32 v150, v161 quad_perm:[2,3,0,1] +v_mov_b32 v144, v162 quad_perm:[2,3,0,1] +v_mov_b32 v148, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v144, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v148, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4424 +s_mov_b32 s56, 0xffffebec +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0xffffed18 +s_setprio 2 +v_pk_fma_f16 v160, v154, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v158, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v152, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v156, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 410, 411, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 414, 415, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 408, 409, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 412, 413, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4350 +s_mov_b32 s56, 0xffffea64 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0xffffeb90 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 410, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 414, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 408, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 412, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v154, v160 quad_perm:[1,0,3,2] +v_mov_b32 v158, v161 quad_perm:[1,0,3,2] +v_mov_b32 v152, v162 quad_perm:[1,0,3,2] +v_mov_b32 v156, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v154, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v158, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v154, v160 quad_perm:[2,3,0,1] +v_mov_b32 v158, v161 quad_perm:[2,3,0,1] +v_mov_b32 v152, v162 quad_perm:[2,3,0,1] +v_mov_b32 v156, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v154, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v158, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4252 +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 88, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 89, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 90, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 91, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 92, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 93, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 94, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 95, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 88, 344, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 89, 345, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 90, 346, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 91, 347, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 92, 348, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 93, 349, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 94, 350, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 95, 351, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v88, v44, 0.5, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v89, v45, 0.5, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v90, v46, 0.5, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v91, v47, 0.5, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v92, v48, 0.5, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v93, v49, 0.5, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v94, v50, 0.5, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v95, v51, 0.5, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v44, -1.0, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v45, -1.0, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v46, -1.0, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v47, -1.0, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v48, -1.0, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v49, -1.0, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v50, -1.0, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v112, v51, -1.0, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v124, v61 +v_mov_b32 v125, v62 +v_mov_b32 v126, v63 +v_mov_b32 v127, v64 +v_mov_b32 v128, v65 +v_mov_b32 v129, v66 +v_mov_b32 v130, v67 +v_mov_b32 v131, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 96, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 97, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 98, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 99, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 100, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 101, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 102, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 103, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 96, 352, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 97, 353, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 98, 354, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 99, 355, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 100, 356, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 101, 357, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 102, 358, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 103, 359, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v96, v44, 0.5, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v97, v45, 0.5, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v98, v46, 0.5, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v99, v47, 0.5, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v100, v48, 0.5, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v101, v49, 0.5, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v102, v50, 0.5, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v103, v51, 0.5, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v44, -1.0, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v45, -1.0, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v46, -1.0, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v47, -1.0, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v48, -1.0, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v49, -1.0, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v50, -1.0, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v120, v51, -1.0, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v132, v61 +v_mov_b32 v133, v62 +v_mov_b32 v134, v63 +v_mov_b32 v135, v64 +v_mov_b32 v136, v65 +v_mov_b32 v137, v66 +v_mov_b32 v138, v67 +v_mov_b32 v139, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 88, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 89, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 90, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 91, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 92, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 93, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 94, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 95, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 88, 344, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 89, 345, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 90, 346, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 91, 347, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 92, 348, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 93, 349, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 94, 350, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 95, 351, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v88, v44, 0.5, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v89, v45, 0.5, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v90, v46, 0.5, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v91, v47, 0.5, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v92, v48, 0.5, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v93, v49, 0.5, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v94, v50, 0.5, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v95, v51, 0.5, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v44, -1.0, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v45, -1.0, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v46, -1.0, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v47, -1.0, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v48, -1.0, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v49, -1.0, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v50, -1.0, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v112, v51, -1.0, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v124, v61 +v_mov_b32 v125, v62 +v_mov_b32 v126, v63 +v_mov_b32 v127, v64 +v_mov_b32 v128, v65 +v_mov_b32 v129, v66 +v_mov_b32 v130, v67 +v_mov_b32 v131, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 96, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 97, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 98, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 99, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 100, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 101, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 102, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 103, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 96, 352, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 97, 353, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 98, 354, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 99, 355, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 100, 356, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 101, 357, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 102, 358, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 103, 359, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v96, v44, 0.5, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v97, v45, 0.5, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v98, v46, 0.5, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v99, v47, 0.5, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v100, v48, 0.5, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v101, v49, 0.5, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v102, v50, 0.5, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v103, v51, 0.5, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v44, -1.0, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v45, -1.0, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v46, -1.0, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v47, -1.0, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v48, -1.0, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v49, -1.0, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v50, -1.0, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v120, v51, -1.0, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v132, v61 +v_mov_b32 v133, v62 +v_mov_b32 v134, v63 +v_mov_b32 v135, v64 +v_mov_b32 v136, v65 +v_mov_b32 v137, v66 +v_mov_b32 v138, v67 +v_mov_b32 v139, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +v_bfe_u32 v21, v0, 6, 1 +v_and_b32 v16, 63, v0 +v_cmp_eq_u32 vcc, v21, 1 +v_cndmask_b32 v23, 0, 0x800, vcc +v_cndmask_b32 v21, 0, 0x400, vcc +v_cndmask_b32 v22, 0, 0x100, vcc +v_lshl_add_u32 v14, v16, 3, v23 +v_lshl_add_u32 v17, v16, 2, v22 +v_lshl_add_u32 v18, v16, 2, 0 +v_lshl_add_u32 v16, v16, 4, v21 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s91, 0, 0x11014000 +s_and_b32 s31, s31, 0xffff +s_add_u32 s31, s31, 0x20000 +s_mov_b64 s[88:89], s[30:31] +s_mov_b32 s90, 0x80000000 +v_and_b32 v21, v0, 63 +v_lshlrev_b32 v21, 1, v21 +v_cmp_lt_u32 vcc, v21, s12 +v_add_nc_u32 v22, v21, 1 +v_cndmask_b32 v21, 0x80000000, v21, vcc +v_cmp_lt_u32 vcc, v22, s12 +v_cndmask_b32 v22, 0x80000000, v22, vcc +buffer_load_d16_b16 v23, v21, s[88:91], 0 idxen +buffer_load_d16_hi_b16 v23, v22, s[88:91], 0 idxen +s_waitcnt vmcnt(0) +v_readlane_b32 s56, v23, 0 +v_readlane_b32 s57, v23, 1 +v_readlane_b32 s59, v23, 2 +v_readlane_b32 s64, v23, 3 +v_readlane_b32 s65, v23, 4 +v_readlane_b32 s66, v23, 5 +v_readlane_b32 s67, v23, 6 +v_readlane_b32 s68, v23, 7 +s_bfe_u32 s88, s58, 0x80000 +s_cmp_eq_u32 s88, 2 +s_cbranch_scc1 20 +s_cmp_eq_u32 s88, 0 +s_cselect_b32 s32, 1.0, s32 +v_cvt_f16_f32 v21, s32 +v_readfirstlane_b32 s32, v21 +v_cvt_f16_f32 v21, s33 +v_readfirstlane_b32 s33, v21 +_v_cmp_gt_f16__vop3_s_lit 106, 32, 0x3c00, 0x0, 0x0 +s_pack_ll_b32_b16 s32, s32, s32 +s_pack_ll_b32_b16 s33, s33, s33 +s_cmp_eq_u32 s88, 3 +s_cbranch_scc1 10 +s_cbranch_vccnz 3 +s_mov_b32 s84, 0x424c +s_branch 8 +s_mov_b32 s84, 0x45e4 +s_branch 5 +s_mov_b32 s84, 0x497c +s_branch 2 +s_mov_b32 s84, 0x4f94 +s_add_u32 s86, s6, 0x3c90 +s_addc_u32 s87, s7, 0 +s_mov_b32 s82, 0xbc00c000 +s_mov_b32 s40, 0x10000 +s_mov_b32 s41, 0x30002 +s_mov_b32 s45, 0x10000 +v_readfirstlane_b32 s88, v0 +s_and_b32 null, 64, s88 +s_cmov_b32 s82, 0x3c00c000 +s_cmov_b32 s40, 0x20003 +s_cmov_b32 s41, 1 +s_cmov_b32 s45, 1 +s_and_b32 s21, s21, 0xffff +s_add_u32 s21, s21, 0x20000 +s_lshl_b32 s80, s51, 1 +s_lshl_b32 s81, s52, 1 +s_mov_b64 s[72:73], s[20:21] +s_mov_b32 s74, 0x80000000 +s_mov_b32 s75, 0 +s_sub_u32 s89, s25, 1 +s_bitcmp1_b32 s14, 1 +s_cselect_b32 s89, s89, 0 +s_cselect_b32 s88, -1, 1 +s_sub_u32 s91, s24, 1 +s_bitcmp1_b32 s14, 0 +s_cselect_b32 s91, s91, 0 +s_cselect_b32 s90, -1, 1 +v_bfe_u32 v24, v0, 6, 1 +v_bfe_u32 v25, v0, 4, 1 +v_bfe_u32 v21, v0, 5, 1 +v_lshl_add_u32 v24, v24, 2, 0 +v_lshl_add_u32 v25, v25, 3, v24 +v_bfe_u32 v23, v0, 2, 2 +v_bfe_u32 v24, v0, 3, 1 +v_xor_b32 v22, v0, v0 quad_perm:[0,0,3,1] +v_lshl_add_u32 v21, v21, 1, v25 +v_xor_b32 v23, v23, v24 +v_add_nc_u32 v24, v21, 1 +v_mad_i32_i16 v19, v23, s88, s89 op_sel:[0,0,0,0] +v_mad_i32_i16 v25, v22, s90, s91 op_sel:[0,0,0,0] +v_mad_u32_u16 v19, v25, s48, v19 op_sel:[0,0,0,0] +v_cmp_lt_u32 vcc, v23, s25 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_cmp_lt_u32 vcc, v22, s24 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_mad_u32_u24 v20, v24, s46, v19 +v_mad_u32_u24 v19, v21, s46, v19 +v_cmp_lt_u32 vcc, v24, s12 +v_cndmask_b32 v20, 0x80000000, v20, vcc +v_cmp_lt_u32 vcc, v21, s12 +v_cndmask_b32 v19, 0x80000000, v19, vcc +s_add_u32 s89, s28, 1 +s_lshr_b32 s89, s89, 1 +s_lshl_b32 s90, s89, 1 +s_add_u32 s91, s29, 1 +s_lshr_b32 s91, s91, 1 +s_lshl1_add_u32 s91, s91, 2 +s_pack_ll_b32_b16 s22, s91, s89 +s_pack_ll_b32_b16 s34, s11, s10 +s_sub_u32 s35, s90, s26 +s_sub_u32 s88, s91, s27 +s_pack_ll_b32_b16 s35, s88, s35 +s_pack_ll_b32_b16 s37, s29, s28 +s_sub_u32 s88, s91, 1 +s_pack_ll_b32_b16 s38, s88, s90 +v_lshrrev_b32 v24, 16, s22 +v_bfi_b32 v25, 0xffff, s22, 0 +v_and_b32 v27, 1, v0 +v_bfe_u32 v33, v0, 6, 1 +v_and_b32 v22, 63, v0 +v_mad_u32_u16 v28, 0x7c, s1, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v33, 2, s5, v33 op_sel:[0,0,0,0] +v_mad_u32_u16 v26, v24, v25, 0 op_sel:[0,0,0,0] +v_cmp_eq_u32 vcc, 0, v27 +v_cndmask_b32 v34, v26, v25, vcc +v_mad_u32_u16 v23, 62, v33, v22 op_sel:[0,0,0,0] +v_cndmask_b32 v23, v28, v23, vcc +v_clz_i32_u32 v40, v34 +v_lshlrev_b32 v41, v40, v34 +v_and_b32 v39, 0xffffff00, v41 +v_cmp_eq_u32 vcc, 0x80000000, v41 +v_cvt_f32_u32 v39, v39 +v_rcp_f32 v35, v39 +v_sub_co_ci_u32 v36, vcc, 32, v40, vcc +v_cvt_f32_ubyte0 v40, v41 +v_fma_f32 v39, v39, v35, -1.0 +v_fma_f32 v39, v40, v35, v39 +v_fmaak_f32 v39, v39, v35, 0x9f000000 +v_mul_f32 v39, 0x5f800000, v39 +v_mov_b32 v40, 0 +v_cvt_floor_i32_f32 v39, -v39 +v_lshl_add_u32 v35, v35, 9, v39 +v_mad_u64_u32 v[40:41], vcc, v41, v35, v[40:41] +v_sub_co_ci_u32 v35, vcc, v35, -1, vcc +v_mov_b32 v38, v36 quad_perm:[1,1,1,1] +v_mov_b32 v36, v36 quad_perm:[0,0,0,0] +v_mov_b32 v37, v35 quad_perm:[1,1,1,1] +v_mov_b32 v35, v35 quad_perm:[0,0,0,0] +v_mul_hi_u32 v39, v23, v35 +v_add_co_u32 v21, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v36 +v_cndmask_b32 v21, v21, v39, vcc +v_alignbit_b32 v21, v39, v21, v36 +v_mul_hi_u32 v39, v23, v37 +v_add_co_u32 v4, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v38 +v_cndmask_b32 v4, v4, v39, vcc +v_alignbit_b32 v4, v39, v4, v38 +v_mad_u32_u16 v32, v21, v25, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v31, v4, v24, 0 op_sel:[0,0,0,0] +v_sub_nc_u32 v32, v23, v32 +v_sub_nc_u32 v31, v21, v31 +v_readlane_b32 s92, v32, 1 +v_sub_nc_u32 v32, v32, v25 +v_readlane_b32 s23, v31, 1 +v_sub_nc_u32 v31, v31, v24 +v_readlane_b32 s15, v4, 1 +v_sub_nc_u32 v4, v4, s8 +s_lshl_b32 s23, s23, 16 +s_and_b32 s92, s92, 0xffff +s_add_u32 s23, s23, s92 +v_mov_b32 v32, v32 quad_perm:[0,0,2,2] +v_mov_b32 v31, v31 quad_perm:[0,0,2,2] +v_mov_b32 v4, v4 quad_perm:[0,0,2,2] +v_add_co_u32 v32, vcc, v32, v27 +v_cndmask_b32 v30, 0, v25, vcc +v_add_co_ci_u32 v31, vcc, v31, 0, vcc +v_cndmask_b32 v29, 0, v24, vcc +v_add_co_ci_u32 v4, vcc, v4, 0, vcc +v_min_u32 v27, v22, 63 +v_sub_nc_u32 v32, v32, v30 +v_sub_nc_u32 v31, v31, v29 +v_cmp_eq_u32 vcc, v22, v27 +v_lshlrev_b32 v5, 16, v31 +v_bfi_b32 v5, 0xffff, v32, v5 +v_add_nc_u32 v42, v4, s8 +v_med3_u32 v27, v22, 1, 62 +v_mul_lo_u32 v6, v42, s42 +v_mul_lo_u32 v11, v42, s50 +s_mul_i32 s36, s15, s42 +s_mul_i32 s39, s15, s50 +v_cndmask_b32 v6, 0x80000000, v6, vcc +v_cmp_eq_u32 vcc, v22, v27 +v_cndmask_b32 v11, 0x80000000, v11, vcc +v_cmp_ge_u32 s[54:55], v42, s8 +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +s_mov_b32 s49, 3 +s_lshl_b32 s53, s49, 9 +v_add_nc_u32 v15, s53, v14 +s_bfe_u32 s10, s58, 0x80008 +s_bfe_u32 s11, s58, 0x80010 +s_cmp_eq_u32 s11, 0 +s_cmov_b32 s26, 0 +s_cbranch_scc1 108 +s_add_u32 s11, s11, 0xffffff00 +s_add_u32 s60, s60, 0 +s_addc_u32 s61, s61, 0 +s_lshr_b32 s91, s13, 2 +s_or_b32 s91, s91, 0x21010000 +v_cmp_eq_u32 vcc, v0, 0x100 +s_cmp_eq_u64 vcc, 0 +s_cselect_b32 s91, 0, s91 +s_cselect_b32 s90, 0, 0x1010101 +s_sub_u32 s10, 0, s10 +s_mov_b64 s[88:89], s[60:61] +s_and_b32 s89, s89, 0xffff +s_or_b32 s89, s89, 0x40000 +s_and_b32 s29, s22, 0xffff +s_lshr_b32 s28, s22, 16 +s_lshr_b32 s29, s29, 1 +s_mul_i32 s27, s29, s28 +s_mul_i32 s27, s27, s8 +s_add_u32 s27, s27, 61 +v_writelane_b32 v22, 62, 0 +v_writelane_b32 v22, s1, 1 +v_writelane_b32 v22, 5, 2 +v_clz_i32_u32 v26, v22 +v_lshlrev_b32 v27, v26, v22 +v_and_b32 v28, 0xffffff00, v27 +v_cmp_eq_u32 vcc, 0x80000000, v27 +v_cvt_f32_u32 v28, v28 +v_rcp_f32 v24, v28 +v_sub_co_ci_u32 v25, vcc, 32, v26, vcc +v_cvt_f32_ubyte0 v26, v27 +v_fma_f32 v28, v28, v24, -1.0 +v_fma_f32 v28, v26, v24, v28 +v_fmaak_f32 v28, v28, v24, 0x9f000000 +v_mul_f32 v28, 0x5f800000, v28 +v_mov_b32 v26, 0 +v_cvt_floor_i32_f32 v28, -v28 +v_lshl_add_u32 v24, v24, 9, v28 +v_mad_u64_u32 v[26:27], vcc, v27, v24, v[26:27] +v_sub_co_ci_u32 v24, vcc, v24, -1, vcc +v_mul_hi_u32 v26, s27, v24 +v_add_co_u32 v23, vcc, v26, s27 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_mov_b32 v23, v23 quad_perm:[0,0,0,0] +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v22, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v22, v22, v26, vcc +v_alignbit_b32 v22, v26, v22, v25 +v_mov_b32 v22, v22 quad_perm:[1,1,1,1] +v_add_nc_u32 v23, v22, 4 +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v23, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_readlane_b32 s28, v22, 1 +v_readlane_b32 s29, v23, 2 +s_add_u32 s27, s9, 15 +s_lshr_b32 s27, s27, 4 +s_cmp_eq_u32 s27, 1 +s_cmov_b32 s29, 1 +s_add_u32 s26, s28, s29 +s_mul_i32 s26, s27, s26 +s_add_u32 s26, 6, s26 +s_sub_u32 s26, s26, 1 +s_mov_b32 s92, 0 +s_mov_b32 s93, 0 +s_mov_b32 s94, 0 +s_mov_b32 s95, 0 +s_mov_b32 s96, 0 +s_mov_b32 s97, 0 +s_mov_b32 s28, 0 +s_mov_b32 s27, 8 +s_cmp_gt_u32 s28, 0 +s_cbranch_scc1 4 +v_mov_b32 v58, v4 +v_mov_b32 v63, v5 +v_mov_b32 v145, v6 +v_mov_b32 v146, v11 +v_mov_b32 v4, v58 +v_mov_b32 v5, v63 +v_mov_b32 v6, v145 +v_mov_b32 v11, v146 +s_add_u32 s28, s28, 16 +s_cmp_ge_u32 s28, s9 +s_cmov_b32 s28, 0 +s_cselect_b32 s29, 6, 2 +s_cselect_b32 s98, 9, 0 +s_pack_lh_b32_b16 s29, s29, s27 +s_pack_ll_b32_b16 s98, s98, s28 +v_mov_b32 v144, s29 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v7, v19 +v_mov_b32 v8, v20 +v_mov_b32 v9, 0x80000000 +v_mov_b32 v10, 0x80000000 +v_mov_b32 v12, 0x80000000 +v_mov_b32 v13, 0x80000000 +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v144 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v165, v163 +v_mov_b32 v166, v164 +v_pk_fma_f16 v147, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v148, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v149, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v150, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 151, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[163:164], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s96 +s_mov_b32 s96, s97 +s_mov_b32 s97, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2473 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 3, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3c94 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v155, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v156, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v157, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v158, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 159, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v144, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v144 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3d54 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_cmp_le_u32 s9, 16 +s_cselect_b32 s99, -1, 4 +s_sub_u32 s99, s99, 1 +s_cselect_b32 s29, s98, s29 +s_bitset0_b32 s29, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v144, s29 +v_add_co_u32 v33, vcc, v5, s23 +v_pk_mad_u16 v23, v5, 0x20001, s35 +v_pk_mad_u16 v28, v5, 0x20001, s38 +_v_pk_min_u16__vop3p 22, 289, 261, 0x0, 0x3, 0x0, 0x0 +v_cndmask_b32 v43, 0, s42, vcc +v_cndmask_b32 v167, 0, s50, vcc +v_mad_u32_u16 v7, v23, 1, v6 op_sel:[0,0,0,0] +v_mad_u32_u16 v12, v28, 1, v11 op_sel:[0,0,0,0] +v_add3_u32 v6, v6, s36, v43 +v_add3_u32 v11, v11, s39, v167 +_v_pk_sub_u16__vop3p 22, 261, 278, 0x0, 0x3, 0x0, 0x0 +v_add_co_ci_u32 v4, s[54:55], v4, s15, vcc +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +v_cmp_lt_u16 vcc, v23, s34 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_cmp_lt_u16 vcc, v28, s37 +v_cndmask_b32 v12, 0x80000000, v12, vcc +_v_pk_ashrrev_i16__vop3p 22, 143, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_u16__vop3p 53, 279, 41, 0x1, 0x3, 0x0, 0x0 +_v_pk_add_u16__vop3p 48, 279, 40, 0x1, 0x3, 0x0, 0x0 +v_mad_u32_u16 v10, v53, s44, v7 op_sel:[1,0,0,0] +v_mad_u32_u16 v8, v48, s44, v7 op_sel:[1,0,0,0] +_v_pk_add_u16__vop3p 38, 284, 45, 0x1, 0x3, 0x0, 0x0 +_v_cmp_lt_u16__vop3 106, 53, 34, 0x3 +v_cndmask_b32 v10, 0x80000000, v10, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x3 +v_cndmask_b32 v8, 0x80000000, v8, vcc +v_mad_u32_u16 v13, v38, s52, v12 op_sel:[1,0,0,0] +v_mad_u32_u16 v9, v53, s44, v7 op_sel:[0,0,0,0] +v_mad_u32_u16 v7, v48, s44, v7 op_sel:[0,0,0,0] +_v_cmp_lt_u16__vop3 106, 38, 37, 0x3 +v_cndmask_b32 v13, 0x80000000, v13, vcc +_v_cmp_lt_u16__vop3 106, 53, 34, 0x2 +v_cndmask_b32 v9, 0x80000000, v9, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x2 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_mad_u32_u16 v12, v38, s52, v12 op_sel:[0,0,0,0] +v_pk_mad_u16 v5, v22, s22, v33 +_v_cmp_lt_u16__vop3 106, 38, 37, 0x2 +v_cndmask_b32 v12, 0x80000000, v12, vcc +v_add_co_u32 v22, vcc, v4, s8 +v_cndmask_b32 v144, s98, v144, vcc +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v144 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v165, v163 +v_mov_b32 v166, v164 +v_pk_fma_f16 v147, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v148, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v149, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v150, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 151, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[163:164], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s96 +s_mov_b32 s96, s97 +s_mov_b32 s97, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2181 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 3, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3c94 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v155, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v156, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v157, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v158, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 159, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v144, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v144 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3d54 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s27, 1 +s_cbranch_scc1 65244 +s_branch 65016 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 40 +v_mov_b32 v64, 0 +v_mov_b32 v68, 0 +v_mov_b32 v65, 0 +v_mov_b32 v69, 0 +v_mov_b32 v66, 0 +v_mov_b32 v70, 0 +v_mov_b32 v67, 0 +v_mov_b32 v71, 0 +v_mov_b32 v80, 0 +v_mov_b32 v84, 0 +v_mov_b32 v81, 0 +v_mov_b32 v85, 0 +v_mov_b32 v82, 0 +v_mov_b32 v86, 0 +v_mov_b32 v83, 0 +v_mov_b32 v87, 0 +v_mov_b32 v96, 0 +v_mov_b32 v100, 0 +v_mov_b32 v97, 0 +v_mov_b32 v101, 0 +v_mov_b32 v98, 0 +v_mov_b32 v102, 0 +v_mov_b32 v99, 0 +v_mov_b32 v103, 0 +v_mov_b32 v112, 0 +v_mov_b32 v116, 0 +v_mov_b32 v113, 0 +v_mov_b32 v117, 0 +v_mov_b32 v114, 0 +v_mov_b32 v118, 0 +v_mov_b32 v115, 0 +v_mov_b32 v119, 0 +v_mov_b32 v128, 0 +v_mov_b32 v132, 0 +v_mov_b32 v129, 0 +v_mov_b32 v133, 0 +v_mov_b32 v130, 0 +v_mov_b32 v134, 0 +v_mov_b32 v131, 0 +v_mov_b32 v135, 0 +s_mov_b32 s85, 0x3e14 +s_cmp_le_u32 s9, 16 +s_cmov_b32 s85, 0x3c90 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 40 +v_mov_b32 v72, 0 +v_mov_b32 v76, 0 +v_mov_b32 v73, 0 +v_mov_b32 v77, 0 +v_mov_b32 v74, 0 +v_mov_b32 v78, 0 +v_mov_b32 v75, 0 +v_mov_b32 v79, 0 +v_mov_b32 v88, 0 +v_mov_b32 v92, 0 +v_mov_b32 v89, 0 +v_mov_b32 v93, 0 +v_mov_b32 v90, 0 +v_mov_b32 v94, 0 +v_mov_b32 v91, 0 +v_mov_b32 v95, 0 +v_mov_b32 v104, 0 +v_mov_b32 v108, 0 +v_mov_b32 v105, 0 +v_mov_b32 v109, 0 +v_mov_b32 v106, 0 +v_mov_b32 v110, 0 +v_mov_b32 v107, 0 +v_mov_b32 v111, 0 +v_mov_b32 v120, 0 +v_mov_b32 v124, 0 +v_mov_b32 v121, 0 +v_mov_b32 v125, 0 +v_mov_b32 v122, 0 +v_mov_b32 v126, 0 +v_mov_b32 v123, 0 +v_mov_b32 v127, 0 +v_mov_b32 v136, 0 +v_mov_b32 v140, 0 +v_mov_b32 v137, 0 +v_mov_b32 v141, 0 +v_mov_b32 v138, 0 +v_mov_b32 v142, 0 +v_mov_b32 v139, 0 +v_mov_b32 v143, 0 +s_mov_b32 s85, 0x3e14 +s_cmp_le_u32 s9, 16 +s_cmov_b32 s85, 0x3c90 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 325, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 326, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 327, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v64, v147 +v_mov_b32 v65, v148 +v_mov_b32 v66, v149 +v_mov_b32 v67, v150 +v_mov_b32 v68, v151 +v_mov_b32 v69, v152 +v_mov_b32 v70, v153 +v_mov_b32 v71, v154 +s_mov_b32 s85, 0x3e80 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 328, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 329, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 330, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 331, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 332, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 333, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 334, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 335, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v72, v155 +v_mov_b32 v73, v156 +v_mov_b32 v74, v157 +v_mov_b32 v75, v158 +v_mov_b32 v76, v159 +v_mov_b32 v77, v160 +v_mov_b32 v78, v161 +v_mov_b32 v79, v162 +s_mov_b32 s85, 0x3eec +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 336, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 337, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 338, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 339, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 340, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 341, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 342, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 343, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v80, v147 +v_mov_b32 v81, v148 +v_mov_b32 v82, v149 +v_mov_b32 v83, v150 +v_mov_b32 v84, v151 +v_mov_b32 v85, v152 +v_mov_b32 v86, v153 +v_mov_b32 v87, v154 +s_mov_b32 s85, 0x3f58 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 344, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 345, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 346, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 347, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 348, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 349, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 350, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 351, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v88, v155 +v_mov_b32 v89, v156 +v_mov_b32 v90, v157 +v_mov_b32 v91, v158 +v_mov_b32 v92, v159 +v_mov_b32 v93, v160 +v_mov_b32 v94, v161 +v_mov_b32 v95, v162 +s_mov_b32 s85, 0x3fc4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 352, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 353, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 354, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 355, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 356, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 357, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 358, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 359, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v96, v147 +v_mov_b32 v97, v148 +v_mov_b32 v98, v149 +v_mov_b32 v99, v150 +v_mov_b32 v100, v151 +v_mov_b32 v101, v152 +v_mov_b32 v102, v153 +v_mov_b32 v103, v154 +s_mov_b32 s85, 0x4030 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 360, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 361, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 362, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 363, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 364, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 365, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 366, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 367, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v104, v155 +v_mov_b32 v105, v156 +v_mov_b32 v106, v157 +v_mov_b32 v107, v158 +v_mov_b32 v108, v159 +v_mov_b32 v109, v160 +v_mov_b32 v110, v161 +v_mov_b32 v111, v162 +s_mov_b32 s85, 0x409c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 368, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 369, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 370, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 371, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 372, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 373, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 374, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 375, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v112, v147 +v_mov_b32 v113, v148 +v_mov_b32 v114, v149 +v_mov_b32 v115, v150 +v_mov_b32 v116, v151 +v_mov_b32 v117, v152 +v_mov_b32 v118, v153 +v_mov_b32 v119, v154 +s_mov_b32 s85, 0x4108 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 376, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 377, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 378, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 379, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 380, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 381, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 382, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 383, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v120, v155 +v_mov_b32 v121, v156 +v_mov_b32 v122, v157 +v_mov_b32 v123, v158 +v_mov_b32 v124, v159 +v_mov_b32 v125, v160 +v_mov_b32 v126, v161 +v_mov_b32 v127, v162 +s_mov_b32 s85, 0x4174 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 384, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 385, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 386, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 387, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 388, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 389, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 390, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 391, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v128, v147 +v_mov_b32 v129, v148 +v_mov_b32 v130, v149 +v_mov_b32 v131, v150 +v_mov_b32 v132, v151 +v_mov_b32 v133, v152 +v_mov_b32 v134, v153 +v_mov_b32 v135, v154 +s_mov_b32 s85, 0x41e0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 392, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 393, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 394, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 395, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 396, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 397, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 398, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 399, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v136, v155 +v_mov_b32 v137, v156 +v_mov_b32 v138, v157 +v_mov_b32 v139, v158 +v_mov_b32 v140, v159 +v_mov_b32 v141, v160 +v_mov_b32 v142, v161 +v_mov_b32 v143, v162 +s_mov_b32 s85, 0x3e14 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 403, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 404, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 405, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 406, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 147, 403, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 148, 404, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 149, 405, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 150, 406, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 407, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 408, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 409, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 410, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 151, 407, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 152, 408, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 153, 409, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 154, 410, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v147, v165, s[72:75], 0 idxen +buffer_store_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v147, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v148, v165, s[72:75], 0 idxen +buffer_store_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v148, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v149, v165, s[72:75], 0 idxen +buffer_store_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v149, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v150, v165, s[72:75], 0 idxen +buffer_store_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v150, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4418 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 411, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 412, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 413, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 414, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 155, 411, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 156, 412, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 157, 413, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 158, 414, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 415, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 416, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 417, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 418, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 159, 415, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 160, 416, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 161, 417, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 162, 418, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v155, v165, s[72:75], 0 idxen +buffer_store_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v155, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v156, v165, s[72:75], 0 idxen +buffer_store_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v156, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v157, v165, s[72:75], 0 idxen +buffer_store_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v157, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v158, v165, s[72:75], 0 idxen +buffer_store_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v158, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x424c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 403, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 404, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 405, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 406, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 147, 403, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 148, 404, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 149, 405, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 150, 406, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 407, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 408, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 409, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 410, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 151, 407, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 152, 408, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 153, 409, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 154, 410, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v147, v165, s[72:75], 0 idxen +buffer_store_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v147, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v148, v165, s[72:75], 0 idxen +buffer_store_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v148, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v149, v165, s[72:75], 0 idxen +buffer_store_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v149, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v150, v165, s[72:75], 0 idxen +buffer_store_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v150, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x47b0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 411, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 412, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 413, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 414, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 155, 411, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 156, 412, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 157, 413, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 158, 414, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 415, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 416, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 417, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 418, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 159, 415, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 160, 416, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 161, 417, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 162, 418, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v155, v165, s[72:75], 0 idxen +buffer_store_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v155, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v156, v165, s[72:75], 0 idxen +buffer_store_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v156, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v157, v165, s[72:75], 0 idxen +buffer_store_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v157, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v158, v165, s[72:75], 0 idxen +buffer_store_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v158, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x45e4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 147, 0xbdc5bdc5, 403, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 148, 0xbdc5bdc5, 404, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 149, 0xbdc5bdc5, 405, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 150, 0xbdc5bdc5, 406, 0x0, 0x3 +v_exp_f16 v147, v147 +v_exp_f16 v148, v148 +v_exp_f16 v149, v149 +v_exp_f16 v150, v150 +_v_exp_f16__vop3 147, 147, 0x9 +_v_exp_f16__vop3 148, 148, 0x9 +_v_exp_f16__vop3 149, 149, 0x9 +_v_exp_f16__vop3 150, 150, 0x9 +_v_pk_add_f16__vop3p_lit 147, 0x3c003c00, 403, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 148, 0x3c003c00, 404, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 149, 0x3c003c00, 405, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 150, 0x3c003c00, 406, 0x0, 0x3 +v_rcp_f16 v147, v147 +v_rcp_f16 v148, v148 +v_rcp_f16 v149, v149 +v_rcp_f16 v150, v150 +_v_rcp_f16__vop3 147, 147, 0x9 +_v_rcp_f16__vop3 148, 148, 0x9 +_v_rcp_f16__vop3 149, 149, 0x9 +_v_rcp_f16__vop3 150, 150, 0x9 +_v_pk_mul_f16__vop3p_lit 151, 0xbdc5bdc5, 407, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 152, 0xbdc5bdc5, 408, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 153, 0xbdc5bdc5, 409, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 154, 0xbdc5bdc5, 410, 0x0, 0x3 +v_exp_f16 v151, v151 +v_exp_f16 v152, v152 +v_exp_f16 v153, v153 +v_exp_f16 v154, v154 +_v_exp_f16__vop3 151, 151, 0x9 +_v_exp_f16__vop3 152, 152, 0x9 +_v_exp_f16__vop3 153, 153, 0x9 +_v_exp_f16__vop3 154, 154, 0x9 +_v_pk_add_f16__vop3p_lit 151, 0x3c003c00, 407, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 152, 0x3c003c00, 408, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 153, 0x3c003c00, 409, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 154, 0x3c003c00, 410, 0x0, 0x3 +v_rcp_f16 v151, v151 +v_rcp_f16 v152, v152 +v_rcp_f16 v153, v153 +v_rcp_f16 v154, v154 +_v_rcp_f16__vop3 151, 151, 0x9 +_v_rcp_f16__vop3 152, 152, 0x9 +_v_rcp_f16__vop3 153, 153, 0x9 +_v_rcp_f16__vop3 154, 154, 0x9 +buffer_store_b16 v147, v165, s[72:75], 0 idxen +buffer_store_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v147, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v148, v165, s[72:75], 0 idxen +buffer_store_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v148, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v149, v165, s[72:75], 0 idxen +buffer_store_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v149, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v150, v165, s[72:75], 0 idxen +buffer_store_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v150, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4c88 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 155, 0xbdc5bdc5, 411, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 156, 0xbdc5bdc5, 412, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 157, 0xbdc5bdc5, 413, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 158, 0xbdc5bdc5, 414, 0x0, 0x3 +v_exp_f16 v155, v155 +v_exp_f16 v156, v156 +v_exp_f16 v157, v157 +v_exp_f16 v158, v158 +_v_exp_f16__vop3 155, 155, 0x9 +_v_exp_f16__vop3 156, 156, 0x9 +_v_exp_f16__vop3 157, 157, 0x9 +_v_exp_f16__vop3 158, 158, 0x9 +_v_pk_add_f16__vop3p_lit 155, 0x3c003c00, 411, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 156, 0x3c003c00, 412, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 157, 0x3c003c00, 413, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 158, 0x3c003c00, 414, 0x0, 0x3 +v_rcp_f16 v155, v155 +v_rcp_f16 v156, v156 +v_rcp_f16 v157, v157 +v_rcp_f16 v158, v158 +_v_rcp_f16__vop3 155, 155, 0x9 +_v_rcp_f16__vop3 156, 156, 0x9 +_v_rcp_f16__vop3 157, 157, 0x9 +_v_rcp_f16__vop3 158, 158, 0x9 +_v_pk_mul_f16__vop3p_lit 159, 0xbdc5bdc5, 415, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 160, 0xbdc5bdc5, 416, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 161, 0xbdc5bdc5, 417, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 162, 0xbdc5bdc5, 418, 0x0, 0x3 +v_exp_f16 v159, v159 +v_exp_f16 v160, v160 +v_exp_f16 v161, v161 +v_exp_f16 v162, v162 +_v_exp_f16__vop3 159, 159, 0x9 +_v_exp_f16__vop3 160, 160, 0x9 +_v_exp_f16__vop3 161, 161, 0x9 +_v_exp_f16__vop3 162, 162, 0x9 +_v_pk_add_f16__vop3p_lit 159, 0x3c003c00, 415, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 160, 0x3c003c00, 416, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 161, 0x3c003c00, 417, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 162, 0x3c003c00, 418, 0x0, 0x3 +v_rcp_f16 v159, v159 +v_rcp_f16 v160, v160 +v_rcp_f16 v161, v161 +v_rcp_f16 v162, v162 +_v_rcp_f16__vop3 159, 159, 0x9 +_v_rcp_f16__vop3 160, 160, 0x9 +_v_rcp_f16__vop3 161, 161, 0x9 +_v_rcp_f16__vop3 162, 162, 0x9 +buffer_store_b16 v155, v165, s[72:75], 0 idxen +buffer_store_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v155, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v156, v165, s[72:75], 0 idxen +buffer_store_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v156, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v157, v165, s[72:75], 0 idxen +buffer_store_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v157, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v158, v165, s[72:75], 0 idxen +buffer_store_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v158, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x497c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 147, 403, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 148, 404, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 149, 405, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 150, 406, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 151, 407, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 152, 408, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 153, 409, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 154, 410, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 147, 403, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 148, 404, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 149, 405, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 150, 406, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v147 +v_and_b32 v28, 0x7fff7fff, v148 +v_and_b32 v38, 0x7fff7fff, v149 +v_and_b32 v48, 0x7fff7fff, v150 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 147, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 148, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 149, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 150, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 147, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 148, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 149, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 150, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v147, 0x7fff7fff, v23, v147 +v_bfi_b32 v148, 0x7fff7fff, v33, v148 +v_bfi_b32 v149, 0x7fff7fff, v43, v149 +v_bfi_b32 v150, 0x7fff7fff, v53, v150 +_v_pk_mul_f16__vop3p 147, 403, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 148, 404, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 149, 405, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 150, 406, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 151, 407, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 152, 408, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 153, 409, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 154, 410, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v151 +v_and_b32 v28, 0x7fff7fff, v152 +v_and_b32 v38, 0x7fff7fff, v153 +v_and_b32 v48, 0x7fff7fff, v154 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 151, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 152, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 153, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 154, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 151, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 152, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 153, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 154, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v151, 0x7fff7fff, v23, v151 +v_bfi_b32 v152, 0x7fff7fff, v33, v152 +v_bfi_b32 v153, 0x7fff7fff, v43, v153 +v_bfi_b32 v154, 0x7fff7fff, v53, v154 +_v_pk_mul_f16__vop3p 151, 407, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 152, 408, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 153, 409, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 154, 410, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v147, v165, s[72:75], 0 idxen +buffer_store_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v147, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v151, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v148, v165, s[72:75], 0 idxen +buffer_store_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v148, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v152, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v149, v165, s[72:75], 0 idxen +buffer_store_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v149, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v153, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v150, v165, s[72:75], 0 idxen +buffer_store_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v150, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v154, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5620 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 155, 411, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 156, 412, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 157, 413, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 158, 414, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 159, 415, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 160, 416, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 161, 417, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 162, 418, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 155, 411, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 156, 412, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 157, 413, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 158, 414, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v155 +v_and_b32 v28, 0x7fff7fff, v156 +v_and_b32 v38, 0x7fff7fff, v157 +v_and_b32 v48, 0x7fff7fff, v158 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 155, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 156, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 157, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 158, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 155, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 156, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 157, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 158, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v155, 0x7fff7fff, v23, v155 +v_bfi_b32 v156, 0x7fff7fff, v33, v156 +v_bfi_b32 v157, 0x7fff7fff, v43, v157 +v_bfi_b32 v158, 0x7fff7fff, v53, v158 +_v_pk_mul_f16__vop3p 155, 411, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 156, 412, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 157, 413, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 158, 414, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 159, 415, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 160, 416, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 417, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 418, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v159 +v_and_b32 v28, 0x7fff7fff, v160 +v_and_b32 v38, 0x7fff7fff, v161 +v_and_b32 v48, 0x7fff7fff, v162 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 159, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 160, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 161, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 162, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 159, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 160, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 161, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 162, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v159, 0x7fff7fff, v23, v159 +v_bfi_b32 v160, 0x7fff7fff, v33, v160 +v_bfi_b32 v161, 0x7fff7fff, v43, v161 +v_bfi_b32 v162, 0x7fff7fff, v53, v162 +_v_pk_mul_f16__vop3p 159, 415, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 160, 416, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 417, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 418, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v155, v165, s[72:75], 0 idxen +buffer_store_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v155, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v159, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v156, v165, s[72:75], 0 idxen +buffer_store_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v156, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v160, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v157, v165, s[72:75], 0 idxen +buffer_store_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v157, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v161, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v158, v165, s[72:75], 0 idxen +buffer_store_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v158, v165, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v162, v166, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4f94 +s_setpc_b64 s[86:87] +s_endpgm +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end 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+s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end + diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c16_stride1.inc b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c16_stride1.inc new file mode 100644 index 0000000000..e1afab3c1d --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c16_stride1.inc @@ -0,0 +1,4640 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +.macro _sop1_lit op:req, sdst:req, lit:req + .long (0b101111101 << 23) | (\sdst << 16) | (\op << 8) | 255 + .long \lit +.endm + +.macro _s_mov_b32__sop1_lit sdst:req, lit:req + _sop1_lit 0, \sdst, \lit +.endm + +.macro _vop1 op:req, vdst:req, src:req + .long (0b0111111 << 25) | (\vdst << 17) | (\op << 9) | \src +.endm + +.macro _v_cvt_f16_i16__vop1 vdst:req, vsrc:req + _vop1 81, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_rcp_f16__vop1 vdst:req, vsrc:req + _vop1 84, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_exp_f16__vop1 vdst:req, vsrc:req + _vop1 88, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _vop3 op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_cvt_f16_i16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 465, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_rcp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 468, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_exp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 472, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_cndmask_b16__vop3 vdst:req, vsrc0:req, vsrc1:req, src2:req, opsel:req + _vop3 605, \vdst, (\vsrc0 + /*VGPR*/ 256), (\vsrc1 + /*VGPR*/ 256), \src2, \opsel, 0, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_s_lit sdst:req, ssrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, \ssrc0, 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_v_lit sdst:req, vsrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, (\vsrc0 + /*VGPR*/ 256), 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_lt_u16__vop3 sdst:req, vsrc0:req, ssrc1:req, opsel:req + _vop3 57, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, \opsel, 0, 0 +.endm + +.macro _v_cmpx_lt_u32__vop3 sdst:req, vsrc0:req, ssrc1:req + _vop3 201, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, 0, 0, 0 +.endm + +.macro _vop3p op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3p_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_pk_ashrrev_i16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 6, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 10, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_sub_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 11, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_min_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 13, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 15, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 15, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_mul_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 16, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_mul_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 16, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_min_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 17, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_max_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 18, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +s_version 0x2006 +s_set_inst_prefetch_distance 0x3 +s_mov_b32 s0, 0 +v_lshlrev_b32 v1, 7, v0 +s_getpc_b64 s[8:9] +s_mov_b32 s10, 0x6244 +s_mov_b32 s11, 0x31014000 +buffer_load_b32 v2, v1, s[8:11], 0 offen +s_waitcnt vmcnt(0) +s_getpc_b64 s[6:7] +s_load_b512 s[8:23], s[2:3], null +s_load_b512 s[24:39], s[2:3], 0x40 +s_load_b512 s[40:55], s[2:3], 0x80 +s_load_b256 s[56:63], s[2:3], 0xc0 +s_load_b64 s[64:65], s[2:3], 0xe0 +v_and_b32 v8, 0xff, v0 +v_lshrrev_b32 v9, 1, v8 +v_and_b32 v10, 1, v0 +v_add_nc_u32 v5, v9, 32 +v_bfi_b32 v6, 31, v8, v9 +v_bfe_u32 v4, v8, 5, 1 +v_bfi_b32 v6, 0xbf, v6, v5 +v_and_b32 v2, 31, v8 +v_lshrrev_b32 v6, 5, v6 +v_lshrrev_b32 v7, 6, v8 +v_lshlrev_b32 v2, 4, v2 +v_and_b32 v3, 31, v9 +v_mad_u32_u24 v2, v4, 0x900, v2 +v_lshlrev_b32 v3, 4, v3 +v_xor_b32 v5, 3, v6 +v_mad_u32_u16 v3, 0x480, v7, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v1, v5, 0x240, v2 +v_mad_u32_u16 v3, 0x1240, v10, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v2, v6, 0x240, v2 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 6 +s_cbranch_scc0 14 +s_load_b64 s[16:17], s[16:17], null +s_load_b64 s[20:21], s[20:21], null +s_load_b64 s[18:19], s[18:19], null +s_cmp_eq_u64 0, s[60:61] +s_cbranch_scc1 2 +s_load_b64 s[60:61], s[60:61], null +s_cmp_eq_u64 0, s[30:31] +s_cbranch_scc1 2 +s_load_b64 s[30:31], s[30:31], null +s_bitcmp1_b32 s14, 3 +s_cbranch_scc0 2 +s_setreg_imm32_b32 hwreg(HW_REG_MODE, 0, 8), 0xf0 +s_cmp_eq_u32 s13, 0x60 +s_cbranch_scc0 16 +s_mul_i32 s1, s4, 0xab +s_lshr_b32 s1, s1, 10 +s_mul_i32 s23, s1, 6 +s_sub_u32 s23, s4, s23 +s_bfe_u32 s15, s1, 0x20000 +s_bfe_u32 s22, s1, 0x10002 +s_bfe_u32 s5, s1, 0x10003 +s_mov_b32 s45, s23 +s_lshl1_add_u32 s45, s45, s22 +s_lshl2_add_u32 s45, s45, s15 +s_lshl1_add_u32 s45, s45, s5 +s_mov_b32 s4, s45 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 13 +s_cbranch_scc0 10 +s_add_u32 s16, s16, s34 +s_addc_u32 s17, s17, s35 +s_add_u32 s20, s20, s38 +s_addc_u32 s21, s21, s39 +s_add_u32 s18, s18, s36 +s_addc_u32 s19, s19, s37 +s_cmp_eq_u64 0, s[30:31] +s_cselect_b64 s[40:41], 0, s[40:41] +s_add_u32 s30, s30, s40 +s_addc_u32 s31, s31, s41 +s_add_u32 s15, s12, 15 +s_lshr_b32 s15, s15, 4 +v_cvt_f32_u32 v4, s15 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v5, v4, s13, s13 +v_lshrrev_b32 v5, 16, v5 +v_cvt_f32_u32 v4, v5 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v6, v4, s4, s4 +v_lshrrev_b32 v6, 16, v6 +v_readfirstlane_b32 s1, v5 +v_readfirstlane_b32 s22, v6 +s_mul_i32 s5, s22, s1 +s_sub_u32 s5, s4, s5 +s_cmp_ge_u32 s22, s15 +s_cbranch_scc1 6159 +s_mul_i32 s13, s1, s15 +s_mul_i32 s23, s22, 16 +s_sub_u32 s12, s12, s23 +s_min_u32 s12, s12, 16 +s_mul_i32 s34, s23, s46 +s_mul_hi_u32 s35, s23, s46 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s18, s34, s18 +s_addc_u32 s19, s35, s19 +s_lshr_b32 s35, s23, 0 +s_mul_i32 s34, s35, s51 +s_mul_hi_u32 s35, s35, s51 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s20, s34, s20 +s_addc_u32 s21, s35, s21 +s_lshl_b32 s34, s23, 1 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s34, 0, s34 +s_add_u32 s30, s30, s34 +s_addc_u32 s31, s31, 0 +v_cmp_lt_u32 vcc, v0, 0x100 +s_cbranch_vccz 2749 +v_and_b32 v20, 0xff, v0 +v_lshrrev_b32 v21, 1, v20 +v_bfe_u32 v17, v20, 3, 1 +v_bfe_u32 v16, v20, 2, 1 +v_mad_u32_u16 v17, v17, 16, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v14, v16, 0x1240, v17 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 0, 2 +v_mad_u32_u16 v14, v16, 0x90, v14 op_sel:[0,0,0,0] +v_bfe_u32 v17, v20, 4, 2 +v_mad_u32_u16 v14, v17, 32, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 6, 1 +v_mad_u32_u16 v14, v16, 0x480, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 7, 1 +v_mad_u32_u16 v14, v16, 0x900, v14 op_sel:[0,0,0,0] +v_bfe_u32 v18, v20, 1, 2 +v_mad_u32_u16 v13, v18, 32, 0 op_sel:[0,0,0,0] +v_bfe_u32 v19, v20, 3, 1 +v_mad_u32_u16 v13, v19, 0x480, v13 op_sel:[0,0,0,0] +v_add_nc_u32 v18, v21, 32 +v_bfi_b32 v18, 0xbf, v20, v18 +v_bfe_u32 v18, v18, 6, 2 +v_mad_u32_u16 v13, v18, 0x90, v13 op_sel:[0,0,0,0] +v_xor_b32 v16, v0, v0 quad_perm:[2,3,2,1] +v_xor_b32 v17, v0, v0 quad_perm:[0,0,3,3] +v_sub_nc_u16 v16, v16, v17 op_sel:[0,0,0] +v_cvt_f16_i16 v15, v16 +_v_cvt_f16_i16__vop1 (15 | /*op_sel*/ 0x80), 17 +_v_pk_mul_f16__vop3p 15, 271, 240, 0x0, 0x1, 0x0, 0x0 +v_bfe_u32 v16, v0, 6, 1 +v_and_b32 v5, 63, v0 +v_cmp_eq_u32 vcc, v16, 1 +v_cndmask_b32 v16, 0, 0x400, vcc +v_cndmask_b32 v17, 0, 0x100, vcc +v_lshl_add_u32 v6, v5, 2, 0 +v_lshl_add_u32 v5, v5, 4, v16 +s_mov_b32 s23, 4 +s_mov_b32 s34, 0 +s_mov_b32 s40, 0xbc00c000 +v_readfirstlane_b32 s74, v0 +s_and_b32 null, 64, s74 +s_cmov_b32 s40, 0x3c00c000 +s_lshl_b32 s49, s43, 1 +s_lshl_b32 s53, s47, 1 +s_lshl_b32 s75, s49, 3 +s_lshl_b32 s76, s53, 3 +s_and_b32 null, 0x80, s74 +s_cselect_b32 s75, s75, 0 +s_cselect_b32 s76, s76, 0 +s_cselect_b32 s22, 8, 0 +s_sub_u32 s22, s9, s22 +s_cmov_b32 s22, 0 +s_mov_b32 s35, 0x11014000 +s_bitcmp1_b32 s14, 4 +s_cselect_b32 s77, 0, 0x8000000 +s_and_b32 s35, 0xf7ffffff, s35 +s_or_b32 s35, s35, s77 +s_and_b32 s17, s17, 0xffff +s_add_u32 s17, s17, 0x20000 +s_and_b32 s19, s19, 0xffff +s_add_u32 s19, s19, 0x20000 +s_add_u32 s16, s16, s75 +s_addc_u32 s17, s17, 0 +s_add_u32 s18, s18, s76 +s_addc_u32 s19, s19, 0 +s_mov_b64 s[36:37], s[16:17] +s_mov_b32 s38, 0x80000000 +s_mov_b32 s39, 0 +s_getpc_b64 s[64:65] +v_cmp_lt_u32 vcc, v0, 0x80 +s_cmp_gt_u32 vcc_lo, 0 +s_mov_b32 s74, 0x23d8 +s_mov_b32 s76, 0x1a58 +s_cmov_b32 s74, 0x1e98 +s_cmov_b32 s76, 0x1618 +s_mov_b32 s75, 0x2654 +s_mov_b32 s77, 0x1c54 +s_cmov_b32 s75, 0x2114 +s_cmov_b32 s77, 0x1814 +s_add_u32 s66, s64, s74 +s_addc_u32 s67, s65, 0 +s_add_u32 s70, s64, s76 +s_addc_u32 s71, s65, 0 +s_add_u32 s68, s64, s75 +s_addc_u32 s69, s65, 0 +s_add_u32 s72, s64, s77 +s_addc_u32 s73, s65, 0 +s_mov_b32 s45, 0 +v_mov_b32 v4, 0 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 308, 341, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 360, 377, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 396, 397, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v16, s40, v34 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v52, s40, v86 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v104, s40, v122 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v140, s40, v142 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5911 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 341, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 377, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 397, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v160 quad_perm:[1,0,3,2] +v_mov_b32 v85, v161 quad_perm:[1,0,3,2] +v_mov_b32 v121, v162 quad_perm:[1,0,3,2] +v_mov_b32 v141, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v121, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v141, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v160 quad_perm:[2,3,0,1] +v_mov_b32 v85, v161 quad_perm:[2,3,0,1] +v_mov_b32 v121, v162 quad_perm:[2,3,0,1] +v_mov_b32 v141, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v121, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v141, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v121, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v141, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5812 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v34, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v86, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v122, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v142, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 290, 291, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 342, 343, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 378, 379, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 398, 399, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5738 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 342, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 378, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 398, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v34, v160 quad_perm:[1,0,3,2] +v_mov_b32 v86, v161 quad_perm:[1,0,3,2] +v_mov_b32 v122, v162 quad_perm:[1,0,3,2] +v_mov_b32 v142, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v34, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v86, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v34, v160 quad_perm:[2,3,0,1] +v_mov_b32 v86, v161 quad_perm:[2,3,0,1] +v_mov_b32 v122, v162 quad_perm:[2,3,0,1] +v_mov_b32 v142, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v34, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v86, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v86, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5640 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 403, 402, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 407, 406, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 411, 410, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 415, 414, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v147, s40, v144 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v151, s40, v148 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v155, s40, v152 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v159, s40, v156 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5565 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 402, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 406, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 410, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 414, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v146, v160 quad_perm:[1,0,3,2] +v_mov_b32 v150, v161 quad_perm:[1,0,3,2] +v_mov_b32 v154, v162 quad_perm:[1,0,3,2] +v_mov_b32 v158, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v154, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v158, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v146, v160 quad_perm:[2,3,0,1] +v_mov_b32 v150, v161 quad_perm:[2,3,0,1] +v_mov_b32 v154, v162 quad_perm:[2,3,0,1] +v_mov_b32 v158, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v154, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v158, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v154, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v158, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5466 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v144, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v148, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v152, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v156, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 400, 401, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 404, 405, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 408, 409, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 412, 413, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5392 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 400, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 404, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 408, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 412, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v144, v160 quad_perm:[1,0,3,2] +v_mov_b32 v148, v161 quad_perm:[1,0,3,2] +v_mov_b32 v152, v162 quad_perm:[1,0,3,2] +v_mov_b32 v156, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v144, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v148, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v144, v160 quad_perm:[2,3,0,1] +v_mov_b32 v148, v161 quad_perm:[2,3,0,1] +v_mov_b32 v152, v162 quad_perm:[2,3,0,1] +v_mov_b32 v156, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v144, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v148, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v144, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v148, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5294 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 308, 341, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 291, 290, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 343, 342, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v16, s40, v121 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v52, s40, v141 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v35, s40, v122 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v87, s40, v142 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5219 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 341, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 342, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v160 quad_perm:[1,0,3,2] +v_mov_b32 v85, v161 quad_perm:[1,0,3,2] +v_mov_b32 v34, v162 quad_perm:[1,0,3,2] +v_mov_b32 v86, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v34, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v86, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v160 quad_perm:[2,3,0,1] +v_mov_b32 v85, v161 quad_perm:[2,3,0,1] +v_mov_b32 v34, v162 quad_perm:[2,3,0,1] +v_mov_b32 v86, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v17, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v85, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v34, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v86, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v85, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v86, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v87, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5120 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v160, v121, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v141, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v122, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v142, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 377, 360, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 397, 396, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 378, 379, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 398, 399, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5046 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 377, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 397, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 378, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 398, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v121, v160 quad_perm:[1,0,3,2] +v_mov_b32 v141, v161 quad_perm:[1,0,3,2] +v_mov_b32 v122, v162 quad_perm:[1,0,3,2] +v_mov_b32 v142, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v121, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v141, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v121, v160 quad_perm:[2,3,0,1] +v_mov_b32 v141, v161 quad_perm:[2,3,0,1] +v_mov_b32 v122, v162 quad_perm:[2,3,0,1] +v_mov_b32 v142, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v121, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v141, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v122, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v142, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v121, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v104, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v122, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v123, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v141, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v140, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v142, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v143, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4948 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_add_f16__vop3p 160, 403, 402, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 161, 407, 406, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 162, 401, 400, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 163, 405, 404, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v164, v147, s40, v154 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v165, v151, s40, v158 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v166, v145, s40, v152 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v167, v149, s40, v156 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4873 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[64:65], s[66:67], s[70:71] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(32) +_v_pk_mul_f16__vop3p 160, 402, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 406, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 400, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 404, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v146, v160 quad_perm:[1,0,3,2] +v_mov_b32 v150, v161 quad_perm:[1,0,3,2] +v_mov_b32 v144, v162 quad_perm:[1,0,3,2] +v_mov_b32 v148, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v144, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v148, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v146, v160 quad_perm:[2,3,0,1] +v_mov_b32 v150, v161 quad_perm:[2,3,0,1] +v_mov_b32 v144, v162 quad_perm:[2,3,0,1] +v_mov_b32 v148, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v146, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v150, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v144, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v148, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v146, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v147, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v144, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v145, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v150, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v151, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v148, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v149, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4774 +s_mov_b32 s56, 0xffffebec +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0xffffed18 +s_setprio 2 +v_pk_fma_f16 v160, v154, s40, v164 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v158, s40, v165 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v152, s40, v166 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v156, s40, v167 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 164, 410, 411, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 165, 414, 415, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 166, 408, 409, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 167, 412, 413, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4700 +s_mov_b32 s56, 0xffffea64 +s_bitcmp1_b32 s45, 4 +s_cselect_b64 s[64:65], s[68:69], s[72:73] +s_bitcmp1_b32 s45, 2 +s_cselect_b32 s56, s56, 0xffffeb90 +s_setprio 2 +_v_pk_mul_f16__vop3p 160, 410, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 161, 414, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 162, 408, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 163, 412, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v154, v160 quad_perm:[1,0,3,2] +v_mov_b32 v158, v161 quad_perm:[1,0,3,2] +v_mov_b32 v152, v162 quad_perm:[1,0,3,2] +v_mov_b32 v156, v163 quad_perm:[1,0,3,2] +v_pk_fma_f16 v160, v154, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v158, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v154, v160 quad_perm:[2,3,0,1] +v_mov_b32 v158, v161 quad_perm:[2,3,0,1] +v_mov_b32 v152, v162 quad_perm:[2,3,0,1] +v_mov_b32 v156, v163 quad_perm:[2,3,0,1] +v_pk_fma_f16 v160, v154, v15, v160 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v161, v158, v15, v161 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v162, v152, v15, v162 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v163, v156, v15, v163 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v154, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v155, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v152, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v153, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v158, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v159, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v156, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v157, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 4602 +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 88, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 89, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 90, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 91, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 92, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 93, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 94, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 95, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 88, 344, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 89, 345, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 90, 346, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 91, 347, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 92, 348, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 93, 349, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 94, 350, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 95, 351, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v88, v44, 0.5, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v89, v45, 0.5, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v90, v46, 0.5, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v91, v47, 0.5, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v92, v48, 0.5, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v93, v49, 0.5, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v94, v50, 0.5, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v95, v51, 0.5, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v44, -1.0, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v45, -1.0, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v46, -1.0, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v47, -1.0, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v48, -1.0, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v49, -1.0, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v50, -1.0, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v112, v51, -1.0, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v124, v61 +v_mov_b32 v125, v62 +v_mov_b32 v126, v63 +v_mov_b32 v127, v64 +v_mov_b32 v128, v65 +v_mov_b32 v129, v66 +v_mov_b32 v130, v67 +v_mov_b32 v131, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 96, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 97, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 98, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 99, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 100, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 101, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 102, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 103, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 96, 352, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 97, 353, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 98, 354, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 99, 355, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 100, 356, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 101, 357, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 102, 358, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 103, 359, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v96, v44, 0.5, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v97, v45, 0.5, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v98, v46, 0.5, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v99, v47, 0.5, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v100, v48, 0.5, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v101, v49, 0.5, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v102, v50, 0.5, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v103, v51, 0.5, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v44, -1.0, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v45, -1.0, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v46, -1.0, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v47, -1.0, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v48, -1.0, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v49, -1.0, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v50, -1.0, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v120, v51, -1.0, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v132, v61 +v_mov_b32 v133, v62 +v_mov_b32 v134, v63 +v_mov_b32 v135, v64 +v_mov_b32 v136, v65 +v_mov_b32 v137, v66 +v_mov_b32 v138, v67 +v_mov_b32 v139, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 88, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 89, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 90, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 91, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 92, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 93, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 94, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 95, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 88, 344, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 89, 345, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 90, 346, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 91, 347, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 92, 348, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 93, 349, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 94, 350, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 95, 351, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v88, v44, 0.5, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v89, v45, 0.5, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v90, v46, 0.5, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v91, v47, 0.5, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v92, v48, 0.5, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v93, v49, 0.5, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v94, v50, 0.5, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v95, v51, 0.5, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v44, -1.0, v88 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v45, -1.0, v89 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v46, -1.0, v90 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v47, -1.0, v91 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v48, -1.0, v92 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v49, -1.0, v93 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v50, -1.0, v94 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v112, v51, -1.0, v95 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v124, v61 +v_mov_b32 v125, v62 +v_mov_b32 v126, v63 +v_mov_b32 v127, v64 +v_mov_b32 v128, v65 +v_mov_b32 v129, v66 +v_mov_b32 v130, v67 +v_mov_b32 v131, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 96, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 97, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 98, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 99, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 100, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 101, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 102, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 103, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 96, 352, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 97, 353, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 98, 354, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 99, 355, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 100, 356, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 101, 357, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 102, 358, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 103, 359, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v96, v44, 0.5, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v97, v45, 0.5, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v98, v46, 0.5, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v99, v47, 0.5, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v100, v48, 0.5, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v101, v49, 0.5, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v102, v50, 0.5, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v103, v51, 0.5, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v44, -1.0, v96 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v45, -1.0, v97 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v46, -1.0, v98 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v47, -1.0, v99 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v48, -1.0, v100 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v49, -1.0, v101 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v50, -1.0, v102 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v120, v51, -1.0, v103 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v132, v61 +v_mov_b32 v133, v62 +v_mov_b32 v134, v63 +v_mov_b32 v135, v64 +v_mov_b32 v136, v65 +v_mov_b32 v137, v66 +v_mov_b32 v138, v67 +v_mov_b32 v139, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 vcc, -1, 0 +s_bitcmp1_b32 s45, 3 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[124:131], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[132:139], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[88:95], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[96:103], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[105:112], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[113:120], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[160:163] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[164:167] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +v_bfe_u32 v21, v0, 6, 1 +v_and_b32 v16, 63, v0 +v_cmp_eq_u32 vcc, v21, 1 +v_cndmask_b32 v23, 0, 0x800, vcc +v_cndmask_b32 v21, 0, 0x400, vcc +v_cndmask_b32 v22, 0, 0x100, vcc +v_lshl_add_u32 v14, v16, 3, v23 +v_lshl_add_u32 v17, v16, 2, v22 +v_lshl_add_u32 v18, v16, 2, 0 +v_lshl_add_u32 v16, v16, 4, v21 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s91, 0, 0x11014000 +s_and_b32 s31, s31, 0xffff +s_add_u32 s31, s31, 0x20000 +s_mov_b64 s[88:89], s[30:31] +s_mov_b32 s90, 0x80000000 +v_and_b32 v21, v0, 63 +v_lshlrev_b32 v21, 1, v21 +v_cmp_lt_u32 vcc, v21, s12 +v_add_nc_u32 v22, v21, 1 +v_cndmask_b32 v21, 0x80000000, v21, vcc +v_cmp_lt_u32 vcc, v22, s12 +v_cndmask_b32 v22, 0x80000000, v22, vcc +buffer_load_d16_b16 v23, v21, s[88:91], 0 idxen +buffer_load_d16_hi_b16 v23, v22, s[88:91], 0 idxen +s_waitcnt vmcnt(0) +v_readlane_b32 s56, v23, 0 +v_readlane_b32 s57, v23, 1 +v_readlane_b32 s59, v23, 2 +v_readlane_b32 s64, v23, 3 +v_readlane_b32 s65, v23, 4 +v_readlane_b32 s66, v23, 5 +v_readlane_b32 s67, v23, 6 +v_readlane_b32 s68, v23, 7 +s_bfe_u32 s88, s58, 0x80000 +s_cmp_eq_u32 s88, 2 +s_cbranch_scc1 20 +s_cmp_eq_u32 s88, 0 +s_cselect_b32 s32, 1.0, s32 +v_cvt_f16_f32 v21, s32 +v_readfirstlane_b32 s32, v21 +v_cvt_f16_f32 v21, s33 +v_readfirstlane_b32 s33, v21 +_v_cmp_gt_f16__vop3_s_lit 106, 32, 0x3c00, 0x0, 0x0 +s_pack_ll_b32_b16 s32, s32, s32 +s_pack_ll_b32_b16 s33, s33, s33 +s_cmp_eq_u32 s88, 3 +s_cbranch_scc1 10 +s_cbranch_vccnz 3 +s_mov_b32 s84, 0x47c4 +s_branch 8 +s_mov_b32 s84, 0x4b5c +s_branch 5 +s_mov_b32 s84, 0x4ef4 +s_branch 2 +s_mov_b32 s84, 0x550c +s_add_u32 s86, s6, 0x3c90 +s_addc_u32 s87, s7, 0 +s_mov_b32 s82, 0xbc00c000 +s_mov_b32 s40, 0x10000 +s_mov_b32 s41, 0x30002 +s_mov_b32 s45, 0x10000 +v_readfirstlane_b32 s88, v0 +s_and_b32 null, 64, s88 +s_cmov_b32 s82, 0x3c00c000 +s_cmov_b32 s40, 0x20003 +s_cmov_b32 s41, 1 +s_cmov_b32 s45, 1 +s_and_b32 s21, s21, 0xffff +s_add_u32 s21, s21, 0x20000 +s_lshl_b32 s80, s51, 1 +s_lshl_b32 s81, s52, 1 +s_mov_b64 s[72:73], s[20:21] +s_mov_b32 s74, 0x80000000 +s_mov_b32 s75, 0 +s_sub_u32 s89, s25, 1 +s_bitcmp1_b32 s14, 1 +s_cselect_b32 s89, s89, 0 +s_cselect_b32 s88, -1, 1 +s_sub_u32 s91, s24, 1 +s_bitcmp1_b32 s14, 0 +s_cselect_b32 s91, s91, 0 +s_cselect_b32 s90, -1, 1 +v_bfe_u32 v24, v0, 6, 1 +v_bfe_u32 v25, v0, 4, 1 +v_bfe_u32 v21, v0, 5, 1 +v_lshl_add_u32 v24, v24, 2, 0 +v_lshl_add_u32 v25, v25, 3, v24 +v_bfe_u32 v23, v0, 2, 2 +v_bfe_u32 v24, v0, 3, 1 +v_xor_b32 v22, v0, v0 quad_perm:[0,0,3,1] +v_lshl_add_u32 v21, v21, 1, v25 +v_xor_b32 v23, v23, v24 +v_add_nc_u32 v24, v21, 1 +v_mad_i32_i16 v19, v23, s88, s89 op_sel:[0,0,0,0] +v_mad_i32_i16 v25, v22, s90, s91 op_sel:[0,0,0,0] +v_mad_u32_u16 v19, v25, s48, v19 op_sel:[0,0,0,0] +v_cmp_lt_u32 vcc, v23, s25 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_cmp_lt_u32 vcc, v22, s24 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_mad_u32_u24 v20, v24, s46, v19 +v_mad_u32_u24 v19, v21, s46, v19 +v_cmp_lt_u32 vcc, v24, s12 +v_cndmask_b32 v20, 0x80000000, v20, vcc +v_cmp_lt_u32 vcc, v21, s12 +v_cndmask_b32 v19, 0x80000000, v19, vcc +s_add_u32 s89, s28, 1 +s_lshr_b32 s89, s89, 1 +s_lshl_b32 s90, s89, 1 +s_add_u32 s91, s29, 1 +s_lshr_b32 s91, s91, 1 +s_lshl1_add_u32 s91, s91, 2 +s_pack_ll_b32_b16 s22, s91, s89 +s_pack_ll_b32_b16 s34, s11, s10 +s_sub_u32 s35, s90, s26 +s_sub_u32 s88, s91, s27 +s_pack_ll_b32_b16 s35, s88, s35 +s_pack_ll_b32_b16 s37, s29, s28 +s_sub_u32 s88, s91, 1 +s_pack_ll_b32_b16 s38, s88, s90 +v_lshrrev_b32 v24, 16, s22 +v_bfi_b32 v25, 0xffff, s22, 0 +v_and_b32 v27, 1, v0 +v_bfe_u32 v33, v0, 6, 1 +v_and_b32 v22, 63, v0 +v_mad_u32_u16 v28, 0x7c, s1, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v33, 2, s5, v33 op_sel:[0,0,0,0] +v_mad_u32_u16 v26, v24, v25, 0 op_sel:[0,0,0,0] +v_cmp_eq_u32 vcc, 0, v27 +v_cndmask_b32 v34, v26, v25, vcc +v_mad_u32_u16 v23, 62, v33, v22 op_sel:[0,0,0,0] +v_cndmask_b32 v23, v28, v23, vcc +v_clz_i32_u32 v40, v34 +v_lshlrev_b32 v41, v40, v34 +v_and_b32 v39, 0xffffff00, v41 +v_cmp_eq_u32 vcc, 0x80000000, v41 +v_cvt_f32_u32 v39, v39 +v_rcp_f32 v35, v39 +v_sub_co_ci_u32 v36, vcc, 32, v40, vcc +v_cvt_f32_ubyte0 v40, v41 +v_fma_f32 v39, v39, v35, -1.0 +v_fma_f32 v39, v40, v35, v39 +v_fmaak_f32 v39, v39, v35, 0x9f000000 +v_mul_f32 v39, 0x5f800000, v39 +v_mov_b32 v40, 0 +v_cvt_floor_i32_f32 v39, -v39 +v_lshl_add_u32 v35, v35, 9, v39 +v_mad_u64_u32 v[40:41], vcc, v41, v35, v[40:41] +v_sub_co_ci_u32 v35, vcc, v35, -1, vcc +v_mov_b32 v38, v36 quad_perm:[1,1,1,1] +v_mov_b32 v36, v36 quad_perm:[0,0,0,0] +v_mov_b32 v37, v35 quad_perm:[1,1,1,1] +v_mov_b32 v35, v35 quad_perm:[0,0,0,0] +v_mul_hi_u32 v39, v23, v35 +v_add_co_u32 v21, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v36 +v_cndmask_b32 v21, v21, v39, vcc +v_alignbit_b32 v21, v39, v21, v36 +v_mul_hi_u32 v39, v23, v37 +v_add_co_u32 v4, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v38 +v_cndmask_b32 v4, v4, v39, vcc +v_alignbit_b32 v4, v39, v4, v38 +v_mad_u32_u16 v32, v21, v25, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v31, v4, v24, 0 op_sel:[0,0,0,0] +v_sub_nc_u32 v32, v23, v32 +v_sub_nc_u32 v31, v21, v31 +v_readlane_b32 s92, v32, 1 +v_sub_nc_u32 v32, v32, v25 +v_readlane_b32 s23, v31, 1 +v_sub_nc_u32 v31, v31, v24 +v_readlane_b32 s15, v4, 1 +v_sub_nc_u32 v4, v4, s8 +s_lshl_b32 s23, s23, 16 +s_and_b32 s92, s92, 0xffff +s_add_u32 s23, s23, s92 +v_mov_b32 v32, v32 quad_perm:[0,0,2,2] +v_mov_b32 v31, v31 quad_perm:[0,0,2,2] +v_mov_b32 v4, v4 quad_perm:[0,0,2,2] +v_add_co_u32 v32, vcc, v32, v27 +v_cndmask_b32 v30, 0, v25, vcc +v_add_co_ci_u32 v31, vcc, v31, 0, vcc +v_cndmask_b32 v29, 0, v24, vcc +v_add_co_ci_u32 v4, vcc, v4, 0, vcc +v_min_u32 v27, v22, 63 +v_sub_nc_u32 v32, v32, v30 +v_sub_nc_u32 v31, v31, v29 +v_cmp_eq_u32 vcc, v22, v27 +v_lshlrev_b32 v5, 16, v31 +v_bfi_b32 v5, 0xffff, v32, v5 +v_add_nc_u32 v42, v4, s8 +v_med3_u32 v27, v22, 1, 62 +v_mul_lo_u32 v6, v42, s42 +v_mul_lo_u32 v11, v42, s50 +s_mul_i32 s36, s15, s42 +s_mul_i32 s39, s15, s50 +v_cndmask_b32 v6, 0x80000000, v6, vcc +v_cmp_eq_u32 vcc, v22, v27 +v_cndmask_b32 v11, 0x80000000, v11, vcc +v_cmp_ge_u32 s[54:55], v42, s8 +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +s_mov_b32 s49, 3 +s_lshl_b32 s53, s49, 9 +v_add_nc_u32 v15, s53, v14 +s_bfe_u32 s10, s58, 0x80008 +s_bfe_u32 s11, s58, 0x80010 +s_cmp_eq_u32 s11, 0 +s_cmov_b32 s26, 0 +s_cbranch_scc1 108 +s_add_u32 s11, s11, 0xffffff00 +s_add_u32 s60, s60, 0 +s_addc_u32 s61, s61, 0 +s_lshr_b32 s91, s13, 2 +s_or_b32 s91, s91, 0x21010000 +v_cmp_eq_u32 vcc, v0, 0x100 +s_cmp_eq_u64 vcc, 0 +s_cselect_b32 s91, 0, s91 +s_cselect_b32 s90, 0, 0x1010101 +s_sub_u32 s10, 0, s10 +s_mov_b64 s[88:89], s[60:61] +s_and_b32 s89, s89, 0xffff +s_or_b32 s89, s89, 0x40000 +s_and_b32 s29, s22, 0xffff +s_lshr_b32 s28, s22, 16 +s_lshr_b32 s29, s29, 1 +s_mul_i32 s27, s29, s28 +s_mul_i32 s27, s27, s8 +s_add_u32 s27, s27, 61 +v_writelane_b32 v22, 62, 0 +v_writelane_b32 v22, s1, 1 +v_writelane_b32 v22, 10, 2 +v_clz_i32_u32 v26, v22 +v_lshlrev_b32 v27, v26, v22 +v_and_b32 v28, 0xffffff00, v27 +v_cmp_eq_u32 vcc, 0x80000000, v27 +v_cvt_f32_u32 v28, v28 +v_rcp_f32 v24, v28 +v_sub_co_ci_u32 v25, vcc, 32, v26, vcc +v_cvt_f32_ubyte0 v26, v27 +v_fma_f32 v28, v28, v24, -1.0 +v_fma_f32 v28, v26, v24, v28 +v_fmaak_f32 v28, v28, v24, 0x9f000000 +v_mul_f32 v28, 0x5f800000, v28 +v_mov_b32 v26, 0 +v_cvt_floor_i32_f32 v28, -v28 +v_lshl_add_u32 v24, v24, 9, v28 +v_mad_u64_u32 v[26:27], vcc, v27, v24, v[26:27] +v_sub_co_ci_u32 v24, vcc, v24, -1, vcc +v_mul_hi_u32 v26, s27, v24 +v_add_co_u32 v23, vcc, v26, s27 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_mov_b32 v23, v23 quad_perm:[0,0,0,0] +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v22, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v22, v22, v26, vcc +v_alignbit_b32 v22, v26, v22, v25 +v_mov_b32 v22, v22 quad_perm:[1,1,1,1] +v_add_nc_u32 v23, v22, 9 +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v23, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_readlane_b32 s28, v22, 1 +v_readlane_b32 s29, v23, 2 +s_add_u32 s27, s9, 15 +s_lshr_b32 s27, s27, 4 +s_cmp_eq_u32 s27, 1 +s_cmov_b32 s29, 1 +s_add_u32 s26, s28, s29 +s_mul_i32 s26, s27, s26 +s_add_u32 s26, 6, s26 +s_sub_u32 s26, s26, 1 +s_mov_b32 s92, 0 +s_mov_b32 s93, 0 +s_mov_b32 s94, 0 +s_mov_b32 s95, 0 +s_mov_b32 s96, 0 +s_mov_b32 s97, 0 +s_mov_b32 s28, 0 +s_mov_b32 s27, 8 +s_cmp_gt_u32 s28, 0 +s_cbranch_scc1 4 +v_mov_b32 v58, v4 +v_mov_b32 v63, v5 +v_mov_b32 v225, v6 +v_mov_b32 v226, v11 +v_mov_b32 v4, v58 +v_mov_b32 v5, v63 +v_mov_b32 v6, v225 +v_mov_b32 v11, v226 +s_add_u32 s28, s28, 16 +s_cmp_ge_u32 s28, s9 +s_cmov_b32 s28, 0 +s_cselect_b32 s29, 6, 2 +s_cselect_b32 s98, 9, 0 +s_pack_lh_b32_b16 s29, s29, s27 +s_pack_ll_b32_b16 s98, s98, s28 +v_mov_b32 v224, s29 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v7, v19 +v_mov_b32 v8, v20 +v_mov_b32 v9, 0x80000000 +v_mov_b32 v10, 0x80000000 +v_mov_b32 v12, 0x80000000 +v_mov_b32 v13, 0x80000000 +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v224 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v245, v243 +v_mov_b32 v246, v244 +v_pk_fma_f16 v227, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v228, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 231, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[243:244], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s96 +s_mov_b32 s96, s97 +s_mov_b32 s97, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2823 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 3, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3c94 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v235, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v236, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 239, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v224, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v224 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3df4 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_cmp_le_u32 s9, 16 +s_cselect_b32 s99, -1, 9 +s_sub_u32 s99, s99, 1 +s_cselect_b32 s29, s98, s29 +s_bitset0_b32 s29, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v224, s29 +v_add_co_u32 v33, vcc, v5, s23 +v_pk_mad_u16 v23, v5, 0x20001, s35 +v_pk_mad_u16 v28, v5, 0x20001, s38 +_v_pk_min_u16__vop3p 22, 289, 261, 0x0, 0x3, 0x0, 0x0 +v_cndmask_b32 v43, 0, s42, vcc +v_cndmask_b32 v247, 0, s50, vcc +v_mad_u32_u16 v7, v23, 1, v6 op_sel:[0,0,0,0] +v_mad_u32_u16 v12, v28, 1, v11 op_sel:[0,0,0,0] +v_add3_u32 v6, v6, s36, v43 +v_add3_u32 v11, v11, s39, v247 +_v_pk_sub_u16__vop3p 22, 261, 278, 0x0, 0x3, 0x0, 0x0 +v_add_co_ci_u32 v4, s[54:55], v4, s15, vcc +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +v_cmp_lt_u16 vcc, v23, s34 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_cmp_lt_u16 vcc, v28, s37 +v_cndmask_b32 v12, 0x80000000, v12, vcc +_v_pk_ashrrev_i16__vop3p 22, 143, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_u16__vop3p 53, 279, 41, 0x1, 0x3, 0x0, 0x0 +_v_pk_add_u16__vop3p 48, 279, 40, 0x1, 0x3, 0x0, 0x0 +v_mad_u32_u16 v10, v53, s44, v7 op_sel:[1,0,0,0] +v_mad_u32_u16 v8, v48, s44, v7 op_sel:[1,0,0,0] +_v_pk_add_u16__vop3p 38, 284, 45, 0x1, 0x3, 0x0, 0x0 +_v_cmp_lt_u16__vop3 106, 53, 34, 0x3 +v_cndmask_b32 v10, 0x80000000, v10, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x3 +v_cndmask_b32 v8, 0x80000000, v8, vcc +v_mad_u32_u16 v13, v38, s52, v12 op_sel:[1,0,0,0] +v_mad_u32_u16 v9, v53, s44, v7 op_sel:[0,0,0,0] +v_mad_u32_u16 v7, v48, s44, v7 op_sel:[0,0,0,0] +_v_cmp_lt_u16__vop3 106, 38, 37, 0x3 +v_cndmask_b32 v13, 0x80000000, v13, vcc +_v_cmp_lt_u16__vop3 106, 53, 34, 0x2 +v_cndmask_b32 v9, 0x80000000, v9, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x2 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_mad_u32_u16 v12, v38, s52, v12 op_sel:[0,0,0,0] +v_pk_mad_u16 v5, v22, s22, v33 +_v_cmp_lt_u16__vop3 106, 38, 37, 0x2 +v_cndmask_b32 v12, 0x80000000, v12, vcc +v_add_co_u32 v22, vcc, v4, s8 +v_cndmask_b32 v224, s98, v224, vcc +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v224 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v245, v243 +v_mov_b32 v246, v244 +v_pk_fma_f16 v227, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v228, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 231, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[243:244], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s96 +s_mov_b32 s96, s97 +s_mov_b32 s97, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2531 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 3, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3c94 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v235, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v236, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 239, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v224, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x3c90 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v224 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x3df4 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s27, 1 +s_cbranch_scc1 65244 +s_branch 65016 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 80 +v_mov_b32 v64, 0 +v_mov_b32 v68, 0 +v_mov_b32 v65, 0 +v_mov_b32 v69, 0 +v_mov_b32 v66, 0 +v_mov_b32 v70, 0 +v_mov_b32 v67, 0 +v_mov_b32 v71, 0 +v_mov_b32 v80, 0 +v_mov_b32 v84, 0 +v_mov_b32 v81, 0 +v_mov_b32 v85, 0 +v_mov_b32 v82, 0 +v_mov_b32 v86, 0 +v_mov_b32 v83, 0 +v_mov_b32 v87, 0 +v_mov_b32 v96, 0 +v_mov_b32 v100, 0 +v_mov_b32 v97, 0 +v_mov_b32 v101, 0 +v_mov_b32 v98, 0 +v_mov_b32 v102, 0 +v_mov_b32 v99, 0 +v_mov_b32 v103, 0 +v_mov_b32 v112, 0 +v_mov_b32 v116, 0 +v_mov_b32 v113, 0 +v_mov_b32 v117, 0 +v_mov_b32 v114, 0 +v_mov_b32 v118, 0 +v_mov_b32 v115, 0 +v_mov_b32 v119, 0 +v_mov_b32 v128, 0 +v_mov_b32 v132, 0 +v_mov_b32 v129, 0 +v_mov_b32 v133, 0 +v_mov_b32 v130, 0 +v_mov_b32 v134, 0 +v_mov_b32 v131, 0 +v_mov_b32 v135, 0 +v_mov_b32 v144, 0 +v_mov_b32 v148, 0 +v_mov_b32 v145, 0 +v_mov_b32 v149, 0 +v_mov_b32 v146, 0 +v_mov_b32 v150, 0 +v_mov_b32 v147, 0 +v_mov_b32 v151, 0 +v_mov_b32 v160, 0 +v_mov_b32 v164, 0 +v_mov_b32 v161, 0 +v_mov_b32 v165, 0 +v_mov_b32 v162, 0 +v_mov_b32 v166, 0 +v_mov_b32 v163, 0 +v_mov_b32 v167, 0 +v_mov_b32 v176, 0 +v_mov_b32 v180, 0 +v_mov_b32 v177, 0 +v_mov_b32 v181, 0 +v_mov_b32 v178, 0 +v_mov_b32 v182, 0 +v_mov_b32 v179, 0 +v_mov_b32 v183, 0 +v_mov_b32 v192, 0 +v_mov_b32 v196, 0 +v_mov_b32 v193, 0 +v_mov_b32 v197, 0 +v_mov_b32 v194, 0 +v_mov_b32 v198, 0 +v_mov_b32 v195, 0 +v_mov_b32 v199, 0 +v_mov_b32 v208, 0 +v_mov_b32 v212, 0 +v_mov_b32 v209, 0 +v_mov_b32 v213, 0 +v_mov_b32 v210, 0 +v_mov_b32 v214, 0 +v_mov_b32 v211, 0 +v_mov_b32 v215, 0 +s_mov_b32 s85, 0x3f54 +s_cmp_le_u32 s9, 16 +s_cmov_b32 s85, 0x3c90 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 80 +v_mov_b32 v72, 0 +v_mov_b32 v76, 0 +v_mov_b32 v73, 0 +v_mov_b32 v77, 0 +v_mov_b32 v74, 0 +v_mov_b32 v78, 0 +v_mov_b32 v75, 0 +v_mov_b32 v79, 0 +v_mov_b32 v88, 0 +v_mov_b32 v92, 0 +v_mov_b32 v89, 0 +v_mov_b32 v93, 0 +v_mov_b32 v90, 0 +v_mov_b32 v94, 0 +v_mov_b32 v91, 0 +v_mov_b32 v95, 0 +v_mov_b32 v104, 0 +v_mov_b32 v108, 0 +v_mov_b32 v105, 0 +v_mov_b32 v109, 0 +v_mov_b32 v106, 0 +v_mov_b32 v110, 0 +v_mov_b32 v107, 0 +v_mov_b32 v111, 0 +v_mov_b32 v120, 0 +v_mov_b32 v124, 0 +v_mov_b32 v121, 0 +v_mov_b32 v125, 0 +v_mov_b32 v122, 0 +v_mov_b32 v126, 0 +v_mov_b32 v123, 0 +v_mov_b32 v127, 0 +v_mov_b32 v136, 0 +v_mov_b32 v140, 0 +v_mov_b32 v137, 0 +v_mov_b32 v141, 0 +v_mov_b32 v138, 0 +v_mov_b32 v142, 0 +v_mov_b32 v139, 0 +v_mov_b32 v143, 0 +v_mov_b32 v152, 0 +v_mov_b32 v156, 0 +v_mov_b32 v153, 0 +v_mov_b32 v157, 0 +v_mov_b32 v154, 0 +v_mov_b32 v158, 0 +v_mov_b32 v155, 0 +v_mov_b32 v159, 0 +v_mov_b32 v168, 0 +v_mov_b32 v172, 0 +v_mov_b32 v169, 0 +v_mov_b32 v173, 0 +v_mov_b32 v170, 0 +v_mov_b32 v174, 0 +v_mov_b32 v171, 0 +v_mov_b32 v175, 0 +v_mov_b32 v184, 0 +v_mov_b32 v188, 0 +v_mov_b32 v185, 0 +v_mov_b32 v189, 0 +v_mov_b32 v186, 0 +v_mov_b32 v190, 0 +v_mov_b32 v187, 0 +v_mov_b32 v191, 0 +v_mov_b32 v200, 0 +v_mov_b32 v204, 0 +v_mov_b32 v201, 0 +v_mov_b32 v205, 0 +v_mov_b32 v202, 0 +v_mov_b32 v206, 0 +v_mov_b32 v203, 0 +v_mov_b32 v207, 0 +v_mov_b32 v216, 0 +v_mov_b32 v220, 0 +v_mov_b32 v217, 0 +v_mov_b32 v221, 0 +v_mov_b32 v218, 0 +v_mov_b32 v222, 0 +v_mov_b32 v219, 0 +v_mov_b32 v223, 0 +s_mov_b32 s85, 0x3f54 +s_cmp_le_u32 s9, 16 +s_cmov_b32 s85, 0x3c90 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 325, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 326, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 327, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v64, v227 +v_mov_b32 v65, v228 +v_mov_b32 v66, v229 +v_mov_b32 v67, v230 +v_mov_b32 v68, v231 +v_mov_b32 v69, v232 +v_mov_b32 v70, v233 +v_mov_b32 v71, v234 +s_mov_b32 s85, 0x3fc0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 328, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 329, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 330, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 331, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 332, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 333, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 334, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 335, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v72, v235 +v_mov_b32 v73, v236 +v_mov_b32 v74, v237 +v_mov_b32 v75, v238 +v_mov_b32 v76, v239 +v_mov_b32 v77, v240 +v_mov_b32 v78, v241 +v_mov_b32 v79, v242 +s_mov_b32 s85, 0x402c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 336, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 337, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 338, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 339, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 340, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 341, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 342, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 343, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v80, v227 +v_mov_b32 v81, v228 +v_mov_b32 v82, v229 +v_mov_b32 v83, v230 +v_mov_b32 v84, v231 +v_mov_b32 v85, v232 +v_mov_b32 v86, v233 +v_mov_b32 v87, v234 +s_mov_b32 s85, 0x4098 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 344, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 345, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 346, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 347, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 348, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 349, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 350, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 351, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v88, v235 +v_mov_b32 v89, v236 +v_mov_b32 v90, v237 +v_mov_b32 v91, v238 +v_mov_b32 v92, v239 +v_mov_b32 v93, v240 +v_mov_b32 v94, v241 +v_mov_b32 v95, v242 +s_mov_b32 s85, 0x4104 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 352, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 353, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 354, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 355, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 356, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 357, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 358, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 359, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v96, v227 +v_mov_b32 v97, v228 +v_mov_b32 v98, v229 +v_mov_b32 v99, v230 +v_mov_b32 v100, v231 +v_mov_b32 v101, v232 +v_mov_b32 v102, v233 +v_mov_b32 v103, v234 +s_mov_b32 s85, 0x4170 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 360, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 361, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 362, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 363, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 364, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 365, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 366, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 367, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v104, v235 +v_mov_b32 v105, v236 +v_mov_b32 v106, v237 +v_mov_b32 v107, v238 +v_mov_b32 v108, v239 +v_mov_b32 v109, v240 +v_mov_b32 v110, v241 +v_mov_b32 v111, v242 +s_mov_b32 s85, 0x41dc +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 368, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 369, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 370, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 371, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 372, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 373, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 374, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 375, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v112, v227 +v_mov_b32 v113, v228 +v_mov_b32 v114, v229 +v_mov_b32 v115, v230 +v_mov_b32 v116, v231 +v_mov_b32 v117, v232 +v_mov_b32 v118, v233 +v_mov_b32 v119, v234 +s_mov_b32 s85, 0x4248 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 376, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 377, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 378, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 379, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 380, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 381, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 382, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 383, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v120, v235 +v_mov_b32 v121, v236 +v_mov_b32 v122, v237 +v_mov_b32 v123, v238 +v_mov_b32 v124, v239 +v_mov_b32 v125, v240 +v_mov_b32 v126, v241 +v_mov_b32 v127, v242 +s_mov_b32 s85, 0x42b4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 384, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 385, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 386, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 387, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 388, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 389, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 390, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 391, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v128, v227 +v_mov_b32 v129, v228 +v_mov_b32 v130, v229 +v_mov_b32 v131, v230 +v_mov_b32 v132, v231 +v_mov_b32 v133, v232 +v_mov_b32 v134, v233 +v_mov_b32 v135, v234 +s_mov_b32 s85, 0x4320 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 392, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 393, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 394, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 395, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 396, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 397, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 398, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 399, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v136, v235 +v_mov_b32 v137, v236 +v_mov_b32 v138, v237 +v_mov_b32 v139, v238 +v_mov_b32 v140, v239 +v_mov_b32 v141, v240 +v_mov_b32 v142, v241 +v_mov_b32 v143, v242 +s_mov_b32 s85, 0x438c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 400, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 401, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 402, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 403, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 404, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 405, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 406, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 407, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v144, v227 +v_mov_b32 v145, v228 +v_mov_b32 v146, v229 +v_mov_b32 v147, v230 +v_mov_b32 v148, v231 +v_mov_b32 v149, v232 +v_mov_b32 v150, v233 +v_mov_b32 v151, v234 +s_mov_b32 s85, 0x43f8 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 408, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 409, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 410, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 411, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 412, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 413, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 414, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 415, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v152, v235 +v_mov_b32 v153, v236 +v_mov_b32 v154, v237 +v_mov_b32 v155, v238 +v_mov_b32 v156, v239 +v_mov_b32 v157, v240 +v_mov_b32 v158, v241 +v_mov_b32 v159, v242 +s_mov_b32 s85, 0x4464 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 416, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 417, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 418, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 419, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 420, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 421, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 422, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 423, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v160, v227 +v_mov_b32 v161, v228 +v_mov_b32 v162, v229 +v_mov_b32 v163, v230 +v_mov_b32 v164, v231 +v_mov_b32 v165, v232 +v_mov_b32 v166, v233 +v_mov_b32 v167, v234 +s_mov_b32 s85, 0x44d0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 424, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 425, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 426, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 427, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 428, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 429, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 430, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 431, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v168, v235 +v_mov_b32 v169, v236 +v_mov_b32 v170, v237 +v_mov_b32 v171, v238 +v_mov_b32 v172, v239 +v_mov_b32 v173, v240 +v_mov_b32 v174, v241 +v_mov_b32 v175, v242 +s_mov_b32 s85, 0x453c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 432, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 433, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 434, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 435, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 436, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 437, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 438, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 439, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v176, v227 +v_mov_b32 v177, v228 +v_mov_b32 v178, v229 +v_mov_b32 v179, v230 +v_mov_b32 v180, v231 +v_mov_b32 v181, v232 +v_mov_b32 v182, v233 +v_mov_b32 v183, v234 +s_mov_b32 s85, 0x45a8 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 440, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 441, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 442, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 443, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 444, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 445, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 446, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 447, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v184, v235 +v_mov_b32 v185, v236 +v_mov_b32 v186, v237 +v_mov_b32 v187, v238 +v_mov_b32 v188, v239 +v_mov_b32 v189, v240 +v_mov_b32 v190, v241 +v_mov_b32 v191, v242 +s_mov_b32 s85, 0x4614 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 448, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 449, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 450, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 451, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 452, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 453, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 454, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 455, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v192, v227 +v_mov_b32 v193, v228 +v_mov_b32 v194, v229 +v_mov_b32 v195, v230 +v_mov_b32 v196, v231 +v_mov_b32 v197, v232 +v_mov_b32 v198, v233 +v_mov_b32 v199, v234 +s_mov_b32 s85, 0x4680 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 456, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 457, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 458, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 459, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 460, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 461, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 462, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 463, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v200, v235 +v_mov_b32 v201, v236 +v_mov_b32 v202, v237 +v_mov_b32 v203, v238 +v_mov_b32 v204, v239 +v_mov_b32 v205, v240 +v_mov_b32 v206, v241 +v_mov_b32 v207, v242 +s_mov_b32 s85, 0x46ec +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 464, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 465, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 466, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 467, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 468, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 469, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 470, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 471, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v208, v227 +v_mov_b32 v209, v228 +v_mov_b32 v210, v229 +v_mov_b32 v211, v230 +v_mov_b32 v212, v231 +v_mov_b32 v213, v232 +v_mov_b32 v214, v233 +v_mov_b32 v215, v234 +s_mov_b32 s85, 0x4758 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 472, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 473, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 474, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 475, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 476, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 477, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 478, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 479, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v216, v235 +v_mov_b32 v217, v236 +v_mov_b32 v218, v237 +v_mov_b32 v219, v238 +v_mov_b32 v220, v239 +v_mov_b32 v221, v240 +v_mov_b32 v222, v241 +v_mov_b32 v223, v242 +s_mov_b32 s85, 0x3f54 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 483, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 484, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 485, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 486, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 227, 483, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 228, 484, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 229, 485, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 230, 486, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 487, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 488, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 489, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 490, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 231, 487, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 232, 488, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 233, 489, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 234, 490, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4990 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 491, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 492, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 493, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 494, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 235, 491, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 236, 492, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 237, 493, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 238, 494, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 495, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 496, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 497, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 498, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 239, 495, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 240, 496, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 241, 497, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 242, 498, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x47c4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 483, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 484, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 485, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 486, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 227, 483, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 228, 484, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 229, 485, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 230, 486, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 487, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 488, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 489, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 490, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 231, 487, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 232, 488, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 233, 489, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 234, 490, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4d28 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 491, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 492, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 493, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 494, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 235, 491, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 236, 492, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 237, 493, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 238, 494, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 495, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 496, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 497, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 498, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 239, 495, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 240, 496, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 241, 497, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 242, 498, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4b5c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 227, 0xbdc5bdc5, 483, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 228, 0xbdc5bdc5, 484, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 229, 0xbdc5bdc5, 485, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 230, 0xbdc5bdc5, 486, 0x0, 0x3 +v_exp_f16 v227, v227 +v_exp_f16 v228, v228 +v_exp_f16 v229, v229 +v_exp_f16 v230, v230 +_v_exp_f16__vop3 227, 227, 0x9 +_v_exp_f16__vop3 228, 228, 0x9 +_v_exp_f16__vop3 229, 229, 0x9 +_v_exp_f16__vop3 230, 230, 0x9 +_v_pk_add_f16__vop3p_lit 227, 0x3c003c00, 483, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 228, 0x3c003c00, 484, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 229, 0x3c003c00, 485, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 230, 0x3c003c00, 486, 0x0, 0x3 +v_rcp_f16 v227, v227 +v_rcp_f16 v228, v228 +v_rcp_f16 v229, v229 +v_rcp_f16 v230, v230 +_v_rcp_f16__vop3 227, 227, 0x9 +_v_rcp_f16__vop3 228, 228, 0x9 +_v_rcp_f16__vop3 229, 229, 0x9 +_v_rcp_f16__vop3 230, 230, 0x9 +_v_pk_mul_f16__vop3p_lit 231, 0xbdc5bdc5, 487, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 232, 0xbdc5bdc5, 488, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 233, 0xbdc5bdc5, 489, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 234, 0xbdc5bdc5, 490, 0x0, 0x3 +v_exp_f16 v231, v231 +v_exp_f16 v232, v232 +v_exp_f16 v233, v233 +v_exp_f16 v234, v234 +_v_exp_f16__vop3 231, 231, 0x9 +_v_exp_f16__vop3 232, 232, 0x9 +_v_exp_f16__vop3 233, 233, 0x9 +_v_exp_f16__vop3 234, 234, 0x9 +_v_pk_add_f16__vop3p_lit 231, 0x3c003c00, 487, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 232, 0x3c003c00, 488, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 233, 0x3c003c00, 489, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 234, 0x3c003c00, 490, 0x0, 0x3 +v_rcp_f16 v231, v231 +v_rcp_f16 v232, v232 +v_rcp_f16 v233, v233 +v_rcp_f16 v234, v234 +_v_rcp_f16__vop3 231, 231, 0x9 +_v_rcp_f16__vop3 232, 232, 0x9 +_v_rcp_f16__vop3 233, 233, 0x9 +_v_rcp_f16__vop3 234, 234, 0x9 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5200 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 235, 0xbdc5bdc5, 491, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 236, 0xbdc5bdc5, 492, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 237, 0xbdc5bdc5, 493, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 238, 0xbdc5bdc5, 494, 0x0, 0x3 +v_exp_f16 v235, v235 +v_exp_f16 v236, v236 +v_exp_f16 v237, v237 +v_exp_f16 v238, v238 +_v_exp_f16__vop3 235, 235, 0x9 +_v_exp_f16__vop3 236, 236, 0x9 +_v_exp_f16__vop3 237, 237, 0x9 +_v_exp_f16__vop3 238, 238, 0x9 +_v_pk_add_f16__vop3p_lit 235, 0x3c003c00, 491, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 236, 0x3c003c00, 492, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 237, 0x3c003c00, 493, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 238, 0x3c003c00, 494, 0x0, 0x3 +v_rcp_f16 v235, v235 +v_rcp_f16 v236, v236 +v_rcp_f16 v237, v237 +v_rcp_f16 v238, v238 +_v_rcp_f16__vop3 235, 235, 0x9 +_v_rcp_f16__vop3 236, 236, 0x9 +_v_rcp_f16__vop3 237, 237, 0x9 +_v_rcp_f16__vop3 238, 238, 0x9 +_v_pk_mul_f16__vop3p_lit 239, 0xbdc5bdc5, 495, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 240, 0xbdc5bdc5, 496, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 241, 0xbdc5bdc5, 497, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 242, 0xbdc5bdc5, 498, 0x0, 0x3 +v_exp_f16 v239, v239 +v_exp_f16 v240, v240 +v_exp_f16 v241, v241 +v_exp_f16 v242, v242 +_v_exp_f16__vop3 239, 239, 0x9 +_v_exp_f16__vop3 240, 240, 0x9 +_v_exp_f16__vop3 241, 241, 0x9 +_v_exp_f16__vop3 242, 242, 0x9 +_v_pk_add_f16__vop3p_lit 239, 0x3c003c00, 495, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 240, 0x3c003c00, 496, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 241, 0x3c003c00, 497, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 242, 0x3c003c00, 498, 0x0, 0x3 +v_rcp_f16 v239, v239 +v_rcp_f16 v240, v240 +v_rcp_f16 v241, v241 +v_rcp_f16 v242, v242 +_v_rcp_f16__vop3 239, 239, 0x9 +_v_rcp_f16__vop3 240, 240, 0x9 +_v_rcp_f16__vop3 241, 241, 0x9 +_v_rcp_f16__vop3 242, 242, 0x9 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x4ef4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 483, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 228, 484, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 229, 485, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 230, 486, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v227 +v_and_b32 v28, 0x7fff7fff, v228 +v_and_b32 v38, 0x7fff7fff, v229 +v_and_b32 v48, 0x7fff7fff, v230 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 227, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 228, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 229, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 230, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 227, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 228, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 229, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 230, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v227, 0x7fff7fff, v23, v227 +v_bfi_b32 v228, 0x7fff7fff, v33, v228 +v_bfi_b32 v229, 0x7fff7fff, v43, v229 +v_bfi_b32 v230, 0x7fff7fff, v53, v230 +_v_pk_mul_f16__vop3p 227, 483, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 228, 484, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 229, 485, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 230, 486, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 231, 487, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 232, 488, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 489, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 490, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v231 +v_and_b32 v28, 0x7fff7fff, v232 +v_and_b32 v38, 0x7fff7fff, v233 +v_and_b32 v48, 0x7fff7fff, v234 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 231, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 232, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 233, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 234, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 231, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 232, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 233, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 234, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v231, 0x7fff7fff, v23, v231 +v_bfi_b32 v232, 0x7fff7fff, v33, v232 +v_bfi_b32 v233, 0x7fff7fff, v43, v233 +v_bfi_b32 v234, 0x7fff7fff, v53, v234 +_v_pk_mul_f16__vop3p 231, 487, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 232, 488, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 489, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 490, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5b98 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 491, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 236, 492, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 237, 493, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 238, 494, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v235 +v_and_b32 v28, 0x7fff7fff, v236 +v_and_b32 v38, 0x7fff7fff, v237 +v_and_b32 v48, 0x7fff7fff, v238 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 235, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 236, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 237, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 238, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 235, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 236, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 237, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 238, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v235, 0x7fff7fff, v23, v235 +v_bfi_b32 v236, 0x7fff7fff, v33, v236 +v_bfi_b32 v237, 0x7fff7fff, v43, v237 +v_bfi_b32 v238, 0x7fff7fff, v53, v238 +_v_pk_mul_f16__vop3p 235, 491, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 236, 492, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 237, 493, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 238, 494, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 239, 495, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 240, 496, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 241, 497, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 242, 498, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v239 +v_and_b32 v28, 0x7fff7fff, v240 +v_and_b32 v38, 0x7fff7fff, v241 +v_and_b32 v48, 0x7fff7fff, v242 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 239, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 240, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 241, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 242, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 239, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 240, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 241, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 242, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v239, 0x7fff7fff, v23, v239 +v_bfi_b32 v240, 0x7fff7fff, v33, v240 +v_bfi_b32 v241, 0x7fff7fff, v43, v241 +v_bfi_b32 v242, 0x7fff7fff, v53, v242 +_v_pk_mul_f16__vop3p 239, 495, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 240, 496, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 241, 497, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 242, 498, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x550c +s_setpc_b64 s[86:87] +s_endpgm +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end 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+s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end + diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c32_stride1.inc b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c32_stride1.inc new file mode 100644 index 0000000000..4934d4900f --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_gfx11_1536vgprs_fp16_fp16acc_f2x3_c32_stride1.inc @@ -0,0 +1,5160 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +.macro _sop1_lit op:req, sdst:req, lit:req + .long (0b101111101 << 23) | (\sdst << 16) | (\op << 8) | 255 + .long \lit +.endm + +.macro _s_mov_b32__sop1_lit sdst:req, lit:req + _sop1_lit 0, \sdst, \lit +.endm + +.macro _vop1 op:req, vdst:req, src:req + .long (0b0111111 << 25) | (\vdst << 17) | (\op << 9) | \src +.endm + +.macro _v_cvt_f16_i16__vop1 vdst:req, vsrc:req + _vop1 81, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_rcp_f16__vop1 vdst:req, vsrc:req + _vop1 84, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _v_exp_f16__vop1 vdst:req, vsrc:req + _vop1 88, \vdst, (\vsrc + /*VGPR*/ 256) +.endm + +.macro _vop3 op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, abs:req, neg:req + .long (0b110101 << 26) | (\op << 16) | (\opsel << 11) | (\abs << 8) | \vdst + .long (\neg << 29) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_cvt_f16_i16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 465, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_rcp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 468, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_exp_f16__vop3 vdst:req, vsrc:req, opsel:req + _vop3 472, \vdst, (\vsrc + /*VGPR*/ 256), 0, 0, \opsel, 0, 0 +.endm + +.macro _v_cndmask_b16__vop3 vdst:req, vsrc0:req, vsrc1:req, src2:req, opsel:req + _vop3 605, \vdst, (\vsrc0 + /*VGPR*/ 256), (\vsrc1 + /*VGPR*/ 256), \src2, \opsel, 0, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_s_lit sdst:req, ssrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, \ssrc0, 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_gt_f16__vop3_v_lit sdst:req, vsrc0:req, lit:req, opsel:req, abs:req + _vop3_lit 4, \sdst, (\vsrc0 + /*VGPR*/ 256), 255, 0, \lit, \opsel, \abs, 0 +.endm + +.macro _v_cmp_lt_u16__vop3 sdst:req, vsrc0:req, ssrc1:req, opsel:req + _vop3 57, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, \opsel, 0, 0 +.endm + +.macro _v_cmpx_lt_u32__vop3 sdst:req, vsrc0:req, ssrc1:req + _vop3 201, \sdst, (\vsrc0 + /*VGPR*/ 256), \ssrc1, 0, 0, 0, 0 +.endm + +.macro _vop3p op:req, vdst:req, src0:req, src1:req, src2:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 +.endm + +.macro _vop3p_lit op:req, vdst:req, src0:req, src1:req, src2:req, lit:req, opsel:req, opsel_hi:req, opsel_hi2:req, neg:req, neg_hi:req + .long (0b11001100 << 24) | (\op << 16) | (\opsel_hi2 << 14) | (\opsel << 11) | (\neg_hi << 8) | \vdst + .long (\neg << 29) | (\opsel_hi << 27) | (\src2 << 18) | (\src1 << 9) | \src0 + .long \lit +.endm + +.macro _v_pk_ashrrev_i16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 6, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 10, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_sub_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 11, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_min_u16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 13, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 15, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_add_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 15, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_mul_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 16, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_mul_f16__vop3p_lit vdst:req, lit:req, src1:req, opsel:req, opsel_hi:req + _vop3p_lit 16, \vdst, 255, \src1, 0, \lit, \opsel, \opsel_hi, 0, 0, 0 +.endm + +.macro _v_pk_min_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 17, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +.macro _v_pk_max_f16__vop3p vdst:req, src0:req, src1:req, opsel:req, opsel_hi:req, neg:req, neg_hi:req + _vop3p 18, \vdst, \src0, \src1, 0, \opsel, \opsel_hi, 0, \neg, \neg_hi +.endm + +s_version 0x2006 +s_set_inst_prefetch_distance 0x3 +s_mov_b32 s0, 0 +v_lshlrev_b32 v1, 7, v0 +s_getpc_b64 s[8:9] +s_mov_b32 s10, 0x70cc +s_mov_b32 s11, 0x31014000 +buffer_load_b32 v2, v1, s[8:11], 0 offen +s_waitcnt vmcnt(0) +s_getpc_b64 s[6:7] +s_load_b512 s[8:23], s[2:3], null +s_load_b512 s[24:39], s[2:3], 0x40 +s_load_b512 s[40:55], s[2:3], 0x80 +s_load_b256 s[56:63], s[2:3], 0xc0 +s_load_b64 s[64:65], s[2:3], 0xe0 +v_and_b32 v8, 0xff, v0 +v_lshrrev_b32 v9, 1, v8 +v_and_b32 v10, 1, v0 +v_add_nc_u32 v5, v9, 32 +v_bfi_b32 v6, 31, v8, v9 +v_bfe_u32 v4, v8, 5, 1 +v_bfi_b32 v6, 0xbf, v6, v5 +v_and_b32 v2, 31, v8 +v_lshrrev_b32 v6, 5, v6 +v_lshrrev_b32 v7, 6, v8 +v_lshlrev_b32 v2, 4, v2 +v_and_b32 v3, 31, v9 +v_mad_u32_u24 v2, v4, 0x900, v2 +v_lshlrev_b32 v3, 4, v3 +v_xor_b32 v5, 3, v6 +v_mad_u32_u16 v3, 0x480, v7, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v1, v5, 0x240, v2 +v_mad_u32_u16 v3, 0x1240, v10, v3 op_sel:[0,0,0,0] +v_mad_u32_u24 v2, v6, 0x240, v2 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 6 +s_cbranch_scc0 14 +s_load_b64 s[16:17], s[16:17], null +s_load_b64 s[20:21], s[20:21], null +s_load_b64 s[18:19], s[18:19], null +s_cmp_eq_u64 0, s[60:61] +s_cbranch_scc1 2 +s_load_b64 s[60:61], s[60:61], null +s_cmp_eq_u64 0, s[30:31] +s_cbranch_scc1 2 +s_load_b64 s[30:31], s[30:31], null +s_bitcmp1_b32 s14, 3 +s_cbranch_scc0 2 +s_setreg_imm32_b32 hwreg(HW_REG_MODE, 0, 8), 0xf0 +s_cmp_eq_u32 s13, 0x60 +s_cbranch_scc0 16 +s_mul_i32 s1, s4, 0xab +s_lshr_b32 s1, s1, 10 +s_mul_i32 s23, s1, 6 +s_sub_u32 s23, s4, s23 +s_bfe_u32 s15, s1, 0x20000 +s_bfe_u32 s22, s1, 0x10002 +s_bfe_u32 s5, s1, 0x10003 +s_mov_b32 s45, s23 +s_lshl1_add_u32 s45, s45, s22 +s_lshl2_add_u32 s45, s45, s15 +s_lshl1_add_u32 s45, s45, s5 +s_mov_b32 s4, s45 +s_waitcnt expcnt(0) lgkmcnt(0) vmcnt(0) +s_bitcmp1_b32 s14, 13 +s_cbranch_scc0 10 +s_add_u32 s16, s16, s34 +s_addc_u32 s17, s17, s35 +s_add_u32 s20, s20, s38 +s_addc_u32 s21, s21, s39 +s_add_u32 s18, s18, s36 +s_addc_u32 s19, s19, s37 +s_cmp_eq_u64 0, s[30:31] +s_cselect_b64 s[40:41], 0, s[40:41] +s_add_u32 s30, s30, s40 +s_addc_u32 s31, s31, s41 +s_add_u32 s15, s12, 15 +s_lshr_b32 s15, s15, 4 +v_cvt_f32_u32 v4, s15 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v5, v4, s13, s13 +v_lshrrev_b32 v5, 16, v5 +v_cvt_f32_u32 v4, v5 +v_rcp_f32 v4, v4 +v_mul_f32 v4, 0x47800000, v4 +v_cvt_floor_i32_f32 v4, v4 +v_mad_u32_u24 v6, v4, s4, s4 +v_lshrrev_b32 v6, 16, v6 +v_readfirstlane_b32 s1, v5 +v_readfirstlane_b32 s22, v6 +s_mul_i32 s5, s22, s1 +s_sub_u32 s5, s4, s5 +s_cmp_ge_u32 s22, s15 +s_cbranch_scc1 7089 +s_mul_i32 s13, s1, s15 +s_mul_i32 s23, s22, 16 +s_sub_u32 s12, s12, s23 +s_min_u32 s12, s12, 16 +s_mul_i32 s34, s23, s46 +s_mul_hi_u32 s35, s23, s46 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s18, s34, s18 +s_addc_u32 s19, s35, s19 +s_lshr_b32 s35, s23, 0 +s_mul_i32 s34, s35, s51 +s_mul_hi_u32 s35, s35, s51 +s_lshl_b64 s[34:35], s[34:35], 1 +s_add_u32 s20, s34, s20 +s_addc_u32 s21, s35, s21 +s_lshl_b32 s34, s23, 1 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s34, 0, s34 +s_add_u32 s30, s30, s34 +s_addc_u32 s31, s31, 0 +v_cmp_lt_u32 vcc, v0, 0x100 +s_cbranch_vccz 3677 +v_and_b32 v20, 0xff, v0 +v_lshrrev_b32 v21, 1, v20 +v_bfe_u32 v17, v20, 3, 1 +v_bfe_u32 v16, v20, 2, 1 +v_mad_u32_u16 v17, v17, 16, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v14, v16, 0x1240, v17 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 0, 2 +v_mad_u32_u16 v14, v16, 0x90, v14 op_sel:[0,0,0,0] +v_bfe_u32 v17, v20, 4, 2 +v_mad_u32_u16 v14, v17, 32, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 6, 1 +v_mad_u32_u16 v14, v16, 0x480, v14 op_sel:[0,0,0,0] +v_bfe_u32 v16, v20, 7, 1 +v_mad_u32_u16 v14, v16, 0x900, v14 op_sel:[0,0,0,0] +v_bfe_u32 v18, v20, 1, 2 +v_mad_u32_u16 v13, v18, 32, 0 op_sel:[0,0,0,0] +v_bfe_u32 v19, v20, 3, 1 +v_mad_u32_u16 v13, v19, 0x480, v13 op_sel:[0,0,0,0] +v_add_nc_u32 v18, v21, 32 +v_bfi_b32 v18, 0xbf, v20, v18 +v_bfe_u32 v18, v18, 6, 2 +v_mad_u32_u16 v13, v18, 0x90, v13 op_sel:[0,0,0,0] +v_xor_b32 v16, v0, v0 quad_perm:[2,3,2,1] +v_xor_b32 v17, v0, v0 quad_perm:[0,0,3,3] +v_sub_nc_u16 v16, v16, v17 op_sel:[0,0,0] +v_cvt_f16_i16 v15, v16 +_v_cvt_f16_i16__vop1 (15 | /*op_sel*/ 0x80), 17 +_v_pk_mul_f16__vop3p 15, 271, 240, 0x0, 0x1, 0x0, 0x0 +v_bfe_u32 v16, v0, 6, 1 +v_and_b32 v5, 63, v0 +v_cmp_eq_u32 vcc, v16, 1 +v_cndmask_b32 v16, 0, 0x400, vcc +v_cndmask_b32 v17, 0, 0x100, vcc +v_lshl_add_u32 v6, v5, 2, 0 +v_lshl_add_u32 v5, v5, 4, v16 +s_mov_b32 s23, 2 +s_mov_b32 s34, 0 +s_mov_b32 s40, 0xbc00c000 +v_readfirstlane_b32 s82, v0 +s_and_b32 null, 64, s82 +s_cmov_b32 s40, 0x3c00c000 +s_lshl_b32 s49, s43, 1 +s_lshl_b32 s53, s47, 1 +s_lshl_b32 s83, s49, 4 +s_lshl_b32 s84, s53, 4 +s_and_b32 null, 0x80, s82 +s_cselect_b32 s83, s83, 0 +s_cselect_b32 s84, s84, 0 +s_cselect_b32 s22, 16, 0 +s_sub_u32 s22, s9, s22 +s_cmov_b32 s22, 0 +s_mov_b32 s35, 0x11014000 +s_bitcmp1_b32 s14, 4 +s_cselect_b32 s85, 0, 0x8000000 +s_and_b32 s35, 0xf7ffffff, s35 +s_or_b32 s35, s35, s85 +s_and_b32 s17, s17, 0xffff +s_add_u32 s17, s17, 0x20000 +s_and_b32 s19, s19, 0xffff +s_add_u32 s19, s19, 0x20000 +s_add_u32 s16, s16, s83 +s_addc_u32 s17, s17, 0 +s_add_u32 s18, s18, s84 +s_addc_u32 s19, s19, 0 +s_mov_b64 s[36:37], s[16:17] +s_mov_b32 s38, 0x80000000 +s_mov_b32 s39, 0 +s_getpc_b64 s[64:65] +v_cmp_lt_u32 vcc, v0, 0x80 +s_cmp_gt_u32 vcc_lo, 0 +s_mov_b32 s82, 0x2ed0 +s_mov_b32 s86, 0x1e40 +s_cmov_b32 s82, 0x2608 +s_cmov_b32 s86, 0x1678 +s_mov_b32 s83, 0x30b4 +s_mov_b32 s87, 0x2024 +s_cmov_b32 s83, 0x27ec +s_cmov_b32 s87, 0x185c +s_mov_b32 s84, 0x3364 +s_mov_b32 s88, 0x2254 +s_cmov_b32 s84, 0x2a9c +s_cmov_b32 s88, 0x1a8c +s_mov_b32 s85, 0x3548 +s_mov_b32 s89, 0x2438 +s_cmov_b32 s85, 0x2c80 +s_cmov_b32 s89, 0x1c70 +s_add_u32 s66, s64, s82 +s_addc_u32 s67, s65, 0 +s_add_u32 s74, s64, s86 +s_addc_u32 s75, s65, 0 +s_add_u32 s68, s64, s83 +s_addc_u32 s69, s65, 0 +s_add_u32 s76, s64, s87 +s_addc_u32 s77, s65, 0 +s_add_u32 s70, s64, s84 +s_addc_u32 s71, s65, 0 +s_add_u32 s78, s64, s88 +s_addc_u32 s79, s65, 0 +s_add_u32 s72, s64, s85 +s_addc_u32 s73, s65, 0 +s_add_u32 s80, s64, s89 +s_addc_u32 s81, s65, 0 +s_mov_b32 s45, 0 +v_mov_b32 v4, 0 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[66:67], s[74:75] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x2b4 +s_setprio 2 +v_pk_fma_f16 v224, v34, s40, v228 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v225, v102, s40, v229 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v226, v170, s40, v230 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v227, v206, s40, v231 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 228, 290, 291, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 229, 358, 359, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 230, 426, 427, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 231, 462, 463, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v102, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v103, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v102, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v103, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6818 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[66:67], s[74:75] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +_v_pk_mul_f16__vop3p 224, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 225, 358, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 226, 426, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 462, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v34, v224 quad_perm:[1,0,3,2] +v_mov_b32 v102, v225 quad_perm:[1,0,3,2] +v_mov_b32 v170, v226 quad_perm:[1,0,3,2] +v_mov_b32 v206, v227 quad_perm:[1,0,3,2] +v_pk_fma_f16 v224, v34, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v102, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v170, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v206, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v34, v224 quad_perm:[2,3,0,1] +v_mov_b32 v102, v225 quad_perm:[2,3,0,1] +v_mov_b32 v170, v226 quad_perm:[2,3,0,1] +v_mov_b32 v206, v227 quad_perm:[2,3,0,1] +v_pk_fma_f16 v224, v34, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v102, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v170, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v206, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v34, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v102, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v103, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v102, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v103, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6720 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[68:69], s[76:77] +s_bitcmp1_b32 s45, 0 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v232, v208, s40, v236 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v233, v212, s40, v237 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v234, v216, s40, v238 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v235, v220, s40, v239 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 236, 464, 465, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 237, 468, 469, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 238, 472, 473, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 239, 476, 477, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v208, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v209, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v208, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v209, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v212, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v213, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v212, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v213, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6646 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[68:69], s[76:77] +s_bitcmp1_b32 s45, 0 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 232, 464, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 468, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 472, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 476, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v208, v232 quad_perm:[1,0,3,2] +v_mov_b32 v212, v233 quad_perm:[1,0,3,2] +v_mov_b32 v216, v234 quad_perm:[1,0,3,2] +v_mov_b32 v220, v235 quad_perm:[1,0,3,2] +v_pk_fma_f16 v232, v208, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v212, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v216, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v220, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v208, v232 quad_perm:[2,3,0,1] +v_mov_b32 v212, v233 quad_perm:[2,3,0,1] +v_mov_b32 v216, v234 quad_perm:[2,3,0,1] +v_mov_b32 v220, v235 quad_perm:[2,3,0,1] +v_pk_fma_f16 v232, v208, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v212, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v216, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v220, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v208, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v209, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v208, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v209, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v212, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v213, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v212, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v213, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6548 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[70:71], s[78:79] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x2bc +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_add_f16__vop3p 224, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 225, 308, 357, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 226, 291, 290, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 227, 359, 358, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v228, v16, s40, v169 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v52, s40, v205 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v35, s40, v170 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v231, v103, s40, v206 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v102, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v103, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v102, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v103, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6473 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[70:71], s[78:79] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_mul_f16__vop3p 224, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 225, 357, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 226, 290, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 358, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v224 quad_perm:[1,0,3,2] +v_mov_b32 v101, v225 quad_perm:[1,0,3,2] +v_mov_b32 v34, v226 quad_perm:[1,0,3,2] +v_mov_b32 v102, v227 quad_perm:[1,0,3,2] +v_pk_fma_f16 v224, v17, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v101, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v34, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v102, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v224 quad_perm:[2,3,0,1] +v_mov_b32 v101, v225 quad_perm:[2,3,0,1] +v_mov_b32 v34, v226 quad_perm:[2,3,0,1] +v_mov_b32 v102, v227 quad_perm:[2,3,0,1] +v_pk_fma_f16 v224, v17, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v101, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v34, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v102, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v34, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v35, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v102, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v103, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v102, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v103, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6374 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[72:73], s[80:81] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_add_f16__vop3p 232, 467, 466, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 233, 471, 470, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 234, 465, 464, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 235, 469, 468, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v236, v211, s40, v218 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v215, s40, v222 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v209, s40, v216 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v239, v213, s40, v220 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v208, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v209, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v208, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v209, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v212, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v213, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v212, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v213, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6299 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[72:73], s[80:81] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_mul_f16__vop3p 232, 466, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 470, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 464, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 468, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v210, v232 quad_perm:[1,0,3,2] +v_mov_b32 v214, v233 quad_perm:[1,0,3,2] +v_mov_b32 v208, v234 quad_perm:[1,0,3,2] +v_mov_b32 v212, v235 quad_perm:[1,0,3,2] +v_pk_fma_f16 v232, v210, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v214, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v208, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v212, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v210, v232 quad_perm:[2,3,0,1] +v_mov_b32 v214, v233 quad_perm:[2,3,0,1] +v_mov_b32 v208, v234 quad_perm:[2,3,0,1] +v_mov_b32 v212, v235 quad_perm:[2,3,0,1] +v_pk_fma_f16 v232, v210, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v214, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v208, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v212, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v208, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v209, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v208, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v209, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v212, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v213, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v212, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v213, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6200 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[66:67], s[74:75] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x2b4 +s_setprio 2 +v_pk_fma_f16 v224, v169, s40, v228 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v225, v205, s40, v229 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v226, v170, s40, v230 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v227, v206, s40, v231 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 228, 425, 392, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 229, 461, 460, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 230, 426, 427, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 231, 462, 463, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v169, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v136, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v169, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v136, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v205, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v204, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v205, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v204, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6126 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[66:67], s[74:75] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x12c +s_setprio 2 +_v_pk_mul_f16__vop3p 224, 425, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 225, 461, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 226, 426, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 462, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v169, v224 quad_perm:[1,0,3,2] +v_mov_b32 v205, v225 quad_perm:[1,0,3,2] +v_mov_b32 v170, v226 quad_perm:[1,0,3,2] +v_mov_b32 v206, v227 quad_perm:[1,0,3,2] +v_pk_fma_f16 v224, v169, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v205, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v170, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v206, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v169, v224 quad_perm:[2,3,0,1] +v_mov_b32 v205, v225 quad_perm:[2,3,0,1] +v_mov_b32 v170, v226 quad_perm:[2,3,0,1] +v_mov_b32 v206, v227 quad_perm:[2,3,0,1] +v_pk_fma_f16 v224, v169, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v205, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v170, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v206, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v169, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v136, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v169, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v136, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v170, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v171, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v205, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v204, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v205, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v204, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v206, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v207, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 6028 +s_mov_b32 s56, 0x18c +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[68:69], s[76:77] +s_bitcmp1_b32 s45, 0 +s_cselect_b32 s56, s56, 0x2b8 +s_setprio 2 +v_pk_fma_f16 v232, v218, s40, v236 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v233, v222, s40, v237 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v234, v216, s40, v238 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v235, v220, s40, v239 op_sel:[0,0,0] op_sel_hi:[1,0,1] +_v_pk_add_f16__vop3p 236, 474, 475, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 237, 478, 479, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 238, 472, 473, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 239, 476, 477, 0x0, 0x3, 0x1, 0x1 +buffer_load_d16_b16 v218, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v219, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v218, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v219, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v222, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v223, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v222, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v223, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5954 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[64:65], s[68:69], s[76:77] +s_bitcmp1_b32 s45, 0 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +_v_pk_mul_f16__vop3p 232, 474, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 478, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 472, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 476, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v218, v232 quad_perm:[1,0,3,2] +v_mov_b32 v222, v233 quad_perm:[1,0,3,2] +v_mov_b32 v216, v234 quad_perm:[1,0,3,2] +v_mov_b32 v220, v235 quad_perm:[1,0,3,2] +v_pk_fma_f16 v232, v218, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v222, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v216, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v220, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v218, v232 quad_perm:[2,3,0,1] +v_mov_b32 v222, v233 quad_perm:[2,3,0,1] +v_mov_b32 v216, v234 quad_perm:[2,3,0,1] +v_mov_b32 v220, v235 quad_perm:[2,3,0,1] +v_pk_fma_f16 v232, v218, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v222, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v216, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v220, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v218, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v219, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v218, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v219, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v216, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v217, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v222, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v223, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v222, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v223, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v220, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v221, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5856 +s_mov_b32 s56, 0x190 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[70:71], s[78:79] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x2bc +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_add_f16__vop3p 224, 272, 273, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 225, 308, 357, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 226, 392, 425, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 227, 460, 461, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v228, v16, s40, v34 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v52, s40, v102 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v136, s40, v170 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v231, v204, s40, v206 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v169, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v136, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v169, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v136, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v205, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v204, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v205, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v204, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5781 +_s_mov_b32__sop1_lit 56, 0x4 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[70:71], s[78:79] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0x130 +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_mul_f16__vop3p 224, 273, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 225, 357, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 226, 425, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 461, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v17, v224 quad_perm:[1,0,3,2] +v_mov_b32 v101, v225 quad_perm:[1,0,3,2] +v_mov_b32 v169, v226 quad_perm:[1,0,3,2] +v_mov_b32 v205, v227 quad_perm:[1,0,3,2] +v_pk_fma_f16 v224, v17, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v101, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v169, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v205, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v17, v224 quad_perm:[2,3,0,1] +v_mov_b32 v101, v225 quad_perm:[2,3,0,1] +v_mov_b32 v169, v226 quad_perm:[2,3,0,1] +v_mov_b32 v205, v227 quad_perm:[2,3,0,1] +v_pk_fma_f16 v224, v17, v15, v224 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v225, v101, v15, v225 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v226, v169, v15, v226 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v227, v205, v15, v227 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v169, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v136, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v17, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v16, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v169, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v136, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v205, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v204, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v101, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v52, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v205, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v204, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5682 +s_mov_b32 s56, 0xffffebf0 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[72:73], s[80:81] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0xffffed18 +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_add_f16__vop3p 232, 467, 466, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 233, 471, 470, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 234, 475, 474, 0x0, 0x3, 0x1, 0x1 +_v_pk_add_f16__vop3p 235, 479, 478, 0x0, 0x3, 0x1, 0x1 +v_pk_fma_f16 v236, v211, s40, v208 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v215, s40, v212 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v219, s40, v216 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v239, v223, s40, v220 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v218, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v219, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v218, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v219, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v222, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v223, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v222, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v223, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5607 +s_mov_b32 s56, 0xffffea64 +s_bitcmp1_b32 s45, 2 +s_cselect_b64 s[64:65], s[72:73], s[80:81] +s_bitcmp1_b32 s45, 1 +s_cselect_b32 s56, s56, 0xffffeb8c +s_setprio 2 +s_waitcnt vmcnt(16) +_v_pk_mul_f16__vop3p 232, 466, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 470, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 474, 271, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 478, 271, 0x0, 0x1, 0x0, 0x0 +v_mov_b32 v210, v232 quad_perm:[1,0,3,2] +v_mov_b32 v214, v233 quad_perm:[1,0,3,2] +v_mov_b32 v218, v234 quad_perm:[1,0,3,2] +v_mov_b32 v222, v235 quad_perm:[1,0,3,2] +v_pk_fma_f16 v232, v210, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v214, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v218, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v222, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v210, v232 quad_perm:[2,3,0,1] +v_mov_b32 v214, v233 quad_perm:[2,3,0,1] +v_mov_b32 v218, v234 quad_perm:[2,3,0,1] +v_mov_b32 v222, v235 quad_perm:[2,3,0,1] +v_pk_fma_f16 v232, v210, v15, v232 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v233, v214, v15, v233 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v234, v218, v15, v234 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v235, v222, v15, v235 op_sel:[0,1,0] op_sel_hi:[1,1,1] +buffer_load_d16_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v218, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v219, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v210, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v211, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v218, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v219, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_b16 v222, v8, s[36:39], 0 idxen +buffer_load_d16_b16 v223, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +buffer_load_d16_hi_b16 v214, v7, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v215, v9, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v222, v8, s[36:39], 0 idxen +buffer_load_d16_hi_b16 v223, v10, s[36:39], 0 idxen +s_sub_u32 s15, s15, 1 +s_cselect_b32 s39, 0, s39 +s_add_u32 s36, s36, s59 +s_addc_u32 s37, s37, 0 +s_swappc_b64 s[64:65], s[64:65] +s_branch 5508 +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 104, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 105, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 106, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 107, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 108, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 109, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 110, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 111, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 104, 360, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 105, 361, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 106, 362, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 107, 363, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 108, 364, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 109, 365, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 110, 366, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 111, 367, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v104, v44, 0.5, v104 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v45, 0.5, v105 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v46, 0.5, v106 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v47, 0.5, v107 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v48, 0.5, v108 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v49, 0.5, v109 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v50, 0.5, v110 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v51, 0.5, v111 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v137, v44, -1.0, v104 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v138, v45, -1.0, v105 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v139, v46, -1.0, v106 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v140, v47, -1.0, v107 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v141, v48, -1.0, v108 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v142, v49, -1.0, v109 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v143, v50, -1.0, v110 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v144, v51, -1.0, v111 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v172, v61 +v_mov_b32 v173, v62 +v_mov_b32 v174, v63 +v_mov_b32 v175, v64 +v_mov_b32 v176, v65 +v_mov_b32 v177, v66 +v_mov_b32 v178, v67 +v_mov_b32 v179, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v12, v14, v3, s[54:55] +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +v_mov_b32 v85, v36 +v_mov_b32 v86, v37 +v_mov_b32 v87, v38 +v_mov_b32 v88, v39 +v_mov_b32 v89, v40 +v_mov_b32 v90, v41 +v_mov_b32 v91, v42 +v_mov_b32 v92, v43 +_v_pk_add_f16__vop3p 120, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 121, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 122, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 123, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 124, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 125, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 126, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 127, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 120, 376, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 121, 377, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 122, 378, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 123, 379, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 124, 380, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 125, 381, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 126, 382, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 127, 383, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v120, v44, 0.5, v120 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v121, v45, 0.5, v121 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v122, v46, 0.5, v122 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v123, v47, 0.5, v123 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v124, v48, 0.5, v124 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v125, v49, 0.5, v125 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v126, v50, 0.5, v126 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v127, v51, 0.5, v127 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v153, v44, -1.0, v120 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v154, v45, -1.0, v121 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v155, v46, -1.0, v122 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v156, v47, -1.0, v123 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v157, v48, -1.0, v124 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v158, v49, -1.0, v125 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v159, v50, -1.0, v126 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v160, v51, -1.0, v127 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v188, v61 +v_mov_b32 v189, v62 +v_mov_b32 v190, v63 +v_mov_b32 v191, v64 +v_mov_b32 v192, v65 +v_mov_b32 v193, v66 +v_mov_b32 v194, v67 +v_mov_b32 v195, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 112, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 113, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 114, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 115, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 116, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 117, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 118, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 119, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 112, 368, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 113, 369, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 114, 370, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 115, 371, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 116, 372, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 117, 373, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 118, 374, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 119, 375, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v112, v44, 0.5, v112 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v45, 0.5, v113 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v46, 0.5, v114 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v47, 0.5, v115 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v48, 0.5, v116 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v49, 0.5, v117 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v50, 0.5, v118 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v51, 0.5, v119 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v145, v44, -1.0, v112 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v146, v45, -1.0, v113 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v147, v46, -1.0, v114 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v148, v47, -1.0, v115 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v149, v48, -1.0, v116 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v150, v49, -1.0, v117 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v151, v50, -1.0, v118 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v152, v51, -1.0, v119 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v180, v61 +v_mov_b32 v181, v62 +v_mov_b32 v182, v63 +v_mov_b32 v183, v64 +v_mov_b32 v184, v65 +v_mov_b32 v185, v66 +v_mov_b32 v186, v67 +v_mov_b32 v187, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v12, v14, v3, s[54:55] +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_mov_b32 v93, v36 +v_mov_b32 v94, v37 +v_mov_b32 v95, v38 +v_mov_b32 v96, v39 +v_mov_b32 v97, v40 +v_mov_b32 v98, v41 +v_mov_b32 v99, v42 +v_mov_b32 v100, v43 +_v_pk_add_f16__vop3p 128, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 129, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 130, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 131, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 132, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 133, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 134, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 135, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 128, 384, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 129, 385, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 130, 386, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 131, 387, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 132, 388, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 133, 389, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 134, 390, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 135, 391, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v128, v44, 0.5, v128 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v129, v45, 0.5, v129 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v130, v46, 0.5, v130 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v131, v47, 0.5, v131 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v132, v48, 0.5, v132 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v133, v49, 0.5, v133 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v134, v50, 0.5, v134 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v135, v51, 0.5, v135 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v44, -1.0, v128 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v45, -1.0, v129 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v46, -1.0, v130 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v164, v47, -1.0, v131 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v165, v48, -1.0, v132 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v166, v49, -1.0, v133 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v167, v50, -1.0, v134 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v168, v51, -1.0, v135 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v196, v61 +v_mov_b32 v197, v62 +v_mov_b32 v198, v63 +v_mov_b32 v199, v64 +v_mov_b32 v200, v65 +v_mov_b32 v201, v66 +v_mov_b32 v202, v67 +v_mov_b32 v203, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_mov_b32 v69, v36 +v_mov_b32 v70, v37 +v_mov_b32 v71, v38 +v_mov_b32 v72, v39 +v_mov_b32 v73, v40 +v_mov_b32 v74, v41 +v_mov_b32 v75, v42 +v_mov_b32 v76, v43 +_v_pk_add_f16__vop3p 104, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 105, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 106, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 107, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 108, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 109, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 110, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 111, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 104, 360, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 105, 361, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 106, 362, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 107, 363, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 108, 364, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 109, 365, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 110, 366, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 111, 367, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v104, v44, 0.5, v104 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v105, v45, 0.5, v105 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v106, v46, 0.5, v106 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v107, v47, 0.5, v107 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v108, v48, 0.5, v108 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v109, v49, 0.5, v109 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v110, v50, 0.5, v110 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v111, v51, 0.5, v111 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v137, v44, -1.0, v104 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v138, v45, -1.0, v105 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v139, v46, -1.0, v106 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v140, v47, -1.0, v107 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v141, v48, -1.0, v108 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v142, v49, -1.0, v109 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v143, v50, -1.0, v110 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v144, v51, -1.0, v111 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v172, v61 +v_mov_b32 v173, v62 +v_mov_b32 v174, v63 +v_mov_b32 v175, v64 +v_mov_b32 v176, v65 +v_mov_b32 v177, v66 +v_mov_b32 v178, v67 +v_mov_b32 v179, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +v_mov_b32 v85, v36 +v_mov_b32 v86, v37 +v_mov_b32 v87, v38 +v_mov_b32 v88, v39 +v_mov_b32 v89, v40 +v_mov_b32 v90, v41 +v_mov_b32 v91, v42 +v_mov_b32 v92, v43 +_v_pk_add_f16__vop3p 120, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 121, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 122, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 123, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 124, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 125, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 126, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 127, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 120, 376, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 121, 377, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 122, 378, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 123, 379, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 124, 380, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 125, 381, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 126, 382, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 127, 383, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v120, v44, 0.5, v120 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v121, v45, 0.5, v121 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v122, v46, 0.5, v122 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v123, v47, 0.5, v123 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v124, v48, 0.5, v124 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v125, v49, 0.5, v125 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v126, v50, 0.5, v126 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v127, v51, 0.5, v127 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v153, v44, -1.0, v120 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v154, v45, -1.0, v121 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v155, v46, -1.0, v122 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v156, v47, -1.0, v123 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v157, v48, -1.0, v124 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v158, v49, -1.0, v125 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v159, v50, -1.0, v126 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v160, v51, -1.0, v127 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v188, v61 +v_mov_b32 v189, v62 +v_mov_b32 v190, v63 +v_mov_b32 v191, v64 +v_mov_b32 v192, v65 +v_mov_b32 v193, v66 +v_mov_b32 v194, v67 +v_mov_b32 v195, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v1, vcc +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_mov_b32 v77, v36 +v_mov_b32 v78, v37 +v_mov_b32 v79, v38 +v_mov_b32 v80, v39 +v_mov_b32 v81, v40 +v_mov_b32 v82, v41 +v_mov_b32 v83, v42 +v_mov_b32 v84, v43 +_v_pk_add_f16__vop3p 112, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 113, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 114, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 115, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 116, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 117, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 118, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 119, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 112, 368, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 113, 369, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 114, 370, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 115, 371, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 116, 372, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 117, 373, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 118, 374, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 119, 375, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v112, v44, 0.5, v112 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v113, v45, 0.5, v113 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v114, v46, 0.5, v114 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v115, v47, 0.5, v115 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v116, v48, 0.5, v116 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v117, v49, 0.5, v117 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v118, v50, 0.5, v118 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v119, v51, 0.5, v119 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v145, v44, -1.0, v112 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v146, v45, -1.0, v113 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v147, v46, -1.0, v114 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v148, v47, -1.0, v115 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v149, v48, -1.0, v116 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v150, v49, -1.0, v117 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v151, v50, -1.0, v118 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v152, v51, -1.0, v119 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v180, v61 +v_mov_b32 v181, v62 +v_mov_b32 v182, v63 +v_mov_b32 v183, v64 +v_mov_b32 v184, v65 +v_mov_b32 v185, v66 +v_mov_b32 v186, v67 +v_mov_b32 v187, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_mov_b32 v93, v36 +v_mov_b32 v94, v37 +v_mov_b32 v95, v38 +v_mov_b32 v96, v39 +v_mov_b32 v97, v40 +v_mov_b32 v98, v41 +v_mov_b32 v99, v42 +v_mov_b32 v100, v43 +_v_pk_add_f16__vop3p 128, 292, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 129, 293, 318, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 130, 294, 319, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 131, 295, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 132, 296, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 133, 297, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 134, 298, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 135, 299, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 128, 384, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 129, 385, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 130, 386, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 131, 387, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 132, 388, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 133, 389, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 134, 390, 240, 0x0, 0x1, 0x0, 0x0 +_v_pk_mul_f16__vop3p 135, 391, 240, 0x0, 0x1, 0x0, 0x0 +v_pk_fma_f16 v128, v44, 0.5, v128 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v129, v45, 0.5, v129 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v130, v46, 0.5, v130 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v131, v47, 0.5, v131 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v132, v48, 0.5, v132 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v133, v49, 0.5, v133 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v134, v50, 0.5, v134 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v135, v51, 0.5, v135 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v161, v44, -1.0, v128 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v162, v45, -1.0, v129 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v163, v46, -1.0, v130 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v164, v47, -1.0, v131 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v165, v48, -1.0, v132 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v166, v49, -1.0, v133 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v167, v50, -1.0, v134 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_pk_fma_f16 v168, v51, -1.0, v135 op_sel:[0,0,0] op_sel_hi:[1,0,1] +v_mov_b32 v196, v61 +v_mov_b32 v197, v62 +v_mov_b32 v198, v63 +v_mov_b32 v199, v64 +v_mov_b32 v200, v65 +v_mov_b32 v201, v66 +v_mov_b32 v202, v67 +v_mov_b32 v203, v68 +s_mov_b32 exec_hi, -1 +v_cndmask_b32 v11, v13, v2, vcc +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[172:179], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[180:187], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[104:111], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[112:119], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[137:144], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[145:152], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_cndmask_b32 v12, v14, v3, s[54:55] +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[85:92], v[36:43], v[18:21] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[93:100], v[36:43], v[18:21] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[188:195], v[61:68], v[30:33] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[196:203], v[61:68], v[30:33] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[120:127], v[36:43], v[22:25] op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[128:135], v[36:43], v[22:25] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[153:160], v[61:68], v[26:29] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[161:168], v[61:68], v[26:29] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[172:179], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[180:187], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[104:111], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[112:119], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[137:144], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[145:152], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_cndmask_b32 v12, v14, v3, s[54:55] +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +s_barrier +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[85:92], v[36:43], v[18:21] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[93:100], v[36:43], v[18:21] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[188:195], v[61:68], v[30:33] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[196:203], v[61:68], v[30:33] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[120:127], v[36:43], v[22:25] op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[128:135], v[36:43], v[22:25] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[153:160], v[61:68], v[26:29] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[161:168], v[61:68], v[26:29] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v1, v[18:21] offset:4672 +ds_store_b128 v1, v[30:33] offset:16 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[172:179], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[180:187], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[104:111], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[112:119], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[137:144], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[145:152], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_sub_u32 s23, s23, s34 +s_cselect_b64 s[56:57], 0, s[56:57] +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s41, v4 +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[85:92], v[36:43], v[18:21] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[93:100], v[36:43], v[18:21] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[188:195], v[61:68], v[30:33] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[196:203], v[61:68], v[30:33] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[120:127], v[36:43], v[22:25] op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[128:135], v[36:43], v[22:25] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[153:160], v[61:68], v[26:29] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[161:168], v[61:68], v[26:29] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v1, vcc +s_barrier +s_bitcmp1_b32 s41, 1 +s_addc_u32 s45, s45, s45 +s_bitcmp1_b32 s41, 0 +s_cselect_b32 s35, 0, s35 +s_cselect_b32 s34, 1, s34 +s_lshr_b32 s39, s41, 16 +ds_load_b128 v[7:10], v5 offset:37120 +ds_load_b32 v4, v6 offset:39168 +s_bitcmp1_b32 s41, 1 +s_cselect_b32 s59, s49, s53 +s_cselect_b64 s[36:37], s[16:17], s[18:19] +s_mul_i32 s56, s39, s59 +s_mul_hi_u32 s57, s39, s59 +s_add_u32 s15, s39, 1 +s_sub_u32 s15, s22, s15 +s_cselect_b32 s39, 0, s35 +s_add_u32 s36, s36, s56 +s_addc_u32 s37, s37, s57 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_waitcnt lgkmcnt(10) +s_swappc_b64 s[64:65], s[64:65] +ds_store_b128 v2, v[18:21] offset:13952 +ds_store_b128 v2, v[30:33] offset:9296 +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 s[54:55], -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[69:76], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[77:84], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[172:179], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[180:187], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[104:111], v[36:43], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[112:119], v[36:43], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[137:144], v[61:68], 0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[145:152], v[61:68], 0 op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_cndmask_b32 v12, v14, v3, s[54:55] +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:27840 +ds_load_b128 v[40:43], v11 offset:30144 +ds_load_b128 v[44:47], v11 offset:32512 +ds_load_b128 v[48:51], v11 offset:34816 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[224:227] offset:18560 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:27856 +ds_load_b128 v[57:60], v11 offset:30160 +ds_load_b128 v[61:64], v11 offset:32528 +ds_load_b128 v[65:68], v11 offset:34832 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[228:231] offset:19136 +s_swappc_b64 s[64:65], s[64:65] +s_setprio 1 +s_ashr_i32 s57, s56, 31 +s_add_u32 s64, s64, s56 +s_addc_u32 s65, s65, s57 +s_bitcmp1_b32 s45, 1 +s_cselect_b64 vcc, -1, 0 +s_mov_b32 exec_hi, 0 +s_waitcnt lgkmcnt(0) +_v_pk_add_f16__vop3p 36, 292, 309, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 293, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 38, 294, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 39, 295, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 40, 296, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 41, 297, 314, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 42, 298, 315, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 43, 299, 316, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 61, 317, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 318, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 319, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 320, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 321, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 322, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 323, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 324, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[18:21], v[85:92], v[36:43], v[18:21] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[18:21], v[93:100], v[36:43], v[18:21] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[30:33], v[188:195], v[61:68], v[30:33] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[30:33], v[196:203], v[61:68], v[30:33] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 36, 300, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 37, 301, 310, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 302, 311, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 39, 303, 312, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 40, 304, 313, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 41, 305, 314, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 42, 306, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 43, 307, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 61, 309, 300, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 62, 310, 301, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 63, 311, 302, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 64, 312, 303, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 65, 313, 304, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 66, 314, 305, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 67, 315, 306, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 68, 316, 307, 0x0, 0x3, 0x2, 0x2 +v_wmma_f16_16x16x16_f16 v[22:25], v[120:127], v[36:43], v[22:25] op_sel:[0,0,0] op_sel_hi:[1,1,0] +s_mov_b32 exec_hi, -1 +v_wmma_f16_16x16x16_f16 v[22:25], v[128:135], v[36:43], v[22:25] op_sel:[0,0,1] op_sel_hi:[1,1,1] +v_wmma_f16_16x16x16_f16 v[26:29], v[153:160], v[61:68], v[26:29] op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_wmma_f16_16x16x16_f16 v[26:29], v[161:168], v[61:68], v[26:29] op_sel:[0,0,1] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 18, 274, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 280, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 281, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 278, 286, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 31, 279, 287, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 32, 280, 288, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 33, 281, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 18, 274, 282, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 19, 275, 283, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 20, 276, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 21, 277, 285, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 30, 286, 282, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 31, 287, 283, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 32, 288, 284, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 33, 289, 285, 0x0, 0x3, 0x2, 0x2 +v_cndmask_b32 v11, v13, v2, vcc +s_barrier +s_mov_b32 exec_hi, 0 +ds_load_b128 v[36:39], v11 offset:18560 +ds_load_b128 v[40:43], v11 offset:20864 +ds_load_b128 v[44:47], v11 offset:23232 +ds_load_b128 v[48:51], v11 offset:25536 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[232:235] offset:27840 +s_mov_b32 exec_hi, 0 +ds_load_b128 v[53:56], v11 offset:18576 +ds_load_b128 v[57:60], v11 offset:20880 +ds_load_b128 v[61:64], v11 offset:23248 +ds_load_b128 v[65:68], v11 offset:25552 +s_mov_b32 exec_hi, -1 +ds_store_b128 v12, v[236:239] offset:28416 +s_swappc_b64 s[64:65], s[64:65] +v_bfe_u32 v21, v0, 6, 1 +v_and_b32 v16, 63, v0 +v_cmp_eq_u32 vcc, v21, 1 +v_cndmask_b32 v23, 0, 0x400, vcc +v_cndmask_b32 v21, 0, 0x400, vcc +v_cndmask_b32 v22, 0, 0x100, vcc +v_lshl_add_u32 v14, v16, 3, v23 +v_lshl_add_u32 v17, v16, 2, v22 +v_lshl_add_u32 v18, v16, 2, 0 +v_lshl_add_u32 v16, v16, 4, v21 +s_cmp_eq_u64 s[30:31], 0 +s_cselect_b32 s91, 0, 0x11014000 +s_and_b32 s31, s31, 0xffff +s_add_u32 s31, s31, 0x20000 +s_mov_b64 s[88:89], s[30:31] +s_mov_b32 s90, 0x80000000 +v_and_b32 v21, v0, 63 +v_lshlrev_b32 v21, 1, v21 +v_cmp_lt_u32 vcc, v21, s12 +v_add_nc_u32 v22, v21, 1 +v_cndmask_b32 v21, 0x80000000, v21, vcc +v_cmp_lt_u32 vcc, v22, s12 +v_cndmask_b32 v22, 0x80000000, v22, vcc +buffer_load_d16_b16 v23, v21, s[88:91], 0 idxen +buffer_load_d16_hi_b16 v23, v22, s[88:91], 0 idxen +s_waitcnt vmcnt(0) +v_readlane_b32 s56, v23, 0 +v_readlane_b32 s57, v23, 1 +v_readlane_b32 s59, v23, 2 +v_readlane_b32 s64, v23, 3 +v_readlane_b32 s65, v23, 4 +v_readlane_b32 s66, v23, 5 +v_readlane_b32 s67, v23, 6 +v_readlane_b32 s68, v23, 7 +s_bfe_u32 s88, s58, 0x80000 +s_cmp_eq_u32 s88, 2 +s_cbranch_scc1 20 +s_cmp_eq_u32 s88, 0 +s_cselect_b32 s32, 1.0, s32 +v_cvt_f16_f32 v21, s32 +v_readfirstlane_b32 s32, v21 +v_cvt_f16_f32 v21, s33 +v_readfirstlane_b32 s33, v21 +_v_cmp_gt_f16__vop3_s_lit 106, 32, 0x3c00, 0x0, 0x0 +s_pack_ll_b32_b16 s32, s32, s32 +s_pack_ll_b32_b16 s33, s33, s33 +s_cmp_eq_u32 s88, 3 +s_cbranch_scc1 10 +s_cbranch_vccnz 3 +s_mov_b32 s84, 0x564c +s_branch 8 +s_mov_b32 s84, 0x59e4 +s_branch 5 +s_mov_b32 s84, 0x5d7c +s_branch 2 +s_mov_b32 s84, 0x6394 +s_add_u32 s86, s6, 0x4b18 +s_addc_u32 s87, s7, 0 +s_mov_b32 s82, 0xbc00c000 +s_mov_b32 s40, 0x10000 +s_mov_b32 s41, 0x30002 +s_mov_b32 s45, 0x10000 +v_readfirstlane_b32 s88, v0 +s_and_b32 null, 64, s88 +s_cmov_b32 s82, 0x3c00c000 +s_cmov_b32 s40, 0x20003 +s_cmov_b32 s41, 1 +s_cmov_b32 s45, 1 +s_and_b32 s21, s21, 0xffff +s_add_u32 s21, s21, 0x20000 +s_lshl_b32 s80, s51, 1 +s_lshl_b32 s81, s52, 1 +s_mov_b64 s[72:73], s[20:21] +s_mov_b32 s74, 0x80000000 +s_mov_b32 s75, 0 +s_sub_u32 s89, s25, 1 +s_bitcmp1_b32 s14, 1 +s_cselect_b32 s89, s89, 0 +s_cselect_b32 s88, -1, 1 +s_sub_u32 s91, s24, 1 +s_bitcmp1_b32 s14, 0 +s_cselect_b32 s91, s91, 0 +s_cselect_b32 s90, -1, 1 +v_bfe_u32 v24, v0, 6, 1 +v_bfe_u32 v25, v0, 4, 1 +v_bfe_u32 v21, v0, 5, 1 +v_lshl_add_u32 v24, v24, 2, 0 +v_lshl_add_u32 v25, v25, 3, v24 +v_bfe_u32 v23, v0, 2, 2 +v_bfe_u32 v24, v0, 3, 1 +v_xor_b32 v22, v0, v0 quad_perm:[0,0,3,1] +v_lshl_add_u32 v21, v21, 1, v25 +v_xor_b32 v23, v23, v24 +v_add_nc_u32 v24, v21, 1 +v_mad_i32_i16 v19, v23, s88, s89 op_sel:[0,0,0,0] +v_mad_i32_i16 v25, v22, s90, s91 op_sel:[0,0,0,0] +v_mad_u32_u16 v19, v25, s48, v19 op_sel:[0,0,0,0] +v_cmp_lt_u32 vcc, v23, s25 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_cmp_lt_u32 vcc, v22, s24 +v_cndmask_b32 v19, 0x80000000, v19, vcc +v_mad_u32_u24 v20, v24, s46, v19 +v_mad_u32_u24 v19, v21, s46, v19 +v_cmp_lt_u32 vcc, v24, s12 +v_cndmask_b32 v20, 0x80000000, v20, vcc +v_cmp_lt_u32 vcc, v21, s12 +v_cndmask_b32 v19, 0x80000000, v19, vcc +s_add_u32 s89, s28, 1 +s_lshr_b32 s89, s89, 1 +s_lshl_b32 s90, s89, 1 +s_add_u32 s91, s29, 1 +s_lshr_b32 s91, s91, 1 +s_lshl1_add_u32 s91, s91, 2 +s_pack_ll_b32_b16 s22, s91, s89 +s_pack_ll_b32_b16 s34, s11, s10 +s_sub_u32 s35, s90, s26 +s_sub_u32 s88, s91, s27 +s_pack_ll_b32_b16 s35, s88, s35 +s_pack_ll_b32_b16 s37, s29, s28 +s_sub_u32 s88, s91, 1 +s_pack_ll_b32_b16 s38, s88, s90 +v_lshrrev_b32 v24, 16, s22 +v_bfi_b32 v25, 0xffff, s22, 0 +v_and_b32 v27, 1, v0 +v_bfe_u32 v33, v0, 6, 1 +v_and_b32 v22, 63, v0 +v_mad_u32_u16 v28, 0x7c, s1, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v33, 2, s5, v33 op_sel:[0,0,0,0] +v_mad_u32_u16 v26, v24, v25, 0 op_sel:[0,0,0,0] +v_cmp_eq_u32 vcc, 0, v27 +v_cndmask_b32 v34, v26, v25, vcc +v_mad_u32_u16 v23, 62, v33, v22 op_sel:[0,0,0,0] +v_cndmask_b32 v23, v28, v23, vcc +v_clz_i32_u32 v40, v34 +v_lshlrev_b32 v41, v40, v34 +v_and_b32 v39, 0xffffff00, v41 +v_cmp_eq_u32 vcc, 0x80000000, v41 +v_cvt_f32_u32 v39, v39 +v_rcp_f32 v35, v39 +v_sub_co_ci_u32 v36, vcc, 32, v40, vcc +v_cvt_f32_ubyte0 v40, v41 +v_fma_f32 v39, v39, v35, -1.0 +v_fma_f32 v39, v40, v35, v39 +v_fmaak_f32 v39, v39, v35, 0x9f000000 +v_mul_f32 v39, 0x5f800000, v39 +v_mov_b32 v40, 0 +v_cvt_floor_i32_f32 v39, -v39 +v_lshl_add_u32 v35, v35, 9, v39 +v_mad_u64_u32 v[40:41], vcc, v41, v35, v[40:41] +v_sub_co_ci_u32 v35, vcc, v35, -1, vcc +v_mov_b32 v38, v36 quad_perm:[1,1,1,1] +v_mov_b32 v36, v36 quad_perm:[0,0,0,0] +v_mov_b32 v37, v35 quad_perm:[1,1,1,1] +v_mov_b32 v35, v35 quad_perm:[0,0,0,0] +v_mul_hi_u32 v39, v23, v35 +v_add_co_u32 v21, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v36 +v_cndmask_b32 v21, v21, v39, vcc +v_alignbit_b32 v21, v39, v21, v36 +v_mul_hi_u32 v39, v23, v37 +v_add_co_u32 v4, vcc, v39, v23 +v_add_co_ci_u32 v39, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v38 +v_cndmask_b32 v4, v4, v39, vcc +v_alignbit_b32 v4, v39, v4, v38 +v_mad_u32_u16 v32, v21, v25, 0 op_sel:[0,0,0,0] +v_mad_u32_u16 v31, v4, v24, 0 op_sel:[0,0,0,0] +v_sub_nc_u32 v32, v23, v32 +v_sub_nc_u32 v31, v21, v31 +v_readlane_b32 s92, v32, 1 +v_sub_nc_u32 v32, v32, v25 +v_readlane_b32 s23, v31, 1 +v_sub_nc_u32 v31, v31, v24 +v_readlane_b32 s15, v4, 1 +v_sub_nc_u32 v4, v4, s8 +s_lshl_b32 s23, s23, 16 +s_and_b32 s92, s92, 0xffff +s_add_u32 s23, s23, s92 +v_mov_b32 v32, v32 quad_perm:[0,0,2,2] +v_mov_b32 v31, v31 quad_perm:[0,0,2,2] +v_mov_b32 v4, v4 quad_perm:[0,0,2,2] +v_add_co_u32 v32, vcc, v32, v27 +v_cndmask_b32 v30, 0, v25, vcc +v_add_co_ci_u32 v31, vcc, v31, 0, vcc +v_cndmask_b32 v29, 0, v24, vcc +v_add_co_ci_u32 v4, vcc, v4, 0, vcc +v_min_u32 v27, v22, 63 +v_sub_nc_u32 v32, v32, v30 +v_sub_nc_u32 v31, v31, v29 +v_cmp_eq_u32 vcc, v22, v27 +v_lshlrev_b32 v5, 16, v31 +v_bfi_b32 v5, 0xffff, v32, v5 +v_add_nc_u32 v42, v4, s8 +v_med3_u32 v27, v22, 1, 62 +v_mul_lo_u32 v6, v42, s42 +v_mul_lo_u32 v11, v42, s50 +s_mul_i32 s36, s15, s42 +s_mul_i32 s39, s15, s50 +v_cndmask_b32 v6, 0x80000000, v6, vcc +v_cmp_eq_u32 vcc, v22, v27 +v_cndmask_b32 v11, 0x80000000, v11, vcc +v_cmp_ge_u32 s[54:55], v42, s8 +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +s_mov_b32 s49, 1 +s_lshl_b32 s53, s49, 9 +v_add_nc_u32 v15, s53, v14 +s_bfe_u32 s10, s58, 0x80008 +s_bfe_u32 s11, s58, 0x80010 +s_cmp_eq_u32 s11, 0 +s_cmov_b32 s26, 0 +s_cbranch_scc1 108 +s_add_u32 s11, s11, 0xffffff00 +s_add_u32 s60, s60, 0 +s_addc_u32 s61, s61, 0 +s_lshr_b32 s91, s13, 2 +s_or_b32 s91, s91, 0x21010000 +v_cmp_eq_u32 vcc, v0, 0x100 +s_cmp_eq_u64 vcc, 0 +s_cselect_b32 s91, 0, s91 +s_cselect_b32 s90, 0, 0x1010101 +s_sub_u32 s10, 0, s10 +s_mov_b64 s[88:89], s[60:61] +s_and_b32 s89, s89, 0xffff +s_or_b32 s89, s89, 0x40000 +s_and_b32 s29, s22, 0xffff +s_lshr_b32 s28, s22, 16 +s_lshr_b32 s29, s29, 1 +s_mul_i32 s27, s29, s28 +s_mul_i32 s27, s27, s8 +s_add_u32 s27, s27, 61 +v_writelane_b32 v22, 62, 0 +v_writelane_b32 v22, s1, 1 +v_writelane_b32 v22, 10, 2 +v_clz_i32_u32 v26, v22 +v_lshlrev_b32 v27, v26, v22 +v_and_b32 v28, 0xffffff00, v27 +v_cmp_eq_u32 vcc, 0x80000000, v27 +v_cvt_f32_u32 v28, v28 +v_rcp_f32 v24, v28 +v_sub_co_ci_u32 v25, vcc, 32, v26, vcc +v_cvt_f32_ubyte0 v26, v27 +v_fma_f32 v28, v28, v24, -1.0 +v_fma_f32 v28, v26, v24, v28 +v_fmaak_f32 v28, v28, v24, 0x9f000000 +v_mul_f32 v28, 0x5f800000, v28 +v_mov_b32 v26, 0 +v_cvt_floor_i32_f32 v28, -v28 +v_lshl_add_u32 v24, v24, 9, v28 +v_mad_u64_u32 v[26:27], vcc, v27, v24, v[26:27] +v_sub_co_ci_u32 v24, vcc, v24, -1, vcc +v_mul_hi_u32 v26, s27, v24 +v_add_co_u32 v23, vcc, v26, s27 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_mov_b32 v23, v23 quad_perm:[0,0,0,0] +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v22, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v22, v22, v26, vcc +v_alignbit_b32 v22, v26, v22, v25 +v_mov_b32 v22, v22 quad_perm:[1,1,1,1] +v_add_nc_u32 v23, v22, 9 +v_mul_hi_u32 v26, v23, v24 +v_add_co_u32 v23, vcc, v26, v23 +v_add_co_ci_u32 v26, vcc, 0, 0, vcc +v_cmp_eq_u32 vcc, 32, v25 +v_cndmask_b32 v23, v23, v26, vcc +v_alignbit_b32 v23, v26, v23, v25 +v_readlane_b32 s28, v22, 1 +v_readlane_b32 s29, v23, 2 +s_add_u32 s27, s9, 31 +s_lshr_b32 s27, s27, 5 +s_cmp_eq_u32 s27, 1 +s_cmov_b32 s29, 1 +s_add_u32 s26, s28, s29 +s_mul_i32 s26, s27, s26 +s_add_u32 s26, 4, s26 +s_sub_u32 s26, s26, 1 +s_mov_b32 s92, 0 +s_mov_b32 s93, 0 +s_mov_b32 s94, 0 +s_mov_b32 s95, 0 +s_mov_b32 s28, 0 +s_mov_b32 s27, 8 +s_cmp_gt_u32 s28, 0 +s_cbranch_scc1 4 +v_mov_b32 v58, v4 +v_mov_b32 v63, v5 +v_mov_b32 v225, v6 +v_mov_b32 v226, v11 +v_mov_b32 v4, v58 +v_mov_b32 v5, v63 +v_mov_b32 v6, v225 +v_mov_b32 v11, v226 +s_add_u32 s28, s28, 32 +s_cmp_ge_u32 s28, s9 +s_cmov_b32 s28, 0 +s_cselect_b32 s29, 6, 2 +s_cselect_b32 s96, 9, 0 +s_pack_lh_b32_b16 s29, s29, s27 +s_pack_ll_b32_b16 s96, s96, s28 +v_mov_b32 v224, s29 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v7, v19 +v_mov_b32 v8, v20 +v_mov_b32 v9, 0x80000000 +v_mov_b32 v10, 0x80000000 +v_mov_b32 v12, 0x80000000 +v_mov_b32 v13, 0x80000000 +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v224 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x4b18 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v245, v243 +v_mov_b32 v246, v244 +v_pk_fma_f16 v227, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v228, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 231, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[243:244], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_barrier +s_nop 15 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2827 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 1, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x4b1c +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v235, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v236, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 239, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v224, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x4b18 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v224 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_barrier +s_nop 15 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x4c7c +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_cmp_le_u32 s9, 32 +s_cselect_b32 s97, -1, 9 +s_sub_u32 s97, s97, 1 +s_cselect_b32 s29, s96, s29 +s_bitset0_b32 s29, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v44, v49, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v45, v50, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v46, v51, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v47, v52, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_mov_b32 v224, s29 +v_add_co_u32 v33, vcc, v5, s23 +v_pk_mad_u16 v23, v5, 0x20001, s35 +v_pk_mad_u16 v28, v5, 0x20001, s38 +_v_pk_min_u16__vop3p 22, 289, 261, 0x0, 0x3, 0x0, 0x0 +v_cndmask_b32 v43, 0, s42, vcc +v_cndmask_b32 v247, 0, s50, vcc +v_mad_u32_u16 v7, v23, 1, v6 op_sel:[0,0,0,0] +v_mad_u32_u16 v12, v28, 1, v11 op_sel:[0,0,0,0] +v_add3_u32 v6, v6, s36, v43 +v_add3_u32 v11, v11, s39, v247 +_v_pk_sub_u16__vop3p 22, 261, 278, 0x0, 0x3, 0x0, 0x0 +v_add_co_ci_u32 v4, s[54:55], v4, s15, vcc +v_cndmask_b32 v6, v6, 0x80000000, s[54:55] +v_cndmask_b32 v11, v11, 0x80000000, s[54:55] +v_cmp_lt_u16 vcc, v23, s34 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_cmp_lt_u16 vcc, v28, s37 +v_cndmask_b32 v12, 0x80000000, v12, vcc +_v_pk_ashrrev_i16__vop3p 22, 143, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_u16__vop3p 53, 279, 41, 0x1, 0x3, 0x0, 0x0 +_v_pk_add_u16__vop3p 48, 279, 40, 0x1, 0x3, 0x0, 0x0 +v_mad_u32_u16 v10, v53, s44, v7 op_sel:[1,0,0,0] +v_mad_u32_u16 v8, v48, s44, v7 op_sel:[1,0,0,0] +_v_pk_add_u16__vop3p 38, 284, 45, 0x1, 0x3, 0x0, 0x0 +_v_cmp_lt_u16__vop3 106, 53, 34, 0x3 +v_cndmask_b32 v10, 0x80000000, v10, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x3 +v_cndmask_b32 v8, 0x80000000, v8, vcc +v_mad_u32_u16 v13, v38, s52, v12 op_sel:[1,0,0,0] +v_mad_u32_u16 v9, v53, s44, v7 op_sel:[0,0,0,0] +v_mad_u32_u16 v7, v48, s44, v7 op_sel:[0,0,0,0] +_v_cmp_lt_u16__vop3 106, 38, 37, 0x3 +v_cndmask_b32 v13, 0x80000000, v13, vcc +_v_cmp_lt_u16__vop3 106, 53, 34, 0x2 +v_cndmask_b32 v9, 0x80000000, v9, vcc +_v_cmp_lt_u16__vop3 106, 48, 34, 0x2 +v_cndmask_b32 v7, 0x80000000, v7, vcc +v_mad_u32_u16 v12, v38, s52, v12 op_sel:[0,0,0,0] +v_pk_mad_u16 v5, v22, s22, v33 +_v_cmp_lt_u16__vop3 106, 38, 37, 0x2 +v_cndmask_b32 v12, 0x80000000, v12, vcc +v_add_co_u32 v22, vcc, v4, s8 +v_cndmask_b32 v224, s96, v224, vcc +s_setprio 0 +ds_load_b128 v[34:37], v3 +ds_store_b128 v16, v[7:10] offset:37120 +ds_load_b128 v[39:42], v3 offset:576 +ds_store_b32 v17, v224 offset:39168 +s_setprio 2 +s_sub_u32 s26, s26, 1 +s_cselect_b32 s91, 0x21010000, s91 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x4b18 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_add_nc_u32 v15, s53, v14 +v_mov_b32 v245, v243 +v_mov_b32 v246, v244 +v_pk_fma_f16 v227, v34, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v228, v35, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v229, v36, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v230, v37, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 34, 285, 290, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 35, 286, 291, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 36, 287, 292, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 37, 288, 293, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 231, 290, 295, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 291, 296, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 292, 297, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 293, 298, 0x0, 0x3, 0x0, 0x0 +s_setprio 0 +ds_load_b64 v[243:244], v15 offset:39680 +ds_load_b128 v[54:57], v3 offset:2304 +ds_load_b128 v[59:62], v3 offset:2880 +s_barrier +s_nop 15 +s_setprio 2 +s_mov_b32 s92, s93 +s_mov_b32 s93, s94 +s_mov_b32 s94, s95 +s_mov_b32 s95, s27 +s_bitcmp1_b32 s92, 0 +s_cbranch_scc1 2533 +s_sub_u32 s49, s49, 1 +s_cselect_b32 s49, 1, s49 +s_lshl_b32 s53, s49, 9 +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x4b1c +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s75, 0x11014000, 0 +s_sub_u32 s69, s12, 1 +s_cselect_b32 s75, 0, s75 +s_mov_b64 s[72:73], s[20:21] +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +s_barrier +v_pk_fma_f16 v235, v54, s82, v44 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v236, v55, s82, v45 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v237, v56, s82, v46 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v238, v57, s82, v47 op_sel:[0,1,0] op_sel_hi:[1,1,1] +_v_pk_add_f16__vop3p 54, 305, 310, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 55, 306, 311, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 56, 307, 312, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 57, 308, 313, 0x0, 0x3, 0x2, 0x2 +_v_pk_add_f16__vop3p 239, 310, 315, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 311, 316, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 312, 317, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 313, 318, 0x0, 0x3, 0x0, 0x0 +s_add_u32 s11, s11, 0x100 +s_cbranch_scc0 7 +s_bitset0_b32 s91, 23 +s_lshl_b64 exec, 1, s90 +buffer_store_b8 v0, off, s[88:91], s4 +s_mov_b64 exec, -1 +s_mul_i32 s11, s11, 0xffffff01 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 3 +s_bitset1_b32 s91, 23 +buffer_load_b32 v21, off, s[88:91], null glc +s_setprio 0 +s_nop 1 +ds_load_b128 v[24:27], v3 offset:9280 +ds_store_b64 v15, v[12:13] offset:39680 +ds_load_b128 v[29:32], v3 offset:9856 +ds_load_b32 v224, v18 offset:39168 +s_setprio 2 +s_bitcmp1_b32 s92, 2 +s_cselect_b32 s86, s84, 0x4b18 +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_swappc_b64 s[86:87], s[86:87] +s_waitcnt lgkmcnt(0) +v_readfirstlane_b32 s27, v224 +v_pk_fma_f16 v24, v29, s82, v24 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v25, v30, s82, v25 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v26, v31, s82, v26 op_sel:[0,1,0] op_sel_hi:[1,1,1] +v_pk_fma_f16 v27, v32, s82, v27 op_sel:[0,1,0] op_sel_hi:[1,1,1] +s_setprio 0 +ds_load_b128 v[44:47], v3 offset:11584 +ds_load_b128 v[49:52], v3 offset:12160 +s_barrier +s_nop 15 +s_setprio 2 +s_and_not1_b32 null, 0xffffff00, s11 +s_cbranch_scc1 25 +s_pack_ll_b32_b16 s10, s10, s10 +s_bfm_b64 exec, s91, 0 +v_cmp_ne_u32 vcc, v21, s90 +s_cbranch_vccz 12 +buffer_load_b32 v21, off, s[88:91], null glc +s_cmp_eq_u32 s10, 0 +s_cselect_b32 vcc_lo, 0, 0x10000 +s_add_u32 s10, s10, vcc_lo +s_cbranch_scc1 2 +s_waitcnt vmcnt(0) +s_branch 65524 +s_and_b32 s91, 0xffff0000, s91 +s_mov_b32 s10, 0 +s_mov_b64 exec, -1 +s_mul_i32 s90, s90, 3 +s_and_b32 s90, s90, 0x3f3f3f3f +s_add_u32 s88, s88, 0x100 +s_and_b32 s88, s88, 0xfffff7ff +s_bitcmp1_b32 s92, 1 +s_cselect_b32 s86, s85, 0x4c7c +s_add_u32 s86, s6, s86 +s_addc_u32 s87, s7, 0 +s_bitcmp1_b32 s27, 1 +s_cbranch_scc1 65242 +s_branch 65012 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 80 +v_mov_b32 v64, 0 +v_mov_b32 v68, 0 +v_mov_b32 v65, 0 +v_mov_b32 v69, 0 +v_mov_b32 v66, 0 +v_mov_b32 v70, 0 +v_mov_b32 v67, 0 +v_mov_b32 v71, 0 +v_mov_b32 v80, 0 +v_mov_b32 v84, 0 +v_mov_b32 v81, 0 +v_mov_b32 v85, 0 +v_mov_b32 v82, 0 +v_mov_b32 v86, 0 +v_mov_b32 v83, 0 +v_mov_b32 v87, 0 +v_mov_b32 v96, 0 +v_mov_b32 v100, 0 +v_mov_b32 v97, 0 +v_mov_b32 v101, 0 +v_mov_b32 v98, 0 +v_mov_b32 v102, 0 +v_mov_b32 v99, 0 +v_mov_b32 v103, 0 +v_mov_b32 v112, 0 +v_mov_b32 v116, 0 +v_mov_b32 v113, 0 +v_mov_b32 v117, 0 +v_mov_b32 v114, 0 +v_mov_b32 v118, 0 +v_mov_b32 v115, 0 +v_mov_b32 v119, 0 +v_mov_b32 v128, 0 +v_mov_b32 v132, 0 +v_mov_b32 v129, 0 +v_mov_b32 v133, 0 +v_mov_b32 v130, 0 +v_mov_b32 v134, 0 +v_mov_b32 v131, 0 +v_mov_b32 v135, 0 +v_mov_b32 v144, 0 +v_mov_b32 v148, 0 +v_mov_b32 v145, 0 +v_mov_b32 v149, 0 +v_mov_b32 v146, 0 +v_mov_b32 v150, 0 +v_mov_b32 v147, 0 +v_mov_b32 v151, 0 +v_mov_b32 v160, 0 +v_mov_b32 v164, 0 +v_mov_b32 v161, 0 +v_mov_b32 v165, 0 +v_mov_b32 v162, 0 +v_mov_b32 v166, 0 +v_mov_b32 v163, 0 +v_mov_b32 v167, 0 +v_mov_b32 v176, 0 +v_mov_b32 v180, 0 +v_mov_b32 v177, 0 +v_mov_b32 v181, 0 +v_mov_b32 v178, 0 +v_mov_b32 v182, 0 +v_mov_b32 v179, 0 +v_mov_b32 v183, 0 +v_mov_b32 v192, 0 +v_mov_b32 v196, 0 +v_mov_b32 v193, 0 +v_mov_b32 v197, 0 +v_mov_b32 v194, 0 +v_mov_b32 v198, 0 +v_mov_b32 v195, 0 +v_mov_b32 v199, 0 +v_mov_b32 v208, 0 +v_mov_b32 v212, 0 +v_mov_b32 v209, 0 +v_mov_b32 v213, 0 +v_mov_b32 v210, 0 +v_mov_b32 v214, 0 +v_mov_b32 v211, 0 +v_mov_b32 v215, 0 +s_mov_b32 s85, 0x4ddc +s_cmp_le_u32 s9, 32 +s_cmov_b32 s85, 0x4b18 +s_setpc_b64 s[86:87] +s_bitcmp1_b32 s92, 3 +s_cbranch_scc0 80 +v_mov_b32 v72, 0 +v_mov_b32 v76, 0 +v_mov_b32 v73, 0 +v_mov_b32 v77, 0 +v_mov_b32 v74, 0 +v_mov_b32 v78, 0 +v_mov_b32 v75, 0 +v_mov_b32 v79, 0 +v_mov_b32 v88, 0 +v_mov_b32 v92, 0 +v_mov_b32 v89, 0 +v_mov_b32 v93, 0 +v_mov_b32 v90, 0 +v_mov_b32 v94, 0 +v_mov_b32 v91, 0 +v_mov_b32 v95, 0 +v_mov_b32 v104, 0 +v_mov_b32 v108, 0 +v_mov_b32 v105, 0 +v_mov_b32 v109, 0 +v_mov_b32 v106, 0 +v_mov_b32 v110, 0 +v_mov_b32 v107, 0 +v_mov_b32 v111, 0 +v_mov_b32 v120, 0 +v_mov_b32 v124, 0 +v_mov_b32 v121, 0 +v_mov_b32 v125, 0 +v_mov_b32 v122, 0 +v_mov_b32 v126, 0 +v_mov_b32 v123, 0 +v_mov_b32 v127, 0 +v_mov_b32 v136, 0 +v_mov_b32 v140, 0 +v_mov_b32 v137, 0 +v_mov_b32 v141, 0 +v_mov_b32 v138, 0 +v_mov_b32 v142, 0 +v_mov_b32 v139, 0 +v_mov_b32 v143, 0 +v_mov_b32 v152, 0 +v_mov_b32 v156, 0 +v_mov_b32 v153, 0 +v_mov_b32 v157, 0 +v_mov_b32 v154, 0 +v_mov_b32 v158, 0 +v_mov_b32 v155, 0 +v_mov_b32 v159, 0 +v_mov_b32 v168, 0 +v_mov_b32 v172, 0 +v_mov_b32 v169, 0 +v_mov_b32 v173, 0 +v_mov_b32 v170, 0 +v_mov_b32 v174, 0 +v_mov_b32 v171, 0 +v_mov_b32 v175, 0 +v_mov_b32 v184, 0 +v_mov_b32 v188, 0 +v_mov_b32 v185, 0 +v_mov_b32 v189, 0 +v_mov_b32 v186, 0 +v_mov_b32 v190, 0 +v_mov_b32 v187, 0 +v_mov_b32 v191, 0 +v_mov_b32 v200, 0 +v_mov_b32 v204, 0 +v_mov_b32 v201, 0 +v_mov_b32 v205, 0 +v_mov_b32 v202, 0 +v_mov_b32 v206, 0 +v_mov_b32 v203, 0 +v_mov_b32 v207, 0 +v_mov_b32 v216, 0 +v_mov_b32 v220, 0 +v_mov_b32 v217, 0 +v_mov_b32 v221, 0 +v_mov_b32 v218, 0 +v_mov_b32 v222, 0 +v_mov_b32 v219, 0 +v_mov_b32 v223, 0 +s_mov_b32 s85, 0x4ddc +s_cmp_le_u32 s9, 32 +s_cmov_b32 s85, 0x4b18 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 320, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 321, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 322, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 323, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 324, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 325, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 326, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 327, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v64, v227 +v_mov_b32 v65, v228 +v_mov_b32 v66, v229 +v_mov_b32 v67, v230 +v_mov_b32 v68, v231 +v_mov_b32 v69, v232 +v_mov_b32 v70, v233 +v_mov_b32 v71, v234 +s_mov_b32 s85, 0x4e48 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 328, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 329, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 330, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 331, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 332, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 333, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 334, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 335, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v72, v235 +v_mov_b32 v73, v236 +v_mov_b32 v74, v237 +v_mov_b32 v75, v238 +v_mov_b32 v76, v239 +v_mov_b32 v77, v240 +v_mov_b32 v78, v241 +v_mov_b32 v79, v242 +s_mov_b32 s85, 0x4eb4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 336, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 337, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 338, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 339, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 340, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 341, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 342, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 343, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v80, v227 +v_mov_b32 v81, v228 +v_mov_b32 v82, v229 +v_mov_b32 v83, v230 +v_mov_b32 v84, v231 +v_mov_b32 v85, v232 +v_mov_b32 v86, v233 +v_mov_b32 v87, v234 +s_mov_b32 s85, 0x4f20 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 344, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 345, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 346, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 347, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 348, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 349, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 350, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 351, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v88, v235 +v_mov_b32 v89, v236 +v_mov_b32 v90, v237 +v_mov_b32 v91, v238 +v_mov_b32 v92, v239 +v_mov_b32 v93, v240 +v_mov_b32 v94, v241 +v_mov_b32 v95, v242 +s_mov_b32 s85, 0x4f8c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 352, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 353, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 354, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 355, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 356, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 357, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 358, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 359, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v96, v227 +v_mov_b32 v97, v228 +v_mov_b32 v98, v229 +v_mov_b32 v99, v230 +v_mov_b32 v100, v231 +v_mov_b32 v101, v232 +v_mov_b32 v102, v233 +v_mov_b32 v103, v234 +s_mov_b32 s85, 0x4ff8 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 360, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 361, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 362, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 363, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 364, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 365, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 366, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 367, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v104, v235 +v_mov_b32 v105, v236 +v_mov_b32 v106, v237 +v_mov_b32 v107, v238 +v_mov_b32 v108, v239 +v_mov_b32 v109, v240 +v_mov_b32 v110, v241 +v_mov_b32 v111, v242 +s_mov_b32 s85, 0x5064 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 368, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 369, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 370, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 371, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 372, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 373, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 374, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 375, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v112, v227 +v_mov_b32 v113, v228 +v_mov_b32 v114, v229 +v_mov_b32 v115, v230 +v_mov_b32 v116, v231 +v_mov_b32 v117, v232 +v_mov_b32 v118, v233 +v_mov_b32 v119, v234 +s_mov_b32 s85, 0x50d0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 376, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 377, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 378, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 379, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 380, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 381, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 382, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 383, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v120, v235 +v_mov_b32 v121, v236 +v_mov_b32 v122, v237 +v_mov_b32 v123, v238 +v_mov_b32 v124, v239 +v_mov_b32 v125, v240 +v_mov_b32 v126, v241 +v_mov_b32 v127, v242 +s_mov_b32 s85, 0x513c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 384, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 385, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 386, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 387, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 388, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 389, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 390, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 391, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v128, v227 +v_mov_b32 v129, v228 +v_mov_b32 v130, v229 +v_mov_b32 v131, v230 +v_mov_b32 v132, v231 +v_mov_b32 v133, v232 +v_mov_b32 v134, v233 +v_mov_b32 v135, v234 +s_mov_b32 s85, 0x51a8 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 392, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 393, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 394, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 395, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 396, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 397, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 398, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 399, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v136, v235 +v_mov_b32 v137, v236 +v_mov_b32 v138, v237 +v_mov_b32 v139, v238 +v_mov_b32 v140, v239 +v_mov_b32 v141, v240 +v_mov_b32 v142, v241 +v_mov_b32 v143, v242 +s_mov_b32 s85, 0x5214 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 400, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 401, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 402, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 403, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 404, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 405, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 406, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 407, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v144, v227 +v_mov_b32 v145, v228 +v_mov_b32 v146, v229 +v_mov_b32 v147, v230 +v_mov_b32 v148, v231 +v_mov_b32 v149, v232 +v_mov_b32 v150, v233 +v_mov_b32 v151, v234 +s_mov_b32 s85, 0x5280 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 408, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 409, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 410, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 411, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 412, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 413, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 414, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 415, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v152, v235 +v_mov_b32 v153, v236 +v_mov_b32 v154, v237 +v_mov_b32 v155, v238 +v_mov_b32 v156, v239 +v_mov_b32 v157, v240 +v_mov_b32 v158, v241 +v_mov_b32 v159, v242 +s_mov_b32 s85, 0x52ec +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 416, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 417, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 418, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 419, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 420, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 421, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 422, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 423, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v160, v227 +v_mov_b32 v161, v228 +v_mov_b32 v162, v229 +v_mov_b32 v163, v230 +v_mov_b32 v164, v231 +v_mov_b32 v165, v232 +v_mov_b32 v166, v233 +v_mov_b32 v167, v234 +s_mov_b32 s85, 0x5358 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 424, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 425, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 426, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 427, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 428, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 429, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 430, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 431, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v168, v235 +v_mov_b32 v169, v236 +v_mov_b32 v170, v237 +v_mov_b32 v171, v238 +v_mov_b32 v172, v239 +v_mov_b32 v173, v240 +v_mov_b32 v174, v241 +v_mov_b32 v175, v242 +s_mov_b32 s85, 0x53c4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 432, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 433, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 434, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 435, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 436, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 437, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 438, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 439, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v176, v227 +v_mov_b32 v177, v228 +v_mov_b32 v178, v229 +v_mov_b32 v179, v230 +v_mov_b32 v180, v231 +v_mov_b32 v181, v232 +v_mov_b32 v182, v233 +v_mov_b32 v183, v234 +s_mov_b32 s85, 0x5430 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 440, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 441, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 442, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 443, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 444, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 445, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 446, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 447, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v184, v235 +v_mov_b32 v185, v236 +v_mov_b32 v186, v237 +v_mov_b32 v187, v238 +v_mov_b32 v188, v239 +v_mov_b32 v189, v240 +v_mov_b32 v190, v241 +v_mov_b32 v191, v242 +s_mov_b32 s85, 0x549c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 448, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 449, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 450, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 451, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 452, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 453, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 454, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 455, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v192, v227 +v_mov_b32 v193, v228 +v_mov_b32 v194, v229 +v_mov_b32 v195, v230 +v_mov_b32 v196, v231 +v_mov_b32 v197, v232 +v_mov_b32 v198, v233 +v_mov_b32 v199, v234 +s_mov_b32 s85, 0x5508 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 456, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 457, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 458, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 459, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 460, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 461, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 462, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 463, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v200, v235 +v_mov_b32 v201, v236 +v_mov_b32 v202, v237 +v_mov_b32 v203, v238 +v_mov_b32 v204, v239 +v_mov_b32 v205, v240 +v_mov_b32 v206, v241 +v_mov_b32 v207, v242 +s_mov_b32 s85, 0x5574 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 464, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 465, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 466, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 467, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 468, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 469, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 470, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 471, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v208, v227 +v_mov_b32 v209, v228 +v_mov_b32 v210, v229 +v_mov_b32 v211, v230 +v_mov_b32 v212, v231 +v_mov_b32 v213, v232 +v_mov_b32 v214, v233 +v_mov_b32 v215, v234 +s_mov_b32 s85, 0x55e0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 472, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 473, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 474, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 475, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 476, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 477, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 478, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 479, 0x0, 0x3, 0x0, 0x0 +v_mov_b32 v216, v235 +v_mov_b32 v217, v236 +v_mov_b32 v218, v237 +v_mov_b32 v219, v238 +v_mov_b32 v220, v239 +v_mov_b32 v221, v240 +v_mov_b32 v222, v241 +v_mov_b32 v223, v242 +s_mov_b32 s85, 0x4ddc +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 483, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 484, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 485, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 486, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 227, 483, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 228, 484, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 229, 485, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 230, 486, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 487, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 488, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 489, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 490, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 231, 487, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 232, 488, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 233, 489, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 234, 490, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5818 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 491, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 492, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 493, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 494, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 235, 491, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 236, 492, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 237, 493, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 238, 494, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 495, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 496, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 497, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 498, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 239, 495, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 240, 496, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 241, 497, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_max_f16__vop3p 242, 498, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x564c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 483, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 484, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 485, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 486, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 227, 483, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 228, 484, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 229, 485, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 230, 486, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 487, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 488, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 489, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 490, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 231, 487, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 232, 488, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 233, 489, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 234, 490, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5bb0 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 491, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 492, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 493, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 494, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 235, 491, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 236, 492, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 237, 493, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 238, 494, 304, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 22, 32, 495, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 28, 32, 496, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 38, 32, 497, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 48, 32, 498, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 239, 495, 278, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 240, 496, 284, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 241, 497, 294, 0x0, 0x3, 0x0, 0x0 +_v_pk_min_f16__vop3p 242, 498, 304, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x59e4 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 227, 0xbdc5bdc5, 483, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 228, 0xbdc5bdc5, 484, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 229, 0xbdc5bdc5, 485, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 230, 0xbdc5bdc5, 486, 0x0, 0x3 +v_exp_f16 v227, v227 +v_exp_f16 v228, v228 +v_exp_f16 v229, v229 +v_exp_f16 v230, v230 +_v_exp_f16__vop3 227, 227, 0x9 +_v_exp_f16__vop3 228, 228, 0x9 +_v_exp_f16__vop3 229, 229, 0x9 +_v_exp_f16__vop3 230, 230, 0x9 +_v_pk_add_f16__vop3p_lit 227, 0x3c003c00, 483, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 228, 0x3c003c00, 484, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 229, 0x3c003c00, 485, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 230, 0x3c003c00, 486, 0x0, 0x3 +v_rcp_f16 v227, v227 +v_rcp_f16 v228, v228 +v_rcp_f16 v229, v229 +v_rcp_f16 v230, v230 +_v_rcp_f16__vop3 227, 227, 0x9 +_v_rcp_f16__vop3 228, 228, 0x9 +_v_rcp_f16__vop3 229, 229, 0x9 +_v_rcp_f16__vop3 230, 230, 0x9 +_v_pk_mul_f16__vop3p_lit 231, 0xbdc5bdc5, 487, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 232, 0xbdc5bdc5, 488, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 233, 0xbdc5bdc5, 489, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 234, 0xbdc5bdc5, 490, 0x0, 0x3 +v_exp_f16 v231, v231 +v_exp_f16 v232, v232 +v_exp_f16 v233, v233 +v_exp_f16 v234, v234 +_v_exp_f16__vop3 231, 231, 0x9 +_v_exp_f16__vop3 232, 232, 0x9 +_v_exp_f16__vop3 233, 233, 0x9 +_v_exp_f16__vop3 234, 234, 0x9 +_v_pk_add_f16__vop3p_lit 231, 0x3c003c00, 487, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 232, 0x3c003c00, 488, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 233, 0x3c003c00, 489, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 234, 0x3c003c00, 490, 0x0, 0x3 +v_rcp_f16 v231, v231 +v_rcp_f16 v232, v232 +v_rcp_f16 v233, v233 +v_rcp_f16 v234, v234 +_v_rcp_f16__vop3 231, 231, 0x9 +_v_rcp_f16__vop3 232, 232, 0x9 +_v_rcp_f16__vop3 233, 233, 0x9 +_v_rcp_f16__vop3 234, 234, 0x9 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x6088 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 235, 0xbdc5bdc5, 491, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 236, 0xbdc5bdc5, 492, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 237, 0xbdc5bdc5, 493, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 238, 0xbdc5bdc5, 494, 0x0, 0x3 +v_exp_f16 v235, v235 +v_exp_f16 v236, v236 +v_exp_f16 v237, v237 +v_exp_f16 v238, v238 +_v_exp_f16__vop3 235, 235, 0x9 +_v_exp_f16__vop3 236, 236, 0x9 +_v_exp_f16__vop3 237, 237, 0x9 +_v_exp_f16__vop3 238, 238, 0x9 +_v_pk_add_f16__vop3p_lit 235, 0x3c003c00, 491, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 236, 0x3c003c00, 492, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 237, 0x3c003c00, 493, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 238, 0x3c003c00, 494, 0x0, 0x3 +v_rcp_f16 v235, v235 +v_rcp_f16 v236, v236 +v_rcp_f16 v237, v237 +v_rcp_f16 v238, v238 +_v_rcp_f16__vop3 235, 235, 0x9 +_v_rcp_f16__vop3 236, 236, 0x9 +_v_rcp_f16__vop3 237, 237, 0x9 +_v_rcp_f16__vop3 238, 238, 0x9 +_v_pk_mul_f16__vop3p_lit 239, 0xbdc5bdc5, 495, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 240, 0xbdc5bdc5, 496, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 241, 0xbdc5bdc5, 497, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 242, 0xbdc5bdc5, 498, 0x0, 0x3 +v_exp_f16 v239, v239 +v_exp_f16 v240, v240 +v_exp_f16 v241, v241 +v_exp_f16 v242, v242 +_v_exp_f16__vop3 239, 239, 0x9 +_v_exp_f16__vop3 240, 240, 0x9 +_v_exp_f16__vop3 241, 241, 0x9 +_v_exp_f16__vop3 242, 242, 0x9 +_v_pk_add_f16__vop3p_lit 239, 0x3c003c00, 495, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 240, 0x3c003c00, 496, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 241, 0x3c003c00, 497, 0x0, 0x3 +_v_pk_add_f16__vop3p_lit 242, 0x3c003c00, 498, 0x0, 0x3 +v_rcp_f16 v239, v239 +v_rcp_f16 v240, v240 +v_rcp_f16 v241, v241 +v_rcp_f16 v242, v242 +_v_rcp_f16__vop3 239, 239, 0x9 +_v_rcp_f16__vop3 240, 240, 0x9 +_v_rcp_f16__vop3 241, 241, 0x9 +_v_rcp_f16__vop3 242, 242, 0x9 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x5d7c +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 227, 483, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 228, 484, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 229, 485, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 230, 486, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 231, 487, 56, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 232, 488, 57, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 233, 489, 59, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 234, 490, 64, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 227, 483, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 228, 484, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 229, 485, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 230, 486, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v227 +v_and_b32 v28, 0x7fff7fff, v228 +v_and_b32 v38, 0x7fff7fff, v229 +v_and_b32 v48, 0x7fff7fff, v230 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 227, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 228, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 229, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 230, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 227, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 228, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 229, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 230, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v227, 0x7fff7fff, v23, v227 +v_bfi_b32 v228, 0x7fff7fff, v33, v228 +v_bfi_b32 v229, 0x7fff7fff, v43, v229 +v_bfi_b32 v230, 0x7fff7fff, v53, v230 +_v_pk_mul_f16__vop3p 227, 483, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 228, 484, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 229, 485, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 230, 486, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 231, 487, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 232, 488, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 489, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 490, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v231 +v_and_b32 v28, 0x7fff7fff, v232 +v_and_b32 v38, 0x7fff7fff, v233 +v_and_b32 v48, 0x7fff7fff, v234 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 231, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 232, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 233, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 234, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 231, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 232, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 233, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 234, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v231, 0x7fff7fff, v23, v231 +v_bfi_b32 v232, 0x7fff7fff, v33, v232 +v_bfi_b32 v233, 0x7fff7fff, v43, v233 +v_bfi_b32 v234, 0x7fff7fff, v53, v234 +_v_pk_mul_f16__vop3p 231, 487, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 232, 488, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 233, 489, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 234, 490, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v227, v245, s[72:75], 0 idxen +buffer_store_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v227, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v231, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v228, v245, s[72:75], 0 idxen +buffer_store_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v228, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v232, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v229, v245, s[72:75], 0 idxen +buffer_store_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v229, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v233, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v230, v245, s[72:75], 0 idxen +buffer_store_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v230, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v234, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x6a20 +s_setpc_b64 s[86:87] +_v_pk_add_f16__vop3p 235, 491, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 236, 492, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 237, 493, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 238, 494, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 239, 495, 65, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 240, 496, 66, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 241, 497, 67, 0x0, 0x3, 0x0, 0x0 +_v_pk_add_f16__vop3p 242, 498, 68, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 235, 491, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 236, 492, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 237, 493, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 238, 494, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v235 +v_and_b32 v28, 0x7fff7fff, v236 +v_and_b32 v38, 0x7fff7fff, v237 +v_and_b32 v48, 0x7fff7fff, v238 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 235, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 236, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 237, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 238, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 235, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 236, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 237, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 238, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v235, 0x7fff7fff, v23, v235 +v_bfi_b32 v236, 0x7fff7fff, v33, v236 +v_bfi_b32 v237, 0x7fff7fff, v43, v237 +v_bfi_b32 v238, 0x7fff7fff, v53, v238 +_v_pk_mul_f16__vop3p 235, 491, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 236, 492, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 237, 493, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 238, 494, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 239, 495, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 240, 496, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 241, 497, 33, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 242, 498, 33, 0x0, 0x3, 0x0, 0x0 +v_and_b32 v22, 0x7fff7fff, v239 +v_and_b32 v28, 0x7fff7fff, v240 +v_and_b32 v38, 0x7fff7fff, v241 +v_and_b32 v48, 0x7fff7fff, v242 +v_mov_b32 v23, 0xb5f8b5f8 +v_mov_b32 v33, 0xb5f8b5f8 +v_mov_b32 v43, 0xb5f8b5f8 +v_mov_b32 v53, 0xb5f8b5f8 +v_pk_fma_f16 v23, v22, 0x2ff12ff1, v23 +v_pk_fma_f16 v33, v28, 0x2ff12ff1, v33 +v_pk_fma_f16 v43, v38, 0x2ff12ff1, v43 +v_pk_fma_f16 v53, v48, 0x2ff12ff1, v53 +v_pk_fma_f16 v23, v22, v23, 0x1c571c57 +v_pk_fma_f16 v33, v28, v33, 0x1c571c57 +v_pk_fma_f16 v43, v38, v43, 0x1c571c57 +v_pk_fma_f16 v53, v48, v53, 0x1c571c57 +v_pk_fma_f16 v23, v22, v23, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v33, v28, v33, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v43, v38, v43, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +v_pk_fma_f16 v53, v48, v53, 1.0 op_sel:[0,0,0] op_sel_hi:[1,1,0] +_v_pk_mul_f16__vop3p 23, 278, 279, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 33, 284, 289, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 43, 294, 299, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 53, 304, 309, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p_lit 22, 0x41c541c5, 278, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 28, 0x41c541c5, 284, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 38, 0x41c541c5, 294, 0x0, 0x3 +_v_pk_mul_f16__vop3p_lit 48, 0x41c541c5, 304, 0x0, 0x3 +v_exp_f16 v22, v22 +v_exp_f16 v28, v28 +v_exp_f16 v38, v38 +v_exp_f16 v48, v48 +_v_exp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_exp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +_v_pk_add_f16__vop3p 22, 242, 278, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 28, 242, 284, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 38, 242, 294, 0x0, 0x2, 0x0, 0x0 +_v_pk_add_f16__vop3p 48, 242, 304, 0x0, 0x2, 0x0, 0x0 +v_rcp_f16 v22, v22 +v_rcp_f16 v28, v28 +v_rcp_f16 v38, v38 +v_rcp_f16 v48, v48 +_v_rcp_f16__vop1 (22 | /*op_sel*/ 0x80), (22 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (28 | /*op_sel*/ 0x80), (28 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (38 | /*op_sel*/ 0x80), (38 | /*op_sel*/ 0x80) +_v_rcp_f16__vop1 (48 | /*op_sel*/ 0x80), (48 | /*op_sel*/ 0x80) +v_pk_fma_f16 v22, v22, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v28, v28, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v38, v38, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +v_pk_fma_f16 v48, v48, 2.0, 1.0 op_sel:[0,0,0] op_sel_hi:[1,0,0] neg_lo:[1,0,0] neg_hi:[1,0,0] +_v_cmp_gt_f16__vop3_v_lit 106, 239, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v23, v23, v22, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 240, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v33, v33, v28, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 241, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v43, v43, v38, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 242, 0x38b838b8, 0x0, 0x1 +v_cndmask_b16 v53, v53, v48, vcc +_v_cmp_gt_f16__vop3_v_lit 106, 239, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 23, 23, 22, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 240, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 33, 33, 28, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 241, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 43, 43, 38, 106, 0xb +_v_cmp_gt_f16__vop3_v_lit 106, 242, 0x38b838b8, 0x3, 0x1 +_v_cndmask_b16__vop3 53, 53, 48, 106, 0xb +v_bfi_b32 v239, 0x7fff7fff, v23, v239 +v_bfi_b32 v240, 0x7fff7fff, v33, v240 +v_bfi_b32 v241, 0x7fff7fff, v43, v241 +v_bfi_b32 v242, 0x7fff7fff, v53, v242 +_v_pk_mul_f16__vop3p 239, 495, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 240, 496, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 241, 497, 32, 0x0, 0x3, 0x0, 0x0 +_v_pk_mul_f16__vop3p 242, 498, 32, 0x0, 0x3, 0x0, 0x0 +buffer_store_b16 v235, v245, s[72:75], 0 idxen +buffer_store_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v235, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v239, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v236, v245, s[72:75], 0 idxen +buffer_store_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v236, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v240, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v237, v245, s[72:75], 0 idxen +buffer_store_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v237, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v241, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_b16 v238, v245, s[72:75], 0 idxen +buffer_store_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +buffer_store_d16_hi_b16 v238, v245, s[72:75], 0 idxen +buffer_store_d16_hi_b16 v242, v246, s[72:75], 0 idxen +s_sub_u32 s69, s69, 1 +s_cselect_b32 s75, 0, s75 +s_add_u32 s72, s72, s80 +s_addc_u32 s73, s73, 0 +s_mov_b32 s84, 0x6394 +s_setpc_b64 s[86:87] +s_endpgm +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end 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+s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end +s_code_end + diff --git a/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_metadata.inc b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_metadata.inc new file mode 100644 index 0000000000..bd29eb5c30 --- /dev/null +++ b/src/kernels/winograd/Conv_Winograd_Fury_v2_4_1_metadata.inc @@ -0,0 +1,192 @@ +/******************************************************************************* + * + * MIT License + * + * Copyright (c) 2024 Advanced Micro Devices, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + * + *******************************************************************************/ + +.macro PROLOG_KERNEL_DESCRIPTOR kernel_name:req +.text +.globl \kernel_name +.p2align 8 +.type \kernel_name,@function +\kernel_name: +.endm + +.macro METADATA sc:req, wc:req, wg_x:req, kernel_name:req +.amdgpu_metadata +--- +amdhsa.version: [ 1, 2 ] +amdhsa.kernels: + - .name: \kernel_name + .symbol: \kernel_name\().kd + .sgpr_count: \sc + .vgpr_count: \wc // Unused in the kernel descriptor + .group_segment_fixed_size: 65536 + .private_segment_fixed_size: 0 + .kernarg_segment_size: 232 + .kernarg_segment_align: 8 + .reqd_workgroup_size: [ \wg_x, 1, 1 ] + .max_flat_workgroup_size: \wg_x + .wavefront_size: 64 + .workgroup_processor_mode: 0 + .args: + - { .size: 4, .offset: 0, .value_kind: by_value, .name: N_ } + - { .size: 4, .offset: 4, .value_kind: by_value, .name: C } + - { .size: 4, .offset: 8, .value_kind: by_value, .name: H } + - { .size: 4, .offset: 12, .value_kind: by_value, .name: W } + + - { .size: 4, .offset: 16, .value_kind: by_value, .name: K } + - { .size: 4, .offset: 20, .value_kind: by_value, .name: n_groups } + + - { .size: 8, .offset: 24, .value_kind: by_value, .name: flags64 } + + - { .size: 8, .offset: 32, .value_kind: global_buffer, .name: data_addr, .address_space: global, .is_const: true } + - { .size: 8, .offset: 40, .value_kind: global_buffer, .name: filter_addr, .address_space: global, .is_const: true } + - { .size: 8, .offset: 48, .value_kind: global_buffer, .name: output_addr, .address_space: global, .is_const: false } + - { .size: 8, .offset: 56, .value_kind: by_value } + + - { .size: 4, .offset: 64, .value_kind: by_value, .name: R } + - { .size: 4, .offset: 68, .value_kind: by_value, .name: S } + - { .size: 4, .offset: 72, .value_kind: by_value, .name: pad_h } + - { .size: 4, .offset: 76, .value_kind: by_value, .name: pad_w } + - { .size: 4, .offset: 80, .value_kind: by_value, .name: out_h } + - { .size: 4, .offset: 84, .value_kind: by_value, .name: out_w } + + - { .size: 8, .offset: 88, .value_kind: global_buffer, .name: bias_addr, .address_space: global, .is_const: true } + - { .size: 4, .offset: 96, .value_kind: by_value, .name: alpha } + - { .size: 4, .offset: 100, .value_kind: by_value, .name: beta } + + - { .size: 8, .offset: 104, .value_kind: by_value, .name: d_offset } + - { .size: 8, .offset: 112, .value_kind: by_value, .name: f_offset } + - { .size: 8, .offset: 120, .value_kind: by_value, .name: o_offset } + - { .size: 8, .offset: 128, .value_kind: by_value, .name: b_offset } + + - { .size: 4, .offset: 136, .value_kind: by_value, .name: d_N_stride } + - { .size: 4, .offset: 140, .value_kind: by_value, .name: d_C_stride } + - { .size: 4, .offset: 144, .value_kind: by_value, .name: d_H_stride } + - { .size: 4, .offset: 148, .value_kind: by_value } + + - { .size: 4, .offset: 152, .value_kind: by_value, .name: f_K_stride } + - { .size: 4, .offset: 156, .value_kind: by_value, .name: f_C_stride } + - { .size: 4, .offset: 160, .value_kind: by_value, .name: f_R_stride } + - { .size: 4, .offset: 164, .value_kind: by_value } + + - { .size: 4, .offset: 168, .value_kind: by_value, .name: o_N_stride } + - { .size: 4, .offset: 172, .value_kind: by_value, .name: o_K_stride } + - { .size: 4, .offset: 176, .value_kind: by_value, .name: o_H_stride } + - { .size: 4, .offset: 180, .value_kind: by_value } + + - { .size: 4, .offset: 184, .value_kind: by_value, .name: G } + - { .size: 4, .offset: 188, .value_kind: by_value, .name: d_G_stride } + - { .size: 4, .offset: 192, .value_kind: by_value, .name: f_G_stride } + - { .size: 4, .offset: 196, .value_kind: by_value, .name: o_G_stride } + + - { .size: 1, .offset: 200, .value_kind: by_value, .name: activation_mode } + - { .size: 1, .offset: 201, .value_kind: by_value, .name: sync_limit } + - { .size: 1, .offset: 202, .value_kind: by_value, .name: sync_period } + - { .size: 1, .offset: 203, .value_kind: by_value } + + - { .size: 4, .offset: 204, .value_kind: by_value } + - { .size: 8, .offset: 208, .value_kind: global_buffer, .name: sync_addr, .address_space: global, .is_const: false } + + - { .size: 8, .offset: 216, .value_kind: global_buffer, .name: acc_addr, .address_space: global, .is_const: false } + - { .size: 8, .offset: 224, .value_kind: by_value, .name: a_offset } +... +.end_amdgpu_metadata +.endm // METADATA + +.altmacro +.macro METADATA_WRAPPER sc:req, wc:req, wg_x:req, kernel_name:req + METADATA %\sc, %\wc, %\wg_x, \kernel_name +.endm + +.macro kernel_end kernel_name:req +.Lfunc_end0: + .size \kernel_name, .Lfunc_end0 - \kernel_name +.endm + +.macro EPILOG_KERNEL_DESCRIPTOR kernel_name:req + +kernel_end \kernel_name + +.if (.amdgcn.gfx_generation_number == 11) + .if ((.amdgcn.gfx_generation_minor == 0 && (.amdgcn.gfx_generation_stepping == 0 || .amdgcn.gfx_generation_stepping == 1)) || (.amdgcn.gfx_generation_minor == 5 && .amdgcn.gfx_generation_stepping == 1)) + // gfx1100, gfx1101, gfx1151 + vgpr_cnt = 252 + .else + // gfx1102, gfx1103, gfx1150 + vgpr_cnt = 168 + .endif + sgpr_cnt = 0 // 128 SGPRs always allocated for gfx10-gfx11 + workgroup_size_x = 384 +.endif + +.amdgcn.next_free_sgpr = sgpr_cnt +.amdgcn.next_free_vgpr = vgpr_cnt + +__group_segment_fixed_size = 65536 +__sgpr_dispatch_ptr = 1 +__sgpr_kernarg_segment_ptr = 1 +__ieee_mode = 0 +__dx10_clamp = 0 + +.rodata +.p2align 6 +.if (.amdgcn.gfx_generation_number == 11) +.amdhsa_kernel \kernel_name + .amdhsa_group_segment_fixed_size __group_segment_fixed_size + .amdhsa_user_sgpr_dispatch_ptr __sgpr_dispatch_ptr // s[0:1] + .amdhsa_user_sgpr_kernarg_segment_ptr __sgpr_kernarg_segment_ptr // s[2:3] + .amdhsa_next_free_vgpr .amdgcn.next_free_vgpr + .amdhsa_next_free_sgpr .amdgcn.next_free_sgpr + .amdhsa_ieee_mode __ieee_mode + .amdhsa_dx10_clamp __dx10_clamp + .amdhsa_wavefront_size32 0 + .amdhsa_workgroup_processor_mode 0 +.end_amdhsa_kernel +.endif + +METADATA_WRAPPER sgpr_cnt, 0, workgroup_size_x, <\kernel_name> + +.endm + +.macro PROLOG_KERNEL_DESCRIPTOR_WRAPPER machine_version:req, kernel_name_postfix:req + PROLOG_KERNEL_DESCRIPTOR miopenSp3AsmConvFury_v2_4_1_gfx\machine_version\()\kernel_name_postfix +.endm + +.macro EPILOG_KERNEL_DESCRIPTOR_WRAPPER machine_version:req, kernel_name_postfix:req + EPILOG_KERNEL_DESCRIPTOR miopenSp3AsmConvFury_v2_4_1_gfx\machine_version\()\kernel_name_postfix +.endm + +.macro KERNEL_PROLOG kernel_name_postfix:req + PROLOG_KERNEL_DESCRIPTOR_WRAPPER %.amdgcn.gfx_generation_number, \kernel_name_postfix +.endm + +.macro KERNEL_EPILOG kernel_name_postfix:req + EPILOG_KERNEL_DESCRIPTOR_WRAPPER %.amdgcn.gfx_generation_number, \kernel_name_postfix +.endm + +.if (.amdgcn.gfx_generation_number != 11) + .error "Unsupported gfx generation" + .end +.endif diff --git a/src/layer_norm.cpp b/src/layernorm.cpp similarity index 100% rename from src/layer_norm.cpp rename to src/layernorm.cpp diff --git a/src/layernorm/problem_description.cpp b/src/layernorm/problem_description.cpp index 0d56e98a8b..657b5c26ca 100644 --- a/src/layernorm/problem_description.cpp +++ b/src/layernorm/problem_description.cpp @@ -39,23 +39,39 @@ NetworkConfig ProblemDescription::MakeNetworkConfig() const size_t outer_size = 1; size_t inner_size = 1; - for(size_t i = 0ULL; i < dims.size(); ++i) + if((mode == MIOPEN_WEIGHT_BIAS_T5) || (mode == MIOPEN_ELEMENTWISE_AFFINE_T5)) { - if(i < normalized_dim) - outer_size *= dims[i]; - else - inner_size *= dims[i]; + inner_size = dims[dims.size() - 1]; + outer_size = std::accumulate(dims.begin(), dims.end() - 1, 1ULL, std::multiplies()); + } + else + { + outer_size = std::accumulate( + dims.begin(), dims.begin() + normalized_dim, 1ULL, std::multiplies()); + inner_size = std::accumulate( + dims.begin() + normalized_dim, dims.end(), 1ULL, std::multiplies()); } - auto dtype = xDesc.GetType(); std::ostringstream ss; ss << "dtype" << dtype; - ss << "normalized_dim" << normalized_dim; + if((mode == MIOPEN_WEIGHT_BIAS_T5) || (mode == MIOPEN_ELEMENTWISE_AFFINE_T5)) + { + ss << "normalized_dim" << dims.size() - 1; + } + else + { + ss << "normalized_dim" << normalized_dim; + } ss << "outer_size" << outer_size; ss << "inner_size" << inner_size; + if((mode == MIOPEN_WEIGHT_BIAS_FUSED_ADD) || (mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD)) + ss << "addlayernorm"; + if((mode == MIOPEN_WEIGHT_BIAS_T5) || (mode == MIOPEN_ELEMENTWISE_AFFINE_T5)) + ss << "t5layernorm"; + return NetworkConfig{ss.str()}; } diff --git a/src/load_file.cpp b/src/load_file.cpp index 3da8b7a5b6..50e5b4b092 100644 --- a/src/load_file.cpp +++ b/src/load_file.cpp @@ -26,10 +26,12 @@ #include #include + #include #include #include #include +#include namespace miopen { diff --git a/src/lock_file.cpp b/src/lock_file.cpp index 4b000b8094..ad55c45c59 100644 --- a/src/lock_file.cpp +++ b/src/lock_file.cpp @@ -28,6 +28,7 @@ #include #include #include +#include namespace miopen { @@ -40,7 +41,7 @@ inline void LogFsError(const fs::filesystem_error& ex, const std::string_view fr // clang-format on } -std::string LockFilePath(const fs::path& filename_) +fs::path LockFilePath(const fs::path& filename_) { try { @@ -54,7 +55,7 @@ std::string LockFilePath(const fs::path& filename_) const auto hash = md5(filename_.parent_path().string()); const auto file = directory / (hash + "_" + filename_.filename() + ".lock"); - return file.string(); + return file; } catch(const fs::filesystem_error& ex) { @@ -63,17 +64,17 @@ std::string LockFilePath(const fs::path& filename_) } } -LockFile::LockFile(const char* path_, PassKey) : path(path_) +LockFile::LockFile(const fs::path& path_, PassKey) : path(path_) { try { if(!fs::exists(path)) { if(!std::ofstream{path}) - MIOPEN_THROW(std::string("Error creating file <") + path + "> for locking."); + MIOPEN_THROW("Error creating file <" + path + "> for locking."); fs::permissions(path, fs::perms::all); } - flock = path; + flock = path.string().c_str(); } catch(const fs::filesystem_error& ex) { @@ -87,8 +88,15 @@ LockFile::LockFile(const char* path_, PassKey) : path(path_) } } -LockFile& LockFile::Get(const char* path) +LockFile& LockFile::Get(const fs::path& file) { +#ifdef _WIN32 + // The character ':' is reserved on Windows and cannot be used for constructing + // a path except when it follows a drive letter. + fs::path path{ReplaceString(file.string(), ":memory:", "memory_")}; +#else +#define path file +#endif // NOLINTNEXTLINE (cppcoreguidelines-avoid-non-const-global-variables) static std::mutex mutex; std::lock_guard lock(mutex); diff --git a/src/logger.cpp b/src/logger.cpp index 41301920a6..cf07b4525d 100644 --- a/src/logger.cpp +++ b/src/logger.cpp @@ -31,6 +31,7 @@ #include #include #include +#include #ifdef __linux__ #include @@ -119,22 +120,22 @@ inline float GetTimeDiff() bool IsLoggingDebugQuiet() { - return debug::LoggingQuiet && !miopen::IsEnabled(ENV(MIOPEN_DEBUG_LOGGING_QUIETING_DISABLE)); + return debug::LoggingQuiet && !env::enabled(MIOPEN_DEBUG_LOGGING_QUIETING_DISABLE); } bool IsLoggingFunctionCalls() { - return miopen::IsEnabled(ENV(MIOPEN_ENABLE_LOGGING)) && !IsLoggingDebugQuiet(); + return env::enabled(MIOPEN_ENABLE_LOGGING) && !IsLoggingDebugQuiet(); } bool IsLoggingToRoctx() { - return miopen::IsEnabled(ENV(MIOPEN_ENABLE_LOGGING_ROCTX)) && !IsLoggingDebugQuiet(); + return env::enabled(MIOPEN_ENABLE_LOGGING_ROCTX) && !IsLoggingDebugQuiet(); } bool IsLogging(const LoggingLevel level, const bool disableQuieting) { - auto enabled_level = miopen::Value(ENV(MIOPEN_LOG_LEVEL)); + auto enabled_level = env::value(MIOPEN_LOG_LEVEL); if(IsLoggingDebugQuiet() && !disableQuieting) { // Disable all levels higher than fatal. @@ -150,6 +151,13 @@ bool IsLogging(const LoggingLevel level, const bool disableQuieting) #endif } +std::string LoggingLevelToCustomString(const LoggingLevel level, const char* custom) +{ + std::ostringstream oss; + oss << custom << " " << LoggingLevelToCString(level); + return oss.str(); +} + const char* LoggingLevelToCString(const LoggingLevel level) { switch(level) @@ -165,15 +173,13 @@ const char* LoggingLevelToCString(const LoggingLevel level) default: return ""; } } -bool IsLoggingCmd() -{ - return miopen::IsEnabled(ENV(MIOPEN_ENABLE_LOGGING_CMD)) && !IsLoggingDebugQuiet(); -} + +bool IsLoggingCmd() { return env::enabled(MIOPEN_ENABLE_LOGGING_CMD) && !IsLoggingDebugQuiet(); } std::string LoggingPrefix() { std::stringstream ss; - if(miopen::IsEnabled(ENV(MIOPEN_ENABLE_LOGGING_MPMT))) + if(env::enabled(MIOPEN_ENABLE_LOGGING_MPMT)) { ss << GetProcessAndThreadId() << ' '; } @@ -183,7 +189,7 @@ std::string LoggingPrefix() #elif MIOPEN_BACKEND_HIP ss << "(HIP)"; #endif - if(miopen::IsEnabled(ENV(MIOPEN_ENABLE_LOGGING_ELAPSED_TIME))) + if(env::enabled(MIOPEN_ENABLE_LOGGING_ELAPSED_TIME)) { ss << std::fixed << std::setprecision(3) << std::setw(8) << GetTimeDiff(); } diff --git a/src/lrn_api.cpp b/src/lrn_api.cpp index fa7f966992..3dab2889ac 100644 --- a/src/lrn_api.cpp +++ b/src/lrn_api.cpp @@ -32,7 +32,10 @@ extern "C" miopenStatus_t miopenCreateLRNDescriptor(miopenLRNDescriptor_t* lrnDesc) { - return miopen::try_([&] { miopen::deref(lrnDesc) = new miopen::LRNDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(lrnDesc); + desc = new miopen::LRNDescriptor(); + }); } extern "C" miopenStatus_t miopenSetLRNDescriptor(miopenLRNDescriptor_t lrnDesc, diff --git a/src/mha/problem_description.cpp b/src/mha/problem_description.cpp index cd1e19d731..7e88eb41bd 100644 --- a/src/mha/problem_description.cpp +++ b/src/mha/problem_description.cpp @@ -37,11 +37,52 @@ NetworkConfig ProblemDescription::MakeNetworkConfig() const { std::ostringstream ss; - ss << "mhafwd-"; + ss << "mha"; + + auto print_strides = [&ss](const TensorDescriptor& desc) { + for(const auto& d : desc.GetStrides()) + { + ss << d << "x"; + } + }; if(isForward) { - // TODO Implement + ss << "fwd-"; + for(auto s : mhaInputDescsForwardPtr->oDesc.GetLengths()) + { + ss << s << "x"; + } + + print_strides(mhaInputDescsForwardPtr->kDesc); + print_strides(mhaInputDescsForwardPtr->qDesc); + print_strides(mhaInputDescsForwardPtr->vDesc); + print_strides(mhaInputDescsForwardPtr->oDesc); + print_strides(mhaInputDescsForwardPtr->mDesc); + print_strides(mhaInputDescsForwardPtr->zInvDesc); + + ss << mhaInputDescsForwardPtr->oDesc.GetType(); + } + else + { + ss << "bwd-"; + + for(auto s : mhaInputDescsBackwardPtr->oDesc.GetLengths()) + { + ss << s << "x"; + } + print_strides(mhaInputDescsBackwardPtr->kDesc); + print_strides(mhaInputDescsBackwardPtr->qDesc); + print_strides(mhaInputDescsBackwardPtr->vDesc); + print_strides(mhaInputDescsBackwardPtr->oDesc); + print_strides(mhaInputDescsBackwardPtr->doDesc); + print_strides(mhaInputDescsBackwardPtr->mDesc); + print_strides(mhaInputDescsBackwardPtr->zInvDesc); + print_strides(mhaInputDescsBackwardPtr->dqDesc); + print_strides(mhaInputDescsBackwardPtr->dkDesc); + print_strides(mhaInputDescsBackwardPtr->dvDesc); + + ss << mhaInputDescsBackwardPtr->oDesc.GetType(); } return NetworkConfig{ss.str()}; diff --git a/src/mlo_dir_conv.cpp b/src/mlo_dir_conv.cpp index c498414d0f..1b80d0d729 100644 --- a/src/mlo_dir_conv.cpp +++ b/src/mlo_dir_conv.cpp @@ -49,15 +49,6 @@ #include #endif -// Only select the first applicable igemm solver due to long compilation time -// (JIRA SWDEV-227826) -/// \todo enable all applicable solvers of igemm after fixing slow compilation -#define WORKAROUND_SWDEV_227826 0 - -#if WORKAROUND_SWDEV_227826 -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_IMPLICIT_GEMM_FIND_ALL_SOLUTIONS) -#endif - miopen::PerformanceDb miopen::GetDb(const miopen::ExecutionContext& ctx) { return {DbKinds::PerfDb, ctx.GetPerfDbPath(), ctx.GetUserPerfDbPath()}; @@ -190,7 +181,8 @@ static auto GetWindogradWrWSolvers() miopen::solver::conv::ConvWinograd3x3MultipassWrW<1, 1, 7, 2>, miopen::solver::conv::ConvWinograd3x3MultipassWrW<1, 1, 7, 3>, miopen::solver::conv::ConvWinograd3x3MultipassWrW<5, 3>, - miopen::solver::conv::ConvWinograd3x3MultipassWrW<5, 4>>{}; + miopen::solver::conv::ConvWinograd3x3MultipassWrW<5, 4>, + miopen::solver::conv::ConvWinoFuryRxS<2, 3>>{}; } static auto GetBwdWrW2DSolvers() @@ -260,14 +252,7 @@ std::vector> FindAllImplicitGemmWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem) { -#if WORKAROUND_SWDEV_227826 - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_IMPLICIT_GEMM_FIND_ALL_SOLUTIONS))) - return GetImplicitGemmSolvers().GetWorkspaceSizes(ctx, problem); - else - return GetImplicitGemmSolvers().GetWorkspaceSizes(ctx, problem, 1); -#else return GetImplicitGemmSolvers().GetWorkspaceSizes(ctx, problem); -#endif } std::vector @@ -275,15 +260,7 @@ FindAllImplicitGemmSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx) { -#if WORKAROUND_SWDEV_227826 - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_IMPLICIT_GEMM_FIND_ALL_SOLUTIONS))) - return GetImplicitGemmSolvers().SearchForAllSolutions(ctx, problem, GetDb(ctx), invoke_ctx); - else - return GetImplicitGemmSolvers().SearchForAllSolutions( - ctx, problem, GetDb(ctx), invoke_ctx, 1); -#else return GetImplicitGemmSolvers().SearchForAllSolutions(ctx, problem, GetDb(ctx), invoke_ctx); -#endif } std::vector @@ -313,14 +290,7 @@ std::vector> FindImplicitGemmWrWWorkspaceSizes(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem) { -#if WORKAROUND_SWDEV_227826 - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_IMPLICIT_GEMM_FIND_ALL_SOLUTIONS))) - return GetImplicitGemmWrWSolvers().GetWorkspaceSizes(ctx, problem); - else - return GetImplicitGemmWrWSolvers().GetWorkspaceSizes(ctx, problem, 1); -#else return GetImplicitGemmWrWSolvers().GetWorkspaceSizes(ctx, problem); -#endif } std::vector @@ -328,16 +298,7 @@ FindImplicitGemmWrWAllSolutions(const miopen::ExecutionContext& ctx, const miopen::conv::ProblemDescription& problem, const miopen::AnyInvokeParams& invoke_ctx) { -#if WORKAROUND_SWDEV_227826 - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_IMPLICIT_GEMM_FIND_ALL_SOLUTIONS))) - return GetImplicitGemmWrWSolvers().SearchForAllSolutions( - ctx, problem, GetDb(ctx), invoke_ctx); - else - return GetImplicitGemmWrWSolvers().SearchForAllSolutions( - ctx, problem, GetDb(ctx), invoke_ctx, 1); -#else return GetImplicitGemmWrWSolvers().SearchForAllSolutions(ctx, problem, GetDb(ctx), invoke_ctx); -#endif } std::vector diff --git a/src/nogpu/handle.cpp b/src/nogpu/handle.cpp index 91d16db3bc..6d3af3a9f2 100644 --- a/src/nogpu/handle.cpp +++ b/src/nogpu/handle.cpp @@ -55,6 +55,10 @@ #include #include +#if MIOPEN_USE_HIPBLASLT +#include +#endif + namespace miopen { Handle::Handle(miopenAcceleratorQueue_t /* stream */) : Handle::Handle() {} @@ -104,7 +108,7 @@ void Handle::Copy(ConstData_t /* src */, Data_t /* dest */, std::size_t /* size KernelInvoke Handle::AddKernel(const std::string& algorithm, const std::string& network_config, - const std::string& program_name, + const fs::path& program_name, const std::string& kernel_name, const std::vector& vld, const std::vector& vgd, @@ -126,12 +130,21 @@ KernelInvoke Handle::AddKernel(const std::string& algorithm, } Invoker Handle::PrepareInvoker(const InvokerFactory& factory, - const std::vector& kernels) const + const std::vector& kernels, + std::vector* programs_out) const { std::vector built; - for(auto& k : kernels) + built.reserve(kernels.size()); + if(programs_out != nullptr) + programs_out->resize(kernels.size()); + + for(auto i = 0; i < kernels.size(); ++i) { + auto& k = kernels[i]; + Program* program_out = programs_out != nullptr ? &(*programs_out)[i] : nullptr; + MIOPEN_LOG_I2("Preparing kernel: " << k.kernel_name); + const auto kernel = this->impl->cache.AddKernel(*this, "", "", @@ -140,7 +153,9 @@ Invoker Handle::PrepareInvoker(const InvokerFactory& factory, k.l_wk, k.g_wk, k.comp_options, - kernels.size()); + kernels.size(), + "", + program_out); built.push_back(kernel); } return factory(built); @@ -150,7 +165,7 @@ void Handle::ClearKernels(const std::string& algorithm, const std::string& netwo { this->impl->cache.ClearKernels(algorithm, network_config); } -void Handle::ClearProgram(const std::string& program_name, const std::string& params) const +void Handle::ClearProgram(const fs::path& program_name, const std::string& params) const { this->impl->cache.ClearProgram(program_name, params); } @@ -161,19 +176,22 @@ const std::vector& Handle::GetKernelsImpl(const std::string& algorithm, return this->impl->cache.GetKernels(algorithm, network_config); } -KernelInvoke Handle::Run(Kernel /* k */) const { return {}; } +KernelInvoke Handle::Run(Kernel /*k*/, bool /*coop_launch*/) const { return {}; } -Program Handle::LoadProgram(const std::string& program_name, +Program Handle::LoadProgram(const fs::path& program_name, std::string params, - const std::string& kernel_src) const + const std::string& kernel_src, + bool force_attach_binary) const { - if(!miopen::EndsWith(program_name, ".mlir")) + std::ignore = force_attach_binary; + + if(program_name.extension() == ".mlir") { params += " -mcpu=" + this->GetTargetProperties().Name(); } - auto hsaco = miopen::LoadBinary( - this->GetTargetProperties(), this->GetMaxComputeUnits(), program_name, params); + auto hsaco = + miopen::LoadBinary(GetTargetProperties(), GetMaxComputeUnits(), program_name, params); auto pgmImpl = std::make_shared(); pgmImpl->program = program_name; pgmImpl->target = this->GetTargetProperties(); @@ -199,7 +217,7 @@ Program Handle::LoadProgram(const std::string& program_name, miopen::WriteFile(p.GetCodeObjectBlob(), path); else fs::copy_file(p.GetCodeObjectPathname(), path); - miopen::SaveBinary(path, this->GetTargetProperties(), program_name, params); + miopen::SaveBinary(path, GetTargetProperties(), program_name, params); #endif } else @@ -210,14 +228,12 @@ Program Handle::LoadProgram(const std::string& program_name, return p; } -bool Handle::HasProgram(const std::string& program_name, const std::string& params) const +bool Handle::HasProgram(const fs::path& program_name, const std::string& params) const { return this->impl->cache.HasProgram(program_name, params); } -void Handle::AddProgram(Program prog, - const std::string& program_name, - const std::string& params) const +void Handle::AddProgram(Program prog, const fs::path& program_name, const std::string& params) const { this->impl->cache.AddProgram(prog, program_name, params); } @@ -250,6 +266,8 @@ std::size_t Handle::GetMaxMemoryAllocSize() return this->impl->max_mem_alloc_size; } +bool Handle::CooperativeLaunchSupported() const { return false; } + const TargetProperties& Handle::GetTargetProperties() const { return this->impl->target_properties; @@ -288,4 +306,19 @@ rocblas_handle_ptr Handle::CreateRocblasHandle(miopenAcceleratorQueue_t) const return result; } #endif + +#if MIOPEN_USE_HIPBLASLT +const hipblasLt_handle_ptr& Handle::HipblasLtHandle() const { return impl->hip_blasLt_handle; } + +hipblasLt_handle_ptr Handle::CreateHipblasLtHandle() const +{ + hipblasLtHandle_t handle = nullptr; + auto status = hipblasLtCreate(&handle); + if(status != HIPBLAS_STATUS_SUCCESS) + { + MIOPEN_THROW(miopenStatusInternalError, "hipBLASLt error encountered"); + } + return hipblasLt_handle_ptr{handle}; +} +#endif } // namespace miopen diff --git a/src/ocl/batchnormocl.cpp b/src/ocl/batchnormocl.cpp index 6147a827b8..40bcd34935 100644 --- a/src/ocl/batchnormocl.cpp +++ b/src/ocl/batchnormocl.cpp @@ -26,6 +26,7 @@ #include #include +#include #include #include #include @@ -39,12 +40,21 @@ #include #include #include +#include #include #include namespace miopen { +namespace batchnorm { +miopen::PerformanceDb GetDb(const miopen::ExecutionContext& ctx, + const miopen::batchnorm::ProblemDescriptionTag&) +{ + return {DbKinds::PerfDb, ctx.GetPerfDbPath("batchnorm"), ctx.GetUserPerfDbPath("batchnorm")}; +} +} // namespace batchnorm + void BatchNormForwardTraining(Handle& handle, miopenBatchNormMode_t bn_mode, const void* alpha, @@ -68,7 +78,8 @@ void BatchNormForwardTraining(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() != yDesc.GetSize() || xDesc.GetSize() != bnScaleBiasMeanVarDesc.GetSize()) + if(xDesc.GetNumDims() != yDesc.GetNumDims() || + xDesc.GetNumDims() != bnScaleBiasMeanVarDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -81,7 +92,7 @@ void BatchNormForwardTraining(Handle& handle, MIOPEN_LOG_E("Only fully packed tensors supported."); MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() < 3) + if(xDesc.GetNumDims() < 3) { MIOPEN_THROW(miopenStatusBadParm); } @@ -185,8 +196,8 @@ void BatchNormForwardInference(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() != yDesc.GetSize() || - xDesc.GetSize() != bnScaleBiasMeanVarDesc.GetSize()) + if(xDesc.GetNumDims() != yDesc.GetNumDims() || + xDesc.GetNumDims() != bnScaleBiasMeanVarDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -194,7 +205,7 @@ void BatchNormForwardInference(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() < 3) + if(xDesc.GetNumDims() < 3) { MIOPEN_THROW(miopenStatusBadParm); } @@ -297,7 +308,8 @@ void BatchNormBackward(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() != dyDesc.GetSize() || xDesc.GetSize() != bnScaleBiasDiffDesc.GetSize()) + if(xDesc.GetNumDims() != dyDesc.GetNumDims() || + xDesc.GetNumDims() != bnScaleBiasDiffDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -305,7 +317,7 @@ void BatchNormBackward(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() < 3) + if(xDesc.GetNumDims() < 3) { MIOPEN_THROW(miopenStatusBadParm); } diff --git a/src/ocl/clhelper.cpp b/src/ocl/clhelper.cpp index 291b9789f1..66691dd38c 100644 --- a/src/ocl/clhelper.cpp +++ b/src/ocl/clhelper.cpp @@ -144,33 +144,30 @@ ClProgramPtr LoadProgram(cl_context ctx, const std::string& kernel_src) { std::string source; - std::string program_name; + fs::path program_name{program}; - program_name = program; // For mlir build, leave both source and kernel_src to be empty - if((kernel_src.empty()) && !(miopen::EndsWith(program_name, ".mlir"))) + if((kernel_src.empty()) && program_name.extension() != ".mlir") source = miopen::GetKernelSrc(program_name); else source = kernel_src; bool load_binary = false; - if(miopen::EndsWith(program_name, ".s")) + if(program_name.extension() == ".s") { source = ClAssemble(device, source, params, target); // Puts output binary into source. load_binary = true; } - if(load_binary || miopen::EndsWith(program_name, ".so")) + if(load_binary || program_name.extension() == dynamic_library_postfix) return LoadBinaryProgram(ctx, device, source); - if(miopen::EndsWith(program_name, ".cpp")) + if(program_name.extension() == ".cpp") { boost::optional dir(program_name); #if MIOPEN_BUILD_DEV params += " -Werror"; -#ifdef __linux__ params += HipKernelWarningsString(); -#endif #endif auto hsaco_file = HipBuild(dir, program_name, source, params, target); // load the hsaco file as a data stream and then load the binary @@ -179,7 +176,7 @@ ClProgramPtr LoadProgram(cl_context ctx, return LoadBinaryProgram(ctx, device, buf); } #if MIOPEN_USE_MLIR - else if(miopen::EndsWith(program_name, ".mlir")) + else if(program_name.extension() == ".mlir") { std::vector buffer; MiirGenBin(params, buffer); @@ -189,13 +186,11 @@ ClProgramPtr LoadProgram(cl_context ctx, else // OpenCL programs. { ClProgramPtr result{CreateProgram(ctx, source.data(), source.size())}; - if(miopen::IsEnabled(ENV(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP))) + if(env::enabled(MIOPEN_DEBUG_OPENCL_WAVE64_NOWGP)) params += " -Wf,-mwavefrontsize64 -Wf,-mcumode"; #if MIOPEN_BUILD_DEV params += " -Werror"; -#ifdef __linux__ params += OclKernelWarningsString(); -#endif #endif params += " -cl-std=CL2.0"; MIOPEN_LOG_I2("Building OpenCL program: '" << program_name << "', options: '" << params); diff --git a/src/ocl/convolutionocl.cpp b/src/ocl/convolutionocl.cpp index d7cbedf77e..3ce997e2c2 100644 --- a/src/ocl/convolutionocl.cpp +++ b/src/ocl/convolutionocl.cpp @@ -23,12 +23,14 @@ * SOFTWARE. * *******************************************************************************/ + +#include + #include #include #include #include #include -#include #include #include #include @@ -39,6 +41,7 @@ #include #include #include +#include #include #include #include @@ -57,7 +60,6 @@ #include -MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_CONV_PRECISE_ROCBLAS_TIMING) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_CONV_IMMED_FALLBACK) MIOPEN_DECLARE_ENV_VAR_STR(MIOPEN_DUMP_TENSOR_PATH) MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_ENABLE_AI_IMMED_MODE_FALLBACK) @@ -186,27 +188,31 @@ CompileSolution(solver::Id solver_id, ExecutionContext ctx, const conv::ProblemD } /// Keep only the best within algorithm, remove all others. -static void ShrinkToFind10Results(std::vector& found) +static void ShrinkToFind10Results(std::vector& found) { - std::vector out; - std::sort(begin(found), end(found)); + std::sort(std::begin(found), std::end(found), [](auto&& l, auto&& r) { + return l.GetTime() < r.GetTime(); + }); + + std::vector out; for(const auto& f : found) { // If an algo already resides in out, then skip solver. - if(std::find_if(out.begin(), out.end(), [&](const auto& o) { - return o.algorithm == f.algorithm; - }) != out.end()) + auto algo_eq = [&](auto&& o) { return o.GetSolver().GetAlgo() == f.GetSolver().GetAlgo(); }; + if(std::find_if(std::begin(out), std::end(out), algo_eq) != std::end(out)) continue; out.emplace_back(f); } - found = out; + found = std::move(out); } -static inline std::vector FindConvolution(const ExecutionContext& ctx, - const conv::ProblemDescription& problem, - const AnyInvokeParams& invoke_ctx) +std::vector FindConvolution(const ExecutionContext& ctx, + const conv::ProblemDescription& problem, + const AnyInvokeParams& invoke_ctx, + int requestAlgoCount, + bool force_attach_binary) { - auto results = std::vector{}; + auto results = std::vector{}; auto sol = boost::optional{}; const auto& conv = problem.GetConv(); const auto& findMode = conv.findMode; @@ -214,10 +220,10 @@ static inline std::vector FindConvolution(const ExecutionContext& ctx if(findMode.IsFast(ctx) || findMode.IsHybrid(ctx)) { auto fallback = bool{}; - auto sols = conv.GetSolutions(ctx, problem, 1, &fallback); + auto sols = conv.GetSolutions(ctx, problem, 1, &fallback, &invoke_ctx); // override the normal find with immed mode with env var if(!sols.empty() && (!(findMode.IsHybrid(ctx) && fallback) || - miopen::IsEnabled(ENV(MIOPEN_DEBUG_FORCE_IMMED_MODE_FALLBACK)))) + env::enabled(MIOPEN_DEBUG_FORCE_IMMED_MODE_FALLBACK))) sol = sols.front(); // In Hybrid Find mode, we use Normal Find instead of Immediate fallback kernels. } @@ -229,24 +235,27 @@ static inline std::vector FindConvolution(const ExecutionContext& ctx const auto id = solver::Id{sol->solution_id}; const auto& s = id.GetSolver(); CompileSolution(id, ctx, problem); - results.push_back({id.GetAlgo(problem.GetDirection()), - id.ToString(), - sol->time, - s.GetWorkspaceSize(ctx, problem)}); + results.push_back({id, sol->time, s.GetWorkspaceSize(ctx, problem)}); } else { - results = UserFindDbRecord::TryLoad(ctx.GetStream(), problem, [&](DbRecord& record) { + results = UserFindDbRecord::TryLoad(ctx.GetStream(), problem, [&]() { auto ctx_copy = ctx; ctx_copy.use_dynamic_solutions_only = findMode.IsDynamicHybrid(ctx); const auto params = conv::ConvFindParameters{conv.IsWinograd3x3SupportedAndFast(ctx_copy, problem)}; - FindCore(invoke_ctx, record, ctx_copy, problem, params, conv::GetConvSolverFinders()); + return FindCore(invoke_ctx, + ctx_copy, + problem, + params, + conv::GetConvSolverFinders(), + std::nullopt, + force_attach_binary); }); } - if(IsEnabled(ENV(MIOPEN_DEBUG_COMPILE_ONLY))) + if(env::enabled(MIOPEN_DEBUG_COMPILE_ONLY)) { MIOPEN_THROW( miopenStatusGpuOperationsSkipped, @@ -254,13 +263,35 @@ static inline std::vector FindConvolution(const ExecutionContext& ctx } ShrinkToFind10Results(results); + results.resize(std::min(results.size(), requestAlgoCount)); for(const auto& entry : results) - MIOPEN_LOG_I(entry.algorithm << "\t" << entry.time << "\t" << entry.workspace); + MIOPEN_LOG_I(entry.GetSolver().GetAlgo(problem.GetDirection()) + << "\t" << entry.GetTime() << "\t" << entry.GetWorkspaceSize()); return results; } +template +static inline void FillFindReturnParameters(const std::vector& results, + FieldType miopenConvAlgoPerf_t::*field, + const char* log_start, + int* const returned_algo_count, + miopenConvAlgoPerf_t* perf_results) +{ + *returned_algo_count = static_cast(results.size()); + + for(int i = 0; i < *returned_algo_count; i++) + { + perf_results[i].*field = static_cast(results[i].GetSolver().GetAlgo()); + perf_results[i].time = results[i].GetTime(); + perf_results[i].memory = results[i].GetWorkspaceSize(); + } + + MIOPEN_LOG_I(log_start << " Chosen Algorithm: " << results[0].GetSolver().ToString() << " , " + << results[0].GetWorkspaceSize() << ", " << results[0].GetTime()); +} + void ConvolutionDescriptor::FindConvFwdAlgorithm(Handle& handle, const TensorDescriptor& xDesc, ConstData_t x, @@ -297,13 +328,10 @@ void ConvolutionDescriptor::FindConvFwdAlgorithm(Handle& handle, return tmp; }(); - const auto invoke_ctx = conv::DataInvokeParams{InvokeType::Evaluate, - {xDesc, x, wDesc, w, yDesc, y}, - workSpace, - workSpaceSize, - attribute.gfx90aFp16alt.GetFwd()}; + const auto invoke_ctx = conv::DataInvokeParams{ + {xDesc, x, wDesc, w, yDesc, y}, workSpace, workSpaceSize, attribute.gfx90aFp16alt.GetFwd()}; - const auto results = FindConvolution(ctx, problem, invoke_ctx); + const auto results = FindConvolution(ctx, problem, invoke_ctx, requestAlgoCount, false); if(results.empty()) { @@ -313,27 +341,19 @@ void ConvolutionDescriptor::FindConvFwdAlgorithm(Handle& handle, MIOPEN_THROW("No suitable algorithm was found to execute the required convolution"); } - *returnedAlgoCount = std::min(requestAlgoCount, static_cast(results.size())); - - for(int i = 0; i < *returnedAlgoCount; i++) - { - perfResults[i].fwd_algo = StringToConvolutionFwdAlgo(results[i].algorithm); - perfResults[i].time = results[i].time; - perfResults[i].memory = results[i].workspace; - } - - MIOPEN_LOG_I("FW Chosen Algorithm: " << results[0].solver_id << " , " << results[0].workspace - << ", " << results[0].time); + FillFindReturnParameters( + results, &miopenConvAlgoPerf_t::fwd_algo, "FW", returnedAlgoCount, perfResults); } namespace { -void ValidateAlphaBeta(const void* alpha, const void* beta) +// Currently 2D case only support default (alpha = 1.0 and beta = 0.0) +void ValidateAlphaBeta(const conv::ProblemDescription& problem) { - if(!float_equal(*(static_cast(alpha)), 1.0) || - !float_equal(*(static_cast(beta)), 0)) + if(problem.Is2d() && problem.GetAlphaBetaCase() != DEFAULT) { - MIOPEN_THROW(miopenStatusNotImplemented, "Only alpha=1 and beta=0 is supported"); + MIOPEN_THROW(miopenStatusNotImplemented, + "Only alpha=1 and beta=0 is supported for 2D cases."); } } @@ -342,7 +362,7 @@ void ValidateAlphaBeta(const void* alpha, const void* beta) void DumpTensorToFileFromDevice(const miopen::Handle& handle, const miopen::TensorDescriptor& tDesc, ConstData_t dData, - const std::string& filename) + const fs::path& filename) { if(dData == nullptr) { @@ -350,25 +370,19 @@ void DumpTensorToFileFromDevice(const miopen::Handle& handle, return; } - fs::path file_name_with_path(filename); - fs::path path = file_name_with_path.parent_path(); + fs::path path = filename.has_parent_path() ? filename : fs::current_path() / filename; - // dump to current folder if full path not provided. - if(path.empty()) - { - path = fs::current_path(); - file_name_with_path = path / file_name_with_path; // append paths - } - if(!fs::exists(path)) + if(!fs::is_directory(path.parent_path())) { MIOPEN_LOG_E("Directory does not exists : " << path); return; } - std::ofstream file_stream{file_name_with_path}; + std::ofstream file_stream{path, std::ios::binary}; + if(!file_stream.is_open()) { - MIOPEN_LOG_E("Cannot write to file : " << file_name_with_path); + MIOPEN_LOG_E("Cannot write to file : " << path); return; } @@ -379,10 +393,9 @@ void DumpTensorToFileFromDevice(const miopen::Handle& handle, handle.ReadTo(hdata.data(), dData, num_bytes); MIOPEN_LOG_I2("Done bringing tensor from device to host"); // write tensor data to file - const char* pointer = hdata.data(); - file_stream.write(pointer, num_bytes); + file_stream.write(hdata.data(), num_bytes); file_stream.close(); - MIOPEN_LOG_I("Dumping tensor to file : " << file_name_with_path); + MIOPEN_LOG_I("Dumping tensor to file : " << path); } static void ConvForwardCheckNumerics(const Handle& handle, @@ -404,7 +417,7 @@ static void ConvForwardCheckNumerics(const Handle& handle, flag |= miopen::checkNumericsOutput(handle, tensors.yDesc, tensors.y); - const auto& file_name = miopen::GetStringEnv(ENV(MIOPEN_DUMP_TENSOR_PATH)); + const auto& file_name = env::value(MIOPEN_DUMP_TENSOR_PATH); if(flag && !file_name.empty()) { DumpTensorToFileFromDevice(handle, tensors.xDesc, tensors.x, file_name + "_x.bin"); @@ -453,14 +466,14 @@ void ConvolutionDescriptor::ValidateTensors(const ConvTensors& tensors) const } // x_tensor_invalid = - if(tensors.xDesc.GetSize() < 3) + if(tensors.xDesc.GetNumDims() < 3) { MIOPEN_THROW(miopenStatusBadParm, "input tensor's number of dimensions is wrong"); } // tensor_sizes_not_matched = - if(tensors.xDesc.GetSize() != tensors.yDesc.GetSize() || - tensors.xDesc.GetSize() != tensors.wDesc.GetSize()) + if(tensors.xDesc.GetNumDims() != tensors.yDesc.GetNumDims() || + tensors.xDesc.GetNumDims() != tensors.wDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm, "number of dimensions mismatch between input, output and weights tensors"); @@ -494,6 +507,18 @@ void ConvolutionDescriptor::ValidateTensors(const ConvTensors& tensors) const //} } +miopenDataType_t GetScalarDataType(const TensorDescriptor& xDesc) +{ + if(xDesc.GetType() == miopenDataType_t::miopenDouble) + { + return miopenDataType_t::miopenDouble; + } + else + { + return miopenDataType_t::miopenFloat; + } +} + void ConvolutionDescriptor::ConvolutionForward(Handle& handle, const void* alpha, const TensorDescriptor& xDesc, @@ -512,23 +537,28 @@ void ConvolutionDescriptor::ConvolutionForward(Handle& handle, const auto tensors = ConvFwdTensors{xDesc, x, wDesc, w, yDesc, y}; ValidateTensors(tensors); - ValidateAlphaBeta(alpha, beta); + Scalar alpha_val(alpha, GetScalarDataType(yDesc)); + Scalar beta_val(beta, GetScalarDataType(yDesc)); + const auto problem = conv::ProblemDescription{ + xDesc, wDesc, yDesc, *this, conv::Direction::Forward, 0, alpha_val, beta_val}; + ValidateAlphaBeta(problem); ConvForwardCheckNumerics(handle, tensors, [&]() { ValidateGroupCount(xDesc, wDesc, *this); const auto algorithm_name = AlgorithmName{ConvolutionAlgoToDirectionalString( static_cast(algo), conv::Direction::Forward)}; - - const auto problem = - conv::ProblemDescription{xDesc, wDesc, yDesc, *this, conv::Direction::Forward}; const auto network_config = problem.MakeNetworkConfig(); const auto& invoker = handle.GetInvoker(network_config, {}, algorithm_name); if(invoker) { - const auto& invoke_ctx = conv::DataInvokeParams{ - tensors, workSpace, workSpaceSize, this->attribute.gfx90aFp16alt.GetFwd()}; + const auto& invoke_ctx = conv::DataInvokeParams{tensors, + workSpace, + workSpaceSize, + this->attribute.gfx90aFp16alt.GetFwd(), + alpha_val, + beta_val}; (*invoker)(handle, invoke_ctx); return; } @@ -600,12 +630,25 @@ struct SolutionTimeComparator } }; +namespace { + +std::ostream& operator<<(std::ostream& os, const miopenConvSolution_t& s) +{ + return os << "id: " << s.solution_id // + << ", algo: " << s.algorithm // + << ", time: " << s.time << ", ws: " << s.workspace_size // + << ", name: " << miopen::solver::Id(s.solution_id).ToString(); +} + +} // namespace + std::vector ConvolutionDescriptor::GetSolutionsFallback(const ExecutionContext& ctx, const conv::ProblemDescription& problem, - const size_t maxSolutionCount) const + const size_t maxSolutionCount, + const AnyInvokeParams* const invokeParams) const { - if(miopen::IsDisabled(ENV(MIOPEN_DEBUG_CONV_IMMED_FALLBACK))) + if(env::disabled(MIOPEN_DEBUG_CONV_IMMED_FALLBACK)) { MIOPEN_LOG_I("Disabled via environment"); return {}; @@ -623,7 +666,7 @@ ConvolutionDescriptor::GetSolutionsFallback(const ExecutionContext& ctx, // TunaNet Fallback #if MIOPEN_ENABLE_AI_IMMED_MODE_FALLBACK - if(!miopen::IsDisabled(ENV(MIOPEN_DEBUG_ENABLE_AI_IMMED_MODE_FALLBACK))) + if(!env::disabled(MIOPEN_DEBUG_ENABLE_AI_IMMED_MODE_FALLBACK)) { const static std::string arch = ctx.GetStream().GetDeviceName(); auto solvers = ai::immed_mode::PredictSolver(problem, ctx, arch); @@ -645,8 +688,11 @@ ConvolutionDescriptor::GetSolutionsFallback(const ExecutionContext& ctx, continue; // branch should never be taken if(!sol.IsApplicable(ctx, problem)) continue; - interim.emplace_back(miopenConvSolution_t{ - ai_time(idx), sol.GetWorkspaceSize(ctx, problem), solver_id.Value(), algo}); + const auto ws = sol.GetWorkspaceSize(ctx, problem); + if(!conv::IsEnoughWorkspace("GetSolutionsFallback AI", solver_id, ws, invokeParams)) + continue; + interim.emplace_back( + miopenConvSolution_t{ai_time(idx), ws, solver_id.Value(), algo}); ++idx; } } @@ -676,22 +722,21 @@ ConvolutionDescriptor::GetSolutionsFallback(const ExecutionContext& ctx, // Let's allow non-dynamic later, if necessary. if(s.IsEmpty() || !s.IsDynamic() || !s.IsApplicable(ctx, problem)) continue; + const auto ws = s.GetWorkspaceSize(ctx, problem); + if(!conv::IsEnoughWorkspace("GetSolutionsFallback WTI", solver_id, ws, invokeParams)) + continue; const auto wti = s.GetWti(ctx, problem); MIOPEN_LOG_I2(solver_id.ToString() << " Estimated WTI = " << wti); if(wti < 0.0f) // Skip unknown WTIs. continue; - interim.emplace_back(miopenConvSolution_t{ - wti2time(wti), s.GetWorkspaceSize(ctx, problem), solver_id.Value(), algo}); + interim.emplace_back(miopenConvSolution_t{wti2time(wti), ws, solver_id.Value(), algo}); } } MIOPEN_LOG_I2("maxSolutionCount = " << maxSolutionCount << ", available = " << interim.size()); for(const auto& s : interim) - { - MIOPEN_LOG_I2("id: " << s.solution_id << " algo: " << s.algorithm << ", time: " << s.time - << " ms, ws: " << s.workspace_size - << ", name: " << miopen::solver::Id(s.solution_id).ToString()); - } + MIOPEN_LOG_I2(s); + std::sort(begin(interim), end(interim), SolutionTimeComparator{}); interim.resize(std::min(maxSolutionCount, interim.size())); @@ -702,7 +747,8 @@ namespace { std::vector GetSolutions(const ExecutionContext& ctx, const conv::ProblemDescription& problem, - const size_t maxSolutionCount) + const size_t maxSolutionCount, + const AnyInvokeParams* const invokeParams) { auto algo_resolver = std::function{}; @@ -730,6 +776,7 @@ std::vector GetSolutions(const ExecutionContext& ctx, continue; const auto solver_id = solver::Id{pair.first}; + // Wrong IDs can't be used to call IsApplicable(), so let's // ignore obsolete or invalid IDs read from find-db first. if(!solver_id.IsValid()) @@ -743,20 +790,41 @@ std::vector GetSolutions(const ExecutionContext& ctx, miopenConvSolution_t{pair.second.time, pair.second.workspace, solver_id.Value(), algo}); } + /// Non-zero InvokeParams means that this function is used in Find to optimize host-side + /// performance (see Hybrid Find modes). Note that maxSolutionCount is usually 1 in this case. + /// + /// The size of the provided workspace in Hybrid Find modes is often smaller than necessary for + /// Normal Find, because GWSS in these modes return size suitable only for the "best" solver + /// \ref ffind_gwss_why_not_0. If we check IsEnoughWorkspace() for all solvers, then many false + /// warnings may be produced. That is why we have to check IsEnoughWorkspace for the + /// maxSolutionCount "best" solvers only. + /// + /// It is also highly desirable to avoid IsApplicable() checks for solutions that go beyond + /// maxSolutionCount, i.e. those that are not needed anyway. This optimization is important, for + /// example, to avoid applicability checks for MLIR solvers, since these may involve running the + /// MIIR compiler, which is very slow. + /// + /// The loop below does all the above at once. std::sort(begin(interim), end(interim), SolutionTimeComparator{}); + auto out = std::vector{}; + out.reserve(maxSolutionCount); + auto n_copied = 0; + for(const auto& s : interim) + { + const auto solver_id = solver::Id{s.solution_id}; + if(!solver_id.GetSolver().IsApplicable(ctx, problem)) + continue; + if(!conv::IsEnoughWorkspace("GetSolutions", solver_id, s.workspace_size, invokeParams)) + continue; + out.push_back(s); + if(++n_copied >= maxSolutionCount) + break; + } - // Let's avoid checks of solvers that reside beyond maxSolutionCount, - // i.e. those that unnecessary anyway. This optimization is important - // because applicability check may involve running MIIR compiler - // (for MLIR solvers), which can be very slow. - interim.resize(std::min(interim.size(), maxSolutionCount)); - const auto to_erase_from = std::remove_if(interim.begin(), interim.end(), [&](auto&& entry) { - const auto solver_id = solver::Id{entry.solution_id}; - return !solver_id.GetSolver().IsApplicable(ctx, problem); - }); - interim.erase(to_erase_from, interim.end()); + for(const auto& s : out) + MIOPEN_LOG_I2(s); - return interim; + return out; } } // namespace @@ -769,10 +837,11 @@ std::vector ConvolutionDescriptor::GetSolutions(const ExecutionContext& ctx, const conv::ProblemDescription& problem, size_t maxSolutionCount, - bool* fallbackPathTaken) const + bool* fallbackPathTaken, + const AnyInvokeParams* const invokeParams) const { MIOPEN_LOG_I(""); - auto solutions = miopen::GetSolutions(ctx, problem, maxSolutionCount); + auto solutions = miopen::GetSolutions(ctx, problem, maxSolutionCount, invokeParams); if(fallbackPathTaken != nullptr) *fallbackPathTaken = solutions.empty(); @@ -780,7 +849,7 @@ ConvolutionDescriptor::GetSolutions(const ExecutionContext& ctx, if(!solutions.empty()) return solutions; - return GetSolutionsFallback(ctx, problem, maxSolutionCount); + return GetSolutionsFallback(ctx, problem, maxSolutionCount, invokeParams); } std::size_t ConvolutionDescriptor::GetForwardSolutionWorkspaceSize(Handle& handle, @@ -885,13 +954,12 @@ void ConvolutionDescriptor::FindConvBwdDataAlgorithm(Handle& handle, return tmp; }(); - const auto invoke_ctx = conv::DataInvokeParams{InvokeType::Evaluate, - {dyDesc, dy, wDesc, w, dxDesc, dx}, + const auto invoke_ctx = conv::DataInvokeParams{{dyDesc, dy, wDesc, w, dxDesc, dx}, workSpace, workSpaceSize, this->attribute.gfx90aFp16alt.GetBwd()}; - const auto results = FindConvolution(ctx, problem, invoke_ctx); + const auto results = FindConvolution(ctx, problem, invoke_ctx, requestAlgoCount, false); if(results.empty()) { @@ -901,17 +969,8 @@ void ConvolutionDescriptor::FindConvBwdDataAlgorithm(Handle& handle, MIOPEN_THROW("No suitable algorithm was found to execute the required convolution"); } - *returnedAlgoCount = std::min(requestAlgoCount, static_cast(results.size())); - - for(int i = 0; i < *returnedAlgoCount; i++) - { - perfResults[i].bwd_data_algo = StringToConvolutionBwdDataAlgo(results[i].algorithm); - perfResults[i].time = results[i].time; - perfResults[i].memory = results[i].workspace; - } - - MIOPEN_LOG_I("BWD Chosen Algorithm: " << results[0].solver_id << " , " << results[0].workspace - << ", " << results[0].time); + FillFindReturnParameters( + results, &miopenConvAlgoPerf_t::bwd_data_algo, "BWD", returnedAlgoCount, perfResults); } static void ConvBwdCheckNumerics(const Handle& handle, @@ -936,7 +995,7 @@ static void ConvBwdCheckNumerics(const Handle& handle, flag |= miopen::checkNumericsOutput(handle, tensors.dxDesc, tensors.dx); - const auto& file_name = miopen::GetStringEnv(ENV(MIOPEN_DUMP_TENSOR_PATH)); + const auto& file_name = env::value(MIOPEN_DUMP_TENSOR_PATH); if(flag && !file_name.empty()) { DumpTensorToFileFromDevice(handle, tensors.dyDesc, tensors.dy, file_name + "_dy.bin"); @@ -965,7 +1024,11 @@ void ConvolutionDescriptor::ConvolutionBackwardData(Handle& handle, auto tensors = ConvBwdTensors{dyDesc, dy, wDesc, w, dxDesc, dx}; ValidateTensors(tensors); - ValidateAlphaBeta(alpha, beta); + Scalar alpha_val(alpha, GetScalarDataType(dxDesc)); + Scalar beta_val(beta, GetScalarDataType(dxDesc)); + const auto problem = conv::ProblemDescription{ + dyDesc, wDesc, dxDesc, *this, conv::Direction::BackwardData, 0, alpha_val, beta_val}; + ValidateAlphaBeta(problem); ConvBwdCheckNumerics(handle, tensors, beta, [&]() { if(dyDesc.GetLengths()[1] != wDesc.GetLengths()[0]) @@ -977,16 +1040,18 @@ void ConvolutionDescriptor::ConvolutionBackwardData(Handle& handle, const auto algorithm_name = AlgorithmName{ConvolutionAlgoToDirectionalString( static_cast(algo), conv::Direction::BackwardData)}; - const auto problem = - conv::ProblemDescription{dyDesc, wDesc, dxDesc, *this, conv::Direction::BackwardData}; const auto network_config = problem.MakeNetworkConfig(); const auto& invoker = handle.GetInvoker(network_config, {}, algorithm_name); if(!invoker) MIOPEN_THROW("No invoker was registered for convolution backward. Was find executed?"); - const auto& invoke_ctx = conv::DataInvokeParams{ - tensors, workSpace, workSpaceSize, this->attribute.gfx90aFp16alt.GetBwd()}; + const auto& invoke_ctx = conv::DataInvokeParams{tensors, + workSpace, + workSpaceSize, + this->attribute.gfx90aFp16alt.GetBwd(), + alpha_val, + beta_val}; (*invoker)(handle, invoke_ctx); }); } @@ -1096,13 +1161,12 @@ void ConvolutionDescriptor::FindConvBwdWeightsAlgorithm(Handle& handle, return tmp; }(); - const auto invoke_ctx = conv::WrWInvokeParams{InvokeType::Evaluate, - {dyDesc, dy, xDesc, x, dwDesc, dw}, + const auto invoke_ctx = conv::WrWInvokeParams{{dyDesc, dy, xDesc, x, dwDesc, dw}, workSpace, workSpaceSize, attribute.gfx90aFp16alt.GetWrW()}; - const auto results = FindConvolution(ctx, problem, invoke_ctx); + const auto results = FindConvolution(ctx, problem, invoke_ctx, requestAlgoCount, false); if(results.empty()) { @@ -1112,16 +1176,8 @@ void ConvolutionDescriptor::FindConvBwdWeightsAlgorithm(Handle& handle, MIOPEN_THROW("No suitable algorithm was found to execute the required convolution"); } - *returnedAlgoCount = std::min(requestAlgoCount, static_cast(results.size())); - - for(int i = 0; i < *returnedAlgoCount; i++) - { - perfResults[i].bwd_weights_algo = StringToConvolutionBwdWeightsAlgo(results[i].algorithm); - perfResults[i].time = results[i].time; - perfResults[i].memory = results[i].workspace; - } - MIOPEN_LOG_I("BWrW Chosen Algorithm: " << results[0].solver_id << " , " << results[0].workspace - << ", " << results[0].time); + FillFindReturnParameters( + results, &miopenConvAlgoPerf_t::bwd_data_algo, "BWrW", returnedAlgoCount, perfResults); } static void ConvWrwCheckNumerics(const Handle& handle, @@ -1146,7 +1202,7 @@ static void ConvWrwCheckNumerics(const Handle& handle, flag |= miopen::checkNumericsOutput(handle, tensors.dwDesc, tensors.dw); - const auto& file_name = miopen::GetStringEnv(ENV(MIOPEN_DUMP_TENSOR_PATH)); + const auto& file_name = env::value(MIOPEN_DUMP_TENSOR_PATH); if(flag && !file_name.empty()) { DumpTensorToFileFromDevice(handle, tensors.dyDesc, tensors.dy, file_name + "_dy.bin"); @@ -1173,7 +1229,12 @@ void ConvolutionDescriptor::ConvolutionBackwardWeights(const Handle& handle, ValidateWorkspace(workSpace, workSpaceSize); decltype(auto) tensors = ConvWrwTensors{dyDesc, dy, xDesc, x, dwDesc, dw}; ValidateTensors(tensors); - ValidateAlphaBeta(alpha, beta); + decltype(auto) direction = conv::Direction::BackwardWeights; + Scalar alpha_val(alpha, GetScalarDataType(dwDesc)); + Scalar beta_val(beta, GetScalarDataType(dwDesc)); + decltype(auto) problem = + conv::ProblemDescription{dyDesc, dwDesc, xDesc, *this, direction, 0, alpha_val, beta_val}; + ValidateAlphaBeta(problem); if(xDesc.GetType() == miopenInt8) MIOPEN_THROW(miopenStatusBadParm); @@ -1181,18 +1242,20 @@ void ConvolutionDescriptor::ConvolutionBackwardWeights(const Handle& handle, ConvWrwCheckNumerics(handle, tensors, beta, [&]() { ValidateGroupCount(xDesc, dwDesc, *this); - decltype(auto) direction = conv::Direction::BackwardWeights; decltype(auto) algorithm_name = AlgorithmName{ConvolutionAlgoToDirectionalString( static_cast(algo), direction)}; - decltype(auto) problem = conv::ProblemDescription{dyDesc, dwDesc, xDesc, *this, direction}; decltype(auto) network_config = problem.MakeNetworkConfig(); - decltype(auto) invoker = handle.GetInvoker(network_config, boost::none, algorithm_name); + decltype(auto) invoker = handle.GetInvoker(network_config, std::nullopt, algorithm_name); if(!invoker) MIOPEN_THROW("No invoker was registered for convolution weights. Was find executed?"); - const auto invoke_ctx = conv::WrWInvokeParams{ - tensors, workSpace, workSpaceSize, this->attribute.gfx90aFp16alt.GetWrW()}; + const auto invoke_ctx = conv::WrWInvokeParams{tensors, + workSpace, + workSpaceSize, + this->attribute.gfx90aFp16alt.GetWrW(), + alpha_val, + beta_val}; (*invoker)(handle, invoke_ctx); }); } diff --git a/src/ocl/dropoutocl.cpp b/src/ocl/dropoutocl.cpp index f8472fcbb7..fa490cdd0a 100644 --- a/src/ocl/dropoutocl.cpp +++ b/src/ocl/dropoutocl.cpp @@ -181,12 +181,12 @@ void DropoutDescriptor::DropoutForward(const Handle& handle, MIOPEN_THROW(miopenStatusBadParm); } - if(xDesc.GetSize() != yDesc.GetSize()) + if(xDesc.GetNumDims() != yDesc.GetNumDims()) { MIOPEN_THROW("Input/Output dimension does not match"); } - if(xDesc.GetSize() > 5) + if(xDesc.GetNumDims() > 5) { MIOPEN_THROW("Only support 1D to 5D tensors"); } @@ -197,7 +197,7 @@ void DropoutDescriptor::DropoutForward(const Handle& handle, } if(xDesc.GetElementSize() != noise_shape.GetElementSize() || - xDesc.GetSize() != noise_shape.GetSize()) + xDesc.GetNumDims() != noise_shape.GetNumDims()) { MIOPEN_THROW("Only support dropout with regular noise shape currently"); } @@ -277,7 +277,7 @@ void DropoutDescriptor::DropoutForward(const Handle& handle, std::to_string(wk_grp_num) /* + "-noise" + std::to_string(noise_shape.GetLengths()[0])*/; // TODO: Add noise shape - // for(int i = 1; i < noise_shape.GetSize(); i++) + // for(int i = 1; i < noise_shape.GetNumDims(); i++) // network_config += "x" + std::to_string(noise_shape.GetLengths()[i]); auto&& kernels = handle.GetKernels(kernel_name, network_config); @@ -385,12 +385,12 @@ void DropoutDescriptor::DropoutBackward(const Handle& handle, MIOPEN_THROW(miopenStatusBadParm); } - if(dxDesc.GetSize() != dyDesc.GetSize()) + if(dxDesc.GetNumDims() != dyDesc.GetNumDims()) { MIOPEN_THROW("Input/Output dimension does not match"); } - if(dyDesc.GetSize() > 5) + if(dyDesc.GetNumDims() > 5) { MIOPEN_THROW("Only support 1D to 5D tensors"); } @@ -401,7 +401,7 @@ void DropoutDescriptor::DropoutBackward(const Handle& handle, } if(dxDesc.GetElementSize() != noise_shape.GetElementSize() || - dxDesc.GetSize() != noise_shape.GetSize()) + dxDesc.GetNumDims() != noise_shape.GetNumDims()) { MIOPEN_THROW("Only support dropout with regular noise shape currently"); } @@ -482,7 +482,7 @@ void DropoutDescriptor::DropoutBackward(const Handle& handle, std::to_string(wk_grp_num) /* + "-noise" + std::to_string(noise_shape.GetLengths()[0]) */; // TODO: Add noise shape - // for(int i = 1; i < noise_shape.GetSize(); i++) + // for(int i = 1; i < noise_shape.GetNumDims(); i++) // network_config += "x" + std::to_string(noise_shape.GetLengths()[i]); auto&& kernels = handle.GetKernels(kernel_name, network_config); diff --git a/src/ocl/fusionopbiasbnactivocl.cpp b/src/ocl/fusionopbiasbnactivocl.cpp index f0e912b3f1..60ba1dcd92 100644 --- a/src/ocl/fusionopbiasbnactivocl.cpp +++ b/src/ocl/fusionopbiasbnactivocl.cpp @@ -30,17 +30,6 @@ namespace miopen { -namespace fusion { - -bool IsWinograd(const std::vector& ss) -{ - assert(ss.size() == 1); - auto solverId = ss[0].GetSolverDbId(); - return (solverId == "ConvBinWinogradRxSFused" || solverId == "ConvBinWinogradRxSf2x3g1Fused"); -} - -} // namespace fusion - miopenStatus_t FusionOpDescriptor::GetNetworkConfig(std::ostringstream& /*network_config*/) { return miopenStatusSuccess; diff --git a/src/ocl/gcn_asm_utils.cpp b/src/ocl/gcn_asm_utils.cpp index 42269fc911..cb586d8c6f 100644 --- a/src/ocl/gcn_asm_utils.cpp +++ b/src/ocl/gcn_asm_utils.cpp @@ -23,6 +23,7 @@ * SOFTWARE. * *******************************************************************************/ +#include #include #include @@ -44,11 +45,10 @@ bool ValidateGcnAssembler() { return true; } #include #include #include +#include #include #ifdef __linux__ -#include - #include #include #include @@ -73,7 +73,7 @@ static std::string CleanupPath(const char* p); std::string GetGcnAssemblerPathImpl() { - const auto& asm_path_env_p = miopen::GetStringEnv(ENV(MIOPEN_EXPERIMENTAL_GCN_ASM_PATH)); + const auto& asm_path_env_p = miopen::env::value(MIOPEN_EXPERIMENTAL_GCN_ASM_PATH); if(!asm_path_env_p.empty()) { return CleanupPath(asm_path_env_p.c_str()); @@ -93,7 +93,6 @@ std::string GetGcnAssemblerPath() bool ValidateGcnAssemblerImpl() { -#ifdef __linux__ const auto path = GetGcnAssemblerPath(); if(path.empty()) { @@ -134,7 +133,6 @@ bool ValidateGcnAssemblerImpl() return true; #endif } -#endif // __linux__ MIOPEN_LOG_NQE("Specified assembler does not support AMDGPU. Expect performance degradation."); return false; } @@ -173,17 +171,18 @@ static std::string CleanupPath(const char* p) * Not intended to be used in production code, so error handling is very straghtforward, * just catch whatever possible and throw an exception. */ -std::string AmdgcnAssemble(const std::string& source, - const std::string& params, +std::string AmdgcnAssemble(std::string_view source, + std::string_view params, const miopen::TargetProperties& target) { -#ifdef __linux__ miopen::TempFile outfile("assembly"); std::ostringstream options; options << " -x assembler -target amdgcn--amdhsa"; +#if WORKAROUND_ISSUE_3001 if(target.Xnack() && !*target.Xnack()) options << " -mno-xnack"; +#endif /// \todo Hacky way to find out which CO version we need to assemble for. if(params.find("ROCM_METADATA_VERSION=5", 0) == std::string::npos) // Assume that !COv3 == COv2. if(GcnAssemblerSupportsNoCOv3()) // If assembling for COv2, then disable COv3. @@ -196,7 +195,7 @@ std::string AmdgcnAssemble(const std::string& source, options << " - -o " << outfile.Path(); MIOPEN_LOG_I2("'" << options.str() << "'"); - std::istringstream clang_stdin(source); + std::istringstream clang_stdin(source.data()); const auto clang_path = GetGcnAssemblerPath(); const auto clang_rc = miopen::exec::Run(clang_path + " " + options.str(), &clang_stdin, nullptr); @@ -207,7 +206,7 @@ std::string AmdgcnAssemble(const std::string& source, } std::string out; - std::ifstream file(outfile, std::ios::binary | std::ios::ate); + std::ifstream file(outfile.Path(), std::ios::binary | std::ios::ate); bool outfile_read_failed = false; do { @@ -236,16 +235,10 @@ std::string AmdgcnAssemble(const std::string& source, MIOPEN_THROW("Error: X-AMDGCN-ASM: outfile_read_failed"); } return out; -#else - (void)source; // -warning - (void)params; // -warning - MIOPEN_THROW("Error: X-AMDGCN-ASM: online assembly under Windows is not supported"); -#endif //__linux__ } -static void AmdgcnAssembleQuiet(const std::string& source, const std::string& params) +static void AmdgcnAssembleQuiet(std::string_view source, std::string_view params) { -#ifdef __linux__ std::stringstream clang_stdout_unused; const auto clang_path = GetGcnAssemblerPath(); const auto args = std::string(" -x assembler -target amdgcn--amdhsa") // @@ -257,11 +250,6 @@ static void AmdgcnAssembleQuiet(const std::string& source, const std::string& pa const int clang_rc = miopen::exec::Run(clang_path + " " + args, nullptr, &clang_stdout_unused); if(clang_rc != 0) MIOPEN_THROW("Assembly error(" + std::to_string(clang_rc) + ")"); -#else - (void)source; // -warning - (void)params; // -warning - MIOPEN_THROW("Error: X-AMDGCN-ASM: online assembly under Windows is not supported"); -#endif //__linux__ } static bool GcnAssemblerHasBug34765Impl() diff --git a/src/ocl/handleocl.cpp b/src/ocl/handleocl.cpp index d08edc4896..e4f314a1a9 100644 --- a/src/ocl/handleocl.cpp +++ b/src/ocl/handleocl.cpp @@ -334,8 +334,11 @@ KernelInvoke Handle::AddKernel(const std::string& algorithm, } Invoker Handle::PrepareInvoker(const InvokerFactory& factory, - const std::vector& kernels) const + const std::vector& kernels, + std::vector* programs_out) const { + std::ignore = programs_out; + std::vector built; for(auto& k : kernels) { @@ -366,8 +369,11 @@ const std::vector& Handle::GetKernelsImpl(const std::string& algorithm, return this->impl->cache.GetKernels(algorithm, network_config); } -KernelInvoke Handle::Run(Kernel k) const +KernelInvoke Handle::Run(Kernel k, bool coop_launch) const { + if(coop_launch) + MIOPEN_THROW(miopenStatusInternalError); + auto q = this->GetStream(); if(this->impl->enable_profiling || MIOPEN_GPU_SYNC) { @@ -384,8 +390,12 @@ KernelInvoke Handle::Run(Kernel k) const Program Handle::LoadProgram(const std::string& program_name, std::string params, - const std::string& kernel_src) const + const std::string& kernel_src, + bool force_attach_binary) const { + // Binary serialization is not supported on OpenCL anyway + std::ignore = force_attach_binary; + auto hsaco = miopen::LoadBinary( this->GetTargetProperties(), this->GetMaxComputeUnits(), program_name, params); if(hsaco.empty()) @@ -408,7 +418,7 @@ Program Handle::LoadProgram(const std::string& program_name, #else auto path = miopen::GetCachePath(false) / boost::filesystem::unique_path().string(); miopen::SaveProgramBinary(p, path.string()); - miopen::SaveBinary(path.string(), this->GetTargetProperties(), program_name, params); + miopen::SaveBinary(path, this->GetTargetProperties(), program_name, params); #endif return p; } @@ -480,6 +490,8 @@ std::size_t Handle::GetMaxMemoryAllocSize() return m_MaxMemoryAllocSizeCached; } +bool Handle::CooperativeLaunchSupported() const { return false; } + std::size_t Handle::GetMaxComputeUnits() const { return miopen::GetDeviceInfo(miopen::GetDevice(this->GetStream())); diff --git a/src/ocl/pooling_ocl.cpp b/src/ocl/pooling_ocl.cpp index 86fca9004b..9881c1596f 100644 --- a/src/ocl/pooling_ocl.cpp +++ b/src/ocl/pooling_ocl.cpp @@ -80,7 +80,7 @@ miopenStatus_t PoolingDescriptor::Forward(Handle& handle, } } - int pool_dim = xDesc.GetSize(); + unsigned pool_dim = xDesc.GetNumDims(); if(pool_dim != 4 && pool_dim != 5) { MIOPEN_THROW("Unsupported pooling dimension"); @@ -171,7 +171,7 @@ miopenStatus_t PoolingDescriptor::Backward(Handle& handle, assert(yDesc.GetElementSize() == dyDesc.GetElementSize() && xDesc.GetElementSize() == dxDesc.GetElementSize()); - int pool_dim = dyDesc.GetSize(); + unsigned pool_dim = dyDesc.GetNumDims(); if(pool_dim != 4 && pool_dim != 5) { MIOPEN_THROW("Unsupported pooling dimension"); diff --git a/src/ocl/rnnocl.cpp b/src/ocl/rnnocl.cpp index 9d042c12b0..1757358544 100644 --- a/src/ocl/rnnocl.cpp +++ b/src/ocl/rnnocl.cpp @@ -37,18 +37,24 @@ #include MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_RNNFWD_exp) +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_RNNFWD_MS_DISPATCH) +MIOPEN_DECLARE_ENV_VAR_UINT64(MIOPEN_RNN_MS_STREAM_CNT) + +MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_MS_WA_FIX) namespace miopen { namespace { +#if MIOPEN_USE_ROCBLAS + bool RNNForwardMSIsSupported([[maybe_unused]] const RNNDescriptor& desctiptor, [[maybe_unused]] bool use_dropout) { #if MIOPEN_USE_GEMM && MIOPEN_BACKEND_HIP if(desctiptor.rnnMode == miopenLSTM && desctiptor.algoMode == miopenRNNdefault && !use_dropout && desctiptor.nLayers > 1 && desctiptor.dirMode == miopenRNNunidirection && - desctiptor.inputMode != miopenRNNskip && !(miopen::IsDisabled(ENV(MIOPEN_RNNFWD_exp)))) + desctiptor.inputMode != miopenRNNskip) { return true; } @@ -56,6 +62,16 @@ bool RNNForwardMSIsSupported([[maybe_unused]] const RNNDescriptor& desctiptor, return false; } +bool RNNForwardMSIsFast(const int seqLen) +{ + if(env::enabled(MIOPEN_RNNFWD_exp)) + return true; + + if(seqLen >= 32 && !env::disabled(MIOPEN_RNNFWD_exp)) + return true; + return false; +} + void checkGemmStatusAndLog(miopenStatus_t gemm_status) { if(gemm_status != miopenStatusSuccess) @@ -233,6 +249,8 @@ miopenStatus_t ReducAddBias(miopen::Handle& handle, return miopenStatusSuccess; } +#endif // MIOPEN_USE_ROCBLAS + } // namespace void RNNDescriptor::RNNForwardMS(Handle& handle, @@ -263,18 +281,80 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, std::tie(std::ignore, max_batch, hidden_size) = miopen::tien<3>(hxDesc.GetLengths()); - auto extra_stream_cnt = 2; - handle.ReserveExtraStreamsInPool(extra_stream_cnt); + const int extra_stream_cnt = env::value_or(MIOPEN_RNN_MS_STREAM_CNT, 4); - auto root_stream_id = 0; - std::vector stream_pull; - for(int i = 0; i <= extra_stream_cnt; i++) + class MultiStreamController { - handle.SetStreamFromPool(i); - stream_pull.push_back(handle.GetStream()); - } + public: + MultiStreamController(Handle& handle, int extra_stream_cnt) + : streamPoolIdsMapping{init_stream_pool_ids(handle, extra_stream_cnt)}, + streamPoolCache{init_stream_pool(handle, streamPoolIdsMapping)}, + activeHandle{handle} + { + } + + hipStream_t getStream(int stream_id) const { return streamPoolCache[stream_id]; } + + void ChangeActiveStream(int stream_id) const + { + activeHandle.SetStreamFromPool(streamPoolIdsMapping[stream_id]); + } + + hipError_t RecordEvent(const hipEvent_t event, int stream_id) const + { + return hipEventRecord(event, getStream(stream_id)); + } + + hipError_t SetWaitEvent(const hipEvent_t event, int stream_id) const + { + return hipStreamWaitEvent(getStream(stream_id), event, 0); + } + + size_t size() const { return streamPoolIdsMapping.size(); } + + private: + static std::vector init_stream_pool_ids(const Handle& handle, int extra_stream_cnt) + { + std::vector ids; + ids.reserve(extra_stream_cnt + 1); + + bool ms_wa_fix_active = extra_stream_cnt > 2 && !env::disabled(MIOPEN_MS_WA_FIX); + int wa_steams = ms_wa_fix_active ? 2 : 0; + + handle.ReserveExtraStreamsInPool(extra_stream_cnt + wa_steams); + + for(int i = 0; i <= extra_stream_cnt + wa_steams; i++) + if(!(ms_wa_fix_active && (i == 3 || i == 4))) + ids.push_back(i); + + return ids; + } + + static std::vector init_stream_pool(const Handle& handle, + const std::vector& pool_ids) + { + std::vector pool; + pool.reserve(pool_ids.size()); + + for(auto id : pool_ids) + { + handle.SetStreamFromPool(id); + pool.push_back(handle.GetStream()); + } + handle.SetStreamFromPool(0); + + return pool; + } + + const std::vector streamPoolIdsMapping; + const std::vector streamPoolCache; + const Handle& activeHandle; + }; + + MultiStreamController ms_controller{handle, extra_stream_cnt}; - handle.SetStreamFromPool(root_stream_id); + constexpr auto root_stream_id = 0; + ms_controller.ChangeActiveStream(root_stream_id); int total_batch_size = 0; std::vector bacc_per_time(seq_len + 1); @@ -753,14 +833,17 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, &in_n, &handle, &wDesc, + &ms_controller, extra_space, hy, cy, max_batch, hidden_size, - seq_len](int layer_id) { + seq_len](int layer_id, int extra_stream_id) { if(hy != nullptr || (cy != nullptr)) { + ms_controller.ChangeActiveStream(extra_stream_id); + auto hcy_layer_offset = get_HxBuff_offset(layer_id); const std::vector hcy_src_stride{ @@ -836,26 +919,26 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, } }; - auto call_sync_all_stream_pull_to_root_stream = [&stream_pull, root_stream_id]() { + auto call_sync_all_stream_pool_to_root_stream = [&ms_controller]() { const miopen::HipEventPtr main_event = make_hip_fast_event(); - hipEventRecord(main_event.get(), stream_pull[root_stream_id]); - for(int i = 0; i < stream_pull.size(); i++) + ms_controller.RecordEvent(main_event.get(), root_stream_id); + + for(size_t i = 0; i < ms_controller.size(); i++) { if(i != root_stream_id) - hipStreamWaitEvent(stream_pull[i], main_event.get(), 0); + ms_controller.SetWaitEvent(main_event.get(), i); } }; - auto sync_root_to_all_stream_pull = [&stream_pull, root_stream_id]() { - hipStream_t root_stream = stream_pull[root_stream_id]; - for(int i = 0; i < stream_pull.size(); i++) + auto sync_root_to_all_stream_pool = [&ms_controller]() { + for(size_t i = 0; i < ms_controller.size(); i++) { if(i != root_stream_id) { const miopen::HipEventPtr sync_event = make_hip_fast_event(); - hipEventRecord(sync_event.get(), stream_pull[i]); - hipStreamWaitEvent(root_stream, sync_event.get(), 0); + ms_controller.RecordEvent(sync_event.get(), i); + ms_controller.SetWaitEvent(sync_event.get(), root_stream_id); } } }; @@ -863,9 +946,9 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, if(seq_len == 0) return; - const int try_chunks_cnt = 16; - const int time_chunk_sz = ((seq_len + try_chunks_cnt - 1) / try_chunks_cnt); - const int chunks_cnt = (seq_len + time_chunk_sz - 1) / time_chunk_sz; + constexpr int try_chunks_cnt = 16; + const int time_chunk_sz = ((seq_len + try_chunks_cnt - 1) / try_chunks_cnt); + const int chunks_cnt = (seq_len + time_chunk_sz - 1) / time_chunk_sz; std::vector layer_inx_cur_time(nLayers, 0); std::vector layer_hx_cur_time(nLayers, 0); @@ -881,15 +964,12 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, layer_chunk_end_event[layer_id][chunk_id] = make_hip_fast_event(); } - std::vector layer_stream_id(nLayers, 2); - layer_stream_id[0] = 1; - auto call_inx_next_chunk_preload = [&](int layer_id) { auto start_time = layer_inx_cur_time[layer_id]; auto time_cnt = std::min(time_chunk_sz, seq_len - start_time); call_x_gemm(layer_id, start_time, time_cnt); - layer_inx_cur_time[layer_id] += time_chunk_sz; + layer_inx_cur_time[layer_id] += time_cnt; }; auto call_hx_next_gemm = [&](int layer_id) { @@ -910,28 +990,19 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, } }; - auto call_next_chunk_compute = [&handle, - &stream_pull, - &layer_stream_id, - &call_next_hidden_state_update, + auto call_next_chunk_compute = [&call_next_hidden_state_update, &call_hx_next_gemm, &call_inx_next_chunk_preload, &layer_upd_cur_time, &layer_chunk_end_event, + &ms_controller, time_chunk_sz, - seq_len](int layer_id) { - auto stream_id = layer_stream_id[layer_id]; - handle.SetStreamFromPool(stream_id); + seq_len](int layer_id, int stream_id) { + ms_controller.ChangeActiveStream(stream_id); const int chunk_id = layer_upd_cur_time[layer_id] / time_chunk_sz; const int chunk_time = std::min(time_chunk_sz, seq_len - chunk_id * time_chunk_sz); - if(layer_id > 0 && layer_stream_id[layer_id - 1] != stream_id) - { - hipStreamWaitEvent( - stream_pull[stream_id], layer_chunk_end_event[layer_id - 1][chunk_id].get(), 0); - } - if(!(layer_id == 0 && chunk_id == 1)) { call_inx_next_chunk_preload(layer_id); @@ -942,9 +1013,27 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, call_hx_next_gemm(layer_id); call_next_hidden_state_update(layer_id); } - hipEventRecord(layer_chunk_end_event[layer_id][chunk_id].get(), stream_pull[stream_id]); + ms_controller.RecordEvent(layer_chunk_end_event[layer_id][chunk_id].get(), stream_id); }; + auto sync_next_chunk_across_time = [&layer_chunk_end_event, + &ms_controller](int stream_id, int layer_id, int chunk_id) { + if(chunk_id > 0) + { + ms_controller.SetWaitEvent(layer_chunk_end_event[layer_id][chunk_id - 1].get(), + stream_id); + } + }; + + auto sync_next_chunk_across_layers = + [&layer_chunk_end_event, &ms_controller](int stream_id, int layer_id, int chunk_id) { + if(layer_id > 0) + { + ms_controller.SetWaitEvent(layer_chunk_end_event[layer_id - 1][chunk_id].get(), + stream_id); + } + }; + { // extra_space clean set 0 const int fill_val = 0; // if(biasMode == 0u) req @@ -954,19 +1043,19 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, // stage 0 bias and input preload // stage 0.2 first chunk compute and preload { - call_sync_all_stream_pull_to_root_stream(); + call_sync_all_stream_pool_to_root_stream(); const auto first_layer_id = 0; - const auto stream_id = layer_stream_id[first_layer_id]; // 1 + const auto stream_id = 1; // 1 const auto extra_stream_id = 2; - handle.SetStreamFromPool(stream_id); + ms_controller.ChangeActiveStream(stream_id); if(biasMode != 0u) call_bias_add(first_layer_id); - call_next_chunk_compute(first_layer_id); + call_next_chunk_compute(first_layer_id, stream_id); - handle.SetStreamFromPool(extra_stream_id); + ms_controller.ChangeActiveStream(extra_stream_id); if(biasMode != 0u) { @@ -978,62 +1067,156 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, // sync first to second stream const miopen::HipEventPtr next_chunk_inx = make_hip_fast_event(); - hipEventRecord(next_chunk_inx.get(), stream_pull[extra_stream_id]); - hipStreamWaitEvent(stream_pull[stream_id], next_chunk_inx.get(), 0); + ms_controller.RecordEvent(next_chunk_inx.get(), extra_stream_id); + ms_controller.SetWaitEvent(next_chunk_inx.get(), stream_id); } - for(int layer_id = 0; layer_id < nLayers; layer_id++) - { + auto spiral_dispatch = [&](int first_stream, int last_stream) { + auto layers_last_state = layer_upd_cur_time; - const auto main_stream_id = 1; - handle.SetStreamFromPool(main_stream_id); + auto update_last_state = [&layer_upd_cur_time, &layers_last_state]() { + std::copy( + layer_upd_cur_time.begin(), layer_upd_cur_time.end(), layers_last_state.begin()); + }; + + auto is_dispatchable = [&layers_last_state, seq_len, time_chunk_sz](int layer, + int dispatch_chunks) { + auto cur_seq_time = layers_last_state[layer]; + return seq_len <= cur_seq_time ? false + : layer == 0 + ? true + : layers_last_state[layer - 1] >= + std::min(cur_seq_time + (dispatch_chunks * time_chunk_sz), seq_len); + }; + + auto try_dispatch_next_chunk = + [&layer_upd_cur_time, + &sync_next_chunk_across_time, + &sync_next_chunk_across_layers, + &call_next_chunk_compute, + &is_dispatchable, + time_chunk_sz](int layer_id, int stream_id, int chunk_to_dispatch) -> bool { + if(!is_dispatchable(layer_id, chunk_to_dispatch)) + return false; - // check for wich stream was assigned this layer. If it differs from current - set stream - // wait event - if(layer_stream_id[layer_id] != main_stream_id) - { auto chunk_id = layer_upd_cur_time[layer_id] / time_chunk_sz; - if(chunk_id > 0) + + sync_next_chunk_across_time(stream_id, layer_id, chunk_id); + sync_next_chunk_across_layers(stream_id, layer_id, chunk_id); + + call_next_chunk_compute(layer_id, stream_id); + return true; + }; + + auto try_dispatch_hy_cy_printout = [&layer_upd_cur_time, &call_hy_cy_update, seq_len]( + int layer_id, int stream_id) -> bool { + if(layer_upd_cur_time[layer_id] < seq_len) + return false; + + call_hy_cy_update(layer_id, stream_id); + return true; + }; + + const auto stream_round = last_stream - first_stream + 1; + bool nothing_to_dispatch = false; + while(!nothing_to_dispatch) + { + update_last_state(); + nothing_to_dispatch = true; + int stream_it = 0; + + for(int cur_layer = 0; cur_layer < nLayers; cur_layer++) { - hipStreamWaitEvent(stream_pull[main_stream_id], - layer_chunk_end_event[layer_id][chunk_id - 1].get(), - 0); + const auto dispatch_stream = first_stream + stream_it; + if(try_dispatch_next_chunk(cur_layer, dispatch_stream, 1)) + { + try_dispatch_hy_cy_printout(cur_layer, dispatch_stream); + stream_it = (stream_it + 1) % stream_round; + nothing_to_dispatch = false; + } } - - layer_stream_id[layer_id] = main_stream_id; } + }; - const int start_chunk = layer_upd_cur_time[layer_id] / time_chunk_sz; + enum class DispatchStrategy + { + OldMasterSlave = 0, + Spiral = 1, + } dispatch_strategy = + static_cast(env::value_or( + MIOPEN_RNNFWD_MS_DISPATCH, + static_cast(DispatchStrategy::Spiral))); // what am I doing wrong? + + if(dispatch_strategy == DispatchStrategy::Spiral) + { + const auto first_stream = extra_stream_cnt > 0 ? 1 : 0; + const auto last_stream = extra_stream_cnt > 0 ? extra_stream_cnt : 0; - const int extra_layer_max_chunks = - start_chunk + - ((layer_id + 1 < nLayers - 1) ? (chunks_cnt - start_chunk) / 2 : chunks_cnt); + spiral_dispatch(first_stream, last_stream); + } + else + { + std::vector layer_stream_id(nLayers, 2); + layer_stream_id[0] = 1; + + auto dispatch_next_chunk = [&layer_upd_cur_time, + sync_next_chunk_across_layers, + call_next_chunk_compute, + time_chunk_sz](int layer_id, int stream_id) { + auto chunk_id = layer_upd_cur_time[layer_id] / time_chunk_sz; - for(int chunk_id = start_chunk; chunk_id < chunks_cnt; chunk_id++) + sync_next_chunk_across_layers(stream_id, layer_id, chunk_id); + + call_next_chunk_compute(layer_id, stream_id); + }; + + for(int layer_id = 0; layer_id < nLayers; layer_id++) { + const auto main_stream_id = 1; + ms_controller.ChangeActiveStream(main_stream_id); - call_next_chunk_compute(layer_id); + // check for wich stream was assigned this layer. If it differs from current - set + // stream wait event + if(layer_stream_id[layer_id] != main_stream_id) + { + auto chunk_id = layer_upd_cur_time[layer_id] / time_chunk_sz; + + sync_next_chunk_across_time(main_stream_id, layer_id, chunk_id); - int extra_compute_layer = layer_id + 1; - for(; extra_compute_layer < nLayers; extra_compute_layer++) + layer_stream_id[layer_id] = main_stream_id; + } + + const int start_chunk = layer_upd_cur_time[layer_id] / time_chunk_sz; + + const int extra_layer_max_chunks = + start_chunk + + ((layer_id + 1 < nLayers - 1) ? (chunks_cnt - start_chunk) / 2 : chunks_cnt); + + for(int chunk_id = start_chunk; chunk_id < chunks_cnt; chunk_id++) { - auto extra_chunk_id = layer_upd_cur_time[extra_compute_layer] / time_chunk_sz; - if(extra_chunk_id < extra_layer_max_chunks && extra_chunk_id <= chunk_id) - break; + dispatch_next_chunk(layer_id, layer_stream_id[layer_id]); + + int extra_compute_layer = layer_id + 1; + for(; extra_compute_layer < nLayers; extra_compute_layer++) + { + auto extra_chunk_id = layer_upd_cur_time[extra_compute_layer] / time_chunk_sz; + if(extra_chunk_id < extra_layer_max_chunks && extra_chunk_id <= chunk_id) + break; + } + + if(extra_compute_layer < nLayers) + dispatch_next_chunk(extra_compute_layer, layer_stream_id[extra_compute_layer]); } - if(extra_compute_layer < nLayers) - call_next_chunk_compute(extra_compute_layer); + // update hy, cy + call_hy_cy_update(layer_id, main_stream_id); } - - handle.SetStreamFromPool(main_stream_id); - // update hy, cy - call_hy_cy_update(layer_id); } - handle.SetStreamFromPool(root_stream_id); - hipStreamWaitEvent( - stream_pull[root_stream_id], layer_chunk_end_event[nLayers - 1][chunks_cnt - 1].get(), 0); + ms_controller.ChangeActiveStream(root_stream_id); + + ms_controller.SetWaitEvent(layer_chunk_end_event[nLayers - 1][chunks_cnt - 1].get(), + root_stream_id); // output tensor copy { @@ -1053,7 +1236,7 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, handle, src_desc, extra_space, y_dst_desc, y, RBuff.ht_offset(nLayers - 1, 0), 0); } - sync_root_to_all_stream_pull(); + sync_root_to_all_stream_pool(); #else (void)handle; (void)seq_array; @@ -1068,8 +1251,6 @@ void RNNDescriptor::RNNForwardMS(Handle& handle, (void)y; (void)hy; (void)cy; - (void)reserveSpace; - (void)reserveSpaceSize; MIOPEN_THROW("GEMM is not supported"); #endif @@ -1099,8 +1280,8 @@ void RNNDescriptor::RNNForwardInference(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(hxDesc.GetSize() != cxDesc.GetSize() || hxDesc.GetSize() != hyDesc.GetSize() || - hxDesc.GetSize() != cyDesc.GetSize()) + if(hxDesc.GetNumDims() != cxDesc.GetNumDims() || hxDesc.GetNumDims() != hyDesc.GetNumDims() || + hxDesc.GetNumDims() != cyDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -1300,7 +1481,7 @@ void RNNDescriptor::RNNForwardInferencePacked(Handle& handle, } // input check end - if(RNNForwardMSIsSupported(*this, false) && xDesc[0].GetType() == miopenFloat && seqLen >= 32) + if(RNNForwardMSIsSupported(*this, false) && RNNForwardMSIsFast(seqLen)) { return RNNForwardMS(handle, in_n, @@ -2488,8 +2669,8 @@ void RNNDescriptor::RNNForwardTraining(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(hxDesc.GetSize() != cxDesc.GetSize() || hxDesc.GetSize() != hyDesc.GetSize() || - hxDesc.GetSize() != cyDesc.GetSize()) + if(hxDesc.GetNumDims() != cxDesc.GetNumDims() || hxDesc.GetNumDims() != hyDesc.GetNumDims() || + hxDesc.GetNumDims() != cyDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -2694,8 +2875,7 @@ void RNNDescriptor::RNNForwardTrainingPackedTensors( // input check end bool use_dropout = !float_equal(miopen::deref(dropoutDesc).dropout, 0); - if(RNNForwardMSIsSupported(*this, use_dropout) && xDesc[0].GetType() == miopenFloat && - seqLen >= 32) + if(RNNForwardMSIsSupported(*this, false) && RNNForwardMSIsFast(seqLen)) { return RNNForwardMS(handle, in_n, @@ -3973,9 +4153,9 @@ void RNNDescriptor::RNNBackwardData(Handle& handle, { MIOPEN_THROW(miopenStatusBadParm); } - if(dhyDesc.GetSize() != dcyDesc.GetSize() || dhyDesc.GetSize() != hxDesc.GetSize() || - dhyDesc.GetSize() != cxDesc.GetSize() || dhyDesc.GetSize() != dhxDesc.GetSize() || - dhyDesc.GetSize() != dcxDesc.GetSize()) + if(dhyDesc.GetNumDims() != dcyDesc.GetNumDims() || + dhyDesc.GetNumDims() != hxDesc.GetNumDims() || dhyDesc.GetNumDims() != cxDesc.GetNumDims() || + dhyDesc.GetNumDims() != dhxDesc.GetNumDims() || dhyDesc.GetNumDims() != dcxDesc.GetNumDims()) { MIOPEN_THROW(miopenStatusBadParm); } @@ -4108,7 +4288,8 @@ void RNNDescriptor::RNNBackwardDataPackedTensors( // if projections supported, dcxDesc.GetLengths()[2] should be used for hidden_size, // dhxDesc.GetLengths()[2] for proj_size. - if(dhxDesc.GetSize() != dcxDesc.GetSize() || dhxDesc.GetLengths()[2] != dcxDesc.GetLengths()[2]) + if(dhxDesc.GetNumDims() != dcxDesc.GetNumDims() || + dhxDesc.GetLengths()[2] != dcxDesc.GetLengths()[2]) { MIOPEN_THROW(miopenStatusBadParm); } diff --git a/src/ocl/tensorocl.cpp b/src/ocl/tensorocl.cpp index 152da125b0..74717f50ea 100644 --- a/src/ocl/tensorocl.cpp +++ b/src/ocl/tensorocl.cpp @@ -1430,15 +1430,14 @@ void SetTensor(const Handle& handle, const TensorDescriptor yDesc_flat = GetFlattenedTensorDescriptor(yDesc); #ifndef NDEBUG - if(yDesc.GetSize() != yDesc_flat.GetSize()) + if(yDesc.GetNumDims() != yDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "real descriptor: " << yDesc << std::endl - << "flat descriptor: " << yDesc_flat << std::endl); + MIOPEN_LOG_I2("real descriptor: " << yDesc); + MIOPEN_LOG_I2("flat descriptor: " << yDesc_flat); } #endif - const std::size_t yDim_flat = yDesc_flat.GetSize(); + const std::size_t yDim_flat = yDesc_flat.GetNumDims(); assert(yDim_flat > 0 && yDim_flat <= 5); @@ -1584,15 +1583,14 @@ void ScaleTensor(const Handle& handle, const TensorDescriptor yDesc_flat = GetFlattenedTensorDescriptor(yDesc); #ifndef NDEBUG - if(yDesc.GetSize() != yDesc_flat.GetSize()) + if(yDesc.GetNumDims() != yDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "real descriptor: " << yDesc << std::endl - << "flat descriptor: " << yDesc_flat << std::endl); + MIOPEN_LOG_I2("real descriptor: " << yDesc); + MIOPEN_LOG_I2("flat descriptor: " << yDesc_flat); } #endif - const std::size_t yDim_flat = yDesc_flat.GetSize(); + const std::size_t yDim_flat = yDesc_flat.GetNumDims(); assert(yDim_flat > 0 && yDim_flat <= 5); @@ -1763,17 +1761,16 @@ void CopyTensor(const Handle& handle, const TensorDescriptor& dstDesc_flat = std::get<1>(flat_descriptors); #ifndef NDEBUG - if(srcDesc.GetSize() != srcDesc_flat.GetSize()) + if(srcDesc.GetNumDims() != srcDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "src real descriptor: " << srcDesc << std::endl - << "src flat descriptor: " << srcDesc_flat << std::endl - << "dst real descriptor: " << dstDesc << std::endl - << "dst flat descriptor: " << dstDesc_flat << std::endl); + MIOPEN_LOG_I2("src real descriptor: " << srcDesc); + MIOPEN_LOG_I2("src flat descriptor: " << srcDesc_flat); + MIOPEN_LOG_I2("dst real descriptor: " << dstDesc); + MIOPEN_LOG_I2("dst flat descriptor: " << dstDesc_flat); } #endif - std::size_t srcDim_flat = srcDesc_flat.GetSize(); + std::size_t srcDim_flat = srcDesc_flat.GetNumDims(); if(srcDim_flat < 1 || srcDim_flat > 5) { @@ -1944,6 +1941,8 @@ std::string GetCastTensorBuildOptionFromType(const std::string& buildOption, mio case miopenDouble: // TODO MIOPEN_THROW(miopenStatusBadParm, "miopenDouble data type not supported in cast tensor."); + case miopenInt64: + MIOPEN_THROW(miopenStatusBadParm, "miopenInt64 data type not supported in cast tensor."); default: MIOPEN_THROW(miopenStatusBadParm, "Invalid data type in cast tensor desc."); } } @@ -1973,17 +1972,16 @@ void CastTensor(const Handle& handle, const TensorDescriptor& dstDesc_flat = std::get<1>(flat_descriptors); #ifndef NDEBUG - if(srcDesc.GetSize() != srcDesc_flat.GetSize()) + if(srcDesc.GetNumDims() != srcDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "src real descriptor: " << srcDesc << std::endl - << "src flat descriptor: " << srcDesc_flat << std::endl - << "dst real descriptor: " << dstDesc << std::endl - << "dst flat descriptor: " << dstDesc_flat << std::endl); + MIOPEN_LOG_I2("src real descriptor: " << srcDesc); + MIOPEN_LOG_I2("src flat descriptor: " << srcDesc_flat); + MIOPEN_LOG_I2("dst real descriptor: " << dstDesc); + MIOPEN_LOG_I2("dst flat descriptor: " << dstDesc_flat); } #endif - std::size_t srcDim_flat = srcDesc_flat.GetSize(); + std::size_t srcDim_flat = srcDesc_flat.GetNumDims(); if(srcDim_flat < 1 || srcDim_flat > 5) { @@ -2257,22 +2255,20 @@ void TransformTensor(const Handle& handle, const TensorDescriptor& yDesc_flat = std::get<1>(flat_descriptors); #ifndef NDEBUG - if(xDesc.GetSize() != xDesc_flat.GetSize()) + if(xDesc.GetNumDims() != xDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "real descriptor: " << xDesc << std::endl - << "flat descriptor: " << xDesc_flat << std::endl); + MIOPEN_LOG_I2("x real descriptor: " << xDesc); + MIOPEN_LOG_I2("x flat descriptor: " << xDesc_flat); } - if(yDesc.GetSize() != yDesc_flat.GetSize()) + if(yDesc.GetNumDims() != yDesc_flat.GetNumDims()) { - MIOPEN_LOG_I2(__func__ << std::endl - << "real descriptor: " << yDesc << std::endl - << "flat descriptor: " << yDesc_flat << std::endl); + MIOPEN_LOG_I2("y real descriptor: " << yDesc); + MIOPEN_LOG_I2("y flat descriptor: " << yDesc_flat); } #endif - const std::size_t yDim_flat = yDesc_flat.GetSize(); + const std::size_t yDim_flat = yDesc_flat.GetNumDims(); assert(yDim_flat > 0 && yDim_flat <= 5); diff --git a/src/ocl/utilocl.cpp b/src/ocl/utilocl.cpp index 027e28975b..d5dd4661e5 100644 --- a/src/ocl/utilocl.cpp +++ b/src/ocl/utilocl.cpp @@ -23,15 +23,17 @@ * SOFTWARE. * *******************************************************************************/ -#include +#include +#include #include -#include #include -#include -#include +#include #include +#include +#include + #define WG_SIZE (static_cast(256)) #define MAX_ACTIVE_THREADS (64 * 4 * 64) #define MAX_LOCAL_MEM 65536 @@ -40,7 +42,7 @@ namespace miopen { float Im2d2ColGPU(const Handle& handle, ConstData_t im, - const int im_offset, + const size_t im_offset, const int c, const int in_h, const int in_w, @@ -58,7 +60,7 @@ float Im2d2ColGPU(const Handle& handle, miopenDataType_t type) { std::string program_name = "MIOpenIm2d2Col.cl"; - std::string kernel_name = "Im2d2Col"; + std::string kernel_name = "Im2d2Col_v2"; // clang-format off std::string network_config = @@ -78,17 +80,14 @@ float Im2d2ColGPU(const Handle& handle, auto&& kernels = handle.GetKernels("miopenIm2d2Col", network_config); - int data_size_bound = c * in_h * in_w; - - int data_size_bound_pack = data_size_bound; - int im_offset_pack = im_offset; + const int data_size_bound = c * in_h * in_w; if(!kernels.empty()) { auto kernel = kernels.front(); - kernel(data_size_bound_pack, + kernel(data_size_bound, im, - im_offset_pack, + im_offset, in_h, in_w, wei_h, @@ -254,9 +253,9 @@ float Im2d2ColGPU(const Handle& handle, handle.AddKernel( "miopenIm2Col", network_config, program_name, kernel_name, vld, vgd, params)( - data_size_bound_pack, + data_size_bound, im, - im_offset_pack, + im_offset, in_h, in_w, wei_h, @@ -403,25 +402,25 @@ float Im3d2ColGPU(const Handle& handle, float Col2Im2dGPU(const Handle& handle, ConstData_t col, - const int out_h, - const int out_w, - const int wei_h, - const int wei_w, - const int pad_h, - const int pad_w, - const int stride_h, - const int stride_w, - const int dilation_h, - const int dilation_w, - const int in_c, - const int in_h, - const int in_w, + const uint32_t out_h, + const uint32_t out_w, + const uint32_t wei_h, + const uint32_t wei_w, + const uint32_t pad_h, + const uint32_t pad_w, + const uint32_t stride_h, + const uint32_t stride_w, + const uint32_t dilation_h, + const uint32_t dilation_w, + const uint32_t in_c, + const uint32_t in_h, + const uint32_t in_w, Data_t im, - int im_offset, + uint32_t im_offset, miopenDataType_t type) { std::string program_name = "MIOpenCol2Im2d.cl"; - std::string kernel_name = "Col2Im2d"; + std::string kernel_name = "Col2Im2dU"; // clang-format off std::string network_config = @@ -468,6 +467,17 @@ float Col2Im2dGPU(const Handle& handle, const std::vector vgd{global_threads, 1, 1}; const std::vector vld{std::min(WG_SIZE, global_threads), 1, 1}; + auto Is64BitIndexRequired = [&]() -> int { + const auto im_ch_max = global_threads / static_cast(in_w * in_h); + const auto ch_offset_max = im_ch_max * out_w * out_h * wei_w * wei_h; + MIOPEN_LOG_T("global_threads, out_h, out_w, wei_h, wei_w = " + << '{' << global_threads << ',' << out_h << ',' << out_w << ',' << wei_h + << ',' << wei_w << '}' << " ch_offset_max = " << ch_offset_max); + return (ch_offset_max > 0xffffffffULL) ? 1 : 0; + }; + + params += " -DMIOPEN_USE_64BIT_INDEX=" + std::to_string(Is64BitIndexRequired()); + handle.AddKernel( "miopenCol2Im2d", network_config, program_name, kernel_name, vld, vgd, params)( col, @@ -491,31 +501,31 @@ float Col2Im2dGPU(const Handle& handle, float Col2Im3dGPU(const Handle& handle, ConstData_t col, - const int out_d, - const int out_h, - const int out_w, - const int wei_d, - const int wei_h, - const int wei_w, - const int pad_d, - const int pad_h, - const int pad_w, - const int stride_d, - const int stride_h, - const int stride_w, - const int dilation_d, - const int dilation_h, - const int dilation_w, - const int in_c, - const int in_d, - const int in_h, - const int in_w, + const uint32_t out_d, + const uint32_t out_h, + const uint32_t out_w, + const uint32_t wei_d, + const uint32_t wei_h, + const uint32_t wei_w, + const uint32_t pad_d, + const uint32_t pad_h, + const uint32_t pad_w, + const uint32_t stride_d, + const uint32_t stride_h, + const uint32_t stride_w, + const uint32_t dilation_d, + const uint32_t dilation_h, + const uint32_t dilation_w, + const uint32_t in_c, + const uint32_t in_d, + const uint32_t in_h, + const uint32_t in_w, Data_t im, std::size_t im_offset, miopenDataType_t type) { std::string program_name = "MIOpenCol2Im3d.cl"; - std::string kernel_name = "Col2Im3d"; + std::string kernel_name = "Col2Im3dU"; // clang-format off std::string network_config = @@ -700,12 +710,12 @@ float Col2ImGPU( out_spatial[1], wei_spatial[0], wei_spatial[1], - pad_spatial[0], - pad_spatial[1], - stride_spatial[0], - stride_spatial[1], - dilation_spatial[0], - dilation_spatial[1], + static_cast(pad_spatial[0]), + static_cast(pad_spatial[1]), + static_cast(stride_spatial[0]), + static_cast(stride_spatial[1]), + static_cast(dilation_spatial[0]), + static_cast(dilation_spatial[1]), in_c, in_spatial[0], in_spatial[1], @@ -722,15 +732,15 @@ float Col2ImGPU( wei_spatial[0], wei_spatial[1], wei_spatial[2], - pad_spatial[0], - pad_spatial[1], - pad_spatial[2], - stride_spatial[0], - stride_spatial[1], - stride_spatial[2], - dilation_spatial[0], - dilation_spatial[1], - dilation_spatial[2], + static_cast(pad_spatial[0]), + static_cast(pad_spatial[1]), + static_cast(pad_spatial[2]), + static_cast(stride_spatial[0]), + static_cast(stride_spatial[1]), + static_cast(stride_spatial[2]), + static_cast(dilation_spatial[0]), + static_cast(dilation_spatial[1]), + static_cast(dilation_spatial[2]), in_c, in_spatial[0], in_spatial[1], @@ -802,9 +812,9 @@ float transpose_NCHW2CNHW(const Handle& handle, auto&& kernels = handle.GetKernels(kernel_name, network_config); if(!kernels.empty()) { - auto kernel = kernels.front(); - kernel.ldims = {{vld[0], vld[1], vld[2]}}; - kernel.gdims = {{vgd[0], vgd[1], vgd[2]}}; + auto kernel = kernels.front(); + kernel.SetLocalDims(vld[0], vld[1], vld[2]); + kernel.SetGlobalDims(vgd[0], vgd[1], vgd[2]); kernel(in, out, in_offset, out_offset, RD_BLCK, HW_RD, n, c, h_in, w_in); } else @@ -843,9 +853,9 @@ float transpose_NCHW2CNHW(const Handle& handle, auto&& kernels = handle.GetKernels(kernel_name, network_config); if(!kernels.empty()) { - auto kernel = kernels.front(); - kernel.ldims = {{vld[0], vld[1], vld[2]}}; - kernel.gdims = {{vgd[0], vgd[1], vgd[2]}}; + auto kernel = kernels.front(); + kernel.SetLocalDims(vld[0], vld[1], vld[2]); + kernel.SetGlobalDims(vgd[0], vgd[1], vgd[2]); kernel(in, out, in_offset, @@ -935,9 +945,9 @@ float transpose_CNHW2NCHW(const Handle& handle, auto&& kernels = handle.GetKernels(kernel_name, network_config); if(!kernels.empty()) { - auto kernel = kernels.front(); - kernel.ldims = {{vld[0], vld[1], vld[2]}}; - kernel.gdims = {{vgd[0], vgd[1], vgd[2]}}; + auto kernel = kernels.front(); + kernel.SetLocalDims(vld[0], vld[1], vld[2]); + kernel.SetGlobalDims(vgd[0], vgd[1], vgd[2]); kernel(in, out, in_offset, out_offset, RD_BLCK, HW_RD, n, c, h_out, w_out); } else @@ -980,9 +990,9 @@ float transpose_CNHW2NCHW(const Handle& handle, auto&& kernels = handle.GetKernels(kernel_name, network_config); if(!kernels.empty()) { - auto kernel = kernels.front(); - kernel.ldims = {{vld[0], vld[1], vld[1]}}; - kernel.gdims = {{vgd[0], vgd[1], vgd[2]}}; + auto kernel = kernels.front(); + kernel.SetLocalDims(vld[0], vld[1], vld[2]); + kernel.SetGlobalDims(vgd[0], vgd[1], vgd[2]); kernel(in, out, in_offset, @@ -1177,14 +1187,13 @@ float transpose_packed_MN2NM(const Handle& handle, if(!kernels.empty()) { - auto kernel = kernels.front(); - kernel.ldims = {{vld[0], vld[1], vld[1]}}; - kernel.gdims = {{vgd[0], vgd[1], vgd[2]}}; + auto kernel = kernels.front(); + kernel.SetLocalDims(vld[0], vld[1], vld[2]); + kernel.SetGlobalDims(vgd[0], vgd[1], vgd[2]); kernel(in, out, n, m, in_offset, out_offset); } else { - handle.AddKernel(kernel_name, network_config, program_name, kernel_name, vld, vgd, params)( in, out, n, m, in_offset, out_offset); } diff --git a/src/ocl_kernel.cpp b/src/ocl_kernel.cpp index da047071aa..49efdf5c61 100644 --- a/src/ocl_kernel.cpp +++ b/src/ocl_kernel.cpp @@ -58,7 +58,7 @@ void OCLKernelInvoke::run() const MIOPEN_HANDLE_LOCK - const auto& arch = miopen::GetStringEnv(ENV(MIOPEN_DEVICE_ARCH)); + const auto& arch = env::value(MIOPEN_DEVICE_ARCH); if(!arch.empty()) { MIOPEN_THROW("MIOPEN_DEVICE_ARCH used, escaping launching kernel"); diff --git a/src/operator.cpp b/src/operator.cpp index 72add47e1b..5c2fb34326 100644 --- a/src/operator.cpp +++ b/src/operator.cpp @@ -42,12 +42,6 @@ std::ostream& operator<<(std::ostream& stream, const FusionOpDescriptor& x) return stream; } -std::ostream& operator<<(std::ostream& stream, const MDGraph_op_t& o) -{ - MIOPEN_LOG_ENUM(stream, o, OpEqual, OpNotEqual, OpAny, OpModulo, OpGTE, OpLTE); - return stream; -} - std::ostream& operator<<(std::ostream& stream, const boost::any& a) { // NOLINTBEGIN(*-braces-around-statements) diff --git a/src/pooling.cpp b/src/pooling.cpp index 91a27f324a..a65cb3c0ab 100644 --- a/src/pooling.cpp +++ b/src/pooling.cpp @@ -215,10 +215,10 @@ void PoolingDescriptor::GetForwardOutputDimNd(const TensorDescriptor& xDesc, TensorDescriptor PoolingDescriptor::GetForwardOutputTensor(const TensorDescriptor& xDesc) const { - std::vector out_dim(xDesc.GetSize()); - GetForwardOutputDimNd(xDesc, xDesc.GetSize(), out_dim.data()); + std::vector out_dim(xDesc.GetNumDims()); + GetForwardOutputDimNd(xDesc, xDesc.GetNumDims(), out_dim.data()); - const std::string default_layout = tensor_layout_get_default(xDesc.GetSize()); + const std::string default_layout = tensor_layout_get_default(xDesc.GetNumDims()); const std::string in_layout = xDesc.GetLayout(default_layout); std::vector out_strides; tensor_layout_to_strides(out_dim, default_layout, in_layout, out_strides); @@ -233,7 +233,7 @@ std::size_t PoolingDescriptor::GetWorkSpaceSize(const TensorDescriptor& yDesc) c const auto main_ws = GetMode() == miopenPoolingMax ? y_size * index_e_size : 0; - const auto labels = tensor_layout_get_default(yDesc.GetSize()); + const auto labels = tensor_layout_get_default(yDesc.GetNumDims()); std::size_t transpose_ws = 0; if(yDesc.GetLayout(labels) != labels) diff --git a/src/pooling_api.cpp b/src/pooling_api.cpp index 321194ffe5..8c475b57d5 100644 --- a/src/pooling_api.cpp +++ b/src/pooling_api.cpp @@ -41,13 +41,14 @@ inline void Pooling_logging_cmd(const miopenPoolingDescriptor_t poolDesc, { if(miopen::IsLoggingCmd()) { - auto tensor_dim = miopen::deref(tensorDesc).GetSize(); + auto tensor_dim = miopen::deref(tensorDesc).GetNumDims(); std::stringstream ss; switch(miopen::deref(tensorDesc).GetType()) { case miopenHalf: ss << "poolfp16"; break; case miopenFloat: ss << "pool"; break; + case miopenInt64: case miopenInt32: case miopenInt8: case miopenBFloat16: @@ -124,7 +125,10 @@ inline void Pooling_logging_cmd(const miopenPoolingDescriptor_t poolDesc, extern "C" miopenStatus_t miopenCreatePoolingDescriptor(miopenPoolingDescriptor_t* poolDesc) { MIOPEN_LOG_FUNCTION(poolDesc); - return miopen::try_([&] { miopen::deref(poolDesc) = new miopen::PoolingDescriptor(); }); + return miopen::try_([&] { + auto& desc = miopen::deref(poolDesc); + desc = new miopen::PoolingDescriptor(); + }); } extern "C" miopenStatus_t miopenSetPoolingIndexType(miopenPoolingDescriptor_t poolDesc, diff --git a/src/problem.cpp b/src/problem.cpp index b6d91b8146..4d6dff6515 100644 --- a/src/problem.cpp +++ b/src/problem.cpp @@ -29,6 +29,9 @@ #include #include #include +#include +#include +#include #include #include #include @@ -47,7 +50,6 @@ #include -#include #include namespace miopen::debug { @@ -100,7 +102,7 @@ template