Bump jinja2 from 2.11.3 to 3.1.3 in /build_tools/benchmarks/reporting #74
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Co-authored-by: Elias Joseph <[email protected]>
The conversion pass is enabled with `--iree-flow-enable-conv-nchw-to-nhwc-transform`
Includes partial support for propagating and cancelling transposes generated
when converting from nchw to nhwc. The high level strategy for this pass is
as follows:
1. Do the conversions for all conv_nchw_fchw ops (and pooling ops) and
wrap the converted convolutions in transposes. Each transpose is tagged
to indicate which direction the transpose should propagate through the
graph.
2. Traverse the ops in the function in reverse to propagate transposes
marked for upwards propagation to their parents. Ideally just before ops
such as arith.constant or function arguments.
3. Propagate the transposes marked for downward propagation to its users,
ideally to just before return.
4. Canonicalize out all adjacent cancelling transposes and generalize the
remaining transposes to allow for fusing them with nearby ops.
- Speedup filter transform folding - Add points for 4x4, switch to that tile size - Move winograd after im2col + padding, in im2col do not touch conv if it has been marked as winograd -remove prints/chrono and adjust Attribute rawKernelAttr for windows by Quinn Co-authored-by: Quinn Dawkins <[email protected]>
Add pass to insert markers for function bisecting. Add pass to outline marked operation ranges into separate functions.
Expose a Python binding that has extraction of an operation list from an MLIR file. This list is then used to execute with IREE the entry MLIR while resolving calls to functions in other MLIR files.
This can be useful when trying to do layout propagation and guaranteeing specific fusion at time (use with caution).
This pass is the spiritual successor to `convert-conv-nchw-to-nhwc` focused on generalizing to enable data tiling and more robust layout propagation, as well as supporting non-named convolutions as well. Currently this includes some baked in generalization patterns and does not support padding. Tile size selection currently is pass-wide, but there is limited attribute control to enable fully transposing. Further generalizations should aim to write this pass by allowing per-op tile size control.
<32 bit width types are handled on the SPIR-V side by introducing bitcasts to and from i32 and bubbling them to the center of the kernel hoping to cancel. This adds a pattern for a bitcast on the result of an scf.if, which comes from the way that padding is handled (transfer_read in the `then` branch, else yield a splat constant).
Build Experimental ROCM builds
* Add rudimentary non-production distributed Python API * Distributed execution validation Add functionality that validates distributed StableHLO is producing the same results as non-distributed. * Add execution time measurement * Distributed Python API: add call_count to run_ranks * Add setup script for distributed Python API * Add JAX to install setup --------- Co-authored-by: Boian Petkantchin <[email protected]>
…f-hosted, clean macos bindist Drop instrumented builds and Python < 3.11 Add Upstream sync CI This fixes the problem of potentially dropping commits that have been submitted while an automatic rebase with upstream IREE is goining on. [CI] Fix macos clean up logic Fixes the macos builder.
This reverts commit a6512dc.
The semantics for specifying different kinds of advice is unclear so I set it in two stages.
…uped qmm MegaPR
[LLVMCPU] Allow parallel tiling in LLVMCPUSplitReduction, tile reduction by 2
This commit enables tiling of parallel dimensions in LLVMCPUSplitReduction,
as well as changing the tile size of the resulting reduction to 2. The latter
change is an x86 specific optimization that allows targeting specific
instructions through VectorContractCustomKernels.
[LLVMCPU] Add support for vecmat cases in VectorContractCustomKernel
This commit introduces some new functionality to VectorContractCustomKernels:
1. Matching for vecmat kernels that have 1D vector shapes
2. Support for `vector.contract` ops with split reduction dimensions
3. Ability to allow promoting smaller bitwidth inputs with `arith.extui` or
`arith.extsi` before passing into the `llvm.inline_asm` op
4. Ability to specify explicit constraint strings per register input in a
VectorContractCustomKernel
5. Support for `i4` and `i8` input types
6. New x86 AVX512VNNI i16xi16->i32 vecmat kernel with split reduction
This commit also adds `vector.transfer_read` flattening patterns and
VectorContractCustomKernel lowering patterns to LLVMCPUVectorLowering.
[LLVMCPU] Add pass to breakdown subbyte `arith.extui`
This pass breaks down `arith.extui` ops that have `i4` inputs into a
sequence of `vector.shuffle->arith.andi->arith.shrui`. This avoids bad
lowering of subbyte extends in x86 backend. This pass is somewhat
specific to some work on vecmat VectorContractCustomKernels right now,
and has some unique matchings.
The pass also attempts to make use of AVX512 registers, so the vector
size for the resulting IR is hardcoded as 512 bits. This needs to
change before landing. This pass in general needs some refactoring
before landing.
[LLVMCPU] Add pass to fold away unit dimensions on `vector.contract` ops
This pass folds away unit dimensions on `vector.contract` ops to get these
ops into a form that is recognizable by the VectorContractCustomKernels
patterns.
This pass also hoists `vector.shape_cast` ops out of containing
`scf.for` ops if possible when the shape cast operates on the accumulator
of a `vector.contract` op. This pattern may be better off somewhere else,
but for now it is here because the unit dim folding pattern can produce
a hoistable `vector.shape_cast` op in cases with split reduction.
[LLVMCPU] Add flag to restrict reassociated quantized matmul optimizations
[LLVMCPU] Add additional Memref alias foldings
[LLVMCPU] Simplify VectorContractCustomKernels x86 constraint codes, add new AVX512 kernel
Co-authored-by: Max Dawkins <[email protected]>
This commit adds a new tiling configuration pass in LLVMCPU. This pass sets a special tiling configuration for reassociated quantized matmuls, since the non-root op of these dispatches require specific tiling to target certain x86 instructions. This pass is a place to set abnormal tile sizes on non-root ops for specific types of workloads.
This is to speed up matvec. The new configuration is experimental and only applied on ROCm targets.
Bumps [jinja2](https://github.com/pallets/jinja) from 2.11.3 to 3.1.3. - [Release notes](https://github.com/pallets/jinja/releases) - [Changelog](https://github.com/pallets/jinja/blob/main/CHANGES.rst) - [Commits](pallets/jinja@2.11.3...3.1.3) --- updated-dependencies: - dependency-name: jinja2 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <[email protected]>
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Bumps jinja2 from 2.11.3 to 3.1.3.
Release notes
Sourced from jinja2's releases.
Changelog
Sourced from jinja2's changelog.
... (truncated)
Commits
d9de4bbrelease version 3.1.350124e1skip test pypi9ea7222use trusted publishingda703f7use trusted publishingbce1746use trusted publishing7277d80update pre-commit hooks5c8a105Make nested-trans-block exceptions nicer (#1918)19a55dbMake nested-trans-block exceptions nicer7167953Merge pull request from GHSA-h5c8-rqwp-cp957dd3680xmlattr filter disallows keys with spacesDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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