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Summary: Building a simple AIT cast operator that supports `float16`, `bfloat16`, and `float32`. This first version does not support Vectorization in order to simplify this task Differential Revision: D46603937 fbshipit-source-id: 2a72752341d04d1127c8bf161476fd12fa48d625
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# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# | ||
# 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. | ||
# | ||
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from typing import Any, Dict | ||
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import jinja2 | ||
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from aitemplate.backend import registry | ||
from aitemplate.backend.backend_spec import CUDASpec | ||
from aitemplate.backend.common.elementwise_common import gen_int_var_product_str | ||
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CUDA_HEADER_FILES = """ | ||
#include <cuda_fp16.h> | ||
#include <cuda_bf16.h> | ||
#include <cuda_runtime.h> | ||
""" | ||
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CONSTANT_TEMPLATE = jinja2.Template( | ||
""" | ||
#define N_THREADS_PER_BLOCK 256 | ||
""" | ||
) | ||
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FUNC_DECL_TEMPLATE = jinja2.Template( | ||
""" | ||
void invoke_{{func_name}}( | ||
void* y, | ||
const void* x, | ||
{{index_type}} n_elements, | ||
{{prefix}}Stream_t stream); | ||
""" | ||
) | ||
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FUNC_CALL_TEMPLATE = jinja2.Template( | ||
""" | ||
{{indent}}{ | ||
{{indent}}const {{index_type}} {{func_name}}_n_elements = {{calculate_n}}; | ||
{{indent}}invoke_{{func_name}}({{output}}, {{input}}, {{func_name}}_n_elements, stream); | ||
{{indent}}} | ||
""" | ||
) | ||
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FUNC_TEMPLATE = jinja2.Template( | ||
""" | ||
{{header_files}} | ||
namespace { | ||
{{constant}} | ||
__global__ void cast_op( | ||
{{output_type}}* output, | ||
const {{input_type}}* input, | ||
{{index_type}} n_elements | ||
) { | ||
const {{index_type}} idx = (blockIdx.x * blockDim.x + threadIdx.x); | ||
if (idx >= n_elements) { | ||
return; | ||
} | ||
output[idx] = {{cast_func_call}} | ||
} | ||
} // namespace | ||
void invoke_{{func_name}}(void* output, const void* input, | ||
{{index_type}} n_elements, {{prefix}}Stream_t stream) { | ||
if (n_elements == 0) { | ||
return; | ||
} | ||
int grid_size = static_cast<int>(std::ceil(static_cast<double>(n_elements) / N_THREADS_PER_BLOCK)); | ||
cast_op<<<grid_size, N_THREADS_PER_BLOCK, 0, stream>>>( | ||
reinterpret_cast<{{output_type}}*>(output), | ||
reinterpret_cast<const {{input_type}}*>(input), | ||
n_elements | ||
); | ||
} | ||
""" | ||
) | ||
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CAST_FUNCS = { | ||
"half": { | ||
"bfloat16": "__float2bfloat16_rn(__half2float(input[idx]));", | ||
"float": "__half2float(input[idx]);", | ||
}, | ||
"bfloat16": { | ||
"half": "__float2half_rn(__bfloat162float(input[idx]));", | ||
"float": "__bfloat162float(input[idx]);", | ||
}, | ||
"float": { | ||
"bfloat16": "__float2bfloat16_rn(input[idx]);", | ||
"half": "__float2half_rn(input[idx]);", | ||
}, | ||
} | ||
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@registry.reg("cuda.cast.gen_function") | ||
def gen_function(func_attrs: Dict[str, Any]) -> str: | ||
input_ = func_attrs["inputs"][0] | ||
output = func_attrs["outputs"][0] | ||
backend_spec = CUDASpec() | ||
output_dtype = output.dtype() | ||
output_type = backend_spec.dtype_to_backend_type(output_dtype) | ||
input_type = backend_spec.dtype_to_backend_type(input_.dtype()) | ||
cast_func_call = CAST_FUNCS[input_type][output_type] | ||
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return FUNC_TEMPLATE.render( | ||
header_files=backend_spec.header_src_template.render( | ||
extra_header=CUDA_HEADER_FILES | ||
), | ||
constant=CONSTANT_TEMPLATE.render(), | ||
func_name=func_attrs["name"], | ||
input_type=input_type, | ||
output_type=output_type, | ||
index_type=backend_spec.index_type, | ||
cast_func_call=cast_func_call, | ||
prefix=backend_spec.prefix, | ||
) | ||
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@registry.reg("cuda.cast.func_decl") | ||
def gen_function_decl(func_attrs: Dict[str, Any]) -> str: | ||
backend_spec = CUDASpec() | ||
return FUNC_DECL_TEMPLATE.render( | ||
func_name=func_attrs["name"], | ||
prefix=backend_spec.prefix, | ||
index_type=backend_spec.index_type, | ||
) | ||
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@registry.reg("cuda.cast.func_call") | ||
def gen_function_call(func_attrs: Dict[str, Any], indent=" ") -> str: | ||
backend_spec = CUDASpec() | ||
return FUNC_CALL_TEMPLATE.render( | ||
func_name=func_attrs["name"], | ||
output=func_attrs["outputs"][0]._attrs["name"], | ||
input=func_attrs["inputs"][0]._attrs["name"], | ||
calculate_n=gen_int_var_product_str(func_attrs["inputs"][0].shape()), | ||
index_type=backend_spec.index_type, | ||
indent=indent, | ||
) |
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# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# | ||
# 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. | ||
# | ||
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from aitemplate import backend | ||
from aitemplate.backend import registry | ||
from aitemplate.compiler.base import Operator, Tensor | ||
from aitemplate.compiler.dtype import normalize_dtype | ||
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class cast(Operator): | ||
""" | ||
Returns the cast of input tensor to specified type. | ||
Only the conversion between any pair of float16, bfloat16, | ||
and float32 dtypes is supported. | ||
Args: | ||
x (Tensor): the source tensor | ||
dtype (str): the target type for the cast operator | ||
Returns: | ||
Tensor: a tensor with the type converted to the | ||
specified dtype. | ||
""" | ||
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def __init__(self) -> None: | ||
super().__init__() | ||
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self._attrs["op"] = "cast" | ||
self._attrs["has_profiler"] = False | ||
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def __call__( | ||
self, | ||
x: Tensor, | ||
dtype: str = "bfloat16", | ||
) -> Tensor: | ||
x_dtype = normalize_dtype(x._attrs["dtype"]) | ||
dtype = normalize_dtype(dtype) | ||
if x_dtype not in ("float16", "bfloat16", "float32"): | ||
raise TypeError( | ||
f"Expected dtype for x must be float16,bfloat16 or float32 , but got {x_dtype}." | ||
) | ||
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if dtype not in ("float16", "bfloat16", "float32"): | ||
raise TypeError( | ||
f"Expected dtype to cast must be float16,bfloat16 or float32 , but got {dtype}." | ||
) | ||
if dtype == x_dtype: | ||
return x | ||
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self._attrs["inputs"] = [x] | ||
self._attrs["cast_dtype"] = dtype | ||
self._set_depth() | ||
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output_shape = x._attrs["shape"] | ||
output = Tensor( | ||
output_shape, | ||
src_ops={self}, | ||
dtype=dtype, | ||
) | ||
self._attrs["outputs"] = [output] | ||
return output | ||
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def gen_function(self) -> str: | ||
target = backend.target.Target.current() | ||
func_key = f"{target.name()}.{self._attrs['op']}.gen_function" | ||
func = registry.get(func_key) | ||
return func(self._attrs) |
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@@ -0,0 +1,97 @@ | ||
# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# | ||
# 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. | ||
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import unittest | ||
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import torch | ||
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from aitemplate.compiler import compile_model, ops | ||
from aitemplate.frontend import Tensor | ||
from aitemplate.testing import detect_target | ||
from aitemplate.testing.test_utils import ( | ||
get_random_torch_tensor, | ||
get_torch_empty_tensor, | ||
) | ||
from aitemplate.utils.torch_utils import string_to_torch_dtype | ||
from parameterized import param, parameterized | ||
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class TestCast(unittest.TestCase): | ||
def __init__(self, *args, **kwargs): | ||
super().__init__(*args, **kwargs) | ||
self._test_id = 0 | ||
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def _test_cast( | ||
self, | ||
shape, | ||
dtype="float32", | ||
cast_dtype="bfloat16", | ||
test_name="cast", | ||
) -> None: | ||
if not isinstance(shape, list): | ||
shape = [shape] | ||
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X = Tensor( | ||
shape=shape, | ||
name="X", | ||
dtype=dtype, | ||
is_input=True, | ||
) | ||
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Y = ops.cast()(X, cast_dtype) | ||
Y._attrs["name"] = "Y" | ||
Y._attrs["is_output"] = True | ||
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target = detect_target() | ||
module = compile_model(Y, target, "./tmp", f"{test_name}_{self._test_id}") | ||
self._test_id += 1 | ||
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x = get_random_torch_tensor(shape, dtype=dtype) | ||
y = get_torch_empty_tensor(shape, dtype=cast_dtype) | ||
inputs = {"X": x} | ||
outputs = {"Y": y} | ||
module.run_with_tensors(inputs, outputs) | ||
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y_pt = x.to(string_to_torch_dtype(cast_dtype)) | ||
torch.testing.assert_close(y, y_pt, atol=1e-2, rtol=1e-2) | ||
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@parameterized.expand( | ||
[ | ||
param(1, "float16", "bfloat16", [1], "float16_to_bfloat16"), | ||
param(2, "float16", "float32", [10, 20], "float16_to_float32"), | ||
param(3, "bfloat16", "float16", [10, 20, 30], "bfloat16_to_float16"), | ||
param(4, "bfloat16", "float32", 123, "bfloat16_to_float32"), | ||
param(5, "float32", "float16", [20, 30], "float32_to_float16"), | ||
param(6, "float32", "bfloat16", [1, 128], "float32_to_bfloat16"), | ||
] | ||
) | ||
def test_cast( | ||
self, | ||
i, | ||
dtype, | ||
cast_dtype, | ||
shape, | ||
test_name, | ||
): | ||
self._test_cast( | ||
shape=shape, | ||
dtype=dtype, | ||
cast_dtype=cast_dtype, | ||
test_name=test_name, | ||
) | ||
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if __name__ == "__main__": | ||
torch.manual_seed(0) | ||
unittest.main() |