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CUTLASS 2.10 (#615)

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Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
This commit is contained in:
ANIKET SHIVAM
2022-09-03 18:48:46 -04:00
committed by GitHub
co-authored by Aniket Shivam
parent ca23ff7924
commit b72cbf957d
289 changed files with 43708 additions and 2513 deletions
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tools/library/scripts/pycutlass/profile/conv/conv2d_f16_sm80.py
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#################################################################################################
#
# Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. 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.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# 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.
#
#################################################################################################
from pycutlass import *
import pycutlass
from pycutlass.test.conv2d_testbed import Conv2dLauncher
if __name__ == "__main__":
pycutlass.get_memory_pool(2**33, 2**33)
pycutlass.compiler.nvcc()
math_inst = MathInstruction(
instruction_shape=[16, 8, 16],
element_a=cutlass.float16, element_b=cutlass.float16,
element_accumulator=cutlass.float32, opcode_class=cutlass.OpClass.TensorOp,
math_operation=MathOperation.multiply_add
)
A = TensorDescription(
element=math_inst.element_a,
layout=cutlass.TensorNHWC,
alignment=8)
B = TensorDescription(
element=math_inst.element_b,
layout=cutlass.TensorNHWC,
alignment=8)
C = TensorDescription(
element=cutlass.float32,
layout=cutlass.TensorNHWC,
alignment=8)
tile_description = TileDescription(
threadblock_shape=[128, 128, 64], stages=4,
warp_count=[2, 2, 1],
math_instruction=math_inst,
min_compute=80, max_compute=80
)
operation = Conv2dOperation(
conv_kind=cutlass.conv.Operator.fprop, iterator_algorithm=cutlass.conv.IteratorAlgorithm.optimized,
arch=80, tile_description=tile_description, A=A, B=B, C=C,
element_epilogue=cutlass.float32, stride_support=StrideSupport.Strided,
epilogue_functor=EpilogueFunctor.LinearCombination,
swizzling_functor=cutlass.IdentitySwizzle1
)
profiler = Conv2dLauncher(operation, verification=False, profiling=True)
python_runtime = profiler.run(
problem_size = cutlass.conv.Conv2dProblemSize(
cutlass.Tensor4DCoord(32, 224, 224, 128),
cutlass.Tensor4DCoord(128, 3, 3, 128),
cutlass.Tensor4DCoord(1, 1, 1, 1),
cutlass.MatrixCoord(1, 1),
cutlass.MatrixCoord(1, 1),
cutlass.conv.Mode.cross_correlation,
1, 1
), split_k_mode=cutlass.conv.SplitKMode.Serial
)
cpp_runtime = profiler.run_cutlass_profiler(
problem_size = cutlass.conv.Conv2dProblemSize(
cutlass.Tensor4DCoord(32, 224, 224, 128),
cutlass.Tensor4DCoord(128, 3, 3, 128),
cutlass.Tensor4DCoord(1, 1, 1, 1),
cutlass.MatrixCoord(1, 1),
cutlass.MatrixCoord(1, 1),
cutlass.conv.Mode.cross_correlation,
1, 1
), split_k_mode=cutlass.conv.SplitKMode.Serial
)
print(cpp_runtime / python_runtime)
tools/library/scripts/pycutlass/profile/gemm/gemm_f32_sm80.py
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#################################################################################################
#
# Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. 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.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# 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.
#
#################################################################################################
import pycutlass
from pycutlass import *
from pycutlass.test import *
from pycutlass.test.gemm_testbed import GemmUniversalLauncher
if __name__ == '__main__':
pycutlass.get_memory_pool(2**32, 2**32)
pycutlass.compiler.nvcc()
math_inst = MathInstruction(
instruction_shape=[16, 8, 16],
element_a=cutlass.float16, element_b=cutlass.float16,
element_accumulator=cutlass.float32, opcode_class=cutlass.OpClass.TensorOp,
math_operation=MathOperation.multiply_add
)
tile_description = TileDescription(
threadblock_shape=[256, 128, 32],
stages=3, warp_count=[4, 2, 1],
math_instruction=math_inst, min_compute=80, max_compute=80
)
A = TensorDescription(
element=cutlass.float16, layout=cutlass.RowMajor,
alignment=4
)
B = TensorDescription(
element=cutlass.float16, layout=cutlass.RowMajor,
alignment=4
)
C = TensorDescription(
element=cutlass.float32, layout=cutlass.ColumnMajor,
alignment=4
)
element_epilogue = cutlass.float32
epilogue_functor = EpilogueFunctor.LinearCombination
swizzling_functor = cutlass.IdentitySwizzle1
operation = GemmOperationUniversal(
arch=80, tile_description=tile_description,
A=A, B=B, C=C, element_epilogue=element_epilogue,
epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor
)
profiler = GemmUniversalLauncher(operation, verification=False, profiling=True)
python_runtime = profiler.run(
mode=cutlass.gemm.Mode.Gemm,
problem_size=cutlass.gemm.GemmCoord(4096, 4096, 4096)
)
cpp_runtime = profiler.run_cutlass_profiler(
mode=cutlass.gemm.Mode.Gemm,
problem_size=cutlass.gemm.GemmCoord(4096, 4096, 4096),
)
print(cpp_runtime / python_runtime)