107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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from pycutlass import *
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import pycutlass
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from pycutlass.epilogue import LinearCombination
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from pycutlass.test.conv2d_testbed import Conv2dLauncher
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if __name__ == "__main__":
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pycutlass.get_memory_pool(2**33, 2**33)
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pycutlass.compiler.nvcc()
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math_inst = MathInstruction(
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instruction_shape=[16, 8, 16],
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element_a=cutlass.float16, element_b=cutlass.float16,
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element_accumulator=cutlass.float32, opcode_class=cutlass.OpClass.TensorOp,
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math_operation=MathOperation.multiply_add
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)
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A = TensorDescription(
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element=math_inst.element_a,
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layout=cutlass.TensorNHWC,
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alignment=8)
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B = TensorDescription(
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element=math_inst.element_b,
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layout=cutlass.TensorNHWC,
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alignment=8)
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C = TensorDescription(
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element=cutlass.float32,
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layout=cutlass.TensorNHWC,
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alignment=8)
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tile_description = TileDescription(
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threadblock_shape=[128, 128, 64], stages=4,
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warp_count=[2, 2, 1],
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math_instruction=math_inst
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)
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epilogue_functor = LinearCombination(cutlass.float32, 4, cutlass.float32, cutlass.float32)
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operation = Conv2dOperation(
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conv_kind=cutlass.conv.Operator.fprop, iterator_algorithm=cutlass.conv.IteratorAlgorithm.optimized,
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arch=80, tile_description=tile_description, A=A, B=B, C=C,
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element_epilogue=cutlass.float32, stride_support=StrideSupport.Strided,
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epilogue_functor=epilogue_functor,
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swizzling_functor=cutlass.IdentitySwizzle1
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)
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profiler = Conv2dLauncher(operation, verification=False, profiling=True)
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python_runtime = profiler.run(
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problem_size = cutlass.conv.Conv2dProblemSize(
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cutlass.Tensor4DCoord(32, 224, 224, 128),
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cutlass.Tensor4DCoord(128, 3, 3, 128),
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cutlass.Tensor4DCoord(1, 1, 1, 1),
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cutlass.MatrixCoord(1, 1),
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cutlass.MatrixCoord(1, 1),
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cutlass.conv.Mode.cross_correlation,
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1, 1
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), split_k_mode=cutlass.conv.SplitKMode.Serial
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)
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cpp_runtime = profiler.run_cutlass_profiler(
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problem_size = cutlass.conv.Conv2dProblemSize(
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cutlass.Tensor4DCoord(32, 224, 224, 128),
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cutlass.Tensor4DCoord(128, 3, 3, 128),
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cutlass.Tensor4DCoord(1, 1, 1, 1),
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cutlass.MatrixCoord(1, 1),
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cutlass.MatrixCoord(1, 1),
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cutlass.conv.Mode.cross_correlation,
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1, 1
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), split_k_mode=cutlass.conv.SplitKMode.Serial
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)
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print(cpp_runtime / python_runtime)
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