147 lines
5.5 KiB
Python
147 lines
5.5 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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## Test case for Pytorch
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import pycutlass
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import unittest
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from pycutlass import *
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from pycutlass.utils.device import device_cc
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import torch
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import cupy as cp
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class Test_Frontend(unittest.TestCase):
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def setUp(self) -> None:
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#
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# define the cutlass operator
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#
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cc = device_cc()
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math_inst = MathInstruction(
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[1, 1, 1], cutlass.float32, cutlass.float32, cutlass.float32,
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cutlass.OpClass.Simt, MathOperation.multiply_add
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)
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# Stages > 2 is supported only for compute capability 80 and beyond
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stages = 4 if cc >= 80 else 2
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tile_description = TileDescription(
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[128, 128, 8], stages, [2, 4, 1],
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math_inst
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)
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A = TensorDescription(
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cutlass.float32, cutlass.RowMajor, 1
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)
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B = TensorDescription(
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cutlass.float32, cutlass.RowMajor, 1
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)
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C = TensorDescription(
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cutlass.float32, cutlass.RowMajor, 1
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)
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epilogue_functor = LinearCombination(
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C.element, C.alignment,
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math_inst.element_accumulator, cutlass.float32)
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self.operation = GemmOperationUniversal(
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arch=cc, tile_description=tile_description,
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A=A, B=B, C=C,
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epilogue_functor=epilogue_functor,
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swizzling_functor=cutlass.IdentitySwizzle1
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)
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pycutlass.compiler.add_module([self.operation,])
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def test_torch_frontend(self):
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problem_size = cutlass.gemm.GemmCoord(512, 256, 128)
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tensor_A = torch.ceil(torch.empty(size=(problem_size.m(), problem_size.k()), dtype=torch.float32, device="cuda").uniform_(-8.5, 7.5))
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tensor_B = torch.ceil(torch.empty(size=(problem_size.k(), problem_size.n()), dtype=torch.float32, device="cuda").uniform_(-8.5, 7.5))
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tensor_C = torch.ceil(torch.empty(size=(problem_size.m(), problem_size.n()), dtype=torch.float32, device="cuda").uniform_(-8.5, 7.5))
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tensor_D = torch.empty_like(tensor_C)
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alpha = 1.0
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beta = 0.0
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arguments = GemmArguments(
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operation=self.operation, problem_size=problem_size,
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A=tensor_A, B=tensor_B, C=tensor_C, D=tensor_D,
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output_op=self.operation.epilogue_type(alpha, beta),
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gemm_mode=cutlass.gemm.Mode.Gemm, split_k_splices=1
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)
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self.operation.run(arguments)
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arguments.sync()
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tensor_D_ref = alpha * tensor_A @ tensor_B + beta * tensor_C
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self.assertTrue(torch.equal(tensor_D, tensor_D_ref))
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def test_cupy_frontend(self):
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cp.cuda.set_allocator(rmm.rmm_cupy_allocator)
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problem_size = cutlass.gemm.GemmCoord(512, 256, 128)
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tensor_A = cp.ceil(cp.random.uniform(low=-8.5, high=7.5, size=(problem_size.m(), problem_size.k()), dtype=cp.float32))
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tensor_B = cp.ceil(cp.random.uniform(low=-8.5, high=7.5, size=(problem_size.k(), problem_size.n()), dtype=cp.float32))
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tensor_C = cp.ceil(cp.random.uniform(low=-8.5, high=7.5, size=(problem_size.m(), problem_size.n()), dtype=cp.float32))
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tensor_D = cp.ones_like(tensor_C)
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alpha = 1.0
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beta = 1.0
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tensor_D_ref = alpha * tensor_A @ tensor_B + beta * tensor_C
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arguments = GemmArguments(
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operation=self.operation, problem_size=problem_size,
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A=tensor_A, B=tensor_B, C=tensor_C, D=tensor_D,
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output_op=self.operation.epilogue_type(alpha, beta),
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gemm_mode=cutlass.gemm.Mode.Gemm, split_k_splices=1
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)
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self.operation.run(arguments)
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arguments.sync()
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self.assertTrue(cp.array_equal(tensor_D, tensor_D_ref))
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if __name__ == '__main__':
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pycutlass.get_memory_pool(2**32, 2**32)
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unittest.main()
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