################################################################################################# # # Copyright (c) 2017 - 2023 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.epilogue import LinearCombinationClamp from pycutlass.test import * import unittest from pycutlass.test.gemm_testbed import test_all_gemm from pycutlass.utils.device import device_cc @unittest.skipIf(device_cc() < 80, "Device compute capability is insufficient for SM80 tests.") class GemmS8TensorOpF32Sm80(unittest.TestCase): def test_SM80_Device_Gemm_s8t_s8n_s8t_tensor_op_s32_64x64x64_32x32x64(self): math_inst = MathInstruction( instruction_shape=[16, 8, 32], element_a=cutlass.int8, element_b=cutlass.int8, element_accumulator=cutlass.int32, opcode_class=cutlass.OpClass.TensorOp, math_operation=MathOperation.multiply_add_saturate ) tile_description = TileDescription( threadblock_shape=[64, 64, 64], stages=6, warp_count=[2, 2, 1], math_instruction=math_inst ) A = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajorInterleaved32, alignment=16 ) B = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajorInterleaved32, alignment=16 ) C = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajorInterleaved32, alignment=8 ) epilogue_functor = FastLinearCombinationClamp( C.element, C.alignment ) swizzling_functor = cutlass.IdentitySwizzle1 operation = GemmOperationUniversal( arch=80, tile_description=tile_description, A=A, B=B, C=C, epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor ) self.assertTrue(test_all_gemm(operation, "interleaved")) def test_SM80_Device_Gemm_s8t_s8n_s8t_tensor_op_s32_256x128x128_64x64x128(self): math_inst = MathInstruction( instruction_shape=[16, 8, 32], element_a=cutlass.int8, element_b=cutlass.int8, element_accumulator=cutlass.int32, opcode_class=cutlass.OpClass.TensorOp, math_operation=MathOperation.multiply_add ) tile_description = TileDescription( threadblock_shape=[128, 128, 128], stages=3, warp_count=[2, 2, 1], math_instruction=math_inst ) A = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajor, alignment=16 ) B = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajor, alignment=16 ) C = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajor, alignment=16 ) epilogue_functor = FastLinearCombinationClamp( C.element, C.alignment ) swizzling_functor = cutlass.IdentitySwizzle1 operation = GemmOperationUniversal( arch=80, tile_description=tile_description, A=A, B=B, C=C, epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor ) self.assertTrue(test_all_gemm(operation, "multistage")) def test_SM80_Device_Gemm_s8t_s8n_s8n_tensor_op_s32_128x128x128_64x64x128(self): math_inst = MathInstruction( instruction_shape=[16, 8, 32], element_a=cutlass.int8, element_b=cutlass.int8, element_accumulator=cutlass.int32, opcode_class=cutlass.OpClass.TensorOp, math_operation=MathOperation.multiply_add ) tile_description = TileDescription( threadblock_shape=[128, 128, 128], stages=3, warp_count=[2, 2, 1], math_instruction=math_inst ) A = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajor, alignment=16 ) B = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajor, alignment=16 ) C = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajor, alignment=16 ) epilogue_functor = FastLinearCombinationClamp( C.element, C.alignment ) swizzling_functor = cutlass.IdentitySwizzle1 operation = GemmOperationUniversal( arch=80, tile_description=tile_description, A=A, B=B, C=C, epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor ) self.assertTrue(test_all_gemm(operation, "multistage")) def test_SM80_Device_Gemm_s8t_s8n_s32n_tensor_op_s32_128x128x128_64x64x128(self): math_inst = MathInstruction( instruction_shape=[16, 8, 32], element_a=cutlass.int8, element_b=cutlass.int8, element_accumulator=cutlass.int32, opcode_class=cutlass.OpClass.TensorOp, math_operation=MathOperation.multiply_add ) tile_description = TileDescription( threadblock_shape=[128, 128, 128], stages=3, warp_count=[2, 2, 1], math_instruction=math_inst ) A = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajor, alignment=16 ) B = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajor, alignment=16 ) C = TensorDescription( element=cutlass.int32, layout=cutlass.ColumnMajor, alignment=4 ) element_epilogue = cutlass.int32 epilogue_functor = LinearCombinationClamp( C.element, C.alignment, math_inst.element_accumulator, element_epilogue ) swizzling_functor = cutlass.IdentitySwizzle1 operation = GemmOperationUniversal( arch=80, tile_description=tile_description, A=A, B=B, C=C, epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor ) self.assertTrue(test_all_gemm(operation, "multistage")) def test_SM80_Device_Gemm_s8t_s8n_s32t_tensor_op_s32_128x128x128_64x64x128(self): math_inst = MathInstruction( instruction_shape=[16, 8, 32], element_a=cutlass.int8, element_b=cutlass.int8, element_accumulator=cutlass.int32, opcode_class=cutlass.OpClass.TensorOp, math_operation=MathOperation.multiply_add ) tile_description = TileDescription( threadblock_shape=[128, 128, 128], stages=3, warp_count=[2, 2, 1], math_instruction=math_inst ) A = TensorDescription( element=cutlass.int8, layout=cutlass.RowMajor, alignment=16 ) B = TensorDescription( element=cutlass.int8, layout=cutlass.ColumnMajor, alignment=16 ) C = TensorDescription( element=cutlass.int32, layout=cutlass.RowMajor, alignment=4 ) element_epilogue = cutlass.int32 epilogue_functor = LinearCombinationClamp( C.element, C.alignment, math_inst.element_accumulator, element_epilogue ) swizzling_functor = cutlass.IdentitySwizzle1 operation = GemmOperationUniversal( arch=80, tile_description=tile_description, A=A, B=B, C=C, epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor ) self.assertTrue(test_all_gemm(operation, "multistage")) if __name__ == '__main__': pycutlass.get_memory_pool(2**30, 2**30) unittest.main()