3073 lines
83 KiB
Plaintext
3073 lines
83 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, 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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/*! \file
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\brief Unit tests for thread-level GEMM
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*/
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#include <fstream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/aligned_buffer.h"
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#include "cutlass/half.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/epilogue/thread/linear_combination_clamp.h"
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#include "cutlass/gemm/warp/default_mma_tensor_op.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "testbed.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_64x64x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_32x32x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x128_64x64x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<128, 128, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x64_64x32x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<128, 64, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x128_32x64x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 128, 32>;
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using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_32x128_32x64x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<32, 128, 32>;
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using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x32_64x32x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<128, 32, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
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kElementsPerAccess
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>::Epilogue;
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//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_256x128_64x64x32) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<256, 128, 32>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using Element = ElementOutput;
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using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
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cutlass::sizeof_bits<Element>::value, 64>;
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using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
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WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
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cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kElementsPerAccess,
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ElementAccumulator,
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ElementCompute
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>;
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//
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// Define the epilogue
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//
|
|
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using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputOp,
|
|
kElementsPerAccess
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>::Epilogue;
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|
//
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// Instantiate epilogue
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//
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EpilogueTestbed<Epilogue> testbed;
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bool passed = testbed.run_all();
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EXPECT_TRUE(passed);
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}
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|
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TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x256_64x64x32) {
|
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|
//
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// Define the warp-level matrix multiply
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//
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|
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|
using ElementOutput = cutlass::int4b_t;
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using ElementAccumulator = int;
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using ElementCompute = float;
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int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 32>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_64x64x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_32x3216) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x128_64x64x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_64x64x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x64_64x32x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_32x64x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_32x128_32x64x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x32_64x32x16) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = int8_t;
|
|
using ElementAccumulator = int;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 32, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
|
|
using Element = ElementOutput;
|
|
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
|
|
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// Mixed precision tests
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// F16 acumulation
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x64_32x32x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x64_64x32x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x128_32x64x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x128_32x64x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, vec1_mixed_f16_f32_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, vec1_mixed_f16_f32_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, vec1_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, vec1_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::Type;
|
|
|
|
//
|
|
// Output operator
|
|
//
|
|
|
|
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
|
ElementOutput,
|
|
kElementsPerAccess,
|
|
ElementAccumulator,
|
|
ElementCompute
|
|
>;
|
|
|
|
//
|
|
// Define the epilogue
|
|
//
|
|
|
|
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
Shape,
|
|
WarpMmaTensorOp,
|
|
kPartitionsK,
|
|
OutputOp,
|
|
kElementsPerAccess
|
|
>::Epilogue;
|
|
|
|
//
|
|
// Instantiate epilogue
|
|
//
|
|
|
|
EpilogueTestbed<Epilogue> testbed;
|
|
|
|
bool passed = testbed.run_all();
|
|
|
|
EXPECT_TRUE(passed);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|