511 lines
14 KiB
Plaintext
511 lines
14 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Unit tests for thread-level GEMM
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*/
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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_planar_complex.h"
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// Tensor Op
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#include "cutlass/gemm/warp/default_mma_tensor_op.h"
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// Volta Tensor Op
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#include "cutlass/gemm/warp/mma_tensor_op_sm70.h"
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#include "cutlass/epilogue/warp/fragment_iterator_volta_tensor_op.h"
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// Simt
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#include "cutlass/gemm/warp/mma_simt.h"
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#include "cutlass/gemm/warp/mma_simt_policy.h"
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// Epilogue components
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#include "cutlass/epilogue/threadblock/default_epilogue_planar_complex.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_planar_complex.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(Epilogue_threadblock_epilogue, planar_complex_f32_f32_tensor_op_64x64_32x32x8) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = float;
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using ElementAccumulator = float;
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using ElementCompute = float;
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int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
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using Element = cutlass::half_t;
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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,
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InstructionShape,
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Element, LayoutA,
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Element, LayoutB,
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ElementAccumulator, cutlass::layout::RowMajor
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>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaTensorOp,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm75,
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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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EpiloguePlanarComplexTestbed<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(Epilogue_threadblock_epilogue, planar_complex_f16_f32_tensor_op_64x64_32x32x8) {
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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::half_t;
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using ElementAccumulator = float;
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using ElementCompute = float;
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int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
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using Element = cutlass::half_t;
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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,
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InstructionShape,
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Element, LayoutA,
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Element, LayoutB,
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ElementAccumulator, cutlass::layout::RowMajor
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>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaTensorOp,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm75,
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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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EpiloguePlanarComplexTestbed<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(Epilogue_threadblock_epilogue, planar_complex_f16_f16_tensor_op_64x64_32x32x8) {
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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::half_t;
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using ElementAccumulator = cutlass::half_t;
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using ElementCompute = cutlass::half_t;
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int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
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using Element = cutlass::half_t;
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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,
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InstructionShape,
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Element, LayoutA,
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Element, LayoutB,
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ElementAccumulator, cutlass::layout::RowMajor
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>::Type;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaTensorOp,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm75,
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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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EpiloguePlanarComplexTestbed<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(Epilogue_threadblock_epilogue, planar_complex_f32_f32_volta_tensor_op_64x64_32x32x4) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = float;
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using ElementAccumulator = float;
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using ElementCompute = float;
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int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<32, 32, 4>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 4>;
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using Element = cutlass::half_t;
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using LayoutA = cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous<cutlass::sizeof_bits<Element>::value>;
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using LayoutB = cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous<cutlass::sizeof_bits<Element>::value>;
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using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<
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cutlass::arch::Mma<
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cutlass::gemm::GemmShape<16, 16, 4>,
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32,
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Element,
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cutlass::layout::ColumnMajor,
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Element,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::layout::RowMajor,
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cutlass::arch::OpMultiplyAdd
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>,
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cutlass::MatrixShape<1, 1>
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>;
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using WarpMmaTensorOp = cutlass::gemm::warp::MmaVoltaTensorOp<
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WarpShape,
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Element,
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LayoutA,
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Element,
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LayoutB,
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ElementAccumulator,
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cutlass::layout::RowMajor,
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Policy
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>;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaTensorOp,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm70,
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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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EpiloguePlanarComplexTestbed<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(Epilogue_threadblock_epilogue, planar_complex_simt_f32_64x64_32x32x8) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = float;
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using ElementAccumulator = float;
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using ElementCompute = float;
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int const kElementsPerAccess = 1;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
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using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
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using Element = float;
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using ElementC = ElementAccumulator;
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using LayoutA = cutlass::layout::ColumnMajor;
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using LayoutB = cutlass::layout::RowMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using ElementOutput = Element;
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using ElementAccumulator = Element;
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using ElementCompute = Element;
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using WarpMmaSimt = cutlass::gemm::warp::MmaSimt<
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WarpShape,
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Element,
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LayoutA,
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Element,
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LayoutB,
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Element,
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LayoutC,
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cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<4, 8>,
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cutlass::layout::RowMajorInterleaved<2>,
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cutlass::gemm::GemmShape<4, 4, 1>
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>
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>;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaSimt,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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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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EpiloguePlanarComplexTestbed<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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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(Epilogue_threadblock_epilogue, planar_complex_simt_f64_64x64_16x32x8) {
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//
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// Define the warp-level matrix multiply
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//
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using ElementOutput = double;
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using ElementAccumulator = double;
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using ElementCompute = double;
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int const kElementsPerAccess = 1;
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int const kPartitionsK = 1;
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using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
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using WarpShape = cutlass::gemm::GemmShape<16, 32, 8>;
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using Element = double;
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using ElementC = ElementAccumulator;
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using LayoutA = cutlass::layout::ColumnMajor;
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using LayoutB = cutlass::layout::RowMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using ElementOutput = Element;
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using ElementAccumulator = Element;
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using ElementCompute = Element;
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using WarpMmaSimt = cutlass::gemm::warp::MmaSimt<
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WarpShape,
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Element,
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LayoutA,
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Element,
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LayoutB,
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Element,
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LayoutC,
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cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<4, 8>,
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cutlass::layout::RowMajorInterleaved<2>,
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cutlass::gemm::GemmShape<4, 4, 1>
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>
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>;
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//
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// Output operator
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//
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using OutputOp = cutlass::epilogue::thread::LinearCombinationPlanarComplex<
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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::DefaultEpiloguePlanarComplex<
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Shape,
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WarpMmaSimt,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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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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EpiloguePlanarComplexTestbed<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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