fb335f6a5f
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
193 lines
6.1 KiB
Plaintext
193 lines
6.1 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017-2019, 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 TOR (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/gemm/gemm.h"
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#include "cutlass/gemm/warp/mma_simt.h"
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#include "testbed.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_warp_gemm_int8_col_row, col_row_8x4x8_1x1x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<1, 1, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<8, 4, 8>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<8, 4, 8> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_8x4x4_1x1x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<1, 1, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<8, 4, 8>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<128, 64, 8> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_16x4x4_2x1x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<2, 1, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<16, 4, 4>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<16, 4, 4> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_16x4x4_2x2x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<2, 2, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<16, 8, 4>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<16, 8, 4> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_32x16x4_4x4x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<4, 4, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<32, 16, 16>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<128, 64, 16> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_128x64x4_16x16x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<16, 16, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<128, 64, 4>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<128, 64, 4> >().run();
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}
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TEST(SM61_warp_gemm_int8_col_row, col_row_64x64x4_4x4x4) {
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using Policy = cutlass::gemm::warp::MmaSimtPolicy<
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cutlass::MatrixShape<8, 4>,
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cutlass::layout::ColumnMajorInterleaved<2>,
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cutlass::gemm::GemmShape<4, 4, 4>
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>;
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using Mma = cutlass::gemm::warp::MmaSimt<
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cutlass::gemm::GemmShape<64, 64, 8>,
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int8_t,
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cutlass::layout::ColumnMajorInterleaved<4>,
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int8_t,
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cutlass::layout::RowMajorInterleaved<4>,
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int,
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cutlass::layout::ColumnMajor,
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Policy
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>;
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test::gemm::warp::Testbed<Mma, cutlass::gemm::GemmShape<64, 64, 8> >().run();
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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