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.
158 lines
7.0 KiB
Plaintext
158 lines
7.0 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 Tests for device-wide GEMM interface
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*/
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#include <iostream>
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/device/gemm.h"
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#include "../../common/cutlass_unit_test.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 "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "testbed.h"
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#define N cutlass::layout::ColumnMajor
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#define T cutlass::layout::RowMajor
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#define RUN_GEMM(X, Y) \
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using ElementOutput = int8_t; \
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using ElementAccumulator = int32_t; \
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using ElementCompute = float; \
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using InstructionShape = cutlass::gemm::GemmShape<1, 1, 4>; \
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using Gemm = cutlass::gemm::device::Gemm< \
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int8_t, \
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X, \
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int8_t, \
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Y, \
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ElementOutput, \
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cutlass::layout::RowMajor, \
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int32_t, \
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cutlass::arch::OpClassSimt, \
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cutlass::arch::Sm61, \
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ThreadBlockShape, \
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WarpShape, \
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InstructionShape, \
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cutlass::epilogue::thread::LinearCombinationClamp< \
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ElementOutput, \
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1, \
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ElementAccumulator, \
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ElementCompute \
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>, \
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle, \
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2 \
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>; \
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EXPECT_TRUE(test::gemm::device::TestAllGemm<Gemm>()); \
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////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_Device_Gemm_s8n_s8t_simt_op_dp4a, 64x64x16_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 16>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
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RUN_GEMM(N, T)
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}
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TEST(SM61_Device_Gemm_s8n_s8t_simt_op_dp4a, 256x128x64_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 128, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 64>;
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RUN_GEMM(N, T)
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}
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TEST(SM61_Device_Gemm_s8n_s8t_simt_op_dp4a, 256x256x16_128x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 256, 16>;
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using WarpShape = cutlass::gemm::GemmShape<128, 64, 16>;
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RUN_GEMM(N, T)
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}
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////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_Device_Gemm_s8t_s8n_simt_op_dp4a, 64x64x16_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 16>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
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RUN_GEMM(T, N)
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}
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TEST(SM61_Device_Gemm_s8t_s8n_simt_op_dp4a, 256x128x64_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 128, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 64>;
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RUN_GEMM(T, N)
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}
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TEST(SM61_Device_Gemm_s8t_s8n_simt_op_dp4a, 256x256x16_128x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 256, 16>;
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using WarpShape = cutlass::gemm::GemmShape<128, 64, 16>;
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RUN_GEMM(T, N)
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}
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////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_Device_Gemm_s8n_s8n_simt_op_dp4a, 64x64x16_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 16>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
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RUN_GEMM(N, N)
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}
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TEST(SM61_Device_Gemm_s8n_s8n_simt_op_dp4a, 256x128x64_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 128, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 64>;
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RUN_GEMM(N, N)
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}
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TEST(SM61_Device_Gemm_s8n_s8n_simt_op_dp4a, 256x256x16_128x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 256, 16>;
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using WarpShape = cutlass::gemm::GemmShape<128, 64, 16>;
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RUN_GEMM(N, N)
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}
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////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_Device_Gemm_s8t_s8t_simt_op_dp4a, 64x64x16_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 16>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
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RUN_GEMM(T, T)
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}
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TEST(SM61_Device_Gemm_s8t_s8t_simt_op_dp4a, 256x128x64_64x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 128, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 64>;
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RUN_GEMM(T, T)
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}
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TEST(SM61_Device_Gemm_s8t_s8t_simt_op_dp4a, 256x256x16_128x64x4) {
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using ThreadBlockShape = cutlass::gemm::GemmShape<256, 256, 16>;
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using WarpShape = cutlass::gemm::GemmShape<128, 64, 16>;
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RUN_GEMM(T, T)
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}
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