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.
165 lines
6.3 KiB
Plaintext
165 lines
6.3 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 "cutlass/arch/wmma.h"
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#if defined(CUTLASS_ARCH_WMMA_SM75_ENABLED)
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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/gemm/warp/default_mma_wmma_tensor_op.h"
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#include "cutlass/core_io.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_compare.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "testbed.h"
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///////////////////////////////////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////////////////// SUBBYTE wmma.mma ////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM75_warp_wmma_row_col_s4, 64x64x32_8x8x32_8x8x32) {
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using WarpShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using ElementA = cutlass::int4b_t;
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using ElementB = cutlass::int4b_t;
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using ElementC = int32_t;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using WmmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
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WarpShape,
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InstructionShape,
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC>::Type;
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test::gemm::warp::Testbed<WmmaTensorOp, cutlass::gemm::GemmShape<64, 64, 32> >().run();
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}
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TEST(SM75_warp_wmma_row_col_s4, 64x64x32_64x64x32_8x8x32) {
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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 ElementA = cutlass::int4b_t;
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using ElementB = cutlass::int4b_t;
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using ElementC = int32_t;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using WmmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
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WarpShape,
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InstructionShape,
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC>::Type;
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test::gemm::warp::Testbed<WmmaTensorOp, cutlass::gemm::GemmShape<64, 64, 32> >().run();
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}
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TEST(SM75_warp_wmma_row_col_s4, 64x64x64_8x8x64_8x8x32) {
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using WarpShape = cutlass::gemm::GemmShape<8, 8, 64>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
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using ElementA = cutlass::int4b_t;
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using ElementB = cutlass::int4b_t;
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using ElementC = int32_t;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using WmmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
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WarpShape,
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InstructionShape,
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC>::Type;
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test::gemm::warp::Testbed<WmmaTensorOp, cutlass::gemm::GemmShape<64, 64, 64> >().run();
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}
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TEST(SM75_warp_wmma_row_col_b1, 64x64x128_8x8x128_8x8x128) {
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using WarpShape = cutlass::gemm::GemmShape<8, 8, 128>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
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using ElementA = cutlass::uint1b_t;
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using ElementB = cutlass::uint1b_t;
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using ElementC = int32_t;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using WmmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
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WarpShape,
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InstructionShape,
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC,
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cutlass::arch::OpXorPopc>::Type;
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test::gemm::warp::Testbed<WmmaTensorOp, cutlass::gemm::GemmShape<64, 64, 128>, cutlass::arch::OpXorPopc>().run();
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}
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TEST(SM75_warp_wmma_row_col_b1, 64x64x128_64x64x128_8x8x128) {
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using WarpShape = cutlass::gemm::GemmShape<64, 64, 128>;
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using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
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using ElementA = cutlass::uint1b_t;
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using ElementB = cutlass::uint1b_t;
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using ElementC = int32_t;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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using WmmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOpWmma<
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WarpShape,
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InstructionShape,
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC,
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cutlass::arch::OpXorPopc>::Type;
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test::gemm::warp::Testbed<WmmaTensorOp, cutlass::gemm::GemmShape<64, 64, 128>, cutlass::arch::OpXorPopc>().run();
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}
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#endif //CUTLASS_ARCH_WMMA_SM75_ENABLED
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