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.
126 lines
5.0 KiB
C++
126 lines
5.0 KiB
C++
/***************************************************************************************************
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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 Matrix multiply
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*/
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#pragma once
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#include <assert.h>
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#include "cutlass/layout/matrix.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace arch {
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////////////////////////////////////////////////////////////////////////////////
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//
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// WMMA template structure defines nvcuda::wmma::fragments and static assert for
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// wmma native instruction sizes supported for half
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//
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////////////////////////////////////////////////////////////////////////////////
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template <
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typename Shape_,
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typename LayoutA_,
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typename LayoutB_,
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typename ElementC_,
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typename LayoutC_>
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struct Wmma<
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Shape_, ///< Size of the matrix product (concept: GemmShape)
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cutlass::half_t, ///< ElementA
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LayoutA_, ///< LayoutA
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cutlass::half_t, ///< ElementB
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LayoutB_, ///< LayoutB
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ElementC_, ///< ElementC
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LayoutC_, ///< LayoutC
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cutlass::arch::OpMultiplyAdd ///< Operator (multiply-add, xor.popc)
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> {
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#if defined(CUTLASS_ARCH_WMMA_SM70_ENABLED)
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using Shape = Shape_;
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using ElementA = cutlass::half_t;
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using LayoutA = LayoutA_;
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using ElementB = cutlass::half_t;
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using LayoutB = LayoutB_;
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using ElementC = ElementC_;
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using LayoutC = LayoutC_;
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using Operator = cutlass::arch::OpMultiplyAdd;
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// check supported wmma shape for the given multiplicand data types
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static_assert(
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platform::is_same<cutlass::gemm::GemmShape<16, 16, 16>, Shape>::value ||
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platform::is_same<cutlass::gemm::GemmShape< 8, 32, 16>, Shape>::value ||
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platform::is_same<cutlass::gemm::GemmShape<32, 8, 16>, Shape>::value,
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"Supported list of wmma operator shape for f16 multiplicands are: 16x16x16, 8x328x16, and 32x8x16");
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// check supported wmma output data type for the given multiplicand data types
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static_assert(
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platform::is_same<cutlass::half_t, ElementC>::value || platform::is_same<float, ElementC>::value,
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"Supported of wmma output data type for f16 multiplicands are: f16 and f32");
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// Wmma Fragment
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using FragmentA = nvcuda::wmma::fragment<
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nvcuda::wmma::matrix_a,
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Shape::kM,
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Shape::kN,
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Shape::kK,
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typename CutlassToWmmaDataType<ElementA>::Type,
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typename CutlassToWmmaLayout<LayoutA>::Layout>;
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using FragmentB = nvcuda::wmma::fragment<
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nvcuda::wmma::matrix_b,
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Shape::kM,
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Shape::kN,
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Shape::kK,
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typename CutlassToWmmaDataType<ElementB>::Type,
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typename CutlassToWmmaLayout<LayoutB>::Layout>;
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using FragmentC = nvcuda::wmma::fragment<
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nvcuda::wmma::accumulator,
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Shape::kM,
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Shape::kN,
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Shape::kK,
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typename CutlassToWmmaDataType<ElementC>::Type>;
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/// Performs a nvcuda::wmma matrix multiply-accumulate operation
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CUTLASS_DEVICE
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void operator()(
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FragmentC &D,
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FragmentA const &A,
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FragmentB const &B,
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FragmentC const &C) const {
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nvcuda::wmma::mma_sync(D, A, B, C);
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}
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#else
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static_assert(false, "wmma.mma.sync for floating point multiplicands is avialable only for SM70 and beyond");
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#endif
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};
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} // namespace arch
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} // namespace cutlass
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