Add support for sparse GEMM with visitor epilogue (#1189)
* Add support for sparse GEMM with visitor epilogue * Refactor changes at the kernel level
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Aleksandar Samardžić
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GitHub
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5c756eb774
@@ -33,3 +33,8 @@ cutlass_example_add_executable(
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ampere_sparse_tensorop_gemm.cu
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)
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cutlass_example_add_executable(
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15_ampere_sparse_tensorop_gemm_with_visitor
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ampere_sparse_tensorop_gemm_with_visitor.cu
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)
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@@ -0,0 +1,379 @@
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/***************************************************************************************************
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* Copyright (c) 2017 - 2023 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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/**
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Please check example 07, 08 and 17 for the basics of dense tensor op gemm kernels. NVIDIA Ampere
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architecture also supports structured sparse tensor op for tf32, fp16, int8 and int4.
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Sparse GEMM kernels needs to takes an additional E matrix which stores the meta data. The format of
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meta data is different for every data types. CUTLASS templates can automatically infer it based on
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input A and B. Check code below.
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Moreover, matrix E needs to be preprocessed so that it can use ldmatrix to load into the registers
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efficiently.
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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_sparse_with_visitor.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "cutlass/util/host_reorder.h"
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#include "cutlass/util/host_uncompress.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/epilogue/threadblock/fusion/visitors.hpp"
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#include "helper.h"
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// The code section below describes datatype for input, output matrices and computation between
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// elements in input matrices.
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using ElementAccumulator = int32_t; // <- data type of accumulator
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using ElementComputeEpilogue = ElementAccumulator; // <- data type of epilogue operations
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using ElementInputA = int8_t; // <- data type of elements in input matrix A
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using ElementInputB = int8_t; // <- data type of elements in input matrix B
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using ElementOutput = int32_t; // <- data type of elements in output matrix D
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// The code section below describes matrix layout of input and output matrices. Row Major for
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// Matrix A, Column Major for Matrix B and Row Major for Matrix C
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using LayoutInputA = cutlass::layout::RowMajor;
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using LayoutInputB = cutlass::layout::ColumnMajor;
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using LayoutOutput = cutlass::layout::RowMajor;
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// The number of elements per vectorized memory access.
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constexpr int AlignmentInputA = 128 / cutlass::sizeof_bits<ElementInputA>::value;
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constexpr int AlignmentInputB = 128 / cutlass::sizeof_bits<ElementInputB>::value;
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constexpr int AlignmentComputeEpilogue = 128 / cutlass::sizeof_bits<ElementComputeEpilogue>::value;
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constexpr int AlignmentOutput = 128 / cutlass::sizeof_bits<ElementOutput>::value;
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// This code section describes whether you want to use tensor cores or regular SIMT cores on GPU SM
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using MMAOp = cutlass::arch::OpClassTensorOp;
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// This code section describes CUDA SM architecture number
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using SmArch = cutlass::arch::Sm80;
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// This code section describes the tile size a thread block will compute
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using ShapeMMAThreadBlock =
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cutlass::gemm::GemmShape<128, 128, 128>; // <- threadblock tile M = 128, N = 128, K = 128
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// This code section describes tile size a warp will compute
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using ShapeMMAWarp = cutlass::gemm::GemmShape<64, 64, 128>; // <- warp tile M = 64, N = 64, K = 128
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// This code section describes the size of MMA op
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using ShapeMMAOp = cutlass::gemm::GemmShape<16, 8, 64>; // <- MMA Op tile M = 16, N = 8, K = 64
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// This code section describes how threadblocks are scheduled on GPU
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using SwizzleThreadBlock = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
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using Operator = cutlass::arch::OpMultiplyAdd;
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// Number of pipelines you want to use
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constexpr int NumStages = 3;
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constexpr auto NumEVTEpilogueStages = 1;
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using Accum = cutlass::epilogue::threadblock::VisitorAccFetch;
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using BiasTileThreadMap = cutlass::epilogue::threadblock::OutputTileThreadLayout<
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ShapeMMAThreadBlock,
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ShapeMMAWarp,
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ElementComputeEpilogue,
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AlignmentComputeEpilogue,
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NumEVTEpilogueStages>;
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using Bias = cutlass::epilogue::threadblock::VisitorAuxLoad<
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BiasTileThreadMap,
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ElementComputeEpilogue,
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cute::Stride<int64_t, cute::_1, int64_t>>;
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using ApplyBias = cutlass::epilogue::threadblock::VisitorCompute<
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cutlass::plus, ElementComputeEpilogue, ElementComputeEpilogue,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTApplyBias = cutlass::epilogue::threadblock::Sm80EVT<
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ApplyBias,
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Accum,
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Bias>;
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using OutputTileThreadMap = cutlass::epilogue::threadblock::OutputTileThreadLayout<
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ShapeMMAThreadBlock,
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ShapeMMAWarp,
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ElementOutput,
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AlignmentOutput,
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NumEVTEpilogueStages>;
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using Output = cutlass::epilogue::threadblock::VisitorAuxStore<
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OutputTileThreadMap, ElementOutput,
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cutlass::FloatRoundStyle::round_to_nearest,
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cute::Stride<int64_t, cute::_1, int64_t>>;
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using EVTOutput = cutlass::epilogue::threadblock::Sm80EVT<
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Output,
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EVTApplyBias>;
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// Use element type in EVT with the smallest bitwidth as ElementC.
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using ElementC = ElementComputeEpilogue;
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using LayoutC = LayoutOutput;
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using Gemm =
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typename cutlass::gemm::device::SparseGemmWithVisitor<
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ElementInputA, LayoutInputA,
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ElementInputB, LayoutInputB,
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ElementC, LayoutC,
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ElementAccumulator,
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MMAOp,
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SmArch,
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ShapeMMAThreadBlock,
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ShapeMMAWarp,
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ShapeMMAOp,
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EVTOutput,
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SwizzleThreadBlock,
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NumStages,
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AlignmentInputA,
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AlignmentInputB,
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Operator,
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NumEVTEpilogueStages>;
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// Data type and layout of meta data matrix E can be inferred from template Gemm.
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using ElementInputE = typename Gemm::GemmKernel::ElementE;
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using LayoutInputE = cutlass::layout::RowMajor;
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using ReorderedLayoutInputE = typename Gemm::GemmKernel::LayoutE;
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// Blow property is defined in include/cutlass/arch/sp_mma_sm80.h
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// 50% Sparsity on Ampere
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constexpr int kSparse = Gemm::kSparse;
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// How many elements of A are covered per ElementE
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constexpr int kElementsPerElementE = Gemm::kElementsPerElementE;
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// The size of individual meta data
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constexpr int kMetaSizeInBits = Gemm::kMetaSizeInBits;
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int run() {
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const int length_m = 512;
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const int length_n = 512;
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const int length_k = 1024;
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// Create a tuple of problem size for matrix multiplication
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cutlass::gemm::GemmCoord problem_size(length_m, length_n, length_k);
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// Initialize tensors using CUTLASS helper functions
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cutlass::HostTensor<ElementInputA, LayoutInputA> tensor_a(
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cutlass::make_Coord(problem_size.m(), problem_size.k() / kSparse)); // <- Create matrix A with dimensions M x (K / 2)
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cutlass::HostTensor<ElementInputA, LayoutInputA> tensor_a_uncompressed(
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problem_size.mk()); // <- Create uncompressed matrix A with dimensions M x K for reference computing
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cutlass::HostTensor<ElementInputB, LayoutInputB> tensor_b(
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problem_size.kn()); // <- Create matrix B with dimensions K x N
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cutlass::HostTensor<ElementComputeEpilogue, LayoutOutput> tensor_c(
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problem_size.mn()); // <- Create matrix C with dimensions M x N
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cutlass::HostTensor<ElementOutput, LayoutOutput> tensor_d(
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problem_size.mn()); // <- Create matrix D with dimensions M x N used to store output from
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// CUTLASS kernel
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cutlass::HostTensor<ElementOutput, LayoutOutput> tensor_ref_d(
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problem_size.mn()); // <- Create matrix D with dimensions M x N used to store output from
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// reference kernel
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// Create matrix E with dimensions M x (K / 2 / kElementsPerElementE). This one is used by reference computing.
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cutlass::HostTensor<ElementInputE, LayoutInputE> tensor_e(
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cutlass::make_Coord(problem_size.m(), problem_size.k() / kSparse / kElementsPerElementE));
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// Same size as the above. The above one needs to be reordered and stored in this one.
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cutlass::HostTensor<ElementInputE, ReorderedLayoutInputE> tensor_e_reordered(
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cutlass::make_Coord(problem_size.m(), problem_size.k() / kSparse / kElementsPerElementE));
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// Fill input and output matrices on host using CUTLASS helper functions
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cutlass::reference::host::TensorFillRandomUniform(
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tensor_a.host_view(),
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1,
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ElementInputA(8),
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ElementInputA(-8),
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0); // <- Fill matrix A on host with uniform-distribution random data
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cutlass::reference::host::TensorFillRandomUniform(
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tensor_b.host_view(),
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1,
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ElementInputB(8),
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ElementInputB(-8),
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0); // <- Fill matrix B on host with uniform-distribution random data
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cutlass::reference::host::TensorFillRandomUniform(
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tensor_c.host_view(),
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1,
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ElementOutput(8),
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ElementOutput(-8),
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0); // <- Fill matrix C on host with uniform-distribution random data
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cutlass::reference::host::TensorFillRandomSparseMeta(
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tensor_e.host_view(),
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1,
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kMetaSizeInBits); // <- Fill matrix E on host with uniform-distribution random meta data
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cutlass::reference::host::TensorFill(
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tensor_d.host_view()); // <- fill matrix D on host with zeros
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cutlass::reference::host::TensorFill(
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tensor_ref_d.host_view()); // <- fill matrix D for reference on host with zeros
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// Reorder the meta data matrix so that we can use ldmatrix to load them to tensor core
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// instructions.
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cutlass::reorder_meta(tensor_e_reordered.host_ref(), tensor_e.host_ref(),
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{problem_size.m(), problem_size.n(),
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problem_size.k() / kSparse / kElementsPerElementE});
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// Copy data from host to GPU
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tensor_a.sync_device();
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tensor_b.sync_device();
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tensor_c.sync_device();
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tensor_d.sync_device();
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tensor_e_reordered.sync_device();
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tensor_ref_d.sync_device();
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// Initialize alpha and beta for dot product computation
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ElementComputeEpilogue alpha = ElementComputeEpilogue(1);
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ElementComputeEpilogue beta = ElementComputeEpilogue(1);
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typename Bias::Arguments bias_arguments{
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tensor_c.device_data(),
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ElementComputeEpilogue(0),
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{problem_size.n(), cute::_1{}, problem_size.mn().product()}
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};
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typename Output::Arguments output_arguments{
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tensor_d.device_data(),
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{problem_size.n(), cute::_1{}, problem_size.mn().product()}
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};
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typename EVTOutput::Arguments callback_arguments{
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{
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{}, // Accum
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bias_arguments, // Bias
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{} // ApplyBias
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}, // EVTApplyBias
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output_arguments // Output
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}; // EVTOutput
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// Create a tuple of gemm kernel arguments. This is later passed as arguments to launch
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// instantiated CUTLASS kernel
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typename Gemm::Arguments arguments{problem_size, // <- problem size of matrix multiplication
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tensor_a.device_ref(), // <- reference to matrix A on device
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tensor_b.device_ref(), // <- reference to matrix B on device
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tensor_e_reordered.device_ref(), // <- reference to matrix E on device
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callback_arguments}; // <- epilogue arguments
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// Using the arguments, query for extra workspace required for matrix multiplication computation
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size_t workspace_size = Gemm::get_workspace_size(arguments);
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// Allocate workspace memory
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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// Instantiate CUTLASS kernel depending on templates
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Gemm gemm_op;
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// Check the problem size is supported or not
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cutlass::Status status = gemm_op.can_implement(arguments);
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CUTLASS_CHECK(status);
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// Initialize CUTLASS kernel with arguments and workspace pointer
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status = gemm_op.initialize(arguments, workspace.get());
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CUTLASS_CHECK(status);
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// Launch initialized CUTLASS kernel
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status = gemm_op();
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CUTLASS_CHECK(status);
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// uncompress tensor_a based on meta data tensor_e. We need it for reference computing.
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cutlass::uncompress(tensor_a_uncompressed.host_ref(), tensor_a.host_ref(),
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tensor_e.host_ref(), problem_size.m(), problem_size.k());
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// Create instantiation for host reference gemm kernel
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cutlass::reference::host::Gemm<ElementInputA,
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LayoutInputA,
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ElementInputB,
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LayoutInputB,
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ElementOutput,
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LayoutOutput,
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ElementComputeEpilogue,
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ElementComputeEpilogue,
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typename Gemm::Operator>
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gemm_host;
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// Launch host reference gemm kernel
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gemm_host(problem_size,
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alpha,
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tensor_a_uncompressed.host_ref(),
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tensor_b.host_ref(),
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beta,
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tensor_c.host_ref(),
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tensor_ref_d.host_ref());
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// Copy output data from CUTLASS host for comparison
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tensor_d.sync_host();
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// Check if output from CUTLASS kernel and reference kernel are equal or not
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bool passed = cutlass::reference::host::TensorEquals(
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tensor_d.host_view(),
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tensor_ref_d.host_view());
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std::cout << (passed ? "Passed" : "Failed") << std::endl;
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return (passed ? 0 : -1);
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}
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int main() {
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bool notSupported = false;
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// Ampere Sparse Tensor Core operations exposed with mma.sync and ldmatrix are first available
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// in CUDA 11.1.
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//
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// CUTLASS must be compiled with CUDA 11.1 Toolkit to run these examples.
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if (!(__CUDACC_VER_MAJOR__ > 11 || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 1))) {
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std::cerr << "Ampere Tensor Core operations must be compiled with CUDA 11.1 Toolkit or later." << std::endl;
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notSupported = true;
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}
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cudaDeviceProp props;
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cudaError_t error = cudaGetDeviceProperties(&props, 0);
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if (error != cudaSuccess) {
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std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
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return -1;
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}
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if (props.major * 10 + props.minor < 80) {
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std::cerr << "Ampere Tensor Core operations must be run on a machine with compute capability at least 80."
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<< std::endl;
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notSupported = true;
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}
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if (notSupported) {
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// Returning zero so this test passes on older Toolkits. Its actions are no-op.
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return 0;
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}
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return run();
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}
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