Add support for sparse GEMM with visitor epilogue (#1189)
* Add support for sparse GEMM with visitor epilogue * Refactor changes at the kernel level
This commit is contained in:
Aleksandar Samardžić
GitHub
parent
8236f30675
commit
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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342
include/cutlass/gemm/device/gemm_sparse_with_visitor.h
Normal file
342
include/cutlass/gemm/device/gemm_sparse_with_visitor.h
Normal file
@ -0,0 +1,342 @@
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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
|
||||
* 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
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* 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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/*! \file
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\brief Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/arch.h"
|
||||
#include "cutlass/device_kernel.h"
|
||||
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
#include "cutlass/gemm/kernel/sparse_gemm.h"
|
||||
|
||||
#include "cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h"
|
||||
#include "cutlass/gemm/device/default_gemm_configuration.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/fusion/visitor_2x.hpp"
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace device {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/*! Sparse GEMM with visitor
|
||||
*/
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA_,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA_,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB_,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB_,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC_,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC_,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator_ = ElementC_,
|
||||
/// Operator class tag
|
||||
typename OperatorClass_ = arch::OpClassSimt,
|
||||
/// Tag indicating architecture to tune for
|
||||
typename ArchTag_ = arch::Sm70,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::WarpShape,
|
||||
/// Instruction-level tile size (concept: GemmShape)
|
||||
typename InstructionShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename FusionCallbacks_ =
|
||||
typename cutlass::epilogue::threadblock::detail::EmptyCallbacks,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle_ =
|
||||
typename threadblock::GemmIdentityThreadblockSwizzle<>,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kStages,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int AlignmentA =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kAlignmentA,
|
||||
/// Access granularity of B matrix in units of elements
|
||||
int AlignmentB =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kAlignmentB,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::Operator,
|
||||
/// Number of stages used in the pipelined epilogue
|
||||
int EpilogueStages = 1>
|
||||
class SparseGemmWithVisitor {
|
||||
public:
|
||||
|
||||
using ElementA = ElementA_;
|
||||
using LayoutA = LayoutA_;
|
||||
using TensorRefA = TensorRef<ElementA const, LayoutA>;
|
||||
using ElementB = ElementB_;
|
||||
using LayoutB = LayoutB_;
|
||||
using TensorRefB = TensorRef<ElementB const, LayoutB>;
|
||||
using ElementC = ElementC_;
|
||||
using LayoutC = LayoutC_;
|
||||
using ElementAccumulator = ElementAccumulator_;
|
||||
using OperatorClass = OperatorClass_;
|
||||
using ArchTag = ArchTag_;
|
||||
using ThreadblockShape = ThreadblockShape_;
|
||||
using WarpShape = WarpShape_;
|
||||
using InstructionShape = InstructionShape_;
|
||||
using FusionCallbacks = FusionCallbacks_;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
using Operator = Operator_;
|
||||
using MathOperator = Operator;
|
||||
static int const kStages = Stages;
|
||||
static int const kAlignmentA = AlignmentA;
|
||||
static int const kAlignmentB = AlignmentB;
|
||||
|
||||
/// Define the kernel
|
||||
using GemmKernel = typename kernel::DefaultSparseGemmWithVisitor<
|
||||
ElementA,
|
||||
LayoutA,
|
||||
kAlignmentA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
kAlignmentB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
FusionCallbacks,
|
||||
ThreadblockSwizzle,
|
||||
kStages,
|
||||
Operator,
|
||||
EpilogueStages
|
||||
>::GemmKernel;
|
||||
|
||||
using ElementE = typename GemmKernel::ElementE;
|
||||
|
||||
using LayoutE = typename GemmKernel::LayoutE;
|
||||
|
||||
static int const kAlignmentE = 128 / sizeof_bits<ElementE>::value;
|
||||
|
||||
static int const kSparse = GemmKernel::kSparse;
|
||||
static int const kMetaSizeInBits = GemmKernel::kMetaSizeInBits;
|
||||
static int const kElementsPerElementE = GemmKernel::kElementsPerElementE;
|
||||
|
||||
/// Argument structure
|
||||
struct Arguments {
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
GemmCoord problem_size;
|
||||
TensorRef<ElementA const, LayoutA> ref_A;
|
||||
TensorRef<ElementB const, LayoutB> ref_B;
|
||||
TensorRef<ElementE const, LayoutE> ref_E;
|
||||
typename FusionCallbacks::Arguments epilogue;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
/// Default ctor
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(): problem_size(0, 0, 0) {
|
||||
|
||||
}
|
||||
|
||||
/// Constructs an Arguments structure
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(
|
||||
GemmCoord problem_size_,
|
||||
TensorRef<ElementA const, LayoutA> ref_A_,
|
||||
TensorRef<ElementB const, LayoutB> ref_B_,
|
||||
TensorRef<ElementE, LayoutE> ref_E_,
|
||||
typename FusionCallbacks::Arguments epilogue_ =
|
||||
typename FusionCallbacks::Arguments()
|
||||
):
|
||||
problem_size(problem_size_),
|
||||
ref_A(ref_A_),
|
||||
ref_B(ref_B_),
|
||||
ref_E(ref_E_),
|
||||
epilogue(epilogue_) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
/// Kernel parameters object
|
||||
typename GemmKernel::Params params_;
|
||||
|
||||
public:
|
||||
|
||||
/// Constructs the GEMM.
|
||||
SparseGemmWithVisitor() { }
|
||||
|
||||
/// Determines whether the GEMM can execute the given problem.
|
||||
static Status can_implement(Arguments const &args) {
|
||||
|
||||
Status status = GemmKernel::can_implement(
|
||||
args.problem_size,
|
||||
args.ref_A.non_const_ref(),
|
||||
args.ref_B.non_const_ref(),
|
||||
cutlass::TensorRef<ElementC, LayoutC>(), // It only matters that it's empty.
|
||||
cutlass::TensorRef<ElementC, LayoutC>(), // Same as above.
|
||||
args.ref_E.non_const_ref()
|
||||
);
|
||||
|
||||
if (status != Status::kSuccess) {
|
||||
return status;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Gets the workspace size
|
||||
static size_t get_workspace_size(Arguments const &args) {
|
||||
|
||||
size_t bytes = 0;
|
||||
|
||||
return bytes;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
|
||||
|
||||
constexpr int SplitKSlices = 1;
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
|
||||
args.problem_size,
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
SplitKSlices);
|
||||
|
||||
// Initialize the Params structure
|
||||
params_ = typename GemmKernel::Params{
|
||||
args.problem_size,
|
||||
grid_shape,
|
||||
args.ref_A.non_const_ref(),
|
||||
args.ref_B.non_const_ref(),
|
||||
args.ref_E.non_const_ref(),
|
||||
args.epilogue
|
||||
};
|
||||
|
||||
int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
|
||||
if (smem_size >= (48 << 10)) {
|
||||
cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
smem_size);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Lightweight update given a subset of arguments
|
||||
Status update(Arguments const &args, void *workspace = nullptr) {
|
||||
|
||||
params_.ref_A.reset(args.ref_A.non_const_ref().data());
|
||||
params_.ref_B.reset(args.ref_B.non_const_ref().data());
|
||||
params_.ref_E.reset(args.ref_E.non_const_ref().data());
|
||||
params_.output_op = args.epilogue;
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status run(cudaStream_t stream = nullptr) {
|
||||
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
|
||||
dim3 block(GemmKernel::kThreadCount, 1, 1);
|
||||
|
||||
int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
|
||||
|
||||
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);
|
||||
|
||||
cudaError_t result = cudaGetLastError();
|
||||
|
||||
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(cudaStream_t stream = nullptr) {
|
||||
return run(stream);
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
Status status = initialize(args, workspace, stream);
|
||||
|
||||
if (status == Status::kSuccess) {
|
||||
status = run(stream);
|
||||
}
|
||||
|
||||
return status;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
198
include/cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h
Normal file
198
include/cutlass/gemm/kernel/default_gemm_sparse_with_visitor.h
Normal file
@ -0,0 +1,198 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Default sparse GEMM with visitor.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/wmma.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/epilogue.h"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/kernel/gemm.h"
|
||||
#include "cutlass/gemm/kernel/default_gemm_sparse.h"
|
||||
#include "cutlass/gemm/kernel/sparse_gemm_with_visitor.h"
|
||||
#include "cutlass/gemm/kernel/gemm_pipelined.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_sparse_mma.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
|
||||
#include "cutlass/epilogue/threadblock/epilogue_with_visitor_callbacks.h"
|
||||
#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
|
||||
|
||||
#if defined(CUTLASS_ARCH_WMMA_ENABLED)
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"
|
||||
#endif //CUTLASS_ARCH_WMMA_ENABLED
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
/// Access granularity of B matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Operator class tag
|
||||
typename OperatorClass,
|
||||
/// Tag indicating architecture to tune for
|
||||
typename ArchTag,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename FusionCallbacks,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator,
|
||||
/// Number of stages used in the pipelined epilogue
|
||||
int EpilogueStages = 1>
|
||||
struct DefaultSparseGemmWithVisitor;
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Partial specialization for Ampere Architecture
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename FusionCallbacks,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator,
|
||||
/// Number of stages used in the pipelined epilogue
|
||||
int EpilogueStages>
|
||||
struct DefaultSparseGemmWithVisitor<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementC, LayoutC, ElementAccumulator, arch::OpClassTensorOp,
|
||||
arch::Sm80, ThreadblockShape, WarpShape, InstructionShape,
|
||||
FusionCallbacks, ThreadblockSwizzle, Stages, Operator,
|
||||
EpilogueStages> {
|
||||
/// Define the threadblock-scoped matrix multiply-accumulate
|
||||
using Mma = typename cutlass::gemm::threadblock::DefaultSparseMma<
|
||||
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
|
||||
ThreadblockShape, WarpShape, InstructionShape, Stages,
|
||||
Operator>::ThreadblockMma;
|
||||
|
||||
static constexpr int kAlignmentC = 128 / sizeof_bits<ElementC>::value;;
|
||||
using ElementEpilogue = ElementAccumulator;
|
||||
|
||||
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
|
||||
using EpilogueOutputOp =
|
||||
typename epilogue::thread::LinearCombination<
|
||||
ElementC, kAlignmentC,
|
||||
ElementAccumulator, ElementEpilogue>;
|
||||
using BaseEpilogue =
|
||||
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape, typename Mma::Operator, kPartitionsK,
|
||||
EpilogueOutputOp, EpilogueOutputOp::kCount>::Epilogue;
|
||||
|
||||
// Define epilogue
|
||||
using Epilogue = cutlass::epilogue::threadblock::EpilogueWithVisitorCallbacks<
|
||||
BaseEpilogue,
|
||||
FusionCallbacks,
|
||||
EpilogueStages>;
|
||||
|
||||
/// Define the kernel-level GEMM operator.
|
||||
using GemmKernel = kernel::SparseGemmWithEpilogueVisitor<Mma, Epilogue, ThreadblockSwizzle>;
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
117
include/cutlass/gemm/kernel/params_sparse_base.h
Normal file
117
include/cutlass/gemm/kernel/params_sparse_base.h
Normal file
@ -0,0 +1,117 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
|
||||
/*! \file
|
||||
\brief Base functionality for common types of sparse GEMM kernel parameters
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Parameters structure
|
||||
template <
|
||||
typename ThreadblockSwizzle,
|
||||
typename ParamsA,
|
||||
typename TensorRefA,
|
||||
typename ParamsB,
|
||||
typename TensorRefB,
|
||||
typename ParamsE,
|
||||
typename TensorRefE>
|
||||
struct SparseParamsBase
|
||||
{
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
ParamsA params_A;
|
||||
TensorRefA ref_A;
|
||||
ParamsB params_B;
|
||||
TensorRefB ref_B;
|
||||
ParamsE params_E;
|
||||
TensorRefE ref_E;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Host dispatch API
|
||||
//
|
||||
|
||||
/// Default constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseParamsBase() : swizzle_log_tile(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseParamsBase(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
TensorRefA ref_A,
|
||||
TensorRefB ref_B,
|
||||
TensorRefE ref_E,
|
||||
int const mma_shape_k)
|
||||
:
|
||||
problem_size(problem_size),
|
||||
grid_tiled_shape(grid_tiled_shape),
|
||||
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
||||
params_A(ref_A.layout()),
|
||||
ref_A(ref_A),
|
||||
params_B(ref_B.layout()),
|
||||
ref_B(ref_B),
|
||||
params_E(ref_E.layout()),
|
||||
ref_E(ref_E)
|
||||
{
|
||||
int total_gemm_k_iterations = (problem_size.k() + mma_shape_k - 1) / mma_shape_k;
|
||||
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
||||
|
||||
gemm_k_size = gemm_k_iterations * mma_shape_k;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@ -37,6 +37,7 @@
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/kernel/params_sparse_base.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/semaphore.h"
|
||||
|
||||
@ -74,66 +75,58 @@ struct SparseGemm {
|
||||
using WarpCount = typename Mma::WarpCount;
|
||||
static int const kThreadCount = 32 * WarpCount::kCount;
|
||||
|
||||
using ParamsA = typename Mma::IteratorA::Params;
|
||||
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
||||
using ParamsB = typename Mma::IteratorB::Params;
|
||||
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
||||
using ParamsE = typename Mma::IteratorE::Params;
|
||||
using TensorRefE = typename Mma::IteratorE::TensorRef;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params {
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
typename Mma::IteratorA::Params params_A;
|
||||
typename Mma::IteratorA::TensorRef ref_A;
|
||||
typename Mma::IteratorB::Params params_B;
|
||||
typename Mma::IteratorB::TensorRef ref_B;
|
||||
struct Params : public SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE> {
|
||||
|
||||
using Base = SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
typename Epilogue::OutputTileIterator::Params params_C;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C;
|
||||
typename Epilogue::OutputTileIterator::Params params_D;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D;
|
||||
typename Mma::IteratorE::Params params_E;
|
||||
typename Mma::IteratorE::TensorRef ref_E;
|
||||
typename OutputOp::Params output_op;
|
||||
int *semaphore;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
TensorRefA ref_A,
|
||||
TensorRefB ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
TensorRefE ref_E,
|
||||
typename OutputOp::Params output_op = typename OutputOp::Params(),
|
||||
int *workspace = nullptr
|
||||
):
|
||||
problem_size(problem_size),
|
||||
grid_tiled_shape(grid_tiled_shape),
|
||||
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
||||
params_A(ref_A.layout()),
|
||||
ref_A(ref_A),
|
||||
params_B(ref_B.layout()),
|
||||
ref_B(ref_B),
|
||||
Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
|
||||
params_C(ref_C.layout()),
|
||||
ref_C(ref_C),
|
||||
params_D(ref_D.layout()),
|
||||
ref_D(ref_D),
|
||||
params_E(ref_E.layout()),
|
||||
ref_E(ref_E),
|
||||
output_op(output_op) {
|
||||
|
||||
int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
||||
|
||||
gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
|
||||
|
||||
semaphore = workspace;
|
||||
output_op(output_op),
|
||||
semaphore(workspace) {
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
237
include/cutlass/gemm/kernel/sparse_gemm_with_visitor.h
Normal file
237
include/cutlass/gemm/kernel/sparse_gemm_with_visitor.h
Normal file
@ -0,0 +1,237 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Sparse GEMM with visitor.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/kernel/sparse_gemm.h"
|
||||
#include "cutlass/gemm/kernel/params_sparse_base.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Sparse Gemm that compute the epilogue visitor functor
|
||||
template <
|
||||
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
||||
typename Epilogue_, ///! Epilogue
|
||||
typename ThreadblockSwizzle_ ///! Threadblock swizzling function
|
||||
>
|
||||
struct SparseGemmWithEpilogueVisitor : public SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false> {
|
||||
|
||||
using Base = SparseGemm<Mma_, Epilogue_, ThreadblockSwizzle_, false>;
|
||||
|
||||
using Mma = Mma_;
|
||||
using Epilogue = Epilogue_;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
|
||||
using FusionCallbacks = typename Epilogue::FusionCallbacks;
|
||||
|
||||
using ParamsA = typename Mma::IteratorA::Params;
|
||||
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
||||
using ParamsB = typename Mma::IteratorB::Params;
|
||||
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
||||
using ParamsE = typename Mma::IteratorE::Params;
|
||||
using TensorRefE = typename Mma::IteratorE::TensorRef;
|
||||
|
||||
static int const kSparse = Base::kSparse;
|
||||
static int const kElementsPerElementE = Base::kElementsPerElementE;
|
||||
using SharedStorage = typename Base::SharedStorage;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params : public SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE> {
|
||||
|
||||
using Base = SparseParamsBase<
|
||||
ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
|
||||
ParamsE, TensorRefE>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
typename FusionCallbacks::Params output_op;
|
||||
cute::Shape<int32_t,int32_t,int32_t> problem_shape;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
typename FusionCallbacks::Arguments output_op = typename FusionCallbacks::Arguments()
|
||||
):
|
||||
Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
|
||||
output_op(FusionCallbacks::to_underlying_arguments(problem_size, output_op, nullptr /*workspace*/)),
|
||||
problem_shape(problem_size.m(), problem_size.n(), 1) {
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseGemmWithEpilogueVisitor() { }
|
||||
|
||||
/// Executes one GEMM
|
||||
CUTLASS_DEVICE
|
||||
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
||||
|
||||
// Compute threadblock location
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
// Early exit if CTA is out of range
|
||||
if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
|
||||
params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// Compute initial location in logical coordinates
|
||||
cutlass::MatrixCoord tb_offset_A{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_B{
|
||||
threadblock_tile_offset.k() * params.gemm_k_size,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_E{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
// Problem size is a function of threadblock index in the K dimension
|
||||
int problem_size_k = min(
|
||||
params.problem_size.k(),
|
||||
(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
|
||||
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
|
||||
// Compute position within threadblock
|
||||
int thread_idx = threadIdx.x;
|
||||
|
||||
// Construct iterators to A, B, and E operands
|
||||
typename Mma::IteratorA iterator_A(
|
||||
params.params_A,
|
||||
params.ref_A.data(),
|
||||
{params.problem_size.m(), problem_size_k / kSparse},
|
||||
thread_idx,
|
||||
tb_offset_A);
|
||||
|
||||
typename Mma::IteratorB iterator_B(
|
||||
params.params_B,
|
||||
params.ref_B.data(),
|
||||
{problem_size_k, params.problem_size.n()},
|
||||
thread_idx,
|
||||
tb_offset_B);
|
||||
|
||||
typename Mma::IteratorE iterator_E(
|
||||
params.params_E, params.ref_E.data(),
|
||||
{params.problem_size.m(),
|
||||
problem_size_k / kSparse / kElementsPerElementE},
|
||||
thread_idx, tb_offset_E);
|
||||
|
||||
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
||||
// is compiled as warp-uniform.
|
||||
int warp_idx = canonical_warp_idx_sync();
|
||||
int lane_idx = threadIdx.x % 32;
|
||||
|
||||
//
|
||||
// Main loop
|
||||
//
|
||||
|
||||
// Construct thread-scoped matrix multiply
|
||||
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
||||
|
||||
typename Mma::FragmentC accumulators;
|
||||
|
||||
accumulators.clear();
|
||||
|
||||
if (gemm_k_iterations > 0) {
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
|
||||
}
|
||||
|
||||
//
|
||||
// Masked tile iterators constructed from members
|
||||
//
|
||||
|
||||
threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
||||
|
||||
//
|
||||
// Epilogue
|
||||
//
|
||||
|
||||
Epilogue epilogue(
|
||||
params.output_op,
|
||||
shared_storage.epilogue,
|
||||
thread_idx,
|
||||
warp_idx,
|
||||
lane_idx);
|
||||
|
||||
// Execute the epilogue operator to update the destination tensor.
|
||||
epilogue(accumulators, threadblock_tile_offset, params.problem_shape, thread_idx);
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
Loading…
Reference in New Issue
Block a user