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Wavelet (inter-wave consumer-producer) GEMM (#310)
* wavelet gemm programming model support for CK * GEMM pipeline update for wavelet progrmmaing model * Updated wavelet programming pipeline * fixes for global-write for math-wave * fixed bug in global writes * Updated comments for better readability * fixed clang format errors * added block_lds without barrier sync * clean * clean * clean * clean * refactor * prototype 4 layouts fix default stride all problem sizes tidy move file update build script restore old file fix build * refactor standalone test to use gemm test harness * simplify gemm test * update build script * remove redundant * early return when cmd arg doesn't match * tidy * report failure when result not validated * tidy * Add comment depicting B2C mapping pattern. * Formatting & comments. * Comparison with custom B2C mapping pattern. * Example for wavelet gemm. * Add wavelet to Gemm standalone test. * Remove debug code. * Remove dangling #endif directive. Co-authored-by: root <Raman Jana> Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: Adam Osewski <aosewski@amd.com> Co-authored-by: Anthony Chang <ac.chang@outlook.com> Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
This commit is contained in:
Raman R jana
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_waveletmodel_cshuffle.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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template <typename GridwiseGemm,
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typename ABDataType,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename EElementwiseOperation,
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typename AGridDesc_AK0_M_AK1,
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typename BGridDesc_BK0_N_BK1,
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typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename Block2ETileMap,
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bool HasMainKBlockLoop>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_WAVELET_MAX_THREAD_PER_BLOCK, CK_WAVELET_MIN_BLOCK_PER_CU)
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#endif
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kernel_gemm_xdl_waveletmodel_cshuffle(
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const ABDataType* __restrict__ p_a_grid,
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const ABDataType* __restrict__ p_b_grid,
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EDataType* __restrict__ p_e_grid,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const EElementwiseOperation e_element_op,
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const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
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const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
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const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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const Block2ETileMap block_2_etile_map)
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{
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#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
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p_b_grid,
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p_e_grid,
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p_shared,
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a_element_op,
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b_element_op,
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e_element_op,
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a_grid_desc_ak0_m_ak1,
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b_grid_desc_bk0_n_bk1,
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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block_2_etile_map);
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#else
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ignore = p_a_grid;
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ignore = p_b_grid;
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ignore = p_e_grid;
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ignore = a_element_op;
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ignore = b_element_op;
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ignore = e_element_op;
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ignore = a_grid_desc_ak0_m_ak1;
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ignore = b_grid_desc_bk0_n_bk1;
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ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
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ignore = block_2_etile_map;
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#endif
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}
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} // namespace ck
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename ALayout,
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typename BLayout,
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typename ELayout,
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typename ADataType,
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typename BDataType,
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typename GemmAcEDataType,
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typename CShuffleDataType,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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GemmSpecialization GemmSpec,
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index_t NumGemmKPrefetchStage,
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index_t TileLoadThreadGroupSize,
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index_t TileMathThreadGroupSize,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1,
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index_t BK1,
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index_t MPerXDL,
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index_t NPerXDL,
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index_t MXdlPerWave,
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index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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bool ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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bool BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
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struct DeviceGemm_Xdl_WaveletModel_CShuffle : public DeviceGemm<ALayout,
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BLayout,
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ELayout,
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ADataType,
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BDataType,
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation>
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{
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using DeviceOp = DeviceGemm_Xdl_WaveletModel_CShuffle;
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto matrix_padder =
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MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
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static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
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{
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const auto a_grid_desc_mraw_kraw = [&]() {
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if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(StrideA, I1));
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}
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else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(I1, StrideA));
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}
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}();
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return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
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}
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static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
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{
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const auto b_grid_desc_nraw_kraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(I1, StrideB));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(StrideB, I1));
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}
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}();
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return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
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}
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template <typename ELay>
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static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
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{
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const auto e_grid_desc_mraw_nraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(StrideE, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(I1, StrideE));
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}
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}();
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return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
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}
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using AGridDesc_M_K = decltype(MakeAGridDescriptor_M_K(1, 1, 1));
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using BGridDesc_N_K = decltype(MakeBGridDescriptor_N_K(1, 1, 1));
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using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdl_waveletmodel_cshuffle<
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ADataType, // TODO: distinguish A/B datatype
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GemmAcEDataType,
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CShuffleDataType,
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation,
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InMemoryDataOperationEnum::Set,
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AGridDesc_M_K,
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BGridDesc_N_K,
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EGridDesc_M_N,
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NumGemmKPrefetchStage,
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TileLoadThreadGroupSize,
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TileMathThreadGroupSize,
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MPerBlock,
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NPerBlock,
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KPerBlock,
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AK1,
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BK1,
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MPerXDL,
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NPerXDL,
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MXdlPerWave,
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NXdlPerWave,
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ABlockTransferThreadClusterLengths_AK0_M_AK1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_AK1,
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false,
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ABlockLdsExtraM,
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BBlockTransferThreadClusterLengths_BK0_N_BK1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_BK1,
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false,
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BBlockLdsExtraN,
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CShuffleMXdlPerWavePerShuffle,
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CShuffleNXdlPerWavePerShuffle,
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CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CShuffleBlockTransferScalarPerVector_NPerBlock>;
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using AGridDesc_AK0_M_AK1 = remove_cvref_t<decltype(
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GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
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using BGridDesc_BK0_N_BK1 = remove_cvref_t<decltype(
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GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
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using Block2ETileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
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// Argument
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struct Argument : public BaseArgument
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{
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Argument(const ADataType* p_a_grid,
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const BDataType* p_b_grid,
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EDataType* p_e_grid,
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index_t MRaw,
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index_t NRaw,
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index_t KRaw,
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index_t StrideA,
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index_t StrideB,
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index_t StrideE,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op)
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: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
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p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
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p_e_grid_{static_cast<EDataType*>(p_e_grid)},
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a_grid_desc_m_k_{DeviceOp::MakeAGridDescriptor_M_K(MRaw, KRaw, StrideA)},
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b_grid_desc_n_k_{DeviceOp::MakeBGridDescriptor_N_K(KRaw, NRaw, StrideB)},
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e_grid_desc_m_n_{DeviceOp::MakeEGridDescriptor_M_N<ELayout>(MRaw, NRaw, StrideE)},
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a_grid_desc_ak0_m_ak1_{
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GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k_)},
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b_grid_desc_bk0_n_bk1_{
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GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k_)},
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e_grid_desc_mblock_mperblock_nblock_nperblock_{},
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block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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cde_element_op_{cde_element_op}
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{
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if(GridwiseGemm::CheckValidity(
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a_grid_desc_m_k_, b_grid_desc_n_k_, e_grid_desc_m_n_, block_2_etile_map_))
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{
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e_grid_desc_mblock_mperblock_nblock_nperblock_ =
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GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
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e_grid_desc_m_n_);
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}
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}
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void Print() const
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{
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std::cout << "A[M, K]: " << a_grid_desc_m_k_ << std::endl;
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std::cout << "B[N, K]: " << b_grid_desc_n_k_ << std::endl;
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std::cout << "E[M, N]: " << e_grid_desc_m_n_ << std::endl;
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}
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// private:
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// pointers
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const ADataType* p_a_grid_;
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const BDataType* p_b_grid_;
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EDataType* p_e_grid_;
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// tensor descriptors for problem definiton
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AGridDesc_M_K a_grid_desc_m_k_;
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BGridDesc_N_K b_grid_desc_n_k_;
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EGridDesc_M_N e_grid_desc_m_n_;
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// tensor descriptors for block/thread-wise copy
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AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
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BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
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typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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e_grid_desc_mblock_mperblock_nblock_nperblock_;
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// block-to-e-tile map
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Block2ETileMap block_2_etile_map_;
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// element-wise op
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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CDEElementwiseOperation cde_element_op_;
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};
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// Invoker
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struct Invoker : public BaseInvoker
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{
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using Argument = DeviceOp::Argument;
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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#if 0
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{
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std::cout << "arg.a_grid_desc_ak0_m_ak1_{"
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<< arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) << ", "
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<< arg.a_grid_desc_ak0_m_ak1_.GetLength(I1) << ", "
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<< arg.a_grid_desc_ak0_m_ak1_.GetLength(I2) << "}" << std::endl;
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std::cout << "arg.b_grid_desc_bk0_n_bk1_{"
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<< arg.b_grid_desc_bk0_n_bk1_.GetLength(I0) << ", "
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<< arg.b_grid_desc_bk0_n_bk1_.GetLength(I1) << ", "
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<< arg.b_grid_desc_bk0_n_bk1_.GetLength(I2) << "}" << std::endl;
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std::cout << "arg.e_grid_desc_m_n_{ " << arg.e_grid_desc_m_n_.GetLength(I0) << ", "
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<< arg.e_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
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}
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#endif
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if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
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arg.b_grid_desc_n_k_,
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arg.e_grid_desc_m_n_,
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arg.block_2_etile_map_))
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{
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throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
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}
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const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.e_grid_desc_m_n_);
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const auto K = arg.a_grid_desc_m_k_.GetLength(I1);
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auto launch_kernel = [&](auto has_main_k_block_loop) {
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constexpr bool has_main_loop = has_main_k_block_loop.value;
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const auto kernel = kernel_gemm_xdl_waveletmodel_cshuffle<
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GridwiseGemm,
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ADataType, // TODO: distiguish A/B datatype
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation,
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DeviceOp::AGridDesc_AK0_M_AK1,
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DeviceOp::BGridDesc_BK0_N_BK1,
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typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename GridwiseGemm::DefaultBlock2ETileMap,
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has_main_loop>;
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return launch_and_time_kernel(
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stream_config,
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kernel,
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dim3(grid_size),
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dim3(TileLoadThreadGroupSize + TileMathThreadGroupSize),
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0,
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arg.p_a_grid_,
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arg.p_b_grid_,
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arg.p_e_grid_,
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arg.a_element_op_,
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arg.b_element_op_,
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arg.cde_element_op_,
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arg.a_grid_desc_ak0_m_ak1_,
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arg.b_grid_desc_bk0_n_bk1_,
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arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.block_2_etile_map_);
|
||||
};
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
|
||||
{
|
||||
return launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
return launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
|
||||
arg.b_grid_desc_n_k_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.block_2_etile_map_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const BDataType* p_b,
|
||||
EDataType* p_e,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_e,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
void* p_e,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<EDataType*>(p_e),
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemm_Xdl_WaveletModel_CShuffle"
|
||||
<< "<"
|
||||
<< TileLoadThreadGroupSize << ", "
|
||||
<< TileMathThreadGroupSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -431,9 +431,6 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout,
|
||||
const index_t grid_size =
|
||||
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
|
||||
|
||||
const auto K =
|
||||
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop) {
|
||||
constexpr bool has_main_loop = has_main_k_block_loop.value;
|
||||
|
||||
@@ -471,6 +468,8 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout,
|
||||
arg.block_2_etile_map_);
|
||||
};
|
||||
|
||||
const auto K = arg.a_grid_desc_m_k_.GetLength(I1);
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
|
||||
{
|
||||
return launch_kernel(integral_constant<bool, true>{});
|
||||
|
||||
@@ -486,7 +486,6 @@ struct DeviceGemm_Xdl_CShuffle : public DeviceGemm<ALayout,
|
||||
|
||||
const index_t grid_size =
|
||||
arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
|
||||
|
||||
const auto K =
|
||||
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
|
||||
|
||||
|
||||
Reference in New Issue
Block a user