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Ding, Yi
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iostream>
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#include <string>
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/common.hpp"
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#include "ck_tile/host/concat.hpp"
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#include "ck_tile/core/utility/env.hpp"
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#include "ck_tile/host/convolution_parameter.hpp"
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#include "ck_tile/ops/grouped_convolution/utils/transform_conv_bwd_weight_to_gemm.hpp"
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#include "ck_tile/ops/grouped_convolution/utils/grouped_convolution_utils.hpp"
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#include "ck_tile/ops/grouped_convolution/utils/split_k_utils.hpp"
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#ifdef CK_EXPERIMENTAL_BUILDER
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#include "ck_tile/builder/reflect/instance_traits_tile_grouped_convolution_backward_weight.hpp"
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#endif
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namespace ck_tile {
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template <typename... Args>
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CK_TILE_HOST void LogInfo(Args&&... args) noexcept
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{
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if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
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{
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CK_TILE_INFO(std::forward<Args>(args)...);
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}
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}
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/// @brief The Grouped Convolution kernel device arguments.
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template <typename GroupedConvTraitsType_>
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struct GroupedConvBwdWeightKernelArgs
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{
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using ConvToGemmTransformer =
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TransformConvBwdWeightToGemm<GroupedConvTraitsType_::NDimSpatial,
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GroupedConvTraitsType_::ConvSpecialization,
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GroupedConvTraitsType_::VectorSizeA,
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GroupedConvTraitsType_::VectorSizeB,
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GroupedConvTraitsType_::VectorSizeC,
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GroupedConvTraitsType_::NumGroupsToMerge>;
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static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
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template <
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typename InLay = typename GroupedConvTraitsType_::InLayout,
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typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
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typename OutLay = typename GroupedConvTraitsType_::OutLayout,
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typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NWGC> &&
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std::is_same_v<WeiLay, tensor_layout::convolution::GKXC> &&
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std::is_same_v<OutLay, tensor_layout::convolution::NWGK>,
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bool>::type = false>
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CK_TILE_HOST GroupedConvBwdWeightKernelArgs(const GroupedConvBwdWeightHostArgs& args)
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{
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in_g_n_c_wis_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.input_spatial_lengths_[0])};
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wei_g_k_c_xs_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.filter_spatial_lengths_[0])};
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out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.output_spatial_lengths_[0])};
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conv_filter_strides = {static_cast<index_t>(args.conv_filter_strides_[0])};
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conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0])};
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input_left_pads = {static_cast<index_t>(args.input_left_pads_[0])};
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input_right_pads = {static_cast<index_t>(args.input_right_pads_[0])};
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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for(index_t d = 0; d < NumDTensor; d++)
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{
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ds_ptr[d] = args.ds_ptr[d];
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}
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out_ptr = args.out_ptr;
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ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
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wei_g_k_c_xs_lengths,
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out_g_n_k_wos_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads};
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// tuple
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auto grid_descs =
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conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
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GroupedConvTraitsType_::NDimSpatial>();
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a_grid_desc_k_m = grid_descs.at(number<0>{});
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b_grid_desc_k_n = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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NumGroupsPerBatch = GroupedConvTraitsType_::NumGroupsToMerge;
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group_stride_a = args.K_ * NumGroupsPerBatch; // A: Out NWGK
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group_stride_b = args.C_ * NumGroupsPerBatch; // B: In NWGC
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group_stride_c = args.K_ * args.C_ // C: Wei GKXC
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* NumGroupsPerBatch *
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std::accumulate(args.filter_spatial_lengths_.begin(),
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args.filter_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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GemmM = a_grid_desc_k_m.get_length(number<1>{});
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GemmN = b_grid_desc_k_n.get_length(number<1>{});
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GemmK = a_grid_desc_k_m.get_length(number<0>{});
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GemmBatch = integer_divide_ceil(args.G_, NumGroupsPerBatch);
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k_batch = args.k_batch;
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LogInfo("GemmM: ",
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GemmM,
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", GemmN: ",
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GemmN,
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", GemmK: ",
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GemmK,
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", GemmBatch: ",
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GemmBatch,
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", NumGroupsPerBatch: ",
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NumGroupsPerBatch,
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", k_batch: ",
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k_batch);
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}
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template <
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typename InLay = typename GroupedConvTraitsType_::InLayout,
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typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
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typename OutLay = typename GroupedConvTraitsType_::OutLayout,
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typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NHWGC> &&
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std::is_same_v<WeiLay, tensor_layout::convolution::GKYXC> &&
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std::is_same_v<OutLay, tensor_layout::convolution::NHWGK>,
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bool>::type = false>
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CK_TILE_HOST GroupedConvBwdWeightKernelArgs(const GroupedConvBwdWeightHostArgs& args)
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{
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in_g_n_c_wis_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.input_spatial_lengths_[0]),
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static_cast<index_t>(args.input_spatial_lengths_[1])};
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wei_g_k_c_xs_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.filter_spatial_lengths_[0]),
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static_cast<index_t>(args.filter_spatial_lengths_[1])};
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out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.output_spatial_lengths_[0]),
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static_cast<index_t>(args.output_spatial_lengths_[1])};
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conv_filter_strides = {static_cast<index_t>(args.conv_filter_strides_[0]),
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static_cast<index_t>(args.conv_filter_strides_[1])};
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conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0]),
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static_cast<index_t>(args.conv_filter_dilations_[1])};
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input_left_pads = {static_cast<index_t>(args.input_left_pads_[0]),
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static_cast<index_t>(args.input_left_pads_[1])};
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input_right_pads = {static_cast<index_t>(args.input_right_pads_[0]),
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static_cast<index_t>(args.input_right_pads_[1])};
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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for(index_t d = 0; d < NumDTensor; d++)
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{
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ds_ptr[d] = args.ds_ptr[d];
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}
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out_ptr = args.out_ptr;
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ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
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wei_g_k_c_xs_lengths,
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out_g_n_k_wos_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads};
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// tuple
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auto grid_descs =
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conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
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GroupedConvTraitsType_::NDimSpatial>();
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a_grid_desc_k_m = grid_descs.at(number<0>{});
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b_grid_desc_k_n = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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NumGroupsPerBatch = GroupedConvTraitsType_::NumGroupsToMerge;
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group_stride_a = args.K_ * NumGroupsPerBatch; // A: Out NHWGK
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group_stride_b = args.C_ * NumGroupsPerBatch; // B: In NHWGC
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group_stride_c = args.K_ * args.C_ // C: Wei GKYXC
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* NumGroupsPerBatch *
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std::accumulate(args.filter_spatial_lengths_.begin(),
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args.filter_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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GemmM = a_grid_desc_k_m.get_length(number<1>{});
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GemmN = b_grid_desc_k_n.get_length(number<1>{});
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GemmK = a_grid_desc_k_m.get_length(number<0>{});
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GemmBatch = integer_divide_ceil(args.G_, NumGroupsPerBatch);
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k_batch = args.k_batch;
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LogInfo("GemmM: ",
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GemmM,
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", GemmN: ",
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GemmN,
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", GemmK: ",
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GemmK,
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", GemmBatch: ",
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GemmBatch,
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", NumGroupsPerBatch: ",
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NumGroupsPerBatch,
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", k_batch: ",
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k_batch);
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}
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template <
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typename InLay = typename GroupedConvTraitsType_::InLayout,
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typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
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typename OutLay = typename GroupedConvTraitsType_::OutLayout,
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typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NDHWGC> &&
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std::is_same_v<WeiLay, tensor_layout::convolution::GKZYXC> &&
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std::is_same_v<OutLay, tensor_layout::convolution::NDHWGK>,
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bool>::type = false>
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CK_TILE_HOST GroupedConvBwdWeightKernelArgs(const GroupedConvBwdWeightHostArgs& args)
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{
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in_g_n_c_wis_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.input_spatial_lengths_[0]),
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static_cast<index_t>(args.input_spatial_lengths_[1]),
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static_cast<index_t>(args.input_spatial_lengths_[2])};
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wei_g_k_c_xs_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.C_),
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static_cast<index_t>(args.filter_spatial_lengths_[0]),
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static_cast<index_t>(args.filter_spatial_lengths_[1]),
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static_cast<index_t>(args.filter_spatial_lengths_[2])};
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out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
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static_cast<index_t>(args.N_),
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static_cast<index_t>(args.K_),
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static_cast<index_t>(args.output_spatial_lengths_[0]),
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static_cast<index_t>(args.output_spatial_lengths_[1]),
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static_cast<index_t>(args.output_spatial_lengths_[2])};
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conv_filter_strides = {static_cast<index_t>(args.conv_filter_strides_[0]),
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static_cast<index_t>(args.conv_filter_strides_[1]),
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static_cast<index_t>(args.conv_filter_strides_[2])};
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conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0]),
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static_cast<index_t>(args.conv_filter_dilations_[1]),
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static_cast<index_t>(args.conv_filter_dilations_[2])};
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input_left_pads = {static_cast<index_t>(args.input_left_pads_[0]),
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static_cast<index_t>(args.input_left_pads_[1]),
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static_cast<index_t>(args.input_left_pads_[2])};
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input_right_pads = {static_cast<index_t>(args.input_right_pads_[0]),
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static_cast<index_t>(args.input_right_pads_[1]),
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static_cast<index_t>(args.input_right_pads_[2])};
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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for(index_t d = 0; d < NumDTensor; d++)
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{
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ds_ptr[d] = args.ds_ptr[d];
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}
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out_ptr = args.out_ptr;
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ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
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wei_g_k_c_xs_lengths,
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out_g_n_k_wos_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads};
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// tuple
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auto grid_descs =
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conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
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GroupedConvTraitsType_::NDimSpatial>();
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a_grid_desc_k_m = grid_descs.at(number<0>{});
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b_grid_desc_k_n = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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NumGroupsPerBatch = GroupedConvTraitsType_::NumGroupsToMerge;
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group_stride_a = args.K_ * NumGroupsPerBatch; // A: Out NDHWGK
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group_stride_b = args.C_ * NumGroupsPerBatch; // B: In NDHWGC
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group_stride_c = args.K_ * args.C_ // C: Wei GKZYXC
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* NumGroupsPerBatch *
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std::accumulate(args.filter_spatial_lengths_.begin(),
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args.filter_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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GemmM = a_grid_desc_k_m.get_length(number<1>{});
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GemmN = b_grid_desc_k_n.get_length(number<1>{});
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GemmK = a_grid_desc_k_m.get_length(number<0>{});
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GemmBatch = integer_divide_ceil(args.G_, NumGroupsPerBatch);
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k_batch = args.k_batch;
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LogInfo("GemmM: ",
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GemmM,
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", GemmN: ",
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GemmN,
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", GemmK: ",
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GemmK,
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", GemmBatch: ",
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GemmBatch,
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", NumGroupsPerBatch: ",
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NumGroupsPerBatch,
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||||
", k_batch: ",
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k_batch);
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||||
}
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||||
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||||
using ABCGridDescs = remove_cvref_t<
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decltype(ConvToGemmTransformer{}.MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N())>;
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using AGridDescKM = remove_cvref_t<decltype(ABCGridDescs{}[number<0>{}])>;
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using BGridDescKN = remove_cvref_t<decltype(ABCGridDescs{}[number<1>{}])>;
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using CGridDescMN = remove_cvref_t<decltype(ABCGridDescs{}[number<2>{}])>;
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static constexpr index_t NonSpatialDims = 3;
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array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> in_g_n_c_wis_lengths;
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array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> wei_g_k_c_xs_lengths;
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||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> out_g_n_k_wos_lengths;
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array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_strides;
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||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_right_pads;
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||||
|
||||
index_t k_batch;
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||||
index_t GemmM;
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||||
index_t GemmN;
|
||||
index_t GemmK;
|
||||
index_t GemmBatch;
|
||||
index_t NumGroupsPerBatch;
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||||
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||||
const void* out_ptr;
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||||
const void* in_ptr;
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||||
std::array<const void*, NumDTensor> ds_ptr;
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||||
void* wei_ptr;
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||||
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||||
AGridDescKM a_grid_desc_k_m;
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||||
BGridDescKN b_grid_desc_k_n;
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||||
CGridDescMN c_grid_desc_m_n;
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||||
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||||
long_index_t group_stride_a;
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||||
long_index_t group_stride_b;
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||||
long_index_t group_stride_c;
|
||||
};
|
||||
|
||||
/// @brief The Grouped Convolution Backward Weight kernel template.
|
||||
///
|
||||
/// @paragraph Overview Overview
|
||||
/// This class provides the grouped convolution backward weight kernel template. By
|
||||
/// semantic division of Implicit GEMM algorithm into following parts we achieve
|
||||
/// flexible, versatile and robust kernel implementation.
|
||||
///
|
||||
/// @li @b Prolog - The start of GEMM kernel implementation in @ref operator()
|
||||
/// function call operator" which determines the work scope of each workgroup.
|
||||
/// @li @b GemmPipeline - The core part @a "heart" of matrix multiplication algorithm.
|
||||
/// This is the place where each workgroup is loading data from global memory and
|
||||
/// carrying out dot products.
|
||||
/// @li @b Epilogue - The @a "final" part of matrix multiplication implementation
|
||||
/// responsible for storing results to global memory. This is also the place where
|
||||
/// any additional operator fusion may take place.
|
||||
///
|
||||
/// Additionally both @ref GemmPipeline_ "GemmPipeline" and @ref EpiloguePipeline_
|
||||
/// "EpiloguePipeline" are parameterized with so called @a Policy which determines all
|
||||
/// internal details of those functional parts. You can think of it like both gemm and
|
||||
/// epilogue pipelines provides the control-flow logic controlled by policies. Moreover
|
||||
/// the policy is responsible for definition of all necessary data layouts and thread's
|
||||
/// work distribution.
|
||||
///
|
||||
/// @tparam GroupedConvTraitsType_ The type of class providing traits for grouped convolution.
|
||||
/// @tparam TilePartitioner_ The type of class providing mapping of workgroup index into
|
||||
/// the output data tile to be calculated. It determines the
|
||||
/// workgroup to data relationship (or in other words - which
|
||||
/// data would be processed and calculated by which workgroup).
|
||||
/// @tparam GemmPipeline_ The type of class which provides the core part of matrix
|
||||
/// multiplication. This class should provide implementation of
|
||||
/// data loading from global memory and performing block-wise
|
||||
/// matrix multiplication. You can think of it as a work done by
|
||||
/// single workgroup point of view.
|
||||
/// @tparam EpiloguePipeline_ The type of class providing the final part of matrix
|
||||
/// multiplication implementation. It is responsible for storing
|
||||
/// results calculated by @ref GemmPipeline_ "GemmPipeline" to
|
||||
/// the output C tensor in global memory.
|
||||
template <typename GroupedConvTraitsType_,
|
||||
typename TilePartitioner_,
|
||||
typename GemmPipeline_,
|
||||
typename EpiloguePipeline_>
|
||||
struct GroupedConvolutionBackwardWeightKernel
|
||||
{
|
||||
static constexpr index_t NDimSpatial = GroupedConvTraitsType_::NDimSpatial;
|
||||
static constexpr ConvolutionSpecialization ConvSpecialization =
|
||||
GroupedConvTraitsType_::ConvSpecialization;
|
||||
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
|
||||
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
|
||||
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
|
||||
using GemmALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
|
||||
using GemmBLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
|
||||
using GemmCLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
|
||||
|
||||
using InLayout = remove_cvref_t<typename GroupedConvTraitsType_::InLayout>;
|
||||
using WeiLayout = remove_cvref_t<typename GroupedConvTraitsType_::WeiLayout>;
|
||||
using OutLayout = remove_cvref_t<typename GroupedConvTraitsType_::OutLayout>;
|
||||
using DsLayout = remove_cvref_t<typename GroupedConvTraitsType_::DsLayout>;
|
||||
|
||||
using GemmDsLayout = remove_cvref_t<typename EpiloguePipeline::DsLayout>;
|
||||
static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
|
||||
|
||||
static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
|
||||
|
||||
using OutDataType = remove_cvref_t<typename GemmPipeline::ADataType>;
|
||||
using InDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
|
||||
using DsDataType = remove_cvref_t<typename EpiloguePipeline::DsDataType>;
|
||||
using WeiDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
|
||||
|
||||
using GroupedConvBwdWeightKernelArgsSpecialized =
|
||||
GroupedConvBwdWeightKernelArgs<GroupedConvTraitsType_>;
|
||||
|
||||
static constexpr bool IsSplitKSupported = true;
|
||||
|
||||
static constexpr auto I0 = number<0>();
|
||||
static constexpr auto I1 = number<1>();
|
||||
static constexpr auto I2 = number<2>();
|
||||
static constexpr auto I3 = number<3>();
|
||||
|
||||
static_assert(GemmPipeline::kPadM && GemmPipeline::kPadN && GemmPipeline::kPadK,
|
||||
"Not supported!");
|
||||
static_assert(std::is_same_v<GemmALayout, tensor_layout::gemm::ColumnMajor>, "Not supported!");
|
||||
static_assert(std::is_same_v<GemmBLayout, tensor_layout::gemm::RowMajor>, "Not supported!");
|
||||
static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>, "Not supported!");
|
||||
static_assert(GroupedConvTraitsType_::ExplicitGemm == false ||
|
||||
GroupedConvTraitsType_::NumGroupsToMerge == 1,
|
||||
"Not supported!");
|
||||
|
||||
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
|
||||
{
|
||||
static constexpr bool EnableSplitImage = GroupedConvTraitsType_::EnableSplitImage;
|
||||
constexpr auto NumGroupsToMerge = GroupedConvTraitsType_::NumGroupsToMerge;
|
||||
// clang-format off
|
||||
return concat('_', "grouped_convolution_backward_weight",
|
||||
gemm_prec_str<InDataType, WeiDataType>(),
|
||||
InLayout::name,
|
||||
WeiLayout::name,
|
||||
OutLayout::name,
|
||||
"gemm",
|
||||
GemmPipeline::GetName(),
|
||||
"epilogue",
|
||||
EpiloguePipeline::GetName(),
|
||||
getConvSpecializationString(ConvSpecialization),
|
||||
"MergedGroups",
|
||||
NumGroupsToMerge,
|
||||
"SplitImage",
|
||||
EnableSplitImage,
|
||||
"ExplicitGemm",
|
||||
GroupedConvTraitsType_::ExplicitGemm
|
||||
);
|
||||
// clang-format on
|
||||
}
|
||||
|
||||
[[nodiscard]] CK_TILE_HOST static const std::string GetTypeString() { return GetName(); }
|
||||
|
||||
#ifdef CK_EXPERIMENTAL_BUILDER
|
||||
CK_TILE_HOST std::string GetInstanceString() const
|
||||
{
|
||||
static_assert(ck_tile::reflect::HasInstanceTraits<GroupedConvolutionBackwardWeightKernel>,
|
||||
"Specialization of instance_traits not found. Please check that a "
|
||||
"specialization exists in file "
|
||||
"ck_tile/builder/reflect/"
|
||||
"instance_traits_tile_grouped_convolution_backward_weight.hpp "
|
||||
"for the given template parameters.");
|
||||
return ck_tile::reflect::instance_string<GroupedConvolutionBackwardWeightKernel>();
|
||||
}
|
||||
#endif
|
||||
|
||||
CK_TILE_HOST static constexpr auto
|
||||
GridSize(const GroupedConvBwdWeightKernelArgsSpecialized& kargs)
|
||||
{
|
||||
return dim3(
|
||||
TilePartitioner::GridSize(kargs.GemmM, kargs.GemmN), kargs.GemmBatch, kargs.k_batch);
|
||||
}
|
||||
|
||||
CK_TILE_HOST static constexpr auto BlockSize()
|
||||
{
|
||||
return is_wave32() ? dim3(kBlockSize / 2) : dim3(kBlockSize);
|
||||
}
|
||||
|
||||
CK_TILE_HOST static constexpr GroupedConvBwdWeightKernelArgsSpecialized
|
||||
MakeKernelArgs(const GroupedConvBwdWeightHostArgs& hostArgs)
|
||||
{
|
||||
LogInfo("MPerBlock: ",
|
||||
number<TilePartitioner::MPerBlock>{},
|
||||
", NPerBlock: ",
|
||||
number<TilePartitioner::NPerBlock>{},
|
||||
", KPerBlock: ",
|
||||
number<TilePartitioner::KPerBlock>{});
|
||||
|
||||
auto kernel_args = GroupedConvBwdWeightKernelArgsSpecialized(hostArgs);
|
||||
|
||||
using KernelImpl = GroupedConvolutionBackwardWeightKernel<GroupedConvTraitsType_,
|
||||
TilePartitioner_,
|
||||
GemmPipeline_,
|
||||
EpiloguePipeline_>;
|
||||
|
||||
// Negative k_batch value: split-K autodeduction.
|
||||
if(kernel_args.k_batch < 0)
|
||||
{
|
||||
const auto optimal_split_k =
|
||||
calculate_optimal_k_batch<GemmPipeline_::BlockSize, KernelImpl, TilePartitioner_>(
|
||||
kernel_args);
|
||||
kernel_args.k_batch = optimal_split_k;
|
||||
}
|
||||
|
||||
return kernel_args;
|
||||
}
|
||||
|
||||
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
|
||||
{
|
||||
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
|
||||
}
|
||||
|
||||
CK_TILE_HOST static bool
|
||||
IsSupportedArgument(const GroupedConvBwdWeightKernelArgsSpecialized& kargs)
|
||||
{
|
||||
if constexpr(GemmPipeline_::Async)
|
||||
{
|
||||
if(get_device_name() != "gfx950")
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
if(kargs.k_batch < 1)
|
||||
{
|
||||
LogInfo("k_batch must be at least one. Ensure argument is created via MakeKernelArgs.");
|
||||
return false;
|
||||
}
|
||||
|
||||
if constexpr(!std::is_same_v<typename EpiloguePipeline::ODataType, float> &&
|
||||
!std::is_same_v<typename EpiloguePipeline::ODataType, double>)
|
||||
{
|
||||
// The epilogue performs atomic add related to split-K using the ODataType.
|
||||
// If the type is less accurate than float, large split-K values may lead to
|
||||
// accuracy issues. Hence, we limit the maximum split-K value to 128 in such cases.
|
||||
if(kargs.k_batch > 128)
|
||||
{
|
||||
LogInfo("For epilogue output data type that is not float/double, we must have "
|
||||
"k_batch <= 128.");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr((GroupedConvTraitsType_::VectorSizeC % 2 != 0 &&
|
||||
is_any_of<WeiDataType, fp16_t, bf16_t>::value))
|
||||
{
|
||||
if(kargs.k_batch != 1)
|
||||
{
|
||||
LogInfo("Conditions not met for K_batch > 1: VectorSizeC must be a multiple of 2 "
|
||||
"for fp16/bf16 when K_batch > 1.",
|
||||
"Now k_batch is ",
|
||||
kargs.k_batch,
|
||||
", VectorSizeC is ",
|
||||
GroupedConvTraitsType_::VectorSizeC);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if(kargs.GemmK < TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{}) * kargs.k_batch)
|
||||
{
|
||||
LogInfo("KBatch is too large, part of GPU wouldn't be utilized! GemmK: ",
|
||||
kargs.GemmK,
|
||||
", BlockGemmShape K: ",
|
||||
TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{}),
|
||||
", k_batch: ",
|
||||
kargs.k_batch);
|
||||
return false;
|
||||
}
|
||||
|
||||
const index_t ConvK = kargs.wei_g_k_c_xs_lengths[number<1>{}];
|
||||
const index_t ConvC = kargs.wei_g_k_c_xs_lengths[number<2>{}];
|
||||
|
||||
// check ConvSpecialization
|
||||
if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter1x1Stride1Pad0)
|
||||
{
|
||||
// check if it's 1x1, stride=1 conv
|
||||
for(index_t i = 0; i < NDimSpatial; ++i)
|
||||
{
|
||||
const index_t SpatialDim = kargs.wei_g_k_c_xs_lengths[i + 3];
|
||||
const index_t ConvStride = kargs.conv_filter_strides[i];
|
||||
const index_t LeftPad = kargs.input_left_pads[i];
|
||||
const index_t RightPad = kargs.input_right_pads[i];
|
||||
|
||||
if(!(SpatialDim == 1 && ConvStride == 1 && LeftPad == 0 && RightPad == 0))
|
||||
{
|
||||
LogInfo("For Filter1x1Stride1Pad0 specialization, all spatial dimensions must "
|
||||
"be 1, stride must be 1, and padding must be 0. Now for dimension ",
|
||||
i,
|
||||
": SpatialDim is ",
|
||||
SpatialDim,
|
||||
", ConvStride is ",
|
||||
ConvStride,
|
||||
", LeftPad is ",
|
||||
LeftPad,
|
||||
", RightPad is ",
|
||||
RightPad);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
else if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter1x1Pad0)
|
||||
{
|
||||
// check if it's 1x1 conv
|
||||
for(index_t i = 0; i < NDimSpatial; ++i)
|
||||
{
|
||||
const index_t SpatialDim = kargs.wei_g_k_c_xs_lengths[i + 3];
|
||||
const index_t LeftPad = kargs.input_left_pads[i];
|
||||
const index_t RightPad = kargs.input_right_pads[i];
|
||||
|
||||
if(!(SpatialDim == 1 && LeftPad == 0 && RightPad == 0))
|
||||
{
|
||||
LogInfo("For Filter1x1Pad0 specialization, all spatial dimensions must be 1 "
|
||||
"and padding must be 0. Now for dimension ",
|
||||
i,
|
||||
": SpatialDim is ",
|
||||
SpatialDim,
|
||||
", LeftPad is ",
|
||||
LeftPad,
|
||||
", RightPad is ",
|
||||
RightPad);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
else if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter3x3)
|
||||
{
|
||||
if(ConvC != 1)
|
||||
{
|
||||
LogInfo("For Filter3x3 specialization, ConvC must be 1. Now ConvC is ", ConvC);
|
||||
return false;
|
||||
}
|
||||
for(index_t i = 0; i < NDimSpatial; ++i)
|
||||
{
|
||||
const index_t filter_spatial_dim = kargs.wei_g_k_c_xs_lengths[i + I3];
|
||||
|
||||
if(filter_spatial_dim != I3)
|
||||
{
|
||||
LogInfo("For Filter3x3 specialization, all spatial dimensions of the filter "
|
||||
"must be 3. Now for dimension ",
|
||||
i,
|
||||
", filter_spatial_dim is ",
|
||||
filter_spatial_dim);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(GroupedConvTraitsType_::ExplicitGemm &&
|
||||
ConvSpecialization != ConvolutionSpecialization::Filter1x1Stride1Pad0)
|
||||
{
|
||||
LogInfo("ExplicitGemm is only supported for Filter1x1Stride1Pad0 specialization.");
|
||||
return false;
|
||||
}
|
||||
|
||||
namespace ctc = tensor_layout::convolution;
|
||||
|
||||
if constexpr(std::is_same_v<InLayout, ctc::NWGC> || std::is_same_v<InLayout, ctc::NHWGC> ||
|
||||
std::is_same_v<InLayout, ctc::NDHWGC>)
|
||||
{
|
||||
// Check access per C
|
||||
if(ConvC % GroupedConvTraitsType_::VectorSizeB != 0)
|
||||
{
|
||||
LogInfo("Conv C is not a multiple of vector load size for input! ConvC: ",
|
||||
ConvC,
|
||||
", VectorSizeB: ",
|
||||
GroupedConvTraitsType_::VectorSizeB);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
LogInfo("Not supported input layout! Now InLayout is ", InLayout::name);
|
||||
return false;
|
||||
}
|
||||
|
||||
if constexpr(std::is_same_v<WeiLayout, ctc::GKXC> ||
|
||||
std::is_same_v<WeiLayout, ctc::GKYXC> ||
|
||||
std::is_same_v<WeiLayout, ctc::GKZYXC>)
|
||||
{
|
||||
if(ConvC % GroupedConvTraitsType_::VectorSizeC != 0)
|
||||
{
|
||||
LogInfo("Conv C is not a multiple of vector load size for weight! ConvC: ",
|
||||
ConvC,
|
||||
", VectorSizeC: ",
|
||||
GroupedConvTraitsType_::VectorSizeC);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
LogInfo("Not supported weight layout! Now WeiLayout is ", WeiLayout::name);
|
||||
return false;
|
||||
}
|
||||
|
||||
if constexpr(std::is_same_v<OutLayout, ctc::NWGK> ||
|
||||
std::is_same_v<OutLayout, ctc::NHWGK> ||
|
||||
std::is_same_v<OutLayout, ctc::NDHWGK>)
|
||||
{
|
||||
if(ConvK % GroupedConvTraitsType_::VectorSizeA != 0)
|
||||
{
|
||||
LogInfo("Conv K is not a multiple of vector load size for output! ConvK: ",
|
||||
ConvK,
|
||||
", VectorSizeA: ",
|
||||
GroupedConvTraitsType_::VectorSizeA);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
LogInfo("Not supported output layout! Now OutLayout is ", OutLayout::name);
|
||||
return false;
|
||||
}
|
||||
|
||||
if constexpr(GroupedConvTraitsType_::NumGroupsToMerge > 1)
|
||||
{
|
||||
const index_t ConvG = kargs.wei_g_k_c_xs_lengths[number<0>{}];
|
||||
if(ConvG % GroupedConvTraitsType_::NumGroupsToMerge != 0)
|
||||
{
|
||||
LogInfo("Number of groups must be divisible by NumGroupsToMerge! ConvG: ",
|
||||
ConvG,
|
||||
", NumGroupsToMerge: ",
|
||||
GroupedConvTraitsType_::NumGroupsToMerge);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeCBlockWindow(WeiDataType* c_ptr,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n)
|
||||
{
|
||||
const auto& c_tensor_view =
|
||||
make_tensor_view<address_space_enum::global, DstInMemOp>(c_ptr, kargs.c_grid_desc_m_n);
|
||||
|
||||
const auto& c_pad_view = pad_tensor_view(
|
||||
c_tensor_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
|
||||
return make_tile_window(
|
||||
c_pad_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
|
||||
{block_idx_m, block_idx_n});
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeDBlockWindows(const std::array<const void*, NumDTensor>& ds_ptr,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n)
|
||||
{
|
||||
const auto& ds_tensor_view = generate_tuple(
|
||||
[&](auto i) {
|
||||
static_assert(std::is_same_v<std::tuple_element_t<i, DsLayout>, OutLayout>,
|
||||
"Not supported!");
|
||||
static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>,
|
||||
"Not supported!");
|
||||
static_assert(std::is_same_v<std::tuple_element_t<i, DsDataType>, WeiDataType>,
|
||||
"Not supported!");
|
||||
|
||||
return make_tensor_view<address_space_enum::global>(
|
||||
static_cast<WeiDataType*>(ds_ptr[i]), kargs.c_grid_desc_m_n);
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
|
||||
const auto& ds_pad_view = generate_tuple(
|
||||
[&](auto i) {
|
||||
return pad_tensor_view(ds_tensor_view[i],
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_tile_window(ds_pad_view[i],
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
{block_idx_m, block_idx_n});
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeBBlockWindow(const InDataType* b_ptr,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_n,
|
||||
const index_t block_idx_k)
|
||||
{
|
||||
static_assert(!GemmPipeline::BlockGemmShape::PermuteB, "Not implemented!");
|
||||
const auto& b_tensor_view =
|
||||
make_tensor_view<address_space_enum::global>(b_ptr, kargs.b_grid_desc_k_n);
|
||||
|
||||
const auto& b_pad_view =
|
||||
pad_tensor_view(b_tensor_view,
|
||||
make_tuple(number<TilePartitioner::KPerBlock>{} * kargs.k_batch,
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
|
||||
return make_tile_window(
|
||||
b_pad_view,
|
||||
make_tuple(number<TilePartitioner::KPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
|
||||
{block_idx_k, block_idx_n});
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeABlockWindow(const OutDataType* a_ptr,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_k)
|
||||
{
|
||||
static_assert(!GemmPipeline::BlockGemmShape::PermuteA, "Not implemented!");
|
||||
const auto& a_tensor_view =
|
||||
make_tensor_view<address_space_enum::global>(a_ptr, kargs.a_grid_desc_k_m);
|
||||
|
||||
const auto& a_pad_view =
|
||||
pad_tensor_view(a_tensor_view,
|
||||
make_tuple(number<TilePartitioner::KPerBlock>{} * kargs.k_batch,
|
||||
number<TilePartitioner::MPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
|
||||
return make_tile_window(
|
||||
a_pad_view,
|
||||
make_tuple(number<TilePartitioner::KPerBlock>{}, number<TilePartitioner::MPerBlock>{}),
|
||||
{block_idx_k, block_idx_m});
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Runs single GEMM problem cooperatively by whole workgroup.
|
||||
*
|
||||
* @param a_ptr input A pointer
|
||||
* @param b_ptr input B pointer
|
||||
* @param c_ptr output C pointer
|
||||
* @param smem_ptr_0 The start memory pointer of the shared memory block.
|
||||
* @param kargs Grouped Convolution Backward Weight kernel arguments
|
||||
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
|
||||
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
|
||||
*
|
||||
*/
|
||||
CK_TILE_DEVICE static void RunGemm(const OutDataType* a_ptr,
|
||||
const InDataType* b_ptr,
|
||||
const std::array<const void*, NumDTensor>& ds_ptr,
|
||||
WeiDataType* c_ptr,
|
||||
void* smem_ptr_0,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t num_loop,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n,
|
||||
const index_t block_idx_k)
|
||||
{
|
||||
// Create block windows using helper methods
|
||||
const auto& a_block_window = MakeABlockWindow(a_ptr, kargs, block_idx_m, block_idx_k);
|
||||
const auto& b_block_window = MakeBBlockWindow(b_ptr, kargs, block_idx_n, block_idx_k);
|
||||
const auto& d_block_window = MakeDBlockWindows(ds_ptr, kargs, block_idx_m, block_idx_n);
|
||||
|
||||
// Run GEMM cooperatively by whole workgroup.
|
||||
const auto& c_block_tile = GemmPipeline{}.template operator()(
|
||||
a_block_window, b_block_window, num_loop, smem_ptr_0);
|
||||
|
||||
// Run Epilogue Pipeline with k_batch dispatching
|
||||
if(kargs.k_batch == 1)
|
||||
{
|
||||
auto c_block_window = MakeCBlockWindow<memory_operation_enum::set>(
|
||||
c_ptr, kargs, block_idx_m, block_idx_n);
|
||||
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile, d_block_window, smem_ptr_0);
|
||||
}
|
||||
else
|
||||
{
|
||||
if constexpr(!(GroupedConvTraitsType_::VectorSizeC % 2 != 0 &&
|
||||
is_any_of<WeiDataType, fp16_t, bf16_t>::value))
|
||||
{
|
||||
auto c_block_window = MakeCBlockWindow<memory_operation_enum::atomic_add>(
|
||||
c_ptr, kargs, block_idx_m, block_idx_n);
|
||||
|
||||
EpiloguePipeline{}(c_block_window, c_block_tile, d_block_window, smem_ptr_0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void CallExplicitGemm(GroupedConvBwdWeightKernelArgsSpecialized& kargs) const
|
||||
{
|
||||
static_assert(NumDTensor == 0, "Not supported!");
|
||||
using ExplicitBatchedGemmKernel =
|
||||
BatchedGemmKernel<TilePartitioner, GemmPipeline, EpiloguePipeline>;
|
||||
const auto batched_gemm_kargs = typename ExplicitBatchedGemmKernel::BatchedGemmKernelArgs{
|
||||
{{kargs.out_ptr},
|
||||
{kargs.in_ptr},
|
||||
{},
|
||||
kargs.wei_ptr,
|
||||
kargs.GemmM,
|
||||
kargs.GemmN,
|
||||
kargs.GemmK,
|
||||
{kargs.GemmM * kargs.GemmBatch},
|
||||
{kargs.GemmN * kargs.GemmBatch},
|
||||
{},
|
||||
kargs.GemmN,
|
||||
kargs.k_batch},
|
||||
kargs.GemmM,
|
||||
kargs.GemmN,
|
||||
kargs.GemmM * kargs.GemmN,
|
||||
kargs.GemmBatch};
|
||||
ExplicitBatchedGemmKernel{}(batched_gemm_kargs);
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void operator()(GroupedConvBwdWeightKernelArgsSpecialized& kargs) const
|
||||
{
|
||||
if constexpr(GroupedConvTraitsType_::ExplicitGemm)
|
||||
{
|
||||
CallExplicitGemm(kargs);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto blockIdX = amd_wave_read_first_lane(blockIdx.x);
|
||||
const auto [iM, iN] =
|
||||
TilePartitioner{kargs.GemmM, kargs.GemmN}.GetOutputTileIndex(blockIdX);
|
||||
const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
|
||||
const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
|
||||
|
||||
const auto blockIdZ = amd_wave_read_first_lane(blockIdx.z);
|
||||
const index_t num_loop = amd_wave_read_first_lane(ck_tile::integer_divide_ceil(
|
||||
kargs.GemmK, kargs.k_batch * TilePartitioner::KPerBlock));
|
||||
const index_t i_k =
|
||||
amd_wave_read_first_lane(blockIdZ * num_loop * TilePartitioner::KPerBlock);
|
||||
|
||||
const auto blockIdY = amd_wave_read_first_lane(blockIdx.y);
|
||||
const auto group_offset_a = amd_wave_read_first_lane(kargs.group_stride_a * blockIdY);
|
||||
const auto group_offset_b = amd_wave_read_first_lane(kargs.group_stride_b * blockIdY);
|
||||
const auto group_offset_c = amd_wave_read_first_lane(kargs.group_stride_c * blockIdY);
|
||||
|
||||
// options
|
||||
// conv_bwd_weight = Out * In = Weight
|
||||
const OutDataType* a_ptr =
|
||||
static_cast<const OutDataType*>(kargs.out_ptr) + group_offset_a;
|
||||
const InDataType* b_ptr = static_cast<const InDataType*>(kargs.in_ptr) + group_offset_b;
|
||||
WeiDataType* c_ptr = static_cast<WeiDataType*>(kargs.wei_ptr) + group_offset_c;
|
||||
|
||||
__shared__ char smem_ptr[GetSmemSize()];
|
||||
|
||||
// Disable Async for other archs than gfx950
|
||||
if constexpr(GemmPipeline_::Async)
|
||||
{
|
||||
#if defined(__gfx950__)
|
||||
RunGemm(
|
||||
a_ptr, b_ptr, kargs.ds_ptr, c_ptr, smem_ptr, kargs, num_loop, i_m, i_n, i_k);
|
||||
#endif
|
||||
}
|
||||
else
|
||||
{
|
||||
RunGemm(
|
||||
a_ptr, b_ptr, kargs.ds_ptr, c_ptr, smem_ptr, kargs, num_loop, i_m, i_n, i_k);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user