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resolved conflicts
This commit is contained in:
Jakub Piasecki
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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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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namespace ck_tile {
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/// @brief The Grouped Convolution kernel device arguments.
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template <index_t NDimSpatial,
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ConvolutionForwardSpecialization ConvBackwardWeightSpecialization,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t GemmK1Number,
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index_t K0PerBlock,
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index_t NumGroupsToMerge,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout>
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struct GroupedConvBwdWeightKernelArgs
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{
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using ConvToGemmTransformer = TransformConvBwdWeightToGemm<NDimSpatial,
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ConvBackwardWeightSpecialization,
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MPerBlock,
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NPerBlock,
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GemmK1Number,
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K0PerBlock,
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NumGroupsToMerge>;
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template <
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typename InLay = InLayout,
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typename WeiLay = WeiLayout,
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typename OutLay = 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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k_batch = args.k_batch;
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GemmM = args.K_;
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GemmN = args.C_ * 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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GemmK = args.N_ * std::accumulate(args.output_spatial_lengths_.begin(),
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args.output_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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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
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.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>();
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a_grid_desc_m_k = grid_descs.at(number<0>{});
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b_grid_desc_n_k = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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group_stride_a = args.K_; // A: Out NWGK
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group_stride_b = args.C_; // B: In NWGC
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group_stride_c = args.K_ * args.C_ * // C: //GKCX
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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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}
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template <
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typename InLay = InLayout,
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typename WeiLay = WeiLayout,
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typename OutLay = 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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k_batch = args.k_batch;
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GemmM = args.K_;
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GemmN = args.C_ * 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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GemmK = args.N_ * std::accumulate(args.output_spatial_lengths_.begin(),
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args.output_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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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
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.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>();
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a_grid_desc_m_k = grid_descs.at(number<0>{});
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b_grid_desc_n_k = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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group_stride_a = args.K_; // A: Out NHWGK
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group_stride_b = args.C_; // B: In NHWGC
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group_stride_c = args.K_ * args.C_ * // C: //GKCYX
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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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}
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template <
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typename InLay = InLayout,
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typename WeiLay = WeiLayout,
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typename OutLay = 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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k_batch = args.k_batch;
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GemmM = args.K_;
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GemmN = args.C_ * 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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GemmK = args.N_ * std::accumulate(args.output_spatial_lengths_.begin(),
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args.output_spatial_lengths_.end(),
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1,
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std::multiplies<index_t>());
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in_ptr = args.in_ptr;
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wei_ptr = args.wei_ptr;
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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
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.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>();
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a_grid_desc_m_k = grid_descs.at(number<0>{});
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b_grid_desc_n_k = grid_descs.at(number<1>{});
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c_grid_desc_m_n = grid_descs.at(number<2>{});
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group_stride_a = args.K_; // A: Out NDHWGK
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group_stride_b = args.C_; // B: In NDHWGC
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group_stride_c = args.K_ * args.C_ * // C: //GKCZYX
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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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}
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using ABCGridDescs = remove_cvref_t<decltype(
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ConvToGemmTransformer{}.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N())>;
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using AGridDescMK = remove_cvref_t<decltype(ABCGridDescs{}[number<0>{}])>;
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using BGridDescNK = 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 + NDimSpatial> in_g_n_c_wis_lengths;
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array<index_t, NonSpatialDims + NDimSpatial> wei_g_k_c_xs_lengths;
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array<index_t, NonSpatialDims + NDimSpatial> out_g_n_k_wos_lengths;
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array<index_t, NDimSpatial> conv_filter_strides;
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array<index_t, NDimSpatial> conv_filter_dilations;
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array<index_t, NDimSpatial> input_left_pads;
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array<index_t, 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;
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index_t GemmK;
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const void* out_ptr;
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const void* in_ptr;
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void* wei_ptr;
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AGridDescMK a_grid_desc_m_k;
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BGridDescNK b_grid_desc_n_k;
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CGridDescMN c_grid_desc_m_n;
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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;
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};
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/// @brief The Grouped Convolution Forward kernel template.
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///
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/// @paragraph Overview Overview
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/// This class provides the grouped convolution forward kernel template. By semantic
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/// division of Implicit GEMM algorithm into following parts we achieve flexible,
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/// versatile and robust kernel implementation.
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///
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/// @li @b Prolog - The start of GEMM kernel implementation in @ref operator()
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/// function call operator" which determines the work scope of each workgroup.
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/// @li @b GemmPipeline - The core part @a "heart" of matrix multiplication algorithm.
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/// This is the place where each workgroup is loading data from global memory and
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/// carrying out dot products.
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/// @li @b Epilogue - The @a "final" part of matrix multiplication implementation
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/// responsible for storing results to global memory. This is also the place where
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/// any additional operator fusion may take place.
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///
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/// Additionally both @ref GemmPipeline_ "GemmPipeline" and @ref EpiloguePipeline_
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/// "EpiloguePipeline" are parameterized with so called @a Policy which determines all
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/// internal details of those functional parts. You can think of it like both gemm and
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/// epilogue pipelines provides the control-flow logic controlled by policies. Moreover
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/// the policy is responsible for definition of all necessary data layouts and thread's
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/// work distribution.
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///
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/// @tparam NDimSpatial_ Number of spatial dimensions of input image.
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/// tparam ConvBackwardWeightSpecialization Tensor descriptors specialization.
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/// @tparam TilePartitioner_ The type of class providing mapping of workgroup index into
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/// the
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/// output data tile to be calculated. It determines the
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/// workgroup to data relationship (or in other words - which
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/// data would be processed and calculated by which workgroup).
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/// @tparam GemmPipeline_ The type of class which provides the core part of matrix
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/// multiplication. This class should provide implementation of
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/// data loading from global memory and performing block-wise
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/// matrix multiplication. You can think of it as a work done by
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/// single workgroup point of view.
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/// @tparam EpiloguePipeline_ The type of class providing the final part of matrix
|
||||
/// multiplication implementation. It is responsible for storing
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/// results calculated by @ref GemmPipeline_ "GemmPipeline" to
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/// the output C tensor in global memory.
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template <index_t NDimSpatial_,
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ConvolutionBackwardWeightSpecialization ConvBackwardWeightSpecialization_,
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typename InLayout_,
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||||
typename WeiLayout_,
|
||||
typename OutLayout_,
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||||
typename TilePartitioner_,
|
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typename GemmPipeline_,
|
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typename EpiloguePipeline_>
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struct GroupedConvolutionBackwardWeightKernel
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||||
{
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||||
static constexpr index_t NDimSpatial = NDimSpatial_;
|
||||
static constexpr ConvolutionBackwardWeightSpecialization ConvBackwardWeightSpecialization =
|
||||
ConvBackwardWeightSpecialization_;
|
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using TilePartitioner = remove_cvref_t<TilePartitioner_>;
|
||||
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
|
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using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
|
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using GemmALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
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using GemmBLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
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using GemmCLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
|
||||
|
||||
using InLayout = remove_cvref_t<InLayout_>;
|
||||
using WeiLayout = remove_cvref_t<WeiLayout_>;
|
||||
using OutLayout = remove_cvref_t<OutLayout_>;
|
||||
|
||||
static constexpr index_t KernelBlockSize = GemmPipeline::BlockSize;
|
||||
|
||||
using InDataType = remove_cvref_t<typename GemmPipeline::ADataType>;
|
||||
using WeiDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
|
||||
// Below type is actually accumulation data type - the output of block GEMM.
|
||||
using OutDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
|
||||
|
||||
using GroupedConvBwdWeightKernelArgsSpecialized =
|
||||
GroupedConvBwdWeightKernelArgs<NDimSpatial_,
|
||||
ConvBackwardWeightSpecialization,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
8, // GemmK1Number,
|
||||
8, // K0PerBlock,
|
||||
1, // NumGroupsToMerge,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout>;
|
||||
|
||||
// TODO: Enable this
|
||||
static constexpr bool IsSplitKSupported = false;
|
||||
|
||||
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::RowMajor>, "Not supported!");
|
||||
static_assert(std::is_same_v<GemmBLayout, tensor_layout::gemm::ColumnMajor>, "Not supported!");
|
||||
static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>, "Not supported!");
|
||||
|
||||
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
|
||||
{
|
||||
// clang-format off
|
||||
return concat('_', "grouped_convolution_backward_weight", gemm_prec_str<InDataType, WeiDataType>, GemmPipeline::GetName());
|
||||
// clang-format on
|
||||
}
|
||||
|
||||
CK_TILE_HOST static constexpr auto GridSize(const GroupedConvBwdWeightHostArgs& args)
|
||||
{
|
||||
const index_t GemmM = args.K_;
|
||||
const index_t GemmN = args.C_ * std::accumulate(args.filter_spatial_lengths_.begin(),
|
||||
args.filter_spatial_lengths_.end(),
|
||||
1,
|
||||
std::multiplies<index_t>());
|
||||
|
||||
return dim3(TilePartitioner::GridSize(GemmM, GemmN), args.G_, args.k_batch);
|
||||
}
|
||||
|
||||
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
|
||||
|
||||
CK_TILE_HOST static constexpr GroupedConvBwdWeightKernelArgsSpecialized
|
||||
MakeKernelArgs(const GroupedConvBwdWeightHostArgs& hostArgs)
|
||||
{
|
||||
return GroupedConvBwdWeightKernelArgsSpecialized(hostArgs);
|
||||
}
|
||||
|
||||
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((EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
|
||||
is_any_of<OutDataType, fp16_t, bf16_t>::value) ||
|
||||
!IsSplitKSupported)
|
||||
{
|
||||
if(kargs.k_batch != 1)
|
||||
{
|
||||
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
|
||||
{
|
||||
CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
|
||||
}
|
||||
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 ConvBackwardWeightSpecialization
|
||||
if constexpr(ConvBackwardWeightSpecialization ==
|
||||
ConvolutionForwardSpecialization::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))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
else if constexpr(ConvBackwardWeightSpecialization ==
|
||||
ConvolutionForwardSpecialization::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))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
else if constexpr(ConvBackwardWeightSpecialization ==
|
||||
ConvolutionForwardSpecialization::Filter3x3)
|
||||
{
|
||||
if(ConvC != 1)
|
||||
{
|
||||
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)
|
||||
{
|
||||
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 % GemmPipeline::GetVectorSizeA() != 0)
|
||||
{
|
||||
CK_TILE_ERROR("Conv C is not a multiple of vector load size for input image!");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
CK_TILE_ERROR("Not supported input layout!");
|
||||
return false;
|
||||
}
|
||||
|
||||
// check vector access of B
|
||||
// FIXME: layout
|
||||
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 % GemmPipeline::GetVectorSizeB() != 0)
|
||||
{
|
||||
CK_TILE_ERROR("Conv C is not a multiple of vector load size for weight!");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
CK_TILE_ERROR("Not supported weight layout!");
|
||||
return false;
|
||||
}
|
||||
|
||||
// check vector access of E
|
||||
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 % EpiloguePipeline::GetVectorSizeC() != 0)
|
||||
{
|
||||
CK_TILE_ERROR("Conv K is not a multiple of vector store size for output image!");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
CK_TILE_ERROR("Not supported output layout!");
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeGemmTensorViews(const OutDataType* a_ptr,
|
||||
const InDataType* b_ptr,
|
||||
WeiDataType* c_ptr,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs)
|
||||
{
|
||||
static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
|
||||
static_assert(!TilePartitioner::BlockGemmShape::PermuteB, "Not implemented!");
|
||||
const auto& a_tensor_view = [&]() {
|
||||
return make_tensor_view<address_space_enum::global>(a_ptr,
|
||||
kargs.a_grid_desc_m_k); // A: out
|
||||
}();
|
||||
|
||||
const auto& b_tensor_view = [&]() {
|
||||
return make_tensor_view<address_space_enum::global>(b_ptr,
|
||||
kargs.b_grid_desc_n_k); // B: in
|
||||
}();
|
||||
|
||||
const auto& c_tensor_view = [&]() {
|
||||
return make_tensor_view<address_space_enum::global>(c_ptr,
|
||||
kargs.c_grid_desc_m_n); // C: wei
|
||||
}();
|
||||
|
||||
return make_tuple(a_tensor_view, b_tensor_view, c_tensor_view);
|
||||
}
|
||||
|
||||
template <typename TensorView>
|
||||
CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
|
||||
{
|
||||
const auto& a_pad_view = [&]() {
|
||||
const auto& a_tensor_view = views.at(I0);
|
||||
return pad_tensor_view(a_tensor_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
}();
|
||||
|
||||
const auto& b_pad_view = [&]() {
|
||||
const auto& b_tensor_view = views.at(I1);
|
||||
return pad_tensor_view(b_tensor_view,
|
||||
make_tuple(number<TilePartitioner::NPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
}();
|
||||
|
||||
const auto& c_pad_view = [&]() {
|
||||
const auto& c_tensor_view = views.at(I2);
|
||||
return pad_tensor_view(c_tensor_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
sequence<true, true>{});
|
||||
}();
|
||||
|
||||
return make_tuple(a_pad_view, b_pad_view, c_pad_view);
|
||||
}
|
||||
|
||||
template <typename PadView>
|
||||
CK_TILE_DEVICE static auto
|
||||
MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
|
||||
{
|
||||
const auto& a_pad_view = views.at(I0);
|
||||
const auto& b_pad_view = views.at(I1);
|
||||
const auto& c_pad_view = views.at(I2);
|
||||
|
||||
const auto& a_block_window = [&]() {
|
||||
return make_tile_window(a_pad_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
{i_m, 0});
|
||||
}();
|
||||
|
||||
const auto& b_block_window = [&]() {
|
||||
return make_tile_window(b_pad_view,
|
||||
make_tuple(number<TilePartitioner::NPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
{i_n, 0});
|
||||
}();
|
||||
|
||||
auto c_block_window = make_tile_window(
|
||||
c_pad_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
|
||||
{i_m, i_n});
|
||||
|
||||
return make_tuple(a_block_window, b_block_window, c_block_window);
|
||||
}
|
||||
|
||||
/**
|
||||
* @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 Forward 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,
|
||||
WeiDataType* c_ptr,
|
||||
void* smem_ptr_0,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n)
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple =
|
||||
MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(a_ptr, b_ptr, c_ptr, kargs);
|
||||
|
||||
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
|
||||
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
|
||||
|
||||
const index_t num_loop =
|
||||
__builtin_amdgcn_readfirstlane(TilePartitioner::GetLoopNum(kargs.GemmK));
|
||||
|
||||
// Run GEMM cooperatively by whole workgroup.
|
||||
const auto& a_block_window = gemm_tile_windows.at(I0);
|
||||
const auto& b_block_window = gemm_tile_windows.at(I1);
|
||||
|
||||
const auto& c_block_tile = GemmPipeline{}.template operator()(
|
||||
a_block_window, b_block_window, num_loop, smem_ptr_0);
|
||||
|
||||
// Run Epilogue Pipeline
|
||||
auto& c_block_window = gemm_tile_windows.at(I2);
|
||||
|
||||
EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
|
||||
c_block_window, c_block_tile, smem_ptr_0);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Runs single GEMM problem cooperatively by whole workgroup.
|
||||
*
|
||||
* @note RunGEMM2LDS in with two shared memory buffers using the ping pong buffer mechanism.
|
||||
*
|
||||
* @param a_ptr input A pointer
|
||||
* @param b_ptr input B pointer
|
||||
* @param c_ptr output C pointer
|
||||
* @param smem_ptr_0 The starting pointer of 1st shared memory block.
|
||||
* @param smem_ptr_1 The starting pointer of 2nd shared memory block.
|
||||
* @param kargs Grouped Convolution Forward 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 RunGemm2LDS(const OutDataType* a_ptr,
|
||||
const InDataType* b_ptr,
|
||||
WeiDataType* c_ptr,
|
||||
void* __restrict__ smem_ptr_0,
|
||||
void* __restrict__ smem_ptr_1,
|
||||
const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n)
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple =
|
||||
MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(a_ptr, b_ptr, c_ptr, kargs);
|
||||
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
|
||||
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
|
||||
|
||||
const index_t num_loop =
|
||||
__builtin_amdgcn_readfirstlane(TilePartitioner::GetLoopNum(kargs.GemmK));
|
||||
|
||||
// Run GEMM cooperatively by whole workgroup.
|
||||
const auto& a_block_window = gemm_tile_windows.at(I0);
|
||||
const auto& b_block_window = gemm_tile_windows.at(I1);
|
||||
|
||||
const auto& c_block_tile = GemmPipeline{}.template operator()(
|
||||
a_block_window, b_block_window, num_loop, smem_ptr_0, smem_ptr_1);
|
||||
|
||||
// Run Epilogue Pipeline
|
||||
auto& c_block_window = gemm_tile_windows.at(I2);
|
||||
|
||||
EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
|
||||
c_block_window, c_block_tile, smem_ptr_0);
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void operator()(GroupedConvBwdWeightKernelArgsSpecialized kargs) const
|
||||
{
|
||||
const auto blockIdX = __builtin_amdgcn_readfirstlane(blockIdx.x);
|
||||
const auto [iM, iN] =
|
||||
TilePartitioner{kargs.GemmM, kargs.GemmN}.GetOutputTileIndex(blockIdX);
|
||||
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
|
||||
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
|
||||
|
||||
const auto blockIdY = __builtin_amdgcn_readfirstlane(blockIdx.y);
|
||||
const auto group_offset_a = __builtin_amdgcn_readfirstlane(kargs.group_stride_a * blockIdY);
|
||||
const auto group_offset_b = __builtin_amdgcn_readfirstlane(kargs.group_stride_b * blockIdY);
|
||||
const auto group_offset_c = __builtin_amdgcn_readfirstlane(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;
|
||||
|
||||
// allocate LDS
|
||||
__shared__ char smem_ptr_0[GetSmemSize()];
|
||||
|
||||
if constexpr(GemmPipeline::DoubleSmemBuffer == true)
|
||||
{
|
||||
__shared__ char smem_ptr_1[GetSmemSize()];
|
||||
if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
|
||||
EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
|
||||
is_any_of<OutDataType, fp16_t, bf16_t>::value))
|
||||
{
|
||||
RunGemm2LDS(a_ptr, b_ptr, c_ptr, smem_ptr_0, smem_ptr_1, kargs, i_m, i_n);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
|
||||
EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
|
||||
is_any_of<OutDataType, fp16_t, bf16_t>::value))
|
||||
{
|
||||
RunGemm(a_ptr, b_ptr, c_ptr, smem_ptr_0, kargs, i_m, i_n);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user