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https://github.com/ROCm/composable_kernel.git
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[CK Tile] Grouped convolution backward data (#2652)
* base working version for single groupped conv bwd data * Fix 2d descriptor * fix groups * Add 3d support * fixes * fixes * fixes --------- Co-authored-by: Jakub Piasecki <jakpia21@gmail.com>
This commit is contained in:
Bartłomiej Kocot
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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_data_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 <typename GroupedConvTraitsType_, typename TilePartitioner_>
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struct GroupedConvBwdDataKernelArgs
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{
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using TilePartitioner = remove_cvref_t<TilePartitioner_>;
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using ConvToGemmTransformer =
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TransformConvBwdDataToGemm<GroupedConvTraitsType_::NDimSpatial,
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GroupedConvTraitsType_::ConvSpecialization>;
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static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
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static constexpr auto I0 = number<0>();
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static constexpr auto I1 = number<1>();
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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 GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& 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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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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const index_t X = wei_g_k_c_xs_lengths[3];
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const index_t ConvStrideW = conv_filter_strides[0];
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const index_t ConvDilationW = conv_filter_dilations[0];
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const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
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const auto XTilde = ConvStrideW / GcdStrideDilationW;
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for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
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{
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const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
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if(XDotSlice <= 0)
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{
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continue;
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}
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if(gemm_count >= MaxGroupedGemmGroupsNum)
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{
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gemm_count++;
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// Avoid array segfault
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continue;
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}
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tildes = {i_xtilde};
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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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tildes};
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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>(1);
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a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
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b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
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c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
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const index_t grid_size_grp =
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TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
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c_grid_descs_m_n[gemm_count].get_length(I1));
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block_starts[gemm_count] = grid_size_;
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block_ends[gemm_count] = grid_size_ + grid_size_grp;
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grid_size_ += grid_size_grp;
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++gemm_count;
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}
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group_stride_a = args.K_; // A: Out NWGK
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group_stride_b = args.K_ * args.C_ *
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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>()); // B: Wei GKXC
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group_stride_c = args.C_; // C: In NWGC
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GemmBatch = args.G_;
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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 GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& 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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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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const index_t Y = wei_g_k_c_xs_lengths[3];
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const index_t X = wei_g_k_c_xs_lengths[4];
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const index_t ConvStrideH = conv_filter_strides[0];
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const index_t ConvStrideW = conv_filter_strides[1];
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const index_t ConvDilationH = conv_filter_dilations[0];
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const index_t ConvDilationW = conv_filter_dilations[1];
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const auto GcdStrideDilationH = gcd(ConvStrideH, ConvDilationH);
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const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
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const auto YTilde = ConvStrideH / GcdStrideDilationH;
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const auto XTilde = ConvStrideW / GcdStrideDilationW;
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for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
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{
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for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
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{
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const auto YDotSlice = integer_divide_ceil(Y - i_ytilde, YTilde);
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const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
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if(XDotSlice * YDotSlice <= 0)
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{
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continue;
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}
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if(gemm_count >= MaxGroupedGemmGroupsNum)
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{
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gemm_count++;
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// Avoid array segfault
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continue;
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}
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tildes = {i_ytilde, i_xtilde};
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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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tildes};
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auto grid_descs = conv_to_gemm_transformer
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.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
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GroupedConvTraitsType_::NDimSpatial>(1);
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a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
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b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
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c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
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const index_t grid_size_grp =
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TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
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c_grid_descs_m_n[gemm_count].get_length(I1));
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block_starts[gemm_count] = grid_size_;
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block_ends[gemm_count] = grid_size_ + grid_size_grp;
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grid_size_ += grid_size_grp;
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++gemm_count;
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}
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}
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group_stride_a = args.K_; // A: Out NWGK
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group_stride_b = args.K_ * args.C_ *
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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>()); // B: Wei GKXC
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group_stride_c = args.C_; // C: In NWGC
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GemmBatch = args.G_;
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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 GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& 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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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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const index_t Z = wei_g_k_c_xs_lengths[3];
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const index_t Y = wei_g_k_c_xs_lengths[4];
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const index_t X = wei_g_k_c_xs_lengths[5];
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const index_t ConvStrideD = conv_filter_strides[0];
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const index_t ConvStrideH = conv_filter_strides[1];
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const index_t ConvStrideW = conv_filter_strides[2];
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const index_t ConvDilationD = conv_filter_dilations[0];
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const index_t ConvDilationH = conv_filter_dilations[1];
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const index_t ConvDilationW = conv_filter_dilations[2];
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const auto GcdStrideDilationD = gcd(ConvStrideD, ConvDilationD);
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const auto GcdStrideDilationH = gcd(ConvStrideH, ConvDilationH);
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const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
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const auto ZTilde = ConvStrideD / GcdStrideDilationD;
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const auto YTilde = ConvStrideH / GcdStrideDilationH;
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const auto XTilde = ConvStrideW / GcdStrideDilationW;
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for(index_t i_ztilde = 0; i_ztilde < ZTilde; ++i_ztilde)
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{
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for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
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{
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for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
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{
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const auto ZDotSlice = integer_divide_ceil(Z - i_ztilde, ZTilde);
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const auto YDotSlice = integer_divide_ceil(Y - i_ytilde, YTilde);
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const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
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if(ZDotSlice * XDotSlice * YDotSlice <= 0)
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{
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continue;
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}
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if(gemm_count >= MaxGroupedGemmGroupsNum)
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{
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gemm_count++;
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// Avoid array segfault
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continue;
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}
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tildes = {i_ztilde, i_ytilde, i_xtilde};
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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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tildes};
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auto grid_descs = conv_to_gemm_transformer
|
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.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
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GroupedConvTraitsType_::NDimSpatial>(1);
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a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
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b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
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c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
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const index_t grid_size_grp =
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TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
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c_grid_descs_m_n[gemm_count].get_length(I1));
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block_starts[gemm_count] = grid_size_;
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block_ends[gemm_count] = grid_size_ + grid_size_grp;
|
||||
|
||||
grid_size_ += grid_size_grp;
|
||||
|
||||
++gemm_count;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
group_stride_a = args.K_; // A: Out NWGK
|
||||
group_stride_b = args.K_ * args.C_ *
|
||||
std::accumulate(args.filter_spatial_lengths_.begin(),
|
||||
args.filter_spatial_lengths_.end(),
|
||||
1,
|
||||
std::multiplies<index_t>()); // B: Wei GKXC
|
||||
group_stride_c = args.C_; // C: In NWGC
|
||||
|
||||
GemmBatch = args.G_; // C: In NWGC
|
||||
}
|
||||
|
||||
static constexpr index_t MaxGroupedGemmGroupsNum = 128;
|
||||
|
||||
using ABCGridDescs =
|
||||
remove_cvref_t<decltype(ConvToGemmTransformer{}
|
||||
.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(1))>;
|
||||
|
||||
using AGridDescMK = remove_cvref_t<decltype(ABCGridDescs{}[number<0>{}])>;
|
||||
using BGridDescNK = remove_cvref_t<decltype(ABCGridDescs{}[number<1>{}])>;
|
||||
using CGridDescMN = remove_cvref_t<decltype(ABCGridDescs{}[number<2>{}])>;
|
||||
|
||||
static constexpr index_t NonSpatialDims = 3;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> in_g_n_c_wis_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> wei_g_k_c_xs_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> out_g_n_k_wos_lengths;
|
||||
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_strides;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_right_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> tildes;
|
||||
|
||||
index_t k_batch;
|
||||
index_t GemmBatch;
|
||||
index_t grid_size_ = 0;
|
||||
index_t gemm_count = 0;
|
||||
|
||||
const void* out_ptr;
|
||||
void* in_ptr;
|
||||
std::array<const void*, NumDTensor> ds_ptr;
|
||||
const void* wei_ptr;
|
||||
|
||||
array<AGridDescMK, MaxGroupedGemmGroupsNum> a_grid_descs_m_k;
|
||||
array<BGridDescNK, MaxGroupedGemmGroupsNum> b_grid_descs_n_k;
|
||||
array<CGridDescMN, MaxGroupedGemmGroupsNum> c_grid_descs_m_n;
|
||||
|
||||
array<index_t, MaxGroupedGemmGroupsNum> block_starts;
|
||||
array<index_t, MaxGroupedGemmGroupsNum> block_ends;
|
||||
|
||||
long_index_t group_stride_a;
|
||||
long_index_t group_stride_b;
|
||||
long_index_t group_stride_c;
|
||||
};
|
||||
|
||||
/// @brief The Grouped Convolution Backward Data kernel template.
|
||||
///
|
||||
/// @paragraph Overview Overview
|
||||
/// This class provides the grouped convolution backward data 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 GroupedConvolutionBackwardDataKernel
|
||||
{
|
||||
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 KernelBlockSize = GemmPipeline::BlockSize;
|
||||
|
||||
using InDataType = remove_cvref_t<typename GemmPipeline::ADataType>;
|
||||
using WeiDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
|
||||
using DsDataType = remove_cvref_t<typename EpiloguePipeline::DsDataType>;
|
||||
|
||||
using OutDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
|
||||
|
||||
using GroupedConvBwdDataKernelArgsSpecialized =
|
||||
GroupedConvBwdDataKernelArgs<GroupedConvTraitsType_, TilePartitioner>;
|
||||
static constexpr index_t MaxGroupedGemmGroupsNum =
|
||||
GroupedConvBwdDataKernelArgsSpecialized::MaxGroupedGemmGroupsNum;
|
||||
|
||||
// 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 A GEMM layout!");
|
||||
static_assert(std::is_same_v<GemmBLayout, tensor_layout::gemm::ColumnMajor>,
|
||||
"Not supported B GEMM layout!");
|
||||
static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>,
|
||||
"Not supported C GEMM layout!");
|
||||
|
||||
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
|
||||
{
|
||||
// clang-format off
|
||||
return concat('_', "grouped_convolution_backward_data", gemm_prec_str<InDataType, WeiDataType>, GemmPipeline::GetName());
|
||||
// clang-format on
|
||||
}
|
||||
|
||||
CK_TILE_HOST static auto GridSize(const GroupedConvBwdDataKernelArgsSpecialized& kargs)
|
||||
{
|
||||
// enable batched grouped gemm
|
||||
return dim3(kargs.grid_size_, kargs.GemmBatch, kargs.k_batch);
|
||||
}
|
||||
|
||||
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(KernelBlockSize); }
|
||||
|
||||
CK_TILE_HOST static constexpr GroupedConvBwdDataKernelArgsSpecialized
|
||||
MakeKernelArgs(const GroupedConvBwdDataHostArgs& hostArgs)
|
||||
{
|
||||
return GroupedConvBwdDataKernelArgsSpecialized(hostArgs);
|
||||
}
|
||||
|
||||
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
|
||||
{
|
||||
return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
|
||||
}
|
||||
|
||||
CK_TILE_HOST static bool
|
||||
IsSupportedArgument(const GroupedConvBwdDataKernelArgsSpecialized& 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;
|
||||
}
|
||||
}
|
||||
|
||||
if(kargs.gemm_count > MaxGroupedGemmGroupsNum)
|
||||
{
|
||||
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))
|
||||
{
|
||||
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))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
else if constexpr(ConvSpecialization == ConvolutionSpecialization::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::GetVectorSizeB() != 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 % EpiloguePipeline::GetVectorSizeC() != 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 % GemmPipeline::GetVectorSizeA() != 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,
|
||||
const std::array<const void*, NumDTensor>& ds_ptr,
|
||||
WeiDataType* c_ptr,
|
||||
const GroupedConvBwdDataKernelArgsSpecialized& kargs,
|
||||
const index_t group_id)
|
||||
{
|
||||
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_descs_m_k[group_id]); // A: out
|
||||
}();
|
||||
|
||||
const auto& b_tensor_view = [&]() {
|
||||
return make_tensor_view<address_space_enum::global>(
|
||||
b_ptr,
|
||||
kargs.b_grid_descs_n_k[group_id]); // B: weight
|
||||
}();
|
||||
|
||||
const auto& c_tensor_view = [&]() {
|
||||
return make_tensor_view<address_space_enum::global>(c_ptr,
|
||||
kargs.c_grid_descs_m_n[group_id]);
|
||||
}();
|
||||
|
||||
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>, OutDataType>,
|
||||
"Not supported!");
|
||||
|
||||
return make_tensor_view<address_space_enum::global>(
|
||||
static_cast<OutDataType*>(ds_ptr[i]), kargs.c_grid_descs_m_n[group_id]);
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
|
||||
return make_tuple(a_tensor_view, b_tensor_view, ds_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& ds_tensor_view = views.at(I2);
|
||||
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>{});
|
||||
|
||||
const auto& c_pad_view = [&]() {
|
||||
const auto& c_tensor_view = views.at(I3);
|
||||
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, ds_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 index_t i_k = 0)
|
||||
{
|
||||
const auto& a_pad_view = views.at(I0);
|
||||
const auto& b_pad_view = views.at(I1);
|
||||
const auto& ds_pad_view = views.at(I2);
|
||||
const auto& c_pad_view = views.at(I3);
|
||||
|
||||
const auto& a_block_window = [&]() {
|
||||
return make_tile_window(a_pad_view,
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
{i_m, i_k});
|
||||
}();
|
||||
|
||||
const auto& b_block_window = [&]() {
|
||||
return make_tile_window(b_pad_view,
|
||||
make_tuple(number<TilePartitioner::NPerBlock>{},
|
||||
number<TilePartitioner::KPerBlock>{}),
|
||||
{i_n, i_k});
|
||||
}();
|
||||
|
||||
const auto ds_block_window = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_tile_window(ds_pad_view[i],
|
||||
make_tuple(number<TilePartitioner::MPerBlock>{},
|
||||
number<TilePartitioner::NPerBlock>{}),
|
||||
{i_m, i_n});
|
||||
},
|
||||
number<NumDTensor>{});
|
||||
|
||||
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, ds_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 Backward Data 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 GroupedConvBwdDataKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n,
|
||||
const index_t group_id)
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple =
|
||||
MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
|
||||
a_ptr, b_ptr, ds_ptr, c_ptr, kargs, group_id);
|
||||
|
||||
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(
|
||||
gemm_pad_views.at(I0).get_tensor_descriptor().get_length(I1)));
|
||||
|
||||
// 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& d_block_window = gemm_tile_windows.at(I2);
|
||||
|
||||
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(I3);
|
||||
|
||||
EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
|
||||
c_block_window, c_block_tile, d_block_window, 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 Backward Data 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,
|
||||
const std::array<const void*, NumDTensor>& ds_ptr,
|
||||
WeiDataType* c_ptr,
|
||||
void* __restrict__ smem_ptr_0,
|
||||
void* __restrict__ smem_ptr_1,
|
||||
const GroupedConvBwdDataKernelArgsSpecialized& kargs,
|
||||
const index_t block_idx_m,
|
||||
const index_t block_idx_n,
|
||||
const index_t group_id)
|
||||
{
|
||||
// Create Gemm tensor views, pad views and tile windows
|
||||
const auto& gemm_tensor_views_tuple =
|
||||
MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
|
||||
a_ptr, b_ptr, ds_ptr, c_ptr, kargs, group_id);
|
||||
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(gemm_tile_windows.at(I0).get_length(I1)));
|
||||
|
||||
// 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& d_block_window = gemm_tile_windows.at(I2);
|
||||
|
||||
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(I3);
|
||||
|
||||
EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
|
||||
c_block_window, c_block_tile, d_block_window, smem_ptr_0);
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE index_t FindGroupId(const GroupedConvBwdDataKernelArgsSpecialized& kargs,
|
||||
index_t block_id) const
|
||||
{
|
||||
index_t left = 0;
|
||||
index_t right = kargs.gemm_count;
|
||||
index_t group_id = index_t((left + right) >> 1);
|
||||
|
||||
while((!(block_id >= kargs.block_starts[group_id] &&
|
||||
block_id < kargs.block_ends[group_id])) &&
|
||||
left <= right)
|
||||
{
|
||||
if(block_id < kargs.block_starts[group_id])
|
||||
{
|
||||
right = group_id;
|
||||
}
|
||||
else
|
||||
{
|
||||
left = group_id;
|
||||
}
|
||||
group_id = index_t((left + right) >> 1);
|
||||
}
|
||||
|
||||
return group_id;
|
||||
}
|
||||
|
||||
CK_TILE_DEVICE void operator()(GroupedConvBwdDataKernelArgsSpecialized kargs) const
|
||||
{
|
||||
const auto blockIdX = __builtin_amdgcn_readfirstlane(blockIdx.x);
|
||||
const index_t group_id = FindGroupId(kargs, blockIdX);
|
||||
|
||||
const auto [iM, iN] = OffsettedTile1DPartitioner<TilePartitioner>::GetOffsetedTileIndex(
|
||||
kargs.block_starts[group_id],
|
||||
kargs.c_grid_descs_m_n[group_id].get_length(I0),
|
||||
kargs.c_grid_descs_m_n[group_id].get_length(I1));
|
||||
|
||||
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_data = Out * Weight = In
|
||||
const OutDataType* a_ptr = static_cast<const OutDataType*>(kargs.out_ptr) + group_offset_a;
|
||||
const WeiDataType* b_ptr = static_cast<const WeiDataType*>(kargs.wei_ptr) + group_offset_b;
|
||||
InDataType* c_ptr = static_cast<InDataType*>(kargs.in_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,
|
||||
kargs.ds_ptr,
|
||||
c_ptr,
|
||||
smem_ptr_0,
|
||||
smem_ptr_1,
|
||||
kargs,
|
||||
i_m,
|
||||
i_n,
|
||||
group_id);
|
||||
}
|
||||
}
|
||||
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, kargs.ds_ptr, c_ptr, smem_ptr_0, kargs, i_m, i_n, group_id);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck_tile
|
||||
@@ -17,19 +17,19 @@
|
||||
namespace ck_tile {
|
||||
|
||||
/// @brief The Grouped Convolution kernel device arguments.
|
||||
template <typename GroupedConvTraitsType>
|
||||
template <typename GroupedConvTraitsType_>
|
||||
struct GroupedConvBwdWeightKernelArgs
|
||||
{
|
||||
|
||||
using ConvToGemmTransformer =
|
||||
TransformConvBwdWeightToGemm<GroupedConvTraitsType::NDimSpatial,
|
||||
GroupedConvTraitsType::ConvSpecialization>;
|
||||
static constexpr index_t NumDTensor = GroupedConvTraitsType::NumDTensor;
|
||||
TransformConvBwdWeightToGemm<GroupedConvTraitsType_::NDimSpatial,
|
||||
GroupedConvTraitsType_::ConvSpecialization>;
|
||||
static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NWGK>,
|
||||
@@ -75,7 +75,7 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
// tuple
|
||||
auto grid_descs =
|
||||
conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
|
||||
GroupedConvTraitsType::NDimSpatial>();
|
||||
GroupedConvTraitsType_::NDimSpatial>();
|
||||
|
||||
a_grid_desc_m_k = grid_descs.at(number<0>{});
|
||||
b_grid_desc_n_k = grid_descs.at(number<1>{});
|
||||
@@ -96,9 +96,9 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
}
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NHWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKYXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NHWGK>,
|
||||
@@ -151,7 +151,7 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
// tuple
|
||||
auto grid_descs =
|
||||
conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
|
||||
GroupedConvTraitsType::NDimSpatial>();
|
||||
GroupedConvTraitsType_::NDimSpatial>();
|
||||
|
||||
a_grid_desc_m_k = grid_descs.at(number<0>{});
|
||||
b_grid_desc_n_k = grid_descs.at(number<1>{});
|
||||
@@ -172,9 +172,9 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
}
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NDHWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKZYXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NDHWGK>,
|
||||
@@ -234,7 +234,7 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
// tuple
|
||||
auto grid_descs =
|
||||
conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
|
||||
GroupedConvTraitsType::NDimSpatial>();
|
||||
GroupedConvTraitsType_::NDimSpatial>();
|
||||
|
||||
a_grid_desc_m_k = grid_descs.at(number<0>{});
|
||||
b_grid_desc_n_k = grid_descs.at(number<1>{});
|
||||
@@ -263,14 +263,14 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
using CGridDescMN = remove_cvref_t<decltype(ABCGridDescs{}[number<2>{}])>;
|
||||
|
||||
static constexpr index_t NonSpatialDims = 3;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> in_g_n_c_wis_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> wei_g_k_c_xs_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> out_g_n_k_wos_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> in_g_n_c_wis_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> wei_g_k_c_xs_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> out_g_n_k_wos_lengths;
|
||||
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> conv_filter_strides;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> input_right_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_strides;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_right_pads;
|
||||
|
||||
index_t k_batch;
|
||||
index_t GemmM;
|
||||
@@ -292,12 +292,12 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
long_index_t group_stride_c;
|
||||
};
|
||||
|
||||
/// @brief The Grouped Convolution Forward kernel template.
|
||||
/// @brief The Grouped Convolution Backward Weight kernel template.
|
||||
///
|
||||
/// @paragraph Overview Overview
|
||||
/// This class provides the grouped convolution forward kernel template. By semantic
|
||||
/// division of Implicit GEMM algorithm into following parts we achieve flexible,
|
||||
/// versatile and robust kernel implementation.
|
||||
/// 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.
|
||||
@@ -315,7 +315,7 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
/// the policy is responsible for definition of all necessary data layouts and thread's
|
||||
/// work distribution.
|
||||
///
|
||||
/// tparam ConvSpecialization Tensor descriptors specialization.
|
||||
/// @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
|
||||
@@ -330,15 +330,15 @@ struct GroupedConvBwdWeightKernelArgs
|
||||
/// multiplication implementation. It is responsible for storing
|
||||
/// results calculated by @ref GemmPipeline_ "GemmPipeline" to
|
||||
/// the output C tensor in global memory.
|
||||
template <typename GroupedConvTraitsType,
|
||||
template <typename GroupedConvTraitsType_,
|
||||
typename TilePartitioner_,
|
||||
typename GemmPipeline_,
|
||||
typename EpiloguePipeline_>
|
||||
struct GroupedConvolutionBackwardWeightKernel
|
||||
{
|
||||
static constexpr index_t NDimSpatial = GroupedConvTraitsType::NDimSpatial_;
|
||||
static constexpr index_t NDimSpatial = GroupedConvTraitsType_::NDimSpatial_;
|
||||
static constexpr ConvolutionSpecialization ConvSpecialization =
|
||||
GroupedConvTraitsType::ConvSpecialization;
|
||||
GroupedConvTraitsType_::ConvSpecialization;
|
||||
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
|
||||
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
|
||||
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
|
||||
@@ -346,13 +346,13 @@ struct GroupedConvolutionBackwardWeightKernel
|
||||
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 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 NumDTensor = GroupedConvTraitsType_::NumDTensor;
|
||||
|
||||
static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
|
||||
|
||||
@@ -363,7 +363,7 @@ struct GroupedConvolutionBackwardWeightKernel
|
||||
using OutDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
|
||||
|
||||
using GroupedConvBwdWeightKernelArgsSpecialized =
|
||||
GroupedConvBwdWeightKernelArgs<GroupedConvTraitsType>;
|
||||
GroupedConvBwdWeightKernelArgs<GroupedConvTraitsType_>;
|
||||
|
||||
// TODO: Enable this
|
||||
static constexpr bool IsSplitKSupported = true;
|
||||
@@ -594,12 +594,9 @@ struct GroupedConvolutionBackwardWeightKernel
|
||||
}();
|
||||
|
||||
const auto& c_tensor_view = [&]() {
|
||||
return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
|
||||
return make_tensor_view<address_space_enum::global, DstInMemOp>(
|
||||
c_ptr,
|
||||
make_tuple(kargs.GemmM, kargs.GemmN),
|
||||
make_tuple(kargs.GemmN, 1),
|
||||
number<EpiloguePipeline::GetVectorSizeC()>{},
|
||||
number<1>{});
|
||||
kargs.c_grid_desc_m_n); // B: in
|
||||
}();
|
||||
|
||||
const auto& ds_tensor_view = generate_tuple(
|
||||
@@ -708,7 +705,7 @@ struct GroupedConvolutionBackwardWeightKernel
|
||||
* @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 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.
|
||||
*
|
||||
@@ -758,7 +755,7 @@ struct GroupedConvolutionBackwardWeightKernel
|
||||
* @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 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.
|
||||
*
|
||||
|
||||
@@ -17,19 +17,19 @@
|
||||
namespace ck_tile {
|
||||
|
||||
/// @brief The Grouped Convolution kernel device arguments.
|
||||
template <typename GroupedConvTraitsType>
|
||||
template <typename GroupedConvTraitsType_>
|
||||
struct GroupedConvFwdKernelArgs
|
||||
{
|
||||
|
||||
using ConvToGemmFwdTransformer =
|
||||
TransformConvFwdToGemm<GroupedConvTraitsType::NDimSpatial,
|
||||
GroupedConvTraitsType::ConvSpecialization>;
|
||||
static constexpr index_t NumDTensor = GroupedConvTraitsType::NumDTensor;
|
||||
TransformConvFwdToGemm<GroupedConvTraitsType_::NDimSpatial,
|
||||
GroupedConvTraitsType_::ConvSpecialization>;
|
||||
static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NWGK>,
|
||||
@@ -79,13 +79,13 @@ struct GroupedConvFwdKernelArgs
|
||||
|
||||
a_grid_desc_m_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType::InLayout>();
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType_::InLayout>();
|
||||
b_grid_desc_n_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType::WeiLayout>();
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType_::WeiLayout>();
|
||||
c_grid_desc_m_n =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType::OutLayout>();
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType_::OutLayout>();
|
||||
|
||||
group_stride_a = args.C_;
|
||||
group_stride_b = args.K_ * args.C_ *
|
||||
@@ -97,9 +97,9 @@ struct GroupedConvFwdKernelArgs
|
||||
}
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NHWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKYXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NHWGK>,
|
||||
@@ -156,13 +156,13 @@ struct GroupedConvFwdKernelArgs
|
||||
|
||||
a_grid_desc_m_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType::InLayout>();
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType_::InLayout>();
|
||||
b_grid_desc_n_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType::WeiLayout>();
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType_::WeiLayout>();
|
||||
c_grid_desc_m_n =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType::OutLayout>();
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType_::OutLayout>();
|
||||
|
||||
group_stride_a = args.C_;
|
||||
group_stride_b = args.K_ * args.C_ *
|
||||
@@ -174,9 +174,9 @@ struct GroupedConvFwdKernelArgs
|
||||
}
|
||||
|
||||
template <
|
||||
typename InLay = typename GroupedConvTraitsType::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType::OutLayout,
|
||||
typename InLay = typename GroupedConvTraitsType_::InLayout,
|
||||
typename WeiLay = typename GroupedConvTraitsType_::WeiLayout,
|
||||
typename OutLay = typename GroupedConvTraitsType_::OutLayout,
|
||||
typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NDHWGC> &&
|
||||
std::is_same_v<WeiLay, tensor_layout::convolution::GKZYXC> &&
|
||||
std::is_same_v<OutLay, tensor_layout::convolution::NDHWGK>,
|
||||
@@ -242,13 +242,13 @@ struct GroupedConvFwdKernelArgs
|
||||
|
||||
a_grid_desc_m_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType::InLayout>();
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType_::InLayout>();
|
||||
b_grid_desc_n_k =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType::WeiLayout>();
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType_::WeiLayout>();
|
||||
c_grid_desc_m_n =
|
||||
conv_to_gemm_transformer
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType::OutLayout>();
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType_::OutLayout>();
|
||||
|
||||
group_stride_a = args.C_;
|
||||
group_stride_b = args.K_ * args.C_ *
|
||||
@@ -261,23 +261,23 @@ struct GroupedConvFwdKernelArgs
|
||||
|
||||
using AGridDescMK = remove_cvref_t<
|
||||
decltype(ConvToGemmFwdTransformer{}
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType::InLayout>())>;
|
||||
.template MakeADescriptor_M_K<typename GroupedConvTraitsType_::InLayout>())>;
|
||||
using BGridDescNK = remove_cvref_t<
|
||||
decltype(ConvToGemmFwdTransformer{}
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType::WeiLayout>())>;
|
||||
.template MakeBDescriptor_N_K<typename GroupedConvTraitsType_::WeiLayout>())>;
|
||||
using CGridDescMN = remove_cvref_t<
|
||||
decltype(ConvToGemmFwdTransformer{}
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType::OutLayout>())>;
|
||||
.template MakeCDescriptor_M_N<typename GroupedConvTraitsType_::OutLayout>())>;
|
||||
|
||||
static constexpr index_t NonSpatialDims = 3;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> in_g_n_c_wis_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> wei_g_k_c_xs_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType::NDimSpatial> out_g_n_k_wos_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> in_g_n_c_wis_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> wei_g_k_c_xs_lengths;
|
||||
array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> out_g_n_k_wos_lengths;
|
||||
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> conv_filter_strides;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType::NDimSpatial> input_right_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_strides;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_dilations;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_left_pads;
|
||||
array<index_t, GroupedConvTraitsType_::NDimSpatial> input_right_pads;
|
||||
|
||||
index_t k_batch;
|
||||
index_t GemmM;
|
||||
@@ -322,7 +322,7 @@ struct GroupedConvFwdKernelArgs
|
||||
/// 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 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
|
||||
@@ -337,15 +337,15 @@ struct GroupedConvFwdKernelArgs
|
||||
/// multiplication implementation. It is responsible for storing
|
||||
/// results calculated by @ref GemmPipeline_ "GemmPipeline" to
|
||||
/// the output C tensor in global memory.
|
||||
template <typename GroupedConvTraitsType,
|
||||
template <typename GroupedConvTraitsType_,
|
||||
typename TilePartitioner_,
|
||||
typename GemmPipeline_,
|
||||
typename EpiloguePipeline_>
|
||||
struct GroupedConvolutionForwardKernel
|
||||
{
|
||||
static constexpr index_t NDimSpatial = GroupedConvTraitsType::NDimSpatial;
|
||||
static constexpr index_t NDimSpatial = GroupedConvTraitsType_::NDimSpatial;
|
||||
static constexpr ConvolutionSpecialization ConvSpecialization =
|
||||
GroupedConvTraitsType::ConvSpecialization;
|
||||
GroupedConvTraitsType_::ConvSpecialization;
|
||||
using TilePartitioner = remove_cvref_t<TilePartitioner_>;
|
||||
using GemmPipeline = remove_cvref_t<GemmPipeline_>;
|
||||
using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
|
||||
@@ -353,13 +353,13 @@ struct GroupedConvolutionForwardKernel
|
||||
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 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 NumDTensor = GroupedConvTraitsType_::NumDTensor;
|
||||
|
||||
static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
|
||||
|
||||
@@ -369,7 +369,7 @@ struct GroupedConvolutionForwardKernel
|
||||
// Below type is actually accumulation data type - the output of block GEMM.
|
||||
using OutDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
|
||||
|
||||
using GroupedConvFwdKernelArgsSpecialized = GroupedConvFwdKernelArgs<GroupedConvTraitsType>;
|
||||
using GroupedConvFwdKernelArgsSpecialized = GroupedConvFwdKernelArgs<GroupedConvTraitsType_>;
|
||||
|
||||
// TODO: Enable this
|
||||
static constexpr bool IsSplitKSupported = false;
|
||||
|
||||
@@ -42,6 +42,7 @@ struct GroupedConvHostArgs : public conv::ConvParam
|
||||
|
||||
using GroupedConvFwdHostArgs = GroupedConvHostArgs<const void*, const void*, void*>;
|
||||
using GroupedConvBwdWeightHostArgs = GroupedConvHostArgs<const void*, void*, const void*>;
|
||||
using GroupedConvBwdDataHostArgs = GroupedConvHostArgs<void*, const void*, const void*>;
|
||||
|
||||
template <index_t NDimSpatial_,
|
||||
ConvolutionSpecialization ConvSpecialization_,
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user