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https://github.com/ROCm/composable_kernel.git
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Merge commit '1fbb47ad304566a90a374cef4731f1a257e5e179' into develop
This commit is contained in:
assistant-librarian[bot]
@@ -2,16 +2,19 @@ set(EXAMPLE_CONV_COMPILE_OPTIONS)
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list(APPEND EXAMPLE_CONV_COMPILE_OPTIONS -mllvm -enable-noalias-to-md-conversion=0)
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add_executable(tile_example_grouped_conv_fwd EXCLUDE_FROM_ALL grouped_convolution_forward.cpp)
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target_compile_options(tile_example_grouped_conv_fwd PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_grouped_conv_fwd PRIVATE ${EXAMPLE_CONV_COMPILE_OPTIONS})
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add_executable(tile_example_grouped_conv_fwd_large_tensor EXCLUDE_FROM_ALL grouped_convolution_forward_large_tensor.cpp)
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target_compile_options(tile_example_grouped_conv_fwd_large_tensor PRIVATE ${EXAMPLE_CONV_COMPILE_OPTIONS})
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add_executable(tile_example_grouped_conv_fwd_bias_clamp EXCLUDE_FROM_ALL grouped_convolution_forward_bias_clamp.cpp)
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target_compile_options(tile_example_grouped_conv_fwd_bias_clamp PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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add_executable(tile_example_grouped_conv_bwd_weight EXCLUDE_FROM_ALL grouped_convolution_backward_weight.cpp)
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target_compile_options(tile_example_grouped_conv_bwd_weight PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_grouped_conv_bwd_weight PRIVATE ${EXAMPLE_CONV_COMPILE_OPTIONS})
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add_executable(tile_example_grouped_conv_bwd_weight_two_stage EXCLUDE_FROM_ALL grouped_convolution_backward_weight_two_stage.cpp)
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target_compile_options(tile_example_grouped_conv_bwd_weight_two_stage PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_grouped_conv_bwd_weight_two_stage PRIVATE ${EXAMPLE_CONV_COMPILE_OPTIONS})
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add_executable(tile_example_grouped_conv_bwd_data EXCLUDE_FROM_ALL grouped_convolution_backward_data.cpp)
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target_compile_options(tile_example_grouped_conv_bwd_data PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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target_compile_options(tile_example_grouped_conv_bwd_data PRIVATE ${EXAMPLE_CONV_COMPILE_OPTIONS})
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@@ -1,5 +1,12 @@
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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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// Regular grouped convolution invoker (no split-image)
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// This invoker demonstrates regular convolution without split-image.
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// It always uses Kernel<false> (split-image disabled).
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// For large images that require split-image, use
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// grouped_convolution_forward_split_image_invoker.hpp
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#pragma once
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#include "grouped_convolution_utils.hpp"
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@@ -21,6 +28,10 @@ struct GroupedConvolutionForwardInvoker
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static float grouped_conv_fwd(const ck_tile::GroupedConvFwdHostArgs<CDElementWise>& args,
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const ck_tile::stream_config& s)
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{
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if(s.log_level_ > 0)
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{
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std::cout << "[INVOKER] grouped_conv_fwd called, NDimSpatial=" << NDimSpatial << "\n";
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}
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constexpr int kBlockPerCu = 1;
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// Implicit GEMM Traits
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@@ -90,6 +101,7 @@ struct GroupedConvolutionForwardInvoker
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1,
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std::multiplies<ck_tile::index_t>());
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// Split-K parameters
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const ck_tile::index_t k_grain = args.k_batch * GemmConfig::K_Tile;
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const ck_tile::index_t K_split = (gemm_k + k_grain - 1) / k_grain * GemmConfig::K_Tile;
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
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@@ -97,100 +109,117 @@ struct GroupedConvolutionForwardInvoker
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const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
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float ave_time{0};
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const auto Run =
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[&](const auto has_hot_loop_, const auto tail_number_, const auto memory_operation_) {
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constexpr bool has_hot_loop_v = has_hot_loop_.value;
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constexpr auto tail_number_v = tail_number_.value;
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constexpr auto scheduler = GemmConfig::Scheduler;
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constexpr auto memory_operation = memory_operation_.value;
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// =====================================================================
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// Regular Convolution: Simple, no split-image
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// =====================================================================
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const auto Run = [&]<bool EnableSplitImage>(const auto has_hot_loop_,
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const auto tail_number_,
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const auto memory_operation_) {
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constexpr bool has_hot_loop_v = has_hot_loop_.value;
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constexpr auto tail_number_v = tail_number_.value;
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constexpr auto scheduler = GemmConfig::Scheduler;
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constexpr auto memory_operation = memory_operation_.value;
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using UniversalGemmProblem =
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ck_tile::UniversalGemmPipelineProblem<InDataType,
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WeiDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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scheduler,
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has_hot_loop_v,
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tail_number_v,
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ck_tile::element_wise::PassThrough,
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ck_tile::element_wise::PassThrough,
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OutDataType,
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true,
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VectorSizeA,
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VectorSizeB>;
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using UniversalGemmProblem =
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ck_tile::UniversalGemmPipelineProblem<InDataType,
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WeiDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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scheduler,
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has_hot_loop_v,
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tail_number_v,
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ck_tile::element_wise::PassThrough,
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ck_tile::element_wise::PassThrough,
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OutDataType,
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true,
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VectorSizeA,
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VectorSizeB>;
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using GemmPipeline = typename PipelineTypeTraits<
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GemmConfig::Pipeline>::template GemmPipeline<UniversalGemmProblem>;
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using GemmPipeline = typename PipelineTypeTraits<
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GemmConfig::Pipeline>::template GemmPipeline<UniversalGemmProblem>;
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using ConvEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
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InDataType,
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WeiDataType,
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DsDataType,
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AccDataType,
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OutDataType,
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typename GroupedConvTraitsType::ImplicitGemmDsLayout,
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ck_tile::tensor_layout::gemm::RowMajor,
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CDElementWise,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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GemmConfig::M_Warp,
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GemmConfig::N_Warp,
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GemmConfig::M_Warp_Tile,
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GemmConfig::N_Warp_Tile,
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GemmConfig::K_Warp_Tile,
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GemmConfig::TransposeC,
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memory_operation,
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1,
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true,
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GroupedConvTraitsType::VectorSizeC>>;
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using ConvEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
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InDataType,
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WeiDataType,
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DsDataType,
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AccDataType,
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OutDataType,
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typename GroupedConvTraitsType::ImplicitGemmDsLayout,
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ck_tile::tensor_layout::gemm::RowMajor,
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CDElementWise,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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GemmConfig::M_Warp,
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GemmConfig::N_Warp,
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GemmConfig::M_Warp_Tile,
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GemmConfig::N_Warp_Tile,
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GemmConfig::K_Warp_Tile,
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GemmConfig::TransposeC,
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memory_operation,
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1,
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true,
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GroupedConvTraitsType::VectorSizeC>>;
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using Kernel = ck_tile::GroupedConvolutionForwardKernel<GroupedConvTraitsType,
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TilePartitioner,
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GemmPipeline,
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ConvEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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using Kernel = ck_tile::GroupedConvolutionForwardKernel<EnableSplitImage,
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GroupedConvTraitsType,
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TilePartitioner,
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GemmPipeline,
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ConvEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args);
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const dim3 grids = Kernel::GridSize(kargs);
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const dim3 blocks = Kernel::BlockSize();
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const dim3 grids = Kernel::GridSize(kargs);
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const dim3 blocks = Kernel::BlockSize();
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if(!Kernel::IsSupportedArgument(kargs))
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{
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throw std::runtime_error("Wrong! Arguments not supported! Skipping conv!\n");
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}
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if(!Kernel::IsSupportedArgument(kargs))
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{
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throw std::runtime_error("Wrong! Arguments not supported! Skipping conv!\n");
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}
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
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<< "shape: " << GemmShape::GetName() << '\n'
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<< "problem: " << UniversalGemmProblem::GetName() << '\n'
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<< "pipeline: " << GemmPipeline::GetName() << '\n'
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<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
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<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
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<< "}" << '\n'
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<< "Vector size A: " << GemmPipeline::GetVectorSizeA()
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<< ", Vector size B: " << GemmPipeline::GetVectorSizeB()
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<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
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}
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if(s.log_level_ > 0)
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{
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std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
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<< "shape: " << GemmShape::GetName() << '\n'
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<< "problem: " << UniversalGemmProblem::GetName() << '\n'
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<< "pipeline: " << GemmPipeline::GetName() << '\n'
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<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
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<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
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<< "}" << '\n'
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<< "Vector size A: " << GemmPipeline::GetVectorSizeA()
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<< ", Vector size B: " << GemmPipeline::GetVectorSizeB()
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<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
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}
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ave_time = ck_tile::launch_kernel(
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s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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ave_time = ck_tile::launch_kernel(
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s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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return ave_time;
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};
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return ave_time;
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};
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// =====================================================================
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// Split-K lambda
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// =====================================================================
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const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
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if(args.k_batch == 1)
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{
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Run(has_hot_loop_, tail_number_, MemoryOpSet{});
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Run.template operator()<false>(has_hot_loop_, tail_number_, MemoryOpSet{});
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}
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else
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{
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Run(has_hot_loop_, tail_number_, MemoryOpAtomicAdd{});
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Run.template operator()<false>(has_hot_loop_, tail_number_, MemoryOpAtomicAdd{});
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}
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};
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// =====================================================================
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// Regular Convolution Example: ALWAYS uses regular path (Kernel<false>)
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// =====================================================================
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// This example demonstrates regular convolution without split-image.
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// For large images that don't fit in memory, use
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// grouped_convolution_forward_split_image.cpp
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// Launch kernel using regular path (no split-image)
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BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
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return ave_time;
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}
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};
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@@ -0,0 +1,63 @@
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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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// Large tensor grouped convolution example
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// This example demonstrates convolution for large tensors that exceed memory limits.
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// It uses automatic tensor splitting when needed to handle large images.
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// For regular convolution without tensor splitting, use grouped_convolution_forward.cpp
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#include <hip/hip_runtime.h>
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#include <cstring>
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#include <iostream>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include "ck_tile/host.hpp"
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#include "grouped_convolution_utils.hpp"
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#include "grouped_convolution_forward_large_tensor_invoker.hpp"
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#include "run_grouped_convolution_fwd_example.inc"
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template <template <typename PrecType> typename GemmConfig>
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int run_grouped_conv_fwd_example(int argc, char* argv[])
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{
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using Invoker = GroupedConvolutionForwardInvoker;
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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std::string data_type = arg_parser.get_str("prec");
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std::string in_layout = arg_parser.get_str("in_layout");
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std::string wei_layout = arg_parser.get_str("wei_layout");
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std::string out_layout = arg_parser.get_str("out_layout");
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if(data_type == "fp16")
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{
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return run_grouped_conv_fwd_example_prec_type<Invoker,
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GemmConfig<ck_tile::half_t>,
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ck_tile::half_t>(
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in_layout, wei_layout, out_layout, argc, argv);
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}
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else if(data_type == "bf16")
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{
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return run_grouped_conv_fwd_example_prec_type<Invoker,
|
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GemmConfig<ck_tile::bf16_t>,
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ck_tile::bf16_t>(
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in_layout, wei_layout, out_layout, argc, argv);
|
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}
|
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else
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{
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throw std::runtime_error("Unsupported data type for this operation !!!");
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}
|
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}
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|
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int main(int argc, char* argv[])
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{
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#if CK_TILE_USE_WMMA
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return !run_grouped_conv_fwd_example<ConvConfigComputeV3_WMMA>(argc, argv);
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#else
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return !run_grouped_conv_fwd_example<ConvConfigComputeV3>(argc, argv);
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#endif
|
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}
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@@ -0,0 +1,388 @@
|
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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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|
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#include "grouped_convolution_utils.hpp"
|
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|
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struct GroupedConvolutionForwardInvoker
|
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{
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template <ck_tile::index_t NDimSpatial,
|
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typename GemmConfig,
|
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typename InDataType,
|
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typename WeiDataType,
|
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typename AccDataType,
|
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typename OutDataType,
|
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typename InLayout,
|
||||
typename WeiLayout,
|
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typename OutLayout,
|
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typename DsDataType = ck_tile::tuple<>,
|
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typename DsLayout = ck_tile::tuple<>,
|
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
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static float grouped_conv_fwd(const ck_tile::GroupedConvFwdHostArgs<CDEElementWise>& args,
|
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const ck_tile::stream_config& s)
|
||||
{
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "[INVOKER] grouped_conv_fwd called, NDimSpatial=" << NDimSpatial << "\n";
|
||||
}
|
||||
constexpr int kBlockPerCu = 1;
|
||||
|
||||
// Implicit GEMM Traits
|
||||
using GemmShape = ck_tile::TileGemmShape<
|
||||
ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
|
||||
ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
|
||||
ck_tile::
|
||||
sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>,
|
||||
GemmConfig::PermuteA,
|
||||
GemmConfig::PermuteB>;
|
||||
|
||||
constexpr ck_tile::index_t VectorSizeA = 8;
|
||||
constexpr ck_tile::index_t VectorSizeB = 8;
|
||||
constexpr ck_tile::index_t VectorSizeC = 8;
|
||||
|
||||
constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
|
||||
using TilePartitioner =
|
||||
ck_tile::GemmSpatiallyLocalTilePartitioner<GemmShape,
|
||||
GemmConfig::TileParitionerGroupNum,
|
||||
GemmConfig::TileParitionerM01>;
|
||||
using GroupedConvTraitsType = ck_tile::GroupedConvTraits<NDimSpatial,
|
||||
ConvSpec,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
DsLayout,
|
||||
OutLayout,
|
||||
VectorSizeA,
|
||||
VectorSizeB,
|
||||
VectorSizeC>;
|
||||
|
||||
using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<
|
||||
GemmConfig::kPadM,
|
||||
GemmConfig::kPadN,
|
||||
GemmConfig::kPadK,
|
||||
GemmConfig::DoubleSmemBuffer,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsFwd::AsLayout,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsFwd::BsLayout,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsFwd::CLayout,
|
||||
GemmConfig::TransposeC,
|
||||
GemmConfig::UseStructuredSparsity,
|
||||
false, // Persistent,
|
||||
GemmConfig::NumWaveGroups,
|
||||
GemmConfig::Preshuffle>;
|
||||
|
||||
using GemmPipelineProblem = ck_tile::GemmPipelineProblem<
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
GemmShape,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsFwd,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
OutDataType,
|
||||
true,
|
||||
VectorSizeA,
|
||||
VectorSizeB>;
|
||||
|
||||
using BaseGemmPipeline = typename PipelineTypeTraits<
|
||||
GemmConfig::Pipeline>::template UniversalGemmPipeline<GemmPipelineProblem>;
|
||||
|
||||
const ck_tile::index_t gemm_k =
|
||||
args.C_ * std::accumulate(args.filter_spatial_lengths_.begin(),
|
||||
args.filter_spatial_lengths_.end(),
|
||||
1,
|
||||
std::multiplies<ck_tile::index_t>());
|
||||
|
||||
// Split-K parameters
|
||||
const ck_tile::index_t k_grain = args.k_batch * GemmConfig::K_Tile;
|
||||
const ck_tile::index_t K_split = (gemm_k + k_grain - 1) / k_grain * GemmConfig::K_Tile;
|
||||
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
|
||||
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
|
||||
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
|
||||
float ave_time{0};
|
||||
|
||||
using TransformType =
|
||||
ck_tile::TransformConvFwdToGemm<NDimSpatial,
|
||||
ck_tile::ConvolutionSpecialization::Default,
|
||||
VectorSizeA,
|
||||
VectorSizeB,
|
||||
VectorSizeC,
|
||||
1, // NumGroupsToMerge
|
||||
false, // SplitN
|
||||
InDataType,
|
||||
OutDataType>;
|
||||
|
||||
// =====================================================================
|
||||
// Step 1: Check if layout supports split-image kernel
|
||||
// =====================================================================
|
||||
// Split-image requires specific memory layouts:
|
||||
// 1D: NWGC (input), GKXC (weight), NWGK (output)
|
||||
// 2D: NHWGC (input), GKYXC (weight), NHWGK (output)
|
||||
// 3D: NDHWGC (input), GKZYXC (weight), NDHWGK (output)
|
||||
constexpr bool is_supported_layout =
|
||||
std::is_same<InLayout, ck_tile::tensor_layout::convolution::NWGC>::value ||
|
||||
std::is_same<InLayout, ck_tile::tensor_layout::convolution::NHWGC>::value ||
|
||||
std::is_same<InLayout, ck_tile::tensor_layout::convolution::NDHWGC>::value;
|
||||
|
||||
// =====================================================================
|
||||
// Step 2: Calculate split-image info (if layout supports it)
|
||||
// =====================================================================
|
||||
// Extract output spatial dimensions
|
||||
const ck_tile::index_t total_d =
|
||||
(NDimSpatial == 3) ? args.output_spatial_lengths_[NDimSpatial - 3] : 1;
|
||||
const ck_tile::index_t total_h =
|
||||
(NDimSpatial >= 2) ? args.output_spatial_lengths_[NDimSpatial - 2] : 1;
|
||||
const ck_tile::index_t total_w = args.output_spatial_lengths_[NDimSpatial - 1];
|
||||
|
||||
auto split_info = TransformType::GetSplitImageInfo(
|
||||
args.G_, args.N_, args.C_, args.K_, total_d, total_h, total_w);
|
||||
|
||||
// =====================================================================
|
||||
// Decide: Split-image or regular kernel?
|
||||
// =====================================================================
|
||||
const bool use_split_image = is_supported_layout && split_info.should_split;
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
if(!is_supported_layout)
|
||||
{
|
||||
std::cout << "[INVOKER] Layout not supported for split-image. "
|
||||
<< "Using regular kernel (Kernel<false>).\n";
|
||||
}
|
||||
else if(!split_info.should_split)
|
||||
{
|
||||
std::cout << "[INVOKER] Image is small (" << total_h << "×" << total_w
|
||||
<< "), split-image not necessary.\n";
|
||||
std::cout << "[INVOKER] Using regular kernel (Kernel<false>).\n";
|
||||
}
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Step 3: Calculate split-image pieces (only if using split-image)
|
||||
// =====================================================================
|
||||
ck_tile::index_t num_d_pieces = 1;
|
||||
ck_tile::index_t num_h_pieces = 1;
|
||||
ck_tile::index_t num_w_pieces = 1;
|
||||
ck_tile::index_t total_pieces = 1;
|
||||
ck_tile::index_t base_piece_d = total_d;
|
||||
ck_tile::index_t base_piece_h = total_h;
|
||||
ck_tile::index_t base_piece_w = total_w;
|
||||
std::array<ck_tile::SplitImagePieceInfo, 64> temp_pieces{};
|
||||
ck_tile::index_t total_blocks = 0;
|
||||
|
||||
if(use_split_image)
|
||||
{
|
||||
num_d_pieces = split_info.num_d_pieces;
|
||||
num_h_pieces = split_info.num_h_pieces;
|
||||
num_w_pieces = split_info.num_w_pieces;
|
||||
total_pieces = num_d_pieces * num_h_pieces * num_w_pieces;
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "\n========================================\n";
|
||||
std::cout << "[SPLIT-IMAGE ENABLED] Large tensor detected\n";
|
||||
std::cout << "========================================\n";
|
||||
if(NDimSpatial == 3)
|
||||
{
|
||||
std::cout << "Total dimensions: D=" << total_d << " H=" << total_h
|
||||
<< " W=" << total_w << "\n";
|
||||
std::cout << "Split into pieces: D=" << num_d_pieces << " × H=" << num_h_pieces
|
||||
<< " × W=" << num_w_pieces << " = " << total_pieces
|
||||
<< " total pieces\n";
|
||||
std::cout << "Base piece size: D=" << (total_d / num_d_pieces)
|
||||
<< " H=" << (total_h / num_h_pieces)
|
||||
<< " W=" << (total_w / num_w_pieces) << "\n";
|
||||
}
|
||||
else if(NDimSpatial == 2)
|
||||
{
|
||||
std::cout << "Total dimensions: H=" << total_h << " W=" << total_w << "\n";
|
||||
std::cout << "Split into pieces: H=" << num_h_pieces << " × W=" << num_w_pieces
|
||||
<< " = " << total_pieces << " total pieces\n";
|
||||
std::cout << "Base piece size: H=" << (total_h / num_h_pieces)
|
||||
<< " W=" << (total_w / num_w_pieces) << "\n";
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Total dimensions: W=" << total_w << "\n";
|
||||
std::cout << "Split into pieces: W=" << num_w_pieces << " = " << total_pieces
|
||||
<< " total pieces\n";
|
||||
std::cout << "Base piece size: W=" << (total_w / num_w_pieces) << "\n";
|
||||
}
|
||||
std::cout << "========================================\n\n";
|
||||
}
|
||||
|
||||
// Base piece size (non-overlapping division)
|
||||
base_piece_d = total_d / num_d_pieces;
|
||||
base_piece_h = total_h / num_h_pieces;
|
||||
base_piece_w = total_w / num_w_pieces;
|
||||
|
||||
// Calculate piece info for all pieces using library utility function
|
||||
for(ck_tile::index_t piece = 0; piece < total_pieces; piece++)
|
||||
{
|
||||
temp_pieces[piece] =
|
||||
ck_tile::calculate_spatial_piece<TilePartitioner>(piece,
|
||||
num_d_pieces,
|
||||
num_h_pieces,
|
||||
num_w_pieces,
|
||||
base_piece_d,
|
||||
base_piece_h,
|
||||
base_piece_w,
|
||||
total_d,
|
||||
total_h,
|
||||
total_w,
|
||||
args.N_,
|
||||
args.K_,
|
||||
total_blocks);
|
||||
total_blocks = temp_pieces[piece].block_end;
|
||||
}
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Kernel launch lambda: Uses EnableSplitImage based on layout support
|
||||
// =====================================================================
|
||||
const auto Run = [&]<bool EnableSplitImage>(const auto has_hot_loop_,
|
||||
const auto tail_number_,
|
||||
const auto memory_operation_) {
|
||||
constexpr bool has_hot_loop_v = has_hot_loop_.value;
|
||||
constexpr auto tail_number_v = tail_number_.value;
|
||||
constexpr auto scheduler = GemmConfig::Scheduler;
|
||||
constexpr auto memory_operation = memory_operation_.value;
|
||||
|
||||
using UniversalGemmProblem =
|
||||
ck_tile::UniversalGemmPipelineProblem<InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
GemmShape,
|
||||
GemmUniversalTraits,
|
||||
scheduler,
|
||||
has_hot_loop_v,
|
||||
tail_number_v,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
OutDataType,
|
||||
true,
|
||||
VectorSizeA,
|
||||
VectorSizeB>;
|
||||
|
||||
using GemmPipeline = typename PipelineTypeTraits<
|
||||
GemmConfig::Pipeline>::template GemmPipeline<UniversalGemmProblem>;
|
||||
|
||||
using ConvEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
typename GroupedConvTraitsType::ImplicitGemmDsLayout,
|
||||
ck_tile::tensor_layout::gemm::RowMajor,
|
||||
CDEElementWise,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
GemmConfig::M_Warp,
|
||||
GemmConfig::N_Warp,
|
||||
GemmConfig::M_Warp_Tile,
|
||||
GemmConfig::N_Warp_Tile,
|
||||
GemmConfig::K_Warp_Tile,
|
||||
GemmConfig::TransposeC,
|
||||
memory_operation,
|
||||
1,
|
||||
true,
|
||||
GroupedConvTraitsType::VectorSizeC>>;
|
||||
|
||||
// Use split-image kernel if layout supports it, otherwise use regular kernel
|
||||
using Kernel = ck_tile::GroupedConvolutionForwardKernel<EnableSplitImage,
|
||||
GroupedConvTraitsType,
|
||||
TilePartitioner,
|
||||
GemmPipeline,
|
||||
ConvEpilogue>;
|
||||
|
||||
// Create kargs
|
||||
auto kargs = Kernel::MakeKernelArgs(args);
|
||||
|
||||
// Populate split-image metadata ONLY if using split-image kernel
|
||||
if constexpr(EnableSplitImage)
|
||||
{
|
||||
kargs.num_spatial_pieces = total_pieces;
|
||||
kargs.split_image.total_d = total_d;
|
||||
kargs.split_image.total_h = total_h;
|
||||
kargs.split_image.total_w = total_w;
|
||||
kargs.split_image.total_spatial = total_d * total_h * total_w; // Pre-calculate
|
||||
kargs.split_image.num_d_pieces = num_d_pieces;
|
||||
kargs.split_image.num_h_pieces = num_h_pieces;
|
||||
kargs.split_image.num_w_pieces = num_w_pieces;
|
||||
|
||||
for(ck_tile::index_t i = 0; i < total_pieces; i++)
|
||||
{
|
||||
kargs.split_image.pieces[i] = {temp_pieces[i].block_start,
|
||||
temp_pieces[i].block_end,
|
||||
temp_pieces[i].d_start,
|
||||
temp_pieces[i].h_start,
|
||||
temp_pieces[i].w_start,
|
||||
temp_pieces[i].d_size,
|
||||
temp_pieces[i].h_size,
|
||||
temp_pieces[i].w_size};
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate grid: use total_blocks for split-image, or normal GridSize for regular
|
||||
const dim3 grids = [&]() {
|
||||
if constexpr(EnableSplitImage)
|
||||
return dim3(total_blocks, kargs.GemmBatch, kargs.n_splits);
|
||||
else
|
||||
return Kernel::GridSize(kargs);
|
||||
}();
|
||||
const dim3 blocks = Kernel::BlockSize();
|
||||
|
||||
if(!Kernel::IsSupportedArgument(kargs))
|
||||
{
|
||||
throw std::runtime_error("Wrong! Arguments not supported! Skipping conv!\n");
|
||||
}
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
|
||||
<< "shape: " << GemmShape::GetName() << '\n'
|
||||
<< "problem: " << UniversalGemmProblem::GetName() << '\n'
|
||||
<< "pipeline: " << GemmPipeline::GetName() << '\n'
|
||||
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
||||
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
|
||||
<< "}" << '\n'
|
||||
<< "Vector size A: " << GemmPipeline::GetVectorSizeA()
|
||||
<< ", Vector size B: " << GemmPipeline::GetVectorSizeB()
|
||||
<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
|
||||
}
|
||||
|
||||
ave_time = ck_tile::launch_kernel(
|
||||
s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
||||
|
||||
return ave_time;
|
||||
};
|
||||
|
||||
// =====================================================================
|
||||
// Step 4: Dispatch kernel (split-image or regular based on decision)
|
||||
// =====================================================================
|
||||
if(use_split_image)
|
||||
{
|
||||
// Use split-image kernel (Kernel<true>)
|
||||
const auto RunSplitImage = [&](const auto has_hot_loop_, const auto tail_number_) {
|
||||
if(args.k_batch == 1)
|
||||
Run.template operator()<true>(has_hot_loop_, tail_number_, MemoryOpSet{});
|
||||
else
|
||||
Run.template operator()<true>(has_hot_loop_, tail_number_, MemoryOpAtomicAdd{});
|
||||
};
|
||||
BaseGemmPipeline::TailHandler(RunSplitImage, has_hot_loop, tail_num);
|
||||
}
|
||||
else
|
||||
{
|
||||
// Use regular kernel (Kernel<false>)
|
||||
const auto RunRegular = [&](const auto has_hot_loop_, const auto tail_number_) {
|
||||
if(args.k_batch == 1)
|
||||
Run.template operator()<false>(has_hot_loop_, tail_number_, MemoryOpSet{});
|
||||
else
|
||||
Run.template operator()<false>(
|
||||
has_hot_loop_, tail_number_, MemoryOpAtomicAdd{});
|
||||
};
|
||||
BaseGemmPipeline::TailHandler(RunRegular, has_hot_loop, tail_num);
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
};
|
||||
Reference in New Issue
Block a user