mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 06:49:15 +00:00
[CK_TILE] Add conv bwd weight two stage support (#2855)
* resolved conflicts * add conv bwd weight twostage * fix one file * fixes after review * fixes * fixes * Fix --------- Co-authored-by: Bartlomiej Kocot <barkocot@amd.com>
This commit is contained in:
jakpiase
@@ -7,5 +7,8 @@ target_compile_options(tile_example_grouped_conv_fwd PRIVATE ${EXAMPLE_GEMM_COMP
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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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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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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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@@ -41,8 +41,8 @@ float grouped_conv_bwd_data(const ck_tile::GroupedConvBwdDataHostArgs& args,
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constexpr ck_tile::index_t N_Warp_Tile = GemmWarpConfig::N_Warp_Tile;
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constexpr ck_tile::index_t K_Warp_Tile = GemmWarpConfig::K_Warp_Tile;
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constexpr ck_tile::index_t VectorSizeA = 8;
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constexpr ck_tile::index_t VectorSizeB = 8;
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constexpr ck_tile::index_t VectorSizeA = 1;
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constexpr ck_tile::index_t VectorSizeB = 1;
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constexpr ck_tile::index_t VectorSizeC = 8;
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// Implicit GEMM Traits
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@@ -51,20 +51,29 @@ float grouped_conv_bwd_data(const ck_tile::GroupedConvBwdDataHostArgs& args,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
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using GroupedConvTraitsType =
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ck_tile::GroupedConvTraits<NDimSpatial, ConvSpec, InLayout, WeiLayout, DsLayout, OutLayout>;
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using CodegenPipelineProblem =
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ck_tile::GemmPipelineProblem<InDataType,
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WeiDataType,
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AccDataType,
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CodegenShape,
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typename GroupedConvTraitsType::GroupedConvImplicitGemmTraits,
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InDataType,
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true,
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VectorSizeA,
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VectorSizeB>;
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constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
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using GroupedConvTraitsType = ck_tile::GroupedConvTraits<NDimSpatial,
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ConvSpec,
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InLayout,
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WeiLayout,
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DsLayout,
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OutLayout,
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VectorSizeA,
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VectorSizeB,
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VectorSizeC>;
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using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<
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InDataType,
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WeiDataType,
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AccDataType,
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CodegenShape,
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typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsBwdData,
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ck_tile::element_wise::PassThrough,
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ck_tile::element_wise::PassThrough,
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InDataType,
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true,
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GroupedConvTraitsType::VectorSizeA,
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GroupedConvTraitsType::VectorSizeB>;
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using CodegenPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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const auto Run = [&](const auto memory_operation_) {
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@@ -90,7 +99,7 @@ float grouped_conv_bwd_data(const ck_tile::GroupedConvBwdDataHostArgs& args,
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memory_operation,
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1,
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true,
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VectorSizeC>>;
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GroupedConvTraitsType::VectorSizeC>>;
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using Kernel = ck_tile::GroupedConvolutionBackwardDataKernel<GroupedConvTraitsType,
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TilePartitioner,
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@@ -11,195 +11,13 @@
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#include "ck_tile/host.hpp"
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#include "grouped_convolution_utils.hpp"
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template <ck_tile::index_t NDimSpatial,
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typename GemmWarpConfig,
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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,
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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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float grouped_conv_bwd_weight(const ck_tile::GroupedConvBwdWeightHostArgs& args,
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const ck_tile::stream_config& s)
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{
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constexpr int kBlockPerCu = 1;
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constexpr ck_tile::index_t M_Tile = 64;
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constexpr ck_tile::index_t N_Tile = 64;
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constexpr ck_tile::index_t K_Tile = 64;
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constexpr ck_tile::index_t M_Warp = 2;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = GemmWarpConfig::M_Warp_Tile;
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constexpr ck_tile::index_t N_Warp_Tile = GemmWarpConfig::N_Warp_Tile;
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constexpr ck_tile::index_t K_Warp_Tile = GemmWarpConfig::K_Warp_Tile;
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constexpr ck_tile::index_t VectorSizeA = 8;
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constexpr ck_tile::index_t VectorSizeB = 8;
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constexpr ck_tile::index_t VectorSizeC = 8;
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// Implicit GEMM Traits
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using CodegenShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
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using GroupedConvTraitsType =
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ck_tile::GroupedConvTraits<NDimSpatial, ConvSpec, InLayout, WeiLayout, DsLayout, OutLayout>;
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using CodegenPipelineProblem =
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ck_tile::GemmPipelineProblem<InDataType,
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WeiDataType,
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AccDataType,
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CodegenShape,
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typename GroupedConvTraitsType::GroupedConvImplicitGemmTraits,
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InDataType,
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true,
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VectorSizeA,
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VectorSizeB>;
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using CodegenPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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const auto Run = [&](const auto memory_operation_) {
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constexpr auto memory_operation = memory_operation_.value;
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using ConvEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<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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CDEElementWise,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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M_Warp,
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N_Warp,
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M_Warp_Tile,
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N_Warp_Tile,
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K_Warp_Tile,
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CodegenPipelineProblem::TransposeC,
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memory_operation,
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1,
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true,
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VectorSizeC>>;
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using Kernel = ck_tile::GroupedConvolutionBackwardWeightKernel<GroupedConvTraitsType,
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TilePartitioner,
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CodegenPipeline,
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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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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: " << CodegenShape::GetName() << '\n'
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<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
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<< "pipeline: " << CodegenPipeline::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: " << CodegenPipeline::GetVectorSizeA()
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<< ", Vector size B: " << CodegenPipeline::GetVectorSizeB()
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<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
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}
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float ave_time = ck_tile::launch_kernel_time_mask(
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s,
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Kernel::Preprocess(kargs, s),
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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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if(args.k_batch == 1)
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{
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return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::set>{});
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}
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else
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{
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return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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ck_tile::memory_operation_enum::atomic_add>{});
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}
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}
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#include "grouped_convolution_backward_weight_invoker.hpp"
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#include "run_grouped_convolution_bwd_weight_example.inc"
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template <typename GemmWarpConfig,
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typename InPrecType,
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typename WeiPrecType = InPrecType,
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typename OutPrecType = InPrecType>
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int run_grouped_conv_bwd_weight_example_prec_type(
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std::string in_layout, std::string wei_layout, std::string out_layout, int argc, char* argv[])
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{
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using NWGC = ck_tile::tensor_layout::convolution::NWGC;
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using NHWGC = ck_tile::tensor_layout::convolution::NHWGC;
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using NDHWGC = ck_tile::tensor_layout::convolution::NDHWGC;
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using GKXC = ck_tile::tensor_layout::convolution::GKXC;
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using GKYXC = ck_tile::tensor_layout::convolution::GKYXC;
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using GKZYXC = ck_tile::tensor_layout::convolution::GKZYXC;
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using NWGK = ck_tile::tensor_layout::convolution::NWGK;
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using NHWGK = ck_tile::tensor_layout::convolution::NHWGK;
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using NDHWGK = ck_tile::tensor_layout::convolution::NDHWGK;
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if(in_layout == "NWGC" && wei_layout == "GKXC" && out_layout == "NWGK")
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{
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return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<1>{},
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GemmWarpConfig,
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InPrecType,
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WeiPrecType,
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OutPrecType>(
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argc, argv, NWGC{}, GKXC{}, NWGK{});
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}
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else if(in_layout == "NHWGC" && wei_layout == "GKYXC" && out_layout == "NHWGK")
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{
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return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<2>{},
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GemmWarpConfig,
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InPrecType,
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WeiPrecType,
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OutPrecType>(
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argc, argv, NHWGC{}, GKYXC{}, NHWGK{});
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}
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else if(in_layout == "NDHWGC" && wei_layout == "GKZYXC" && out_layout == "NDHWGK")
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{
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return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<3>{},
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GemmWarpConfig,
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InPrecType,
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WeiPrecType,
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OutPrecType>(
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argc, argv, NDHWGC{}, GKZYXC{}, NDHWGK{});
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}
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else
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{
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throw std::runtime_error("Unsupported memory layout!");
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}
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}
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template <typename GemmWarpConfig>
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int run_grouped_conv_bwd_weight_example(int argc, char* argv[])
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int run_grouped_conv_bwd_weight_example(ck_tile::ArgParser& arg_parser)
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{
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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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using Invoker = GroupedConvolutionBackwardWeightInvoker;
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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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@@ -208,13 +26,17 @@ int run_grouped_conv_bwd_weight_example(int argc, char* argv[])
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if(data_type == "fp16")
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{
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return run_grouped_conv_bwd_weight_example_prec_type<GemmWarpConfig, ck_tile::half_t>(
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in_layout, wei_layout, out_layout, argc, argv);
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return run_grouped_conv_bwd_weight_example_prec_type<Invoker,
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GemmWarpConfig,
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ck_tile::half_t>(
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in_layout, wei_layout, out_layout, arg_parser);
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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_bwd_weight_example_prec_type<GemmWarpConfig, ck_tile::bf16_t>(
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in_layout, wei_layout, out_layout, argc, argv);
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return run_grouped_conv_bwd_weight_example_prec_type<Invoker,
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GemmWarpConfig,
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ck_tile::bf16_t>(
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in_layout, wei_layout, out_layout, arg_parser);
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}
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else
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{
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@@ -224,9 +46,22 @@ int run_grouped_conv_bwd_weight_example(int argc, char* argv[])
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int main(int argc, char* argv[])
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{
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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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try
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{
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#if CK_TILE_USE_WMMA
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return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Wmma>(argc, argv);
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return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Wmma>(arg_parser);
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#else
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return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Mfma>(argc, argv);
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return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Mfma>(arg_parser);
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#endif
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}
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catch(const std::runtime_error& e)
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{
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std::cerr << "Runtime error: " << e.what() << '\n';
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return EXIT_FAILURE;
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}
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}
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@@ -0,0 +1,145 @@
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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 "grouped_convolution_utils.hpp"
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struct GroupedConvolutionBackwardWeightInvoker
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{
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template <ck_tile::index_t NDimSpatial,
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typename GemmWarpConfig,
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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,
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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_bwd_weight(const ck_tile::GroupedConvBwdWeightHostArgs& args,
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const ck_tile::stream_config& s)
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{
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constexpr int kBlockPerCu = 1;
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constexpr ck_tile::index_t M_Tile = 64;
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constexpr ck_tile::index_t N_Tile = 64;
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constexpr ck_tile::index_t K_Tile = 64;
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constexpr ck_tile::index_t M_Warp = 2;
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constexpr ck_tile::index_t N_Warp = 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = GemmWarpConfig::M_Warp_Tile;
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constexpr ck_tile::index_t N_Warp_Tile = GemmWarpConfig::N_Warp_Tile;
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constexpr ck_tile::index_t K_Warp_Tile = GemmWarpConfig::K_Warp_Tile;
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constexpr ck_tile::index_t VectorSizeA = 1;
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constexpr ck_tile::index_t VectorSizeB = 1;
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constexpr ck_tile::index_t VectorSizeC = 8;
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// Implicit GEMM Traits
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using CodegenShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
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using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
|
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using GroupedConvTraitsType = ck_tile::GroupedConvTraits<NDimSpatial,
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ConvSpec,
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InLayout,
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WeiLayout,
|
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DsLayout,
|
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OutLayout,
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VectorSizeA,
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VectorSizeB,
|
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VectorSizeC>;
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using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<
|
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InDataType,
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WeiDataType,
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AccDataType,
|
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CodegenShape,
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typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsBwdWeight,
|
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ck_tile::element_wise::PassThrough,
|
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ck_tile::element_wise::PassThrough,
|
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InDataType,
|
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true,
|
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GroupedConvTraitsType::VectorSizeA,
|
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GroupedConvTraitsType::VectorSizeB>;
|
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using CodegenPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
|
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|
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const auto Run = [&](const auto memory_operation_) {
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constexpr auto memory_operation = memory_operation_.value;
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|
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using ConvEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
|
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InDataType,
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WeiDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
typename GroupedConvTraitsType::ImplicitGemmDsLayout,
|
||||
ck_tile::tensor_layout::gemm::RowMajor,
|
||||
CDEElementWise,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
M_Warp,
|
||||
N_Warp,
|
||||
M_Warp_Tile,
|
||||
N_Warp_Tile,
|
||||
K_Warp_Tile,
|
||||
CodegenPipelineProblem::TransposeC,
|
||||
memory_operation,
|
||||
1,
|
||||
true,
|
||||
GroupedConvTraitsType::VectorSizeC>>;
|
||||
|
||||
using Kernel = ck_tile::GroupedConvolutionBackwardWeightKernel<GroupedConvTraitsType,
|
||||
TilePartitioner,
|
||||
CodegenPipeline,
|
||||
ConvEpilogue>;
|
||||
auto kargs = Kernel::MakeKernelArgs(args);
|
||||
|
||||
const dim3 grids = 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: " << CodegenShape::GetName() << '\n'
|
||||
<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
|
||||
<< "pipeline: " << CodegenPipeline::GetName() << '\n'
|
||||
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
||||
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
|
||||
<< "}" << '\n'
|
||||
<< "Vector size A: " << CodegenPipeline::GetVectorSizeA()
|
||||
<< ", Vector size B: " << CodegenPipeline::GetVectorSizeB()
|
||||
<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
|
||||
}
|
||||
|
||||
float ave_time = ck_tile::launch_kernel_time_mask(
|
||||
s,
|
||||
Kernel::Preprocess(kargs, s),
|
||||
ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
||||
|
||||
return ave_time;
|
||||
};
|
||||
|
||||
if(args.k_batch == 1)
|
||||
{
|
||||
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::set>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::atomic_add>{});
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -0,0 +1,67 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <hip/hip_runtime.h>
|
||||
|
||||
#include <cstring>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
|
||||
#include "ck_tile/host.hpp"
|
||||
#include "grouped_convolution_utils.hpp"
|
||||
#include "grouped_convolution_backward_weight_two_stage_invoker.hpp"
|
||||
#include "run_grouped_convolution_bwd_weight_example.inc"
|
||||
|
||||
template <typename GemmWarpConfig>
|
||||
int run_grouped_conv_bwd_weight_example(ck_tile::ArgParser& arg_parser)
|
||||
{
|
||||
using Invoker = GroupedConvolutionBackwardWeightTwoStageInvoker;
|
||||
|
||||
std::string data_type = arg_parser.get_str("prec");
|
||||
std::string in_layout = arg_parser.get_str("in_layout");
|
||||
std::string wei_layout = arg_parser.get_str("wei_layout");
|
||||
std::string out_layout = arg_parser.get_str("out_layout");
|
||||
|
||||
if(data_type == "fp16")
|
||||
{
|
||||
return run_grouped_conv_bwd_weight_example_prec_type<Invoker,
|
||||
GemmWarpConfig,
|
||||
ck_tile::half_t>(
|
||||
in_layout, wei_layout, out_layout, arg_parser);
|
||||
}
|
||||
else if(data_type == "bf16")
|
||||
{
|
||||
return run_grouped_conv_bwd_weight_example_prec_type<Invoker,
|
||||
GemmWarpConfig,
|
||||
ck_tile::bf16_t>(
|
||||
in_layout, wei_layout, out_layout, arg_parser);
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data type for this operation!");
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return -1;
|
||||
|
||||
try
|
||||
{
|
||||
#if CK_TILE_USE_WMMA
|
||||
return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Wmma>(arg_parser);
|
||||
#else
|
||||
return !run_grouped_conv_bwd_weight_example<GemmWarpConfig_Mfma>(arg_parser);
|
||||
#endif
|
||||
}
|
||||
catch(const std::runtime_error& e)
|
||||
{
|
||||
std::cerr << "Runtime error: " << e.what() << '\n';
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,215 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
#pragma once
|
||||
|
||||
#include "grouped_convolution_utils.hpp"
|
||||
|
||||
struct GroupedConvolutionBackwardWeightTwoStageInvoker
|
||||
{
|
||||
template <ck_tile::index_t NDimSpatial,
|
||||
typename GemmWarpConfig,
|
||||
typename InDataType,
|
||||
typename WeiDataType,
|
||||
typename AccDataType,
|
||||
typename OutDataType,
|
||||
typename InLayout,
|
||||
typename WeiLayout,
|
||||
typename OutLayout,
|
||||
typename DsDataType = ck_tile::tuple<>,
|
||||
typename DsLayout = ck_tile::tuple<>,
|
||||
typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
||||
static float grouped_conv_bwd_weight(const ck_tile::GroupedConvBwdWeightHostArgs& args,
|
||||
const ck_tile::stream_config& s)
|
||||
{
|
||||
using WorkspaceDataType = float;
|
||||
|
||||
constexpr int kBlockPerCu = 1;
|
||||
|
||||
constexpr ck_tile::index_t M_Tile = 64;
|
||||
constexpr ck_tile::index_t N_Tile = 64;
|
||||
constexpr ck_tile::index_t K_Tile = 64;
|
||||
|
||||
constexpr ck_tile::index_t M_Warp = 2;
|
||||
constexpr ck_tile::index_t N_Warp = 2;
|
||||
constexpr ck_tile::index_t K_Warp = 1;
|
||||
|
||||
constexpr ck_tile::index_t M_Warp_Tile = GemmWarpConfig::M_Warp_Tile;
|
||||
constexpr ck_tile::index_t N_Warp_Tile = GemmWarpConfig::N_Warp_Tile;
|
||||
constexpr ck_tile::index_t K_Warp_Tile = GemmWarpConfig::K_Warp_Tile;
|
||||
|
||||
constexpr ck_tile::index_t VectorSizeA = 1;
|
||||
constexpr ck_tile::index_t VectorSizeB = 1;
|
||||
constexpr ck_tile::index_t VectorSizeC = 1;
|
||||
|
||||
// Implicit GEMM Traits
|
||||
using CodegenShape =
|
||||
ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
|
||||
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
|
||||
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
|
||||
|
||||
constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
|
||||
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
|
||||
using GroupedConvTraitsType = ck_tile::GroupedConvTraits<NDimSpatial,
|
||||
ConvSpec,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
DsLayout,
|
||||
OutLayout,
|
||||
VectorSizeA,
|
||||
VectorSizeB,
|
||||
VectorSizeC>;
|
||||
using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<
|
||||
OutDataType, // A: Out
|
||||
InDataType, // B: In
|
||||
AccDataType,
|
||||
CodegenShape,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsBwdWeight,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
InDataType,
|
||||
true,
|
||||
GroupedConvTraitsType::VectorSizeA,
|
||||
GroupedConvTraitsType::VectorSizeB>;
|
||||
using CodegenPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
|
||||
|
||||
const auto Run = [&](const auto memory_operation_) {
|
||||
constexpr auto memory_operation = memory_operation_.value;
|
||||
|
||||
using ConvEpilogue = ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<
|
||||
OutDataType, // A: Out
|
||||
InDataType, // B: In
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
WorkspaceDataType, // C: Workspace normally Out
|
||||
typename GroupedConvTraitsType::ImplicitGemmDsLayout,
|
||||
ck_tile::tensor_layout::gemm::RowMajor,
|
||||
CDEElementWise,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
M_Warp,
|
||||
N_Warp,
|
||||
M_Warp_Tile,
|
||||
N_Warp_Tile,
|
||||
K_Warp_Tile,
|
||||
CodegenPipelineProblem::TransposeC,
|
||||
memory_operation,
|
||||
1,
|
||||
true,
|
||||
GroupedConvTraitsType::VectorSizeC>>;
|
||||
|
||||
using Kernel = ck_tile::GroupedConvolutionBackwardWeightKernel<GroupedConvTraitsType,
|
||||
TilePartitioner,
|
||||
CodegenPipeline,
|
||||
ConvEpilogue>;
|
||||
|
||||
const ck_tile::index_t spatial_lengths_accum =
|
||||
std::accumulate(args.filter_spatial_lengths_.begin(),
|
||||
args.filter_spatial_lengths_.end(),
|
||||
1,
|
||||
std::multiplies<ck_tile::index_t>());
|
||||
ck_tile::DeviceMem ws_m_n_dev_buf(args.G_ * args.K_ * args.C_ * spatial_lengths_accum *
|
||||
sizeof(WorkspaceDataType));
|
||||
ck_tile::GroupedConvBwdWeightHostArgs ws_args =
|
||||
ck_tile::GroupedConvBwdWeightHostArgs(args);
|
||||
auto c_ptr = ws_args.wei_ptr;
|
||||
ws_args.wei_ptr = ws_m_n_dev_buf.GetDeviceBuffer();
|
||||
auto kargs = Kernel::MakeKernelArgs(ws_args);
|
||||
|
||||
const dim3 grids = Kernel::GridSize(kargs);
|
||||
const dim3 blocks = Kernel::BlockSize();
|
||||
|
||||
if(!Kernel::IsSupportedArgument(kargs))
|
||||
{
|
||||
throw std::runtime_error("Wrong! Arguments not supported! Skipping conv!\n");
|
||||
}
|
||||
|
||||
using XElementwiseOperation = ck_tile::element_wise::UnaryConvert;
|
||||
using BlockTile = ck_tile::sequence<2048>;
|
||||
using BlockWarps = ck_tile::sequence<8>;
|
||||
using WarpTile = ck_tile::sequence<64>;
|
||||
|
||||
using ElementwiseShape =
|
||||
ck_tile::ElementWiseShape<BlockWarps, BlockTile, WarpTile, WorkspaceDataType>;
|
||||
using Problem = ck_tile::ElementWisePipelineProblem<WorkspaceDataType,
|
||||
WorkspaceDataType,
|
||||
WeiDataType,
|
||||
ElementwiseShape,
|
||||
XElementwiseOperation>;
|
||||
using ElementwiseKernel =
|
||||
ck_tile::ElementWiseKernel<Problem, ck_tile::ElementWiseDefaultPolicy>;
|
||||
|
||||
ck_tile::index_t total_elements = 1;
|
||||
std::vector<ck_tile::index_t> shape = {
|
||||
static_cast<ck_tile::index_t>(args.G_ * args.K_),
|
||||
static_cast<ck_tile::index_t>(args.C_ * spatial_lengths_accum)};
|
||||
|
||||
for(auto d : shape)
|
||||
total_elements *= d;
|
||||
|
||||
const ck_tile::index_t kBlockSize = ElementwiseKernel::BlockSize();
|
||||
|
||||
constexpr ck_tile::index_t elements_per_block = BlockTile::at(ck_tile::number<0>{});
|
||||
ck_tile::index_t kGridSize =
|
||||
(total_elements + elements_per_block - 1) / elements_per_block;
|
||||
|
||||
auto input_tensors =
|
||||
ck_tile::make_tuple(static_cast<WorkspaceDataType*>(ws_args.wei_ptr));
|
||||
auto input_size = ck_tile::make_tuple(shape[0], shape[1]);
|
||||
|
||||
// Check if the kernel configuration is supported
|
||||
if(!ElementwiseKernel::IsSupportedArgument(input_size))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"Wrong! Elementwise arguments not supported! Skipping gemm!\n");
|
||||
}
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
|
||||
<< "shape: " << CodegenShape::GetName() << '\n'
|
||||
<< "problem: " << CodegenPipelineProblem::GetName() << '\n'
|
||||
<< "pipeline: " << CodegenPipeline::GetName() << '\n'
|
||||
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
||||
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
|
||||
<< "}" << '\n'
|
||||
<< "Vector size A: " << CodegenPipeline::GetVectorSizeA()
|
||||
<< ", Vector size B: " << CodegenPipeline::GetVectorSizeB()
|
||||
<< ", Vector size C: " << ConvEpilogue::GetVectorSizeC() << std::endl;
|
||||
}
|
||||
|
||||
auto preprocess = [&]() {
|
||||
if(args.k_batch > 1)
|
||||
ck_tile::hip_check_error(
|
||||
hipMemsetAsync(ws_args.wei_ptr,
|
||||
0,
|
||||
shape[0] * shape[1] * sizeof(WorkspaceDataType),
|
||||
s.stream_id_));
|
||||
};
|
||||
|
||||
return ck_tile::launch_kernel_time_mask(
|
||||
s,
|
||||
preprocess,
|
||||
ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs),
|
||||
ck_tile::make_kernel<kBlockPerCu>(ElementwiseKernel{},
|
||||
kGridSize,
|
||||
kBlockSize,
|
||||
0,
|
||||
input_size,
|
||||
ck_tile::make_tuple(shape[1], 1), // Input Stride
|
||||
ck_tile::make_tuple(shape[1], 1), // Output Stride
|
||||
input_tensors,
|
||||
static_cast<WeiDataType*>(c_ptr)));
|
||||
};
|
||||
|
||||
if(args.k_batch == 1)
|
||||
{
|
||||
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::set>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::atomic_add>{});
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -50,20 +50,29 @@ float grouped_conv_fwd(const ck_tile::GroupedConvFwdHostArgs& args, const ck_til
|
||||
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
|
||||
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
|
||||
|
||||
constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
|
||||
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
|
||||
using GroupedConvTraitsType =
|
||||
ck_tile::GroupedConvTraits<NDimSpatial, ConvSpec, InLayout, WeiLayout, DsLayout, OutLayout>;
|
||||
using CodegenPipelineProblem =
|
||||
ck_tile::GemmPipelineProblem<InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
CodegenShape,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraits,
|
||||
InDataType,
|
||||
true,
|
||||
VectorSizeA,
|
||||
VectorSizeB>;
|
||||
constexpr auto ConvSpec = ck_tile::ConvolutionSpecialization::Default;
|
||||
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenShape>;
|
||||
using GroupedConvTraitsType = ck_tile::GroupedConvTraits<NDimSpatial,
|
||||
ConvSpec,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
DsLayout,
|
||||
OutLayout,
|
||||
VectorSizeA,
|
||||
VectorSizeB,
|
||||
VectorSizeC>;
|
||||
using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
CodegenShape,
|
||||
typename GroupedConvTraitsType::GroupedConvImplicitGemmTraitsFwd,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
InDataType,
|
||||
true,
|
||||
GroupedConvTraitsType::VectorSizeA,
|
||||
GroupedConvTraitsType::VectorSizeB>;
|
||||
using CodegenPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
|
||||
|
||||
const auto Run = [&](const auto memory_operation_) {
|
||||
@@ -89,7 +98,7 @@ float grouped_conv_fwd(const ck_tile::GroupedConvFwdHostArgs& args, const ck_til
|
||||
memory_operation,
|
||||
1,
|
||||
true,
|
||||
VectorSizeC>>;
|
||||
GroupedConvTraitsType::VectorSizeC>>;
|
||||
|
||||
using Kernel = ck_tile::GroupedConvolutionForwardKernel<GroupedConvTraitsType,
|
||||
TilePartitioner,
|
||||
|
||||
@@ -4,6 +4,7 @@
|
||||
|
||||
template <ck_tile::index_t NDimSpatial,
|
||||
typename GemmWarpConfig,
|
||||
typename Invoker,
|
||||
typename InDataType,
|
||||
typename WeiDataType,
|
||||
typename AccDataType,
|
||||
@@ -15,43 +16,34 @@ float invoke_grouped_conv_bwd_weight(ck_tile::GroupedConvBwdWeightHostArgs& args
|
||||
int n_warmup,
|
||||
int n_repeat)
|
||||
{
|
||||
float ave_time = grouped_conv_bwd_weight<NDimSpatial,
|
||||
GemmWarpConfig,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout>(
|
||||
float ave_time = Invoker::template grouped_conv_bwd_weight<NDimSpatial,
|
||||
GemmWarpConfig,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout>(
|
||||
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
|
||||
|
||||
std::size_t flop = args.GetFlops();
|
||||
std::size_t num_byte = args.GetByte<InDataType, WeiDataType, OutDataType>();
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< std::endl;
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
template <ck_tile::index_t NDimSpatial,
|
||||
typename GemmWarpConfig,
|
||||
typename Invoker,
|
||||
typename InDataType,
|
||||
typename WeiDataType = InDataType,
|
||||
typename OutDataType = InDataType,
|
||||
typename InLayout,
|
||||
typename WeiLayout,
|
||||
typename OutLayout>
|
||||
int run_grouped_conv_bwd_weight_example_with_layouts(
|
||||
int argc, char* argv[], const InLayout, const WeiLayout, const OutLayout)
|
||||
int run_grouped_conv_bwd_weight_example_with_layouts(ck_tile::ArgParser& arg_parser,
|
||||
const InLayout,
|
||||
const WeiLayout,
|
||||
const OutLayout)
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return -1;
|
||||
|
||||
using AccDataType = float;
|
||||
|
||||
std::vector<ck_tile::index_t> filter_spatial_lengths;
|
||||
@@ -138,17 +130,27 @@ int run_grouped_conv_bwd_weight_example_with_layouts(
|
||||
std::cout << "weight: " << weight.mDesc << std::endl;
|
||||
std::cout << "output: " << output.mDesc << std::endl;
|
||||
|
||||
invoke_grouped_conv_bwd_weight<NDimSpatial,
|
||||
GemmWarpConfig,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout>(args, n_warmup, n_repeat);
|
||||
float ave_time = invoke_grouped_conv_bwd_weight<NDimSpatial,
|
||||
GemmWarpConfig,
|
||||
Invoker,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout>(args, n_warmup, n_repeat);
|
||||
|
||||
weight_dev_buf.FromDevice(weight.data());
|
||||
|
||||
std::size_t flop = args.GetFlops();
|
||||
std::size_t num_byte = args.GetByte<InDataType, WeiDataType, OutDataType>();
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< std::endl;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
if(arg_parser.get_int("v") == 1)
|
||||
@@ -189,3 +191,61 @@ int run_grouped_conv_bwd_weight_example_with_layouts(
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
template <typename Invoker,
|
||||
typename GemmWarpConfig,
|
||||
typename InPrecType,
|
||||
typename WeiPrecType = InPrecType,
|
||||
typename OutPrecType = InPrecType>
|
||||
int run_grouped_conv_bwd_weight_example_prec_type(std::string in_layout,
|
||||
std::string wei_layout,
|
||||
std::string out_layout,
|
||||
ck_tile::ArgParser& arg_parser)
|
||||
{
|
||||
using NWGC = ck_tile::tensor_layout::convolution::NWGC;
|
||||
using NHWGC = ck_tile::tensor_layout::convolution::NHWGC;
|
||||
using NDHWGC = ck_tile::tensor_layout::convolution::NDHWGC;
|
||||
|
||||
using GKXC = ck_tile::tensor_layout::convolution::GKXC;
|
||||
using GKYXC = ck_tile::tensor_layout::convolution::GKYXC;
|
||||
using GKZYXC = ck_tile::tensor_layout::convolution::GKZYXC;
|
||||
|
||||
using NWGK = ck_tile::tensor_layout::convolution::NWGK;
|
||||
using NHWGK = ck_tile::tensor_layout::convolution::NHWGK;
|
||||
using NDHWGK = ck_tile::tensor_layout::convolution::NDHWGK;
|
||||
|
||||
if(in_layout == "NWGC" && wei_layout == "GKXC" && out_layout == "NWGK")
|
||||
{
|
||||
return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<1>{},
|
||||
GemmWarpConfig,
|
||||
Invoker,
|
||||
InPrecType,
|
||||
WeiPrecType,
|
||||
OutPrecType>(
|
||||
arg_parser, NWGC{}, GKXC{}, NWGK{});
|
||||
}
|
||||
else if(in_layout == "NHWGC" && wei_layout == "GKYXC" && out_layout == "NHWGK")
|
||||
{
|
||||
return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<2>{},
|
||||
GemmWarpConfig,
|
||||
Invoker,
|
||||
InPrecType,
|
||||
WeiPrecType,
|
||||
OutPrecType>(
|
||||
arg_parser, NHWGC{}, GKYXC{}, NHWGK{});
|
||||
}
|
||||
else if(in_layout == "NDHWGC" && wei_layout == "GKZYXC" && out_layout == "NDHWGK")
|
||||
{
|
||||
return run_grouped_conv_bwd_weight_example_with_layouts<ck_tile::number<3>{},
|
||||
GemmWarpConfig,
|
||||
Invoker,
|
||||
InPrecType,
|
||||
WeiPrecType,
|
||||
OutPrecType>(
|
||||
arg_parser, NDHWGC{}, GKZYXC{}, NDHWGK{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported memory layout!");
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user