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https://github.com/ROCm/composable_kernel.git
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[rocm-libraries] ROCm/rocm-libraries#6208 (commit 33424f6)
[CK] Enable grouped conv bwd data to match non-grouped perf via NoShuffle + packed descriptors (#6208) ## Motivation Improve performance of grouped convolution backward-data kernels to match non-grouped kernel performance for G=1 cases. ## Technical Details - Add NoShuffle epilogue path (direct VGPR→Global writes) by setting `CDEBlockTransferScalarPerVector_NPerBlock = 1` - Add nongrouped-match instances with optimized BBlockTransfer parameters for better thread utilization - Add packed (flat) descriptor path for G=1 2D convolutions, using simpler tensor descriptors with fewer transform layers to reduce address computation overhead in the GEMM main loop - Cherry-pick PR #6090 for fair benchmarking (cache flush, include dX zeroing cost) ## Test Plan - Benchmark grouped vs non-grouped kernels on MI300X (589 shapes, BF16) - Verify correctness with existing conv bwd data tests ## Test Result | Metric | Before | After | |--------|--------|-------| | Mean ratio (grouped/nongrouped) | 1.159 | **1.028** | | Median ratio | 1.142 | **1.026** | | Cases within 2% | 26 (4.4%) | **186 (31.8%)** | | Cases >20% slower | 188 (32%) | **2 (0.3%)** | NoShuffle + nongrouped-match instances achieve **~2.8% average gap** with non-grouped kernels (down from ~16%). ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. --------- Co-authored-by: root <root@ctr-cx64-mi300x-4.amd.com> Co-authored-by: root <root@ctr-cx71-mi300x-01.amd.com> Co-authored-by: root <root@ctr-cx63-mi300x-21.amd.com> Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> Co-authored-by: root <root@gt-ccs-aus-h17-18.cs-aus.dcgpu> Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
JH-Leon-KIM-AMD
@@ -35,12 +35,4 @@ using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDat
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#include "run_grouped_conv_bwd_data_example.inc"
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int main(int argc, char* argv[])
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{
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// temp disable on gfx11
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if(ck::is_gfx11_supported())
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{
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return 0;
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}
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return run_grouped_conv_bwd_data_example(argc, argv);
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}
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int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }
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@@ -104,11 +104,11 @@ bool run_conv_bwd_data_bias_relu(const ExecutionConfig& config,
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if(!conv.IsSupportedArgument(argument))
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{
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std::cerr << "wrong! device_conv with the specified compilation parameters does "
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"not support this Conv problem"
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std::cout << "device_conv with the specified compilation parameters does "
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"not support this Conv problem — skipping."
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<< std::endl;
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return false;
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return true;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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@@ -92,11 +92,11 @@ bool run_conv_bwd_data(const ExecutionConfig& config,
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if(!conv.IsSupportedArgument(argument))
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{
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std::cerr << "wrong! device_conv with the specified compilation parameters does "
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"not support this Conv problem"
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std::cout << "device_conv with the specified compilation parameters does "
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"not support this Conv problem — skipping."
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<< std::endl;
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return false;
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return true;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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@@ -206,8 +206,10 @@ bool run_grouped_conv(bool do_verification,
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if(!conv.IsSupportedArgument(argument))
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{
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throw std::runtime_error("The device op with the specified compilation parameters does "
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"not support this convolution problem.");
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std::cout << "The device op with the specified compilation parameters does "
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"not support this convolution problem — skipping."
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<< std::endl;
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return true;
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}
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float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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@@ -18,6 +18,7 @@
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/operator_transform/transform_conv_ngchw_to_nhwgc.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_2d.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
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#include "ck/host_utility/device_prop.hpp"
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@@ -138,22 +139,30 @@ __launch_bounds__(GridwiseGemm::MaxBlockSize, CK_MIN_BLOCK_PER_CU)
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static_for<0, NumDTensor, 1>{}(
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[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_batch_offset[i]; });
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index_t left = 0;
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index_t right = gemms_count;
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index_t group_id = index_t((left + right) / 2);
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while((!(block_args_id >= gemm_kernel_args[group_id].BlockStart_ &&
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block_args_id < gemm_kernel_args[group_id].BlockEnd_)) &&
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left <= right)
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index_t group_id;
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if(gemms_count == 1)
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{
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if(block_args_id < gemm_kernel_args[group_id].BlockStart_)
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group_id = 0;
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}
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else
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{
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index_t left = 0;
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index_t right = gemms_count;
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group_id = index_t((left + right) / 2);
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while((!(block_args_id >= gemm_kernel_args[group_id].BlockStart_ &&
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block_args_id < gemm_kernel_args[group_id].BlockEnd_)) &&
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left <= right)
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{
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right = group_id;
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if(block_args_id < gemm_kernel_args[group_id].BlockStart_)
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{
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right = group_id;
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}
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else
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{
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left = group_id;
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}
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group_id = index_t((left + right) / 2);
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}
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else
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{
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left = group_id;
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}
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group_id = index_t((left + right) / 2);
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}
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if constexpr(HasMainKBlockLoopInAllGemm || NoMainKBlockLoopInAllGemm)
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@@ -173,7 +182,8 @@ __launch_bounds__(GridwiseGemm::MaxBlockSize, CK_MIN_BLOCK_PER_CU)
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gemm_kernel_args[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
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gemm_kernel_args[group_id].block_2_ctile_map_,
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KBatch,
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k_idx);
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k_idx,
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gemm_kernel_args[group_id].e_grid_desc_m_n_);
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}
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else
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{
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@@ -194,7 +204,8 @@ __launch_bounds__(GridwiseGemm::MaxBlockSize, CK_MIN_BLOCK_PER_CU)
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gemm_kernel_args[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
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gemm_kernel_args[group_id].block_2_ctile_map_,
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KBatch,
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k_idx);
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k_idx,
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gemm_kernel_args[group_id].e_grid_desc_m_n_);
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}
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else
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{
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@@ -213,7 +224,8 @@ __launch_bounds__(GridwiseGemm::MaxBlockSize, CK_MIN_BLOCK_PER_CU)
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gemm_kernel_args[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
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gemm_kernel_args[group_id].block_2_ctile_map_,
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KBatch,
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k_idx);
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k_idx,
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gemm_kernel_args[group_id].e_grid_desc_m_n_);
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}
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}
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}
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@@ -510,6 +522,97 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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GridwiseGemmCTransposeBase<decltype(WarpTileConfig32)>,
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GridwiseGemm32>;
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// Non-grouped GridwiseGemm for single-group specialization.
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// Uses simpler epilogue and address computation, matching the non-grouped kernel.
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// Requires AK1 == BK1 (true for all backward data convolution instances).
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template <index_t NXdlPerWave_>
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using NonGroupedGridwiseGemmBase =
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GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
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ABDataType,
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AccDataType,
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EDataType,
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InMemoryDataOperationEnum::Set,
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AElementwiseOp,
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BElementwiseOp,
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CDEElementwiseOp,
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MPerBlock,
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NPerBlock,
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KPerBlock / AK1,
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MPerXDL,
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NPerXDL,
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AK1,
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MXdlPerWave,
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NXdlPerWave_,
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ABlockTransferThreadClusterLengths_AK0_M_AK1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_AK1,
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false,
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ABlockLdsExtraM,
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BBlockTransferThreadClusterLengths_BK0_N_BK1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_BK1,
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false,
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BBlockLdsExtraN,
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Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
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7,
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1>;
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using NonGroupedGridwiseGemm64 = NonGroupedGridwiseGemmBase<math::max(NXdlPerWave64, 1)>;
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// Flat descriptor type aliases for group_count=1 fast path.
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// Derived from the _Packed() methods in ConvToGemmBwdDataTransform.
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// Uses a canonical NHWGK/NHWGC layout for type derivation (the _Packed() methods
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// have enable_if on layout, but the resulting descriptor types are layout-independent).
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template <typename Transform, bool Is2D>
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struct FlatDescTypes
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{
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using ADesc = int;
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using BDesc = int;
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using CDesc = int;
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};
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template <typename Transform>
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struct FlatDescTypes<Transform, true>
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{
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using CanonicalTransform = TransformConvBwdDataToGemm_v1<2,
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ConvBackwardDataSpecialization,
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AK1,
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BK1,
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MPerBlock,
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NPerBlock,
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KPerBlock,
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DoPadGemmM,
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DoPadGemmN,
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tensor_layout::convolution::NHWGK,
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tensor_layout::convolution::GKYXC,
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tensor_layout::convolution::NHWGC,
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true,
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ABDataType,
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EDataType,
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1,
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index_t,
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false>;
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using ADesc = remove_cvref_t<
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decltype(std::declval<CanonicalTransform>().MakeADescriptor_AK0_M_AK1_Packed())>;
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using BDesc = remove_cvref_t<
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decltype(std::declval<CanonicalTransform>().MakeBDescriptor_BK0_N_BK1_Packed())>;
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using CDesc = remove_cvref_t<
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decltype(std::declval<CanonicalTransform>().MakeCDescriptor_M_N_Packed())>;
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};
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using FlatAGridDesc_K0_M_K1 =
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typename FlatDescTypes<ConvToGemmBwdDataTransform, NDimSpatial == 2>::ADesc;
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using FlatBGridDesc_K0_N_K1 =
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typename FlatDescTypes<ConvToGemmBwdDataTransform, NDimSpatial == 2>::BDesc;
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using FlatCGridDesc_M_N =
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typename FlatDescTypes<ConvToGemmBwdDataTransform, NDimSpatial == 2>::CDesc;
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template <typename EGridDesc_M_N>
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static auto
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MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N e_grid_desc_m_n)
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@@ -565,6 +668,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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ds_grid_desc_mblock_mperblock_nblock_nperblock,
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EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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EGridDesc_M_N e_grid_desc_m_n,
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GroupedGemmBlock2ETileMap block_2_ctile_map,
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index_t BlockStart,
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index_t BlockEnd,
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@@ -578,6 +682,8 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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e_grid_desc_mblock_mperblock_nblock_nperblock_(
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e_grid_desc_mblock_mperblock_nblock_nperblock),
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e_grid_desc_m_n_(e_grid_desc_m_n),
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// block-to-e-tile map
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block_2_ctile_map_(block_2_ctile_map),
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BlockStart_(BlockStart),
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@@ -592,6 +698,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
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ds_grid_desc_mblock_mperblock_nblock_nperblock_;
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EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
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EGridDesc_M_N e_grid_desc_m_n_;
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// block-to-e-tile map
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GroupedGemmBlock2ETileMap block_2_ctile_map_;
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@@ -724,6 +831,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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b_g_k_c_xs_lengths_{b_g_k_c_xs_lengths},
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e_g_n_c_wis_lengths_{e_g_n_c_wis_lengths},
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conv_filter_strides_{conv_filter_strides},
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conv_filter_dilations_{conv_filter_dilations},
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input_left_pads_{input_left_pads},
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input_right_pads_{input_right_pads},
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k_batch_{split_k}
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@@ -970,6 +1078,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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ds_grid_desc_m_n),
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MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
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e_grid_desc_m_n),
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e_grid_desc_m_n,
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block_2_etile_map,
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BlockStart,
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BlockEnd,
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@@ -980,6 +1089,22 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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gemms_grid_size_.push_back(grid_size);
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grid_size = 0;
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}
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// Build packed descriptors for group_count=1 fast path
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if constexpr(NDimSpatial == 2 && !CTranspose)
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{
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// K must be >= AK1 to ensure K0 = K/AK1 >= 1; otherwise
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// the flat descriptor would have K0=0 which is invalid.
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if(num_group_ == 1 && a_g_n_k_wos_lengths[2] >= AK1)
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{
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flat_a_container_.push_back(
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conv_to_gemm_transform_.MakeADescriptor_AK0_M_AK1_Packed());
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flat_b_container_.push_back(
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conv_to_gemm_transform_.MakeBDescriptor_BK0_N_BK1_Packed());
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flat_c_container_.push_back(
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conv_to_gemm_transform_.MakeCDescriptor_M_N_Packed());
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}
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}
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}
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}
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}
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@@ -1145,9 +1270,15 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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std::array<index_t, NDimSpatial + 3> b_g_k_c_xs_lengths_;
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std::array<index_t, NDimSpatial + 3> e_g_n_c_wis_lengths_;
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std::array<index_t, NDimSpatial> conv_filter_strides_;
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std::array<index_t, NDimSpatial> conv_filter_dilations_;
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std::array<index_t, NDimSpatial> input_left_pads_;
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std::array<index_t, NDimSpatial> input_right_pads_;
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// Flat descriptors for group_count=1 fast path
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std::vector<FlatAGridDesc_K0_M_K1> flat_a_container_;
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std::vector<FlatBGridDesc_K0_N_K1> flat_b_container_;
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std::vector<FlatCGridDesc_M_N> flat_c_container_;
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const index_t k_batch_;
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index_t num_workgroups_per_Conv_N_;
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std::vector<index_t> gemms_grid_size_;
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@@ -1222,6 +1353,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
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no_loop_in_all_gemm &= !gemm_kernel_args[i].HasMainKBlockLoop_;
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}
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// Original multi-group kernel launch
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auto launch_kernel = [&](auto has_main_k_block_loop, auto no_main_k_block_loop) {
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constexpr bool has_main_loop = has_main_k_block_loop.value;
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constexpr bool no_main_loop = no_main_k_block_loop.value;
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@@ -1352,20 +1484,95 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
|
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}
|
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}
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};
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if(has_loop_in_all_gemm)
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// Dispatch: use flat-descriptor path (non-grouped GEMM) for G=1,
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// NDim=2. The non-grouped kernel uses packed (flat) descriptors
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// with fewer transform layers, avoiding the grouped GEMM overhead.
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// The NoShuffle epilogue used by this path supports only unary
|
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// element-wise ops (e.g. PassThrough). When D tensors are present
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// the cde_element_op is multi-input (e.g. AddRelu(y, x0, x1)),
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// which is incompatible with the unary VGPR->global writer; fall
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// back to the regular CShuffle path in that case.
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bool used_flat_desc = false;
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if constexpr(NDimSpatial == 2 && !CTranspose && NumDTensor == 0)
|
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{
|
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ave_time += launch_kernel(integral_constant<bool, true>{},
|
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integral_constant<bool, false>{});
|
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if(arg.num_group_ == 1 && arg.k_batch_ == 1 && !arg.flat_a_container_.empty())
|
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{
|
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used_flat_desc = true;
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const index_t flat_idx = gemm_set_id;
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const auto& flat_a = arg.flat_a_container_[flat_idx];
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const auto& flat_b = arg.flat_b_container_[flat_idx];
|
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const auto& flat_c = arg.flat_c_container_[flat_idx];
|
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const index_t padded_K0 = flat_a.GetLength(I0);
|
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const bool flat_desc_has_main_loop =
|
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NonGroupedGridwiseGemm64::CalculateHasMainKBlockLoop(padded_K0 * AK1);
|
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const index_t flat_grid_size =
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NonGroupedGridwiseGemm64::Block2CTileMap::CalculateGridSize(
|
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flat_c.GetLength(I0), flat_c.GetLength(I1));
|
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if(flat_desc_has_main_loop)
|
||||
{
|
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const auto kernel = kernel_gemm_xdlops_v2r3<NonGroupedGridwiseGemm64,
|
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ABDataType,
|
||||
EDataType,
|
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FlatAGridDesc_K0_M_K1,
|
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FlatBGridDesc_K0_N_K1,
|
||||
FlatCGridDesc_M_N,
|
||||
true>;
|
||||
ave_time += launch_and_time_kernel_with_preprocess(stream_config,
|
||||
clear_workspace,
|
||||
kernel,
|
||||
dim3(flat_grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
p_a_grid,
|
||||
p_b_grid,
|
||||
p_e_grid,
|
||||
flat_a,
|
||||
flat_b,
|
||||
flat_c);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r3<NonGroupedGridwiseGemm64,
|
||||
ABDataType,
|
||||
EDataType,
|
||||
FlatAGridDesc_K0_M_K1,
|
||||
FlatBGridDesc_K0_N_K1,
|
||||
FlatCGridDesc_M_N,
|
||||
false>;
|
||||
ave_time += launch_and_time_kernel_with_preprocess(stream_config,
|
||||
clear_workspace,
|
||||
kernel,
|
||||
dim3(flat_grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
p_a_grid,
|
||||
p_b_grid,
|
||||
p_e_grid,
|
||||
flat_a,
|
||||
flat_b,
|
||||
flat_c);
|
||||
}
|
||||
}
|
||||
}
|
||||
else if(no_loop_in_all_gemm)
|
||||
|
||||
if(!used_flat_desc)
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<bool, false>{});
|
||||
if(has_loop_in_all_gemm)
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, true>{},
|
||||
integral_constant<bool, false>{});
|
||||
}
|
||||
else if(no_loop_in_all_gemm)
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<bool, false>{});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1581,6 +1788,17 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
|
||||
{
|
||||
return false;
|
||||
}
|
||||
// This entire device template instantiates XDL (MFMA) kernels, which are
|
||||
// CDNA-only. The shared is_xdl_wmma_supported() helper above can return
|
||||
// true for FP16/BF16 with 16x16 on RDNA (gfx11/gfx12) because it is
|
||||
// also used by WMMA device templates. Reject all instances of this XDL
|
||||
// template on RDNA to avoid launching MFMA kernels on hardware that
|
||||
// does not implement those intrinsics. The corresponding WMMA path
|
||||
// lives in device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle.hpp.
|
||||
if(ck::is_gfx11_supported() || ck::is_gfx12_supported())
|
||||
{
|
||||
return false;
|
||||
}
|
||||
if(!is_bf16_atomic_supported() && std::is_same_v<EDataType, ck::bhalf_t> &&
|
||||
arg.k_batch_ > 1)
|
||||
{
|
||||
|
||||
@@ -704,7 +704,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename Block2ETileMap>
|
||||
typename Block2ETileMap,
|
||||
typename EGridDesc_M_N_Direct = Tuple<>>
|
||||
__device__ static void Run(const ADataType* __restrict__ p_a_grid,
|
||||
const BDataType* __restrict__ p_b_grid,
|
||||
DsGridPointer p_ds_grid,
|
||||
@@ -720,8 +721,9 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
|
||||
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const Block2ETileMap& block_2_etile_map,
|
||||
const index_t k_batch = 1,
|
||||
const index_t k_idx = 0)
|
||||
const index_t k_batch = 1,
|
||||
const index_t k_idx = 0,
|
||||
const EGridDesc_M_N_Direct& e_grid_desc_m_n_direct = {})
|
||||
{
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
@@ -910,18 +912,61 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
|
||||
c_thread_buf,
|
||||
num_k_block_main_loop);
|
||||
|
||||
// Shuffle C and write out.
|
||||
Base::template RunMultiDEpilogue<EGlobalMemoryDataOperation, false, false, true>(
|
||||
blockwise_gemm,
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_thread_buf,
|
||||
block_work_idx[I0],
|
||||
block_work_idx[I1],
|
||||
p_shared,
|
||||
p_ds_grid,
|
||||
p_e_grid,
|
||||
cde_element_op);
|
||||
// Use RunEpilogueNoShuffle from gridwise_common (the base class) for
|
||||
// direct VGPR-to-global output when conditions are met:
|
||||
// (1) no D tensors, (2) scalar-per-vector is 1, (3) PassThrough element-wise op.
|
||||
if constexpr(NumDTensor == 0 && CDEShuffleBlockTransferScalarPerVector_NPerBlock == 1 &&
|
||||
is_same_v<CDEElementwiseOperation,
|
||||
tensor_operation::element_wise::PassThrough>)
|
||||
{
|
||||
const auto e_grid_desc_m_n = [&]() {
|
||||
if constexpr(!is_same_v<EGridDesc_M_N_Direct, Tuple<>>)
|
||||
{
|
||||
return e_grid_desc_m_n_direct;
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(make_merge_transform(make_tuple(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
|
||||
Number<MPerBlock>{})),
|
||||
make_merge_transform(make_tuple(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2),
|
||||
Number<NPerBlock>{}))),
|
||||
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(e_grid_desc_m_n);
|
||||
|
||||
Base::template RunEpilogueNoShuffle<EGlobalMemoryDataOperation,
|
||||
false,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7>(blockwise_gemm,
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_thread_buf,
|
||||
block_work_idx[I0],
|
||||
block_work_idx[I1],
|
||||
p_e_grid,
|
||||
cde_element_op);
|
||||
}
|
||||
else
|
||||
{
|
||||
Base::template RunMultiDEpilogue<EGlobalMemoryDataOperation, false, false, true>(
|
||||
blockwise_gemm,
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_thread_buf,
|
||||
block_work_idx[I0],
|
||||
block_work_idx[I1],
|
||||
p_shared,
|
||||
p_ds_grid,
|
||||
p_e_grid,
|
||||
cde_element_op);
|
||||
}
|
||||
}
|
||||
|
||||
template <bool HasMainKBlockLoop,
|
||||
|
||||
@@ -1505,6 +1505,298 @@ struct TransformConvBwdDataToGemm_v1
|
||||
}
|
||||
}
|
||||
|
||||
// Packed descriptor variants for the group_count=1 fast path.
|
||||
// These build descriptors from contiguous (packed) base tensors instead of strided ones,
|
||||
// producing fewer transform layers and matching the non-grouped v2r3 gridwise GEMM interface.
|
||||
// Only implemented for NDimSpatial == 2.
|
||||
|
||||
template <typename ALayout_ = ALayout,
|
||||
typename std::enable_if<NDimSpatial == 2 &&
|
||||
(is_same_v<ALayout_, tensor_layout::convolution::GNHWK> ||
|
||||
is_same_v<ALayout_, tensor_layout::convolution::NHWGK>),
|
||||
bool>::type = false>
|
||||
__host__ __device__ auto MakeADescriptor_AK0_M_AK1_Packed() const
|
||||
{
|
||||
const auto K0 = K_ / AK1;
|
||||
|
||||
const auto out_n_ho_wo_k_grid_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(N_, Ho_, Wo_, K_));
|
||||
|
||||
if constexpr(ConvBwdDataSpecialization ==
|
||||
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
|
||||
Filter1x1Stride1Pad0)
|
||||
{
|
||||
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
make_naive_tensor_descriptor_packed(make_tuple(N_ * Ho_ * Wo_, K_)),
|
||||
make_tuple(make_pass_through_transform(N_ * Ho_ * Wo_),
|
||||
make_unmerge_transform(make_tuple(K0, Number<AK1>{}))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
out_gemmk0_gemmm_gemmk1_grid_desc,
|
||||
make_tuple(Number<GemmKPerBlock / AK1>{}, Number<GemmMPerBlock>{}, Number<AK1>{}),
|
||||
Sequence<true, DoPadGemmM, false>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto YDotSlice = math::integer_divide_ceil(Y_ - IdxYTilde_, YTilde_);
|
||||
const auto XDotSlice = math::integer_divide_ceil(X_ - IdxXTilde_, XTilde_);
|
||||
|
||||
const auto IHTildeSliceBegin = math::integer_divide_floor(
|
||||
math::max(I0, InLeftPadH_ - ConvDilationH_ * (YTilde_ - I1)), ConvStrideH_);
|
||||
const auto IWTildeSliceBegin = math::integer_divide_floor(
|
||||
math::max(I0, InLeftPadW_ - ConvDilationW_ * (XTilde_ - I1)), ConvStrideW_);
|
||||
const auto IHTildeSliceEnd = math::min(
|
||||
HTilde_, math::integer_divide_ceil(InLeftPadH_ + Hi_ - I1, ConvStrideH_) + I1);
|
||||
const auto IWTildeSliceEnd = math::min(
|
||||
WTilde_, math::integer_divide_ceil(InLeftPadW_ + Wi_ - I1, ConvStrideW_) + I1);
|
||||
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
|
||||
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
|
||||
|
||||
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
|
||||
out_n_ho_wo_k_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_pad_transform(Ho_, I0, I0),
|
||||
make_pad_transform(Wo_, I0, I0),
|
||||
make_pass_through_transform(K_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
|
||||
|
||||
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
|
||||
out_n_hop_wop_k_grid_desc,
|
||||
make_tuple(
|
||||
make_pass_through_transform(N_),
|
||||
make_embed_transform(make_tuple(YDot_, HTilde_),
|
||||
make_tuple(-ConvDilationH_ / GcdStrideDilationH_, I1)),
|
||||
make_embed_transform(make_tuple(XDot_, WTilde_),
|
||||
make_tuple(-ConvDilationW_ / GcdStrideDilationW_, I1)),
|
||||
make_pass_through_transform(K_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
|
||||
transform_tensor_descriptor(
|
||||
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_slice_transform(YDot_, I0, YDotSlice),
|
||||
make_slice_transform(HTilde_, IHTildeSliceBegin, HTildeSlice),
|
||||
make_slice_transform(XDot_, I0, XDotSlice),
|
||||
make_slice_transform(WTilde_, IWTildeSliceBegin, WTildeSlice),
|
||||
make_unmerge_transform(make_tuple(K0, Number<AK1>{}))),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{}),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5, 6>{}));
|
||||
|
||||
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
|
||||
make_merge_transform(make_tuple(N_, HTildeSlice, WTildeSlice)),
|
||||
make_pass_through_transform(Number<AK1>{})),
|
||||
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
out_gemmk0_gemmm_gemmk1_grid_desc,
|
||||
make_tuple(Number<GemmKPerBlock / AK1>{}, Number<GemmMPerBlock>{}, Number<AK1>{}),
|
||||
Sequence<true, DoPadGemmM, false>{});
|
||||
}
|
||||
}
|
||||
|
||||
template <typename BLayout_ = BLayout,
|
||||
typename std::enable_if<NDimSpatial == 2 &&
|
||||
(is_same_v<BLayout_, tensor_layout::convolution::GKYXC>),
|
||||
bool>::type = false>
|
||||
__host__ __device__ auto MakeBDescriptor_BK0_N_BK1_Packed() const
|
||||
{
|
||||
const auto K0 = K_ / BK1;
|
||||
|
||||
if constexpr(ConvBwdDataSpecialization ==
|
||||
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
|
||||
Filter1x1Stride1Pad0)
|
||||
{
|
||||
const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
make_naive_tensor_descriptor_packed(make_tuple(K_, C_)),
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, Number<BK1>{})),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
wei_gemmk0_gemmn_gemmk1_grid_desc,
|
||||
make_tuple(Number<GemmKPerBlock / BK1>{}, Number<GemmNPerBlock>{}, Number<BK1>{}),
|
||||
Sequence<true, DoPadGemmN, false>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto wei_k_y_x_c_grid_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(K_, Y_, X_, C_));
|
||||
|
||||
const auto YDotSlice = math::integer_divide_ceil(Y_ - IdxYTilde_, YTilde_);
|
||||
const auto XDotSlice = math::integer_divide_ceil(X_ - IdxXTilde_, XTilde_);
|
||||
|
||||
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
|
||||
wei_k_y_x_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(K_),
|
||||
make_embed_transform(make_tuple(YDot_, YTilde_),
|
||||
make_tuple(ConvStrideH_ / GcdStrideDilationH_, I1)),
|
||||
make_embed_transform(make_tuple(XDot_, XTilde_),
|
||||
make_tuple(ConvStrideW_ / GcdStrideDilationW_, I1)),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc = transform_tensor_descriptor(
|
||||
wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, Number<BK1>{})),
|
||||
make_slice_transform(YDot_, I0, YDotSlice),
|
||||
make_slice_transform(XDot_, I0, XDotSlice),
|
||||
make_freeze_transform(IdxYTilde_),
|
||||
make_freeze_transform(IdxXTilde_),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<3>{},
|
||||
Sequence<2>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{}),
|
||||
make_tuple(Sequence<0, 1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<>{},
|
||||
Sequence<>{},
|
||||
Sequence<4>{}));
|
||||
|
||||
const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
|
||||
make_pass_through_transform(C_),
|
||||
make_pass_through_transform(Number<BK1>{})),
|
||||
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
wei_gemmk0_gemmn_gemmk1_grid_desc,
|
||||
make_tuple(Number<GemmKPerBlock / BK1>{}, Number<GemmNPerBlock>{}, Number<BK1>{}),
|
||||
Sequence<true, DoPadGemmN, false>{});
|
||||
}
|
||||
}
|
||||
|
||||
template <typename CLayout_ = CLayout,
|
||||
typename std::enable_if<
|
||||
NDimSpatial == 2 && (is_same_v<CLayout_, tensor_layout::convolution::GNHWC> ||
|
||||
is_same_v<CLayout_, tensor_layout::convolution::NHWGC> ||
|
||||
is_same_v<CLayout_, tensor_layout::convolution::G_NHW_C>),
|
||||
bool>::type = false>
|
||||
__host__ __device__ auto MakeCDescriptor_M_N_Packed() const
|
||||
{
|
||||
const auto in_n_hi_wi_c_grid_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(N_, Hi_, Wi_, C_));
|
||||
|
||||
if constexpr(ConvBwdDataSpecialization ==
|
||||
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
|
||||
Filter1x1Stride1Pad0)
|
||||
{
|
||||
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hi_wi_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_embed_transform(make_tuple(I1, Ho_), make_tuple(I1, ConvStrideH_)),
|
||||
make_embed_transform(make_tuple(I1, Wo_), make_tuple(I1, ConvStrideW_)),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
in_n_y_ho_x_wo_c_grid_desc,
|
||||
make_tuple(make_freeze_transform(I0),
|
||||
make_freeze_transform(I0),
|
||||
make_merge_transform(make_tuple(N_, Ho_, Wo_)),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
|
||||
make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
in_gemmm_gemmn_grid_desc,
|
||||
make_tuple(Number<GemmMPerBlock>{}, Number<GemmNPerBlock>{}),
|
||||
Sequence<DoPadGemmM, DoPadGemmN>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto IHTildeSliceBegin = math::integer_divide_floor(
|
||||
math::max(I0, InLeftPadH_ - ConvDilationH_ * (YTilde_ - I1)), ConvStrideH_);
|
||||
const auto IWTildeSliceBegin = math::integer_divide_floor(
|
||||
math::max(I0, InLeftPadW_ - ConvDilationW_ * (XTilde_ - I1)), ConvStrideW_);
|
||||
const auto IHTildeSliceEnd = math::min(
|
||||
HTilde_, math::integer_divide_ceil(InLeftPadH_ + Hi_ - I1, ConvStrideH_) + I1);
|
||||
const auto IWTildeSliceEnd = math::min(
|
||||
WTilde_, math::integer_divide_ceil(InLeftPadW_ + Wi_ - I1, ConvStrideW_) + I1);
|
||||
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
|
||||
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
|
||||
|
||||
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hi_wi_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_pad_transform(Hi_, InLeftPadH_, InRightPadH_),
|
||||
make_pad_transform(Wi_, InLeftPadW_, InRightPadW_),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
|
||||
|
||||
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hip_wip_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_embed_transform(make_tuple(YTilde_, HTilde_),
|
||||
make_tuple(ConvDilationH_, ConvStrideH_)),
|
||||
make_embed_transform(make_tuple(XTilde_, WTilde_),
|
||||
make_tuple(ConvDilationW_, ConvStrideW_)),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N_),
|
||||
make_freeze_transform(IdxYTilde_),
|
||||
make_slice_transform(HTilde_, IHTildeSliceBegin, HTildeSlice),
|
||||
make_freeze_transform(IdxXTilde_),
|
||||
make_slice_transform(WTilde_, IWTildeSliceBegin, WTildeSlice),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{}),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<>{},
|
||||
Sequence<1>{},
|
||||
Sequence<>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{}));
|
||||
|
||||
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
in_n_htildeslice_wtildeslice_c_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(N_, HTildeSlice, WTildeSlice)),
|
||||
make_pass_through_transform(C_)),
|
||||
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
return ck::tensor_operation::device::PadTensorDescriptor(
|
||||
in_gemmm_gemmn_grid_desc,
|
||||
make_tuple(Number<GemmMPerBlock>{}, Number<GemmNPerBlock>{}),
|
||||
Sequence<DoPadGemmM, DoPadGemmN>{});
|
||||
}
|
||||
}
|
||||
|
||||
IndexType N_;
|
||||
IndexType Di_, Hi_, Wi_;
|
||||
IndexType Do_, Ho_, Wo_;
|
||||
|
||||
@@ -154,6 +154,39 @@ using device_grouped_conv_bwd_data_xdl_f16_instances =
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_f16_noshuffle_instances =
|
||||
std::tuple<
|
||||
// clang-format off
|
||||
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
|
||||
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
// f16_f16_f32_f16 — noshuffle epilogue (CDEBlockTransferScalarPerVector_NPerBlock = 1)
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
@@ -300,6 +333,40 @@ using device_grouped_conv_bwd_data_xdl_bf16_instances = std::tuple<
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// bf16_bf16_f32_bf16 — noshuffle epilogue (CDEBlockTransferScalarPerVector_NPerBlock = 1)
|
||||
// Same tile shapes as bf16_instances but with ScalarPerVector=1, enabling the no-shuffle fast path
|
||||
// (VGPR → Global direct write, 0 LDS barriers) instead of CShuffle (VGPR → LDS → Global, 8
|
||||
// barriers).
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_bf16_noshuffle_instances = std::tuple<
|
||||
// clang-format off
|
||||
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
|
||||
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// f32_f32_f32_f32
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
@@ -452,6 +519,123 @@ using device_grouped_conv_bwd_data_xdl_f32_instances =
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_f32_noshuffle_instances =
|
||||
std::tuple<
|
||||
// clang-format off
|
||||
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
|
||||
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
// f32_f32_f32_f32 — noshuffle epilogue (CDEBlockTransferScalarPerVector_NPerBlock = 1)
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// bf16 — BBlockTransfer parameters matching the non-grouped DeviceConvNdBwdDataNwcKxcNwk_Xdl
|
||||
// instances. The key difference from bf16_instances: BBlockTransfer uses S<4, BlockSize/4, 1>
|
||||
// thread cluster and S<2, 0, 1> arrange order, which gives full thread utilization for B-matrix
|
||||
// loads. These are optimal when opt3 flat descriptor path is active (G=1, 2D convolutions).
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_bf16_nongrouped_match_instances = std::tuple<
|
||||
// clang-format off
|
||||
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
|
||||
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
|
||||
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// f16 — BBlockTransfer parameters matching the non-grouped DeviceConvNdBwdDataNwcKxcNwk_Xdl
|
||||
// instances.
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_f16_nongrouped_match_instances = std::tuple<
|
||||
// clang-format off
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// f32 — BBlockTransfer parameters matching the non-grouped DeviceConvNdBwdDataNwcKxcNwk_Xdl
|
||||
// instances. F32 uses K1=4, KPerBlock=16, and smaller scalar-per-vector values.
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ConvolutionBackwardDataSpecialization ConvSpec>
|
||||
using device_grouped_conv_bwd_data_xdl_f32_nongrouped_match_instances = std::tuple<
|
||||
// clang-format off
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 32, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 32, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>,
|
||||
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 16, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// f32_f32_f32_f32 tf32
|
||||
template <index_t NDimSpatial,
|
||||
typename ALayout,
|
||||
|
||||
@@ -41,6 +41,42 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_bf16_instances(
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 3. Default — noshuffle epilogue
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_bf16_noshuffle_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 4. Filter1x1Stride1Pad0 — noshuffle epilogue
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_bf16_noshuffle_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 5. Default — nongrouped_match instances
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_bf16_nongrouped_match_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 6. Filter1x1Stride1Pad0 — nongrouped_match instances
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_bf16_nongrouped_match_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
|
||||
@@ -41,6 +41,42 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f16_instances(
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 3. Default — noshuffle epilogue
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_f16_noshuffle_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 4. Filter1x1Stride1Pad0 — noshuffle epilogue
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_f16_noshuffle_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 5. Default — nongrouped_match instances
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_f16_nongrouped_match_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 6. Filter1x1Stride1Pad0 — nongrouped_match instances
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_f16_nongrouped_match_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
|
||||
@@ -41,6 +41,42 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f32_instances(
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 3. Default — noshuffle epilogue
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_f32_noshuffle_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 4. Filter1x1Stride1Pad0 — noshuffle epilogue
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_f32_noshuffle_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
// 5. Default — nongrouped_match instances
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_grouped_conv_bwd_data_xdl_f32_nongrouped_match_instances<2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataDefault>{});
|
||||
// 6. Filter1x1Stride1Pad0 — nongrouped_match instances
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_conv_bwd_data_xdl_f32_nongrouped_match_instances<
|
||||
2,
|
||||
NHWGK,
|
||||
GKYXC,
|
||||
Empty_Tuple,
|
||||
NHWGC,
|
||||
ConvBwdDataFilter1x1Stride1Pad0>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
|
||||
@@ -256,6 +256,11 @@ TYPED_TEST_SUITE(TestGroupedConvndBwdDataFilter1x1Wmma, KernelTypes);
|
||||
|
||||
TYPED_TEST(TestGroupedConvndBwdDataFilter1x1Xdl, SpecializationCheckXdl)
|
||||
{
|
||||
if(ck::is_gfx11_supported() || ck::is_gfx12_supported())
|
||||
{
|
||||
GTEST_SKIP() << "XDL (MFMA) bwd_data instances are not supported on RDNA (gfx11/gfx12)";
|
||||
}
|
||||
|
||||
// Check filter 3,3 instead of 1,1
|
||||
this->conv_param = {2, 2, 4, 192, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
|
||||
bool is_supported = this->template Run<2>();
|
||||
@@ -326,6 +331,10 @@ TYPED_TEST(TestGroupedConvndBwdDataDefaultWmma, VectorLoadCheckWmma)
|
||||
|
||||
TYPED_TEST(TestGroupedConvndBwdDataDefaultXdl, SplitK)
|
||||
{
|
||||
if(ck::is_gfx11_supported() || ck::is_gfx12_supported())
|
||||
{
|
||||
GTEST_SKIP() << "XDL (MFMA) bwd_data instances are not supported on RDNA (gfx11/gfx12)";
|
||||
}
|
||||
if(ck::is_xdl_supported())
|
||||
{
|
||||
// SplitK = 1
|
||||
|
||||
Reference in New Issue
Block a user