mirror of
https://github.com/ROCm/composable_kernel.git
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Add basic support for direct loads from global to LDS (#999)
* Add basic support for direct loads from global to LDS * Clean the code and comments * Add support for fp16 * Add comments * Add check for thread cluster lengths * Align non-direct-load fp16 example * Small fixes * Extend IsSupported to check for supported GPU gens * Build examples only on the supported HW * Do not throw when instance not supported in 04 example * Review: Apply review suggestions * Review: small fix * Review: small fix
This commit is contained in:
Bartlomiej Wroblewski
committed by
GitHub
GitHub
parent
e8cddfdc3b
commit
627054b941
@@ -0,0 +1,991 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/multi_index_transform_helper.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
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#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_direct_load.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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namespace ck {
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template <typename GridwiseGemm,
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typename ADataType,
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typename BDataType,
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typename DsPointer,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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typename AGridDesc_AK0_M_AK1,
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typename BGridDesc_BK0_N_BK1,
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typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename Block2ETileMap,
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bool HasMainKBlockLoop>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
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#endif
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kernel_gemm_multiple_d_xdl_cshuffle_lds_direct_load(
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const ADataType* __restrict__ p_a_grid,
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const BDataType* __restrict__ p_b_grid,
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DsPointer p_ds_grid,
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EDataType* __restrict__ p_e_grid,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const CDEElementwiseOperation cde_element_op,
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const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
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const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
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const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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ds_grid_desc_mblock_mperblock_nblock_nperblock,
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const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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const Block2ETileMap block_2_etile_map)
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{
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#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx90a__) || defined(__gfx940__) || \
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defined(__gfx941__) || defined(__gfx942__))
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
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p_b_grid,
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p_ds_grid,
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p_e_grid,
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p_shared,
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a_element_op,
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b_element_op,
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cde_element_op,
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a_grid_desc_ak0_m_ak1,
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b_grid_desc_bk0_n_bk1,
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ds_grid_desc_mblock_mperblock_nblock_nperblock,
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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block_2_etile_map);
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#else
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ignore = p_a_grid;
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ignore = p_b_grid;
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ignore = p_ds_grid;
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ignore = p_e_grid;
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ignore = a_element_op;
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ignore = b_element_op;
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ignore = cde_element_op;
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ignore = a_grid_desc_ak0_m_ak1;
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ignore = b_grid_desc_bk0_n_bk1;
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ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
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ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
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ignore = block_2_etile_map;
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#endif
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}
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// GEMM:
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// input : A[M, K]
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// input : B[N, K]
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// input : D0[M, N], D1[M, N], ...
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// output : E[M, N]
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// C = a_op(A) * b_op(B)
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// E = cde_op(C, D0, D1, ...)
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// Assume:
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// D0, D1, ... and E have the same layout
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template <typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename ELayout,
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typename ADataType,
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typename BDataType,
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typename AComputeDataType_,
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typename AccDataType,
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typename CShuffleDataType,
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typename DsDataType,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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InMemoryDataOperationEnum EGlobalMemoryDataOperation,
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tensor_operation::device::GemmSpecialization GemmSpec,
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index_t NumGemmKPrefetchStage,
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index_t BlockSize,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1Value,
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index_t BK1Value,
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index_t MPerXdl,
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index_t NPerXdl,
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index_t MXdlPerWave,
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index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferScalarPerVector,
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index_t ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferScalarPerVector,
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index_t BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
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LoopScheduler LoopSched,
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PipelineVersion PipelineVer = PipelineVersion::v4,
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typename BComputeDataType = AComputeDataType_>
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struct GridwiseGemmMultipleD_Xdl_CShuffle_LdsDirectLoad
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{
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static constexpr index_t NumDTensor = DsDataType::Size();
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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static constexpr auto I5 = Number<5>{};
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static constexpr auto I6 = Number<6>{};
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static constexpr auto I7 = Number<7>{};
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static constexpr auto AK1 = Number<AK1Value>{};
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static constexpr auto BK1 = Number<BK1Value>{};
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static constexpr auto AK0PerBlock = Number<KPerBlock / AK1Value>{};
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static constexpr auto BK0PerBlock = Number<KPerBlock / BK1Value>{};
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using ThisThreadBlock = ThisThreadBlock<BlockSize>;
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using GridwiseGemmPipe = remove_cvref_t<
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decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
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#if CK_WORKAROUND_DENORM_FIX
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using AComputeDataType =
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conditional_t<is_same_v<AComputeDataType_, ck::half_t>, ck::bhalf_t, AComputeDataType_>;
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#else
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using AComputeDataType = AComputeDataType_;
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#endif
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__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
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{
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// A matrix in LDS memory, destination of blockwise copy.
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return make_naive_tensor_descriptor(
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make_tuple(AK0PerBlock, Number<MPerBlock>{}, AK1),
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make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
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}
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__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
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{
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// B matrix in LDS memory, destination of blockwise copy.
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return make_naive_tensor_descriptor(
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make_tuple(BK0PerBlock, Number<NPerBlock>{}, BK1),
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make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
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}
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__host__ __device__ static constexpr auto
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GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
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{
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constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
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constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
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constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
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make_naive_tensor_descriptor_packed(
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make_tuple(I1,
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Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
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I1,
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Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
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return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
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}
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// ck::Tuple<const D0DataType*, const D1DataType*, ...>
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static constexpr auto MakeDsGridPointer()
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{
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return generate_tuple(
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[&](auto i) {
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using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
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return static_cast<const DDataType*>(nullptr);
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},
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Number<NumDTensor>{});
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}
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__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
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{
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// LDS allocation for A and B: be careful of alignment.
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constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
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constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
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constexpr auto max_lds_align = math::lcm(AK1, BK1);
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constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
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a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
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b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
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// LDS allocation for C shuffle.
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constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
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GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
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constexpr auto c_block_size =
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c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
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return math::max(a_block_space_size_aligned * sizeof(AComputeDataType) +
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b_block_space_size_aligned * sizeof(BComputeDataType),
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c_block_size * sizeof(CShuffleDataType));
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}
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__host__ __device__ static auto
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MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
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{
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constexpr auto matrix_padder =
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ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
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MPerBlock, NPerBlock, KPerBlock};
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const auto a_grid_desc_mraw_kraw = [&]() {
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if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(StrideA, I1));
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}
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else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(I1, StrideA));
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}
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}();
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return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
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}
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__host__ __device__ static auto
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MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
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{
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constexpr auto matrix_padder =
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ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
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MPerBlock, NPerBlock, KPerBlock};
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const auto b_grid_desc_nraw_kraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(I1, StrideB));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(StrideB, I1));
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}
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}();
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return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
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}
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__host__ __device__ static auto
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MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
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{
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constexpr auto matrix_padder =
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ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
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MPerBlock, NPerBlock, KPerBlock};
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const auto e_grid_desc_mraw_nraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, ELayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(StrideE, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(I1, StrideE));
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}
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}();
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return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
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}
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__host__ __device__ static auto
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MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
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const std::array<index_t, NumDTensor>& NRaws,
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const std::array<index_t, NumDTensor>& DsStride)
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{
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return generate_tuple(
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[&](auto i) { return MakeEGridDescriptor_M_N(MRaws[i], NRaws[i], DsStride[i]); },
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Number<NumDTensor>{});
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}
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using AGridDesc_M_K = decltype(MakeAGridDescriptor_M_K(1, 1, 1));
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using BGridDesc_N_K = decltype(MakeBGridDescriptor_N_K(1, 1, 1));
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using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>;
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using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(1, 1, 1));
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// A desc for source in blockwise copy.
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__host__ __device__ static constexpr auto
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MakeDefaultAGridDescriptor_AK0_M_AK1(const AGridDesc_M_K& a_grid_desc_m_k)
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{
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const auto M = a_grid_desc_m_k.GetLength(I0);
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const auto K = a_grid_desc_m_k.GetLength(I1);
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const auto AK0 = K / AK1;
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return transform_tensor_descriptor(a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
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make_pass_through_transform(M)),
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make_tuple(Sequence<1>{}, Sequence<0>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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// B desc for source in blockwise copy.
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__host__ __device__ static constexpr auto
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MakeDefaultBGridDescriptor_BK0_N_BK1(const BGridDesc_N_K& b_grid_desc_n_k)
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{
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const auto N = b_grid_desc_n_k.GetLength(I0);
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const auto K = b_grid_desc_n_k.GetLength(I1);
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const auto BK0 = K / BK1;
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return transform_tensor_descriptor(b_grid_desc_n_k,
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make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
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make_pass_through_transform(N)),
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make_tuple(Sequence<1>{}, Sequence<0>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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// E desc for destination in blockwise copy.
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__host__ __device__ static constexpr auto
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MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
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{
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const auto M = e_grid_desc_m_n.GetLength(I0);
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const auto N = e_grid_desc_m_n.GetLength(I1);
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const auto MBlock = M / MPerBlock;
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const auto NBlock = N / NPerBlock;
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const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
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e_grid_desc_m_n,
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make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
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make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
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return e_grid_desc_mblock_mperblock_nblock_nperblock;
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}
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// Ds desc for source in blockwise copy.
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__host__ __device__ static constexpr auto
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MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const DsGridDesc_M_N& ds_grid_desc_m_n)
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{
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return generate_tuple(
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[&](auto i) {
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return MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n[i]);
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},
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Number<NumDTensor>{});
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}
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__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
|
||||
e_grid_desc_m_n);
|
||||
}
|
||||
|
||||
using AGridDesc_AK0_M_AK1 =
|
||||
remove_cvref_t<decltype(MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
|
||||
using BGridDesc_BK0_N_BK1 =
|
||||
remove_cvref_t<decltype(MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
|
||||
using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
|
||||
remove_cvref_t<decltype(MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
DsGridDesc_M_N{}))>;
|
||||
using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
|
||||
remove_cvref_t<decltype(MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
EGridDesc_M_N{}))>;
|
||||
|
||||
using Block2ETileMap = remove_cvref_t<decltype(MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
|
||||
|
||||
__host__ __device__ static constexpr bool CheckValidity(const AGridDesc_M_K& a_grid_desc_m_k,
|
||||
const BGridDesc_N_K& b_grid_desc_n_k,
|
||||
const DsGridDesc_M_N& ds_grid_desc_m_n,
|
||||
const EGridDesc_M_N& e_grid_desc_m_n,
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
|
||||
(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
|
||||
"Invalid tuning param!");
|
||||
static_assert(KPerBlock % AK1Value == 0 && KPerBlock % BK1Value == 0,
|
||||
"KPerBlock must be divisible by AK1Value and BK1Value!");
|
||||
|
||||
static_assert(
|
||||
std::is_same_v<AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough> &&
|
||||
std::is_same_v<BElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough>,
|
||||
"Direct load transfers do not support elementwise operations other than passthrough.");
|
||||
|
||||
const auto M = a_grid_desc_m_k.GetLength(I0);
|
||||
const auto N = b_grid_desc_n_k.GetLength(I0);
|
||||
const auto AK = a_grid_desc_m_k.GetLength(I1);
|
||||
const auto BK = b_grid_desc_n_k.GetLength(I1);
|
||||
|
||||
// Check the consistency of descriptors.
|
||||
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1) && AK == BK))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
bool valid = true;
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
valid = valid && (M == ds_grid_desc_m_n[i].GetLength(I0) &&
|
||||
N == ds_grid_desc_m_n[i].GetLength(I1));
|
||||
});
|
||||
|
||||
if(!valid)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Check the tile size.
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && AK % KPerBlock == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Check gridwise gemm pipeline.
|
||||
const auto num_k_loop = AK / KPerBlock;
|
||||
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Check block-to-E-tile.
|
||||
if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Check tensor size: cannot exceed 2GB.
|
||||
constexpr long_index_t TwoGB = (long_index_t{1} << 31);
|
||||
|
||||
if(!(a_grid_desc_m_k.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
|
||||
b_grid_desc_n_k.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB &&
|
||||
e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
|
||||
{
|
||||
const index_t num_loop = K / KPerBlock;
|
||||
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
|
||||
}
|
||||
|
||||
using DsGridPointer = decltype(MakeDsGridPointer());
|
||||
|
||||
__device__ __host__ static constexpr auto GetMPerBlock() { return MPerBlock; }
|
||||
|
||||
template <bool HasMainKBlockLoop,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>
|
||||
__device__ static void Run(const ADataType* __restrict__ p_a_grid,
|
||||
const BDataType* __restrict__ p_b_grid,
|
||||
DsGridPointer p_ds_grid,
|
||||
EDataType* __restrict__ p_e_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const CDEElementwiseOperation& cde_element_op,
|
||||
const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
// Elementwise operations are not supported for A and B, arguments left only for the API
|
||||
// consistency.
|
||||
(void)a_element_op;
|
||||
(void)b_element_op;
|
||||
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
const auto ds_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_ds_grid[i],
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
// Divide block work by [M, N].
|
||||
const auto block_work_idx =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
if(!block_2_etile_map.ValidCTileIndex(
|
||||
block_work_idx,
|
||||
make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// This forces m/n_block_data_idx_on_grid into SGPR.
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
|
||||
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
// A matrix in LDS memory, destination of blockwise copy.
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
|
||||
// B matrix in LDS memory, destination of blockwise copy.
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
auto a_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_DirectLoad<ThisThreadBlock,
|
||||
Sequence<AK0PerBlock, MPerBlock, AK1>,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ADataType,
|
||||
AComputeDataType,
|
||||
decltype(a_grid_desc_ak0_m_ak1),
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
ABlockTransferSrcVectorDim,
|
||||
2,
|
||||
ABlockTransferScalarPerVector>(
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
make_multi_index(0, m_block_data_idx_on_grid, 0),
|
||||
a_block_desc_ak0_m_ak1,
|
||||
make_multi_index(0, 0, 0));
|
||||
|
||||
auto b_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_DirectLoad<ThisThreadBlock,
|
||||
Sequence<BK0PerBlock, NPerBlock, BK1>,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BDataType,
|
||||
BComputeDataType,
|
||||
decltype(b_grid_desc_bk0_n_bk1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
BBlockTransferSrcVectorDim,
|
||||
2,
|
||||
BBlockTransferScalarPerVector>(
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
make_multi_index(0, n_block_data_idx_on_grid, 0),
|
||||
b_block_desc_bk0_n_bk1,
|
||||
make_multi_index(0, 0, 0));
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[K0PerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
constexpr index_t KPack = math::max(
|
||||
math::lcm(AK1, BK1),
|
||||
MfmaSelector<AComputeDataType, MPerXdl, NPerXdl, BComputeDataType>::selected_mfma
|
||||
.k_per_blk);
|
||||
|
||||
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
|
||||
BlockSize,
|
||||
AComputeDataType,
|
||||
BComputeDataType,
|
||||
AccDataType,
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
MPerXdl,
|
||||
NPerXdl,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
KPack,
|
||||
LoopSched>();
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment.
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<AComputeDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<BComputeDataType*>(p_shared) + a_block_space_size_aligned,
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
|
||||
|
||||
const auto gridwise_gemm_pipeline =
|
||||
GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>();
|
||||
|
||||
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
|
||||
(a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
|
||||
KPerBlock);
|
||||
|
||||
gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
a_blockwise_copy,
|
||||
a_grid_buf,
|
||||
a_block_buf,
|
||||
a_block_slice_copy_step,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
b_blockwise_copy,
|
||||
b_grid_buf,
|
||||
b_block_buf,
|
||||
b_block_slice_copy_step,
|
||||
blockwise_gemm,
|
||||
c_thread_buf,
|
||||
num_k_block_main_loop);
|
||||
|
||||
// Shuffle C and write out.
|
||||
{
|
||||
static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
|
||||
NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
// c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
|
||||
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
|
||||
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
|
||||
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
|
||||
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
|
||||
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
|
||||
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
|
||||
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
|
||||
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<CShuffleDataType*>(p_shared),
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
|
||||
M1, // M1 = MWave
|
||||
M2, // M2 * M3 * M4 = MPerXdl
|
||||
M3,
|
||||
M4)),
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
|
||||
N1, // N1 = NWave
|
||||
N2))), // N2 = NPerXdl
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(
|
||||
Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
|
||||
|
||||
// Calculate the origin of thread output tensor on global memory.
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
|
||||
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_block_idx =
|
||||
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_block));
|
||||
|
||||
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_block_idx =
|
||||
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_block));
|
||||
|
||||
// Shuffle: threadwise copy C from VGPR to LDS.
|
||||
auto c_thread_copy_vgpr_to_lds =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
|
||||
CShuffleDataType,
|
||||
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
I1,
|
||||
I1,
|
||||
M2,
|
||||
I1,
|
||||
M4,
|
||||
I1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(0,
|
||||
0,
|
||||
m_thread_data_on_block_idx[I1],
|
||||
n_thread_data_on_block_idx[I1],
|
||||
m_thread_data_on_block_idx[I2],
|
||||
m_thread_data_on_block_idx[I3],
|
||||
m_thread_data_on_block_idx[I4],
|
||||
n_thread_data_on_block_idx[I2]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
// A tuple of reference to C/Ds tensor descriptors.
|
||||
const auto c_ds_desc_refs = concat_tuple_of_reference(
|
||||
tie(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
|
||||
generate_tie(
|
||||
[&](auto i) -> const auto& // return type should be reference
|
||||
{ return ds_grid_desc_mblock_mperblock_nblock_nperblock[i]; },
|
||||
Number<NumDTensor>{}));
|
||||
|
||||
// A tuple of reference to C/Ds grid buffers.
|
||||
const auto c_ds_buf_refs = concat_tuple_of_reference(
|
||||
tie(c_shuffle_block_buf),
|
||||
generate_tie(
|
||||
[&](auto i) -> const auto& // return type should be reference
|
||||
{ return ds_grid_buf[i]; },
|
||||
Number<NumDTensor>{}));
|
||||
|
||||
// A tuple of starting index of C/Ds blockwise copy.
|
||||
const auto idx_c_ds_block_begin = container_concat(
|
||||
make_tuple(make_multi_index(0, 0, 0, 0)),
|
||||
generate_tuple(
|
||||
[&](auto) {
|
||||
return make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0);
|
||||
},
|
||||
Number<NumDTensor>{}));
|
||||
|
||||
// Blockwise copy C/D/E between LDS and global.
|
||||
auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7<
|
||||
ThisThreadBlock,
|
||||
decltype(container_concat(make_tuple(CShuffleDataType{}), DsDataType{})),
|
||||
Tuple<EDataType>,
|
||||
decltype(c_ds_desc_refs),
|
||||
decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
|
||||
CDEElementwiseOperation,
|
||||
Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
|
||||
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
|
||||
Sequence<0, 1, 2, 3>, // typename DimAccessOrder,
|
||||
3, // index_t VectorDim,
|
||||
CDEShuffleBlockTransferScalarPerVector_NPerBlock,
|
||||
sequence_merge_t<
|
||||
Sequence<true>,
|
||||
uniform_sequence_gen_t<NumDTensor,
|
||||
false>>, // ThreadTransferSrcResetCoordinateAfterRunFlags
|
||||
Sequence<false>> // ThreadTransferDstResetCoordinateAfterRunFlags
|
||||
{c_ds_desc_refs,
|
||||
idx_c_ds_block_begin,
|
||||
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
make_tuple(make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0)),
|
||||
cde_element_op};
|
||||
|
||||
// Space filling curve for threadwise C in VGPR before shuffle.
|
||||
constexpr auto sfc_c_vgpr =
|
||||
SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
1,
|
||||
1,
|
||||
M2,
|
||||
1,
|
||||
M4,
|
||||
1>>{};
|
||||
|
||||
// Space filling curve for shuffled blockwise C/D/E.
|
||||
constexpr auto sfc_cde_block =
|
||||
SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
|
||||
|
||||
constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
|
||||
|
||||
static_assert(num_access == sfc_cde_block.GetNumOfAccess(), "wrong!");
|
||||
|
||||
static_for<0, num_access, 1>{}([&](auto access_id) {
|
||||
// Make sure it's safe to write to LDS.
|
||||
block_sync_lds();
|
||||
|
||||
// Each thread write its data from VGPR to LDS.
|
||||
c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
|
||||
c_thread_buf,
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_shuffle_block_buf);
|
||||
|
||||
// Make sure it's safe to read from LDS.
|
||||
block_sync_lds();
|
||||
|
||||
// Each block copy its data from LDS to global.
|
||||
cde_block_copy_lds_and_global.Run(
|
||||
c_ds_desc_refs,
|
||||
c_ds_buf_refs,
|
||||
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
tie(e_grid_buf));
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
constexpr auto cde_lds_and_global_step =
|
||||
sfc_cde_block.GetForwardStep(access_id);
|
||||
|
||||
// Move on Ds.
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
cde_block_copy_lds_and_global.MoveSrcSliceWindow(
|
||||
c_ds_desc_refs, i + I1, cde_lds_and_global_step);
|
||||
});
|
||||
|
||||
// Move on E.
|
||||
cde_block_copy_lds_and_global.MoveDstSliceWindow(
|
||||
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
I0,
|
||||
cde_lds_and_global_step);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
struct Argument : public tensor_operation::device::BaseArgument
|
||||
{
|
||||
Argument(const void* p_a_grid,
|
||||
const void* p_b_grid,
|
||||
std::array<const void*, NumDTensor> p_ds_grid,
|
||||
void* p_e_grid,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
|
||||
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
|
||||
p_ds_grid_{},
|
||||
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
|
||||
a_grid_desc_m_k_{MakeAGridDescriptor_M_K(MRaw, KRaw, StrideA)},
|
||||
b_grid_desc_n_k_{MakeBGridDescriptor_N_K(KRaw, NRaw, StrideB)},
|
||||
ds_grid_desc_m_n_{},
|
||||
e_grid_desc_m_n_{MakeEGridDescriptor_M_N(MRaw, NRaw, StrideE)},
|
||||
a_grid_desc_ak0_m_ak1_{MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k_)},
|
||||
b_grid_desc_bk0_n_bk1_{MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k_)},
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
block_2_etile_map_{MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op},
|
||||
MRaw_{MRaw},
|
||||
NRaw_{NRaw},
|
||||
KRaw_{KRaw}
|
||||
{
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
|
||||
ds_grid_desc_m_n_(i) = MakeEGridDescriptor_M_N(MRaw, NRaw, StrideDs[i]);
|
||||
});
|
||||
|
||||
if(CheckValidity(a_grid_desc_m_k_,
|
||||
b_grid_desc_n_k_,
|
||||
ds_grid_desc_m_n_,
|
||||
e_grid_desc_m_n_,
|
||||
block_2_etile_map_))
|
||||
{
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n_);
|
||||
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(e_grid_desc_m_n_);
|
||||
}
|
||||
}
|
||||
|
||||
void Print() const
|
||||
{
|
||||
std::cout << "A[M, K]: " << a_grid_desc_m_k_ << std::endl;
|
||||
std::cout << "B[N, K]: " << b_grid_desc_n_k_ << std::endl;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { std::cout << "Ds[M, N]: " << ds_grid_desc_m_n_[i] << std::endl; });
|
||||
std::cout << "E[M, N]: " << e_grid_desc_m_n_ << std::endl;
|
||||
}
|
||||
|
||||
// Pointers
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
DsGridPointer p_ds_grid_;
|
||||
EDataType* p_e_grid_;
|
||||
|
||||
// Tensor descriptors for problem definiton
|
||||
AGridDesc_M_K a_grid_desc_m_k_;
|
||||
BGridDesc_N_K b_grid_desc_n_k_;
|
||||
DsGridDesc_M_N ds_grid_desc_m_n_;
|
||||
EGridDesc_M_N e_grid_desc_m_n_;
|
||||
|
||||
// Tensor descriptors for block/thread-wise copy
|
||||
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
|
||||
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
|
||||
DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
|
||||
// block-to-e-tile map
|
||||
Block2ETileMap block_2_etile_map_;
|
||||
|
||||
// element-wise ops
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
|
||||
// For checking vector load/store
|
||||
index_t MRaw_;
|
||||
index_t NRaw_;
|
||||
index_t KRaw_;
|
||||
};
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
@@ -7,6 +7,7 @@
|
||||
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v2.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v4_direct_load.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
@@ -14,6 +15,8 @@ enum struct PipelineVersion
|
||||
{
|
||||
v1,
|
||||
v2,
|
||||
// v3 is only used in the Stream-K implementation.
|
||||
v4,
|
||||
};
|
||||
|
||||
template <PipelineVersion PipelineVer,
|
||||
@@ -36,6 +39,10 @@ constexpr auto GridwiseGemmPipeline_Selector()
|
||||
{
|
||||
return GridwiseGemmPipeline_v2{};
|
||||
}
|
||||
else if constexpr(PipelineVer == PipelineVersion::v4)
|
||||
{
|
||||
return GridwiseGemmPipeline_v4<NumPrefetch>{};
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cerr << "GridwiseGemmPipeline configuration is not available" << std::endl;
|
||||
|
||||
@@ -0,0 +1,101 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/utility/loop_scheduler.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <index_t NumPrefetch>
|
||||
struct GridwiseGemmPipeline_v4;
|
||||
|
||||
// 1-stage prefetch
|
||||
template <>
|
||||
struct GridwiseGemmPipeline_v4<1>
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
|
||||
__host__ __device__ static constexpr bool IsSupported(index_t /* num_loop */) { return true; }
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainLoop(index_t num_loop)
|
||||
{
|
||||
return num_loop > 1;
|
||||
}
|
||||
|
||||
template <bool HasMainLoop,
|
||||
typename AGridDesc,
|
||||
typename ABlockDesc,
|
||||
typename ABlockTransfer,
|
||||
typename AGridBuffer,
|
||||
typename ABlockBuffer,
|
||||
typename ABlockTransferStep,
|
||||
typename BGridDesc,
|
||||
typename BBlockDesc,
|
||||
typename BBlockTransfer,
|
||||
typename BGridBuffer,
|
||||
typename BBlockBuffer,
|
||||
typename BBlockTransferStep,
|
||||
typename BlockwiseGemm,
|
||||
typename CThreadBuffer>
|
||||
__device__ static void Run(const AGridDesc& a_grid_desc,
|
||||
const ABlockDesc& a_block_desc,
|
||||
ABlockTransfer& a_blockwise_copy,
|
||||
const AGridBuffer& a_grid_buf,
|
||||
ABlockBuffer& a_block_buf,
|
||||
const ABlockTransferStep& a_block_copy_step,
|
||||
const BGridDesc& b_grid_desc,
|
||||
const BBlockDesc& b_block_desc,
|
||||
BBlockTransfer& b_blockwise_copy,
|
||||
const BGridBuffer& b_grid_buf,
|
||||
BBlockBuffer& b_block_buf,
|
||||
const BBlockTransferStep& b_block_copy_step,
|
||||
const BlockwiseGemm& blockwise_gemm,
|
||||
CThreadBuffer& c_thread_buf,
|
||||
index_t num_loop)
|
||||
{
|
||||
a_blockwise_copy.Run(a_grid_desc, a_grid_buf, a_block_desc, a_block_buf);
|
||||
b_blockwise_copy.Run(b_grid_desc, b_grid_buf, b_block_desc, b_block_buf);
|
||||
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
|
||||
|
||||
// Initialize C
|
||||
c_thread_buf.Clear();
|
||||
|
||||
// main body
|
||||
if constexpr(HasMainLoop)
|
||||
{
|
||||
index_t i = 0;
|
||||
|
||||
do
|
||||
{
|
||||
block_sync_lds_direct_load();
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
|
||||
block_sync_lds_direct_load();
|
||||
|
||||
a_blockwise_copy.Run(a_grid_desc, a_grid_buf, a_block_desc, a_block_buf);
|
||||
b_blockwise_copy.Run(b_grid_desc, b_grid_buf, b_block_desc, b_block_buf);
|
||||
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
|
||||
|
||||
++i;
|
||||
} while(i < (num_loop - 1));
|
||||
}
|
||||
|
||||
// tail
|
||||
{
|
||||
block_sync_lds_direct_load();
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user