mirror of
https://github.com/ROCm/composable_kernel.git
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367 lines
17 KiB
C++
367 lines
17 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
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#include "ck_tile/ops/common/tensor_layout.hpp"
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namespace ck_tile {
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template <typename ADataType_,
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typename BDataType_,
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typename DsDataType_,
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typename AccDataType_,
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typename ODataType_,
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typename DsLayout_,
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typename ELayout_,
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typename CDElementwise_,
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index_t kBlockSize_,
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index_t kM_,
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index_t kN_,
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index_t MWave_,
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index_t NWave_,
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index_t MPerXdl_,
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index_t NPerXdl_,
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index_t KPerXdl_,
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bool isCTransposed_,
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memory_operation_enum MemoryOperation_,
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index_t kNumWaveGroups_ = 1,
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bool FixedVectorSize_ = false,
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index_t VectorSizeC_ = 1>
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struct CShuffleEpilogueProblem
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{
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using ADataType = remove_cvref_t<ADataType_>;
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using BDataType = remove_cvref_t<BDataType_>;
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using AccDataType = remove_cvref_t<AccDataType_>;
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using ODataType = remove_cvref_t<ODataType_>;
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using DsDataType = remove_cvref_t<DsDataType_>;
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using DsLayout = remove_cvref_t<DsLayout_>;
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using ELayout = remove_cvref_t<ELayout_>;
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using CDElementwise = remove_cvref_t<CDElementwise_>;
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static constexpr index_t kBlockSize = kBlockSize_;
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static constexpr index_t kMPerBlock = kM_;
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static constexpr index_t kNPerBlock = kN_;
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static constexpr index_t MWave = MWave_;
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static constexpr index_t NWave = NWave_;
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static constexpr index_t MPerXdl = MPerXdl_;
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static constexpr index_t NPerXdl = NPerXdl_;
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static constexpr index_t KPerXdl = KPerXdl_;
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static constexpr index_t isCTransposed = isCTransposed_;
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static constexpr memory_operation_enum MemoryOperation = MemoryOperation_;
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static constexpr bool FixedVectorSize = FixedVectorSize_;
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static constexpr index_t VectorSizeC = VectorSizeC_;
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static constexpr index_t kNumWaveGroups = kNumWaveGroups_;
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static constexpr index_t NumDTensor = DsDataType::size();
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static_assert(NumDTensor == DsLayout::size(),
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"The size of DsDataType and DsLayout should be the same");
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};
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template <typename Problem_, typename Policy_ = void>
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struct CShuffleEpilogue
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{
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using Problem = remove_cvref_t<Problem_>;
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using ADataType = remove_cvref_t<typename Problem::ADataType>;
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using BDataType = remove_cvref_t<typename Problem::BDataType>;
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using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
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using ODataType = remove_cvref_t<typename Problem::ODataType>;
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using DsDataType = remove_cvref_t<typename Problem::DsDataType>;
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using DsLayout = remove_cvref_t<typename Problem::DsLayout>;
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using ATypeToUse =
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std::conditional_t<std::is_same_v<ADataType, pk_int4_t>, BDataType, ADataType>;
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// Used for weight-only quantization kernel, B would be dequantized to the same data type as A
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using BTypeToUse =
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std::conditional_t<std::is_same_v<BDataType, pk_int4_t>, ADataType, BDataType>;
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using ELayout = remove_cvref_t<typename Problem::ELayout>;
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using CDElementwise = remove_cvref_t<typename Problem::CDElementwise>;
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static constexpr memory_operation_enum MemoryOperation = Problem::MemoryOperation;
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static constexpr index_t kBlockSize = Problem::kBlockSize;
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static constexpr index_t kMPerBlock = Problem::kMPerBlock;
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static constexpr index_t kNPerBlock = Problem::kNPerBlock;
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static constexpr index_t MWave = Problem::MWave;
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static constexpr index_t NWave = Problem::NWave;
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static constexpr index_t MPerXdl = Problem::MPerXdl;
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static constexpr index_t NPerXdl = Problem::NPerXdl;
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static constexpr index_t KPerXdl = Problem::KPerXdl;
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static constexpr index_t isCTransposed = Problem::isCTransposed;
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static constexpr bool FixedVectorSize = Problem::FixedVectorSize;
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static constexpr index_t VectorSizeC = Problem::VectorSizeC;
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static constexpr index_t MPerIteration = MPerXdl * MWave;
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static constexpr index_t NPerIteration = NPerXdl * NWave;
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static constexpr index_t NumDTensor = Problem::NumDTensor;
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static_assert(NumDTensor == DsLayout::size(),
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"The size of DsDataType and DsLayout should be the same");
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/**
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* @brief Get the vector store size for C tensor.
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*
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* @note The vector store size for output C tensor would depend on multiple factors
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* like its data layout and warp gemm C transposition. In general it would
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* be the number of consecutive elements in contiguous C dimension hold by
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* single thread.
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*
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* @return The vector store size for C tensor.
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*/
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CK_TILE_HOST_DEVICE static constexpr index_t GetVectorSizeC()
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{
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if constexpr(FixedVectorSize)
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{
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return VectorSizeC;
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}
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constexpr index_t max_vector_size = 16;
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if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
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{
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return std::min(static_cast<int>(NPerIteration),
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static_cast<int>(max_vector_size / sizeof(ODataType)));
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}
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else if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::ColumnMajor>)
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{
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return std::min(static_cast<int>(MPerIteration),
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static_cast<int>(max_vector_size / sizeof(ODataType)));
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}
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else
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{
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static_assert(false, "Unsupported ELayout!");
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}
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}
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/**
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* @brief Get the vector store size for Di tensor.
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*
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* @return The vector store size for Di tensor.
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*/
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template <index_t I>
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CK_TILE_HOST_DEVICE static constexpr index_t GetVectorSizeD(number<I> index)
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{
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constexpr index_t max_vector_size = 16;
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using DiDataType = remove_cvref_t<std::tuple_element_t<index.value, DsDataType>>;
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using DiLayout = remove_cvref_t<std::tuple_element_t<index.value, DsLayout>>;
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if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
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{
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return std::min(static_cast<int>(NPerIteration),
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static_cast<int>(max_vector_size / sizeof(DiDataType)));
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}
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else if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::ColumnMajor>)
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{
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return std::min(static_cast<int>(MPerIteration),
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static_cast<int>(max_vector_size / sizeof(DiDataType)));
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}
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else
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{
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static_assert(false, "Unsupported DLayout!");
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}
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return max_vector_size / sizeof(DiDataType);
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}
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/**
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* @brief Shuffle tile configuration parameters
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*
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* @details These parameters control the number of XDL tiles processed per wave in each shuffle
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* iteration:
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* - NumMXdlPerWavePerShuffle: Number of XDL tiles in M dimension processed per wave
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* - NumNXdlPerWavePerShuffle: Number of XDL tiles in N dimension processed per wave
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*/
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static constexpr auto shuffle_tile_tuple = [] {
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constexpr index_t elem_per_thread = MPerXdl * NPerXdl / get_warp_size();
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if constexpr(elem_per_thread >= GetVectorSizeC())
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{
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return std::make_tuple(1, 1);
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}
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else
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{
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constexpr index_t num_xdl_shuffles = GetVectorSizeC() / elem_per_thread;
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if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
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{
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static_assert((kMPerBlock % (MPerXdl * MWave) == 0) &&
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(kMPerBlock % num_xdl_shuffles == 0),
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"kMPerBlock must be divisible by MPerXdl*MWave and "
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"num_xdl_shuffles for CShuffleEpilogue");
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return std::make_tuple(min(num_xdl_shuffles, kMPerBlock / (MPerXdl * MWave)), 1);
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}
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else
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{
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static_assert((kNPerBlock % (NPerXdl * NWave) == 0) &&
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(kNPerBlock % num_xdl_shuffles == 0),
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"kNPerBlock must be divisible by NPerXdl*NWave and "
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"num_xdl_shuffles for CShuffleEpilogue");
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return std::make_tuple(1, min(num_xdl_shuffles, kNPerBlock / (NPerXdl * NWave)));
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}
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}
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}();
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static constexpr index_t NumMXdlPerWavePerShuffle = std::get<0>(shuffle_tile_tuple);
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static constexpr index_t NumNXdlPerWavePerShuffle = std::get<1>(shuffle_tile_tuple);
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static constexpr auto MNPerIterationShuffle = [] {
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constexpr index_t m_val = MPerXdl * MWave * NumMXdlPerWavePerShuffle;
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constexpr index_t n_val = NPerXdl * NWave * NumNXdlPerWavePerShuffle;
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if constexpr(kMPerBlock % m_val != 0 || kNPerBlock % n_val != 0)
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return std::make_tuple(MPerXdl * MWave, NPerXdl * NWave);
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else
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return std::make_tuple(m_val, n_val);
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}();
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static constexpr index_t MPerIterationShuffle = std::get<0>(MNPerIterationShuffle);
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static constexpr index_t NPerIterationShuffle = std::get<1>(MNPerIterationShuffle);
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using WG = WarpGemmMfmaDispatcher<ATypeToUse,
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BTypeToUse,
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AccDataType,
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MPerXdl,
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NPerXdl,
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KPerXdl,
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isCTransposed>;
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using CWarpDstr = typename WG::CWarpDstr;
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using CWarpTensor = typename WG::CWarpTensor;
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template <typename Problem>
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CK_TILE_HOST_DEVICE static constexpr auto MakeLdsBlockDescriptor()
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{
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// N is contiguous dimension
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if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
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{
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return make_naive_tensor_descriptor(
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make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
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make_tuple(number<NPerIterationShuffle>{}, number<1>{}));
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}
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// M is contiguous dimension
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else if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::ColumnMajor>)
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{
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return make_naive_tensor_descriptor(
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make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
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make_tuple(number<1>{}, number<MPerIterationShuffle>{}));
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}
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else
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{
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static_assert(false, "Unsupported ELayout!");
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}
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}
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CK_TILE_DEVICE static constexpr auto MakeLdsDistributionEncode()
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{
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constexpr auto block_outer_dstr_encoding =
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tile_distribution_encoding<sequence<>,
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tuple<sequence<NumMXdlPerWavePerShuffle, MWave>,
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sequence<NumNXdlPerWavePerShuffle, NWave>>,
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tuple<sequence<1, 2>>,
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tuple<sequence<1, 1>>,
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sequence<1, 2>,
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sequence<0, 0>>{};
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constexpr auto block_dstr_encoding = detail::make_embed_tile_distribution_encoding(
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block_outer_dstr_encoding, typename CWarpDstr::DstrEncode{});
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return block_dstr_encoding;
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}
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
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{
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return MPerIterationShuffle * NPerIterationShuffle * sizeof(ODataType);
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}
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template <typename ODramWindow, typename OAccTile, typename DsDramWindows>
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CK_TILE_DEVICE auto operator()(ODramWindow& out_dram_window,
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const OAccTile& o_acc_tile,
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const DsDramWindows& ds_dram_windows,
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void* p_smem)
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{
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constexpr auto LdsTileDistr = make_static_tile_distribution(MakeLdsDistributionEncode());
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auto lds_tile = make_static_distributed_tensor<AccDataType>(LdsTileDistr);
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constexpr auto lds_block_desc = MakeLdsBlockDescriptor<Problem>();
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auto o_lds_block = make_tensor_view<address_space_enum::lds>(
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static_cast<ODataType*>(p_smem), lds_block_desc);
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auto in_lds_window = make_tile_window(
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o_lds_block,
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make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
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{0, 0},
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LdsTileDistr);
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auto out_lds_window = make_tile_window(
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o_lds_block,
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make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
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{0, 0});
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using SFC = space_filling_curve<sequence<kMPerBlock, kNPerBlock>,
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sequence<0, 1>,
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sequence<MPerIterationShuffle, NPerIterationShuffle>>;
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constexpr index_t num_access = SFC::get_num_of_access();
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static_assert(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>,
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"Currently, the CShuffle Epilogue only supports the Row Major Output layout");
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using TileEncodingPattern =
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TileDistributionEncodingPattern2D<kBlockSize,
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MPerIterationShuffle,
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NPerIterationShuffle,
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GetVectorSizeC(),
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tile_distribution_pattern::thread_raked,
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Problem::kNumWaveGroups>;
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constexpr auto dram_tile_distribution = TileEncodingPattern::Make2DStaticTileDistribution();
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auto d_dram_windows = generate_tuple(
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[&](auto idx) {
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return make_tile_window(ds_dram_windows[idx], dram_tile_distribution);
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},
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number<NumDTensor>{});
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constexpr auto c_warp_y_lengths =
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to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
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constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
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static_for<0, num_access, 1>{}([&](auto iAccess) {
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block_sync_lds();
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constexpr auto idx_y_start = SFC::get_index(iAccess);
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constexpr auto mIter = number<idx_y_start.at(number<0>{}) / (MPerIterationShuffle)>{};
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constexpr auto nIter = number<idx_y_start.at(number<1>{}) / (NPerIterationShuffle)>{};
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lds_tile.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
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merge_sequences(
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sequence<mIter * NumMXdlPerWavePerShuffle, nIter * NumNXdlPerWavePerShuffle>{},
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c_warp_y_index_zeros),
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merge_sequences(sequence<NumMXdlPerWavePerShuffle, NumNXdlPerWavePerShuffle>{},
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c_warp_y_lengths));
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const auto c_warptile_in_tensor_casted = cast_tile<ODataType>(lds_tile);
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store_tile(in_lds_window, c_warptile_in_tensor_casted);
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block_sync_lds();
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auto c_out_tensor = load_tile(make_tile_window(out_lds_window, dram_tile_distribution));
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const auto ds_tensor = generate_tuple(
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[&](auto idx) { return load_tile(d_dram_windows[idx]); }, number<NumDTensor>{});
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const auto c_ds_tiles = concat_tuple_of_reference(
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tie(c_out_tensor, c_out_tensor),
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generate_tie([&](auto idx) -> const auto& { return ds_tensor[idx]; },
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number<NumDTensor>{}));
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tile_elementwise_inout_unpack(typename Problem::CDElementwise{}, c_ds_tiles);
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if constexpr(MemoryOperation == memory_operation_enum::set)
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{
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store_tile(out_dram_window, c_out_tensor);
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}
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else
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{
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update_tile(out_dram_window, c_out_tensor);
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}
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if constexpr(iAccess != num_access - 1)
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{
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constexpr auto step = SFC::get_forward_step(iAccess);
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move_tile_window(out_dram_window, {step.at(number<0>{}), step.at(number<1>{})});
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static_for<0, NumDTensor, 1>{}([&](auto idx) {
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move_tile_window(d_dram_windows[idx],
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{step.at(number<0>{}), step.at(number<1>{})});
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});
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}
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});
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}
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};
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} // namespace ck_tile
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