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[CK_TILE] Separate PermuteN epilogue from CShuffle epilogue into standalone file (#5863) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Motivation The PermuteN epilogue was previously embedded within cshuffle_epilogue.hpp, despite having fundamentally different behaviour. Coupling these two independent strategies in one file introduced unnecessary complexity, SFINAE guards, and a dual operator() overload selected at compile time via TiledMMAPermuteN_ template parameter. This PR separates PermuteN into its own standalone file(pertmuten_epilogue.hpp), simplifying both implementations and making the codebase easier to maintain and extend independently. ## Technical Details **New file: permuten_epilogue.hpp:** contains PermuteNEpilogueProblem and PermuteNEpilogue, extracted from the permuteN code path in cshuffle_epilogue.hpp. **Cleanup of cshuffle_epilogue.hpp:** - Removed the TiledMMAPermuteN_ template parameter from [CShuffleEpilogueProblem] - Removed the SFINAE-guarded permuteN operator() overload - Removed the EnablePermuateN_ SFINAE alias - CShuffle now only contains CShuffle logic; EightWave support (independent feature) is retained **Consumer migration :** All consumer files now use compile-time epilogue selection via [std::conditional_t] `using GemmEpilogue = std::conditional_t< TiledMMAPermuteN, PermuteNEpilogue<PermuteNEpilogueProblem<...>>, CShuffleEpilogue<CShuffleEpilogueProblem<...>>>;` **Files modified:** - flatmm_basic.cpp, moe_flatmm.cpp, a16w4_moe_flatmm.cpp, mixed_prec_flatmm.cpp, mx_flatmm_instance.hpp — flatmm examples - run_gemm_quant_example.inc — block-scale GEMM example - gemm_weight_preshuffle_invoker.hpp — weight preshuffle invoker - test_gemm_quant_fixtures.hpp, test_gemm_persistent_async_input.cpp, test_gemm_pipeline_util.hpp — test utilities - universal_gemm_invoker.hpp — universal GEMM invoker - epilogue.hpp — add header updated to include permuten_epilogue.hpp ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
376 lines
16 KiB
C++
376 lines
16 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck_tile/host/concat.hpp"
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/common/utils.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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#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
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#include <type_traits>
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namespace ck_tile {
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template <typename AsDataType_,
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typename BsDataType_,
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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 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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bool FixedVectorSize_ = false,
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index_t VectorSizeC_ = 1>
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struct PermuteNEpilogueProblem
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{
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using AsDataType = remove_cvref_t<AsDataType_>;
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using BsDataType = remove_cvref_t<BsDataType_>;
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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 = MWave_ * NWave_ * get_warp_size();
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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 bool FixedVectorSize = FixedVectorSize_;
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static constexpr index_t VectorSizeC = VectorSizeC_;
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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 PermuteNEpilogue
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{
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using Problem = remove_cvref_t<Problem_>;
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using AsDataType = remove_cvref_t<typename Problem::AsDataType>;
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using BsDataType = remove_cvref_t<typename Problem::BsDataType>;
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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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static constexpr bool ADataTypeIsTuple = is_detected<is_tuple, AsDataType>::value;
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static constexpr bool BDataTypeIsTuple = is_detected<is_tuple, BsDataType>::value;
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using AsDataTypeTuple = std::conditional_t<ADataTypeIsTuple,
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remove_cvref_t<AsDataType>,
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remove_cvref_t<tuple<AsDataType>>>;
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using BsDataTypeTuple = std::conditional_t<BDataTypeIsTuple,
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remove_cvref_t<BsDataType>,
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remove_cvref_t<tuple<BsDataType>>>;
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using ADataType = remove_cvref_t<std::tuple_element_t<number<0>{}, AsDataTypeTuple>>;
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using BDataType = remove_cvref_t<std::tuple_element_t<number<0>{}, BsDataTypeTuple>>;
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using ATypeToUse = std::conditional_t<std::is_same_v<ADataType, pk_int4_t> ||
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std::is_same_v<ADataType, pk_fp4_t>,
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BDataType,
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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 = std::conditional_t<std::is_same_v<BDataType, pk_int4_t> ||
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std::is_same_v<BDataType, pk_fp4_t> ||
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sizeof(BDataType) < sizeof(ADataType),
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ADataType,
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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 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 constexpr index_t MRepeat = kMPerBlock / (MPerXdl * MWave);
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static constexpr index_t NRepeat = kNPerBlock / (NPerXdl * NWave);
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CDElementwise elfunc_;
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// PermuteN epilogue does not support D tensors or non-passthrough elementwise operations.
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// If D tensor support is needed, use CShuffleEpilogue instead.
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static_assert(NumDTensor == 0,
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"PermuteNEpilogue does not support D tensors. Use CShuffleEpilogue instead.");
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static_assert(std::is_same_v<CDElementwise, element_wise::PassThrough>,
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"PermuteNEpilogue only supports PassThrough elementwise. "
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"Use CShuffleEpilogue for custom elementwise operations.");
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CK_TILE_DEVICE PermuteNEpilogue(CDElementwise elfunc = CDElementwise{}) : elfunc_(elfunc) {};
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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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[[nodiscard]] CK_TILE_HOST static const std::string GetName()
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{
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// clang-format off
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return concat('_', "PermuteNEpilogue",
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concat('x', MWave, NWave),
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concat('x', MPerXdl, NPerXdl, KPerXdl),
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VectorSizeC,
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isCTransposed ? "CTransposed" : "CNotTransposed");
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// clang-format on
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}
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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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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return 0; }
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using WG = WarpGemmDispatcher<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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using CWarpDstrEncoding = typename WG::CWarpDstrEncoding;
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// TODO: Check if there would be nicer ways to overload rather than with EmptyScale or nullptr_t
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struct EmptyScale
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{
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};
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template <typename, typename = void>
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struct ScaleDataType
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{
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using DataType = float;
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};
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template <typename T>
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struct ScaleDataType<T, std::void_t<typename T::DataType>>
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{
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using DataType = typename T::DataType;
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};
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template <typename ODramWindow,
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typename OAccTile,
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typename DsDramWindows,
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typename ScaleM = EmptyScale,
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typename ScaleN = EmptyScale>
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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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const ScaleM& scale_m = {},
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const ScaleN& scale_n = {})
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{
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static constexpr int RowsPerLane = CWarpTensor::get_thread_buffer_size();
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static_assert(MPerXdl % RowsPerLane == 0,
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"PermuteN: MPerXdl must be divisible by per-lane row count.");
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constexpr int kM0 = MWave;
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constexpr int kM2 = RowsPerLane;
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constexpr int kM1 = MPerXdl / kM2;
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constexpr int kN0 = NWave;
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constexpr int kN1 = NPerXdl;
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constexpr int kN2 = NRepeat;
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using IntrThreadShuffleEncode =
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tile_distribution_encoding<sequence<>,
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tuple<sequence<kM0, kM1, kM2>, sequence<kN0, kN1, kN2>>,
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tuple<sequence<1, 2>, sequence<1, 2>>,
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tuple<sequence<0, 0>, sequence<1, 1>>,
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sequence<1, 2>,
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sequence<2, 2>>;
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constexpr auto dram_tile_distribution =
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make_static_tile_distribution(IntrThreadShuffleEncode{});
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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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auto shuffle_acc = make_static_distributed_tensor<AccDataType>(dram_tile_distribution);
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auto c_out_tensor = make_static_distributed_tensor<ODataType>(dram_tile_distribution);
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// Optional scales (must share the same distribution to match per-thread indexing)
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constexpr bool has_scales =
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!std::is_same<ScaleM, EmptyScale>::value && !std::is_same<ScaleN, EmptyScale>::value;
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constexpr bool has_scalar_scales =
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std::is_same_v<ScaleM, AccDataType> && std::is_same_v<ScaleN, AccDataType>;
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// Tiles to hold row/col scales when present
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using SMType = typename ScaleDataType<ScaleM>::DataType;
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using SNType = typename ScaleDataType<ScaleN>::DataType;
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auto sm_tile = make_static_distributed_tensor<SMType>(dram_tile_distribution);
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auto sn_tile = make_static_distributed_tensor<SNType>(dram_tile_distribution);
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// Build windows only if non-scalar scales are provided
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auto scale_m_window = [&]() {
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if constexpr(has_scales && !has_scalar_scales)
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{
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return make_tile_window(scale_m, dram_tile_distribution);
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}
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else
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{
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return EmptyScale{};
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}
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}();
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auto scale_n_window = [&]() {
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if constexpr(has_scales && !has_scalar_scales)
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{
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return make_tile_window(scale_n, dram_tile_distribution);
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}
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else
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{
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return EmptyScale{};
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}
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}();
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static_for<0, MRepeat, 1>{}([&](auto mIter) {
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// Slice accumulators for this M repeat into the permuted layout
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shuffle_acc.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
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merge_sequences(sequence<mIter, 0>{}, c_warp_y_index_zeros),
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merge_sequences(sequence<1, NRepeat>{}, c_warp_y_lengths));
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// If non-scalar scales provided, load them with identical distribution
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if constexpr(has_scales && !has_scalar_scales)
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{
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sm_tile = load_tile(scale_m_window); // row scales in permuted layout
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sn_tile = load_tile(scale_n_window); // col scales in permuted layout
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}
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// Pack "rows per lane" with permuted N layout
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static_for<0, NRepeat, 1>{}([&](auto n_idx) {
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// source indices in shuffle_acc: (n_idx * product(Y) + row)
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const index_t plane = c_warp_y_lengths.product();
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// Fuse scale (if present) and convert
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static_for<0, kM2, 1>{}([&](auto m_lane) {
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const int src = n_idx * plane + m_lane; // source row in this N-plane
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const int dst = n_idx + m_lane * NRepeat; // permuted N layout in output
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AccDataType v = shuffle_acc.get_thread_buffer()[src];
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if constexpr(has_scalar_scales)
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{
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v = static_cast<AccDataType>(v * scale_m * scale_n);
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}
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else if constexpr(has_scales && !has_scalar_scales)
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{
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const auto sm = static_cast<float>(sm_tile.get_thread_buffer()[dst]);
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const auto sn = static_cast<float>(sn_tile.get_thread_buffer()[dst]);
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v = static_cast<AccDataType>(v * sm * sn);
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}
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c_out_tensor.get_thread_buffer()[dst] = type_convert<ODataType>(v);
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});
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});
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// store/update
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if constexpr(decltype(out_dram_window.get_bottom_tensor_view())::DstInMemOp ==
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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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// advance output (and any D-tensors) by one MPerXdl*MWave chunk
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move_tile_window(out_dram_window, {number<MPerXdl * MWave>{}, number<0>{}});
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static_for<0, NumDTensor, 1>{}([&](auto idx) {
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move_tile_window(d_dram_windows[idx], {number<MPerXdl * MWave>{}, number<0>{}});
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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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