212 lines
7.3 KiB
C++
212 lines
7.3 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/pipeline/pipeline.hpp"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cutlass/epilogue/dispatch_policy.hpp"
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#include "cute/tensor.hpp"
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#include "cute/numeric/int.hpp"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace epilogue {
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namespace collective {
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namespace detail {
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template <class T>
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static constexpr int elements_per_access_v = cutlass::sizeof_bits<uint32_t>::value / cutlass::sizeof_bits<T>::value;
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template <class EpilogueSchedule>
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static constexpr bool sm90_is_cooperative_v =
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std::is_base_of_v<cutlass::epilogue::TmaWarpSpecializedCooperative, EpilogueSchedule>;
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template <class EpilogueSchedule>
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static constexpr bool sm90_is_warp_specialized_v =
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std::is_base_of_v<cutlass::epilogue::TmaWarpSpecialized, EpilogueSchedule>;
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template <class T>
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struct EmptyStorage {
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CUTLASS_HOST_DEVICE
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T* data() { return nullptr; }
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};
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template<class EpilogueSchedule, class Stride>
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CUTLASS_HOST_DEVICE
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auto get_epilogue_stride(Stride stride){
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if constexpr (cute::is_base_of_v<cutlass::gemm::EpilogueTransposed, EpilogueSchedule>) {
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return cute::make_stride(cute::get<1>(stride), cute::get<0>(stride), cute::get<2>(stride));
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}
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else {
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return stride;
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}
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}
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template <typename ThreadEpilogueOp, typename = void>
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struct IsThreadEpilogueOpWithBias {
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static constexpr bool value = false;
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using type = typename ThreadEpilogueOp::ElementCompute;
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};
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template <typename ThreadEpilogueOp>
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struct IsThreadEpilogueOpWithBias <ThreadEpilogueOp, cute::void_t<typename ThreadEpilogueOp::ElementBias>> {
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static constexpr bool value = true;
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using type = typename ThreadEpilogueOp::ElementBias;
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};
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// IF_EPILOGUE_USES_TMA<T>::value will be true only if:
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// class T has member CopyOpS2G and T::CopyOpS2G is true
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template <typename T, typename = void>
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struct IF_EPILOGUE_USES_TMA { static constexpr bool value = false; };
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template <typename T>
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struct IF_EPILOGUE_USES_TMA <T, void_t<typename T::CopyOpS2G>>
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{ static constexpr bool value = true; };
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// Wrapper class to use operator-style epilogues in sm90 TMA warp-specialized kernels
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template <class EpilogueOp>
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class Sm90TmaWarpSpecializedAdapter : public EpilogueOp {
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public:
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using LoadPipeline = cutlass::PipelineTransactionAsync<0>; // 0 stage to disable smem alloc
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using LoadPipelineState = cutlass::PipelineState<0>;
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constexpr static uint32_t TmaTransactionBytes = 0;
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using StorePipeline = cutlass::PipelineTmaStore<1>; // tma store pipe has no smem alloc
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using StorePipelineState = cutlass::PipelineState<1>;
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using TensorStorage = typename EpilogueOp::SharedStorage;
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using PipelineStorage = typename LoadPipeline::SharedStorage;
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template<class TileShapeMNK>
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CUTLASS_HOST_DEVICE
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static constexpr int
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get_load_pipe_increment([[maybe_unused]] TileShapeMNK) {
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return 1;
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}
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template<class TileShapeMNK>
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CUTLASS_HOST_DEVICE
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static constexpr int
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get_store_pipe_increment([[maybe_unused]] TileShapeMNK) {
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return 1;
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}
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CUTLASS_DEVICE
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static void prefetch_tma_descriptors([[maybe_unused]] typename EpilogueOp::Params const&)
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{
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}
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// ctor inheritance
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using EpilogueOp::EpilogueOp;
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template<
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class ProblemShapeMNKL,
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class TileShapeMNK,
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class TileCoordMNKL,
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class TiledMma
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>
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CUTLASS_DEVICE void
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load(
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[[maybe_unused]] LoadPipeline load_pipeline,
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[[maybe_unused]] LoadPipelineState load_pipe_producer_state,
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[[maybe_unused]] ProblemShapeMNKL problem_shape_mnkl,
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[[maybe_unused]] TileShapeMNK tile_shape_MNK,
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[[maybe_unused]] TileCoordMNKL tile_coord_mnkl,
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[[maybe_unused]] TiledMma tiled_mma,
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[[maybe_unused]] int thread_idx,
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[[maybe_unused]] TensorStorage& shared_tensors)
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{
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// source load is performed in epilogue operator
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}
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CUTLASS_DEVICE void
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load_tail(
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[[maybe_unused]] LoadPipeline load_pipeline,
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[[maybe_unused]] LoadPipelineState load_pipe_producer_state)
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{
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}
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template<
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class ProblemShapeMNKL,
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class TileShapeMNK,
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class TileCoordMNKL,
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class AccEngine, class AccLayout,
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class TiledMma
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>
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CUTLASS_DEVICE void
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store(
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[[maybe_unused]] LoadPipeline load_pipeline,
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[[maybe_unused]] LoadPipelineState load_pipe_consumer_state,
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[[maybe_unused]] StorePipeline store_pipeline,
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[[maybe_unused]] StorePipelineState store_pipe_producer_state,
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ProblemShapeMNKL problem_shape_mnkl,
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TileShapeMNK tile_shape_MNK,
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TileCoordMNKL tile_coord_mnkl,
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cute::Tensor<AccEngine,AccLayout> accumulators,
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TiledMma tiled_mma,
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int thread_idx,
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TensorStorage& shared_tensors)
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{
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constexpr int BLK_M_RANK = rank<0>(tile_shape_MNK);
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auto m_max_coord = unwrap(cute::transform(make_seq<BLK_M_RANK>{}, [&](auto i) {
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return get<0,i>(problem_shape_mnkl) - get<0,i>(tile_shape_MNK) * get<0,i>(tile_coord_mnkl);
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}));
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constexpr int BLK_N_RANK = rank<1>(tile_shape_MNK);
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auto n_max_coord = unwrap(cute::transform(make_seq<BLK_N_RANK>{}, [&](auto i) {
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return get<1,i>(problem_shape_mnkl) - get<1,i>(tile_shape_MNK) * get<1,i>(tile_coord_mnkl);
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}));
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auto residue_mnk = make_tuple(m_max_coord, n_max_coord, Int<0>{});
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(*this)(
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problem_shape_mnkl,
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tile_shape_MNK,
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tile_coord_mnkl,
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accumulators,
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tiled_mma,
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residue_mnk,
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thread_idx,
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reinterpret_cast<char*>(&shared_tensors));
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}
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};
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} // namespace detail
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} // namespace collective
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} // namespace epilogue
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} // namespace cutlass
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