431 lines
15 KiB
C++
431 lines
15 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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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/*! \file
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\brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
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The epilogue rearranges the result of a matrix product through shared memory to match canonical
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tensor layouts in global memory. Epilogues support conversion and reduction operations.
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*/
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#pragma once
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#if defined(__CUDACC_RTC__)
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#include <cuda/std/cassert>
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#else
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#include <assert.h>
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#endif
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/array.h"
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#include "cutlass/layout/vector.h"
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#include "cutlass/layout/tensor.h"
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#include "cutlass/tensor_coord.h"
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#include "cutlass/aligned_buffer.h"
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#include "cutlass/functional.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/transform/pitch_linear_thread_map.h"
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#include "cutlass/transform/threadblock/regular_tile_iterator.h"
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#include "cutlass/epilogue/threadblock/epilogue_base.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h"
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#include "cutlass/numeric_types.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace epilogue {
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namespace threadblock {
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////////////////////////////////////////////////////////////////////////////////
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/// Epilogue operator
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template <
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typename Shape_, ///< Shape of threadblock tile (concept: GemmShape)
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typename WarpMmaOperator_, ///< Warp-level MMA operator (concept: gemm::warp::MmaTensorOp)
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int PartitionsK, ///< Number of partitions of the K dimension
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typename OutputTileIterator_, ///< Tile iterator reading and writing output tensors
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typename AccumulatorFragmentIterator_, ///< Fragment iterator selecting accumulators
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typename WarpTileIterator_, ///< Warp-scoped tile iterator writing accumulators to SMEM
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typename SharedLoadIterator_, ///< Threadblock-scoped tile iterator loading from SMEM
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///< Output operator
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typename OutputOp0_,
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typename OutputOp1_,
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typename OutputOp2_,
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typename Padding_, ///< Padding added to SMEM allocation to avoid bank conflicts (concept: MatrixShape)
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bool StoreD0 = true,
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bool StoreD1 = true,
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int FragmentsPerPartition = 1, ///< Used to coarsten the epilogue granularity
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int IterationsUnroll = ///< Used to reduce binary size when epilogue op is large
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(!IsEpilogueFunctorHeavy<OutputOp0_>::value)
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>
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class DualEpilogue {
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public:
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using Base = EpilogueBase<
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Shape_,
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typename WarpMmaOperator_::Shape,
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PartitionsK,
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AccumulatorFragmentIterator_,
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WarpTileIterator_,
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Padding_,
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FragmentsPerPartition>;
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using Shape = Shape_;
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using WarpMmaOperator = WarpMmaOperator_;
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static int const kPartitionsK = PartitionsK;
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static bool constexpr kStoreD0 = StoreD0;
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static bool constexpr kStoreD1 = StoreD1;
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using OutputTileIterator = OutputTileIterator_;
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using AccumulatorFragmentIterator = AccumulatorFragmentIterator_;
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using WarpTileIterator = WarpTileIterator_;
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using SharedLoadIterator = SharedLoadIterator_;
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using OutputOp0 = OutputOp0_;
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using OutputOp1 = OutputOp1_;
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using OutputOp2 = OutputOp2_;
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using Padding = Padding_;
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using Layout = layout::RowMajor;
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using LongIndex = typename Layout::LongIndex;
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/// The complete warp-level accumulator tile
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using AccumulatorTile = typename Base::AccumulatorTile;
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/// Accumulator element
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using ElementAccumulator = typename WarpTileIterator::Element;
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/// Output element
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using ElementOutput = typename OutputTileIterator::Element;
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/// Output access size
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static int const kElementsPerAccess = OutputTileIterator::kElementsPerAccess;
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/// Tensor reference to destination tensor
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using TensorRef = typename OutputTileIterator::TensorRef;
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/// Tensor reference to sync tensor
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using SyncTensorRef = typename cutlass::TensorRef<int, cutlass::layout::PackedVectorLayout>;
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/// Const tensor reference to source tensor
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using ConstTensorRef = typename OutputTileIterator::ConstTensorRef;
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/// Array type used to output
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using OutputAccessType = Array<
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typename OutputTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
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/// Array type used by output functor
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using AccumulatorAccessType = Array<typename WarpTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
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/// Number of warps
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using WarpCount = typename Base::WarpCount;
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struct SharedStorage {
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using Element = typename WarpTileIterator::Element;
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/// Tensor reference to shared memory allocation
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using TensorRef = typename WarpTileIterator::TensorRef;
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/// Logical shape of the shared memory tile written to by all warps.
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using Shape = typename Base::Shape;
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/// Shape of the shared memory allocation for the epilogue
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using StorageShape = typename Base::SharedStorage::StorageShape;
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//
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// Data members
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//
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AlignedBuffer<Element, StorageShape::kCount> storage[2];
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//
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// Methods
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//
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/// Returns a tensor reference to the shared memory buffer
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CUTLASS_DEVICE
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TensorRef reference(int i) {
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return TensorRef(
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storage[i].data(),
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Layout::packed({StorageShape::kRow, StorageShape::kColumn}));
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}
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};
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static int constexpr kSmemTiles = Base::kFragmentsPerIteration > 1 ? Base::kFragmentsPerIteration : kPartitionsK;
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static int constexpr kSmemPointerOffset = SharedStorage::StorageShape::kCount / kSmemTiles;
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public:
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static_assert(SharedLoadIterator::Fragment::kElements == OutputTileIterator::Fragment::kElements,
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"Mismatch between shared load iterator and output tile iterator.");
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static_assert(OutputTileIterator::kElementsPerAccess, "OutputTileIterator::kElementsPerAccess must not be zero.");
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static_assert(!(OutputTileIterator::Fragment::kElements % OutputTileIterator::kElementsPerAccess),
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"Divisibility");
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private:
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/// Loads fragment from shared memory aligned with output tensor
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SharedLoadIterator shared_load_iterator0_;
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SharedLoadIterator shared_load_iterator1_;
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/// Stores a warp's fragment of accumulators to SMEM
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WarpTileIterator warp_tile_iterator0_;
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WarpTileIterator warp_tile_iterator1_;
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public:
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/// Constructor
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CUTLASS_DEVICE
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DualEpilogue(
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SharedStorage &shared_storage, ///< Shared storage object
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int thread_idx, ///< ID of a thread within the threadblock
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int warp_idx, ///< ID of warp within threadblock
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int lane_idx ///< Id of thread within warp
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):
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shared_load_iterator0_(shared_storage.reference(0), thread_idx),
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shared_load_iterator1_(shared_storage.reference(1), thread_idx),
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warp_tile_iterator0_(shared_storage.reference(0), lane_idx),
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warp_tile_iterator1_(shared_storage.reference(1), lane_idx)
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{
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int warp_k = warp_idx / (WarpCount::kM * WarpCount::kN);
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int warp_mn = warp_idx % (WarpCount::kM * WarpCount::kN);
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int warp_m = warp_mn % WarpCount::kM;
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int warp_n = warp_mn / WarpCount::kM;
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MatrixCoord warp_offset{warp_k * WarpCount::kM + warp_m, warp_n};
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warp_tile_iterator0_.add_tile_offset(warp_offset);
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warp_tile_iterator1_.add_tile_offset(warp_offset);
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}
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/// Streams the result to global memory
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CUTLASS_DEVICE
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void operator()(
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OutputOp0 const &output_op0,
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OutputOp1 const &output_op1,
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OutputOp2 const &output_op2,
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OutputTileIterator dest0,
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OutputTileIterator dest1,
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OutputTileIterator dest2,
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AccumulatorTile const &accumulator0,
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AccumulatorTile const &accumulator1,
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OutputTileIterator source_iterator[2],
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bool writeToD2 // true if it's the final split-k
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) {
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// TODO: Implement when no source is needed
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typename OutputTileIterator::Fragment source_fragment[2];
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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source_fragment[i].clear();
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}
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//
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// Iterator over warp-level accumulator fragment
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//
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AccumulatorFragmentIterator accum_fragment_iterator[2] = {accumulator0, accumulator1};
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//
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// Iterate over accumulator tile
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//
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#pragma unroll(IterationsUnroll ? OutputTileIterator::kIterations : 1)
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for (int iter = 0; iter < OutputTileIterator::kIterations; ++iter) {
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//
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// Load the source
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//
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < 2; ++i) {
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source_iterator[i].load(source_fragment[i]);
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++source_iterator[i];
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}
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//
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// Convert and store fragment
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//
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__syncthreads();
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acc2smem_source_needed<cutlass::make_index_sequence<OutputTileIterator::kIterations>>::push(
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iter, accum_fragment_iterator[0], this->warp_tile_iterator0_);
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acc2smem_source_needed<cutlass::make_index_sequence<OutputTileIterator::kIterations>>::push(
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iter, accum_fragment_iterator[1], this->warp_tile_iterator1_);
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__syncthreads();
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//
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// Load fragments from shared memory
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//
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typename SharedLoadIterator::Fragment aligned_accum_fragment0[kPartitionsK];
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typename SharedLoadIterator::Fragment aligned_accum_fragment1[kPartitionsK];
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shared_load_iterator0_.load(aligned_accum_fragment0[0]);
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shared_load_iterator1_.load(aligned_accum_fragment1[0]);
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// If the number of k-slices is > 1 - perform a reduction amongst the k-slices
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if (kPartitionsK > 1) {
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plus <typename SharedLoadIterator::Fragment> add_fragments;
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CUTLASS_PRAGMA_UNROLL
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for ( int i = 1; i < kPartitionsK; ++i) {
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shared_load_iterator0_.add_pointer_offset(kSmemPointerOffset);
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shared_load_iterator1_.add_pointer_offset(kSmemPointerOffset);
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shared_load_iterator0_.load(aligned_accum_fragment0[i]);
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shared_load_iterator1_.load(aligned_accum_fragment1[i]);
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aligned_accum_fragment0[0] = add_fragments(aligned_accum_fragment0[0], aligned_accum_fragment0[i]);
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aligned_accum_fragment1[0] = add_fragments(aligned_accum_fragment1[0], aligned_accum_fragment1[i]);
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}
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shared_load_iterator0_.add_pointer_offset((1 - kPartitionsK) * kSmemPointerOffset);
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shared_load_iterator1_.add_pointer_offset((1 - kPartitionsK) * kSmemPointerOffset);
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}
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//
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// Compute the output result
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//
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typename OutputTileIterator::Fragment output_fragment[3];
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apply_output_operator_(output_fragment,
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output_op0, output_op1, output_op2,
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aligned_accum_fragment0[0], aligned_accum_fragment1[0],
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source_fragment);
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//
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// Store the final result
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//
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if (kStoreD0) {
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dest0.store(output_fragment[0]);
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++dest0;
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}
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if (kStoreD1) {
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dest1.store(output_fragment[1]);
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++dest1;
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}
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if (writeToD2) {
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dest2.store(output_fragment[2]);
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++dest2;
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}
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}
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}
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private:
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static_assert(kPartitionsK == 1 || Base::kFragmentsPerIteration == 1, "One of these must be exactly 1.");
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template<class Seq>
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struct acc2smem_source_needed;
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template <size_t... Seq>
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struct acc2smem_source_needed<cutlass::index_sequence<Seq...>> {
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template<int Advance>
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CUTLASS_DEVICE
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static void helper(AccumulatorFragmentIterator accum_fragment_iterator,
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WarpTileIterator &warp_tile_iterator) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Advance; i++) {
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++accum_fragment_iterator;
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}
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typename AccumulatorFragmentIterator::Fragment accum_fragment;
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accum_fragment_iterator.load(accum_fragment);
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warp_tile_iterator.store(accum_fragment);
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}
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CUTLASS_DEVICE
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static void push(size_t pos,
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AccumulatorFragmentIterator const &iterator_begin,
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WarpTileIterator &warp_tile_iterator) {
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int dummy[] = {(pos == Seq) && (helper<Seq>(iterator_begin, warp_tile_iterator), 0)...};
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}
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};
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/// Helper to invoke the output functor over each vector of output
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CUTLASS_DEVICE
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void apply_output_operator_(
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typename OutputTileIterator::Fragment (&output_fragment)[3],
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OutputOp0 const &output_op0,
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OutputOp1 const &output_op1,
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OutputOp2 const &output_op2,
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typename SharedLoadIterator::Fragment const& aligned_accum_fragment0,
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typename SharedLoadIterator::Fragment const& aligned_accum_fragment1,
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typename OutputTileIterator::Fragment const (&source_fragment)[2]) {
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OutputAccessType* output_frag_ptr[3] = {
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reinterpret_cast<OutputAccessType *>(&output_fragment[0]),
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reinterpret_cast<OutputAccessType *>(&output_fragment[1]),
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reinterpret_cast<OutputAccessType *>(&output_fragment[2])
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};
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AccumulatorAccessType const *compute_frag_ptr[2] = {
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reinterpret_cast<AccumulatorAccessType const *>(&aligned_accum_fragment0),
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reinterpret_cast<AccumulatorAccessType const *>(&aligned_accum_fragment1)
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};
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OutputAccessType const *source_frag_ptr[2] = {
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reinterpret_cast<OutputAccessType const *>(&source_fragment[0]),
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reinterpret_cast<OutputAccessType const *>(&source_fragment[1])
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};
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int const kOutputOpIterations =
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OutputTileIterator::Fragment::kElements / OutputTileIterator::kElementsPerAccess;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kOutputOpIterations; ++i) {
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// Call the output operators
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output_frag_ptr[0][i] = output_op0(compute_frag_ptr[0][i], source_frag_ptr[0][i]);
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output_frag_ptr[1][i] = output_op1(compute_frag_ptr[1][i], source_frag_ptr[1][i]);
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output_frag_ptr[2][i] = output_op2(output_frag_ptr[0][i], output_frag_ptr[1][i]);
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}
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace epilogue
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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