510 lines
16 KiB
C++
510 lines
16 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2024 - 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 Sparse GEMM kernel with an epilogue that computes the absolute maximum value of the output
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and a pre-activation-function auxiliary output. The auxiliary output is also (optionally)
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stored to global memory.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/kernel/params_sparse_base.h"
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#include "cutlass/matrix_coord.h"
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#include "cutlass/semaphore.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace kernel {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <
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typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
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typename Epilogue_, ///! Epilogue
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typename ThreadblockSwizzle_, ///! Threadblock swizzling function
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bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
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>
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struct SparseGemmWithAbsmax {
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using OutputOp = typename Epilogue::OutputOp;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static bool const kSplitKSerial = SplitKSerial;
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static int const kSparse = Mma::kSparse;
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static int const kMetaSizeInBits = Mma::kMetaSizeInBits;
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static int const kMaxID2 = Mma::kMaxID2;
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static int const kElementsPerElementE = Mma::kElementsPerElementE;
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using ElementE = typename Mma::ElementE;
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using LayoutE = typename Mma::LayoutE;
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using LayoutC = typename Epilogue::OutputTileIterator::Layout;
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/// Warp count (concept: GemmShape)
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using WarpCount = typename Mma::WarpCount;
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static int const kThreadCount = 32 * WarpCount::kCount;
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using ParamsA = typename Mma::IteratorA::Params;
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using TensorRefA = typename Mma::IteratorA::TensorRef;
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using ParamsB = typename Mma::IteratorB::Params;
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using TensorRefB = typename Mma::IteratorB::TensorRef;
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using ParamsE = typename Mma::IteratorE::Params;
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using TensorRefE = typename Mma::IteratorE::TensorRef;
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using ParamsC = typename Epilogue::OutputTileIterator::Params;
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using TensorRefC = typename Epilogue::OutputTileIterator::TensorRef;
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using ParamsD = typename Epilogue::OutputTileIterator::Params;
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using TensorRefD = typename Epilogue::OutputTileIterator::TensorRef;
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using ParamsAux = typename Epilogue::AuxOutputTileIterator::Params;
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using TensorRefAux = typename Epilogue::AuxOutputTileIterator::TensorRef;
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/// Argument structure
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struct Arguments {
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//
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// Data members
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//
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GemmCoord problem_size;
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TensorRefA ref_A;
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TensorRefB ref_B;
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TensorRefC ref_C;
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TensorRefD ref_D;
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TensorRefE ref_E;
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TensorRefAux ref_Aux;
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void* ptr_Vector;
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typename LayoutC::Stride::Index ldr;
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typename Epilogue::OutputOp::Params epilogue;
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int split_k_slices;
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//
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// Methods
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//
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/// Default ctor
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CUTLASS_HOST_DEVICE
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Arguments(): problem_size(0, 0, 0), split_k_slices(1) {
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}
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/// Constructs an Arguments structure
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CUTLASS_HOST_DEVICE
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Arguments(
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GemmCoord problem_size_,
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TensorRefA ref_A_,
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TensorRefB ref_B_,
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TensorRefC ref_C_,
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TensorRefD ref_D_,
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TensorRefE ref_E_,
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TensorRefAux ref_Aux_,
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void* ptr_Vector_,
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typename LayoutC::Stride::Index ldr_,
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typename OutputOp::Params epilogue_ =
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typename OutputOp::Params(),
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int split_k_slices = 1
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):
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problem_size(problem_size_),
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ref_A(ref_A_),
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ref_B(ref_B_),
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ref_C(ref_C_),
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ref_D(ref_D_),
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ref_E(ref_E_),
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ref_Aux(ref_Aux_),
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ptr_Vector(ptr_Vector_),
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ldr(ldr_),
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epilogue(epilogue_),
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split_k_slices(split_k_slices) {
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}
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};
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/// Parameters structure
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struct Params : public SparseParamsBase<
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ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
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ParamsE, TensorRefE> {
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using Base = SparseParamsBase<
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ThreadblockSwizzle, ParamsA, TensorRefA, ParamsB, TensorRefB,
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ParamsE, TensorRefE>;
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//
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// Data members
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//
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ParamsC params_C;
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TensorRefC ref_C;
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ParamsD params_D;
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TensorRefD ref_D;
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ParamsAux params_Aux;
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TensorRefAux ref_Aux;
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void* ptr_Vector;
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typename LayoutC::Stride::Index ldr;
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typename OutputOp::Params output_op;
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int *semaphore;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params() { }
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CUTLASS_HOST_DEVICE
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Params(
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cutlass::gemm::GemmCoord const & problem_size,
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cutlass::gemm::GemmCoord const & grid_tiled_shape,
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TensorRefA ref_A,
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TensorRefB ref_B,
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TensorRefC ref_C,
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TensorRefD ref_D,
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TensorRefE ref_E,
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TensorRefAux ref_Aux,
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void* ptr_Vector,
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typename LayoutC::Stride::Index ldr,
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typename OutputOp::Params output_op = typename OutputOp::Params(),
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int *workspace = nullptr
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):
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Base(problem_size, grid_tiled_shape, ref_A, ref_B, ref_E, Mma::Shape::kK),
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params_C(ref_C.layout()),
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ref_C(ref_C),
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params_D(ref_D.layout()),
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ref_D(ref_D),
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output_op(output_op),
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ref_Aux(ref_Aux),
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params_Aux(ref_Aux.layout()),
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ptr_Vector(ptr_Vector),
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ldr(ldr) {
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semaphore = workspace;
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}
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};
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/// Shared memory storage structure
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union SharedStorage {
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typename Mma::SharedStorage main_loop;
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typename Epilogue::SharedStorage epilogue;
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};
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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SparseGemmWithAbsmax() { }
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/// Determines whether kernel satisfies alignment
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static Status can_implement(
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cutlass::gemm::GemmCoord const & problem_size,
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typename Mma::IteratorA::TensorRef ref_A,
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typename Mma::IteratorB::TensorRef ref_B,
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typename Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D,
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typename Mma::IteratorE::TensorRef ref_E) {
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static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
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static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
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static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
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static int const kAlignmentE = Mma::IteratorE::AccessType::kElements;
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if (!TensorRef_aligned(ref_A, kAlignmentA)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_B, kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_C, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_D, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_E, kAlignmentE)) {
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return Status::kErrorMisalignedOperand;
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}
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if ((problem_size.m() % kAlignmentA) || ((problem_size.k() / kSparse) % kAlignmentA) ||
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(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
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(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC) ||
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(problem_size.m() % kAlignmentE) || ((problem_size.k() / kSparse) % kAlignmentE)) {
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return Status::kErrorMisalignedOperand;
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}
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// The k dimension has to be the multiple of the Threadblock k because out
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// of bound meta data would be initialized to 0 by acync.zfill but 0 is not
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// a valid meta data.
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if (problem_size.k() % Mma::Shape::kK) {
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return Status::kErrorMisalignedOperand;
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}
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// M dimension has to be multiple of 32 (sparse float) or 16 (sparse int)
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// because of the row reordering of operand E
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static int const kAlignmentM = (sizeof(ElementE) == 2) ? 32 : 16;
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if (problem_size.m() % kAlignmentM) {
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return Status::kErrorMisalignedOperand;
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}
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return Status::kSuccess;
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}
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/// Executes one GEMM
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CUTLASS_DEVICE
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void operator()(Params const ¶ms, SharedStorage &shared_storage) {
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// Compute threadblock location
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord threadblock_tile_offset =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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// Early exit if CTA is out of range
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if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
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params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
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return;
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}
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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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cutlass::MatrixCoord tb_offset_B{
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threadblock_tile_offset.k() * params.gemm_k_size,
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threadblock_tile_offset.n() * Mma::Shape::kN
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};
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cutlass::MatrixCoord tb_offset_E{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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// Problem size is a function of threadblock index in the K dimension
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int problem_size_k = min(
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params.problem_size.k(),
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(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
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// Compute threadblock-scoped matrix multiply-add
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int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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// Compute position within threadblock
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int thread_idx = threadIdx.x;
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// Construct iterators to A, B, and E operands
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typename Mma::IteratorA iterator_A(
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params.params_A,
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params.ref_A.data(),
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{params.problem_size.m(), problem_size_k / kSparse},
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thread_idx,
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tb_offset_A);
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typename Mma::IteratorB iterator_B(
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params.params_B,
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params.ref_B.data(),
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{problem_size_k, params.problem_size.n()},
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thread_idx,
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tb_offset_B);
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typename Mma::IteratorE iterator_E(
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params.params_E, params.ref_E.data(),
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{params.problem_size.m(),
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problem_size_k / kSparse / kElementsPerElementE},
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thread_idx, tb_offset_E);
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// Broadcast the warp_id computed by lane 0 to ensure dependent code
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// is compiled as warp-uniform.
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int warp_idx = canonical_warp_idx_sync();
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int lane_idx = threadIdx.x % 32;
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//
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// Main loop
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//
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// Construct thread-scoped matrix multiply
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Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
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typename Mma::FragmentC accumulators;
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accumulators.clear();
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if (!kSplitKSerial || gemm_k_iterations > 0) {
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// Compute threadblock-scoped matrix multiply-add
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mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
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}
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//
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// Epilogue
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//
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OutputOp output_op(params.output_op);
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//
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// Masked tile iterators constructed from members
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//
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threadblock_tile_offset =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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//assume identity swizzle
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MatrixCoord threadblock_offset(
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.n() * Mma::Shape::kN
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);
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int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
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// Construct the semaphore.
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Semaphore semaphore(params.semaphore + block_idx, thread_idx);
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// If performing a reduction via split-K, fetch the initial synchronization
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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// Fetch the synchronization lock initially but do not block.
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semaphore.fetch();
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// Indicate which position in a serial reduction the output operator is currently updating
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output_op.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
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}
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typename Epilogue::ElementVector *ptr_Vector = static_cast<typename Epilogue::ElementVector *>(params.ptr_Vector);
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// Move to appropriate location for this output tile
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if (ptr_Vector) {
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ptr_Vector += threadblock_offset.column() + threadblock_tile_offset.m() * params.ldr;
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}
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// Tile iterator loading from source tensor.
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typename Epilogue::OutputTileIterator iterator_C(
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params.params_C,
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params.ref_C.data(),
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params.problem_size.mn(),
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thread_idx,
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threadblock_offset
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);
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// Tile iterator writing to destination tensor.
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typename Epilogue::OutputTileIterator iterator_D(
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params.params_D,
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params.ref_D.data(),
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params.problem_size.mn(),
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thread_idx,
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threadblock_offset
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);
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// Tile iterator writing to auxiliary destination tensor.
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typename Epilogue::AuxOutputTileIterator iterator_Aux(
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params.params_Aux,
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// Only the final block writes the auxiliary tensor
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((kSplitKSerial && params.grid_tiled_shape.k() > 1) &&
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(params.grid_tiled_shape.k() != threadblock_tile_offset.k() + 1))
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? nullptr
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: params.ref_Aux.data(),
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params.problem_size.mn(),
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thread_idx,
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threadblock_offset
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);
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Epilogue epilogue(
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shared_storage.epilogue,
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thread_idx,
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warp_idx,
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lane_idx);
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// Wait on the semaphore - this latency may have been covered by iterator construction
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
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if (threadblock_tile_offset.k()) {
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iterator_C = iterator_D;
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}
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semaphore.wait(threadblock_tile_offset.k());
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__threadfence();
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}
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// Execute the epilogue operator to update the destination tensor.
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epilogue(output_op,
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// Only the final block uses Vector
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((kSplitKSerial && params.grid_tiled_shape.k() > 1) &&
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(params.grid_tiled_shape.k() != threadblock_tile_offset.k() + 1))
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? nullptr
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: ptr_Vector,
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iterator_D,
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accumulators,
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iterator_C,
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iterator_Aux,
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params.problem_size.mn(),
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threadblock_offset);
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//
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// Release the semaphore
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//
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if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
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int lock = 0;
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if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
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// The final threadblock resets the semaphore for subsequent grids.
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lock = 0;
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}
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else {
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// Otherwise, the semaphore is incremented
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lock = threadblock_tile_offset.k() + 1;
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}
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__threadfence();
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semaphore.release(lock);
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}
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace gemm
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} // namespace cutlass
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