537 lines
16 KiB
C++
537 lines
16 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 GEMM kernel to support the epilogue visitor model
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for customized softmax partial reduction epilogue fusion.
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This source file will likely be moved to `include/cutlass/gemm/kernel/` in the future once
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its usage has been stabilized. For now, it is included in this example to demonstrate
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some basic output fusion options.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/fast_math.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/matrix_coord.h"
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#include "cutlass/complex.h"
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#include "cutlass/semaphore.h"
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#include "cutlass/trace.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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>
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struct GemmWithEpilogueVisitor {
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public:
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using EpilogueVisitor = typename Epilogue::Visitor;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using ElementA = typename Mma::IteratorA::Element;
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using LayoutA = typename Mma::IteratorA::Layout;
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using TensorRefA = TensorRef<ElementA, LayoutA>;
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using ElementB = typename Mma::IteratorB::Element;
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using LayoutB = typename Mma::IteratorB::Layout;
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using TensorRefB = TensorRef<ElementB, LayoutB>;
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using ElementC = typename EpilogueVisitor::ElementOutput;
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using LayoutC = typename Epilogue::Layout;
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using TensorRefC = TensorRef<ElementC, LayoutC>;
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static ComplexTransform const kTransformA = Mma::kTransformA;
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static ComplexTransform const kTransformB = Mma::kTransformB;
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using Operator = typename Mma::Operator;
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using OperatorClass = typename Mma::Operator::OperatorClass;
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using ThreadblockShape = typename Mma::Shape;
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using WarpShape = typename Mma::Operator::Shape;
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using InstructionShape = typename Mma::Policy::Operator::InstructionShape;
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using ArchTag = typename Mma::ArchTag;
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using ElementNorm = typename EpilogueVisitor::ElementNorm;
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using ElementSum = typename EpilogueVisitor::ElementSum;
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static int const kStages = Mma::kStages;
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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 = EpilogueVisitor::kElementsPerAccess;
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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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/// Split-K preserves splits that are 128b aligned
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static int const kSplitKAlignment = const_max(
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128 / sizeof_bits<ElementA>::value,
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128 / sizeof_bits<ElementB>::value
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);
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//
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// Structures
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//
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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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GemmUniversalMode mode;
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GemmCoord problem_size;
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int batch_count;
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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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TensorRefC ref_D;
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ElementNorm *ptr_Max;
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ElementSum *ptr_Sum;
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int64_t batch_stride_A;
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int64_t batch_stride_B;
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typename EpilogueVisitor::Arguments epilogue_visitor;
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//
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// Methods
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//
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Arguments():
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mode(GemmUniversalMode::kGemm),
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batch_count(1)
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{ }
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/// constructs an arguments structure
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Arguments(
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GemmUniversalMode mode_,
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GemmCoord problem_size_,
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int batch_count_,
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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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TensorRefC ref_D_,
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ElementNorm *ptr_Max_,
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ElementSum *ptr_Sum_,
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int64_t batch_stride_A_,
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int64_t batch_stride_B_,
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typename EpilogueVisitor::Arguments epilogue_visitor_
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):
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mode(mode_),
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problem_size(problem_size_),
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batch_count(batch_count_),
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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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ptr_Max(ptr_Max_),
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ptr_Sum(ptr_Sum_),
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batch_stride_A(batch_stride_A_),
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batch_stride_B(batch_stride_B_),
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epilogue_visitor(epilogue_visitor_)
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{
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}
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};
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//
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// Structure for precomputing values in host memory and passing to kernels
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//
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/// Parameters structure
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struct Params {
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cutlass::gemm::GemmCoord problem_size;
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cutlass::gemm::GemmCoord grid_tiled_shape;
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int swizzle_log_tile;
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typename Mma::IteratorA::Params params_A;
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typename Mma::IteratorB::Params params_B;
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typename EpilogueVisitor::OutputTileIterator::Params params_C;
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typename EpilogueVisitor::OutputTileIterator::Params params_D;
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GemmUniversalMode mode;
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int batch_count;
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int gemm_k_size;
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void * ptr_A;
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void * ptr_B;
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ElementC * ptr_C;
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ElementC * ptr_D;
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ElementNorm * ptr_Max;
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ElementSum * ptr_Sum;
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int64_t batch_stride_A;
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int64_t batch_stride_B;
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typename EpilogueVisitor::Params epilogue_visitor;
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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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swizzle_log_tile(0),
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params_A(0),
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params_B(0),
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params_C(0),
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params_D(0),
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batch_count(0),
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gemm_k_size(0),
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mode(cutlass::gemm::GemmUniversalMode::kGemm),
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ptr_A(nullptr),
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ptr_B(nullptr),
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ptr_C(nullptr),
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ptr_D(nullptr),
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ptr_Max(nullptr),
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ptr_Sum(nullptr),
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batch_stride_A(0),
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batch_stride_B(0)
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{ }
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Params(
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Arguments const &args
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):
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problem_size(args.problem_size),
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swizzle_log_tile(0),
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params_A(args.ref_A.layout()),
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params_B(args.ref_B.layout()),
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params_C(args.ref_C.layout()),
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params_D(args.ref_D.layout()),
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mode(args.mode),
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batch_count(args.batch_count),
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gemm_k_size(args.problem_size.k()),
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ptr_A(args.ref_A.data()),
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ptr_B(args.ref_B.data()),
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ptr_C(args.ref_C.data()),
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ptr_D(args.ref_D.data()),
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ptr_Max(args.ptr_Max),
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ptr_Sum(args.ptr_Sum),
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batch_stride_A(args.batch_stride_A),
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batch_stride_B(args.batch_stride_B),
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epilogue_visitor(args.epilogue_visitor)
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{
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ThreadblockSwizzle threadblock_swizzle;
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grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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args.batch_count);
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if (args.mode == GemmUniversalMode::kGemm || args.mode == GemmUniversalMode::kGemmSplitKParallel) {
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int const kAlignK = const_max(const_max(128 / sizeof_bits<ElementA>::value, 128 / sizeof_bits<ElementB>::value), 1);
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gemm_k_size = round_up(ceil_div(args.problem_size.k(), args.batch_count), kAlignK);
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if (gemm_k_size) {
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grid_tiled_shape.k() = ceil_div(args.problem_size.k(), gemm_k_size);
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}
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}
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swizzle_log_tile = threadblock_swizzle.get_log_tile(grid_tiled_shape);
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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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struct {
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typename Epilogue::SharedStorage epilogue;
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typename EpilogueVisitor::SharedStorage visitor;
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} epilogue;
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};
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public:
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//
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// Methods
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//
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CUTLASS_DEVICE
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GemmWithEpilogueVisitor() { }
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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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CUTLASS_TRACE_HOST("GemmWithEpilogueVisitor::can_implement()");
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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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bool isAMisaligned = false;
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bool isBMisaligned = false;
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bool isCMisaligned = false;
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if (platform::is_same<LayoutA, layout::RowMajor>::value) {
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isAMisaligned = problem_size.k() % kAlignmentA;
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} else if (platform::is_same<LayoutA, layout::ColumnMajor>::value) {
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isAMisaligned = problem_size.m() % kAlignmentA;
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} else if (platform::is_same<LayoutA, layout::ColumnMajorInterleaved<32>>::value
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|| platform::is_same<LayoutA, layout::ColumnMajorInterleaved<64>>::value) {
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isAMisaligned = problem_size.k() % kAlignmentA;
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}
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if (platform::is_same<LayoutB, layout::RowMajor>::value) {
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isBMisaligned = problem_size.n() % kAlignmentB;
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} else if (platform::is_same<LayoutB, layout::ColumnMajor>::value) {
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isBMisaligned = problem_size.k() % kAlignmentB;
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} else if (platform::is_same<LayoutB, layout::RowMajorInterleaved<32>>::value
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|| platform::is_same<LayoutB, layout::RowMajorInterleaved<64>>::value) {
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isBMisaligned = problem_size.k() % kAlignmentB;
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}
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if (platform::is_same<LayoutC, layout::RowMajor>::value) {
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isCMisaligned = problem_size.n() % kAlignmentC;
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} else if (platform::is_same<LayoutC, layout::ColumnMajor>::value) {
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isCMisaligned = problem_size.m() % kAlignmentC;
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} else if (platform::is_same<LayoutC, layout::ColumnMajorInterleaved<32>>::value
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|| platform::is_same<LayoutC, layout::ColumnMajorInterleaved<64>>::value) {
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isCMisaligned = problem_size.n() % kAlignmentC;
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}
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if (isAMisaligned) {
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CUTLASS_TRACE_HOST(" returning kErrorMisalignedOperand for A operand");
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return Status::kErrorMisalignedOperand;
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}
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if (isBMisaligned) {
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CUTLASS_TRACE_HOST(" returning kErrorMisalignedOperand for B operand");
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return Status::kErrorMisalignedOperand;
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}
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if (isCMisaligned) {
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CUTLASS_TRACE_HOST(" returning kErrorMisalignedOperand for C operand");
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return Status::kErrorMisalignedOperand;
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}
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CUTLASS_TRACE_HOST(" returning kSuccess");
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return Status::kSuccess;
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}
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static Status can_implement(Arguments const &args) {
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return can_implement(args.problem_size);
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}
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#define SPLIT_K_ENABLED 1
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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 = 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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int offset_k = 0;
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int problem_size_k = params.problem_size.k();
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ElementA *ptr_A = static_cast<ElementA *>(params.ptr_A);
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ElementB *ptr_B = static_cast<ElementB *>(params.ptr_B);
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#if SPLIT_K_ENABLED
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//
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// Fetch pointers based on mode.
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//
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if (params.mode == GemmUniversalMode::kGemm ||
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params.mode == GemmUniversalMode::kGemmSplitKParallel) {
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if (threadblock_tile_offset.k() + 1 < params.grid_tiled_shape.k()) {
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problem_size_k = (threadblock_tile_offset.k() + 1) * params.gemm_k_size;
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}
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offset_k = threadblock_tile_offset.k() * params.gemm_k_size;
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}
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else if (params.mode == GemmUniversalMode::kBatched) {
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ptr_A += threadblock_tile_offset.k() * params.batch_stride_A;
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ptr_B += threadblock_tile_offset.k() * params.batch_stride_B;
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}
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else if (params.mode == GemmUniversalMode::kArray) {
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ptr_A = static_cast<ElementA * const *>(params.ptr_A)[threadblock_tile_offset.k()];
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ptr_B = static_cast<ElementB * const *>(params.ptr_B)[threadblock_tile_offset.k()];
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}
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#endif
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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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offset_k,
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};
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cutlass::MatrixCoord tb_offset_B{
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offset_k,
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threadblock_tile_offset.n() * Mma::Shape::kN
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};
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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 and B operands
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typename Mma::IteratorA iterator_A(
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params.params_A,
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ptr_A,
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{params.problem_size.m(), problem_size_k},
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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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ptr_B,
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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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// 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 = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
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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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// Compute threadblock-scoped matrix multiply-add
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int gemm_k_iterations = (problem_size_k - offset_k + Mma::Shape::kK - 1) / Mma::Shape::kK;
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// Compute threadblock-scoped matrix multiply-add
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mma(
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gemm_k_iterations,
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accumulators,
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iterator_A,
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iterator_B,
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accumulators);
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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 = 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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//
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// Construct the epilogue visitor
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//
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EpilogueVisitor epilogue_visitor(
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params.epilogue_visitor,
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shared_storage.epilogue.visitor,
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params.problem_size.mn(),
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thread_idx,
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warp_idx,
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lane_idx,
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params.params_C,
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params.params_D,
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params.ptr_C,
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params.ptr_D,
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params.ptr_Max,
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params.ptr_Sum,
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threadblock_offset,
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blockIdx.y *params.problem_size.m() );
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if (params.mode == GemmUniversalMode::kGemm) {
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// Indicate which position in a serial reduction the output operator is currently updating
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epilogue_visitor.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
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}
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else if (params.mode == GemmUniversalMode::kBatched || params.mode == GemmUniversalMode::kArray) {
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epilogue_visitor.set_batch_index(threadblock_tile_offset.k());
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}
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|
// Construct the epilogue
|
|
Epilogue epilogue(
|
|
shared_storage.epilogue.epilogue,
|
|
thread_idx,
|
|
warp_idx,
|
|
lane_idx);
|
|
|
|
// Execute the epilogue operator to update the destination tensor.
|
|
epilogue(epilogue_visitor, accumulators);
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace kernel
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|