480 lines
17 KiB
C++
480 lines
17 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2022 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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/*!
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\file
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\brief Base device-level grouped kernel.
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*/
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#pragma once
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#include <limits>
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#include <numeric>
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#include <vector>
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/arch/arch.h"
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#include "cutlass/device_kernel.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/gemm/kernel/gemm_universal.h"
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#include "cutlass/gemm/kernel/default_gemm_universal.h"
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#include "cutlass/gemm/device/default_gemm_configuration.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 device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// GEMM Grouped
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template <typename BaseKernel_>
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class BaseGrouped {
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public:
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using BaseKernel = BaseKernel_;
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using ElementA = typename BaseKernel::ElementA;
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using LayoutA = typename BaseKernel::LayoutA;
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using TensorRefA = TensorRef<ElementA const, LayoutA>;
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static ComplexTransform const kTransformA = BaseKernel::kTransformA;
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static int const kAlignmentA = BaseKernel::kAlignmentA;
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using ElementB = typename BaseKernel::ElementB;
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using LayoutB = typename BaseKernel::LayoutB;
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using TensorRefB = TensorRef<ElementB const, LayoutB>;
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static ComplexTransform const kTransformB = BaseKernel::kTransformB;
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static int const kAlignmentB = BaseKernel::kAlignmentB;
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using ElementC = typename BaseKernel::ElementC;
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using LayoutC = typename BaseKernel::LayoutC;
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using TensorRefC = TensorRef<ElementC const, LayoutC>;
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using TensorRefD = TensorRef<ElementC, LayoutC>;
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static int const kAlignmentC = BaseKernel::kAlignmentC;
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using ElementAccumulator = typename BaseKernel::Mma::Policy::Operator::ElementC;
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using EpilogueOutputOp = typename BaseKernel::EpilogueOutputOp;
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using ThreadblockSwizzle = typename BaseKernel::ThreadblockSwizzle;
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using Operator = typename BaseKernel::Operator;
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using WarpMmaOperator = typename BaseKernel::Mma::Policy::Operator;
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using ArchMmaOperator = typename WarpMmaOperator::ArchMmaOperator;
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using MathOperator = typename WarpMmaOperator::MathOperator;
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using OperatorClass = typename WarpMmaOperator::OperatorClass;
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using ArchTag = typename WarpMmaOperator::ArchTag;
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using ThreadblockShape = typename BaseKernel::Mma::Shape;
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using WarpShape = typename BaseKernel::WarpShape;
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using InstructionShape = typename BaseKernel::InstructionShape;
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static int const kStages = BaseKernel::Mma::kStages;
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/// Argument structure
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using Arguments = typename BaseKernel::Arguments;
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using ProblemInfo = typename BaseKernel::ProblemVisitor::ProblemInfo;
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protected:
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/// Kernel parameters object
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typename BaseKernel::Params params_;
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private:
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/// Get the number of tiles across all problems in a group
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static int32_t group_tile_count(const cutlass::gemm::GemmCoord* problem_sizes_ptr, int problem_count) {
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int32_t tiles = 0;
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for (int32_t i = 0; i < problem_count; ++i) {
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cutlass::gemm::GemmCoord problem = problem_sizes_ptr[i];
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BaseKernel::ProblemVisitor::possibly_transpose_problem(problem);
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tiles += problem_tile_count(problem);
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}
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return tiles;
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}
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/// Copy from `data` to `workspace`
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Status copy_to_workspace(void* workspace, void* data, size_t bytes) {
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cudaError_t cuda_error = cudaMemcpy(workspace, data, bytes, cudaMemcpyHostToDevice);
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if (cuda_error != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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cuda_error = cudaGetLastError();
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CUTLASS_TRACE_HOST(
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" cudaMemcpy() returned error "
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<< cudaGetErrorString(cuda_error));
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return Status::kErrorInternal;
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}
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return Status::kSuccess;
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}
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/// Precomputes scheduling information for the grouped GEMM
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Status precompute(Arguments const &args, int32_t tile_count, void* workspace) {
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size_t workspace_bytes = get_workspace_size(args);
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std::vector<uint8_t> host_workspace(workspace_bytes);
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BaseKernel::ProblemVisitor::host_precompute(args.host_problem_sizes,
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args.problem_count,
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args.threadblock_count,
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(void*)host_workspace.data());
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return copy_to_workspace(workspace, host_workspace.data(), workspace_bytes);
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}
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/// Reorder `data` according to `indices`
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template <typename T>
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static void reorder_array(T* data, const std::vector<size_t>& indices) {
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// For now, simply create a copy of the data and then copy over to the original.
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std::vector<T> copy(indices.size());
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for (int i = 0; i < indices.size(); ++i) {
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copy.at(i) = data[indices[i]];
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}
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memcpy(data, copy.data(), indices.size() * sizeof(T));
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}
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public:
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/// Constructs the GEMM.
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BaseGrouped() { }
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/// Determines whether the GEMM can execute the given problem.
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static Status can_implement(Arguments const &args) {
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return BaseKernel::can_implement(args);
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}
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/// Get the number of tiles in a problem
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static int32_t problem_tile_count(cutlass::gemm::GemmCoord const &problem) {
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auto grid = BaseKernel::ProblemVisitor::grid_shape(problem);
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return BaseKernel::ProblemVisitor::tile_count(grid);
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}
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/// Get the number of tiles across all problems in a group
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static int32_t group_tile_count(Arguments const &args) {
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if (args.host_problem_sizes == nullptr) {
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CUTLASS_TRACE_HOST("Received nullptr for `args.host_problem_sizes");
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return -1;
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}
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return group_tile_count(args.host_problem_sizes, args.problem_count);
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}
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/// Gets the workspace size
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static size_t get_workspace_size(Arguments const &args) {
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if (BaseKernel::ProblemVisitor::kRequiresPrecomputation) {
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return BaseKernel::ProblemVisitor::get_workspace_size(args.host_problem_sizes,
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args.problem_count,
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args.threadblock_count);
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} else {
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return 0;
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}
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}
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/// Computes the grid shape
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static dim3 get_grid_shape(Arguments const &args) {
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return dim3(args.threadblock_count, 1, 1);
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}
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/// Computes the maximum number of active blocks per multiprocessor
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static int maximum_active_blocks(int smem_capacity = -1) {
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CUTLASS_TRACE_HOST("GemmUniversalBase::maximum_active_blocks()");
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int smem_size = int(sizeof(typename BaseKernel::SharedStorage));
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CUTLASS_TRACE_HOST(" smem_size: " << smem_size << " bytes");
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cudaError_t result;
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if (smem_size > (48 << 10)) {
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result = cudaFuncSetAttribute(Kernel<BaseKernel>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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smem_size);
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if (result != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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result = cudaGetLastError();
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CUTLASS_TRACE_HOST(
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" cudaFuncSetAttribute() returned error "
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<< cudaGetErrorString(result));
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return -1;
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}
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}
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int max_active_blocks = -1;
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result = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
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&max_active_blocks,
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Kernel<BaseKernel>,
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BaseKernel::kThreadCount,
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smem_size);
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if (result != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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result = cudaGetLastError();
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CUTLASS_TRACE_HOST(
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" cudaOccupancyMaxActiveBlocksPerMultiprocessor() returned error "
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<< cudaGetErrorString(result));
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return -1;
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}
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CUTLASS_TRACE_HOST(" max_active_blocks: " << max_active_blocks);
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return max_active_blocks;
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}
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/// Sorts each pointer passed in according to the indices that sort
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/// `problem_sizes_ptr` in descending order of problem-K dimension.
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static void sort_problems(int problem_count,
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cutlass::gemm::GemmCoord* problem_sizes_ptr,
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int64_t* lda_host_ptr,
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int64_t* ldb_host_ptr,
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int64_t* ldc_host_ptr,
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int64_t* ldd_host_ptr,
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int64_t* offset_A_ptr,
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int64_t* offset_B_ptr,
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int64_t* offset_C_ptr,
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int64_t* offset_D_ptr)
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{
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std::vector<size_t> indices(problem_count);
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std::iota(indices.begin(), indices.end(), 0);
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std::stable_sort(indices.begin(), indices.end(),
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[&problem_sizes_ptr](size_t i, size_t j) {
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return problem_sizes_ptr[i].k() > problem_sizes_ptr[j].k();
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});
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reorder_array(problem_sizes_ptr, indices);
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reorder_array(lda_host_ptr, indices);
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reorder_array(ldb_host_ptr, indices);
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reorder_array(ldc_host_ptr, indices);
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reorder_array(ldd_host_ptr, indices);
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reorder_array(offset_A_ptr, indices);
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reorder_array(offset_B_ptr, indices);
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reorder_array(offset_C_ptr, indices);
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reorder_array(offset_D_ptr, indices);
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}
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/// Computes the number of threadblocks to launch for the grouped kernel
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static int sufficient(const cutlass::gemm::GemmCoord* problem_sizes_ptr=nullptr,
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int problem_count=0,
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int available_sm_count=-1) {
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// Determine the number of blocks that would be launched to fill up a single
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// wave on the GPU with each SM having maximum occupancy.
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cudaDeviceProp properties;
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int device_idx;
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cudaError_t result = cudaGetDevice(&device_idx);
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if (result != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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result = cudaGetLastError();
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CUTLASS_TRACE_HOST(" cudaGetDevice() returned error "
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<< cudaGetErrorString(result));
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return 0;
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}
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result = cudaGetDeviceProperties(&properties, device_idx);
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if (result != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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result = cudaGetLastError();
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CUTLASS_TRACE_HOST(" cudaGetDeviceProperties() returned error "
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<< cudaGetErrorString(result));
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return 0;
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}
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bool override_sm_count = (available_sm_count < 0 || available_sm_count > properties.multiProcessorCount);
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if (override_sm_count) {
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available_sm_count = properties.multiProcessorCount;
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}
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int max_active_blocks = maximum_active_blocks();
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if (max_active_blocks <= 0) {
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return 0;
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}
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int occupancy_based_block_count = available_sm_count * max_active_blocks;
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if (problem_sizes_ptr == nullptr || problem_count == 0) {
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return occupancy_based_block_count;
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}
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int total_tiles = group_tile_count(problem_sizes_ptr, problem_count);
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// If the group contains a single problem, launching the exact number of
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// threadblocks needed to cover the problem minimizes the work performed
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// per threadblock in finding the next tile to compute. We return total_tiles
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// unless the user has provided the SM count.
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if (problem_count == 1 && override_sm_count) {
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return total_tiles;
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}
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// Choose between the full wave of threadblocks and the tile count. If there
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// are fewer tiles in the group than threadblocks in the full wave, only
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// some threadblocks will be assigned tiles. Those threadblocks
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// which are not assigned tiles still need to perform the work of iterating through
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// problem sizes to determine that they have no work to do. This competes for cycles
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// with those threadblocks that are assigned tiles to compute.
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return min(total_tiles, occupancy_based_block_count);
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}
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/// Initializes GEMM state from arguments.
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Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
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CUTLASS_TRACE_HOST("GemmUniversalBase::initialize() - workspace "
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<< workspace << ", stream: " << (stream ? "non-null" : "null"));
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// Workspace
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size_t workspace_bytes = get_workspace_size(args);
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if (workspace_bytes && !workspace) {
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return Status::kErrorWorkspaceNull;
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}
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if (BaseKernel::ProblemVisitor::kRequiresPrecomputation) {
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int32_t tile_count = group_tile_count(args);
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Status status = precompute(args, tile_count, workspace);
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if (status != Status::kSuccess) {
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return status;
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}
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params_ = typename BaseKernel::Params(args, workspace, tile_count);
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} else {
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params_ = typename BaseKernel::Params(args, workspace);
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}
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// Specify shared memory capacity for kernel.
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int smem_size = int(sizeof(typename BaseKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(Kernel<BaseKernel>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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smem_size);
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if (result != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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return Status::kSuccess;
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}
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/// Lightweight update given a subset of arguments
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Status update(Arguments const &args, void *workspace = nullptr) {
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size_t workspace_bytes = get_workspace_size(args);
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if (workspace_bytes && !workspace) {
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return Status::kErrorWorkspaceNull;
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}
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if (BaseKernel::ProblemVisitor::kRequiresPrecomputation) {
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int32_t tile_count = group_tile_count(args);
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Status status = precompute(args, tile_count, workspace);
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if (status != Status::kSuccess) {
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return status;
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}
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params_.update(args, workspace, tile_count);
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} else {
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params_.update(args, workspace);
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}
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return Status::kSuccess;
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}
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/// Runs the kernel using initialized state.
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Status run(cudaStream_t stream = nullptr) {
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//
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// Configure grid and block dimensions
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//
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if (!params_.problem_visitor.problem_count) {
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return Status::kSuccess;
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}
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dim3 grid(params_.threadblock_count, 1, 1);
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dim3 block(BaseKernel::kThreadCount, 1, 1);
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int smem_size = int(sizeof(typename BaseKernel::SharedStorage));
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//
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// Launch kernel
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//
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// Launch
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cutlass::Kernel<BaseKernel><<<grid, block, smem_size, stream>>>(params_);
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//
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// Query for errors
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//
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cudaError_t result = cudaGetLastError();
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if (result != cudaSuccess) {
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// Call cudaGetLastError() to clear the error bit
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result = cudaGetLastError();
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CUTLASS_TRACE_HOST(" grid launch failed with error " << cudaGetErrorString(result));
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return Status::kErrorInternal;
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}
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return Status::kSuccess;
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}
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/// Runs the kernel using initialized state.
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Status operator()(cudaStream_t stream = nullptr) {
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return run(stream);
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}
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/// Initializes and runs the kernel.
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Status operator()(
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Arguments const &args,
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void *workspace,
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cudaStream_t stream = nullptr) {
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Status status = initialize(args, workspace, stream);
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if (status == Status::kSuccess) {
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status = run(stream);
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}
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return status;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace device
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} // namespace gemm
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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