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All rights reserved. * * Redistribution and use in source and binary forms, with or without modification, are permitted * provided that the following conditions are met: * * Redistributions of source code must retain the above copyright notice, this list of * conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above copyright notice, this list of * conditions and the following disclaimer in the documentation and/or other materials * provided with the distribution. * * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used * to endorse or promote products derived from this software without specific prior written * permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * **************************************************************************************************/ /*! \file \brief */ #pragma once #include "cutlass/cutlass.h" #include "cutlass/numeric_types.h" #include "cutlass/arch/arch.h" #include "cutlass/device_kernel.h" #include "cutlass/gemm/gemm.h" #include "cutlass/gemm/threadblock/threadblock_swizzle.h" #include "cutlass/gemm/kernel/gemm_universal.h" #include "cutlass/gemm/kernel/default_gemm_universal.h" #include "cutlass/gemm/device/default_gemm_configuration.h" #include "cutlass/gemm/device/gemm_universal_base.h" //////////////////////////////////////////////////////////////////////////////// namespace cutlass { namespace gemm { namespace device { ///////////////////////////////////////////////////////////////////////////////////////////////// /*! The universal GEMM accommodates serial reductions, parallel reductions, batched strided, and batched array variants. */ template < /// Element type for A matrix operand typename ElementA_, /// Layout type for A matrix operand typename LayoutA_, /// Element type for B matrix operand typename ElementB_, /// Layout type for B matrix operand typename LayoutB_, /// Element type for C and D matrix operands typename ElementC_, /// Layout type for C and D matrix operands typename LayoutC_, /// Element type for internal accumulation typename ElementAccumulator_ = ElementC_, /// Operator class tag typename OperatorClass_ = arch::OpClassSimt, /// Tag indicating architecture to tune for. This is the minimum SM that /// supports the intended feature. The device kernel can be built /// targeting any SM larger than this number. typename ArchTag_ = arch::Sm61, /// Threadblock-level tile size (concept: GemmShape) typename ThreadblockShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::ThreadblockShape, /// Warp-level tile size (concept: GemmShape) typename WarpShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::WarpShape, /// Instruction-level tile size (concept: GemmShape) typename InstructionShape_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::InstructionShape, /// Epilogue output operator typename EpilogueOutputOp_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::EpilogueOutputOp, /// Threadblock-level swizzling operator typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle<>, /// Number of stages used in the pipelined mainloop int Stages = DefaultGemmConfiguration::kStages, /// Access granularity of A matrix in units of elements int AlignmentA = DefaultGemmConfiguration::kAlignmentA, /// Access granularity of B matrix in units of elements int AlignmentB = DefaultGemmConfiguration::kAlignmentB, /// Operation performed by GEMM typename Operator_ = typename DefaultGemmConfiguration< OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_, ElementAccumulator_>::Operator, /// Complex elementwise transformation on A operand ComplexTransform TransformA = ComplexTransform::kNone, /// Complex elementwise transformation on B operand ComplexTransform TransformB = ComplexTransform::kNone > class GemmUniversal : GemmUniversalBase< typename kernel::DefaultGemmUniversal< ElementA_, LayoutA_, TransformA, AlignmentA, ElementB_, LayoutB_, TransformB, AlignmentB, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, Operator_ >::GemmKernel > { public: using ElementAccumulator = ElementAccumulator_; using OperatorClass = OperatorClass_; using ArchTag = ArchTag_; using ThreadblockShape = ThreadblockShape_; using WarpShape = WarpShape_; using InstructionShape = InstructionShape_; using EpilogueOutputOp = EpilogueOutputOp_; using ThreadblockSwizzle = ThreadblockSwizzle_; using Operator = Operator_; static int const kStages = Stages; static int const kAlignmentA = AlignmentA; static int const kAlignmentB = AlignmentB; static int const kAlignmentC = EpilogueOutputOp::kCount; static ComplexTransform const kTransformA = TransformA; static ComplexTransform const kTransformB = TransformB; using Base = GemmUniversalBase< typename kernel::DefaultGemmUniversal< ElementA_, LayoutA_, TransformA, AlignmentA, ElementB_, LayoutB_, TransformB, AlignmentB, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, Operator_ >::GemmKernel >; using Arguments = typename Base::Arguments; using GemmKernel = typename Base::GemmKernel; }; //////////////////////////////////////////////////////////////////////////////// /// Parital specialization for column-major output exchanges problem size and operand. template < /// Element type for A matrix operand typename ElementA_, /// Layout type for A matrix operand typename LayoutA_, /// Element type for B matrix operand typename ElementB_, /// Layout type for B matrix operand typename LayoutB_, /// Element type for C and D matrix operands typename ElementC_, /// Element type for internal accumulation typename ElementAccumulator_, /// Operator class tag typename OperatorClass_, /// Tag indicating architecture to tune for. This is the minimum SM that /// supports the intended feature. The device kernel can be built /// targeting any SM larger than this number. typename ArchTag_, /// Threadblock-level tile size (concept: GemmShape) typename ThreadblockShape_, /// Warp-level tile size (concept: GemmShape) typename WarpShape_, /// Instruction-level tile size (concept: GemmShape) typename InstructionShape_, /// Epilogue output operator typename EpilogueOutputOp_, /// Threadblock-level swizzling operator typename ThreadblockSwizzle_, /// Number of stages used in the pipelined mainloop int Stages, /// Access granularity of A matrix in units of elements int AlignmentA, /// Access granularity of B matrix in units of elements int AlignmentB, /// Operation performed by GEMM typename Operator_, /// Complex elementwise transformation on A operand ComplexTransform TransformA, /// Complex elementwise transformation on B operand ComplexTransform TransformB> class GemmUniversal { public: using ElementA = ElementA_; using LayoutA = LayoutA_; using TensorRefA = TensorRef; using ElementB = ElementB_; using LayoutB = LayoutB_; using TensorRefB = TensorRef; using ElementC = ElementC_; using LayoutC = layout::ColumnMajor; using TensorRefC = TensorRef; using TensorRefD = TensorRef; using ElementAccumulator = ElementAccumulator_; using OperatorClass = OperatorClass_; using ArchTag = ArchTag_; using ThreadblockShape = ThreadblockShape_; using WarpShape = WarpShape_; using InstructionShape = InstructionShape_; using EpilogueOutputOp = EpilogueOutputOp_; using ThreadblockSwizzle = ThreadblockSwizzle_; using Operator = Operator_; static int const kStages = Stages; static int const kAlignmentA = AlignmentA; static int const kAlignmentB = AlignmentB; static ComplexTransform const kTransformA = TransformA; static ComplexTransform const kTransformB = TransformB; using UnderlyingOperator = typename GemmUniversal< ElementB, typename layout::LayoutTranspose::type, ElementA, typename layout::LayoutTranspose::type, ElementC, layout::RowMajor, ElementAccumulator, OperatorClass, ArchTag, ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp, ThreadblockSwizzle, Stages, kAlignmentB, kAlignmentA, Operator, kTransformB, kTransformA >::Base; using GemmKernel = typename UnderlyingOperator::GemmKernel; static int const kAlignmentC = EpilogueOutputOp::kCount; /// Argument structure using Arguments = typename UnderlyingOperator::Arguments; private: UnderlyingOperator underlying_operator_; public: /// Constructs the GEMM. GemmUniversal() { } /// Helper to construct a transposed equivalent for the underying GEMM operator static Arguments to_underlying_arguments(Arguments const &args) { return args.transposed_problem(); } /// Determines whether the GEMM can execute the given problem. static Status can_implement(Arguments const &args) { return UnderlyingOperator::can_implement(to_underlying_arguments(args)); } /// Gets the workspace size static size_t get_workspace_size(Arguments const &args) { return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args)); } /// Computes the grid shape static dim3 get_grid_shape(Arguments const &args) { return UnderlyingOperator::get_grid_shape(to_underlying_arguments(args)); } /// Computes the maximum number of active blocks per multiprocessor static int maximum_active_blocks(int smem_capacity = -1) { return UnderlyingOperator::maximum_active_blocks(smem_capacity); } /// Initializes GEMM state from arguments. Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { return underlying_operator_.initialize(to_underlying_arguments(args), workspace, stream); } /// Lightweight update given a subset of arguments Status update(Arguments const &args, void *workspace = nullptr) { return underlying_operator_.update(to_underlying_arguments(args), workspace); } /// Runs the kernel using initialized state. Status run(cudaStream_t stream = nullptr) { return underlying_operator_.run(stream); } /// Runs the kernel using initialized state. Status operator()(cudaStream_t stream = nullptr) { return run(stream); } /// Runs the kernel using initialized state. Status operator()( Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { Status status = initialize(args, workspace, stream); if (status == Status::kSuccess) { status = run(stream); } return status; } }; //////////////////////////////////////////////////////////////////////////////// } // namespace device } // namespace gemm } // namespace cutlass ////////////////////////////////////////////////////////////////////////////////