704 lines
22 KiB
C++
704 lines
22 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 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 Template for a pipelined batch GEMM kernel.
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*/
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#pragma once
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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/threadblock/threadblock_swizzle.h"
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#include "cutlass/gemm/kernel/gemm_batched.h"
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#include "cutlass/gemm/kernel/default_gemm.h"
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#include "cutlass/gemm/device/default_gemm_configuration.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 device-level operator. This is an interface to efficient CUTLASS GEMM kernels that may
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be invoked from host code.
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The contributions of this class are:
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1. At compile time, it maps data types and high-level structural parameters onto
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specific CUTLASS components.
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2. At runtime, it maps logical arguments to GEMM problems to kernel parameters.
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3. At runtime, it launches kernels on the device.
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The intent is to provide a convenient mechanism for interacting with most plausible GEMM
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configurations for each supported architecture. Consequently, not all parameters are exposed
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to the top-level interface. Rather, sensible defaults at each level of the CUTLASS hierarchy
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are selected to tradeoff simplicity of the interface with flexibility. We expect
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most configurations to be specified at this level. Applications with more exotic requirements
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may construct their kernels of interest using CUTLASS components at the threadblock, warp,
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and thread levels of abstraction.
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CUTLASS exposes computations using the functor design pattern in which objects compose some
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internal state with an overloaded function call operator. This enables decoupling of
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initialization from execution, possibly reducing overhead during steady state phases of
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application execution.
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CUTLASS device-level operators expose an Arguments structure encompassing each logical
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input to the computation. This is distinct from the kernel-level Params structure pattern
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which contains application-specific precomputed state needed by the device code.
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Example of a CUTLASS GEMM operator implementing the functionality of cuBLAS's SGEMM NN
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is as follows:
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//
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// Instantiate the CUTLASS GEMM operator.
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//
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cutlass::gemm::device::Gemm<
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::ColumnMajor
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> gemm_op;
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//
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// Launch the GEMM operation on the device
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//
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cutlass::Status status = gemm_op({
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{m, n, k}, // GemmCoord problem_size,
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{A, lda}, // TensorRef<float, layout::ColumnMajor> ref_A,
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{B, ldb}, // TensorRef<float, layout::ColumnMajor> ref_B,
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{C, ldc}, // TensorRef<float, layout::ColumnMajor> ref_C,
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{D, ldd}, // TensorRef<float, layout::ColumnMajor> ref_D,
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{alpha, beta} // EpilogueOutputOp::Params epilogue_op_params
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});
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A simplified view of the template is listed below.
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template <
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/// Element type for A matrix operand
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typename ElementA,
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/// Layout type for A matrix operand
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typename LayoutA,
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/// Element type for B matrix operand
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typename ElementB,
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/// Layout type for B matrix operand
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typename LayoutB,
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/// Element type for C and D matrix operands
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typename ElementC,
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/// Layout type for C and D matrix operands
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typename LayoutC,
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/// Element type for internal accumulation
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typename ElementAccumulator,
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/// Operator class tag
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typename OperatorClass,
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/// Tag indicating architecture to tune for. This is the minimum SM that
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/// supports the intended feature. The device kernel can be built
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/// targeting any SM larger than this number.
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typename ArchTag,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape,
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/// Warp-level tile size (concept: GemmShape)
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typename InstructionShape,
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/// Epilogue output operator
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typename EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle,
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/// Number of stages used in the pipelined mainloop
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int Stages
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>
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class Gemm;
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*/
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template <
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/// Element type for A matrix operand
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typename ElementA_,
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/// Layout type for A matrix operand
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typename LayoutA_,
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/// Element type for B matrix operand
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typename ElementB_,
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/// Layout type for B matrix operand
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typename LayoutB_,
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/// Element type for C and D matrix operands
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typename ElementC_,
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/// Layout type for C and D matrix operands
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typename LayoutC_,
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/// Element type for internal accumulation
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typename ElementAccumulator_ = ElementC_,
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/// Operator class tag
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typename OperatorClass_ = arch::OpClassSimt,
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/// Tag indicating architecture to tune for
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typename ArchTag_ = arch::Sm70,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::WarpShape,
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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::InstructionShape,
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/// Epilogue output operator
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typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_ = threadblock::GemmBatchedIdentityThreadblockSwizzle,
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/// Number of stages used in the pipelined mainloop
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int Stages =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kStages,
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/// Access granularity of A matrix in units of elements
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int AlignmentA =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentB,
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/// Operation performed by GEMM
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typename Operator_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::Operator
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>
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class GemmBatched {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using TensorRefA = TensorRef<ElementA const, LayoutA>;
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using ElementB = ElementB_;
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using LayoutB = LayoutB_;
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using TensorRefB = TensorRef<ElementB const, LayoutB>;
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using ElementC = ElementC_;
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using LayoutC = LayoutC_;
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using TensorRefC = TensorRef<ElementC const, LayoutC>;
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using TensorRefD = TensorRef<ElementC, LayoutC>;
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using ElementAccumulator = ElementAccumulator_;
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using OperatorClass = OperatorClass_;
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using ArchTag = ArchTag_;
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using ThreadblockShape = ThreadblockShape_;
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using WarpShape = WarpShape_;
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using InstructionShape = InstructionShape_;
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using EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static int const kStages = Stages;
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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static int const kAlignmentC = EpilogueOutputOp::kCount;
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using Operator = Operator_;
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/// Define the kernel
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using DefaultGemmKernel = typename kernel::DefaultGemm<
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ElementA,
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LayoutA,
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kAlignmentA,
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ElementB,
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LayoutB,
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kAlignmentB,
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ElementC,
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LayoutC,
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ElementAccumulator,
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OperatorClass,
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ArchTag,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp,
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ThreadblockSwizzle,
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kStages,
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false,
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Operator
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>::GemmKernel;
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using GemmKernel = kernel::GemmBatched<typename DefaultGemmKernel::Mma, typename DefaultGemmKernel::Epilogue, ThreadblockSwizzle>;
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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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TensorRef<ElementA const, LayoutA> ref_A;
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int64_t stride_A;
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TensorRef<ElementB const, LayoutB> ref_B;
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int64_t stride_B;
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TensorRef<ElementC const, LayoutC> ref_C;
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int64_t stride_C;
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TensorRef<ElementC, LayoutC> ref_D;
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int64_t stride_D;
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typename EpilogueOutputOp::Params epilogue;
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int batch_count;
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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() { }
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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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TensorRef<ElementA const, LayoutA> ref_A_,
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int64_t stride_A_,
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TensorRef<ElementB const, LayoutB> ref_B_,
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int64_t stride_B_,
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TensorRef<ElementC const, LayoutC> ref_C_,
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int64_t stride_C_,
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TensorRef<ElementC, LayoutC> ref_D_,
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int64_t stride_D_,
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typename EpilogueOutputOp::Params epilogue_,
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int batch_count_
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):
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problem_size(problem_size_),
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ref_A(ref_A_),
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stride_A(stride_A_),
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ref_B(ref_B_),
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stride_B(stride_B_),
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ref_C(ref_C_),
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stride_C(stride_C_),
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ref_D(ref_D_),
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stride_D(stride_D_),
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epilogue(epilogue_),
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batch_count(batch_count_) { }
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};
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private:
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/// Kernel parameters object
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typename GemmKernel::Params params_;
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public:
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/// Constructs the GEMM.
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GemmBatched() { }
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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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if (!TensorRef_aligned(args.ref_A, kAlignmentA) || (args.stride_A % kAlignmentA)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(args.ref_B, kAlignmentB) || (args.stride_B % kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(args.ref_C, kAlignmentC) || (args.stride_C % kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(args.ref_D, kAlignmentC) || (args.stride_D % kAlignmentC)) {
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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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/// Gets the workspace size
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static size_t get_workspace_size(Arguments const &args) {
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return 0;
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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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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord grid_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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// Initialize the Params structure
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params_ = typename GemmKernel::Params{
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args.problem_size,
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grid_shape,
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args.ref_A.non_const_ref(),
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args.stride_A,
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args.ref_B.non_const_ref(),
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args.stride_B,
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args.ref_C.non_const_ref(),
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args.stride_C,
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args.ref_D,
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args.stride_D,
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args.epilogue,
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args.batch_count
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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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params_.ref_A.reset(args.ref_A.non_const_ref().data());
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params_.ref_B.reset(args.ref_B.non_const_ref().data());
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params_.ref_C.reset(args.ref_C.non_const_ref().data());
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params_.ref_D.reset(args.ref_D.data());
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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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ThreadblockSwizzle threadblock_swizzle;
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dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
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dim3 block(GemmKernel::kThreadCount, 1, 1);
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cudaError_t result;
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int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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result = cudaFuncSetAttribute(Kernel<GemmKernel>,
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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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cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);
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result = cudaGetLastError();
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return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
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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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/// Runs the kernel using initialized state.
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Status operator()(
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Arguments const &args,
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void *workspace = nullptr,
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cudaStream_t stream = nullptr) {
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Status status = initialize(args, workspace);
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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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/// Partial specialization for column-major output exchanges problem size and operand.
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template <
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/// Element type for A matrix operand
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typename ElementA_,
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/// Layout type for A matrix operand
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typename LayoutA_,
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/// Element type for B matrix operand
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typename ElementB_,
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/// Layout type for B matrix operand
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typename LayoutB_,
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/// Element type for C and D matrix operands
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typename ElementC_,
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/// Element type for internal accumulation
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typename ElementAccumulator_,
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/// Operator class tag
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typename OperatorClass_,
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/// Tag indicating architecture to tune for
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typename ArchTag_,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape_,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape_,
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/// Warp-level tile size (concept: GemmShape)
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typename InstructionShape_,
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/// Epilogue output operator
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typename EpilogueOutputOp_,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_,
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/// Number of stages used in the pipelined mainloop
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int Stages,
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/// Access granularity of A matrix in units of elements
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int AlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB,
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typename Operator_
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>
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class GemmBatched<
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ElementA_,
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LayoutA_,
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ElementB_,
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LayoutB_,
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ElementC_,
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layout::ColumnMajor,
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ElementAccumulator_,
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OperatorClass_,
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ArchTag_,
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ThreadblockShape_,
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WarpShape_,
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InstructionShape_,
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EpilogueOutputOp_,
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ThreadblockSwizzle_,
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Stages,
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AlignmentA,
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AlignmentB,
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Operator_
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> {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using TensorRefA = TensorRef<ElementA const, LayoutA>;
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using ElementB = ElementB_;
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using LayoutB = LayoutB_;
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using TensorRefB = TensorRef<ElementB const, LayoutB>;
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using ElementC = ElementC_;
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using LayoutC = layout::ColumnMajor;
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using TensorRefC = TensorRef<ElementC const, LayoutC>;
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using TensorRefD = TensorRef<ElementC, LayoutC>;
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using ElementAccumulator = ElementAccumulator_;
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using OperatorClass = OperatorClass_;
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using ArchTag = ArchTag_;
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using ThreadblockShape = ThreadblockShape_;
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using WarpShape = WarpShape_;
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using InstructionShape = InstructionShape_;
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using EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static int const kStages = Stages;
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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static int const kAlignmentC = EpilogueOutputOp::kCount;
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static bool const kSplitKSerial = false;
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|
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|
//
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using UnderlyingOperator = GemmBatched<
|
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ElementB,
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typename layout::LayoutTranspose<LayoutB>::type,
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ElementA,
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typename layout::LayoutTranspose<LayoutA>::type,
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ElementC,
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|
layout::RowMajor,
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|
ElementAccumulator,
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|
OperatorClass,
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|
ArchTag,
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|
ThreadblockShape,
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|
WarpShape,
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|
InstructionShape,
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|
EpilogueOutputOp,
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|
ThreadblockSwizzle,
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|
Stages,
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|
kAlignmentB,
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|
kAlignmentA
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|
>;
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|
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using UnderlyingArguments = typename UnderlyingOperator::Arguments;
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using GemmKernel = typename UnderlyingOperator::GemmKernel;
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|
|
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/// Argument structure
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|
struct Arguments {
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|
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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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TensorRef<ElementA const, LayoutA> ref_A;
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|
int64_t stride_A;
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TensorRef<ElementB const, LayoutB> ref_B;
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|
int64_t stride_B;
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|
TensorRef<ElementC const, LayoutC> ref_C;
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|
int64_t stride_C;
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|
TensorRef<ElementC, LayoutC> ref_D;
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|
int64_t stride_D;
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|
typename EpilogueOutputOp::Params epilogue;
|
|
int batch_count;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Default ctor
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments() { }
|
|
|
|
/// Constructs an Arguments structure
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(
|
|
GemmCoord problem_size_,
|
|
TensorRef<ElementA const, LayoutA> ref_A_,
|
|
int64_t stride_A_,
|
|
TensorRef<ElementB const, LayoutB> ref_B_,
|
|
int64_t stride_B_,
|
|
TensorRef<ElementC const, LayoutC> ref_C_,
|
|
int64_t stride_C_,
|
|
TensorRef<ElementC, LayoutC> ref_D_,
|
|
int64_t stride_D_,
|
|
typename EpilogueOutputOp::Params epilogue_,
|
|
int batch_count_
|
|
):
|
|
problem_size(problem_size_),
|
|
ref_A(ref_A_),
|
|
stride_A(stride_A_),
|
|
ref_B(ref_B_),
|
|
stride_B(stride_B_),
|
|
ref_C(ref_C_),
|
|
stride_C(stride_C_),
|
|
ref_D(ref_D_),
|
|
stride_D(stride_D_),
|
|
epilogue(epilogue_),
|
|
batch_count(batch_count_) { }
|
|
};
|
|
|
|
private:
|
|
|
|
UnderlyingOperator underlying_operator_;
|
|
|
|
public:
|
|
|
|
/// Constructs the GEMM.
|
|
GemmBatched() { }
|
|
|
|
/// Helper to construct a transposed equivalent for the underying GEMM operator
|
|
static UnderlyingArguments to_underlying_arguments(Arguments const &args) {
|
|
return UnderlyingArguments(
|
|
{args.problem_size.n(), args.problem_size.m(), args.problem_size.k()},
|
|
{args.ref_B.data(), args.ref_B.stride(0)},
|
|
args.stride_B,
|
|
{args.ref_A.data(), args.ref_A.stride(0)},
|
|
args.stride_A,
|
|
{args.ref_C.data(), args.ref_C.stride(0)},
|
|
args.stride_C,
|
|
{args.ref_D.data(), args.ref_D.stride(0)},
|
|
args.stride_D,
|
|
args.epilogue,
|
|
args.batch_count
|
|
);
|
|
}
|
|
|
|
/// 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));
|
|
}
|
|
|
|
/// 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);
|
|
}
|
|
|
|
/// 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
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|