849 lines
27 KiB
C++
849 lines
27 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Template for a Block-Ell sparse 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/ell_gemm.h"
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#include "cutlass/gemm/kernel/default_ell_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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/*! Blocked-Ell sparse gemm device-level operator. This is an interface to efficient CUTLASS
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Blocked-Ell kernels that may 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 Blocked-Ell problems to kernel parameters.
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3. At runtime, it launches kernels on the device.
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Example of a CUTLASS EllGemm operator is as follows:
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//
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// Instantiate the CUTLASS EllGemm operator.
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//
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cutlass::gemm::device::EllGemm<
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cutlass::half_t,
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cutlass::layout::RowMajor,
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cutlass::half_t,
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cutlass::layout::ColumnMajor,
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cutlass::half_t,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::arch::OpClassTensorOp,
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cutlass::arch::Sm80,
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cutlass::gemm::GemmShape<128, 128, 32>,
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cutlass::gemm::GemmShape<64, 64, 32>,
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cutlass::gemm::GemmShape<16, 8, 16>,
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cutlass::epilogue::thread::LinearCombination<
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cutlass::half_t, 128 / cutlass::sizeof_bits<cutlass::half_t>::value,
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float, float>,
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cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<8>,
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4, // Stages
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128 / cutlass::sizeof_bits<cutlass::half_t>::value, // Alignment A
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128 / cutlass::sizeof_bits<cutlass::half_t>::value // Alignment B
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> ellgemm_op;
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//
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// Launch the EllGemm operation on the device
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//
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Description of parameters and tensors used to represent the Blocked-Ellpack (ELL) format:
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a_rows - Rows in the sparse matrix.
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a_cols - Colums in the sparse matrix.
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BlockedEllA - Packed matrix (ellValue matrix) that stores non-zero values in
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consecutive blocks, whose size is (a_rows * a_ell_num_columns)
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ell_idx - Blocked-ELL Column indices (ellColInd) matrix, whose size is
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(a_rows / a_ell_blocksize) * (a_ell_num_columns / a_ell_blocksize)
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a_ell_blocksize - Size of the ELL-Blocks.
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a_ell_num_columns - Number of columns in the Blocked-Ellpack format (ellValue columns)
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B - Input dense matrix whose size is (a_cols * n)
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C/D - Output dense matrix whose size is (a_rows * n)
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cutlass::Status status = ellgemm_op({
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{a_rows, n, a_cols}, // GemmCoord problem_size
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{BlockedEllA, lda}, // TensorRef<cutlass::half_t, layout::RowMajor> ref_BlockedEllA
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{B, ldb}, // TensorRef<cutlass::half_t, 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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ell_idx, // Blocked-ELL Column indices or ellColInd matrix (const int*)
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a_ell_num_columns, // Columns in the Blocked-Ellpack (ellValue) matrix (int)
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a_ell_blocksize, // Size of the ELL-Blocks (int)
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a_ell_base, // Base index of ellColInd (int) - Zero or One
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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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/// 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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/// Supports split-K with serial reduction
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bool SplitKSerial,
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/// Operation performed by GEMM
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typename Operator,
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/// Sparse matrix is A or not
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bool IsASparse
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>
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class EllGemm;
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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::OpClassTensorOp,
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/// Tag indicating architecture to tune for
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typename ArchTag_ = arch::Sm80,
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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_ =
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typename threadblock::GemmIdentityThreadblockSwizzle<>,
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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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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial = false,
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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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/// Sparse matrix is A or not
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bool IsASparse = true
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>
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class EllGemm {
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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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using Operator = Operator_;
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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 = SplitKSerial;
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static ComplexTransform const kTransformA = ComplexTransform::kNone;
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static ComplexTransform const kTransformB = ComplexTransform::kNone;
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static bool const kIsASparse = IsASparse;
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/// Define the kernel
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using GemmKernel = typename kernel::DefaultEllGemm<
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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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kSplitKSerial,
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Operator,
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kIsASparse
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>::GemmKernel;
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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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TensorRef<ElementB const, LayoutB> ref_B;
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TensorRef<ElementC const, LayoutC> ref_C;
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TensorRef<ElementC, LayoutC> ref_D;
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const int* ell_idx;
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int ell_ncol;
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int ell_blocksize;
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int ell_base_idx;
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typename EpilogueOutputOp::Params epilogue;
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int split_k_slices;
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//
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// Methods
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//
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/// Default ctor
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CUTLASS_HOST_DEVICE
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Arguments(): problem_size(0, 0, 0), split_k_slices(1) {
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}
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/// Constructs an Arguments structure
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CUTLASS_HOST_DEVICE
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Arguments(
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GemmCoord problem_size_,
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TensorRef<ElementA const, LayoutA> ref_A_,
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TensorRef<ElementB const, LayoutB> ref_B_,
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TensorRef<ElementC const, LayoutC> ref_C_,
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TensorRef<ElementC, LayoutC> ref_D_,
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const int* ell_idx_,
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int ell_ncol_,
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int ell_blocksize_,
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int ell_base_idx_,
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typename EpilogueOutputOp::Params epilogue_ =
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typename EpilogueOutputOp::Params(),
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int split_k_slices = 1
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):
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problem_size(problem_size_),
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ref_A(ref_A_),
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ref_B(ref_B_),
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ref_C(ref_C_),
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ref_D(ref_D_),
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ell_idx(ell_idx_),
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ell_ncol(ell_ncol_),
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ell_blocksize(ell_blocksize_),
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ell_base_idx(ell_base_idx_),
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epilogue(epilogue_),
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split_k_slices(split_k_slices) {
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}
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};
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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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EllGemm() { }
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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 (!kSplitKSerial && args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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Status status = GemmKernel::can_implement(
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args.problem_size,
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args.ref_A.non_const_ref(),
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args.ref_B.non_const_ref(),
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args.ref_C.non_const_ref(),
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args.ref_D
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);
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if (status != Status::kSuccess) {
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return status;
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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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size_t bytes = 0;
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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size,
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{args.ell_blocksize,
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ThreadblockShape::kN, ThreadblockShape::kK},
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args.split_k_slices);
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tiled_shape.m() *= (args.ell_blocksize + ThreadblockShape::kM - 1 ) / ThreadblockShape::kM;
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if (kSplitKSerial && args.split_k_slices > 1) {
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bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
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}
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return bytes;
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}
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Status set(Arguments const &args, cutlass::gemm::GemmCoord const &grid_shape, void *workspace){
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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.ref_B.non_const_ref(),
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args.ref_C.non_const_ref(),
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args.ref_D,
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args.ell_idx,
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args.ell_ncol,
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args.ell_blocksize,
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args.ell_base_idx,
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args.epilogue,
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static_cast<int *>(workspace)
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};
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return Status::kSuccess;
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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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{args.ell_blocksize, ThreadblockShape::kN, ThreadblockShape::kK},
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args.split_k_slices);
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grid_shape.m() *= (args.ell_blocksize + ThreadblockShape::kM - 1 ) / ThreadblockShape::kM;
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if (kSplitKSerial) {
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if (args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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size_t bytes = get_workspace_size(args);
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cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
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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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}
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else {
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if (args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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}
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return set(args, grid_shape, workspace);
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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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if (kSplitKSerial && args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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}
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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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params_.output_op = args.epilogue;
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params_.semaphore = static_cast<int *>(workspace);
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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_);
|
|
|
|
result = cudaGetLastError();
|
|
|
|
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
|
}
|
|
|
|
/// 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);
|
|
|
|
if (status == Status::kSuccess) {
|
|
status = run(stream);
|
|
}
|
|
|
|
return status;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Partial 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
|
|
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,
|
|
/// If true, kernel supports split-K as a serial reduction
|
|
bool SplitKSerial,
|
|
/// Operation performed by GEMM
|
|
typename Operator_,
|
|
/// Sparse matrix is A or not
|
|
bool IsASparse>
|
|
class EllGemm<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
|
|
layout::ColumnMajor, // partially specialized on LayoutC
|
|
ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_,
|
|
WarpShape_, InstructionShape_, EpilogueOutputOp_,
|
|
ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB,
|
|
SplitKSerial, Operator_, IsASparse> {
|
|
public:
|
|
|
|
using ElementA = ElementA_;
|
|
using LayoutA = LayoutA_;
|
|
using TensorRefA = TensorRef<ElementA const, LayoutA>;
|
|
using ElementB = ElementB_;
|
|
using LayoutB = LayoutB_;
|
|
using TensorRefB = TensorRef<ElementB const, LayoutB>;
|
|
using ElementC = ElementC_;
|
|
using LayoutC = layout::ColumnMajor;
|
|
using TensorRefC = TensorRef<ElementC const, LayoutC>;
|
|
using TensorRefD = TensorRef<ElementC, LayoutC>;
|
|
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 = ComplexTransform::kNone;
|
|
static ComplexTransform const kTransformB = ComplexTransform::kNone;
|
|
static bool const kSplitKSerial = SplitKSerial;
|
|
static bool const kIsASparse = false;
|
|
|
|
using UnderlyingOperator = EllGemm<
|
|
ElementB,
|
|
typename layout::LayoutTranspose<LayoutB>::type,
|
|
ElementA,
|
|
typename layout::LayoutTranspose<LayoutA>::type,
|
|
ElementC,
|
|
layout::RowMajor,
|
|
ElementAccumulator,
|
|
OperatorClass,
|
|
ArchTag,
|
|
ThreadblockShape,
|
|
WarpShape,
|
|
InstructionShape,
|
|
EpilogueOutputOp,
|
|
ThreadblockSwizzle,
|
|
Stages,
|
|
kAlignmentB,
|
|
kAlignmentA,
|
|
SplitKSerial,
|
|
Operator,
|
|
kIsASparse
|
|
>;
|
|
|
|
using UnderlyingArguments = typename UnderlyingOperator::Arguments;
|
|
using GemmKernel = typename UnderlyingOperator::GemmKernel;
|
|
static int const kAlignmentC = UnderlyingOperator::kAlignmentC;
|
|
|
|
/// Argument structure
|
|
struct Arguments {
|
|
|
|
//
|
|
// Data members
|
|
//
|
|
|
|
GemmCoord problem_size;
|
|
TensorRef<ElementA const, LayoutA> ref_A;
|
|
TensorRef<ElementB const, LayoutB> ref_B;
|
|
TensorRef<ElementC const, LayoutC> ref_C;
|
|
TensorRef<ElementC, LayoutC> ref_D;
|
|
const int* ell_idx;
|
|
int ell_ncol;
|
|
int ell_blocksize;
|
|
int ell_base_idx;
|
|
typename EpilogueOutputOp::Params epilogue;
|
|
int split_k_slices;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Default ctor
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments() { }
|
|
|
|
/// Constructs an Arguments structure
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(
|
|
GemmCoord problem_size_,
|
|
TensorRef<ElementA const, LayoutA> ref_A_,
|
|
TensorRef<ElementB const, LayoutB> ref_B_,
|
|
TensorRef<ElementC const, LayoutC> ref_C_,
|
|
TensorRef<ElementC, LayoutC> ref_D_,
|
|
const int* ell_idx_,
|
|
int ell_ncol_,
|
|
int ell_blocksize_,
|
|
int ell_base_idx_,
|
|
typename EpilogueOutputOp::Params epilogue_ =
|
|
typename EpilogueOutputOp::Params(),
|
|
int split_k_slices = 1
|
|
):
|
|
problem_size(problem_size_),
|
|
ref_A(ref_A_),
|
|
ref_B(ref_B_),
|
|
ref_C(ref_C_),
|
|
ref_D(ref_D_),
|
|
ell_idx(ell_idx_),
|
|
ell_ncol(ell_ncol_),
|
|
ell_blocksize(ell_blocksize_),
|
|
ell_base_idx(ell_base_idx_),
|
|
epilogue(epilogue_),
|
|
split_k_slices(split_k_slices) { }
|
|
};
|
|
|
|
private:
|
|
|
|
UnderlyingOperator underlying_operator_;
|
|
|
|
public:
|
|
|
|
/// Constructs the GEMM.
|
|
EllGemm() { }
|
|
|
|
/// 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.ref_A.data(), args.ref_A.stride(0)},
|
|
{args.ref_C.data(), args.ref_C.stride(0)},
|
|
{args.ref_D.data(), args.ref_D.stride(0)},
|
|
args.ell_idx,
|
|
args.ell_ncol,
|
|
args.ell_blocksize,
|
|
args.ell_base_idx,
|
|
args.epilogue,
|
|
args.split_k_slices
|
|
);
|
|
}
|
|
|
|
/// 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) {
|
|
|
|
size_t bytes = 0;
|
|
|
|
// Determine grid shape
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
|
|
args.problem_size,
|
|
{ThreadblockShape::kM, args.ell_blocksize, ThreadblockShape::kK},
|
|
args.split_k_slices);
|
|
|
|
tiled_shape.n() *= (args.ell_blocksize + ThreadblockShape::kN - 1 ) / ThreadblockShape::kN;
|
|
|
|
if (kSplitKSerial && args.split_k_slices > 1) {
|
|
|
|
bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
|
|
}
|
|
|
|
return bytes;
|
|
}
|
|
|
|
Status set(Arguments const &args, cutlass::gemm::GemmCoord const &grid_shape, void *workspace){
|
|
// Initialize the Params structure
|
|
return underlying_operator_.set(to_underlying_arguments(args), grid_shape, workspace);
|
|
}
|
|
|
|
/// Initializes GEMM state from arguments.
|
|
Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
|
|
|
|
// Determine grid shape
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
|
|
{args.problem_size.n(), args.problem_size.m(), args.problem_size.k()},
|
|
{ThreadblockShape::kM, args.ell_blocksize, ThreadblockShape::kK},
|
|
args.split_k_slices);
|
|
|
|
grid_shape.n() *= (args.ell_blocksize + ThreadblockShape::kN - 1 ) / ThreadblockShape::kN;
|
|
|
|
if (kSplitKSerial) {
|
|
if (args.split_k_slices > 1) {
|
|
if (!workspace) {
|
|
return Status::kErrorWorkspaceNull;
|
|
}
|
|
|
|
size_t bytes = get_workspace_size(args);
|
|
|
|
cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
|
|
|
|
if (result != cudaSuccess) {
|
|
return Status::kErrorInternal;
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
|
|
if (args.split_k_slices > 1) {
|
|
return Status::kErrorInvalidProblem;
|
|
}
|
|
}
|
|
|
|
// Initialize the Params structure
|
|
set(args, grid_shape, workspace);
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// 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
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|