197 lines
8.3 KiB
C++
197 lines
8.3 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 pipelined GEMM kernel. Does not compute batching or support split-K.
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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/arch/wmma.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
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#include "cutlass/transform/threadblock/predicated_tile_iterator_2dthreadtile.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
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#if defined(CUTLASS_ARCH_WMMA_ENABLED)
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#include "cutlass/gemm/threadblock/default_mma_core_wmma.h"
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#endif //CUTLASS_ARCH_WMMA_ENABLED
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace threadblock {
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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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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
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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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/// Access granularity of B matrix in units of elements
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int kAlignmentB,
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/// Element type for internal accumulation
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typename ElementAccumulator_,
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/// Layout type for C and D matrix operands
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typename LayoutC_,
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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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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_,
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/// Number of stages used in the pipelined mainloop
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int Stages,
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/// Operation perfomed by GEMM
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typename Operator,
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/// Store the accumulators in row major or column major. Row major is used
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/// when output layout is interleaved.
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bool AccumulatorsInRowMajor = false
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>
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struct DefaultSparseMma;
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////////////////////////////////////////////////////////////////////////////////
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/// Specialization for row-major output (OperatorClass TensorOp)
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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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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
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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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/// Access granularity of B matrix in units of elements
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int kAlignmentB,
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/// Element type for internal accumulation
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typename ElementAccumulator,
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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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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape,
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/// Number of stages used in the multistage mainloop
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int Stages,
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/// Operation perfomed by GEMM
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typename Operator
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>
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struct DefaultSparseMma<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB,
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kAlignmentB, ElementAccumulator, layout::RowMajor,
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arch::OpClassTensorOp, ArchTag, ThreadblockShape, WarpShape,
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InstructionShape, Stages, Operator, false> {
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static cutlass::arch::CacheOperation::Kind const CacheOpA =
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((sizeof_bits<ElementA>::value * kAlignmentA) == 128)
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? cutlass::arch::CacheOperation::Global
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: cutlass::arch::CacheOperation::Always;
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static cutlass::arch::CacheOperation::Kind const CacheOpB =
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((sizeof_bits<ElementB>::value * kAlignmentB) == 128)
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? cutlass::arch::CacheOperation::Global
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: cutlass::arch::CacheOperation::Always;
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// Define the MmaCore components
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using MmaCore = typename cutlass::gemm::threadblock::DefaultSparseMmaCore<
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ThreadblockShape, WarpShape, InstructionShape, ElementA, LayoutA,
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ElementB, LayoutB, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
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Stages, Operator, false, CacheOpA, CacheOpB>;
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static int const kSparse = MmaCore::kSparse;
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// Define iterators over tiles from the A operand
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using ThreadMapA = typename MmaCore::IteratorThreadMapA;
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using AccessTypeA = cutlass::Array<ElementA, kAlignmentA>;
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using IteratorA =
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cutlass::transform::threadblock::PredicatedTileAccessIterator<
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cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK / kSparse>,
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ElementA, LayoutA, 1, ThreadMapA, AccessTypeA>;
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// Define iterators over tiles from the B operand
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using ThreadMapB = typename MmaCore::IteratorThreadMapB;
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using AccessTypeB = cutlass::Array<ElementB, kAlignmentB>;
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using IteratorB =
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cutlass::transform::threadblock::PredicatedTileAccessIterator<
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cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
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ElementB, LayoutB, 0, ThreadMapB, AccessTypeB>;
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// Define iterators over tiles from the E operand
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using ElementE = typename MmaCore::ElementE;
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using LayoutE = typename MmaCore::GmemLayoutE;
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using ThreadMapE = typename MmaCore::IteratorThreadMapE;
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using AccessTypeE =
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cutlass::Array<ElementE, 128 / sizeof_bits<ElementE>::value>;
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using IteratorE =
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cutlass::transform::threadblock::PredicatedTileAccessIterator<
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cutlass::MatrixShape<ThreadblockShape::kM,
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ThreadblockShape::kK / kSparse /
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MmaCore::kElementsPerElementE>,
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ElementE, LayoutE, 1, ThreadMapE, AccessTypeE>;
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// Define the threadblock-scoped multistage matrix multiply
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using ThreadblockMma = cutlass::gemm::threadblock::SparseMmaMultistage<
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typename MmaCore::Shape, IteratorA, typename MmaCore::SmemIteratorA,
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MmaCore::kCacheOpA, IteratorB, typename MmaCore::SmemIteratorB,
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MmaCore::kCacheOpB, ElementAccumulator, layout::RowMajor,
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IteratorE, typename MmaCore::SmemIteratorE, MmaCore::kCacheOpE,
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typename MmaCore::MmaPolicy, Stages>;
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace gemm
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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