CUTLASS
CUDA Templates for Linear Algebra Subroutines and Solvers
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Partial specialization for m-by-n-by-kgroup.
#include <default_mma_tensor_op.h>
Public Types | |
using | Policy = cutlass::gemm::warp::MmaTensorOpPolicy< cutlass::arch::Mma< InstructionShape_, 32, ElementA, cutlass::layout::RowMajor, ElementB, cutlass::layout::ColumnMajor, ElementC, cutlass::layout::RowMajor, Operator_ >, cutlass::MatrixShape< 1, 1 > > |
using | Type = cutlass::gemm::warp::MmaTensorOp< WarpShape_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Policy, PartitionsK, AccumulatorsInRowMajor, PartitionsN > |
using cutlass::gemm::warp::DefaultMmaTensorOp< WarpShape_, InstructionShape_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator_, PartitionsK, AccumulatorsInRowMajor, PartitionsN >::Policy = cutlass::gemm::warp::MmaTensorOpPolicy< cutlass::arch::Mma<InstructionShape_, 32, ElementA, cutlass::layout::RowMajor, ElementB, cutlass::layout::ColumnMajor, ElementC, cutlass::layout::RowMajor, Operator_>, cutlass::MatrixShape<1, 1> > |
using cutlass::gemm::warp::DefaultMmaTensorOp< WarpShape_, InstructionShape_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator_, PartitionsK, AccumulatorsInRowMajor, PartitionsN >::Type = cutlass::gemm::warp::MmaTensorOp< WarpShape_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Policy, PartitionsK, AccumulatorsInRowMajor, PartitionsN> |