PartitionsK Class — pytorch Architecture

Source Code

aten/src/ATen/native/cuda/cutlass_extensions/gemm/warp/default_mma_tensor_op.h lines 51–119

template<
    /// Shape of one matrix production operation (concept: GemmShape)
    typename WarpShape_,
    /// Shape of one matrix production operation (concept: GemmShape)
    typename InstructionShape_,
    /// Data type of A elements,
    typename ElementA,
    /// Layout of A matrix (concept: MatrixLayout)
    typename LayoutA,
    /// Data type of B elements
    typename ElementB,
    /// Layout of B matrix (concept: MatrixLayout)
    typename LayoutB,
    /// Element type of C matrix
    typename ElementC,
    /// Layout of C matrix (concept: MatrixLayout)
    typename LayoutC,
    /// Number of partitions along K dimension
    int PartitionsK,
    /// Store the accumulators in row major or column major.  Row major is used
    /// when output layout is interleaved.
    bool AccumulatorsInRowMajor>
struct DefaultMmaTensorOp<WarpShape_,
                          InstructionShape_,
                          ElementA,
                          LayoutA,
                          ElementB,
                          LayoutB,
                          ElementC,
                          LayoutC,
                          arch::OpMultiplyAddDequantizeInterleavedBToA,
                          PartitionsK,
                          AccumulatorsInRowMajor> {

private:
    // Shape for computing the FP16s
    using ComputeInstructionShape = InstructionShape_;

    // Chosen so we get K=16 for int8 and K=32 for int4.
    static constexpr int LoadInstructionK = 8 * sizeof_bits<ElementA>::value / sizeof_bits<ElementB>::value;

    // Shape for loading the narrow data type from shared memory
    using LoadInstructionShape = GemmShape<InstructionShape_::kM, InstructionShape_::kN, LoadInstructionK>;

public:
    using Policy = cutlass::gemm::warp::MmaTensorOpPolicy<cutlass::arch::Mma<InstructionShape_,
                                                                             32,
                                                                             ElementA,
                                                                             cutlass::layout::RowMajor,
                                                                             ElementA,
                                                                             cutlass::layout::ColumnMajor,
                                                                             ElementC,
                                                                             cutlass::layout::RowMajor,
                                                                             arch::OpMultiplyAdd>,
                                                          cutlass::MatrixShape<1, 1>>;

    // Define the warp-level tensor op
    using Type = cutlass::gemm::warp::MmaTensorOpComputeBWithF16<WarpShape_,
                                                                 ElementA,
                                                                 LayoutA,
                                                                 ElementB,
                                                                 LayoutB,
                                                                 ElementC,
                                                                 LayoutC,
                                                                 Policy,
                                                                 LoadInstructionShape,
                                                                 PartitionsK,
                                                                 AccumulatorsInRowMajor>;
};