CUTLASS 2.0 (#62)
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
This commit is contained in:
Andrew Kerr
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/***************************************************************************************************
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* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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 Defines a matrix object intended for storing data in registers and operations within
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a CUDA thread.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/array.h"
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#include "cutlass/matrix_coord.h"
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namespace cutlass {
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namespace thread {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Per-thread matrix object storing a packed matrix
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template <
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typename Element,
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int Rows,
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int Columns,
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typename Layout = layout::RowMajor
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>
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class Matrix : public Array<Element, Rows * Columns> {
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public:
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// Verify layout refers to a rank=2 matrix.
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static_assert(
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Layout::kRank == 2,
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"Layout type must refer to a rank=2 matrix");
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/// Base type
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using Base = Array<Element, Rows * Columns>;
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/// Element type
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using Element = Element_;
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/// Number of rows
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static int const kRows = Rows;
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/// Number of columns
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static int const kColumns = Columns;
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/// Layout within the array
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using Layout = Layout_;
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/// Reference type to an element
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using Reference = Element &;
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/// Logical rank of tensor index space
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static int const kRank = 2;
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/// Index type
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using Index = typename Layout::Index;
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/// Long index used for pointer offsets
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using LongIndex = typename Layout::LongIndex;
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/// Coordinate in logical tensor space
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using TensorCoord = typename Layout::TensorCoord;
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/// Stride type
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using Stride = typename Layout::Stride;
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/// TensorRef to matrix object
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using TensorRef = TensorRef<Element, kRank, Layout>;
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/// TensorRef to constant matrix object
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using ConstTensorRef = typename TensorRef::ConstTensorRef;
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/// TensorRef to matrix object
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using TensorView = TensorView<Element, kRank, Layout>;
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/// TensorRef to constant matrix object
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using ConstTensorView = typename TensorView::ConstTensorView;
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/// Diagonal vector
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using Diagonal = Vector<Element, __NV_STD_MIN(kRows, kColumns)>;
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private:
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public:
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//
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// Methods
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//
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/// Returns the size of the object
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CUTLASS_HOST_DEVICE
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static MatrixCoord extent() {
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return make_Coord(kRows, kColumns);
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}
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/// Returns the layout object
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CUTLASS_HOST_DEVICE
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static Layout layout() {
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return Layout::packed(extent());
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}
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/// Ctor
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CUTLASS_HOST_DEVICE
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Matrix() { }
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/// Ctor
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CUTLASS_HOST_DEVICE
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Matrix(Diagonal const &diag) {
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// Todo - construct from diagonal
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}
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/// Returns a TensorRef pointing to the first element of the tensor.
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CUTLASS_HOST_DEVICE
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TensorRef ref() {
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return TensorRef(this->data(), layout());
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}
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/// Returns a TensorRef pointing to the first element of the tensor.
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CUTLASS_HOST_DEVICE
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ConstTensorRef const_ref() const {
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return ConstTensorRef(this->data(), layout());
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}
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/// Returns a TensorRef pointing to the first element of the tensor.
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CUTLASS_HOST_DEVICE
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TensorView view() {
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return TensorView(ref(), extent());
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}
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/// Returns a TensorView to const data
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CUTLASS_HOST_DEVICE
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ConstTensorView const_view() const {
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return ConstTensorView(const_ref(), extent());
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}
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/// Returns a reference to the element at a given Coord
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CUTLASS_HOST_DEVICE
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Reference at(MatrixCoord const& coord) const {
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typename Base::size_type offset_(layout().offset(coord));
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return Base::at(offset_);
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}
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/// Returns the number of scalar elements needed to store tensor.
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CUTLASS_HOST_DEVICE
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LongIndex capacity() const {
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return LongIndex(Base::size());
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Column vector defined as a matrix with exactly one column
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template <
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typename Element,
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int Rows,
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typename Layout = layout::ColumnMajor
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>
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using ColumnVector = Matrix<Element, Rows, 1, Layout>;
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/// Row vector defined as a matrix with exactly one row
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template <
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typename Element,
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int Columns,
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typename Layout = layout::RowMajor
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>
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using RowVector = Matrix<Element, 1, Columns, Layout>;
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace thread
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} // namespace cutlass
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