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.
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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 Epilogue for threadblock scoped GEMMs using Tensor Ops.
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The epilogue rearranges the result of a matrix product through shared memory to match canonical
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tensor layouts in global memory. Epilogues support conversion and reduction operations.
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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/array.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/matrix_shape.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/epilogue/threadblock/output_tile_thread_map.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace epilogue {
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namespace threadblock {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Tile iterator used to load output tile from shared memory in epilogue.
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///
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/// Satisfies: ReadableTileIterator
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///
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template <
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typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
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typename Element_, ///< Element data type
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int MaxAlignment = ThreadMap_::kElementsPerAccess * sizeof_bits<Element_>::value / 8
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>
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class SharedLoadIterator {
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public:
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using ThreadMap = ThreadMap_;
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using Shape = typename ThreadMap::Shape;
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using Element = Element_;
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using Layout = layout::RowMajor;
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using TensorRef = TensorRef<Element, Layout>;
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using ConstTensorRef = typename TensorRef::ConstTensorRef;
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using Index = typename Layout::Index;
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using LongIndex = typename Layout::LongIndex;
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using TensorCoord = MatrixCoord;
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static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
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static int const kMinAlignment = ThreadMap_::kElementsPerAccess * sizeof_bits<Element_>::value / 8;
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static int const kAlignment = (MaxAlignment < kMinAlignment ? MaxAlignment : kMinAlignment);
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static int const kThreads = ThreadMap::kThreads;
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/// Fragment object
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using Fragment = Array<
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Element,
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ThreadMap::Iterations::kColumn *
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ThreadMap::Iterations::kRow *
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ThreadMap::Iterations::kGroup *
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ThreadMap::Iterations::kCluster *
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ThreadMap::kElementsPerAccess>;
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/// Memory access size
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using AccessType = AlignedArray<
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Element,
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ThreadMap::kElementsPerAccess,
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kAlignment>;
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private:
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//
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// Data members
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//
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/// Byte-level pointer
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uint8_t *byte_pointer_;
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/// Stride along adjacent rows
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int stride_;
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public:
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//
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// Methods
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//
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/// Constructor
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CUTLASS_DEVICE
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SharedLoadIterator(
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TensorRef ref,
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int thread_idx
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):
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byte_pointer_(reinterpret_cast<uint8_t *>(ref.data())),
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stride_((ref.stride(0) * sizeof_bits<Element>::value) / 8) {
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TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx);
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// Initialize pointer
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byte_pointer_ +=
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thread_offset.row() * stride_ +
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thread_offset.column() * sizeof(AccessType) / kElementsPerAccess;
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int byte_offset = thread_offset.row() * stride_ +
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thread_offset.column() * sizeof(AccessType) / kElementsPerAccess;
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}
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/// Adds a pointer offset in units of Element
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CUTLASS_HOST_DEVICE
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void add_pointer_offset(LongIndex pointer_offset) {
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byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
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}
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CUTLASS_DEVICE
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void add_tile_offset(TensorCoord const &offset) {
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add_pointer_offset(offset.row() * stride_ / (sizeof_bits<Element>::value / 8) + offset.column() * Shape::kColumn);
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}
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/// Loads a fragment from memory
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CUTLASS_DEVICE
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void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
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AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
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CUTLASS_PRAGMA_UNROLL
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for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
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CUTLASS_PRAGMA_UNROLL
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for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
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CUTLASS_PRAGMA_UNROLL
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for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
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uint8_t const *byte_pointer = byte_pointer_ +
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row * ThreadMap::Delta::kRow * stride_ +
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group * ThreadMap::Delta::kGroup* stride_ +
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cluster * ThreadMap::Delta::kCluster * stride_ +
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pointer_offset * sizeof_bits<Element>::value / 8;
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int frag_row_idx =
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(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
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AccessType const *memory_pointer = reinterpret_cast<AccessType const *>(byte_pointer);
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CUTLASS_PRAGMA_UNROLL
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for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
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int frag_idx = frag_row_idx * ThreadMap::Iterations::kColumn + column;
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frag_ptr[frag_idx] =
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memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess];
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}
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}
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}
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}
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}
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/// Loads a fragment
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CUTLASS_DEVICE
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void load(Fragment &frag) {
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load_with_pointer_offset(frag, 0);
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace epilogue
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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