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
@@ -0,0 +1,725 @@
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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/transform/pitch_linear_thread_map.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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////////////////////////////////////////////////////////////////////////////////
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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 | PredicatedTileIterator | ForwardTileIterator
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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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>
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class PredicatedTileIterator {
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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 kThreads = ThreadMap::kThreads;
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static int const kIterations = ThreadMap::Count::kTile;
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static_assert( ThreadMap::Iterations::kRow > 0,"ThreadMap::Iterations::kRow must be > 0");
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static_assert( ThreadMap::Iterations::kGroup > 0,"ThreadMap::Iterations::kGroup must be > 0");
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static_assert( ThreadMap::Iterations::kCluster > 0,"ThreadMap::Iterations::kCluster must be > 0");
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static_assert( ThreadMap::Iterations::kColumn > 0,"ThreadMap::Iterations::kColumn must be > 0");
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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 * ThreadMap::kElementsPerAccess>;
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/// Memory access size
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using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
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//
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// Parameters struct
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//
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struct Params {
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//
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// Data members
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//
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Index stride; ///< stride in bytes between rows
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Index increment_row; ///< increment quantity (in bytes) to advance when moving between rows
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Index increment_group; ///< increment quantity (in bytes) to advance when moving to the next group
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Index increment_cluster; ///< increment quantity (in bytes) to advance when moving to the next cluster
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Index advance_row; ///< amount to add to move to the next 'row' position
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Index advance_group; ///< amount to add to move to the next 'group' position
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Index advance_cluster; ///< amount to add to move to the next 'cluster' position
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Index advance_tile; ///< amount to add to move to the next 'tile'
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Status initialize(Index stride_) {
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stride = stride_;
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increment_row = stride * ThreadMap::Delta::kRow;
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increment_group = stride * ThreadMap::Delta::kGroup
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- stride * ThreadMap::Delta::kRow * (ThreadMap::Iterations::kRow - 1);
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increment_cluster = stride * ThreadMap::Delta::kCluster
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- stride * ThreadMap::Delta::kGroup * (ThreadMap::Iterations::kGroup - 1)
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- stride * ThreadMap::Delta::kRow * (ThreadMap::Iterations::kRow - 1);
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advance_row = stride * ThreadMap::Shape::kRow;
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advance_group = stride * (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
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advance_cluster =
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stride *
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ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;;
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advance_tile =
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stride *
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ThreadMap::Shape::kGroup *
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ThreadMap::Shape::kRow *
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ThreadMap::Shape::kCluster *
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ThreadMap::Shape::kTile;
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return Status::kSuccess;
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}
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CUTLASS_HOST_DEVICE
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Params() {
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initialize(0);
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}
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CUTLASS_HOST_DEVICE
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Params(Layout const &layout) {
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initialize(layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess);
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}
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};
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/// Mask object
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struct Mask {
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static int const kCount = ThreadMap::Iterations::kColumn;
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/// Predicate state
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bool predicates[kCount];
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//
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// Mask
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//
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CUTLASS_HOST_DEVICE
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Mask() {
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enable();
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}
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///< Efficiently disables all accesses guarded by mask
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CUTLASS_HOST_DEVICE void clear() {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kCount; ++i) {
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predicates[i] = false;
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}
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}
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///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
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CUTLASS_DEVICE void enable() {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kCount; ++i) {
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predicates[i] = true;
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}
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}
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};
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private:
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//
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// Data members
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//
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/// Parameters structure containing reference and precomputed state.
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Params params_;
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/// Byte-level pointer
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uint8_t *byte_pointer_;
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/// Array of boolean values to contain steady-state predicates
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Mask mask_;
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/// Extent of the matrix tile in rows
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Index extent_row_;
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/// A thread's starting row position (assuming steady-state predicates have been computed)
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Index thread_start_row_;
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/// Internal state counter
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int state_[3];
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private:
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//
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// Methods
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//
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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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PredicatedTileIterator(
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Params const & params,
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Element *pointer,
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TensorCoord extent,
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int thread_idx,
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TensorCoord threadblock_offset = TensorCoord()
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):
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params_(params) {
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TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
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extent_row_ = extent.row();
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thread_start_row_ = thread_offset.row();
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// Initialize predicates
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CUTLASS_PRAGMA_UNROLL
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for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) {
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mask_.predicates[c] = ((thread_offset.column()
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+ ThreadMap::Delta::kColumn * c) < extent.column());
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}
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// Initialize pointer
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byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
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thread_offset.row() * params_.stride +
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thread_offset.column() * sizeof(AccessType) / kElementsPerAccess;
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// Initialize internal state counter
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state_[0] = state_[1] = state_[2] = 0;
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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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/// Loads a fragment from memory
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CUTLASS_DEVICE
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void load(Fragment &frag) {
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uint8_t *byte_pointer = byte_pointer_;
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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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int frag_row_idx =
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(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
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int row_offset = row * ThreadMap::Delta::kRow
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+ group * ThreadMap::Delta::kGroup
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+ cluster * ThreadMap::Delta::kCluster;
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bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
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AccessType *memory_pointer = reinterpret_cast<AccessType *>(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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bool guard = row_guard && mask_.predicates[column];
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if (guard) {
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frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column] =
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memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess];
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}
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}
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if (row + 1 < ThreadMap::Iterations::kRow) {
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byte_pointer += params_.increment_row;
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}
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}
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if (group + 1 < ThreadMap::Iterations::kGroup) {
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byte_pointer += params_.increment_group;
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}
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}
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if (cluster + 1 < ThreadMap::Iterations::kCluster) {
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byte_pointer += params_.increment_cluster;
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}
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}
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}
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/// Stores a fragment to memory
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CUTLASS_DEVICE
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void store(Fragment const &frag) {
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uint8_t *byte_pointer = byte_pointer_;
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AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&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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int frag_row_idx =
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(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
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int row_offset = row * ThreadMap::Delta::kRow
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+ group * ThreadMap::Delta::kGroup
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+ cluster * ThreadMap::Delta::kCluster;
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bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
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AccessType *memory_pointer = reinterpret_cast<AccessType *>(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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bool guard = row_guard && mask_.predicates[column];
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if (guard) {
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memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess] =
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frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
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}
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}
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if (row + 1 < ThreadMap::Iterations::kRow) {
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byte_pointer += params_.increment_row;
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}
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}
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if (group + 1 < ThreadMap::Iterations::kGroup) {
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byte_pointer += params_.increment_group;
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}
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}
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if (cluster + 1 < ThreadMap::Iterations::kCluster) {
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byte_pointer += params_.increment_cluster;
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}
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}
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}
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/// Advances to the next position to load or store
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CUTLASS_HOST_DEVICE
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PredicatedTileIterator &operator++() {
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++state_[0];
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byte_pointer_ += params_.advance_row;
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thread_start_row_ += ThreadMap::Shape::kRow;
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if (state_[0] == ThreadMap::Count::kRow) {
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state_[0] = 0;
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++state_[1];
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byte_pointer_ += params_.advance_group;
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thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
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ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
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if (state_[1] == ThreadMap::Count::kGroup) {
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state_[1] = 0;
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++state_[2];
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byte_pointer_ += params_.advance_cluster;
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thread_start_row_ += ThreadMap::Count::kGroup *
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ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
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if (state_[2] == ThreadMap::Count::kCluster) {
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state_[2] = 0;
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byte_pointer_ += params_.advance_tile;
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}
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}
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}
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return *this;
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}
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///< Efficiently disables all accesses guarded by mask
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CUTLASS_DEVICE void clear_mask() {
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mask_.clear();
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}
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///< Efficiently enables all accesses guarded by mask
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CUTLASS_DEVICE void enable_mask() {
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mask_.enable();
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}
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///< Sets the mask
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CUTLASS_DEVICE void get_mask(Mask &mask) {
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return mask_;
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}
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///< Sets the mask
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CUTLASS_DEVICE void set_mask(Mask const &mask) {
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mask_ = mask;
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}
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||||
};
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||||
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////////////////////////////////////////////////////////////////////////////////
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||||
/// Tile iterator used to load output tile from shared memory in epilogue.
|
||||
///
|
||||
/// Satisfies: ReadableTileIterator | InterleavedPredicatedTileIterator | ForwardTileIterator
|
||||
///
|
||||
template <
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||||
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
|
||||
typename Element_, ///< Element data type
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||||
int InterleavedK ///< Number of Interleaved K
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||||
>
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||||
class InterleavedPredicatedTileIterator {
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public:
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using ThreadMap = ThreadMap_;
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||||
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||||
using Element = Element_;
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||||
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||||
using Layout = layout::ColumnMajorInterleaved<InterleavedK>;
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||||
using TensorRef = TensorRef<Element, Layout>;
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||||
using ConstTensorRef = typename TensorRef::ConstTensorRef;
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||||
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||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
using TensorCoord = layout::PitchLinearCoord;
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||||
|
||||
static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
|
||||
static int const kThreads = ThreadMap::kThreads;
|
||||
static int const kIterations = ThreadMap::Iterations::kCount;
|
||||
|
||||
/// Fragment object
|
||||
using Fragment = Array<Element, ThreadMap::kElementsPerAccess>;
|
||||
|
||||
/// Memory access size
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
|
||||
//
|
||||
// Parameters struct
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
Index stride; ///< stride in bytes between columns
|
||||
|
||||
Index advance_row; ///< amount to add to move to the next 'row' position
|
||||
Index advance_column; ///< amount to add to move to the next 'column' position
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Status initialize(Index stride_) {
|
||||
stride = stride_;
|
||||
|
||||
advance_row =
|
||||
ThreadMap::Delta::kContiguous * sizeof_bits<Element>::value / 8;
|
||||
|
||||
advance_column =
|
||||
stride_ - ThreadMap::Iterations::kContiguous * kElementsPerAccess *
|
||||
sizeof_bits<Element>::value * ThreadMap::kWarpSize / 8;
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() {
|
||||
initialize(0);
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Layout const &layout) {
|
||||
|
||||
initialize(layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess);
|
||||
}
|
||||
};
|
||||
|
||||
/// Mask object
|
||||
struct Mask {
|
||||
static int const kCount = (ThreadMap::Iterations::kContiguous < 8)
|
||||
? 8
|
||||
: ThreadMap::Iterations::kContiguous;
|
||||
|
||||
/// Predicate state
|
||||
bool predicates[kCount];
|
||||
|
||||
//
|
||||
// Mask
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Mask() {
|
||||
enable();
|
||||
}
|
||||
|
||||
///< Efficiently disables all accesses guarded by mask
|
||||
CUTLASS_HOST_DEVICE void clear() {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int i = 0; i < kCount; ++i) {
|
||||
predicates[i] = false;
|
||||
}
|
||||
}
|
||||
|
||||
///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
|
||||
CUTLASS_DEVICE void enable() {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int i = 0; i < kCount; ++i) {
|
||||
predicates[i] = true;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
/// Parameters structure containing reference and precomputed state.
|
||||
Params params_;
|
||||
|
||||
/// Byte-level pointer
|
||||
uint8_t *byte_pointer_;
|
||||
|
||||
/// Array of boolean values to contain steady-state predicates
|
||||
Mask mask_;
|
||||
|
||||
/// Extent of the matrix tile in columns
|
||||
Index extent_col_;
|
||||
|
||||
/// A thread's starting column position (assuming steady-state predicates have
|
||||
/// been computed)
|
||||
Index thread_start_col_;
|
||||
|
||||
/// Internal iteration counter
|
||||
int iteration_contiguous_;
|
||||
|
||||
int iteration_strided_;
|
||||
|
||||
private:
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
public:
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_DEVICE
|
||||
InterleavedPredicatedTileIterator(
|
||||
Params const & params,
|
||||
Element *pointer,
|
||||
TensorCoord extent,
|
||||
int thread_idx,
|
||||
TensorCoord threadblock_offset
|
||||
):
|
||||
params_(params) {
|
||||
TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) +
|
||||
TensorCoord(threadblock_offset.contiguous() * InterleavedK,
|
||||
threadblock_offset.strided() / InterleavedK);
|
||||
|
||||
extent_col_ = extent.strided() / InterleavedK;
|
||||
thread_start_col_ = thread_offset.strided();
|
||||
|
||||
// Initialize predicates
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
mask_.predicates[c] =
|
||||
((thread_offset.contiguous() + ThreadMap::Delta::kContiguous * c) <
|
||||
(extent.contiguous() * InterleavedK));
|
||||
}
|
||||
|
||||
// Initialize pointer
|
||||
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
|
||||
thread_offset.strided() * params_.stride +
|
||||
thread_offset.contiguous() * sizeof(AccessType) / kElementsPerAccess;
|
||||
|
||||
// Initialize internal state counter
|
||||
iteration_contiguous_ = iteration_strided_ = 0;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
/// Loads a fragment from memory
|
||||
CUTLASS_DEVICE
|
||||
void load(Fragment &frag) {
|
||||
uint8_t *byte_pointer = byte_pointer_;
|
||||
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
|
||||
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);
|
||||
|
||||
int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;
|
||||
|
||||
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
|
||||
|
||||
bool guard = col_guard && mask_.predicates[iteration_contiguous_];
|
||||
|
||||
if (guard) {
|
||||
*frag_ptr = *memory_pointer;
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores a fragment to memory
|
||||
CUTLASS_DEVICE
|
||||
void store(Fragment const &frag) {
|
||||
uint8_t *byte_pointer = byte_pointer_;
|
||||
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
|
||||
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);
|
||||
|
||||
int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;
|
||||
|
||||
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
|
||||
|
||||
bool guard = col_guard && mask_.predicates[iteration_contiguous_];
|
||||
|
||||
if (guard) {
|
||||
*memory_pointer = *frag_ptr;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(int iteration) {
|
||||
iteration_contiguous_ = iteration % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = iteration / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Advances to the next position to load or store
|
||||
CUTLASS_HOST_DEVICE
|
||||
InterleavedPredicatedTileIterator &operator++() {
|
||||
|
||||
++iteration_contiguous_;
|
||||
byte_pointer_ += params_.advance_row;
|
||||
|
||||
if (iteration_contiguous_ == ThreadMap::Iterations::kContiguous) {
|
||||
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
byte_pointer_ += params_.advance_column;
|
||||
|
||||
if (iteration_strided_ == ThreadMap::Iterations::kStrided) {
|
||||
iteration_strided_ = 0;
|
||||
}
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
///< Efficiently disables all accesses guarded by mask
|
||||
CUTLASS_DEVICE void clear_mask() {
|
||||
mask_.clear();
|
||||
}
|
||||
|
||||
///< Efficiently enables all accesses guarded by mask
|
||||
CUTLASS_DEVICE void enable_mask() {
|
||||
mask_.enable();
|
||||
}
|
||||
|
||||
///< Sets the mask
|
||||
CUTLASS_DEVICE void get_mask(Mask &mask) {
|
||||
return mask_;
|
||||
}
|
||||
|
||||
///< Sets the mask
|
||||
CUTLASS_DEVICE void set_mask(Mask const &mask) {
|
||||
mask_ = mask;
|
||||
}
|
||||
};
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace epilogue
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
Reference in New Issue
Block a user