Manish Gupta
@@ -0,0 +1,602 @@
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/***************************************************************************************************
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* Copyright (c) 2017-2021, 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/layout/tensor.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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#include "cutlass/arch/arch.h"
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#include "cutlass/arch/memory.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator_params.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 and store output tile from global memory in epilogue.
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///
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/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
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///
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/// It provides a fast path for the case Rank = 2 which does not need div/rem to
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/// calculate modes.
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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 Rank
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>
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class PredicatedTileIteratorAffineRankN {
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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::AffineRankN<Rank>;
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using TensorRef = TensorRef<Element, Layout>;
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using TensorView = TensorView<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 = typename Layout::TensorCoord;
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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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static_assert( !(Layout::kRank % 2),
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"Layout rank must be even. This assumes the first half of the modes correspond to the 'row' "
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"and the second half of the modes correspond to the 'column'");
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static bool const kBigEndian = false;
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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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/// Parameters structure
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struct Params {
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//
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// Data members
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//
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Layout layout;
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/// Stride in units of bytes along M modes
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Coord<Layout::kRank/2, typename Layout::LongIndex> stride_m;
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/// Stride in units of bytes along N modes
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Coord<Layout::kRank/2, typename Layout::LongIndex> stride_n;
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/// Fast divmod objects divided by tensor extents
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FastDivmod divmod_m[(Layout::kRank == 2) ? 1 : (Layout::kRank/2 - 1)];
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/// Fast divmod objects divided by tensor extents
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FastDivmod divmod_n[(Layout::kRank == 2) ? 1 : (Layout::kRank/2 - 1)];
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int64_t rank2_inc_col;
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int64_t rank2_inc_row;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params() { }
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CUTLASS_HOST_DEVICE
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Params(TensorCoord const &extent, Layout const &layout_): layout(layout_) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Layout::kRank / 2; ++i) {
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stride_m[i] = OffsetBytes<Element>(layout_.stride()[i]);
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stride_n[i] = OffsetBytes<Element>(layout_.stride()[i + Layout::kRank / 2]);
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}
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if (kBigEndian) {
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// "Big Endian" scheme
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Layout::kRank / 2 - 1; ++i) {
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divmod_m[i] = FastDivmod(extent[i + 1]);
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divmod_n[i] = FastDivmod(extent[i + Layout::kRank / 2 + 1]);
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}
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}
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else {
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// "Little Endian" scheme
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Layout::kRank / 2 - 1; ++i) {
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divmod_m[i] = FastDivmod(extent[i]);
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divmod_n[i] = FastDivmod(extent[i + Layout::kRank / 2]);
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}
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}
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#if 0
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//
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// Debug print statements to verify extents and strides are passed correctly.
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//
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printf("PredicatedTileIteratorAffine::Params() entered\n");
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Layout::kRank; ++i) {
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printf(" extent[%d]: %d\n", i, extent[i]);
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}
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for (int i = 0; i < Layout::kRank; ++i) {
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printf(" stride[%d]: %ld\n", i, layout_.stride()[i]);
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}
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printf("PredicatedTileIteratorAffine::Params() returning\n");
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#endif
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}
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CUTLASS_HOST_DEVICE
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Params(Layout const &layout_): layout(layout_) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Layout::kRank / 2; ++i) {
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stride_m[i] = OffsetBytes<Element>(layout_.stride()[i]);
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stride_n[i] = OffsetBytes<Element>(layout_.stride()[i + Layout::kRank / 2]);
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}
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rank2_inc_col = ThreadMap::Delta::kColumn * stride_n[0];
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rank2_inc_row = ThreadMap::Delta::kRow * stride_m[0];
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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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/// Extent of the matrix tile in rows
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Index extent_col_;
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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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/// A thread's starting column position (assuming steady-state predicates have been computed)
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Index thread_start_column_;
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/// Internal state counter
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int state_[3];
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//
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// Static asserts about internal strides
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//
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static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
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static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
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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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PredicatedTileIteratorAffineRankN(
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Params const & params,
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Element *pointer,
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MatrixCoord extent,
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int thread_idx,
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MatrixCoord threadblock_offset = MatrixCoord()
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):
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params_(params)
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{
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MatrixCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
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extent_row_ = extent.row();
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extent_col_ = extent.column();
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thread_start_row_ = thread_offset.row();
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thread_start_column_ = thread_offset.column();
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if (Layout::kRank > 2) {
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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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if (!pointer) {
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mask_.clear();
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}
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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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// 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_with_byte_offset(Fragment &frag, int64_t byte_offset) {
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uint8_t const *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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int row_begin = thread_start_row_ + group * ThreadMap::Delta::kGroup + cluster * ThreadMap::Delta::kCluster;
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int64_t offset_modes_m = row_begin * params_.stride_m[0];
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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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//
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// Compute coordinate and decompose into M modes
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//
|
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int coord_m = row * ThreadMap::Delta::kRow + row_begin;
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Coord<Layout::kRank / 2, Index> modes_m;
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if (Layout::kRank > 2) {
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if (kBigEndian) {
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modes_m = CoordinateDecomposition<Layout::kRank / 2>(coord_m, params_.divmod_m);
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} else {
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modes_m = CoordinateDecompositionLittleEndian<Layout::kRank / 2>(coord_m, params_.divmod_m);
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}
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offset_modes_m = dot(modes_m, params_.stride_m);
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}
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//
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// Compute the offset due to modes M
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//
|
||||
|
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bool row_guard = (coord_m < extent_row_);
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int64_t offset_modes_n = thread_start_column_ * params_.stride_n[0];
|
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|
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CUTLASS_PRAGMA_UNROLL
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for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
|
||||
|
||||
//
|
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// Compute coordinate and decompose into N modes
|
||||
//
|
||||
|
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int coord_n = thread_start_column_ + column * ThreadMap::Delta::kColumn;
|
||||
|
||||
Coord<Layout::kRank / 2, Index> modes_n;
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||||
|
||||
if (Layout::kRank > 2) {
|
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if (kBigEndian) {
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modes_n = CoordinateDecomposition<Layout::kRank / 2>(coord_n, params_.divmod_n);
|
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} else {
|
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modes_n = CoordinateDecompositionLittleEndian<Layout::kRank / 2>(coord_n, params_.divmod_n);
|
||||
}
|
||||
|
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offset_modes_n = dot(modes_n, params_.stride_n);
|
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}
|
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|
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//
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// Compute the pointer and access
|
||||
//
|
||||
bool guard;
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||||
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||||
if (Layout::kRank > 2) {
|
||||
guard = row_guard && mask_.predicates[column];
|
||||
} else {
|
||||
guard = (coord_m < extent_row_) &&
|
||||
((thread_start_column_ + ThreadMap::Delta::kColumn * column) < extent_col_);
|
||||
}
|
||||
|
||||
cutlass::arch::global_load<
|
||||
AccessType,
|
||||
sizeof(AccessType)
|
||||
>(
|
||||
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
|
||||
(void *)(byte_pointer + offset_modes_m + offset_modes_n + byte_offset),
|
||||
guard
|
||||
);
|
||||
|
||||
if (Layout::kRank == 2) {
|
||||
offset_modes_n += params_.rank2_inc_col;
|
||||
}
|
||||
}
|
||||
|
||||
if (Layout::kRank == 2) {
|
||||
offset_modes_m += params_.rank2_inc_row;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Loads a fragment from memory
|
||||
CUTLASS_DEVICE
|
||||
void load(Fragment &frag) {
|
||||
|
||||
load_with_byte_offset(frag, 0);
|
||||
}
|
||||
|
||||
/// Stores a fragment to memory
|
||||
CUTLASS_DEVICE
|
||||
void store_with_byte_offset(Fragment const &frag, int64_t byte_offset) {
|
||||
uint8_t *byte_pointer = byte_pointer_;
|
||||
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
|
||||
|
||||
int row_begin = thread_start_row_ + group * ThreadMap::Delta::kGroup + cluster * ThreadMap::Delta::kCluster;
|
||||
int64_t offset_modes_m = row_begin * params_.stride_m[0];
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
|
||||
|
||||
int frag_row_idx =
|
||||
(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
|
||||
|
||||
//
|
||||
// Compute coordinate and decompose into M modes
|
||||
//
|
||||
|
||||
int coord_m = row * ThreadMap::Delta::kRow + row_begin;
|
||||
|
||||
Coord<Layout::kRank / 2, Index> modes_m;
|
||||
|
||||
if (Layout::kRank > 2) {
|
||||
if (kBigEndian) {
|
||||
modes_m = CoordinateDecomposition<Layout::kRank / 2>(coord_m, params_.divmod_m);
|
||||
} else {
|
||||
modes_m = CoordinateDecompositionLittleEndian<Layout::kRank / 2>(coord_m, params_.divmod_m);
|
||||
}
|
||||
|
||||
offset_modes_m = dot(modes_m, params_.stride_m);
|
||||
}
|
||||
|
||||
//
|
||||
// Compute the offset due to modes M
|
||||
//
|
||||
|
||||
bool row_guard = (coord_m < extent_row_);
|
||||
int64_t offset_modes_n = thread_start_column_ * params_.stride_n[0];
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
|
||||
|
||||
//
|
||||
// Compute coordinate and decompose into N modes
|
||||
//
|
||||
|
||||
int coord_n = thread_start_column_ + column * ThreadMap::Delta::kColumn;
|
||||
|
||||
Coord<Layout::kRank / 2, Index> modes_n;
|
||||
|
||||
if (Layout::kRank > 2) {
|
||||
if (kBigEndian) {
|
||||
modes_n = CoordinateDecomposition<Layout::kRank / 2>(coord_n, params_.divmod_n);
|
||||
}
|
||||
else {
|
||||
modes_n = CoordinateDecompositionLittleEndian<Layout::kRank / 2>(coord_n, params_.divmod_n);
|
||||
}
|
||||
|
||||
offset_modes_n = dot(modes_n, params_.stride_n);
|
||||
}
|
||||
|
||||
//
|
||||
// Compute the pointer and access
|
||||
//
|
||||
bool guard;
|
||||
if (Layout::kRank > 2) {
|
||||
guard = row_guard && mask_.predicates[column];
|
||||
} else {
|
||||
guard = (coord_m < extent_row_) && ((thread_start_column_ + ThreadMap::Delta::kColumn * column) < extent_col_);
|
||||
}
|
||||
|
||||
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
|
||||
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
|
||||
(void *)(byte_pointer + offset_modes_m + offset_modes_n + byte_offset),
|
||||
guard);
|
||||
|
||||
if (Layout::kRank == 2) {
|
||||
offset_modes_n += params_.rank2_inc_col;
|
||||
}
|
||||
}
|
||||
|
||||
if (Layout::kRank == 2) {
|
||||
offset_modes_m += params_.rank2_inc_row;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores a fragment to memory
|
||||
CUTLASS_DEVICE
|
||||
void store(Fragment const &frag) {
|
||||
|
||||
store_with_byte_offset(frag, 0);
|
||||
}
|
||||
|
||||
/// Advances to the next position to load or store
|
||||
CUTLASS_HOST_DEVICE
|
||||
PredicatedTileIteratorAffineRankN &operator++() {
|
||||
|
||||
++state_[0];
|
||||
thread_start_row_ += ThreadMap::Shape::kRow;
|
||||
|
||||
if (state_[0] == ThreadMap::Count::kRow) {
|
||||
|
||||
state_[0] = 0;
|
||||
++state_[1];
|
||||
|
||||
thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
|
||||
ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
|
||||
|
||||
if (state_[1] == ThreadMap::Count::kGroup) {
|
||||
|
||||
state_[1] = 0;
|
||||
++state_[2];
|
||||
|
||||
thread_start_row_ += ThreadMap::Count::kGroup *
|
||||
ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
|
||||
|
||||
if (state_[2] == ThreadMap::Count::kCluster) {
|
||||
state_[2] = 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) {
|
||||
mask = 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