add a new epilogue for the case that the output is not packed
This commit is contained in:
Haicheng Wu
@ -0,0 +1,562 @@
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/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (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/layout/permute.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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#include "cutlass/conv/conv2d_problem_size.h"
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#include "cutlass/conv/conv3d_problem_size.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 | PredicatedTileIteratorConv | 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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bool ScatterD = false, ///< Scatter D operand or not
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typename PermuteDLayout = layout::NoPermute, ///< Permute D operand or not
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bool UseCUDAStore = false,
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int Rank = 4
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>
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class PredicatedTileIteratorConv {
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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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static int const kRank = Rank;
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using Layout = typename platform::conditional<kRank == 4,
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layout::TensorNHWC,
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layout::TensorNDHWC>::type;
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using Stride = typename Layout::Stride;
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static int const kStrideRank = Layout::kStrideRank;
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using TensorRef = TensorRef<Element, Layout>;
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using ConstTensorRef = typename TensorRef::ConstTensorRef;
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using MappedLayout = layout::RowMajor;
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using Index = typename MappedLayout::Index;
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using LongIndex = typename MappedLayout::LongIndex;
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using TensorCoord = typename MappedLayout::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 bool constexpr PermuteD = !layout::is_trivial_permute<PermuteDLayout>;
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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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/// Uses a non-template class
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struct Params : PredicatedTileIteratorParams {
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using Base = PredicatedTileIteratorParams;
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/// Fast divmod objects divided by tensor extents
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FastDivmod divmod[kStrideRank - 1];
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Stride tensor_stride;
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CUTLASS_HOST_DEVICE
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Params() { }
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CUTLASS_HOST_DEVICE
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Params(Layout const &layout, conv::Conv2dProblemSize const &problem_size):
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PredicatedTileIteratorParams(
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layout.stride()[0] * int(sizeof(AccessType)) / kElementsPerAccess,
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make_OutputTileThreadMapDesc<ThreadMap>()
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) {
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divmod[0] = FastDivmod(problem_size.Q);
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divmod[1] = FastDivmod(problem_size.P);
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kStrideRank; ++i) {
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tensor_stride[i] = layout.stride()[i];
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}
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}
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CUTLASS_HOST_DEVICE
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Params(Layout const &layout, conv::Conv3dProblemSize const &problem_size):
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PredicatedTileIteratorParams(
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layout.stride()[0] * int(sizeof(AccessType)) / kElementsPerAccess,
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make_OutputTileThreadMapDesc<ThreadMap>()
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) {
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divmod[0] = FastDivmod(problem_size.Q);
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divmod[1] = FastDivmod(problem_size.P);
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divmod[2] = FastDivmod(problem_size.Z);
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kStrideRank; ++i) {
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tensor_stride[i] = layout.stride()[i];
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}
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}
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CUTLASS_HOST_DEVICE
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Params(Base const &base) :
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Base(base) { }
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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. This pointer is usually for both load() and store(), unless PermuteD is performed. When having PermuteD, byte_pointer_ is only for load().
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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_column_;
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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
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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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static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");
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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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PredicatedTileIteratorConv(
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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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{
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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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extent_column_ = 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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// 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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// Null pointer performs no accesses
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if (!pointer) {
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mask_.clear();
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}
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// Initialize byte_pointer_
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byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
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LongIndex(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_with_byte_offset(Fragment &frag, int64_t byte_offset) const {
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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 + byte_offset);
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Stride tensor_coord = CoordinateDecompositionLittleEndian<kStrideRank>(row_offset + thread_start_row_, params_.divmod);
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LongIndex tensor_offset = dot(tensor_coord, params_.tensor_stride);
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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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cutlass::arch::global_load<
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AccessType,
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sizeof(AccessType)
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>(
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frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
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column],
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(void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
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kElementsPerAccess + tensor_offset / kElementsPerAccess],
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guard);
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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 from memory
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CUTLASS_DEVICE
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void load(Fragment &frag) const {
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load_with_byte_offset(frag, 0);
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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_with_byte_offset(Fragment const &frag, int64_t byte_offset) const {
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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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Stride tensor_coord = CoordinateDecompositionLittleEndian<kStrideRank>((row_offset + thread_start_row_), params_.divmod);
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LongIndex tensor_offset = dot(tensor_coord, params_.tensor_stride);
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AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
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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 (UseCUDAStore) {
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if (guard) {
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memory_pointer[tensor_offset / kElementsPerAccess] =
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frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
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}
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} else {
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cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
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frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
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(void *)&memory_pointer[tensor_offset / kElementsPerAccess],
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guard);
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}
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memory_pointer += (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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/// Stores a fragment to memory
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CUTLASS_DEVICE
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void store(Fragment const &frag) const {
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||||
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||||
store_with_byte_offset(frag, 0);
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||||
}
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CUTLASS_DEVICE
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MatrixCoord thread_start() const {
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return MatrixCoord(thread_start_row_, thread_start_column_);
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}
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||||
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/// Need to get the thread start row from the tile iterator
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CUTLASS_DEVICE
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int32_t thread_start_row() const {
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return thread_start_row_;
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||||
}
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||||
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||||
/// Need to get the thread start row from the tile iterator
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CUTLASS_DEVICE
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int32_t thread_start_column() const {
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return thread_start_column_;
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||||
}
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||||
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||||
/// Extent of the matrix in rows
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CUTLASS_DEVICE
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Index extent_row() const {
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||||
return extent_row_;
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||||
}
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||||
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||||
/// Extent of the matrix in columns
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CUTLASS_DEVICE
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Index extent_column() const {
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||||
return extent_column_;
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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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PredicatedTileIteratorConv &operator++() {
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||||
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||||
++state_[0];
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||||
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thread_start_row_ += ThreadMap::Shape::kRow;
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||||
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||||
if (state_[0] == ThreadMap::Count::kRow) {
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||||
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||||
state_[0] = 0;
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++state_[1];
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||||
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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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||||
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||||
if (state_[1] == ThreadMap::Count::kGroup) {
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||||
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||||
state_[1] = 0;
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||||
++state_[2];
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||||
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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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||||
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||||
if (state_[2] == ThreadMap::Count::kCluster) {
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||||
state_[2] = 0;
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||||
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||||
thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow
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||||
* ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile;
|
||||
}
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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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||||
/// Advances a number of positions to load or store
|
||||
CUTLASS_HOST_DEVICE
|
||||
PredicatedTileIteratorConv &operator+=(int increment)
|
||||
{
|
||||
// Row
|
||||
state_[0] += increment;
|
||||
int increment_row = state_[0] / ThreadMap::Count::kRow;
|
||||
state_[0] = state_[0] % ThreadMap::Count::kRow;
|
||||
|
||||
thread_start_row_ += (ThreadMap::Shape::kRow * increment);
|
||||
|
||||
// Group
|
||||
state_[1] += increment_row;
|
||||
int increment_group = state_[1] / ThreadMap::Count::kGroup;
|
||||
state_[1] = state_[1] % ThreadMap::Count::kGroup;
|
||||
|
||||
thread_start_row_ +=
|
||||
(ThreadMap::Shape::kGroup - 1) *
|
||||
ThreadMap::Shape::kRow *
|
||||
ThreadMap::Count::kRow *
|
||||
increment_row;
|
||||
|
||||
// Cluster
|
||||
state_[2] += increment_group;
|
||||
int increment_cluster = state_[2] / ThreadMap::Count::kCluster;
|
||||
state_[2] = state_[2] % ThreadMap::Count::kCluster;
|
||||
|
||||
thread_start_row_ +=
|
||||
ThreadMap::Count::kGroup *
|
||||
ThreadMap::Shape::kGroup *
|
||||
ThreadMap::Count::kRow *
|
||||
ThreadMap::Shape::kRow *
|
||||
increment_group;
|
||||
|
||||
// Tile
|
||||
thread_start_row_ +=
|
||||
ThreadMap::Shape::kGroup *
|
||||
ThreadMap::Shape::kRow *
|
||||
ThreadMap::Shape::kCluster *
|
||||
ThreadMap::Shape::kTile *
|
||||
increment_cluster;
|
||||
|
||||
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) const {
|
||||
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