1364 lines
40 KiB
C++
1364 lines
40 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 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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////////////////////////////////////////////////////////////////////////////////
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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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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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>
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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 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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CUTLASS_HOST_DEVICE
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Params() { }
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CUTLASS_HOST_DEVICE
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Params(Layout const &layout):
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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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{ }
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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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PredicatedTileIteratorParams 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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/// Byte-level pointer for store(). Due to PermuteD Op, store_byte_pointer_ may be with different address computation compared to byte_pointer_.
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uint8_t *store_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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/// Scatter indices
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int const *indices_;
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/// PermuteDLayout
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PermuteDLayout permute_layout_;
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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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PredicatedTileIterator(
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PredicatedTileIteratorParams 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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int const *indices = nullptr
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):
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params_(params), indices_(indices),
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permute_layout_(PitchLinearCoord(extent.column(), extent.row()), params_.stride * kElementsPerAccess / sizeof(AccessType))
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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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if (ScatterD && !indices) {
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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.row()) * LongIndex(params_.stride) +
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LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
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if (ScatterD) {
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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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}
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// store_byte_pointer_ is set to be the same with byte_pointer_ unless PermuteD is used.
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store_byte_pointer_ = PermuteD ? reinterpret_cast<uint8_t *>(pointer) : byte_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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store_byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
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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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if (ScatterD && row_guard) {
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assert(indices_);
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memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
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LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
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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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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],
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guard);
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}
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if (row + 1 < ThreadMap::Iterations::kRow) {
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if (!ScatterD) {
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byte_pointer += params_.increment_row;
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}
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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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/// 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 = store_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 + byte_offset);
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if (ScatterD && row_guard) {
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assert(indices_);
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memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
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LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
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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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bool guard = row_guard && mask_.predicates[column];
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if (PermuteD) {
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int col_offset = column * ThreadMap::Delta::kColumn;
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int col = col_offset + thread_start_column_;
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int row = row_offset + thread_start_row_;
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// Locate memory_pointer
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memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset
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+ permute_layout_(PitchLinearCoord(col, row)) * sizeof(AccessType) / kElementsPerAccess);
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}
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if (UseCUDAStore) {
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if (guard) {
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memory_pointer[0] =
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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[0],
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guard);
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}
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if (!PermuteD) {
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memory_pointer += (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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if (!ScatterD && !PermuteD) {
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byte_pointer += params_.increment_row;
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}
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}
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}
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if (group + 1 < ThreadMap::Iterations::kGroup) {
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if (!ScatterD && !PermuteD) {
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byte_pointer += params_.increment_group;
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}
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}
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}
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if (cluster + 1 < ThreadMap::Iterations::kCluster) {
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if (!ScatterD && !PermuteD) {
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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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}
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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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store_with_byte_offset(frag, 0);
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}
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/// Loads a fragment from memory
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CUTLASS_DEVICE
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void downsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) 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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int output_row = row_offset + thread_start_row_;
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int output_N = output_row / (convolution_P * convolution_Q);
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int output_PQ = output_row % (convolution_P * convolution_Q);
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int output_P = output_PQ / convolution_Q;
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int output_Q = output_PQ % convolution_Q;
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int input_row = output_N * 2 * convolution_P * 2 * convolution_Q +
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(2 * output_P + add_P) * 2 * convolution_Q + 2 * output_Q + add_Q;
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int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);
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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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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],
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guard);
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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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}
|
|
}
|
|
|
|
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
|
|
byte_pointer += params_.increment_cluster;
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Loads a fragment from memory
|
|
CUTLASS_DEVICE
|
|
void upsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) const {
|
|
|
|
uint8_t *byte_pointer = byte_pointer_;
|
|
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&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) {
|
|
|
|
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));
|
|
|
|
int row_offset = row * ThreadMap::Delta::kRow
|
|
+ group * ThreadMap::Delta::kGroup
|
|
+ cluster * ThreadMap::Delta::kCluster;
|
|
|
|
bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
|
|
|
|
int output_row = row_offset + thread_start_row_;
|
|
int output_N = output_row / (convolution_P * convolution_Q);
|
|
int output_PQ = output_row % (convolution_P * convolution_Q);
|
|
int output_P = output_PQ / convolution_Q;
|
|
int output_Q = output_PQ % convolution_Q;
|
|
int row_add_P = add_P;
|
|
int row_add_Q = add_Q;
|
|
if (output_P > convolution_P - 2) row_add_P = 0;
|
|
if (output_Q > convolution_Q - 2) row_add_Q = 0;
|
|
|
|
int input_row = output_N * (convolution_P/2) * (convolution_Q/2) +
|
|
((output_P + row_add_P)/2) * (convolution_Q/2) + (output_Q + row_add_Q)/2;
|
|
|
|
int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);
|
|
|
|
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
|
|
|
|
bool guard = row_guard && mask_.predicates[column];
|
|
|
|
cutlass::arch::global_load<
|
|
AccessType,
|
|
sizeof(AccessType)
|
|
>(
|
|
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
|
|
column],
|
|
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
|
|
kElementsPerAccess],
|
|
guard);
|
|
}
|
|
|
|
if (row + 1 < ThreadMap::Iterations::kRow) {
|
|
byte_pointer += params_.increment_row;
|
|
}
|
|
}
|
|
|
|
if (group + 1 < ThreadMap::Iterations::kGroup) {
|
|
byte_pointer += params_.increment_group;
|
|
}
|
|
}
|
|
|
|
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
|
|
byte_pointer += params_.increment_cluster;
|
|
}
|
|
}
|
|
}
|
|
|
|
CUTLASS_DEVICE
|
|
MatrixCoord thread_start() const {
|
|
return MatrixCoord(thread_start_row_, thread_start_column_);
|
|
}
|
|
|
|
/// Need to get the thread start row from the tile iterator
|
|
CUTLASS_DEVICE
|
|
int32_t thread_start_row() const {
|
|
return thread_start_row_;
|
|
}
|
|
|
|
/// Need to get the thread start row from the tile iterator
|
|
CUTLASS_DEVICE
|
|
int32_t thread_start_column() const {
|
|
return thread_start_column_;
|
|
}
|
|
|
|
/// Extent of the matrix in rows
|
|
CUTLASS_DEVICE
|
|
Index extent_row() const {
|
|
return extent_row_;
|
|
}
|
|
|
|
/// Extent of the matrix in columns
|
|
CUTLASS_DEVICE
|
|
Index extent_column() const {
|
|
return extent_column_;
|
|
}
|
|
|
|
/// Advances to the next position to load or store
|
|
CUTLASS_HOST_DEVICE
|
|
PredicatedTileIterator &operator++() {
|
|
|
|
++state_[0];
|
|
|
|
if (!ScatterD) {
|
|
byte_pointer_ += params_.advance_row;
|
|
}
|
|
|
|
if (!ScatterD && !PermuteD) {
|
|
store_byte_pointer_ += params_.advance_row;
|
|
}
|
|
|
|
thread_start_row_ += ThreadMap::Shape::kRow;
|
|
|
|
if (state_[0] == ThreadMap::Count::kRow) {
|
|
|
|
state_[0] = 0;
|
|
++state_[1];
|
|
|
|
if (!ScatterD) {
|
|
byte_pointer_ += params_.advance_group;
|
|
}
|
|
|
|
if (!ScatterD && !PermuteD) {
|
|
store_byte_pointer_ += params_.advance_group;
|
|
}
|
|
|
|
thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
|
|
ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
|
|
|
|
if (state_[1] == ThreadMap::Count::kGroup) {
|
|
|
|
state_[1] = 0;
|
|
++state_[2];
|
|
|
|
if (!ScatterD) {
|
|
byte_pointer_ += params_.advance_cluster;
|
|
}
|
|
|
|
if (!ScatterD && !PermuteD) {
|
|
store_byte_pointer_ += params_.advance_cluster;
|
|
}
|
|
|
|
thread_start_row_ += ThreadMap::Count::kGroup *
|
|
ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
|
|
|
|
if (state_[2] == ThreadMap::Count::kCluster) {
|
|
state_[2] = 0;
|
|
|
|
if (!ScatterD) {
|
|
byte_pointer_ += params_.advance_tile;
|
|
}
|
|
|
|
if (!ScatterD && !PermuteD) {
|
|
store_byte_pointer_ += params_.advance_tile;
|
|
}
|
|
|
|
thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow
|
|
* ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile;
|
|
}
|
|
}
|
|
}
|
|
|
|
return *this;
|
|
}
|
|
|
|
/// Advances a number of positions to load or store
|
|
CUTLASS_HOST_DEVICE
|
|
PredicatedTileIterator &operator+=(int increment)
|
|
{
|
|
// Row
|
|
state_[0] += increment;
|
|
int increment_row = state_[0] / ThreadMap::Count::kRow;
|
|
state_[0] = state_[0] % ThreadMap::Count::kRow;
|
|
|
|
byte_pointer_ += (params_.advance_row * increment);
|
|
store_byte_pointer_ += (params_.advance_row * increment);
|
|
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;
|
|
|
|
byte_pointer_ += (params_.advance_group * increment_row);
|
|
store_byte_pointer_ += (params_.advance_group * increment_row);
|
|
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;
|
|
|
|
byte_pointer_ += (params_.advance_cluster * increment_group);
|
|
store_byte_pointer_ += (params_.advance_cluster * increment_group);
|
|
thread_start_row_ +=
|
|
ThreadMap::Count::kGroup *
|
|
ThreadMap::Shape::kGroup *
|
|
ThreadMap::Count::kRow *
|
|
ThreadMap::Shape::kRow *
|
|
increment_group;
|
|
|
|
// Tile
|
|
byte_pointer_ += (params_.advance_tile * increment_cluster);
|
|
store_byte_pointer_ += (params_.advance_tile * increment_cluster);
|
|
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;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Tile iterator used to load output tile from global memory in epilogue.
|
|
///
|
|
/// Satisfies: ReadableTileIterator | InterleavedPredicatedTileIterator | ForwardTileIterator
|
|
///
|
|
template <
|
|
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
|
|
typename Element_, ///< Element data type
|
|
int InterleavedN ///< Number of Interleaved N
|
|
>
|
|
class InterleavedPredicatedTileIterator {
|
|
public:
|
|
using ThreadMap = ThreadMap_;
|
|
|
|
using Element = Element_;
|
|
|
|
using Layout = layout::ColumnMajorInterleaved<InterleavedN>;
|
|
using TensorRef = TensorRef<Element, Layout>;
|
|
using ConstTensorRef = typename TensorRef::ConstTensorRef;
|
|
|
|
using Index = typename Layout::Index;
|
|
using LongIndex = typename Layout::LongIndex;
|
|
using TensorCoord = layout::PitchLinearCoord;
|
|
|
|
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>;
|
|
|
|
/// Uses a non-template class
|
|
struct Params : InterleavedPredicatedTileIteratorParams {
|
|
using Base = InterleavedPredicatedTileIteratorParams;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params() { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(Layout const &layout):
|
|
Base(
|
|
layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
|
|
make_InterleavedPredicatedTileIteratorDesc<Element, ThreadMap>()
|
|
) { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(Base const &base) :
|
|
Base(base) { }
|
|
};
|
|
|
|
/// 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,
|
|
int const *indices = nullptr ///< gather/scatter indices, note no support for gather/scatter at this specialization
|
|
):
|
|
params_(params) {
|
|
TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) +
|
|
TensorCoord(threadblock_offset.contiguous() * InterleavedN,
|
|
threadblock_offset.strided() / InterleavedN);
|
|
|
|
extent_col_ = extent.strided() / InterleavedN;
|
|
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() * InterleavedN));
|
|
}
|
|
|
|
// Initialize pointer
|
|
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
|
|
LongIndex(thread_offset.strided()) * LongIndex(params_.stride) +
|
|
LongIndex(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_];
|
|
|
|
cutlass::arch::global_load<
|
|
AccessType,
|
|
sizeof(AccessType)
|
|
>(
|
|
*frag_ptr,
|
|
(void *)memory_pointer,
|
|
guard);
|
|
}
|
|
|
|
/// 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_];
|
|
|
|
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
|
|
*frag_ptr, (void *)memory_pointer, guard);
|
|
}
|
|
|
|
/// 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;
|
|
}
|
|
|
|
/// Advances a number of positions to load or store
|
|
CUTLASS_HOST_DEVICE
|
|
InterleavedPredicatedTileIterator &operator+=(int increment)
|
|
{
|
|
// Contiguous
|
|
iteration_contiguous_ += increment;
|
|
int increment_strided = iteration_contiguous_ / ThreadMap::Iterations::kContiguous;
|
|
iteration_contiguous_ = iteration_contiguous_ % ThreadMap::Iterations::kContiguous;
|
|
byte_pointer_ += (params_.advance_row * increment);
|
|
|
|
// Strided
|
|
iteration_strided_ += increment_strided;
|
|
byte_pointer_ += (params_.advance_column * increment_strided);
|
|
|
|
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;
|
|
}
|
|
};
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Tile iterator used to load output tile from global memory in epilogue.
|
|
///
|
|
/// Satisfies: ReadableTileIterator | InterleavedMaskedTileIterator | ForwardTileIterator
|
|
///
|
|
template <
|
|
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
|
|
typename Element_, ///< Element data type
|
|
int InterleavedN ///< Number of Interleaved N
|
|
>
|
|
class InterleavedConvPredicatedTileIterator {
|
|
public:
|
|
using ThreadMap = ThreadMap_;
|
|
|
|
using Element = Element_;
|
|
|
|
using Layout = layout::TensorNCxHWx<InterleavedN>;
|
|
using TensorRef = TensorRef<Element, Layout>;
|
|
using ConstTensorRef = typename TensorRef::ConstTensorRef;
|
|
|
|
using Index = typename Layout::Index;
|
|
using LongIndex = typename Layout::LongIndex;
|
|
using TensorCoord = Tensor4DCoord;
|
|
|
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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::Iterations::kCount;
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/// Fragment object
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using Fragment = Array<Element, 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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|
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struct Params {
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//
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// Data members
|
|
//
|
|
|
|
LongIndex stride_col; ///< stride in bytes between columns
|
|
LongIndex stride_row; ///< stride in bytes between rows
|
|
|
|
//
|
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// Methods
|
|
//
|
|
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CUTLASS_HOST_DEVICE
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|
Status initialize(typename Layout::Stride stride_) {
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stride_col = stride_[1];
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stride_row = stride_[2];
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|
|
return Status::kSuccess;
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|
}
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|
CUTLASS_HOST_DEVICE
|
|
Params() {
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|
initialize(cutlass::make_Coord(0, 0, 0));
|
|
}
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|
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CUTLASS_HOST_DEVICE
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|
Params(Layout const &layout) {
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|
|
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initialize(layout.stride());
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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 =
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(ThreadMap::Iterations::kRow < 8) ? 8 : ThreadMap::Iterations::kRow;
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|
|
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/// Predicate state
|
|
bool predicates[kCount];
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|
|
|
//
|
|
// 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_;
|
|
|
|
/// Extent of the matrix tile in rows
|
|
Index extent_row_;
|
|
|
|
/// Extent of the matrix tile in pq
|
|
Index extent_pq_;
|
|
|
|
/// A thread's starting row position (assuming steady-state predicates have
|
|
/// been computed)
|
|
Index thread_start_row_;
|
|
|
|
/// A thread's starting column position (assuming steady-state predicates have
|
|
/// been computed)
|
|
Index thread_start_col_;
|
|
|
|
/// Internal iteration counter
|
|
LongIndex iteration_row_;
|
|
LongIndex iteration_col_;
|
|
|
|
uint32_t pq_mul_;
|
|
|
|
uint32_t pq_shr_;
|
|
|
|
private:
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
public:
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Constructor
|
|
CUTLASS_DEVICE
|
|
InterleavedConvPredicatedTileIterator(
|
|
Params const & params,
|
|
Element *pointer,
|
|
TensorCoord extent,
|
|
int thread_idx,
|
|
MatrixCoord threadblock_offset
|
|
):
|
|
params_(params) {
|
|
MatrixCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
|
|
|
|
extent_col_ = extent.c();
|
|
extent_pq_ = extent.h() * extent.w();
|
|
extent_row_ = extent.n() * extent_pq_;
|
|
|
|
find_divisor(pq_mul_, pq_shr_, extent_pq_);
|
|
|
|
thread_start_row_ = thread_offset.row();
|
|
thread_start_col_ = thread_offset.column();
|
|
|
|
// Initialize predicates
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int r = 0; r < ThreadMap::Iterations::kRow; ++r) {
|
|
mask_.predicates[r] =
|
|
((thread_offset.row() + ThreadMap::Delta::kRow * r) < extent_row_);
|
|
}
|
|
|
|
// Initialize pointer
|
|
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
|
|
((thread_start_col_ / InterleavedN) * params_.stride_col +
|
|
(thread_start_col_ % InterleavedN)) *
|
|
sizeof_bits<Element>::value / 8;
|
|
|
|
// Initialize internal state counter
|
|
iteration_row_ = iteration_col_ = 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) {
|
|
|
|
int col_offset = iteration_col_ * ThreadMap::Delta::kColumn;
|
|
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
|
|
bool guard = col_guard && mask_.predicates[iteration_row_];
|
|
|
|
int n, pq_rem;
|
|
|
|
fast_divmod(n, pq_rem,
|
|
thread_start_row_ + iteration_row_ * ThreadMap::Delta::kRow,
|
|
extent_pq_, pq_mul_, pq_shr_);
|
|
|
|
uint8_t *byte_pointer =
|
|
byte_pointer_ + (n * params_.stride_row + pq_rem * InterleavedN) *
|
|
sizeof_bits<Element>::value / 8;
|
|
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
|
|
AccessType const *memory_pointer =
|
|
reinterpret_cast<AccessType const *>(byte_pointer);
|
|
|
|
cutlass::arch::global_load<
|
|
AccessType,
|
|
sizeof(AccessType)
|
|
>(
|
|
*frag_ptr,
|
|
(void *)memory_pointer,
|
|
guard);
|
|
}
|
|
|
|
/// Stores a fragment to memory
|
|
CUTLASS_DEVICE
|
|
void store(Fragment const &frag) {
|
|
|
|
int col_offset = iteration_col_ * ThreadMap::Delta::kColumn;
|
|
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
|
|
bool guard = col_guard && mask_.predicates[iteration_row_];
|
|
|
|
int n, pq_rem;
|
|
|
|
fast_divmod(n, pq_rem,
|
|
thread_start_row_ + iteration_row_ * ThreadMap::Delta::kRow,
|
|
extent_pq_, pq_mul_, pq_shr_);
|
|
|
|
uint8_t *byte_pointer =
|
|
byte_pointer_ + (n * params_.stride_row + pq_rem * InterleavedN) *
|
|
sizeof_bits<Element>::value / 8;
|
|
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
|
|
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);
|
|
|
|
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
|
|
*frag_ptr, (void *)memory_pointer, guard);
|
|
}
|
|
|
|
/// Overrides the internal iteration index
|
|
CUTLASS_HOST_DEVICE
|
|
void set_iteration_index(int iteration) {
|
|
iteration_row_ = iteration % ThreadMap::Iterations::kRow;
|
|
iteration_col_ = iteration / ThreadMap::Iterations::kRow;
|
|
}
|
|
|
|
/// Advances to the next position to load or store
|
|
CUTLASS_HOST_DEVICE
|
|
InterleavedConvPredicatedTileIterator &operator++() {
|
|
|
|
++iteration_row_;
|
|
|
|
if (iteration_row_ == ThreadMap::Iterations::kRow) {
|
|
|
|
iteration_row_ = 0;
|
|
++iteration_col_;
|
|
byte_pointer_ += params_.stride_col;
|
|
|
|
if (iteration_col_ == ThreadMap::Iterations::kColumn) {
|
|
iteration_col_ = 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
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|