Add support for sparse GEMM with row broadcasted bias vector (#951)
This commit is contained in:
Aleksandar Samardžić
@@ -0,0 +1,183 @@
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/***************************************************************************************************
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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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* 3. Neither the name of the copyright holder nor the names of its
|
||||
* 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
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* 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
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* 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/platform/platform.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/epilogue/thread/linear_combination_clamp.h"
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#include "cutlass/epilogue/thread/linear_combination_relu.h"
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#include "cutlass/epilogue/thread/linear_combination_relu0.h"
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#include "cutlass/epilogue/thread/linear_combination_gelu.h"
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#include "cutlass/epilogue/thread/linear_combination_sigmoid.h"
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#include "cutlass/epilogue/thread/linear_combination_hardswish.h"
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#include "cutlass/epilogue/thread/linear_combination_planar_complex.h"
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#include "cutlass/epilogue/thread/conversion_op.h"
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#include "cutlass/epilogue/thread/reduction_op.h"
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#include "cutlass/transform/threadblock/regular_tile_iterator_pitch_linear.h"
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#include "cutlass/epilogue/warp/fragment_iterator_tensor_op.h"
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#include "cutlass/epilogue/warp/fragment_iterator_complex_tensor_op.h"
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#include "cutlass/epilogue/warp/tile_iterator_tensor_op.h"
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#include "cutlass/epilogue/warp/tile_iterator_tensor_op_mixed.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
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#include "cutlass/epilogue/threadblock/default_thread_map_tensor_op.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator_row_broadcast.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator_strided_dgrad.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator_affine.h"
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#include "cutlass/epilogue/threadblock/shared_load_iterator.h"
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#include "cutlass/epilogue/threadblock/shared_load_iterator_mixed.h"
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#include "cutlass/epilogue/threadblock/epilogue.h"
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#include "cutlass/epilogue/threadblock/interleaved_epilogue.h"
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#include "cutlass/layout/permute.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace epilogue {
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namespace threadblock {
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template <
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typename Shape_,
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typename WarpMmaTensorOp_,
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int PartitionsK,
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typename OutputOp_,
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int ElementsPerAccess,
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bool ScatterD = false,
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typename PermuteDLayout = layout::NoPermute
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>
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struct DefaultEpilogueTensorOpRowBroadcast {
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using Shape = Shape_;
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using WarpMmaTensorOp = WarpMmaTensorOp_;
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static int const kPartitionsK = PartitionsK;
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using OutputOp = OutputOp_;
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static int const kElementsPerAccess = ElementsPerAccess;
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using ElementOutput = typename OutputOp::ElementOutput;
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using LayoutC = typename WarpMmaTensorOp::LayoutC;
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using ElementAccumulator = typename WarpMmaTensorOp::ElementC;
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//
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// Thread map
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//
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using OutputTileThreadMap = typename cutlass::epilogue::threadblock::DefaultThreadMapTensorOp<
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Shape,
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typename WarpMmaTensorOp::Shape,
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kPartitionsK,
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ElementOutput,
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kElementsPerAccess
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>::Type;
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static bool const UseCUDAStore = platform::is_same<ElementOutput, double>::value;
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using OutputTileIterator = cutlass::epilogue::threadblock::PredicatedTileIteratorRowBroadcast<
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OutputTileThreadMap,
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ElementOutput,
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ScatterD,
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PermuteDLayout,
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UseCUDAStore
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>;
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using AccumulatorFragmentIterator = typename platform::conditional<is_complex<ElementOutput>::value,
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cutlass::epilogue::warp::FragmentIteratorComplexTensorOp<
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typename WarpMmaTensorOp::Shape,
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typename WarpMmaTensorOp::Policy::Operator::Shape,
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typename WarpMmaTensorOp::Policy::Operator::ElementC,
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typename WarpMmaTensorOp::Policy::Operator::FragmentC,
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LayoutC>,
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cutlass::epilogue::warp::FragmentIteratorTensorOp<
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typename WarpMmaTensorOp::Shape,
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typename WarpMmaTensorOp::Policy::Operator::Shape,
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typename WarpMmaTensorOp::Policy::Operator::ElementC,
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typename WarpMmaTensorOp::Policy::Operator::FragmentC,
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LayoutC> >::type;
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/// Support several implementations depending on structure of epilogue
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using DefaultIterators = detail::DefaultIteratorsTensorOp<
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ElementOutput,
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ElementAccumulator,
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kElementsPerAccess,
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Shape,
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typename WarpMmaTensorOp::Shape,
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typename WarpMmaTensorOp::Policy::Operator::Shape,
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typename OutputTileThreadMap::CompactedThreadMap
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>;
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using WarpTileIterator = typename DefaultIterators::WarpTileIterator;
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using SharedLoadIterator = typename DefaultIterators::SharedLoadIterator;
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/// Hard-coded padding elements added
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using Padding = cutlass::MatrixShape<0, 64 / sizeof_bits<ElementAccumulator>::value * 4>;
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static int const kFragmentsPerIteration = (kPartitionsK == 1 ? DefaultIterators::kFragmentsPerIteration : 1);
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//
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// Define the epilogue
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//
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using Epilogue = cutlass::epilogue::threadblock::Epilogue<
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Shape,
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WarpMmaTensorOp,
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kPartitionsK,
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OutputTileIterator,
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AccumulatorFragmentIterator,
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WarpTileIterator,
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SharedLoadIterator,
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OutputOp,
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Padding,
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kFragmentsPerIteration
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>;
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};
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} // namespace threadblock
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} // namespace epilogue
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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@@ -0,0 +1,519 @@
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/***************************************************************************************************
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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
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
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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 PredicatedTileIteratorRowBroadcast {
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static_assert(!ScatterD);
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static_assert(std::is_same<PermuteDLayout, layout::NoPermute>::value);
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public:
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using ThreadMap = ThreadMap_;
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using Shape = typename ThreadMap::Shape;
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using Element = Element_;
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using Layout = layout::RowMajor;
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using TensorRef = TensorRef<Element, Layout>;
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using ConstTensorRef = typename TensorRef::ConstTensorRef;
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using Index = typename Layout::Index;
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using LongIndex = typename Layout::LongIndex;
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using TensorCoord = MatrixCoord;
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static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
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static int const kThreads = ThreadMap::kThreads;
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static int const kIterations = ThreadMap::Count::kTile;
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static_assert( ThreadMap::Iterations::kRow > 0,"ThreadMap::Iterations::kRow must be > 0");
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static_assert( ThreadMap::Iterations::kGroup > 0,"ThreadMap::Iterations::kGroup must be > 0");
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static_assert( ThreadMap::Iterations::kCluster > 0,"ThreadMap::Iterations::kCluster must be > 0");
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static_assert( ThreadMap::Iterations::kColumn > 0,"ThreadMap::Iterations::kColumn must be > 0");
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/// Fragment object
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using Fragment = Array<
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Element,
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ThreadMap::Iterations::kColumn *
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ThreadMap::Iterations::kRow *
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ThreadMap::Iterations::kGroup *
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ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;
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/// Memory access size
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using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
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//
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// Parameters struct
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//
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/// 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.
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uint8_t *byte_pointer_;
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/// Byte-level pointer for store().
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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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//
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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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PredicatedTileIteratorRowBroadcast(
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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)
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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.row()) * LongIndex(params_.stride) +
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LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
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// store_byte_pointer_ is set to be the same with byte_pointer_
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store_byte_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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byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
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LongIndex(thread_offset.row()) * LongIndex(params_.stride);
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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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||||
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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 {
|
||||
uint8_t *byte_pointer = byte_pointer_;
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||||
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
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||||
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||||
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_);
|
||||
|
||||
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 (guard) {
|
||||
Element *bias = reinterpret_cast<Element*>(byte_pointer + byte_offset);
|
||||
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column].fill(*bias);
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Loads a fragment from memory
|
||||
CUTLASS_DEVICE
|
||||
void load(Fragment &frag) const {
|
||||
|
||||
load_with_byte_offset(frag, 0);
|
||||
}
|
||||
|
||||
/// Stores a fragment to memory
|
||||
CUTLASS_DEVICE
|
||||
void store_with_byte_offset(Fragment const &frag, int64_t byte_offset) const {
|
||||
uint8_t *byte_pointer = store_byte_pointer_;
|
||||
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
|
||||
|
||||
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_);
|
||||
|
||||
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];
|
||||
|
||||
if (UseCUDAStore) {
|
||||
if (guard) {
|
||||
memory_pointer[0] =
|
||||
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
|
||||
}
|
||||
} else {
|
||||
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
|
||||
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
|
||||
(void *)&memory_pointer[0],
|
||||
guard);
|
||||
}
|
||||
|
||||
memory_pointer += (ThreadMap::Delta::kColumn / kElementsPerAccess);
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores a fragment to memory
|
||||
CUTLASS_DEVICE
|
||||
void store(Fragment const &frag) const {
|
||||
|
||||
store_with_byte_offset(frag, 0);
|
||||
}
|
||||
|
||||
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
|
||||
PredicatedTileIteratorRowBroadcast &operator++() {
|
||||
|
||||
++state_[0];
|
||||
|
||||
store_byte_pointer_ += params_.advance_row;
|
||||
|
||||
byte_pointer_ += params_.advance_row;
|
||||
|
||||
thread_start_row_ += ThreadMap::Shape::kRow;
|
||||
|
||||
if (state_[0] == ThreadMap::Count::kRow) {
|
||||
|
||||
state_[0] = 0;
|
||||
++state_[1];
|
||||
byte_pointer_ += params_.advance_group;
|
||||
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];
|
||||
byte_pointer_ += params_.advance_cluster;
|
||||
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;
|
||||
byte_pointer_ += params_.advance_tile;
|
||||
store_byte_pointer_ += params_.advance_tile;
|
||||
|
||||
thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow
|
||||
* ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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