Add support for sparse GEMM with row broadcasted bias vector (#951)
This commit is contained in:
Aleksandar Samardžić
GitHub
parent
b4ab501767
commit
d3e72719b4
@ -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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*
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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
|
||||
* 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
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* 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/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
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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
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
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* 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.
|
||||
*
|
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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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* 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
|
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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 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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/// 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 =
|
||||
(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
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
514
include/cutlass/gemm/device/gemm_sparse_row_broadcast.h
Normal file
514
include/cutlass/gemm/device/gemm_sparse_row_broadcast.h
Normal file
@ -0,0 +1,514 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* 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.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/arch.h"
|
||||
#include "cutlass/device_kernel.h"
|
||||
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
#include "cutlass/gemm/kernel/sparse_gemm_row_broadcast.h"
|
||||
|
||||
#include "cutlass/gemm/kernel/default_gemm_sparse_row_broadcast.h"
|
||||
#include "cutlass/gemm/device/default_gemm_configuration.h"
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace device {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/*! Gemm device-level operator. This is an interface to efficient CUTLASS GEMM kernels that may
|
||||
be invoked from host code.
|
||||
|
||||
The contributions of this class are:
|
||||
|
||||
1. At compile time, it maps data types and high-level structural parameters onto
|
||||
specific CUTLASS components.
|
||||
|
||||
2. At runtime, it maps logical arguments to GEMM problems to kernel parameters.
|
||||
|
||||
3. At runtime, it launches kernels on the device.
|
||||
|
||||
The intent is to provide a convenient mechanism for interacting with most plausible GEMM
|
||||
configurations for each supported architecture. Consequently, not all parameters are exposed
|
||||
to the top-level interface. Rather, sensible defaults at each level of the CUTLASS hierarchy
|
||||
are selected to tradeoff simplicity of the interface with flexibility. We expect
|
||||
most configurations to be specified at this level. Applications with more exotic requirements
|
||||
may construct their kernels of interest using CUTLASS components at the threadblock, warp,
|
||||
and thread levels of abstraction.
|
||||
|
||||
CUTLASS exposes computations using the functor design pattern in which objects compose some
|
||||
internal state with an overloaded function call operator. This enables decoupling of
|
||||
initialization from execution, possibly reducing overhead during steady state phases of
|
||||
application execution.
|
||||
|
||||
CUTLASS device-level operators expose an Arguments structure encompassing each logical
|
||||
input to the computation. This is distinct from the kernel-level Params structure pattern
|
||||
which contains application-specific precomputed state needed by the device code.
|
||||
|
||||
Example of a CUTLASS GEMM operator implementing the functionality of cuBLAS's SGEMM NN
|
||||
is as follows:
|
||||
|
||||
//
|
||||
// Instantiate the CUTLASS GEMM operator.
|
||||
//
|
||||
|
||||
cutlass::gemm::device::Gemm<
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
> gemm_op;
|
||||
|
||||
//
|
||||
// Launch the GEMM operation on the device
|
||||
//
|
||||
|
||||
cutlass::Status status = gemm_op({
|
||||
{m, n, k}, // GemmCoord problem_size,
|
||||
{A, lda}, // TensorRef<float, layout::ColumnMajor> ref_A,
|
||||
{B, ldb}, // TensorRef<float, layout::ColumnMajor> ref_B,
|
||||
{C, ldc}, // TensorRef<float, layout::ColumnMajor> ref_C,
|
||||
{D, ldd}, // TensorRef<float, layout::ColumnMajor> ref_D,
|
||||
{alpha, beta} // EpilogueOutputOp::Params epilogue_op_params
|
||||
});
|
||||
|
||||
|
||||
A simplified view of the template is listed below.
|
||||
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA,
|
||||
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA,
|
||||
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC,
|
||||
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
|
||||
/// Operator class tag
|
||||
typename OperatorClass,
|
||||
|
||||
/// Tag indicating architecture to tune for. This is the minimum SM that
|
||||
/// supports the intended feature. The device kernel can be built
|
||||
/// targeting any SM larger than this number.
|
||||
typename ArchTag,
|
||||
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp,
|
||||
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages
|
||||
>
|
||||
class Gemm;
|
||||
*/
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA_,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA_,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB_,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB_,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC_,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC_,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator_ = ElementC_,
|
||||
/// Operator class tag
|
||||
typename OperatorClass_ = arch::OpClassSimt,
|
||||
/// Tag indicating architecture to tune for
|
||||
typename ArchTag_ = arch::Sm70,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::WarpShape,
|
||||
/// Instruction-level tile size (concept: GemmShape)
|
||||
typename InstructionShape_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::EpilogueOutputOp,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle_ =
|
||||
typename threadblock::GemmIdentityThreadblockSwizzle<>,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kStages,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int AlignmentA =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kAlignmentA,
|
||||
/// Access granularity of B matrix in units of elements
|
||||
int AlignmentB =
|
||||
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
||||
ElementC_, ElementAccumulator_>::kAlignmentB,
|
||||
/// If true, kernel supports split-K with serial reduction
|
||||
bool SplitKSerial = false,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator_ = typename DefaultGemmConfiguration<
|
||||
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
||||
ElementAccumulator_>::Operator>
|
||||
class SparseGemmRowBroadcast {
|
||||
public:
|
||||
|
||||
using ElementA = ElementA_;
|
||||
using LayoutA = LayoutA_;
|
||||
using TensorRefA = TensorRef<ElementA const, LayoutA>;
|
||||
using ElementB = ElementB_;
|
||||
using LayoutB = LayoutB_;
|
||||
using TensorRefB = TensorRef<ElementB const, LayoutB>;
|
||||
using ElementC = ElementC_;
|
||||
using LayoutC = LayoutC_;
|
||||
using TensorRefC = TensorRef<ElementC const, LayoutC>;
|
||||
using TensorRefD = TensorRef<ElementC, LayoutC>;
|
||||
using ElementAccumulator = ElementAccumulator_;
|
||||
using OperatorClass = OperatorClass_;
|
||||
using ArchTag = ArchTag_;
|
||||
using ThreadblockShape = ThreadblockShape_;
|
||||
using WarpShape = WarpShape_;
|
||||
using InstructionShape = InstructionShape_;
|
||||
using EpilogueOutputOp = EpilogueOutputOp_;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
using Operator = Operator_;
|
||||
using MathOperator = Operator;
|
||||
static int const kStages = Stages;
|
||||
static int const kAlignmentA = AlignmentA;
|
||||
static int const kAlignmentB = AlignmentB;
|
||||
static int const kAlignmentC = EpilogueOutputOp::kCount;
|
||||
static bool const kSplitKSerial = SplitKSerial;
|
||||
static ComplexTransform const kTransformA = ComplexTransform::kNone;
|
||||
static ComplexTransform const kTransformB = ComplexTransform::kNone;
|
||||
|
||||
/// Define the kernel
|
||||
using GemmKernel = typename kernel::DefaultSparseGemmRowBroadcast<
|
||||
ElementA,
|
||||
LayoutA,
|
||||
kAlignmentA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
kAlignmentB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
kStages,
|
||||
kSplitKSerial,
|
||||
Operator
|
||||
>::GemmKernel;
|
||||
|
||||
using ElementE = typename GemmKernel::ElementE;
|
||||
|
||||
using LayoutE = typename GemmKernel::LayoutE;
|
||||
|
||||
static int const kAlignmentE = 128 / sizeof_bits<ElementE>::value;
|
||||
|
||||
static int const kSparse = GemmKernel::kSparse;
|
||||
static int const kMetaSizeInBits = GemmKernel::kMetaSizeInBits;
|
||||
static int const kElementsPerElementE = GemmKernel::kElementsPerElementE;
|
||||
|
||||
/// Argument structure
|
||||
struct Arguments {
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
GemmCoord problem_size;
|
||||
TensorRef<ElementA const, LayoutA> ref_A;
|
||||
TensorRef<ElementB const, LayoutB> ref_B;
|
||||
TensorRef<ElementC const, LayoutC> ref_C;
|
||||
TensorRef<ElementC, LayoutC> ref_D;
|
||||
TensorRef<ElementE const, LayoutE> ref_E;
|
||||
typename EpilogueOutputOp::Params epilogue;
|
||||
int split_k_slices;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
/// Default ctor
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(): problem_size(0, 0, 0), split_k_slices(1) {
|
||||
|
||||
}
|
||||
|
||||
/// Constructs an Arguments structure
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(
|
||||
GemmCoord problem_size_,
|
||||
TensorRef<ElementA const, LayoutA> ref_A_,
|
||||
TensorRef<ElementB const, LayoutB> ref_B_,
|
||||
TensorRef<ElementC const, LayoutC> ref_C_,
|
||||
TensorRef<ElementC, LayoutC> ref_D_,
|
||||
TensorRef<ElementE, LayoutE> ref_E_,
|
||||
typename EpilogueOutputOp::Params epilogue_ =
|
||||
typename EpilogueOutputOp::Params(),
|
||||
int split_k_slices = 1
|
||||
):
|
||||
problem_size(problem_size_),
|
||||
ref_A(ref_A_),
|
||||
ref_B(ref_B_),
|
||||
ref_C(ref_C_),
|
||||
ref_D(ref_D_),
|
||||
ref_E(ref_E_),
|
||||
epilogue(epilogue_),
|
||||
split_k_slices(split_k_slices) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
/// Kernel parameters object
|
||||
typename GemmKernel::Params params_;
|
||||
|
||||
public:
|
||||
|
||||
/// Constructs the GEMM.
|
||||
SparseGemmRowBroadcast() { }
|
||||
|
||||
/// Determines whether the GEMM can execute the given problem.
|
||||
static Status can_implement(Arguments const &args) {
|
||||
|
||||
if (!kSplitKSerial && args.split_k_slices > 1) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
Status status = GemmKernel::can_implement(
|
||||
args.problem_size,
|
||||
args.ref_A.non_const_ref(),
|
||||
args.ref_B.non_const_ref(),
|
||||
args.ref_C.non_const_ref(),
|
||||
args.ref_D,
|
||||
args.ref_E.non_const_ref()
|
||||
);
|
||||
|
||||
if (status != Status::kSuccess) {
|
||||
return status;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Gets the workspace size
|
||||
static size_t get_workspace_size(Arguments const &args) {
|
||||
|
||||
size_t bytes = 0;
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
|
||||
args.problem_size,
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
args.split_k_slices);
|
||||
|
||||
if (kSplitKSerial && args.split_k_slices > 1) {
|
||||
|
||||
bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
|
||||
}
|
||||
|
||||
return bytes;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
|
||||
args.problem_size,
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
args.split_k_slices);
|
||||
|
||||
if (kSplitKSerial) {
|
||||
if (args.split_k_slices > 1) {
|
||||
if (!workspace) {
|
||||
return Status::kErrorWorkspaceNull;
|
||||
}
|
||||
|
||||
size_t bytes = get_workspace_size(args);
|
||||
|
||||
cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
if (args.split_k_slices > 1) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
// Initialize the Params structure
|
||||
params_ = typename GemmKernel::Params{
|
||||
args.problem_size,
|
||||
grid_shape,
|
||||
args.ref_A.non_const_ref(),
|
||||
args.ref_B.non_const_ref(),
|
||||
args.ref_C.non_const_ref(),
|
||||
args.ref_D,
|
||||
args.ref_E.non_const_ref(),
|
||||
args.epilogue,
|
||||
static_cast<int *>(workspace)
|
||||
};
|
||||
|
||||
int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
|
||||
if (smem_size >= (48 << 10)) {
|
||||
cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
smem_size);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Lightweight update given a subset of arguments
|
||||
Status update(Arguments const &args, void *workspace = nullptr) {
|
||||
|
||||
if (kSplitKSerial && args.split_k_slices > 1) {
|
||||
if (!workspace) {
|
||||
return Status::kErrorWorkspaceNull;
|
||||
}
|
||||
}
|
||||
|
||||
params_.ref_A.reset(args.ref_A.non_const_ref().data());
|
||||
params_.ref_B.reset(args.ref_B.non_const_ref().data());
|
||||
params_.ref_C.reset(args.ref_C.non_const_ref().data());
|
||||
params_.ref_D.reset(args.ref_D.data());
|
||||
params_.ref_E.reset(args.ref_E.non_const_ref().data());
|
||||
params_.output_op = args.epilogue;
|
||||
params_.semaphore = static_cast<int *>(workspace);
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status run(cudaStream_t stream = nullptr) {
|
||||
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
|
||||
dim3 block(GemmKernel::kThreadCount, 1, 1);
|
||||
|
||||
int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
|
||||
|
||||
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);
|
||||
|
||||
cudaError_t result = cudaGetLastError();
|
||||
|
||||
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(cudaStream_t stream = nullptr) {
|
||||
return run(stream);
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
Status status = initialize(args, workspace, stream);
|
||||
|
||||
if (status == Status::kSuccess) {
|
||||
status = run(stream);
|
||||
}
|
||||
|
||||
return status;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
191
include/cutlass/gemm/kernel/default_gemm_sparse_row_broadcast.h
Normal file
191
include/cutlass/gemm/kernel/default_gemm_sparse_row_broadcast.h
Normal file
@ -0,0 +1,191 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* 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.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief
|
||||
Default kernel-level GEMM definitions combine threadblock-scoped matrix multiply-add with
|
||||
the appropriate threadblock-scoped epilogue.
|
||||
|
||||
Note, CUTLASS epilogues universally target row-major outputs. Column-major outputs are
|
||||
accommodated by exchanging A and B operands and assuming transposed layouts. Partial
|
||||
specializations here choose 'device::GemmTransposed' to implement this functionality.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/wmma.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/epilogue.h"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/kernel/gemm.h"
|
||||
#include "cutlass/gemm/kernel/sparse_gemm_row_broadcast.h"
|
||||
#include "cutlass/gemm/kernel/gemm_pipelined.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_sparse_mma.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op_row_broadcast.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
|
||||
#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
|
||||
|
||||
#if defined(CUTLASS_ARCH_WMMA_ENABLED)
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"
|
||||
#endif //CUTLASS_ARCH_WMMA_ENABLED
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA_,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA_,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB_,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB_,
|
||||
/// Access granularity of B matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC_,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC_,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Operator class tag
|
||||
typename OperatorClass,
|
||||
/// Tag indicating architecture to tune for
|
||||
typename ArchTag,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// If true, kernel is configured to support serial reduction in the
|
||||
/// epilogue
|
||||
bool SplitKSerial,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator>
|
||||
struct DefaultSparseGemmRowBroadcast;
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Partial specialization for Ampere Architecture
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// If true, kernel is configured to support serial reduction in the
|
||||
/// epilogue
|
||||
bool SplitKSerial,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator>
|
||||
struct DefaultSparseGemmRowBroadcast<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementC,
|
||||
layout::RowMajor, ElementAccumulator, arch::OpClassTensorOp,
|
||||
arch::Sm80, ThreadblockShape, WarpShape, InstructionShape,
|
||||
EpilogueOutputOp, ThreadblockSwizzle, Stages, SplitKSerial,
|
||||
Operator> {
|
||||
/// Define the threadblock-scoped matrix multiply-accumulate
|
||||
using Mma = typename cutlass::gemm::threadblock::DefaultSparseMma<
|
||||
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
|
||||
ThreadblockShape, WarpShape, InstructionShape, Stages,
|
||||
Operator>::ThreadblockMma;
|
||||
|
||||
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
|
||||
|
||||
/// Define the epilogue
|
||||
using Epilogue =
|
||||
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOpRowBroadcast<
|
||||
ThreadblockShape, typename Mma::Operator, kPartitionsK, EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount>::Epilogue;
|
||||
|
||||
/// Define the kernel-level GEMM operator.
|
||||
using GemmKernel = kernel::SparseGemmRowBroadcast<Mma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
400
include/cutlass/gemm/kernel/sparse_gemm_row_broadcast.h
Normal file
400
include/cutlass/gemm/kernel/sparse_gemm_row_broadcast.h
Normal file
@ -0,0 +1,400 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* 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.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/semaphore.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
||||
typename Epilogue_, ///! Epilogue
|
||||
typename ThreadblockSwizzle_, ///! Threadblock swizzling function
|
||||
bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
|
||||
>
|
||||
struct SparseGemmRowBroadcast {
|
||||
|
||||
using Mma = Mma_;
|
||||
using Epilogue = Epilogue_;
|
||||
using OutputOp = typename Epilogue::OutputOp;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
static bool const kSplitKSerial = SplitKSerial;
|
||||
|
||||
static int const kSparse = Mma::kSparse;
|
||||
static int const kMetaSizeInBits = Mma::kMetaSizeInBits;
|
||||
static int const kMaxID2 = Mma::kMaxID2;
|
||||
static int const kElementsPerElementE = Mma::kElementsPerElementE;
|
||||
|
||||
using ElementE = typename Mma::ElementE;
|
||||
using LayoutE = typename Mma::LayoutE;
|
||||
|
||||
/// Warp count (concept: GemmShape)
|
||||
using WarpCount = typename Mma::WarpCount;
|
||||
static int const kThreadCount = 32 * WarpCount::kCount;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params {
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
typename Mma::IteratorA::Params params_A;
|
||||
typename Mma::IteratorA::TensorRef ref_A;
|
||||
typename Mma::IteratorB::Params params_B;
|
||||
typename Mma::IteratorB::TensorRef ref_B;
|
||||
typename Epilogue::OutputTileIterator::Params params_C;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C;
|
||||
typename Epilogue::OutputTileIterator::Params params_D;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D;
|
||||
typename Mma::IteratorE::Params params_E;
|
||||
typename Mma::IteratorE::TensorRef ref_E;
|
||||
typename OutputOp::Params output_op;
|
||||
int *semaphore;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
typename OutputOp::Params output_op = typename OutputOp::Params(),
|
||||
int *workspace = nullptr
|
||||
):
|
||||
problem_size(problem_size),
|
||||
grid_tiled_shape(grid_tiled_shape),
|
||||
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
||||
params_A(ref_A.layout()),
|
||||
ref_A(ref_A),
|
||||
params_B(ref_B.layout()),
|
||||
ref_B(ref_B),
|
||||
params_C(ref_C.layout()),
|
||||
ref_C(ref_C),
|
||||
params_D(ref_D.layout()),
|
||||
ref_D(ref_D),
|
||||
params_E(ref_E.layout()),
|
||||
ref_E(ref_E),
|
||||
output_op(output_op) {
|
||||
|
||||
int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
||||
|
||||
gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
|
||||
|
||||
semaphore = workspace;
|
||||
}
|
||||
};
|
||||
|
||||
/// Shared memory storage structure
|
||||
union SharedStorage {
|
||||
typename Mma::SharedStorage main_loop;
|
||||
typename Epilogue::SharedStorage epilogue;
|
||||
};
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseGemmRowBroadcast() { }
|
||||
|
||||
/// Determines whether kernel satisfies alignment
|
||||
static Status can_implement(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E) {
|
||||
|
||||
static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
|
||||
static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
|
||||
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
|
||||
static int const kAlignmentE = Mma::IteratorE::AccessType::kElements;
|
||||
|
||||
if (!TensorRef_aligned(ref_A, kAlignmentA)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if (!TensorRef_aligned(ref_B, kAlignmentB)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// if (!TensorRef_aligned(ref_C, kAlignmentC)) {
|
||||
// return Status::kErrorMisalignedOperand;
|
||||
// }
|
||||
|
||||
if (!TensorRef_aligned(ref_D, kAlignmentC)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if (!TensorRef_aligned(ref_E, kAlignmentE)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if ((problem_size.m() % kAlignmentA) || ((problem_size.k() / kSparse) % kAlignmentA) ||
|
||||
(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
|
||||
(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC) ||
|
||||
(problem_size.m() % kAlignmentE) || ((problem_size.k() / kSparse) % kAlignmentE)) {
|
||||
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// The k dimension has to be the multiple of the Threadblock k because out
|
||||
// of bound meta data would be initialized to 0 by acync.zfill but 0 is not
|
||||
// a valid meta data.
|
||||
if (problem_size.k() % Mma::Shape::kK) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// M dimension has to be multiple of 32 (sparse float) or 16 (sparse int)
|
||||
// because of the row reordering of operand E
|
||||
static int const kAlignmentM = (sizeof(ElementE) == 2) ? 32 : 16;
|
||||
|
||||
if (problem_size.m() % kAlignmentM) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Executes one GEMM
|
||||
CUTLASS_DEVICE
|
||||
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
||||
|
||||
// Compute threadblock location
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
// Early exit if CTA is out of range
|
||||
if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
|
||||
params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// Compute initial location in logical coordinates
|
||||
cutlass::MatrixCoord tb_offset_A{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_B{
|
||||
threadblock_tile_offset.k() * params.gemm_k_size,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_E{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
// Problem size is a function of threadblock index in the K dimension
|
||||
int problem_size_k = min(
|
||||
params.problem_size.k(),
|
||||
(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
|
||||
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
|
||||
// Compute position within threadblock
|
||||
int thread_idx = threadIdx.x;
|
||||
|
||||
// Construct iterators to A, B, and E operands
|
||||
typename Mma::IteratorA iterator_A(
|
||||
params.params_A,
|
||||
params.ref_A.data(),
|
||||
{params.problem_size.m(), problem_size_k / kSparse},
|
||||
thread_idx,
|
||||
tb_offset_A);
|
||||
|
||||
typename Mma::IteratorB iterator_B(
|
||||
params.params_B,
|
||||
params.ref_B.data(),
|
||||
{problem_size_k, params.problem_size.n()},
|
||||
thread_idx,
|
||||
tb_offset_B);
|
||||
|
||||
typename Mma::IteratorE iterator_E(
|
||||
params.params_E, params.ref_E.data(),
|
||||
{params.problem_size.m(),
|
||||
problem_size_k / kSparse / kElementsPerElementE},
|
||||
thread_idx, tb_offset_E);
|
||||
|
||||
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
||||
// is compiled as warp-uniform.
|
||||
int warp_idx = canonical_warp_idx();
|
||||
int lane_idx = threadIdx.x % 32;
|
||||
|
||||
//
|
||||
// Main loop
|
||||
//
|
||||
|
||||
// Construct thread-scoped matrix multiply
|
||||
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
||||
|
||||
typename Mma::FragmentC accumulators;
|
||||
|
||||
accumulators.clear();
|
||||
|
||||
if (!kSplitKSerial || gemm_k_iterations > 0) {
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
|
||||
}
|
||||
|
||||
//
|
||||
// Epilogue
|
||||
//
|
||||
|
||||
OutputOp output_op(params.output_op);
|
||||
|
||||
//
|
||||
// Masked tile iterators constructed from members
|
||||
//
|
||||
|
||||
threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
//assume identity swizzle
|
||||
MatrixCoord threadblock_offset(
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
);
|
||||
|
||||
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
||||
|
||||
// Construct the semaphore.
|
||||
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
||||
|
||||
// If performing a reduction via split-K, fetch the initial synchronization
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// Fetch the synchronization lock initially but do not block.
|
||||
semaphore.fetch();
|
||||
|
||||
// Indicate which position in a serial reduction the output operator is currently updating
|
||||
output_op.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
|
||||
}
|
||||
|
||||
// Tile iterator loading from source tensor.
|
||||
typename Epilogue::OutputTileIterator iterator_C(
|
||||
params.params_C,
|
||||
params.ref_C.data(),
|
||||
params.problem_size.mn(),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
// Tile iterator writing to destination tensor.
|
||||
typename Epilogue::OutputTileIterator iterator_D(
|
||||
params.params_D,
|
||||
params.ref_D.data(),
|
||||
params.problem_size.mn(),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
Epilogue epilogue(
|
||||
shared_storage.epilogue,
|
||||
thread_idx,
|
||||
warp_idx,
|
||||
lane_idx);
|
||||
|
||||
// Wait on the semaphore - this latency may have been covered by iterator construction
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
||||
if (threadblock_tile_offset.k()) {
|
||||
iterator_C = iterator_D;
|
||||
}
|
||||
|
||||
semaphore.wait(threadblock_tile_offset.k());
|
||||
|
||||
__threadfence();
|
||||
}
|
||||
|
||||
// Execute the epilogue operator to update the destination tensor.
|
||||
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
||||
|
||||
//
|
||||
// Release the semaphore
|
||||
//
|
||||
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
int lock = 0;
|
||||
if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
|
||||
|
||||
// The final threadblock resets the semaphore for subsequent grids.
|
||||
lock = 0;
|
||||
}
|
||||
else {
|
||||
// Otherwise, the semaphore is incremented
|
||||
lock = threadblock_tile_offset.k() + 1;
|
||||
}
|
||||
|
||||
__threadfence();
|
||||
semaphore.release(lock);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
@ -37,6 +37,7 @@
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/gemm/device/gemm_sparse.h"
|
||||
#include "cutlass/gemm/device/gemm_sparse_row_broadcast.h"
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/reference/host/gemm.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
@ -267,6 +268,24 @@ TEST(SM80_Device_Sparse_Gemm_f16n_f16n_f16t_tensor_op_f32, 64x64x128_32x32x128)
|
||||
EXPECT_TRUE(test::gemm::device::TestAllSparseGemm<Gemm>());
|
||||
}
|
||||
|
||||
TEST(SM80_Device_Sparse_Gemm_Row_Broadcast_f16n_f16n_f16t_tensor_op_f32, 64x64x128_32x32x128) {
|
||||
using ElementOutput = cutlass::half_t;
|
||||
using ElementAccumulator = float;
|
||||
|
||||
using Gemm = cutlass::gemm::device::SparseGemmRowBroadcast<
|
||||
cutlass::half_t, cutlass::layout::ColumnMajor, cutlass::half_t,
|
||||
cutlass::layout::ColumnMajor, ElementOutput, cutlass::layout::RowMajor,
|
||||
ElementAccumulator, cutlass::arch::OpClassTensorOp, cutlass::arch::Sm80,
|
||||
cutlass::gemm::GemmShape<64, 64, 128>,
|
||||
cutlass::gemm::GemmShape<32, 32, 128>, cutlass::gemm::GemmShape<16, 8, 32>,
|
||||
cutlass::epilogue::thread::LinearCombination<
|
||||
ElementOutput, 128 / cutlass::sizeof_bits<ElementOutput>::value,
|
||||
ElementAccumulator, ElementAccumulator>,
|
||||
cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>, 6>;
|
||||
|
||||
EXPECT_TRUE(test::gemm::device::TestAllSparseGemm<Gemm>(true));
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_SPARSE_MMA_SM80_SUPPORTED)
|
||||
|
||||
@ -164,14 +164,19 @@ struct SparseTestbed {
|
||||
}
|
||||
|
||||
/// Initializes data structures
|
||||
void initialize(cutlass::gemm::GemmCoord problem_size) {
|
||||
void initialize(cutlass::gemm::GemmCoord problem_size, bool tensor_C_row_broadcast = false) {
|
||||
//
|
||||
// Allocate the GEMM workspace
|
||||
//
|
||||
tensor_A.resize(cutlass::make_Coord(problem_size.m(), problem_size.k() / kSparse));
|
||||
tensor_A_uncompressed.resize(problem_size.mk());
|
||||
tensor_B.resize(problem_size.kn());
|
||||
if (tensor_C_row_broadcast) {
|
||||
tensor_C.resize({problem_size.m(), 1});
|
||||
}
|
||||
else {
|
||||
tensor_C.resize(problem_size.mn());
|
||||
}
|
||||
tensor_D.resize(problem_size.mn());
|
||||
reference_D.resize(problem_size.mn(), false);
|
||||
tensor_E.resize(cutlass::make_Coord(
|
||||
@ -206,7 +211,14 @@ struct SparseTestbed {
|
||||
tensor_B.host_view().at({0, 0}) = typename Gemm::ElementB(1);
|
||||
tensor_C.host_view().at({0, 0}) = typename Gemm::ElementC(1);
|
||||
|
||||
if (tensor_C_row_broadcast) {
|
||||
for (int i = 0; i < problem_size.m(); ++i)
|
||||
for (int j = 0; j < problem_size.n(); ++j)
|
||||
reference_D.host_view().at({i, j}) = tensor_C.host_view().at({i, 0});
|
||||
}
|
||||
else {
|
||||
cutlass::reference::host::TensorCopy(reference_D.host_view(), tensor_C.host_view());
|
||||
}
|
||||
|
||||
tensor_A.sync_device();
|
||||
tensor_B.sync_device();
|
||||
@ -338,7 +350,8 @@ struct SparseTestbed {
|
||||
cutlass::gemm::GemmCoord problem_size,
|
||||
int split_k_slices = 1,
|
||||
ElementCompute alpha = ElementCompute(1),
|
||||
ElementCompute beta = ElementCompute(0)) {
|
||||
ElementCompute beta = ElementCompute(0),
|
||||
bool tensor_C_row_broadcast = false) {
|
||||
|
||||
// Waive test if insufficient CUDA device
|
||||
if (!sufficient()) {
|
||||
@ -348,7 +361,7 @@ struct SparseTestbed {
|
||||
return true;
|
||||
}
|
||||
|
||||
this->initialize(problem_size);
|
||||
this->initialize(problem_size, tensor_C_row_broadcast);
|
||||
|
||||
//
|
||||
// Initialize the GEMM operator
|
||||
@ -403,7 +416,7 @@ struct SparseTestbed {
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <typename Gemm>
|
||||
bool TestAllSparseGemm() {
|
||||
bool TestAllSparseGemm(bool tensor_C_row_broadcast = false) {
|
||||
bool passed = true;
|
||||
|
||||
int const kMinimumOperandElementSize =
|
||||
@ -463,7 +476,8 @@ bool TestAllSparseGemm() {
|
||||
problem_size,
|
||||
split_k,
|
||||
cutlass::from_real<ElementCompute>(alpha),
|
||||
cutlass::from_real<ElementCompute>(beta)
|
||||
cutlass::from_real<ElementCompute>(beta),
|
||||
tensor_C_row_broadcast
|
||||
);
|
||||
|
||||
if (!passed) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user