Add support for sparse GEMM with row broadcasted bias vector (#951)
This commit is contained in:
Aleksandar Samardžić
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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
|
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* 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
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Default kernel-level GEMM definitions combine threadblock-scoped matrix multiply-add with
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the appropriate threadblock-scoped epilogue.
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Note, CUTLASS epilogues universally target row-major outputs. Column-major outputs are
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accommodated by exchanging A and B operands and assuming transposed layouts. Partial
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specializations here choose 'device::GemmTransposed' to implement this functionality.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/arch/wmma.h"
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#include "cutlass/epilogue/threadblock/epilogue.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/kernel/gemm.h"
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#include "cutlass/gemm/kernel/sparse_gemm_row_broadcast.h"
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#include "cutlass/gemm/kernel/gemm_pipelined.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
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#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
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#include "cutlass/gemm/threadblock/default_sparse_mma.h"
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#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op_row_broadcast.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
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#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
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#if defined(CUTLASS_ARCH_WMMA_ENABLED)
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#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"
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#endif //CUTLASS_ARCH_WMMA_ENABLED
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace kernel {
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////////////////////////////////////////////////////////////////////////////////
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template <
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/// Element type for A matrix operand
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typename ElementA_,
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/// Layout type for A matrix operand
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typename LayoutA_,
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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
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/// Element type for B matrix operand
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typename ElementB_,
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/// Layout type for B matrix operand
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typename LayoutB_,
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/// Access granularity of B matrix in units of elements
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int kAlignmentB,
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/// Element type for C and D matrix operands
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typename ElementC_,
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/// Layout type for C and D matrix operands
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typename LayoutC_,
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/// Element type for internal accumulation
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typename ElementAccumulator,
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/// Operator class tag
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typename OperatorClass,
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/// Tag indicating architecture to tune for
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typename ArchTag,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape,
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/// Warp-level tile size (concept: GemmShape)
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typename InstructionShape,
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/// Epilogue output operator
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typename EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle,
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/// Number of stages used in the pipelined mainloop
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int Stages,
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/// If true, kernel is configured to support serial reduction in the
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/// epilogue
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bool SplitKSerial,
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/// Operation performed by GEMM
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typename Operator>
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struct DefaultSparseGemmRowBroadcast;
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////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////
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/// Partial specialization for Ampere Architecture
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template <
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/// Element type for A matrix operand
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typename ElementA,
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/// Layout type for A matrix operand
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typename LayoutA,
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/// Access granularity of A matrix in units of elements
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int kAlignmentA,
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/// Element type for B matrix operand
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typename ElementB,
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/// Layout type for B matrix operand
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typename LayoutB,
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/// Access granularity of A matrix in units of elements
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int kAlignmentB,
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/// Element type for C and D matrix operands
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typename ElementC,
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/// Element type for internal accumulation
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typename ElementAccumulator,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape,
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/// Warp-level tile size (concept: GemmShape)
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typename InstructionShape,
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/// Epilogue output operator
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typename EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle,
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/// Number of stages used in the pipelined mainloop
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int Stages,
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/// If true, kernel is configured to support serial reduction in the
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/// epilogue
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bool SplitKSerial,
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/// Operation performed by GEMM
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typename Operator>
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struct DefaultSparseGemmRowBroadcast<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementC,
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layout::RowMajor, ElementAccumulator, arch::OpClassTensorOp,
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arch::Sm80, ThreadblockShape, WarpShape, InstructionShape,
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EpilogueOutputOp, ThreadblockSwizzle, Stages, SplitKSerial,
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Operator> {
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/// Define the threadblock-scoped matrix multiply-accumulate
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using Mma = typename cutlass::gemm::threadblock::DefaultSparseMma<
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ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
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ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
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ThreadblockShape, WarpShape, InstructionShape, Stages,
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Operator>::ThreadblockMma;
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static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
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/// Define the epilogue
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using Epilogue =
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typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOpRowBroadcast<
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ThreadblockShape, typename Mma::Operator, kPartitionsK, EpilogueOutputOp,
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EpilogueOutputOp::kCount>::Epilogue;
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/// Define the kernel-level GEMM operator.
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using GemmKernel = kernel::SparseGemmRowBroadcast<Mma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace gemm
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} // namespace cutlass
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@@ -0,0 +1,400 @@
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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:
|
||||
*
|
||||
* 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
|
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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 Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/matrix_coord.h"
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#include "cutlass/semaphore.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace kernel {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <
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typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
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typename Epilogue_, ///! Epilogue
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typename ThreadblockSwizzle_, ///! Threadblock swizzling function
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bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
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>
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struct SparseGemmRowBroadcast {
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using Mma = Mma_;
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using Epilogue = Epilogue_;
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using OutputOp = typename Epilogue::OutputOp;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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static bool const kSplitKSerial = SplitKSerial;
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static int const kSparse = Mma::kSparse;
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static int const kMetaSizeInBits = Mma::kMetaSizeInBits;
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static int const kMaxID2 = Mma::kMaxID2;
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static int const kElementsPerElementE = Mma::kElementsPerElementE;
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using ElementE = typename Mma::ElementE;
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using LayoutE = typename Mma::LayoutE;
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/// Warp count (concept: GemmShape)
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using WarpCount = typename Mma::WarpCount;
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static int const kThreadCount = 32 * WarpCount::kCount;
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/// Parameters structure
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struct Params {
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cutlass::gemm::GemmCoord problem_size;
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cutlass::gemm::GemmCoord grid_tiled_shape;
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int swizzle_log_tile;
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typename Mma::IteratorA::Params params_A;
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typename Mma::IteratorA::TensorRef ref_A;
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typename Mma::IteratorB::Params params_B;
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typename Mma::IteratorB::TensorRef ref_B;
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typename Epilogue::OutputTileIterator::Params params_C;
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typename Epilogue::OutputTileIterator::TensorRef ref_C;
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typename Epilogue::OutputTileIterator::Params params_D;
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typename Epilogue::OutputTileIterator::TensorRef ref_D;
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typename Mma::IteratorE::Params params_E;
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typename Mma::IteratorE::TensorRef ref_E;
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typename OutputOp::Params output_op;
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int *semaphore;
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int gemm_k_iterations;
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int gemm_k_size;
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(0), gemm_k_size(0) { }
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CUTLASS_HOST_DEVICE
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Params(
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cutlass::gemm::GemmCoord const & problem_size,
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cutlass::gemm::GemmCoord const & grid_tiled_shape,
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typename Mma::IteratorA::TensorRef ref_A,
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typename Mma::IteratorB::TensorRef ref_B,
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typename Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D,
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typename Mma::IteratorE::TensorRef ref_E,
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typename OutputOp::Params output_op = typename OutputOp::Params(),
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int *workspace = nullptr
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):
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problem_size(problem_size),
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grid_tiled_shape(grid_tiled_shape),
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swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
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params_A(ref_A.layout()),
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ref_A(ref_A),
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params_B(ref_B.layout()),
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ref_B(ref_B),
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params_C(ref_C.layout()),
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ref_C(ref_C),
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params_D(ref_D.layout()),
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ref_D(ref_D),
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params_E(ref_E.layout()),
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ref_E(ref_E),
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output_op(output_op) {
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int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
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gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
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semaphore = workspace;
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}
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};
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/// Shared memory storage structure
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union SharedStorage {
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typename Mma::SharedStorage main_loop;
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typename Epilogue::SharedStorage epilogue;
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};
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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SparseGemmRowBroadcast() { }
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/// Determines whether kernel satisfies alignment
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static Status can_implement(
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cutlass::gemm::GemmCoord const & problem_size,
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typename Mma::IteratorA::TensorRef ref_A,
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typename Mma::IteratorB::TensorRef ref_B,
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typename Epilogue::OutputTileIterator::TensorRef ref_C,
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typename Epilogue::OutputTileIterator::TensorRef ref_D,
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typename Mma::IteratorE::TensorRef ref_E) {
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static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
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static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
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static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
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static int const kAlignmentE = Mma::IteratorE::AccessType::kElements;
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if (!TensorRef_aligned(ref_A, kAlignmentA)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_B, kAlignmentB)) {
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return Status::kErrorMisalignedOperand;
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}
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// if (!TensorRef_aligned(ref_C, kAlignmentC)) {
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// return Status::kErrorMisalignedOperand;
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// }
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if (!TensorRef_aligned(ref_D, kAlignmentC)) {
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return Status::kErrorMisalignedOperand;
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}
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if (!TensorRef_aligned(ref_E, kAlignmentE)) {
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return Status::kErrorMisalignedOperand;
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}
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if ((problem_size.m() % kAlignmentA) || ((problem_size.k() / kSparse) % kAlignmentA) ||
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(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
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(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC) ||
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(problem_size.m() % kAlignmentE) || ((problem_size.k() / kSparse) % kAlignmentE)) {
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return Status::kErrorMisalignedOperand;
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}
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// The k dimension has to be the multiple of the Threadblock k because out
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// of bound meta data would be initialized to 0 by acync.zfill but 0 is not
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// a valid meta data.
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if (problem_size.k() % Mma::Shape::kK) {
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return Status::kErrorMisalignedOperand;
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}
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// M dimension has to be multiple of 32 (sparse float) or 16 (sparse int)
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// because of the row reordering of operand E
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static int const kAlignmentM = (sizeof(ElementE) == 2) ? 32 : 16;
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if (problem_size.m() % kAlignmentM) {
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return Status::kErrorMisalignedOperand;
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}
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return Status::kSuccess;
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}
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/// Executes one GEMM
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CUTLASS_DEVICE
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void operator()(Params const ¶ms, SharedStorage &shared_storage) {
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// Compute threadblock location
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord threadblock_tile_offset =
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threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
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// Early exit if CTA is out of range
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if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
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params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
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return;
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}
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// Compute initial location in logical coordinates
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cutlass::MatrixCoord tb_offset_A{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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cutlass::MatrixCoord tb_offset_B{
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threadblock_tile_offset.k() * params.gemm_k_size,
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threadblock_tile_offset.n() * Mma::Shape::kN
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};
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cutlass::MatrixCoord tb_offset_E{
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threadblock_tile_offset.m() * Mma::Shape::kM,
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threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
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};
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// Problem size is a function of threadblock index in the K dimension
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int problem_size_k = min(
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params.problem_size.k(),
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(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
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// Compute threadblock-scoped matrix multiply-add
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int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
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|
||||
// 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
|
||||
Reference in New Issue
Block a user