Stream-K with broadcast (#892)
* [WIP] GEMM StreamK w/ Fused Epilogue * Adds Gemm Streamk with Fused Epilogue kernel level struct. * Mostly based on Gemm with Fused Epilogue, * Requires a new epilogue * Work in progress * [WIP] StreamK support for GemmUniversalWithBroadcast * Just based off of how StreamK is allowed in GemmUniversal * Untested and a work in progress * Minor fixes * [WIP] It compiles! It is almost certainly incorrect, but we're past getting the templates to match, so checkpointing. * Correction to reference kernel * Fix typo * Added MSE measurement * Switch back to reference kernel + host for loop Still WIP. Now we're getting even a larger MSE, but it's both on basic Split-K and Stream-K. * Fix typos * Fix broadcast vector + requested changes * Comment typo * Small int option and more * Fix incorrect condition on source needed * Requested changes * I think I got it? * Bias vector should be stride 0 * Two source added! * Typos * Merge examples * Bring back vector row offset Just to ensure consistency with universal gemm with fused epilogue * Base arguments and params structs for StreamK * StreamK epilogue with broadcast now inherits the original * undo params_streamk_base.h --------- Co-authored-by: Ali Hassani <ahassanijr@gmail.com> Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
This commit is contained in:
Ali Hassani
co-authored by
Ali Hassani
Haicheng Wu
Ali Hassani
Haicheng Wu
parent
6fbc0d3380
commit
13f413493a
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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
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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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 Stream-K GEMM kernel that can broadcast bias vector in the
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epilogue.
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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/arch/arch.h"
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#include "cutlass/epilogue/thread/linear_combination_bias_elementwise.h"
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#include "cutlass/device_kernel.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/gemm/kernel/gemm_universal.h"
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#include "cutlass/gemm/kernel/default_gemm_universal.h"
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#include "cutlass/gemm/kernel/default_gemm_streamk_with_broadcast.h"
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#include "cutlass/gemm/device/default_gemm_configuration.h"
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#include "cutlass/gemm/device/gemm_universal_base.h"
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////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/*!
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The universal GEMM with a broadcast epilogue.
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Supports
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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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/// 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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/// 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_ = ElementC_,
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/// Operator class tag
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typename OperatorClass_ = arch::OpClassSimt,
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/// Tag indicating architecture to tune for. This is the minimum SM that
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/// supports the intended feature. The device kernel can be built
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/// targeting any SM larger than this number.
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typename ArchTag_ = arch::Sm70,
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/// Threadblock-level tile size (concept: GemmShape)
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typename ThreadblockShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::ThreadblockShape,
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/// Warp-level tile size (concept: GemmShape)
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typename WarpShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::WarpShape,
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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::InstructionShape,
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/// Epilogue output operator - must satisfy concept of 'EpilogueWithBroadcastOp'
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typename EpilogueOutputOp_ = cutlass::epilogue::thread::LinearCombinationBiasElementwise<
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ElementC_, ElementAccumulator_, ElementAccumulator_,
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ElementC_, ElementC_, 128 / cutlass::sizeof_bits<ElementC_>::value>,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle<>,
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/// Number of stages used in the pipelined mainloop
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int Stages =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kStages,
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/// Access granularity of A matrix in units of elements
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int AlignmentA =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentB,
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/// Operation performed by GEMM
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typename Operator_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::Operator,
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/// Complex elementwise transformation on A operand
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ComplexTransform TransformA = ComplexTransform::kNone,
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/// Complex elementwise transformation on B operand
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ComplexTransform TransformB = ComplexTransform::kNone
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>
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class GemmUniversalStreamkWithBroadcast :
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public GemmUniversalBase<
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typename kernel::DefaultGemmStreamkWithBroadcast<
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ElementA_,
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LayoutA_,
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TransformA,
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AlignmentA,
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ElementB_,
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LayoutB_,
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TransformB,
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AlignmentB,
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ElementC_,
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LayoutC_,
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ElementAccumulator_,
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OperatorClass_,
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ArchTag_,
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ThreadblockShape_,
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WarpShape_,
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InstructionShape_,
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EpilogueOutputOp_,
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ThreadblockSwizzle_,
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Stages,
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Operator_
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>::GemmKernel
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> {
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public:
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using ElementAccumulator = ElementAccumulator_;
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using OperatorClass = OperatorClass_;
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using ArchTag = ArchTag_;
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using ThreadblockShape = ThreadblockShape_;
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using WarpShape = WarpShape_;
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using InstructionShape = InstructionShape_;
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using EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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static int const kStages = Stages;
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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static int const kAlignmentC = EpilogueOutputOp::kCount;
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static ComplexTransform const kTransformA = TransformA;
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static ComplexTransform const kTransformB = TransformB;
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using Base = GemmUniversalBase<
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typename kernel::DefaultGemmStreamkWithBroadcast<
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ElementA_,
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LayoutA_,
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TransformA,
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AlignmentA,
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ElementB_,
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LayoutB_,
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TransformB,
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AlignmentB,
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ElementC_,
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LayoutC_,
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ElementAccumulator_,
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OperatorClass_,
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ArchTag_,
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ThreadblockShape_,
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WarpShape_,
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InstructionShape_,
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EpilogueOutputOp_,
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ThreadblockSwizzle_,
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Stages,
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Operator_
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>::GemmKernel
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>;
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using Arguments = typename Base::Arguments;
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using GemmKernel = typename Base::GemmKernel;
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};
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////////////////////////////////////////////////////////////////////////////////
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/// Partial specialization for column-major output exchanges problem size and operand.
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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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/// 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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/// 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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/// Operator class tag
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typename OperatorClass_,
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/// Tag indicating architecture to tune for. This is the minimum SM that
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/// supports the intended feature. The device kernel can be built
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/// targeting any SM larger than this number.
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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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/// Instruction-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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/// Access granularity of A matrix in units of elements
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int AlignmentA,
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/// Access granularity of B matrix in units of elements
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int AlignmentB,
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/// Operation performed by GEMM
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typename Operator_,
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/// Complex elementwise transformation on A operand
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ComplexTransform TransformA,
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/// Complex elementwise transformation on B operand
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ComplexTransform TransformB>
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class GemmUniversalStreamkWithBroadcast<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
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layout::ColumnMajor, // partially specialized on LayoutC
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ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_,
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WarpShape_, InstructionShape_, EpilogueOutputOp_,
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ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB,
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Operator_, TransformA, TransformB> {
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public:
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using ElementA = ElementA_;
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using LayoutA = LayoutA_;
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using TensorRefA = TensorRef<ElementA const, LayoutA>;
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using ElementB = ElementB_;
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using LayoutB = LayoutB_;
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using TensorRefB = TensorRef<ElementB const, LayoutB>;
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using ElementC = ElementC_;
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using LayoutC = layout::ColumnMajor;
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using TensorRefC = TensorRef<ElementC const, LayoutC>;
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using TensorRefD = TensorRef<ElementC, LayoutC>;
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using ElementAccumulator = ElementAccumulator_;
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using OperatorClass = OperatorClass_;
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using ArchTag = ArchTag_;
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using ThreadblockShape = ThreadblockShape_;
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using WarpShape = WarpShape_;
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using InstructionShape = InstructionShape_;
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using EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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static int const kStages = Stages;
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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static ComplexTransform const kTransformA = TransformA;
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static ComplexTransform const kTransformB = TransformB;
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using UnderlyingOperator = typename GemmUniversalStreamkWithBroadcast<
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ElementB,
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typename layout::LayoutTranspose<LayoutB>::type,
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ElementA,
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typename layout::LayoutTranspose<LayoutA>::type,
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ElementC,
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layout::RowMajor,
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ElementAccumulator,
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OperatorClass,
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ArchTag,
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ThreadblockShape,
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WarpShape,
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InstructionShape,
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EpilogueOutputOp,
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ThreadblockSwizzle,
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Stages,
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kAlignmentB,
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kAlignmentA,
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Operator,
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kTransformB,
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kTransformA
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>::Base;
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using GemmKernel = typename UnderlyingOperator::GemmKernel;
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static int const kAlignmentC = EpilogueOutputOp::kCount;
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/// Argument structure
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using Arguments = typename UnderlyingOperator::Arguments;
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private:
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UnderlyingOperator underlying_operator_;
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public:
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/// Constructs the GEMM.
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GemmUniversalStreamkWithBroadcast() { }
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/// Helper to construct a transposed equivalent for the underying GEMM operator
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static Arguments to_underlying_arguments(Arguments const &args) {
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return args.transposed_problem();
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}
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/// Determines whether the GEMM can execute the given problem.
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static Status can_implement(Arguments const &args) {
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return UnderlyingOperator::can_implement(to_underlying_arguments(args));
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}
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/// Gets the workspace size
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static size_t get_workspace_size(Arguments const &args) {
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return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args));
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}
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/// Computes the grid shape
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static dim3 get_grid_shape(Arguments const &args) {
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return UnderlyingOperator::get_grid_shape(to_underlying_arguments(args));
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}
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/// Computes the maximum number of active blocks per multiprocessor
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static int maximum_active_blocks(int smem_capacity = -1) {
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return UnderlyingOperator::maximum_active_blocks(smem_capacity);
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}
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/// Initializes GEMM state from arguments.
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Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
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return underlying_operator_.initialize(to_underlying_arguments(args), workspace, stream);
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}
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/// Lightweight update given a subset of arguments
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Status update(Arguments const &args, void *workspace = nullptr) {
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return underlying_operator_.update(to_underlying_arguments(args), workspace);
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}
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/// Runs the kernel using initialized state.
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Status run(cudaStream_t stream = nullptr) {
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return underlying_operator_.run(stream);
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}
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/// Runs the kernel using initialized state.
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Status operator()(cudaStream_t stream = nullptr) {
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return run(stream);
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}
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/// Runs the kernel using initialized state.
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Status operator()(
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Arguments const &args,
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void *workspace = nullptr,
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cudaStream_t stream = nullptr) {
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Status status = initialize(args, workspace, stream);
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if (status == Status::kSuccess) {
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status = run(stream);
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}
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return status;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace device
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} // namespace gemm
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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