releaase 2.11 (#703)
This commit is contained in:
Aditya Atluri
@@ -0,0 +1,457 @@
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/***************************************************************************************************
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* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Performs a dual gemm in one fused kernel:
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```
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D0 = epilogue0(X @ B0, C0)
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D1 = epilogue1(X @ B1, C1)
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D2 = element_wise(D0, D1)
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```
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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/device_kernel.h"
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#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
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#include "cutlass/gemm/device/default_gemm_configuration.h"
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#include "cutlass/gemm/threadblock/default_mma.h"
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#include "cutlass/epilogue/thread/linear_combination_relu.h"
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#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
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#include "../kernel/dual_gemm.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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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_,
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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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/// Instruction-level tile size (concept: GemmShape)
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typename InstructionShape_,
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/// Epilogue output operator
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typename EpilogueOutputOp0_,
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typename EpilogueOutputOp1_,
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typename EpilogueOutputOp2_,
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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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bool StoreD0 = true,
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bool StoreD1 = true,
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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial = false,
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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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class DualGemm {
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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 = LayoutC_;
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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 EpilogueOutputOp0 = EpilogueOutputOp0_;
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using EpilogueOutputOp1 = EpilogueOutputOp1_;
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using EpilogueOutputOp2 = EpilogueOutputOp2_;
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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 = EpilogueOutputOp1::kCount;
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static bool const kSplitKSerial = SplitKSerial;
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static bool constexpr kStoreD0 = StoreD0;
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static bool constexpr kStoreD1 = StoreD1;
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static ComplexTransform const kTransformA = ComplexTransform::kNone;
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static ComplexTransform const kTransformB = ComplexTransform::kNone;
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using LayoutScaleBias = layout::RowMajor;
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/// Define the kernel
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/// Define the threadblock-scoped matrix multiply-accumulate
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static_assert(ArchTag::kMinComputeCapability >= 80, "Only multistage is implemented");
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static_assert(kStages >= 3, "Only multistage is implemented");
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using Mma = typename cutlass::gemm::threadblock::DefaultMma<
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ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
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ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, ArchTag,
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ThreadblockShape, WarpShape,
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InstructionShape, Stages, Operator>::ThreadblockMma;
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using DualMma = threadblock::DualMmaMultistage<
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typename Mma::Shape,
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typename Mma::IteratorA,
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typename Mma::SmemIteratorA,
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Mma::kCacheOpA,
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typename Mma::IteratorB,
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typename Mma::SmemIteratorB,
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Mma::kCacheOpB,
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typename Mma::ElementC,
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typename Mma::LayoutC,
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typename Mma::Policy,
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Mma::kStages,
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SharedMemoryClearOption::kNone
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>;
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static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
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/// Define the epilogue
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using Epilogue0 =
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typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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ThreadblockShape, typename DualMma::Operator, kPartitionsK, EpilogueOutputOp0,
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EpilogueOutputOp0::kCount>::Epilogue;
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using Epilogue1 =
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typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
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ThreadblockShape, typename DualMma::Operator, kPartitionsK, EpilogueOutputOp1,
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EpilogueOutputOp1::kCount>::Epilogue;
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/// Define the kernel-level GEMM operator.
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using DualGemmKernel = kernel::DualGemm<
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DualMma,
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Epilogue0, Epilogue1, EpilogueOutputOp2,
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ThreadblockSwizzle, kSplitKSerial,
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kStoreD0, kStoreD1>;
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/// Argument structure
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struct Arguments {
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//
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// Data members
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//
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GemmCoord problem_size;
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TensorRef<ElementA const, LayoutA> ref_A0;
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TensorRef<ElementB const, LayoutB> ref_B0;
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TensorRef<ElementC const, LayoutC> ref_C0;
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TensorRef<ElementC, LayoutC> ref_D0;
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TensorRef<ElementB const, LayoutB> ref_B1;
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TensorRef<ElementC const, LayoutC> ref_C1;
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TensorRef<ElementC, LayoutC> ref_D1;
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TensorRef<ElementC, LayoutC> ref_D2;
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typename EpilogueOutputOp0::Params epilogue0;
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typename EpilogueOutputOp1::Params epilogue1;
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typename EpilogueOutputOp2::Params epilogue2;
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int split_k_slices;
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//
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// Methods
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//
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/// Default ctor
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CUTLASS_HOST_DEVICE
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Arguments(): problem_size(0, 0, 0), split_k_slices(1) {
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}
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/// Constructs an Arguments structure
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CUTLASS_HOST_DEVICE
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Arguments(
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GemmCoord problem_size_,
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TensorRef<ElementA const, LayoutA> ref_A0_,
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TensorRef<ElementB const, LayoutB> ref_B0_,
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TensorRef<ElementC const, LayoutC> ref_C0_,
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TensorRef<ElementC, LayoutC> ref_D0_,
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TensorRef<ElementB const, LayoutB> ref_B1_,
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TensorRef<ElementC const, LayoutC> ref_C1_,
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TensorRef<ElementC, LayoutC> ref_D1_,
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TensorRef<ElementC, LayoutC> ref_D2_,
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typename EpilogueOutputOp0::Params epilogue0_ =
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typename EpilogueOutputOp0::Params(),
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typename EpilogueOutputOp1::Params epilogue1_ =
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typename EpilogueOutputOp1::Params(),
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typename EpilogueOutputOp2::Params epilogue2_ =
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typename EpilogueOutputOp2::Params(),
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int split_k_slices_ = 1
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):
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problem_size(problem_size_),
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ref_A0(ref_A0_),
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ref_B0(ref_B0_),
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ref_C0(ref_C0_),
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ref_D0(ref_D0_),
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ref_B1(ref_B1_),
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ref_C1(ref_C1_),
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ref_D1(ref_D1_),
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ref_D2(ref_D2_),
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epilogue0(epilogue0_),
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epilogue1(epilogue1_),
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epilogue2(epilogue2_),
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split_k_slices(split_k_slices_) {
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}
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};
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private:
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/// Kernel parameters object
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typename DualGemmKernel::Params params_;
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public:
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/// Constructs the GEMM.
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DualGemm() = default;
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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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if (!kSplitKSerial && args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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if (kStoreD0 != (args.ref_D0.data() != nullptr)) {
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return Status::kErrorInternal;
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}
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if (kStoreD1 != (args.ref_D1.data() != nullptr)) {
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return Status::kErrorInternal;
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}
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Status status = DualGemmKernel::can_implement(
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args.problem_size,
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args.ref_A0.non_const_ref(),
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args.ref_B0.non_const_ref(),
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args.ref_C0.non_const_ref(),
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args.ref_D0,
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args.ref_B1.non_const_ref(),
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args.ref_C1.non_const_ref(),
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args.ref_D1,
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args.ref_D2
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);
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if (status != Status::kSuccess) {
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return status;
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}
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return Status::kSuccess;
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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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size_t bytes = 0;
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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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args.split_k_slices);
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if (kSplitKSerial && args.split_k_slices > 1) {
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bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
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}
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return bytes;
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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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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size,
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{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
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args.split_k_slices);
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if (kSplitKSerial) {
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if (args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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size_t bytes = get_workspace_size(args);
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cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
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if (result != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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}
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else {
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if (args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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}
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// Initialize the Params structure
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params_ = typename DualGemmKernel::Params{
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args.problem_size,
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grid_shape,
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args.ref_A0.non_const_ref(),
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args.ref_B0.non_const_ref(),
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args.ref_C0.non_const_ref(),
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args.ref_D0,
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args.ref_B1.non_const_ref(),
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args.ref_C1.non_const_ref(),
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args.ref_D1,
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args.ref_D2,
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args.epilogue0,
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args.epilogue1,
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args.epilogue2,
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reinterpret_cast<int *>(workspace),
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};
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return Status::kSuccess;
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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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if (kSplitKSerial && args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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}
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params_.ref_A0.reset(args.ref_A0.non_const_ref().data());
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params_.ref_B0.reset(args.ref_B0.non_const_ref().data());
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params_.ref_C0.reset(args.ref_C0.non_const_ref().data());
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params_.ref_D0.reset(args.ref_D0.data());
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params_.ref_B1.reset(args.ref_B1.non_const_ref().data());
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params_.ref_C1.reset(args.ref_C1.non_const_ref().data());
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params_.ref_D1.reset(args.ref_D1.data());
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params_.ref_D2.reset(args.ref_D2.data());
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params_.output_op_0 = args.epilogue0;
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params_.output_op_1 = args.epilogue1;
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params_.output_op_2 = args.epilogue2;
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params_.semaphore = reinterpret_cast<int *>(workspace);
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return Status::kSuccess;
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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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ThreadblockSwizzle threadblock_swizzle;
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dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
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dim3 block(DualGemmKernel::kThreadCount, 1, 1);
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cudaError_t result;
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int smem_size = int(sizeof(typename DualGemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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result = cudaFuncSetAttribute(Kernel<DualGemmKernel>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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smem_size);
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if (result != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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cutlass::Kernel<DualGemmKernel><<<grid, block, smem_size, stream>>>(params_);
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result = cudaGetLastError();
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return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
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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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} // namespace device
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} // namespace gemm
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////
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