Remove sparse GEMM with row broadcasted bias vector (#1302)
This reverts commit d3e72719b4.
Co-authored-by: Aleksandar Samardžić <asamardzic@matf.bg.ac.rs>
This commit is contained in:
Aleksandar Samardžić
co-authored by
Aleksandar Samardžić
Aleksandar Samardžić
parent
362abbf274
commit
ca37d632c9
@@ -1,514 +0,0 @@
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/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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 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/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/kernel/sparse_gemm_row_broadcast.h"
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#include "cutlass/gemm/kernel/default_gemm_sparse_row_broadcast.h"
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#include "cutlass/gemm/device/default_gemm_configuration.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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/*! Gemm device-level operator. This is an interface to efficient CUTLASS GEMM kernels that may
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be invoked from host code.
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The contributions of this class are:
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1. At compile time, it maps data types and high-level structural parameters onto
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specific CUTLASS components.
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2. At runtime, it maps logical arguments to GEMM problems to kernel parameters.
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3. At runtime, it launches kernels on the device.
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The intent is to provide a convenient mechanism for interacting with most plausible GEMM
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configurations for each supported architecture. Consequently, not all parameters are exposed
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to the top-level interface. Rather, sensible defaults at each level of the CUTLASS hierarchy
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are selected to tradeoff simplicity of the interface with flexibility. We expect
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most configurations to be specified at this level. Applications with more exotic requirements
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may construct their kernels of interest using CUTLASS components at the threadblock, warp,
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and thread levels of abstraction.
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CUTLASS exposes computations using the functor design pattern in which objects compose some
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internal state with an overloaded function call operator. This enables decoupling of
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initialization from execution, possibly reducing overhead during steady state phases of
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application execution.
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CUTLASS device-level operators expose an Arguments structure encompassing each logical
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input to the computation. This is distinct from the kernel-level Params structure pattern
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which contains application-specific precomputed state needed by the device code.
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Example of a CUTLASS GEMM operator implementing the functionality of cuBLAS's SGEMM NN
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is as follows:
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//
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// Instantiate the CUTLASS GEMM operator.
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//
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cutlass::gemm::device::Gemm<
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::ColumnMajor
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> gemm_op;
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//
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// Launch the GEMM operation on the device
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//
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cutlass::Status status = gemm_op({
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{m, n, k}, // GemmCoord problem_size,
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{A, lda}, // TensorRef<float, layout::ColumnMajor> ref_A,
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{B, ldb}, // TensorRef<float, layout::ColumnMajor> ref_B,
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{C, ldc}, // TensorRef<float, layout::ColumnMajor> ref_C,
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{D, ldd}, // TensorRef<float, layout::ColumnMajor> ref_D,
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{alpha, beta} // EpilogueOutputOp::Params epilogue_op_params
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});
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A simplified view of the template is listed below.
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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. 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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/// 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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>
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class Gemm;
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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
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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
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typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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ElementAccumulator_>::EpilogueOutputOp,
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/// Threadblock-level swizzling operator
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typename ThreadblockSwizzle_ =
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typename 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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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial = false,
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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 SparseGemmRowBroadcast {
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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 EpilogueOutputOp = EpilogueOutputOp_;
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using ThreadblockSwizzle = ThreadblockSwizzle_;
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using Operator = Operator_;
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using MathOperator = 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 bool const kSplitKSerial = SplitKSerial;
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static ComplexTransform const kTransformA = ComplexTransform::kNone;
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static ComplexTransform const kTransformB = ComplexTransform::kNone;
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/// Define the kernel
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using GemmKernel = typename kernel::DefaultSparseGemmRowBroadcast<
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ElementA,
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LayoutA,
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kAlignmentA,
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ElementB,
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LayoutB,
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kAlignmentB,
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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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kStages,
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kSplitKSerial,
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Operator
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>::GemmKernel;
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using ElementE = typename GemmKernel::ElementE;
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using LayoutE = typename GemmKernel::LayoutE;
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static int const kAlignmentE = 128 / sizeof_bits<ElementE>::value;
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static int const kSparse = GemmKernel::kSparse;
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static int const kMetaSizeInBits = GemmKernel::kMetaSizeInBits;
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static int const kElementsPerElementE = GemmKernel::kElementsPerElementE;
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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_A;
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TensorRef<ElementB const, LayoutB> ref_B;
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TensorRef<ElementC const, LayoutC> ref_C;
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TensorRef<ElementC, LayoutC> ref_D;
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TensorRef<ElementE const, LayoutE> ref_E;
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typename EpilogueOutputOp::Params epilogue;
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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_A_,
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TensorRef<ElementB const, LayoutB> ref_B_,
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TensorRef<ElementC const, LayoutC> ref_C_,
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TensorRef<ElementC, LayoutC> ref_D_,
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TensorRef<ElementE, LayoutE> ref_E_,
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typename EpilogueOutputOp::Params epilogue_ =
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typename EpilogueOutputOp::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_A(ref_A_),
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ref_B(ref_B_),
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ref_C(ref_C_),
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ref_D(ref_D_),
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ref_E(ref_E_),
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epilogue(epilogue_),
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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 GemmKernel::Params params_;
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public:
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/// Constructs the GEMM.
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SparseGemmRowBroadcast() { }
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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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Status status = GemmKernel::can_implement(
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args.problem_size,
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args.ref_A.non_const_ref(),
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args.ref_B.non_const_ref(),
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args.ref_C.non_const_ref(),
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args.ref_D,
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args.ref_E.non_const_ref()
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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 GemmKernel::Params{
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args.problem_size,
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grid_shape,
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args.ref_A.non_const_ref(),
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args.ref_B.non_const_ref(),
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args.ref_C.non_const_ref(),
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args.ref_D,
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args.ref_E.non_const_ref(),
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args.epilogue,
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static_cast<int *>(workspace)
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};
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int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
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if (smem_size >= (48 << 10)) {
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cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>,
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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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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_A.reset(args.ref_A.non_const_ref().data());
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params_.ref_B.reset(args.ref_B.non_const_ref().data());
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params_.ref_C.reset(args.ref_C.non_const_ref().data());
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params_.ref_D.reset(args.ref_D.data());
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params_.ref_E.reset(args.ref_E.non_const_ref().data());
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params_.output_op = args.epilogue;
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params_.semaphore = static_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(GemmKernel::kThreadCount, 1, 1);
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int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
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cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);
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cudaError_t result = cudaGetLastError();
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return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
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}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(cudaStream_t stream = nullptr) {
|
||||
return run(stream);
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
Status status = initialize(args, workspace, stream);
|
||||
|
||||
if (status == Status::kSuccess) {
|
||||
status = run(stream);
|
||||
}
|
||||
|
||||
return status;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -1,191 +0,0 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief
|
||||
Default kernel-level GEMM definitions combine threadblock-scoped matrix multiply-add with
|
||||
the appropriate threadblock-scoped epilogue.
|
||||
|
||||
Note, CUTLASS epilogues universally target row-major outputs. Column-major outputs are
|
||||
accommodated by exchanging A and B operands and assuming transposed layouts. Partial
|
||||
specializations here choose 'device::GemmTransposed' to implement this functionality.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/wmma.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/epilogue.h"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/kernel/gemm.h"
|
||||
#include "cutlass/gemm/kernel/sparse_gemm_row_broadcast.h"
|
||||
#include "cutlass/gemm/kernel/gemm_pipelined.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_sparse_sm80.h"
|
||||
#include "cutlass/gemm/threadblock/default_sparse_mma.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op_row_broadcast.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
|
||||
#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
|
||||
|
||||
#if defined(CUTLASS_ARCH_WMMA_ENABLED)
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_wmma_tensor_op.h"
|
||||
#endif //CUTLASS_ARCH_WMMA_ENABLED
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA_,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA_,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB_,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB_,
|
||||
/// Access granularity of B matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC_,
|
||||
/// Layout type for C and D matrix operands
|
||||
typename LayoutC_,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Operator class tag
|
||||
typename OperatorClass,
|
||||
/// Tag indicating architecture to tune for
|
||||
typename ArchTag,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// If true, kernel is configured to support serial reduction in the
|
||||
/// epilogue
|
||||
bool SplitKSerial,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator>
|
||||
struct DefaultSparseGemmRowBroadcast;
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Partial specialization for Ampere Architecture
|
||||
template <
|
||||
/// Element type for A matrix operand
|
||||
typename ElementA,
|
||||
/// Layout type for A matrix operand
|
||||
typename LayoutA,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentA,
|
||||
/// Element type for B matrix operand
|
||||
typename ElementB,
|
||||
/// Layout type for B matrix operand
|
||||
typename LayoutB,
|
||||
/// Access granularity of A matrix in units of elements
|
||||
int kAlignmentB,
|
||||
/// Element type for C and D matrix operands
|
||||
typename ElementC,
|
||||
/// Element type for internal accumulation
|
||||
typename ElementAccumulator,
|
||||
/// Threadblock-level tile size (concept: GemmShape)
|
||||
typename ThreadblockShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename WarpShape,
|
||||
/// Warp-level tile size (concept: GemmShape)
|
||||
typename InstructionShape,
|
||||
/// Epilogue output operator
|
||||
typename EpilogueOutputOp,
|
||||
/// Threadblock-level swizzling operator
|
||||
typename ThreadblockSwizzle,
|
||||
/// Number of stages used in the pipelined mainloop
|
||||
int Stages,
|
||||
/// If true, kernel is configured to support serial reduction in the
|
||||
/// epilogue
|
||||
bool SplitKSerial,
|
||||
/// Operation performed by GEMM
|
||||
typename Operator>
|
||||
struct DefaultSparseGemmRowBroadcast<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementC,
|
||||
layout::RowMajor, ElementAccumulator, arch::OpClassTensorOp,
|
||||
arch::Sm80, ThreadblockShape, WarpShape, InstructionShape,
|
||||
EpilogueOutputOp, ThreadblockSwizzle, Stages, SplitKSerial,
|
||||
Operator> {
|
||||
/// Define the threadblock-scoped matrix multiply-accumulate
|
||||
using Mma = typename cutlass::gemm::threadblock::DefaultSparseMma<
|
||||
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
|
||||
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
|
||||
ThreadblockShape, WarpShape, InstructionShape, Stages,
|
||||
Operator>::ThreadblockMma;
|
||||
|
||||
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
|
||||
|
||||
/// Define the epilogue
|
||||
using Epilogue =
|
||||
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOpRowBroadcast<
|
||||
ThreadblockShape, typename Mma::Operator, kPartitionsK, EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount>::Epilogue;
|
||||
|
||||
/// Define the kernel-level GEMM operator.
|
||||
using GemmKernel = kernel::SparseGemmRowBroadcast<Mma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
@@ -1,400 +0,0 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
* SPDX-License-Identifier: BSD-3-Clause
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright notice, this
|
||||
* list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
* this list of conditions and the following disclaimer in the documentation
|
||||
* and/or other materials provided with the distribution.
|
||||
*
|
||||
* 3. Neither the name of the copyright holder nor the names of its
|
||||
* contributors may be used to endorse or promote products derived from
|
||||
* this software without specific prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Template for a pipelined GEMM kernel. Does not compute batching or support split-K.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/semaphore.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace gemm {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
||||
typename Epilogue_, ///! Epilogue
|
||||
typename ThreadblockSwizzle_, ///! Threadblock swizzling function
|
||||
bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
|
||||
>
|
||||
struct SparseGemmRowBroadcast {
|
||||
|
||||
using Mma = Mma_;
|
||||
using Epilogue = Epilogue_;
|
||||
using OutputOp = typename Epilogue::OutputOp;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
static bool const kSplitKSerial = SplitKSerial;
|
||||
|
||||
static int const kSparse = Mma::kSparse;
|
||||
static int const kMetaSizeInBits = Mma::kMetaSizeInBits;
|
||||
static int const kMaxID2 = Mma::kMaxID2;
|
||||
static int const kElementsPerElementE = Mma::kElementsPerElementE;
|
||||
|
||||
using ElementE = typename Mma::ElementE;
|
||||
using LayoutE = typename Mma::LayoutE;
|
||||
|
||||
/// Warp count (concept: GemmShape)
|
||||
using WarpCount = typename Mma::WarpCount;
|
||||
static int const kThreadCount = 32 * WarpCount::kCount;
|
||||
|
||||
/// Parameters structure
|
||||
struct Params {
|
||||
cutlass::gemm::GemmCoord problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
int swizzle_log_tile;
|
||||
typename Mma::IteratorA::Params params_A;
|
||||
typename Mma::IteratorA::TensorRef ref_A;
|
||||
typename Mma::IteratorB::Params params_B;
|
||||
typename Mma::IteratorB::TensorRef ref_B;
|
||||
typename Epilogue::OutputTileIterator::Params params_C;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C;
|
||||
typename Epilogue::OutputTileIterator::Params params_D;
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D;
|
||||
typename Mma::IteratorE::Params params_E;
|
||||
typename Mma::IteratorE::TensorRef ref_E;
|
||||
typename OutputOp::Params output_op;
|
||||
int *semaphore;
|
||||
int gemm_k_iterations;
|
||||
int gemm_k_size;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(0), gemm_k_size(0) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E,
|
||||
typename OutputOp::Params output_op = typename OutputOp::Params(),
|
||||
int *workspace = nullptr
|
||||
):
|
||||
problem_size(problem_size),
|
||||
grid_tiled_shape(grid_tiled_shape),
|
||||
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
||||
params_A(ref_A.layout()),
|
||||
ref_A(ref_A),
|
||||
params_B(ref_B.layout()),
|
||||
ref_B(ref_B),
|
||||
params_C(ref_C.layout()),
|
||||
ref_C(ref_C),
|
||||
params_D(ref_D.layout()),
|
||||
ref_D(ref_D),
|
||||
params_E(ref_E.layout()),
|
||||
ref_E(ref_E),
|
||||
output_op(output_op) {
|
||||
|
||||
int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
||||
|
||||
gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
|
||||
|
||||
semaphore = workspace;
|
||||
}
|
||||
};
|
||||
|
||||
/// Shared memory storage structure
|
||||
union SharedStorage {
|
||||
typename Mma::SharedStorage main_loop;
|
||||
typename Epilogue::SharedStorage epilogue;
|
||||
};
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
SparseGemmRowBroadcast() { }
|
||||
|
||||
/// Determines whether kernel satisfies alignment
|
||||
static Status can_implement(
|
||||
cutlass::gemm::GemmCoord const & problem_size,
|
||||
typename Mma::IteratorA::TensorRef ref_A,
|
||||
typename Mma::IteratorB::TensorRef ref_B,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
||||
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
||||
typename Mma::IteratorE::TensorRef ref_E) {
|
||||
|
||||
static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
|
||||
static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
|
||||
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
|
||||
static int const kAlignmentE = Mma::IteratorE::AccessType::kElements;
|
||||
|
||||
if (!TensorRef_aligned(ref_A, kAlignmentA)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if (!TensorRef_aligned(ref_B, kAlignmentB)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// if (!TensorRef_aligned(ref_C, kAlignmentC)) {
|
||||
// return Status::kErrorMisalignedOperand;
|
||||
// }
|
||||
|
||||
if (!TensorRef_aligned(ref_D, kAlignmentC)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if (!TensorRef_aligned(ref_E, kAlignmentE)) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
if ((problem_size.m() % kAlignmentA) || ((problem_size.k() / kSparse) % kAlignmentA) ||
|
||||
(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
|
||||
(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC) ||
|
||||
(problem_size.m() % kAlignmentE) || ((problem_size.k() / kSparse) % kAlignmentE)) {
|
||||
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// The k dimension has to be the multiple of the Threadblock k because out
|
||||
// of bound meta data would be initialized to 0 by acync.zfill but 0 is not
|
||||
// a valid meta data.
|
||||
if (problem_size.k() % Mma::Shape::kK) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
// M dimension has to be multiple of 32 (sparse float) or 16 (sparse int)
|
||||
// because of the row reordering of operand E
|
||||
static int const kAlignmentM = (sizeof(ElementE) == 2) ? 32 : 16;
|
||||
|
||||
if (problem_size.m() % kAlignmentM) {
|
||||
return Status::kErrorMisalignedOperand;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Executes one GEMM
|
||||
CUTLASS_DEVICE
|
||||
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
||||
|
||||
// Compute threadblock location
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
// Early exit if CTA is out of range
|
||||
if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
|
||||
params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// Compute initial location in logical coordinates
|
||||
cutlass::MatrixCoord tb_offset_A{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_B{
|
||||
threadblock_tile_offset.k() * params.gemm_k_size,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
};
|
||||
|
||||
cutlass::MatrixCoord tb_offset_E{
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.k() * params.gemm_k_size / kSparse,
|
||||
};
|
||||
|
||||
// Problem size is a function of threadblock index in the K dimension
|
||||
int problem_size_k = min(
|
||||
params.problem_size.k(),
|
||||
(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
|
||||
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
int gemm_k_iterations = (problem_size_k - tb_offset_B.row() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
||||
|
||||
// Compute position within threadblock
|
||||
int thread_idx = threadIdx.x;
|
||||
|
||||
// Construct iterators to A, B, and E operands
|
||||
typename Mma::IteratorA iterator_A(
|
||||
params.params_A,
|
||||
params.ref_A.data(),
|
||||
{params.problem_size.m(), problem_size_k / kSparse},
|
||||
thread_idx,
|
||||
tb_offset_A);
|
||||
|
||||
typename Mma::IteratorB iterator_B(
|
||||
params.params_B,
|
||||
params.ref_B.data(),
|
||||
{problem_size_k, params.problem_size.n()},
|
||||
thread_idx,
|
||||
tb_offset_B);
|
||||
|
||||
typename Mma::IteratorE iterator_E(
|
||||
params.params_E, params.ref_E.data(),
|
||||
{params.problem_size.m(),
|
||||
problem_size_k / kSparse / kElementsPerElementE},
|
||||
thread_idx, tb_offset_E);
|
||||
|
||||
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
||||
// is compiled as warp-uniform.
|
||||
int warp_idx = canonical_warp_idx();
|
||||
int lane_idx = threadIdx.x % 32;
|
||||
|
||||
//
|
||||
// Main loop
|
||||
//
|
||||
|
||||
// Construct thread-scoped matrix multiply
|
||||
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
||||
|
||||
typename Mma::FragmentC accumulators;
|
||||
|
||||
accumulators.clear();
|
||||
|
||||
if (!kSplitKSerial || gemm_k_iterations > 0) {
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, iterator_E, accumulators);
|
||||
}
|
||||
|
||||
//
|
||||
// Epilogue
|
||||
//
|
||||
|
||||
OutputOp output_op(params.output_op);
|
||||
|
||||
//
|
||||
// Masked tile iterators constructed from members
|
||||
//
|
||||
|
||||
threadblock_tile_offset =
|
||||
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
||||
|
||||
//assume identity swizzle
|
||||
MatrixCoord threadblock_offset(
|
||||
threadblock_tile_offset.m() * Mma::Shape::kM,
|
||||
threadblock_tile_offset.n() * Mma::Shape::kN
|
||||
);
|
||||
|
||||
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
||||
|
||||
// Construct the semaphore.
|
||||
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
||||
|
||||
// If performing a reduction via split-K, fetch the initial synchronization
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// Fetch the synchronization lock initially but do not block.
|
||||
semaphore.fetch();
|
||||
|
||||
// Indicate which position in a serial reduction the output operator is currently updating
|
||||
output_op.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
|
||||
}
|
||||
|
||||
// Tile iterator loading from source tensor.
|
||||
typename Epilogue::OutputTileIterator iterator_C(
|
||||
params.params_C,
|
||||
params.ref_C.data(),
|
||||
params.problem_size.mn(),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
// Tile iterator writing to destination tensor.
|
||||
typename Epilogue::OutputTileIterator iterator_D(
|
||||
params.params_D,
|
||||
params.ref_D.data(),
|
||||
params.problem_size.mn(),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
Epilogue epilogue(
|
||||
shared_storage.epilogue,
|
||||
thread_idx,
|
||||
warp_idx,
|
||||
lane_idx);
|
||||
|
||||
// Wait on the semaphore - this latency may have been covered by iterator construction
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
||||
if (threadblock_tile_offset.k()) {
|
||||
iterator_C = iterator_D;
|
||||
}
|
||||
|
||||
semaphore.wait(threadblock_tile_offset.k());
|
||||
|
||||
__threadfence();
|
||||
}
|
||||
|
||||
// Execute the epilogue operator to update the destination tensor.
|
||||
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
||||
|
||||
//
|
||||
// Release the semaphore
|
||||
//
|
||||
|
||||
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
int lock = 0;
|
||||
if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
|
||||
|
||||
// The final threadblock resets the semaphore for subsequent grids.
|
||||
lock = 0;
|
||||
}
|
||||
else {
|
||||
// Otherwise, the semaphore is incremented
|
||||
lock = threadblock_tile_offset.k() + 1;
|
||||
}
|
||||
|
||||
__threadfence();
|
||||
semaphore.release(lock);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
Reference in New Issue
Block a user