119 lines
5.3 KiB
C++
119 lines
5.3 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief
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This file contains definitions and utility functions for describing convolution problem sizes in terms of
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activation (NHWC), filter (KRSC), output (NPQK), pading (pad_h, pad_w), stride (stride_h, stride_w),
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dilation (dilation_h, dilation_w). Furthermore, it defines helper functions to map cutlass' implicit gemm
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tensor extents, sizes, data types to that of convolutions extents, sizes, and data types.
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* Mapping convolutions to Gemm computation *
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Cutlass employs ImplicitGemm algorithm to implement convolutions. ImplicitGemm algorithm runs gemm operation
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on convolution tensors Activation, Filter, and Output . The underlying gemm operation follows the standard
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gemm definition:
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C = A * B + C
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A and B are input matrices
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C is source and output matrix
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For the three convolutional operators (Fprop, Dgrad, Wgrad), ImplicitGemm matrices A, B, and C are mapped on
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to convolution tensors Activation, Filter and Output as per the below table:
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___________________________________________________________________________
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ConvolutionalOperator | A | B | C
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___________________________________________________________________________
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| | | | |
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| Fprop | Activation | Filter | Output |
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| Dgrad | Output | Filter | Activation |
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| Wgrad | Output | Activation | Filter |
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___________________________________________________________________________
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In convolution codebase, DO NOT mix using (A, B, C) with (Acvitation, Filter, Output).
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For example, a convolution class/function with A, B, Output is confusing and error-prone. Instead use below
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mapping functions and adhere to using either A, B, C or Acvitation, Filter, Output.
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Map elements' data types (ImplicitGemm -> Conv): GemmToConvElementMap
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Map elements' data types (Conv -> ImplicitGemm): ConvToGemmElementMap
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/tensor_coord.h"
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#include "cutlass/fast_math.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/matrix_coord.h"
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namespace cutlass {
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namespace conv {
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////////////////////////////////////////////////////////////////////////////////////////////////////
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/// Convolutional operator
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enum class Operator {
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kFprop,
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kDgrad,
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kWgrad
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};
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/// Distinguishes convolution from cross correlation
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enum class Mode {
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kCrossCorrelation,
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kConvolution
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};
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/// Selects among several implementation variants trading off performance with simplicity
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enum class IteratorAlgorithm {
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kAnalytic, ///< functionally correct in all cases but lower performance
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kOptimized ///< optimized for R <= 32, S <= 32 and unity-stride dgrad
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};
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/// Distinguishes among partial specializations that accelerate certain problems where convolution
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/// stride is unit.
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enum class StrideSupport {
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kStrided, ///< arbitrary convolution stride
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kUnity ///< unit convolution stride
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};
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/// Identifies split-K mode
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enum class SplitKMode {
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kNone,
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kSerial,
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kParallel
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace conv
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} // namespace cutlass
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////////////////////////////////////////////////////////////////////////////////////////////////////
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