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/***************************************************************************************************
* 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
This file contains definitions and utility functions for describing convolution problem sizes in terms of
activation (NHWC), filter (KRSC), output (NPQK), padding (pad_h, pad_w), stride (stride_h, stride_w), and
dilation (dilation_h, dilation_w). Furthermore, it defines helper functions to map CUTLASS's implicit gemm
tensor extents, sizes, and data types to that of the convolution's extents, sizes, and data types.
* Mapping convolutions to Gemm computation *
Cutlass implements convolutions with the Implicit Gemm algorithm. This algorithm performs a gemm
(general matrix-matrix multiply) on the convolution tensors Activation, Filter, and Output.
The underlying gemm operation follows the standard gemm definition:
C = A * B + C
A and B are input matrices
C is source and output matrix
For the three convolutional operators (Fprop, Dgrad, Wgrad), ImplicitGemm matrices A, B, and C are mapped
to convolution tensors Activation, Filter and Output as described in the table below.
___________________________________________________________________________
ConvolutionalOperator | A | B | C
___________________________________________________________________________
| | | | |
| Fprop | Activation | Filter | Output |
| Dgrad | Output | Filter | Activation |
| Wgrad | Output | Activation | Filter |
___________________________________________________________________________
In convolution codebase, DO NOT mix using (A, B, C) with (Activation, Filter, Output).
For example, it's confusing and error prone to document a convolution class or function
as operating on "A, B, Output." Instead, use the mapping functions below,
and adhere to using either A, B, C or Activation, Filter, Output.
Map elements' data types (ImplicitGemm -> Conv): GemmToConvElementMap
Map elements' data types (Conv -> ImplicitGemm): ConvToGemmElementMap
*/
/*
Note: CUTLASS 3x increases the host compiler requirements to C++17. However, certain
existing integrations of CUTLASS require C++11 host compilers.
Until this requirement can be lifted, certain headers with this annotation are required
to be remain consistent with C++11 syntax.
C++11 compatibility is enforced by `cutlass_test_unit_core_cpp11`.
*/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/tensor_coord.h"
#include "cutlass/fast_math.h"
#include "cutlass/gemm/gemm_enumerated_types.h"
#include "cutlass/matrix_coord.h"
namespace cutlass {
namespace conv {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Convolutional operator
enum class Operator {
kFprop,
kDgrad,
kWgrad
};
/// Distinguishes convolution from cross correlation
enum class Mode {
kCrossCorrelation,
kConvolution
};
/// Selects among several implementation variants trading off performance with simplicity
enum class IteratorAlgorithm {
kAnalytic, ///< functionally correct in all cases but lower performance
kOptimized, ///< optimized for R <= 32, S <= 32 and unity-stride dgrad
kFixedChannels, ///< Analytic algorithm optimized for fixed channel count (C == AccessSize)
kFewChannels, ///< Analytic algorithm optimized for few channels (C divisible by AccessSize)
kFixedStrideDilation ///< Optimized for fixed stride and dilation
};
/// Distinguishes among partial specializations that accelerate certain problems where convolution
/// stride is unit.
enum class StrideSupport {
kStrided, ///< arbitrary convolution stride
kUnity, ///< unit convolution stride
kFixed ///< fixed convolution stride
};
/// Identifies split-K mode
enum class SplitKMode {
kNone,
kSerial,
kParallel
};
/// Identifies group mode
enum class GroupMode {
kNone,
kSingleGroup, ///< One CTA calculates one group or less
kMultipleGroup, ///< One CTA calculates multiple groups
kDepthwise ///< One CTA calculates cta_n groups (problem_size.C == problem_size.K == problem_size.groups)
};
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Shape of a tensor
template <
int N = 1,
int H = 1,
int W = 1,
int C = 1
>
struct TensorNHWCShape {
static int const kN = N;
static int const kH = H;
static int const kW = W;
static int const kC = C;
static int const kHW = H * W;
static int const kNHW = N * kHW;
static int const kNHWC = N * H * W * C;
static int const kCount = kNHWC;
//
// Static member functions
//
/// Returns a Coord object
CUTLASS_HOST_DEVICE
static Coord<4> toCoord() {
return make_Coord(kN, kH, kW, kC);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace conv
} // namespace cutlass
////////////////////////////////////////////////////////////////////////////////////////////////////
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