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/*
* Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
namespace winograd
{
namespace
{
template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T>
class OutputTransformImplTiles
{
public:
typedef void (*TileFn)(
const int n_channels, /** @param[in] Number of channels in output tensor */
const T* const matrix_base, /** @param[in] Base pointer to Winograd output matrices. */
const int matrix_stride, /** @param[in] Stride between matrices in the output space. */
const T* const biases, /** @param[in] Pointer to bias vector (may be nullptr). */
T* const output, /** @param[out] Pointer to output tensor. */
const int output_row_stride, /** @param[in] Stride across rows of the output tensor. */
const int output_col_stride, /** @param[in] Stride between columns of the output tensor. */
const int _pad_bottom, /** @param[in] Bottom padding for unspecialised tiles. */
const int _pad_right /** @param[in] Right padding for unspecialised tiles. */
);
static TileFn get_tile_specialization(
const int pad_bottom,
const int pad_right
);
static constexpr unsigned int OutputTileRows = InnerTileRows - KernelRows + 1;
static constexpr unsigned int OutputTileCols = InnerTileCols - KernelCols + 1;
private:
static constexpr unsigned int n_pad_bottom = OutputTileRows - 1;
static constexpr unsigned int n_pad_right = OutputTileCols - 1;
static const TileFn tilefn_generic; /** Generic tile processing function. */
static const TileFn tilefn_unpadded; /** Tile processor for unpadded tiles. */
static const TileFn tilefn_bottom_padded[n_pad_bottom]; /** Bottom padding only. */
static const TileFn tilefn_right_padded[n_pad_right]; /** Right padding only. */
};
template <int KernelCols, int InnerTileCols, typename T>
class OutputTransformImplTiles<1, KernelCols, 1, InnerTileCols, T>
{
public:
typedef void (*TileFn)(
const int n_channels, /** @param[in] Number of channels in output tensor */
const T* const matrix_base, /** @param[in] Base pointer to Winograd output matrices. */
const int matrix_stride, /** @param[in] Stride between matrices in the output space. */
const T* const biases, /** @param[in] Pointer to bias vector (may be nullptr). */
T* const output, /** @param[out] Pointer to output tensor. */
const int output_row_stride, /** @param[in] Stride across rows of the output tensor. */
const int output_col_stride, /** @param[in] Stride between columns of the output tensor. */
const int _pad_bottom, /** @param[in] Bottom padding for unspecialised tiles. */
const int _pad_right /** @param[in] Right padding for unspecialised tiles. */
);
static TileFn get_tile_specialization(
const int pad_bottom,
const int pad_right
);
static constexpr unsigned int OutputTileRows = 1;
static constexpr unsigned int OutputTileCols = InnerTileCols - KernelCols + 1;
private:
static constexpr unsigned int n_pad_right = OutputTileCols - 1;
static const TileFn tilefn_unpadded; /** Tile processor for unpadded tiles. */
static const TileFn tilefn_right_padded[n_pad_right]; /** Right padding only. */
};
template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T>
class OutputTransformImpl
{
private:
static void process_tile_row(
const int tile_N,
const int n_channels,
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output,
const int output_row_stride,
const int output_col_stride,
const int row_pad_bottom,
const int row_pad_right
);
using Tiles = OutputTransformImplTiles<
KernelRows, KernelCols, InnerTileRows, InnerTileCols, T
>;
public:
/** Apply the output transform to a tensor. */
static void execute(
const int n_batches,
const int out_batch_stride,
const int n_rows,
const int out_row_stride,
const int n_cols,
const int out_col_stride,
const int n_channels,
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output
);
static constexpr unsigned int OutputTileRows = Tiles::OutputTileRows;
static constexpr unsigned int OutputTileCols = Tiles::OutputTileCols;
};
template <int KernelRows, int InnerTileRows, typename T>
class OutputTransformImpl<KernelRows, 1, InnerTileRows, 1, T>
{
public:
/** Apply the output transform to a tensor. */
static void execute(
const int n_batches,
const int out_batch_stride,
const int n_rows,
const int out_row_stride,
const int n_cols,
const int out_col_stride,
const int n_channels,
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output
);
static constexpr unsigned int OutputTileRows = InnerTileRows - KernelRows + 1;
static constexpr unsigned int OutputTileCols = 1;
};
} // namespace (anonymous)
template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T>
class OutputTransform
{
public:
/***********************************************************************/
/** Create an OutputTransform operator fixed on a given problem and set
* of pointers.
*/
OutputTransform(
const T* const matrix_base, /** Pointer to base of matrices. */
const int matrix_stride, /** Stride between matrices. */
const int matrix_row_stride, /** Stride within a matrix. */
const T* const biases, /** Pointer to biases vector. */
T* const output, /** Pointer to output tensor. */
const int n_batches, /** Number of batches in output tensor. */
const int n_rows, /** Number of rows in output tensor. */
const int n_cols, /** Number of columns in output tensor. */
const int n_channels, /** Number of channels in output tensor. */
const int out_batch_stride=0, /** Output batch stride. */
const int out_row_stride=0, /** Output row stride. */
const int out_col_stride=0 /** Output column stride. */
);
/** Get the window of work a given operator can perform. */
unsigned int get_window() const;
static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window
/** Perform work upon a window of the input. */
void run(const unsigned int start, const unsigned int stop);
/** Apply the transform to create a tensor. */
static void execute(
const int n_batches,
const int out_batch_stride,
const int n_rows,
const int out_row_stride,
const int n_cols,
const int out_col_stride,
const int n_channels,
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output
);
private:
using Transform = OutputTransformImpl<
KernelRows, KernelCols, InnerTileRows, InnerTileCols, T
>;
static constexpr unsigned int OutputTileRows = Transform::OutputTileRows;
static constexpr unsigned int OutputTileCols = Transform::OutputTileCols;
/** Member constants for instances of the transform. */
const T* const _matrix_base;
const T* const _biases;
const int _matrix_stride, _matrix_row_stride;
T* const _outptr;
const int _n_batches, _n_rows, _n_cols, _n_channels, _tile_M, _tile_N;
const int _out_col_stride, _out_row_stride, _out_batch_stride;
};
} // namespace winograd
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