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/*
* Copyright (c) 2017 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
#include "../winograd_gemm.hpp"
namespace winograd
{
/***************************************************************************/
/* Instance-less API */
template <int output_tile_rows, int output_tile_cols,
int kernel_rows, int kernel_cols>
template <typename T>
void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::execute(
const T *inptr,
const Tensor4DShape& input_shape,
const PaddingType padding_type,
const int tile_M,
const int tile_N,
T *outptr_base,
const int matrix_stride,
const int matrix_batch_stride,
const int matrix_row_stride
)
{
// Compute the padding required on each edge of the image
const bool base_padding = (padding_type == PADDING_SAME) ? 1 : 0;
const int pad_top = base_padding;
const int pad_left = base_padding;
const int tile_overlap = kernel_rows - 1;
// Compute striding values (assuming NHWC ordered data)
const int input_col_stride = input_shape.n_channels;
const int input_row_stride = input_shape.n_cols * input_col_stride;
const int input_batch_stride = input_shape.n_rows * input_row_stride;
const int output_col_stride = matrix_row_stride;
const int output_row_stride = tile_N * output_col_stride;
// Loop over batches
for (int batch = 0; batch < input_shape.n_batches; batch++)
{
// Pointer to the batch
const T* const input_base_batch = inptr + batch * input_batch_stride;
T* const outptr_base_batch = outptr_base + batch * matrix_batch_stride;
// Loop over rows of tiles
for (int tile_i = 0; tile_i < tile_M; tile_i++)
{
// Pointer to the row
const int row_offset = (tile_i == 0) ?
0 : ((padding_type == PADDING_VALID) ? 0 : 1);
const T* const input_base_row = (
input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*input_row_stride
);
T* const outptr_base_row = outptr_base_batch + tile_i*output_row_stride;
// Padding (top + bottom) for the row
const int row_top = tile_i*(inner_tile_rows - tile_overlap) - pad_top;
const int row_bottom = row_top + inner_tile_rows;
const int row_pad_top = (tile_i == 0) ? pad_top : 0;
const int row_pad_bottom = (row_bottom <= input_shape.n_rows) ? 0 : row_bottom - input_shape.n_rows;
// Process the row
process_tile_row(
tile_N, input_shape.n_channels,
input_base_row, input_row_stride, input_col_stride,
outptr_base_row, matrix_stride, matrix_row_stride,
row_pad_top, pad_left, row_pad_bottom, input_shape.n_cols
);
}
}
}
template <int output_tile_rows, int output_tile_cols,
int kernel_rows, int kernel_cols>
template <typename T>
void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::process_tile_row(
const int tile_N,
int n_channels,
const T* const input_base,
const int input_row_stride,
const int input_col_stride,
T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const int pad_top,
const int row_pad_left,
const int pad_bottom,
const int n_cols
)
{
constexpr int tile_overlap = kernel_cols - 1;
// Loop over columns of tiles
for (int tile_j = 0; tile_j < tile_N; tile_j++)
{
// Padding (left + right) for the tile
const int t_pad_left = (tile_j == 0) ? row_pad_left : 0;
const int t_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_left;
const int t_end = t_start + inner_tile_cols;
const int t_pad_right = (t_end <= n_cols) ? 0 : t_end - n_cols;
// Get pointers into the inputs and outputs
const int col_offset = (tile_j == 0) ? 0 : row_pad_left;
const T* const input_base_col = (
input_base + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*input_col_stride
);
T* const outptr = matrix_base + tile_j*matrix_row_stride;
// Apply the specific tile processing function
tile_fns[pad_top][t_pad_left][pad_bottom][t_pad_right](
n_channels,
input_base_col,
input_row_stride,
input_col_stride,
outptr,
matrix_stride
);
}
}
/***************************************************************************/
template <int otr, int otc, int kr, int kc>
template <typename T>
WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::InputTransform(
const T* const input, /** Input tensor data */
const int n_batches, /** Number of batches in input tensor. */
const int n_rows, /** Number of rows in input tensor. */
const int n_cols, /** Number of columns in input tensor. */
const int n_channels, /** Number of channels in input tensor. */
const PaddingType padding, /** Padding type. */
T* const output, /** Base of output matrices. */
const int matrix_stride, /** Stride between output matrices. */
const int matrix_row_stride /** Stride within matrices. */
) : _inptr(input), _outptr(output),
_n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels),
_matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride),
_tiles_M(iceildiv((padding == PADDING_SAME) ? n_rows : n_rows - 2, output_tile_rows)),
_tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - 2, output_tile_cols)),
_padding_type(padding)
{
}
template <int otr, int otc, int kr, int kc>
template <typename T>
unsigned int WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::get_window() const
{
// TODO When the input transform supports multithreading, return the total
// number of tile rows (allowing for multiple batches). For now we return 1
// to indicate that the activations must be transformed as a single block.
return 1; // TODO _tiles_M * _n_batches;
}
template <int otr, int otc, int kr, int kc>
template <typename T>
void WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::run(
const unsigned int start, const unsigned int stop
)
{
// TODO When the input transform supports multithreading call execute for a
// portion of the tile rows.
(void) start;
(void) stop;
// For now, just do all of the work.
const Tensor4DShape input_shape = {
_n_batches, _n_rows, _n_cols, _n_channels, NHWC
};
execute(
_inptr, input_shape, _padding_type, _tiles_M, _tiles_N, _outptr,
_matrix_stride, _matrix_row_stride * _tiles_M * _tiles_N, _matrix_row_stride
);
}
}
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