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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 "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
namespace winograd
{
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>::OutputTransform<T>::execute(
const Tensor4DShape &output_shape,
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output
)
{
// Compute the number of tiles and hence the padding required on the bottom
// and right of the image.
const int tile_M = iceildiv(output_shape.n_rows, output_tile_rows);
const int tile_N = iceildiv(output_shape.n_cols, output_tile_cols);
const int pad_bottom = output_tile_rows*tile_M - output_shape.n_rows;
const int pad_right = output_tile_cols*tile_N - output_shape.n_cols;
const int matrix_tile_row_stride = tile_N * matrix_row_stride;
const int matrix_batch_stride = tile_M * matrix_tile_row_stride;
const int output_col_stride = output_shape.n_channels;
const int output_row_stride = output_shape.n_cols * output_col_stride;
const int output_batch_stride = output_shape.n_rows * output_row_stride;
// Perform the output transformation for each batch
for (int batch = 0; batch < output_shape.n_batches; batch++)
{
// Get batch offset for input and outputs.
const T* const matrix_batch = matrix_base + batch*matrix_batch_stride;
T* const outptr_batch = output + batch*output_batch_stride;
// Perform the output transformation for each row of the output tensor.
for (int tile_i = 0; tile_i < tile_M; tile_i++)
{
// Compute properties of this row of output tiles
const int row_pad_bottom = (tile_i < tile_M - 1) ? 0: pad_bottom;
const T* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride;
T* const outptr_row = outptr_batch + output_tile_rows*tile_i*output_row_stride;
// Process the row
process_tile_row(
tile_N, output_shape.n_channels, matrix_tile_row, matrix_stride,
matrix_row_stride, biases,
outptr_row, output_row_stride, output_col_stride, row_pad_bottom,
pad_right
);
}
}
}
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>::OutputTransform<T>::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
)
{
// Loop over columns of tiles
for (int tile_j = 0; tile_j < tile_N; tile_j++)
{
// Properties of this tile
const int tile_pad_right = (tile_j < tile_N - 1) ? 0 : row_pad_right;
const T* const matrix_row = matrix_base + tile_j * matrix_row_stride;
T* const outptr = output + output_tile_cols*tile_j*output_col_stride;
// Perform the output transformation
tile_fns[row_pad_bottom][tile_pad_right](
n_channels, matrix_row, matrix_stride, biases,
outptr, output_row_stride, output_col_stride
);
}
}
template <int output_tile_rows, int output_tile_cols, int kr, int kc>
template <typename T>
size_t WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::bytes_read(const Tensor4DShape &shape)
{
const int M = iceildiv(shape.n_rows, output_tile_rows) *
iceildiv(shape.n_cols, output_tile_cols);
const int N = shape.n_channels;
return inner_tile_rows * inner_tile_cols * M * N * sizeof(T);
}
template <int otr, int otc, int kr, int kc>
template <typename T>
size_t WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::bytes_written(const Tensor4DShape &shape)
{
return shape.size() * sizeof(T);
}
template <int output_tile_rows, int output_tile_cols, int kr, int kc>
template <typename T>
WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::OutputTransform(
const T* const matrix_base,
const int matrix_stride,
const int matrix_row_stride,
const T* const biases,
T* const output,
const int n_batches,
const int n_rows,
const int n_cols,
const int n_channels
) : _matrix_base(matrix_base), _biases(biases),
_matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride),
_outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols),
_n_channels(n_channels), _tile_M(iceildiv(n_rows, output_tile_rows)),
_tile_N(iceildiv(n_cols, output_tile_cols))
{
}
template <int otr, int otc, int kr, int kc>
template <typename T>
unsigned int WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::get_window() const
{
// TODO When the output 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 _tile_M * _n_batches;
}
template <int otr, int otc, int kr, int kc>
template <typename T>
void WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::run(
const unsigned int start, const unsigned int stop
)
{
// TODO When the output 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 output_shape = {
_n_batches, _n_rows, _n_cols, _n_channels, NHWC
};
execute(
output_shape, _matrix_base, _matrix_stride, _matrix_row_stride, _biases,
_outptr
);
}
} // namespace winograd
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