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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.
*/
#include "convolution.hpp"
#include "winograd_layer.hpp"
#include "tensor.hpp"
/** Determine how much memory (in units of TIn) to allocate for the transformed
* weights.
*/
template <
int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
typename TIn, typename TOut
>
unsigned int WinogradConvolutionLayer<
OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
>::get_weight_storage_size(
const int n_output_channels, /** Number of output feature maps. */
const int n_input_channels /** Number of input feature maps. */
)
{
const KernelShape shape(
n_output_channels, KernelRows, KernelCols, n_input_channels
);
return static_cast<unsigned int>(
// WinogradConv returns the size in bytes, we divide by `sizeof(TIn)` to
// express that in units of TIn.
WinogradConv::get_kernel_storage_size(shape) / sizeof(TIn)
);
}
/** Determine how much memory (in units of TIn) to allocate for the transformed
* input.
*/
template <
int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
typename TIn, typename TOut
>
unsigned int WinogradConvolutionLayer<
OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
>::get_input_storage_size(
const int n_batches, /** Number of batches in the input tensor. */
const int n_channels, /** Number of feature maps in the input tensor. */
const int n_rows, /** Number of rows in each feature map. */
const int n_cols, /** Number of columns in each feature map. */
const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
)
{
// Construct shapes for the input and kernel tensors.
const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels);
const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels);
const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
// Return the size, converted into units of TIn
return static_cast<unsigned int>(
WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) /
sizeof(TIn)
);
}
/** Determine how much memory (in units of TOut) to allocate for the (Winograd
* domain) output.
*/
template <
int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
typename TIn, typename TOut
>
unsigned int WinogradConvolutionLayer<
OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
>::get_output_storage_size(
const int n_batches, /** Number of batches in the output tensor. */
const int n_rows, /** Number of rows in each feature map of the input tensor. */
const int n_cols, /** Number of columns in each feature map of the input tensor. */
const int n_output_channels, /** Number of feature maps in the output tensor. */
const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
)
{
// Construct shapes for the input and kernel tensors.
const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1);
const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1);
const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
// Return the size, converted into units of TOut
return static_cast<unsigned int>(
WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) /
sizeof(TOut)
);
}
/** Get the shape (rows, cols) of a feature map of the output tensor. */
template <
int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
typename TIn, typename TOut
>
std::pair<int, int> WinogradConvolutionLayer<
OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
>::get_output_feature_map_shape(
const int n_input_rows, /** Number of rows in the input feature map. */
const int n_input_cols, /** Number of columns in the input feature map. */
const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
)
{
// Construct shapes for the input and kernel tensors.
const Tensor4DShape input_shape(1, n_input_rows, n_input_cols, 1);
const KernelShape kern_shape(1, KernelRows, KernelCols, 1);
const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
// Compute the new shape
const auto output_shape = WinogradConv::get_output_shape(
kern_shape, input_shape, padding
);
return std::make_pair(output_shape.n_rows, output_shape.n_cols);
}
/** Create a new Winograd convolution layer.
*/
template <
int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
typename TIn, typename TOut
>
WinogradConvolutionLayer<OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut>::
WinogradConvolutionLayer(
const int n_batches, /** Number of batches in the input and output tensors. */
const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */
const int n_input_rows, /** Number of rows in a feature map of the input tensor. */
const int n_input_cols, /** Number of columns in a feature map of the input tensor. */
const int n_output_channels, /** Number of feature maps in the output tensor. */
const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */
const TIn* const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */
TIn* const winograd_weights, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
const TIn* const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */
TIn* const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */
TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
TOut* const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
) : _kernel_shape(n_output_channels, KernelRows, KernelCols, n_input_channels),
_input_shape(n_batches, n_input_rows, n_input_cols, n_input_channels),
_padding(same_padding ? PADDING_SAME : PADDING_VALID),
_output_shape(WinogradConv::get_output_shape(_kernel_shape, _input_shape, _padding)),
_n_output_rows(_output_shape.n_rows),
_n_output_cols(_output_shape.n_cols),
_kernel_matrix_stride(WinogradConv::get_kernel_matrix_stride(_kernel_shape)),
_kernel_matrix_row_stride(roundup(n_output_channels, WinogradConv::N_BLOCK)),
_input_matrix_stride(WinogradConv::get_input_matrix_stride(_kernel_shape, _input_shape, _padding)),
_input_matrix_row_stride(n_input_channels),
_output_matrix_stride(WinogradConv::get_output_matrix_stride(_kernel_shape, _input_shape, _padding)),
_output_matrix_row_stride(_kernel_matrix_row_stride),
_tile_rows(iceildiv(_n_output_rows, OutputTileRows)),
_tile_cols(iceildiv(_n_output_cols, OutputTileCols)),
_m(n_batches * _tile_rows * _tile_cols),
_k(n_input_channels),
_n(n_output_channels),
weights_transform(
weights, winograd_weights,
_kernel_matrix_stride, _kernel_matrix_row_stride,
n_output_channels, n_input_channels
),
input_transform(
input, n_batches, n_input_rows, n_input_cols, n_input_channels, _padding,
winograd_input, _input_matrix_stride, _input_matrix_row_stride
),
gemms(
WinogradBase::N_GEMMS, _m, _k, _n,
_input_matrix_stride, _input_matrix_row_stride,
_kernel_matrix_stride, _kernel_matrix_row_stride,
_output_matrix_stride, _output_matrix_row_stride,
winograd_input, winograd_weights, winograd_output
),
output_transform(
winograd_output, _output_matrix_stride, _output_matrix_row_stride,
output, n_batches, _n_output_rows, _n_output_cols, n_output_channels
)
{
}
// Instantiate valid implementations.
template class WinogradConvolutionLayer<2, 2, 3, 3, float, float>;
template class WinogradConvolutionLayer<4, 4, 3, 3, float, float>;
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