diff options
Diffstat (limited to 'src/core/NEON/kernels/convolution/depthwise/impl_base.hpp')
| -rw-r--r-- | src/core/NEON/kernels/convolution/depthwise/impl_base.hpp | 505 |
1 files changed, 0 insertions, 505 deletions
diff --git a/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp b/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp deleted file mode 100644 index 266d13d6fc..0000000000 --- a/src/core/NEON/kernels/convolution/depthwise/impl_base.hpp +++ /dev/null @@ -1,505 +0,0 @@ -/* - * Copyright (c) 2018-2019 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. - */ - -/* - * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - * - * NOTE: Header to be included by implementation files only. - * - * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - */ - -#include <algorithm> -#include <cstdint> -#include "depthwise.hpp" -#include "padding.hpp" -#include "utils.hpp" - -#pragma once - -#define MEMBERFN(TOUT) template <\ - unsigned int OutputTileRows, unsigned int OutputTileColumns,\ - unsigned int KernelRows, unsigned int KernelColumns,\ - unsigned int StrideRows, unsigned int StrideColumns,\ - typename TIn, typename TBias, typename TOut,\ - typename Derived\ -> TOUT DepthwiseConvolutionBase<\ - OutputTileRows, OutputTileColumns,\ - KernelRows, KernelColumns,\ - StrideRows, StrideColumns,\ - TIn, TBias, TOut, Derived\ -> - -using namespace neon_convolution_kernels; - -namespace depthwise -{ - -template <unsigned int KernelRows, unsigned int KernelColumns, size_t WeightSize, size_t BiasSize> -struct PackParameters -{ - static void execute( - unsigned int n_channels, - void *buffer, - const void *weights, - unsigned int weight_row_stride, - unsigned int weight_col_stride, - const void *biases - ); -}; - -const unsigned int CHANNEL_BLOCK = 16; - -MEMBERFN(int)::get_output_size( - const int dim_size, const unsigned int padding_before, const unsigned int padding_after -) -{ - return iceildiv(dim_size + padding_before + padding_after - KernelRows + 1, StrideRows); -} - -MEMBERFN(int)::output_size( - const int dim_size, const unsigned int padding_before, const unsigned int padding_after -) const -{ - return get_output_size(dim_size, padding_before, padding_after); -} - -MEMBERFN()::DepthwiseConvolutionBase( - const int n_batches, - const int n_input_rows, - const int n_input_cols, - const int n_channels, - ActivationFunction activation, - const unsigned int padding_top, - const unsigned int padding_left, - const unsigned int padding_bottom, - const unsigned int padding_right -) : DepthwiseConvolutionBase( - n_batches, n_input_rows, n_input_cols, n_channels, - get_output_size(n_input_rows, padding_top, padding_bottom), - get_output_size(n_input_cols, padding_left, padding_right), - activation, - padding_top, padding_left, padding_bottom, padding_right - ) -{ -} - -MEMBERFN()::DepthwiseConvolutionBase( - const int n_batches, - const int n_input_rows, - const int n_input_cols, - const int n_channels, - const int n_output_rows, - const int n_output_cols, - ActivationFunction activation, - const unsigned int padding_top, - const unsigned int padding_left, - const unsigned int padding_bottom, - const unsigned int padding_right -) : _input(nullptr), _output(nullptr), - _packed_parameters(nullptr), - _working_space(nullptr), - _n_batches(n_batches), - _n_input_rows(n_input_rows), - _n_input_cols(n_input_cols), - _n_channels(n_channels), - _n_output_rows(n_output_rows), - _n_output_cols(n_output_cols), - _n_tile_rows(iceildiv(_n_output_rows, output_tile_rows)), - _n_tile_cols(iceildiv(_n_output_cols, output_tile_cols)), - _padding_top(padding_top), - _padding_left(padding_left), - _padding_bottom(padding_bottom), - _padding_right(padding_right), - _activation(activation), - _input_col_stride(0), _input_row_stride(0), _input_batch_stride(0), - _output_col_stride(0), _output_row_stride(0), _output_batch_stride(0) -{ -} - -MEMBERFN(void)::set_input(const void* const inptr) -{ - set_input(inptr, _n_channels); -} - -MEMBERFN(void)::set_input(const void* const inptr, const int ld_col) -{ - set_input(inptr, _n_input_cols * ld_col, ld_col); -} - -MEMBERFN(void)::set_input(const void* const inptr, const int ld_row, const int ld_col) -{ - set_input(inptr, _n_input_rows * ld_row, ld_row, ld_col); -} - -MEMBERFN(void)::set_input(const void* const inptr, const int ld_batch, const int ld_row, const int ld_col) -{ - _input = static_cast<const TIn *>(inptr); - _input_batch_stride = ld_batch; - _input_row_stride = ld_row; - _input_col_stride = ld_col; -} - -MEMBERFN(void)::set_output(void* const outptr) -{ - set_output(outptr, _n_channels); -} - -MEMBERFN(void)::set_output(void* const outptr, const int ld_col) -{ - set_output(outptr, _n_output_cols * ld_col, ld_col); -} - -MEMBERFN(void)::set_output(void* const outptr, const int ld_row, const int ld_col) -{ - set_output(outptr, _n_output_rows * ld_row, ld_row, ld_col); -} - -MEMBERFN(void)::set_output(void* const outptr, const int ld_batch, const int ld_row, const int ld_col) -{ - _output = static_cast<TOut *>(outptr); - _output_batch_stride = ld_batch; - _output_row_stride = ld_row; - _output_col_stride = ld_col; -} - -MEMBERFN(size_t)::get_packed_params_size(void) const -{ - return _n_channels * (sizeof(TIn)*KernelRows*KernelColumns + sizeof(TBias)); -} - -MEMBERFN(void)::set_packed_params_buffer(void *buffer) -{ - _packed_parameters = buffer; -} - -MEMBERFN(void)::pack_params(const void *weights, const void *biases) const -{ - static_cast<const Derived *>(this)->pack_params(_packed_parameters, weights, biases); -} - -MEMBERFN(void)::pack_params(void *buffer, const void *weights, const void *biases) const -{ - const unsigned int weight_col_stride = _n_channels; - const unsigned int weight_row_stride = KernelColumns * weight_col_stride; - static_cast<const Derived *>(this)->pack_params( - buffer, weights, weight_row_stride, weight_col_stride, biases - ); -} - -MEMBERFN(void)::pack_params( - void * const buffer, - const void * const weights, - const unsigned int weight_row_stride, - const unsigned int weight_col_stride, - const void * const biases -) const -{ - static_cast<const Derived *>(this)->_pack_params( - buffer, weights, weight_row_stride, weight_col_stride, biases - ); -} - -MEMBERFN(void)::_pack_params( - void * const buffer, - const void * const weights, - const unsigned int weight_row_stride, - const unsigned int weight_col_stride, - const void * const biases -) const -{ - // Default implementation - PackParameters<KernelRows, KernelColumns, sizeof(TIn), sizeof(TOut)>::execute( - _n_channels, buffer, weights, weight_row_stride, weight_col_stride, biases - ); -} - -MEMBERFN(size_t)::get_working_space_size(const unsigned int nthreads) const -{ - return nthreads * ( - _get_input_working_space_size() + _get_output_working_space_size() - ); -} - -MEMBERFN(void)::set_working_space(void *buffer) -{ - _working_space = buffer; -} - -MEMBERFN(size_t)::_get_input_working_space_size(void) const -{ - return sizeof(TIn) * _n_channels; -} - -MEMBERFN(size_t)::_get_output_working_space_size(void) const -{ - return sizeof(TOut) * _n_channels; -} - -MEMBERFN(void *)::_get_input_working_space(const unsigned int threadid) const -{ - return static_cast<uint8_t*>(_working_space) + threadid * ( - _get_input_working_space_size() + _get_output_working_space_size() - ); -} - -MEMBERFN(void *)::_get_output_working_space(const unsigned int threadid) const -{ - return static_cast<uint8_t*>(_get_input_working_space(threadid)) + _get_input_working_space_size(); -} - -MEMBERFN(unsigned int)::get_window() const -{ - // Parallelise over blocks of channels. - return iceildiv(_n_channels, CHANNEL_BLOCK); -} - -MEMBERFN(void)::run( - const unsigned int start, - const unsigned int stop, - const unsigned int threadid -) -{ - // Clear the input padding buffer - TIn *buf = static_cast<TIn *>(_get_input_working_space(threadid)); - const TIn pad_value = static_cast<Derived *>(this)->_input_padding_value(); - for (int n = 0; n < _n_channels; n++) - { - buf[n] = pad_value; - } - - // Parallelise over blocks of channels - const auto start_channel = CHANNEL_BLOCK * start; - const auto stop_channel = std::min<unsigned int>(_n_channels, CHANNEL_BLOCK * stop); - const auto params_size_per_channel = this->get_packed_params_size()/_n_channels; - - // Compute top and bottom padding for input and output - const int input_pad_top = _padding_top; - const int input_pad_left = _padding_left; - constexpr int tile_overlap = kernel_rows - stride_rows; - - // Perform the convolution by calling `process_tile_row` for each tile row in - // each batch. - for (int batch = 0; batch < _n_batches; batch++) - { - const TIn* const inptr_batch = _input + batch*_input_batch_stride; - TOut* const outptr_batch = _output + batch*_output_batch_stride; - - // Loop over rows of tiles - for (int tile_i = 0; tile_i < _n_tile_rows; tile_i++) - { - // Pointer to the row - const int input_row_offset = (tile_i == 0) ? 0 : input_pad_top; - const TIn* const inptr_row = (inptr_batch + ((inner_tile_rows - tile_overlap)*tile_i - input_row_offset)*_input_row_stride); - TOut* const outptr_row = outptr_batch + output_tile_rows * tile_i * _output_row_stride; - - // Input padding (top + bottom) for the row - const int input_row_top = tile_i*(inner_tile_rows - tile_overlap) - input_pad_top; - const int input_row_bottom = input_row_top + inner_tile_rows; - const int input_row_pad_top = (tile_i == 0) ? input_pad_top : 0; - const int input_row_pad_bottom = std::max(0, input_row_bottom - _n_input_rows); - - // Output padding (bottom) for the row - const int output_row_bottom = (tile_i + 1)*output_tile_rows; - const int output_row_pad_bottom = std::max(0, output_row_bottom - _n_output_rows); - - // Get the offset into the packed parameters - const auto params_ptr = static_cast<const uint8_t*>(_packed_parameters) + - start_channel*params_size_per_channel; - - // Process the row - process_tile_row( - threadid, - stop_channel - start_channel, - params_ptr, - inptr_row + start_channel, - outptr_row + start_channel, - input_row_pad_top, input_pad_left, input_row_pad_bottom, - output_row_pad_bottom, - _n_tile_cols, _n_input_cols, _n_output_cols - ); - } - } -} - -MEMBERFN(void)::process_tile_row( - const unsigned int threadid, - const int n_channels, - const void* const packed_params, - const TIn* const inptr, - TOut* const outptr, - const int row_pad_in_top, - const int row_pad_in_left, - const int row_pad_in_bottom, - const int row_pad_out_bottom, - const int n_tiles, - const int n_input_cols, - const int n_output_cols -) -{ - constexpr int tile_overlap = kernel_cols - stride_cols; - - // Loop over columns of tiles - for (int tile_j = 0; tile_j < n_tiles; tile_j++) - { - // Input padding (left + right) for the tile - const int t_pad_in_left = (tile_j == 0) ? row_pad_in_left : 0; - const int t_in_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_in_left; - const int t_in_end = t_in_start + inner_tile_cols; - const int t_pad_in_right = std::max(0, t_in_end - n_input_cols); - - // Output padding (right) for the tile - const int t_out_end = (tile_j + 1) * output_tile_cols; - const int t_pad_out_right = std::max(0, t_out_end - n_output_cols); - - // Get pointers into the inputs and outputs - const int col_offset = (tile_j == 0) ? 0 : row_pad_in_left; - const TIn* const inptr_col = (inptr + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*_input_col_stride); - TOut* const outptr_col = outptr + tile_j * output_tile_cols * _output_col_stride; - - // Process just this tile - process_tile( - threadid, n_channels, packed_params, inptr_col, outptr_col, - row_pad_in_top, t_pad_in_left, row_pad_in_bottom, t_pad_in_right, // Input paddings - row_pad_out_bottom, t_pad_out_right // Output paddings - ); - } -} - -MEMBERFN(TIn)::_input_padding_value(void) const -{ - return static_cast<TIn>(0); -} - -MEMBERFN(void)::process_tile( - const unsigned int threadid, - const int n_channels, - const void* const packed_params, - const TIn* const inptr, - TOut* const outptr, - const int pad_in_top, - const int pad_in_left, - const int pad_in_bottom, - const int pad_in_right, - const int pad_out_bottom, - const int pad_out_right -) -{ - Derived * dthis = static_cast<Derived *>(this); - const bool pad_input = pad_in_top || pad_in_left || pad_in_bottom || pad_in_right; - const bool pad_output = pad_out_bottom || pad_out_right; - - if (!pad_input && !pad_output) - { - switch(_activation) - { - case ActivationFunction::ReLU: - dthis->template execute_tile<ActivationFunction::ReLU>( - n_channels, packed_params, - inptr, _input_row_stride, _input_col_stride, - outptr, _output_row_stride, _output_col_stride - ); - break; - case ActivationFunction::ReLU6: - dthis->template execute_tile<ActivationFunction::ReLU6>( - n_channels, packed_params, - inptr, _input_row_stride, _input_col_stride, - outptr, _output_row_stride, _output_col_stride - ); - break; - default: - dthis->template execute_tile<ActivationFunction::None>( - n_channels, packed_params, - inptr, _input_row_stride, _input_col_stride, - outptr, _output_row_stride, _output_col_stride - ); - break; - } - } - else - { - // Create arrays of input and output pointers, pointing padded elements to - // the working space padding buffers provided. - const TIn *inptrs[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - if (i < pad_in_top || (inner_tile_rows - pad_in_bottom) <= i || - j < pad_in_left || (inner_tile_cols - pad_in_right) <= j) - { - // Padded input - inptrs[i][j] = static_cast<const TIn *>(_get_input_working_space(threadid)); - } - else - { - inptrs[i][j] = inptr + (i - pad_in_top)*_input_row_stride + (j - pad_in_left)*_input_col_stride; - } - } - } - - TOut *outptrs[output_tile_rows][output_tile_cols]; - for (int i = 0; i < output_tile_rows; i++) - { - for (int j = 0; j < output_tile_cols; j++) - { - if (i < (output_tile_rows - pad_out_bottom) && - j < (output_tile_cols - pad_out_right)) - { - outptrs[i][j] = outptr + i*_output_row_stride + j*_output_col_stride; - } - else - { - outptrs[i][j] = static_cast<TOut *>(_get_output_working_space(threadid)); - } - } - } - - switch(_activation) - { - case ActivationFunction::ReLU: - dthis->template execute_tile<ActivationFunction::ReLU>( - n_channels, packed_params, inptrs, outptrs - ); - break; - case ActivationFunction::ReLU6: - dthis->template execute_tile<ActivationFunction::ReLU6>( - n_channels, packed_params, inptrs, outptrs - ); - break; - default: - dthis->template execute_tile<ActivationFunction::None>( - n_channels, packed_params, inptrs, outptrs - ); - break; - } - } -} - -MEMBERFN(int)::n_channels(void) const -{ - return _n_channels; -} - -} // namespace depthwise |