| /* |
| * Copyright (c) 2021 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 "pool_common.hpp" |
| #include "utils.hpp" |
| |
| namespace arm_conv { |
| namespace pooling { |
| |
| template <class strategy> |
| class PoolingDepthfirstGeneric : public PoolingCommon<typename strategy::operand_type, typename strategy::return_type> |
| { |
| using TInput = typename strategy::operand_type; |
| using TOutput = typename strategy::return_type; |
| |
| const PoolingArgs m_args; // Copy of arguments |
| |
| unsigned int input_rows(void) const |
| { |
| return m_args.pool_window.rows; |
| } |
| |
| unsigned int input_cols(void) const |
| { |
| return m_args.pool_window.cols; |
| } |
| |
| public: |
| PoolingDepthfirstGeneric(const PoolingArgs &args) : m_args(args) |
| { |
| } |
| |
| PoolingDepthfirstGeneric(PoolingDepthfirstGeneric &) = delete; |
| PoolingDepthfirstGeneric &operator=(PoolingDepthfirstGeneric &) = delete; |
| |
| size_t sizeof_input_pointer_array(void) const |
| { |
| return sizeof(TInput *) * input_rows() * input_cols(); |
| } |
| |
| size_t get_working_size(unsigned int num_threads) const override |
| { |
| return num_threads * sizeof_input_pointer_array(); |
| } |
| |
| void execute( |
| const void *const input, |
| void *const output, |
| void *const working_space, |
| unsigned int thread_id, |
| unsigned int num_threads |
| ) const override |
| { |
| const size_t ld_input_col = m_args.n_channels; |
| const size_t ld_input_row = ld_input_col * m_args.input_cols; |
| const size_t ld_input_batch = ld_input_row * m_args.input_rows; |
| const size_t ld_output_col = ld_input_col; |
| const size_t ld_output_row = ld_output_col * m_args.output_cols; |
| const size_t ld_output_batch = ld_output_row * m_args.output_rows; |
| |
| execute( |
| input, ld_input_col, ld_input_row, ld_input_batch, |
| output, ld_output_col, ld_output_row, ld_output_batch, |
| working_space, |
| thread_id, num_threads |
| ); |
| } |
| |
| void execute( |
| const void *const input, |
| size_t ld_input_col, |
| size_t ld_input_row, |
| size_t ld_input_batch, |
| void *const output, |
| size_t ld_output_col, |
| size_t ld_output_row, |
| size_t ld_output_batch, |
| void *const working_space, |
| unsigned int thread_id, |
| unsigned int num_threads |
| ) const override |
| { |
| execute( |
| m_args.n_batches, m_args.input_rows, m_args.input_cols, |
| m_args.n_channels, |
| input, ld_input_col, ld_input_row, ld_input_batch, |
| m_args.padding, |
| m_args.output_rows, m_args.output_cols, |
| output, ld_output_col, ld_output_row, ld_output_batch, |
| working_space, |
| thread_id, num_threads |
| ); |
| } |
| |
| void execute( |
| unsigned int batches, |
| unsigned int height, |
| unsigned int width, |
| unsigned int channels, |
| const void *const _input, |
| size_t ld_input_col, |
| size_t ld_input_row, |
| size_t ld_input_batch, |
| const PaddingValues &padding, |
| unsigned int output_height, |
| unsigned int output_width, |
| void *const _output, |
| size_t ld_output_col, |
| size_t ld_output_row, |
| size_t ld_output_batch, |
| void *const _working_space, |
| unsigned int thread_id, |
| unsigned int num_threads |
| ) const override |
| { |
| strategy strat(m_args.cpu_info); |
| #ifdef CYCLE_PROFILING |
| arm_gemm::profiler prof; |
| #endif // CYCLE_PROFILING |
| |
| const unsigned int roundup_output_rows = roundup(output_height, num_threads); |
| const unsigned int rows_per_thread = roundup_output_rows / num_threads; |
| int start_out_height = static_cast<int>(thread_id * rows_per_thread); |
| int end_out_height = std::min<int>(output_height, static_cast<int>((thread_id + 1) * rows_per_thread)); |
| |
| unsigned int start_channel = 0; |
| unsigned int end_channel = channels; |
| if(output_height == 1) |
| { |
| const unsigned int channels_per_thread = roundup(channels, num_threads) / num_threads; |
| start_channel = thread_id * channels_per_thread; |
| end_channel = std::min(start_channel + channels_per_thread, channels); |
| |
| // Reset start and end rows |
| start_out_height = 0; |
| end_out_height = output_height; |
| } |
| |
| if(start_channel >= end_channel) |
| { |
| // Early exit in case of multiple threads parallelising on channels |
| return; |
| } |
| |
| // Cast input and output pointers into the right types |
| const TInput *const inptr = static_cast<const TInput *>(_input) + start_channel; |
| TOutput *const outptr = static_cast<TOutput *>(_output) + start_channel; |
| |
| // Grab the input pointer array |
| uint8_t *const working_space = static_cast<uint8_t *>(_working_space); |
| const TInput **const inptr_array = reinterpret_cast<const TInput **>(working_space + thread_id * sizeof_input_pointer_array()); |
| |
| // For each output tile, construct the requisite set of pointers and call |
| // into the kernel. |
| for (unsigned int batch = 0; batch < batches; batch++) |
| { |
| // Get batch pointers |
| const auto inptr_batch = inptr + batch * ld_input_batch; |
| auto outptr_row = outptr + batch * ld_output_batch + start_out_height * ld_output_row; |
| |
| for (int out_i = start_out_height; out_i < end_out_height; out_i++) |
| { |
| const int start_in_i = out_i * m_args.pool_stride.rows - padding.top; |
| const int end_in_i = start_in_i + m_args.pool_window.rows; |
| |
| // Compute top/bottom padding |
| const auto pad_top = static_cast<unsigned int>(std::max(0 - start_in_i, 0)); |
| const auto pad_bottom = static_cast<unsigned int>(std::max<int>(end_in_i - height, 0)); |
| const auto valid_rows = input_rows() - pad_top - pad_bottom; |
| |
| // Compute the number of pooling window rows which are contained in |
| // either the valid region of the input tensor, or the padding. |
| const auto padded_bottom = std::min<unsigned int>( |
| start_in_i + m_args.pool_window.rows, height + padding.bottom |
| ); |
| const auto n_total_rows = padded_bottom - start_in_i; |
| |
| auto outptr_col = outptr_row; |
| auto inptr_row = inptr_batch + (start_in_i + pad_top) * ld_input_row; |
| |
| for (int out_j = 0, start_in_j = -padding.left; |
| out_j < static_cast<int>(output_width); |
| out_j++, start_in_j += m_args.pool_stride.cols) |
| { |
| const int end_in_j = start_in_j + m_args.pool_window.cols; |
| |
| // Compute left/right padding |
| const auto pad_left = static_cast<unsigned int>(std::max(0 - start_in_j, 0)); |
| const auto pad_right = static_cast<unsigned int>(std::max<int>(0, end_in_j - width)); |
| const auto valid_cols = input_cols() - pad_left - pad_right; |
| |
| // Compute the number of pooling window columns which are contained |
| // in either the valid region of the input tensor, or the padding. |
| const auto padded_right = std::min<unsigned int>( |
| start_in_j + m_args.pool_window.cols, width + padding.right |
| ); |
| const auto n_total_cols = padded_right - start_in_j; |
| |
| // Construct the input pointer array - fill in all valid points |
| // contiguously. |
| const TInput **ptrs = inptr_array; |
| const TInput *rowptr = inptr_row + (start_in_j + pad_left) * ld_input_col; |
| for (auto i = 0u; i < valid_rows; i++) |
| { |
| const TInput *colptr = rowptr; |
| for (auto j = 0u; j < valid_cols; j++) |
| { |
| *(ptrs++) = colptr; |
| colptr += ld_input_col; |
| } |
| rowptr += ld_input_row; |
| } |
| |
| // Compute the number of valid cells |
| const auto valid_cells = valid_rows * valid_cols; |
| const auto cells_in_range = n_total_rows * n_total_cols; |
| const auto window_cells = m_args.exclude_padding ? valid_cells : cells_in_range; |
| |
| // Get the output pointer for this call |
| TOutput *outptr = outptr_col; |
| outptr_col += ld_output_col; |
| |
| #ifdef CYCLE_PROFILING |
| // TODO Work number |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(strategy::out_rows() * strategy::out_cols() * strategy::pool_rows() * strategy::pool_cols())); |
| #endif // CYCLE_PROFILING |
| strat.kernel(window_cells, valid_cells, end_channel - start_channel, inptr_array, outptr); |
| } |
| |
| outptr_row += ld_output_row; |
| } |
| } |
| } |
| }; |
| |
| } // namespace pooling |
| } // namespace arm_conv |