| /* |
| * Copyright (c) 2021-2022 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 "depthfirst_driver.hpp" |
| #include "src/core/NEON/kernels/arm_conv/addressing.hpp" |
| #include "utils.hpp" |
| #if !defined(_WIN64) && !defined(__OpenBSD__) |
| #include <alloca.h> |
| #endif /* !defined(_WIN64) && !defined(__OpenBSD__) */ |
| #include <limits> |
| |
| namespace arm_conv { |
| namespace pooling { |
| |
| template <typename TInput, typename TOutput> |
| class DepthfirstStrategy : public IDepthfirstStrategy |
| { |
| unsigned int input_rows, input_cols, output_rows, output_cols; |
| |
| public: |
| DepthfirstStrategy(unsigned int window_rows, unsigned int window_cols, |
| unsigned int stride_rows, unsigned int stride_cols, |
| unsigned int output_rows, unsigned int output_cols) |
| : input_rows(output_rows + (window_rows - 1) * stride_rows), |
| input_cols(output_cols + (window_cols - 1) * stride_cols), |
| output_rows(output_rows), output_cols(output_cols) |
| { |
| } |
| |
| unsigned int get_input_rows() const override { return input_rows; } |
| unsigned int get_input_cols() const override { return input_cols; } |
| unsigned int get_output_rows() const override { return output_rows; } |
| unsigned int get_output_cols() const override { return output_cols; } |
| |
| typedef void (*KernelType)( |
| unsigned int n_channels, |
| const TInput *const *, |
| TOutput *const *, |
| bool exclude_padding, |
| unsigned int pad_left, |
| unsigned int pad_top, |
| unsigned int pad_right, |
| unsigned int pad_bottom |
| ); |
| virtual KernelType get_kernel(void) const = 0; |
| }; |
| |
| |
| struct WorkingSpace |
| { |
| void *input_buffer; |
| void *output_buffer; |
| }; |
| |
| |
| template <typename TInput, typename TOutput=TInput, class OutputStage=Nothing> |
| class PoolingDepthfirst : public DepthfirstDriver<TInput, TOutput> |
| { |
| size_t sizeof_input_buffer(void) const |
| { |
| return sizeof(TInput) * this->m_args.n_channels; |
| } |
| |
| size_t sizeof_output_buffer(void) const |
| { |
| return sizeof(TOutput) * this->m_args.n_channels; |
| } |
| |
| protected: |
| /* Compute the amount of working space required for a single thread. */ |
| size_t get_working_size_per_thread(unsigned int n_channels) const override |
| { |
| return sizeof(WorkingSpace) + n_channels * (sizeof(TInput) + sizeof(TOutput)); |
| } |
| |
| /* Initialise the working space for a thread. */ |
| void initialise_working_space(void *raw_ws, unsigned int n_channels) const override |
| { |
| auto ws = reinterpret_cast<WorkingSpace *>(raw_ws); |
| ws->input_buffer = ws + 1; |
| ws->output_buffer = reinterpret_cast<TInput *>(ws + 1) + n_channels; |
| |
| // Fill the input buffer with an appropriate value |
| TInput fill_val = 0; |
| if (this->m_args.pool_type == PoolingType::MAX) |
| { |
| using limits = std::numeric_limits<TInput>; |
| if (limits::has_infinity) |
| { |
| fill_val = -limits::infinity(); |
| } |
| else |
| { |
| fill_val = limits::min(); |
| } |
| } |
| |
| auto ptr = reinterpret_cast<TInput *>(ws->input_buffer); |
| for (; n_channels; n_channels--) |
| { |
| *(ptr++) = fill_val; |
| } |
| } |
| |
| /* Compute a portion of the output tensor with padding. */ |
| void compute_tile_padded( |
| unsigned int output_i, unsigned int output_j, |
| unsigned int channel_start, unsigned int channel_end, |
| const TensorSpec<const TInput *> &input, |
| const TensorSpec<TOutput *> &output, |
| void *working_space |
| ) const override |
| { |
| const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>( |
| this->m_strat.get())->get_kernel(); |
| |
| // Get the working space, and some space on the stack for pointer arrays |
| auto ws = reinterpret_cast<WorkingSpace *>(working_space); |
| auto inptr_array = reinterpret_cast<const TInput **>(alloca( |
| sizeof(TInput *) * this->m_strat->get_input_rows() * this->m_strat->get_input_cols())); |
| auto outptr_array = reinterpret_cast<TOutput **>(alloca( |
| sizeof(TOutput *) * this->m_strat->get_output_rows() * this->m_strat->get_output_cols())); |
| |
| // Prepare the input pointers |
| const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top; |
| const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0); |
| const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii); |
| |
| const unsigned int end_ii = ii + this->m_strat->get_input_rows(); |
| const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows; |
| |
| const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left; |
| const auto input_pad_left = static_cast<unsigned int>(ij < 0 ? -ij : 0); |
| const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij); |
| |
| const unsigned int end_ij = ij + this->m_strat->get_input_cols(); |
| const auto input_pad_right = end_ij < this->m_args.input_cols ? 0 : end_ij - this->m_args.input_cols; |
| |
| fill_pointer_array<const TInput>( |
| inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), |
| input.base + input_i*input.ld_row + input_j*input.ld_col + channel_start, |
| input.ld_row, input.ld_col, |
| reinterpret_cast<const TInput *>(ws->input_buffer), |
| input_pad_top, this->m_args.input_rows - input_i, |
| input_pad_left, this->m_args.input_cols - input_j |
| ); |
| |
| // Prepare the output pointers |
| fill_pointer_array( |
| outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(), |
| output.base + output_i*output.ld_row + output_j*output.ld_col + channel_start, |
| output.ld_row, output.ld_col, |
| reinterpret_cast<TOutput *>(ws->output_buffer), |
| 0, this->m_args.output_rows - output_i, // Top padding, # valid rows |
| 0, this->m_args.output_cols - output_j // Left padding, # valid columns |
| ); |
| |
| // Call the kernel |
| kern( |
| channel_end - channel_start, inptr_array, outptr_array, |
| this->m_args.exclude_padding, |
| input_pad_left, input_pad_top, |
| input_pad_right, input_pad_bottom |
| ); |
| } |
| |
| // Compute a portion of the work with only top/bottom padding. |
| void compute_row_padded_tile_row( |
| const unsigned int output_i, unsigned int output_j, unsigned int n_tile_cols, |
| const unsigned int channel_start, const unsigned int channel_end, |
| const TensorSpec<const TInput *> &input, |
| const TensorSpec<TOutput *> &output, |
| void *working_space |
| ) const override |
| { |
| const auto kern = reinterpret_cast<const DepthfirstStrategy<TInput, TOutput> *>( |
| this->m_strat.get())->get_kernel(); |
| |
| // Get the working space, and some space on the stack for pointer arrays |
| auto ws = reinterpret_cast<WorkingSpace *>(working_space); |
| auto inptr_array = reinterpret_cast<const TInput **>(alloca( |
| sizeof(TInput *) * this->m_strat->get_input_rows() * this->m_strat->get_input_cols())); |
| auto outptr_array = reinterpret_cast<TOutput **>(alloca( |
| sizeof(TOutput *) * this->m_strat->get_output_rows() * this->m_strat->get_output_cols())); |
| |
| // Prepare the initial input pointers |
| const int ii = static_cast<int>(output_i * this->m_args.pool_stride.rows) - this->m_args.padding.top; |
| const auto input_pad_top = static_cast<unsigned int>(ii < 0 ? -ii : 0); |
| const auto input_i = static_cast<unsigned int>(ii < 0 ? 0 : ii); |
| |
| const unsigned int end_ii = ii + this->m_strat->get_input_rows(); |
| const auto input_pad_bottom = end_ii < this->m_args.input_rows ? 0 : end_ii - this->m_args.input_rows; |
| |
| const int ij = static_cast<int>(output_j * this->m_args.pool_stride.cols) - this->m_args.padding.left; |
| const auto input_j = static_cast<unsigned int>(ij < 0 ? 0 : ij); |
| |
| const auto end_oi = output_i + this->m_strat->get_output_cols(); |
| const auto output_pad_bottom = end_oi < this->m_args.output_rows ? 0 : end_oi - this->m_args.output_rows; |
| |
| fill_pointer_array<const TInput>( |
| inptr_array, this->m_strat->get_input_rows(), this->m_strat->get_input_cols(), |
| input.base + input_i*input.ld_row + input_j*input.ld_col + channel_start, |
| input.ld_row, input.ld_col, |
| reinterpret_cast<const TInput *>(ws->input_buffer), |
| input_pad_top, this->m_args.input_rows - input_i, |
| 0, this->m_args.input_cols - input_j |
| ); |
| |
| // Prepare the initial output pointers |
| fill_pointer_array( |
| outptr_array, this->m_strat->get_output_rows(), this->m_strat->get_output_cols(), |
| output.base + output_i*output.ld_row + output_j*output.ld_col + channel_start, |
| output.ld_row, output.ld_col, |
| reinterpret_cast<TOutput *>(ws->output_buffer), |
| 0, this->m_args.output_rows - output_i, // Top padding, # valid rows |
| 0, this->m_args.output_cols - output_j // Left padding, # valid columns |
| ); |
| |
| // Call the kernel |
| for (; n_tile_cols; n_tile_cols--) |
| { |
| kern( |
| channel_end - channel_start, inptr_array, outptr_array, |
| this->m_args.exclude_padding, |
| 0, input_pad_top, |
| 0, input_pad_bottom |
| ); |
| |
| // Progress the input and output pointer arrays |
| const auto input_col_stride = input.ld_col * this->m_strat->get_output_cols() * this->m_args.pool_stride.cols; |
| for ( |
| auto n = input_pad_top * this->m_strat->get_input_cols(); |
| n < (this->m_strat->get_input_rows() - input_pad_bottom) * this->m_strat->get_input_cols(); |
| n++ |
| ) |
| { |
| inptr_array[n] += input_col_stride; |
| } |
| |
| const auto output_col_stride = output.ld_col * this->m_strat->get_output_cols(); |
| for ( |
| auto n = 0u; |
| n < (this->m_strat->get_output_rows() - output_pad_bottom) * this->m_strat->get_output_cols(); |
| n++ |
| ) |
| { |
| outptr_array[n] += output_col_stride; |
| } |
| } |
| } |
| |
| public: |
| PoolingDepthfirst(const DepthfirstStrategy<TInput, TOutput> *strat, |
| const PoolingArgs &args, const OutputStage &os = {}) |
| : DepthfirstDriver<TInput, TOutput>(strat, args) |
| { |
| ARM_COMPUTE_UNUSED(os); |
| } |
| }; |
| |
| } // namespace pooling |
| } // namespace arm_conv |