| /* |
| * Copyright (c) 2022-2023 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 "winograd.hpp" |
| #include <algorithm> |
| #include <functional> |
| |
| namespace arm_conv { |
| namespace winograd { |
| namespace weight_transform { |
| |
| /* Driver class for the Winograd weight transforms. |
| */ |
| template <typename TIn, typename TOut=TIn> |
| class Transform : public ITransform |
| { |
| using Kernel = std::function<void( |
| unsigned int n_channels, // Number of channels to transform |
| const TIn *inptr, size_t ld_in_row, size_t ld_in_col, |
| TOut *outptr, size_t ld_out_matrix |
| )>; |
| |
| const std::string m_name; |
| const unsigned int m_kernel_rows, m_kernel_cols; |
| const unsigned int m_transformed_tile_rows, m_transformed_tile_cols; |
| const Kernel m_kernel; |
| |
| void execute_internal( |
| const ConvolutionArgs &args, |
| const TIn *inptr, size_t ld_in_row, size_t ld_in_col, size_t ld_input_channel, |
| TOut *outptr, size_t ld_out_matrix, size_t ld_out_row, |
| unsigned int thread_id, unsigned int n_threads |
| ) const |
| { |
| // Stripe groups of input channels over threads, this should reduce false |
| // sharing of the output matrix. |
| constexpr auto n_input_channels_per_thread = 16u; |
| |
| // Get the initial offset for the input and output pointers |
| const auto offset = thread_id * n_input_channels_per_thread; |
| inptr += offset * ld_input_channel; |
| outptr += offset * ld_out_row; |
| |
| for (auto start_ic = thread_id * n_input_channels_per_thread; |
| start_ic < args.n_input_channels; |
| start_ic += n_threads * n_input_channels_per_thread) |
| { |
| // Now iterate over the input channels assigned to this thread. |
| const auto end_ic = std::min(args.n_input_channels, |
| start_ic + n_input_channels_per_thread); |
| for (auto ic = start_ic; ic < end_ic; ic++) |
| { |
| m_kernel(args.n_output_channels, inptr, ld_in_row, ld_in_col, |
| outptr, ld_out_matrix); |
| inptr += ld_input_channel; |
| outptr += ld_out_row; |
| } |
| |
| // Progress the pointers to the account for the work not performed by |
| // this thread. |
| const auto skip = (n_threads - 1) * n_input_channels_per_thread; |
| inptr += skip * ld_input_channel; |
| outptr += skip * ld_out_row; |
| } |
| } |
| |
| public: |
| Transform( |
| const std::string &name, |
| unsigned int kernel_rows, unsigned int kernel_cols, |
| unsigned int transformed_tile_rows, unsigned int transformed_tile_cols, |
| const Kernel kernel |
| ) |
| : m_name(name), |
| m_kernel_rows(kernel_rows), m_kernel_cols(kernel_cols), |
| m_transformed_tile_rows(transformed_tile_rows), m_transformed_tile_cols(transformed_tile_cols), |
| m_kernel(kernel) |
| { |
| } |
| |
| const std::string &get_name(void) const override { return m_name; } |
| |
| unsigned int get_kernel_rows(void) const override { return m_kernel_rows; } |
| unsigned int get_kernel_cols(void) const override { return m_kernel_cols; } |
| |
| unsigned int get_transformed_tile_rows(void) const override { return m_transformed_tile_rows; } |
| unsigned int get_transformed_tile_cols(void) const override { return m_transformed_tile_cols; } |
| |
| void execute( |
| const ConvolutionArgs &args, |
| const void *inptr, size_t ld_in_row, size_t ld_in_col, size_t ld_input_channel, |
| void *outptr, size_t ld_out_matrix, size_t ld_out_row, |
| unsigned int thread_id, unsigned int n_threads |
| ) const override |
| { |
| execute_internal( |
| args, |
| reinterpret_cast<const TIn *>(inptr), ld_in_row, ld_in_col, ld_input_channel, |
| reinterpret_cast<TOut *>(outptr), ld_out_matrix, ld_out_row, |
| thread_id, n_threads |
| ); |
| } |
| |
| /* Utility method to get a transposed variant of a kernel, this transposed |
| * version simply calls the original kernel with the input row and column |
| * strides swapped. |
| */ |
| static constexpr Kernel get_transposed_kernel(const Kernel &kernel) |
| { |
| return [kernel] ( |
| const unsigned int n_channels, |
| const TIn *const inptr, const size_t ld_in_row, const size_t ld_in_col, |
| TOut *const outptr, const size_t ld_out |
| ) { |
| kernel(n_channels, inptr, ld_in_col, ld_in_row, outptr, ld_out); |
| }; |
| } |
| }; |
| |
| } // namespace weight_transform |
| } // namespace winograd |
| } // namespace arm_conv |