| /* |
| * 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 "src/core/NEON/kernels/arm_gemm/utils.hpp" |
| |
| #ifdef CYCLE_PROFILING |
| #include "profiler.hpp" |
| #endif |
| |
| #include <limits> |
| |
| namespace arm_conv { |
| namespace depthwise { |
| |
| struct IDepthwiseDepthfirstStrategy |
| { |
| virtual arm_gemm::VLType get_vl_type() const = 0; |
| |
| virtual unsigned int get_input_rows() const = 0; |
| virtual unsigned int get_input_cols() const = 0; |
| |
| virtual unsigned int get_output_rows() const = 0; |
| virtual unsigned int get_output_cols() const = 0; |
| |
| virtual unsigned int get_kernel_rows() const = 0; |
| virtual unsigned int get_kernel_cols() const = 0; |
| |
| virtual unsigned int get_stride_rows() const = 0; |
| virtual unsigned int get_stride_cols() const = 0; |
| |
| virtual void indirect_kernel( |
| const void *const *const input_ptrs, |
| void *const *const output_ptrs, |
| const void *params, |
| unsigned int n_channels, |
| const void *activation_min, |
| const void *activation_max |
| ) const = 0; |
| |
| virtual void direct_kernel( |
| const unsigned int n_tile_rows, const unsigned int n_tile_cols, |
| const void *inptr, int64_t ld_input_row, int64_t ld_input_col, |
| void *outptr, int64_t ld_output_row, int64_t ld_output_col, |
| const void *params, unsigned int n_channels, |
| const void *activation_min, |
| const void *activation_max |
| ) const = 0; |
| |
| virtual ~IDepthwiseDepthfirstStrategy() {} |
| }; |
| |
| template <typename TInput, typename TWeight, typename TOutput, typename TAccum> |
| class DepthwiseDepthfirst : public DepthwiseCommon<TInput, TWeight, TOutput> |
| { |
| const std::unique_ptr<IDepthwiseDepthfirstStrategy> m_strat; |
| |
| size_t sizeof_inptr_array(void) const |
| { |
| return sizeof(TInput *) * m_strat->get_input_rows() * m_strat->get_input_cols(); |
| } |
| |
| size_t sizeof_input_buffer(unsigned int n_input_channels) const |
| { |
| return sizeof(TInput) * n_input_channels; |
| } |
| |
| size_t sizeof_outptr_array(void) const |
| { |
| return sizeof(TInput *) * m_strat->get_output_rows() * m_strat->get_output_cols(); |
| } |
| |
| size_t sizeof_output_buffer(unsigned int n_output_channels) const |
| { |
| return sizeof(TOutput) * n_output_channels; |
| } |
| |
| public: |
| DepthwiseDepthfirst( |
| IDepthwiseDepthfirstStrategy *const strat, |
| const DepthwiseArgs &args |
| ) : DepthwiseCommon<TInput, TWeight, TOutput>(args), m_strat(strat) |
| { |
| } |
| |
| DepthwiseDepthfirst(DepthwiseDepthfirst &) = delete; |
| DepthwiseDepthfirst &operator=(DepthwiseDepthfirst &) = delete; |
| |
| size_t get_storage_size(void) const override |
| { |
| // TODO What if we insert extra padding? Biases are a different size to the inputs, ... |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TInput>(m_strat->get_vl_type()); |
| const auto rounded_channels = arm_gemm::roundup(this->m_args.input_channels, vl); |
| return (1 + this->m_args.kernel_rows * this->m_args.kernel_cols) * rounded_channels * sizeof(TWeight); |
| } |
| |
| void pack_parameters(void *_buffer, const void *_biases, const void *_weights, size_t ld_weight_col, size_t ld_weight_row) override |
| { |
| // TODO What if the kernel needs a different packing function? |
| |
| // Cast the pointers |
| uint8_t *buffer = static_cast<uint8_t *>(_buffer); |
| const TAccum *biases = static_cast<const TAccum *>(_biases); |
| const TWeight *const weights = static_cast<const TWeight *>(_weights); |
| |
| const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(m_strat->get_vl_type()); |
| ld_weight_col = (ld_weight_col == 0) ? this->m_args.input_channels : ld_weight_col; |
| ld_weight_row = (ld_weight_row == 0) ? this->m_args.kernel_cols * ld_weight_col : ld_weight_row; |
| |
| for (unsigned int n = 0; n < this->m_args.input_channels; n += vl) |
| { |
| const unsigned int todo = std::min(vl, this->m_args.input_channels - n); |
| |
| // Copy across the correct amount of bias (or 0) |
| for (unsigned int i = 0; i < todo; i++) |
| { |
| reinterpret_cast<TAccum *>(buffer)[i] = (biases == nullptr) ? 0 : biases[n + i]; |
| } |
| buffer += vl * sizeof(TAccum); |
| |
| // Copy each of the weights in turn |
| auto weights_row = weights + n; |
| for (unsigned int i = 0; i < this->m_args.kernel_rows; i++) |
| { |
| auto weights_col = weights_row; |
| |
| for (unsigned int j = 0; j < this->m_args.kernel_cols; j++) |
| { |
| for (unsigned int m = 0; m < todo; m++) |
| { |
| reinterpret_cast<TWeight *>(buffer)[m] = weights_col[m]; |
| } |
| buffer += vl * sizeof(TWeight); |
| |
| weights_col += ld_weight_col; |
| } |
| |
| weights_row += ld_weight_row; |
| } |
| } |
| } |
| |
| size_t get_working_size(const unsigned int n_threads, const unsigned int n_channels) const override |
| { |
| const unsigned int n_output_channels = n_channels * this->m_args.channel_multiplier; |
| return n_threads * (sizeof_inptr_array() + sizeof_outptr_array() + |
| sizeof_output_buffer(n_output_channels) + |
| sizeof_input_buffer(n_channels)); |
| } |
| |
| using DepthwiseCommon<TInput, TWeight, TOutput>::execute; |
| void execute( |
| const unsigned int batches, |
| const unsigned int input_height, |
| const unsigned int input_width, |
| const unsigned int input_channels, |
| const PaddingValues &padding, |
| const void *const _input, |
| const size_t ld_input_col, |
| const size_t ld_input_row, |
| const size_t ld_input_batch, |
| const void *const parameters, |
| const unsigned int output_height, |
| const unsigned int output_width, |
| void *const _output, |
| const size_t ld_output_col, |
| const size_t ld_output_row, |
| const size_t ld_output_batch, |
| void *const _working_space, |
| const unsigned int thread_id, |
| const unsigned int n_threads |
| ) const override |
| { |
| #ifdef CYCLE_PROFILING |
| arm_gemm::profiler prof; |
| #endif |
| |
| // Compute activation values |
| TAccum activation_min, activation_max; |
| std::tie(activation_min, activation_max) = get_default_activation_values<TAccum>(); |
| |
| switch (this->m_args.activation.type) |
| { |
| case arm_gemm::Activation::Type::BoundedReLU: |
| activation_max = static_cast<TAccum>(this->m_args.activation.param1); |
| // Fall through |
| case arm_gemm::Activation::Type::ReLU: |
| activation_min = static_cast<TAccum>(0); |
| break; |
| default: |
| break; |
| } |
| |
| // Determine what portion of the work to do. |
| const unsigned int n_rows_per_thread = arm_gemm::iceildiv(output_height, n_threads); |
| const int start_out_height = std::min(thread_id * n_rows_per_thread, output_height); |
| const int end_out_height = std::min(start_out_height + n_rows_per_thread, output_height); |
| |
| // Cast input and output pointers into the right types |
| const TInput *const inptr = static_cast<const TInput *>(_input); |
| TOutput *const outptr = static_cast<TOutput *>(_output); |
| |
| // Allocate portions of the working space |
| uint8_t *working_space = static_cast<uint8_t *>(_working_space) + get_working_size(thread_id, input_channels); |
| |
| const void **const inptr_array = reinterpret_cast<const void **>(working_space); |
| working_space += sizeof_inptr_array(); |
| |
| void **const outptr_array = reinterpret_cast<void **>(working_space); |
| working_space += sizeof_outptr_array(); |
| |
| TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space); |
| working_space += sizeof_output_buffer(input_channels * this->m_args.channel_multiplier); |
| |
| TInput *const input_buffer = reinterpret_cast<TInput *>(working_space); |
| |
| // Initialise the input buffer |
| for (unsigned int c = 0; c < input_channels; c++) |
| { |
| input_buffer[c] = static_cast<TInput>(0); |
| } |
| |
| // 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; |
| const auto outptr_batch = outptr + batch * ld_output_batch; |
| |
| for (int start_out_i = start_out_height; |
| start_out_i < end_out_height; |
| start_out_i += static_cast<int>(m_strat->get_output_rows())) |
| { |
| const int end_out_i = start_out_i + m_strat->get_output_rows(); |
| const int start_in_i = start_out_i * m_strat->get_stride_rows() - padding.top; |
| const int end_in_i = start_in_i + m_strat->get_input_rows(); |
| |
| // Compute top/bottom padding |
| const auto pad_top = static_cast<unsigned int>(-std::min(start_in_i, 0)); |
| const auto pad_bottom = static_cast<unsigned int>(-std::min(static_cast<int>(input_height) - end_in_i, 0)); |
| const unsigned int valid_output_rows = std::min( |
| end_out_i - start_out_i, |
| static_cast<int>(output_height) - start_out_i |
| ); |
| |
| // Fill the input pointer array with padding values |
| for (auto index = 0u; index < m_strat->get_input_rows() * m_strat->get_input_cols(); index++) |
| { |
| inptr_array[index] = input_buffer; |
| } |
| |
| for (int start_out_j = 0; start_out_j < static_cast<int>(output_width);) |
| { |
| const int start_in_j = start_out_j * m_strat->get_stride_cols() - this->m_args.padding.left; |
| const int pad_left = -std::min(0, start_in_j); |
| |
| // Compute how many output tiles we can compute with the direct kernel. |
| int n_direct_tiles = 0; |
| if (!pad_top && !pad_bottom && !pad_left) |
| { |
| // Determine the maximum number of tiles we could handle. |
| n_direct_tiles = (output_width - start_out_j) / m_strat->get_output_cols(); |
| |
| // Continue to reduce this number as required to avoid reading |
| // padding on the right edge. |
| int end_in_j = start_in_j + n_direct_tiles * m_strat->get_input_cols(); |
| int pad_right = std::max(0, end_in_j - static_cast<int>(input_width)); |
| |
| while (pad_right && n_direct_tiles) |
| { |
| n_direct_tiles--; |
| end_in_j -= m_strat->get_input_cols(); |
| pad_right = std::max(0, end_in_j - static_cast<int>(input_width)); |
| } |
| } |
| |
| // Use the unpadded kernel if we can, otherwise use the padded one. |
| if (n_direct_tiles) |
| { |
| auto inptr = inptr_batch + start_in_i*ld_input_row + start_in_j*ld_input_col; |
| auto outptr = outptr_batch + start_out_i*ld_output_row + start_out_j*ld_output_col; |
| start_out_j += n_direct_tiles*m_strat->get_output_cols(); |
| |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, 0); |
| #endif |
| m_strat->direct_kernel(1, n_direct_tiles, |
| inptr, ld_input_row, ld_input_col, |
| outptr, ld_output_row, ld_output_col, |
| parameters, this->m_args.input_channels, |
| &activation_min, &activation_max); |
| continue; |
| } |
| |
| const int end_out_j = start_out_j + m_strat->get_output_cols(); |
| const int end_in_j = start_in_j + m_strat->get_input_cols(); |
| |
| const auto pad_right = static_cast<unsigned int>(-std::min(static_cast<int>(input_width) - end_in_j, 0)); |
| const unsigned int valid_output_cols = std::min( |
| end_out_j - start_out_j, |
| static_cast<int>(output_width) - start_out_j |
| ); |
| |
| // Construct the input pointer array - fill the array with pointers to |
| // the input buffer and then fill in the required values. |
| for (auto i = pad_top; i < m_strat->get_input_rows() - pad_bottom; i++) |
| { |
| // Can skip over the left padding because we will have either the |
| // same or less than the previous tile. |
| unsigned int j = pad_left; |
| const TInput *colptr = inptr_batch + (start_in_i + i) * ld_input_row + (start_in_j + j) * ld_input_col; |
| const void **ptrs = inptr_array + i * m_strat->get_input_cols() + j; |
| for (; j < m_strat->get_input_cols() - pad_right; j++) |
| { |
| *(ptrs++) = colptr; |
| colptr += ld_input_col; |
| } |
| for (; j < m_strat->get_input_cols(); j++) |
| { |
| *(ptrs++) = input_buffer; |
| } |
| } |
| |
| // Construct the output pointer array. |
| void **outptr_pos = outptr_array; |
| for (auto i = 0u; i < valid_output_rows; i++) |
| { |
| unsigned int j = 0u; |
| TOutput *colptr = outptr_batch + (start_out_i + i) * ld_output_row + start_out_j * ld_output_col; |
| for (; j < valid_output_cols; j++) |
| { |
| *(outptr_pos++) = colptr; |
| colptr += ld_output_col; |
| } |
| for (; j < m_strat->get_output_cols(); j++) |
| { |
| *(outptr_pos++) = output_buffer; |
| } |
| } |
| for (auto i = valid_output_rows; i < m_strat->get_output_rows(); i++) |
| { |
| for (auto j = 0u; j < m_strat->get_output_cols(); j++) |
| { |
| *(outptr_pos++) = output_buffer; |
| } |
| } |
| |
| start_out_j += m_strat->get_output_cols(); |
| |
| #ifdef CYCLE_PROFILING |
| // TODO Work number |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(0)); |
| #endif |
| m_strat->indirect_kernel(inptr_array, outptr_array, parameters, |
| this->m_args.input_channels, |
| &activation_min, &activation_max); |
| } |
| } |
| } |
| } |
| }; |
| |
| } // namespace depthwise |
| } // namespace arm_conv |