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Diffstat (limited to 'src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp')
| -rw-r--r-- | src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp | 312 |
1 files changed, 312 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp b/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp new file mode 100644 index 0000000000..ad95207fb3 --- /dev/null +++ b/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst.hpp @@ -0,0 +1,312 @@ +/* + * 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" + +#include "arm_compute/core/Types.h" +#include <limits> + +namespace arm_conv { +namespace pooling { + +template <class strategy> +class PoolingDepthfirst : 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 + + constexpr static unsigned int input_rows(void) + { + return (strategy::out_rows() - 1)*strategy::stride_rows() + strategy::pool_rows(); + } + + constexpr static unsigned int input_cols(void) + { + return (strategy::out_cols() - 1)*strategy::stride_cols() + strategy::pool_cols(); + } + + size_t sizeof_input_buffer(void) const + { + return sizeof(TInput) * m_args.n_channels; + } + + size_t sizeof_output_buffer(void) const + { + return sizeof(TOutput) * m_args.n_channels; + } + + public: + PoolingDepthfirst(const PoolingArgs &args) : m_args(args) + { + } + + PoolingDepthfirst(PoolingDepthfirst &) = delete; + PoolingDepthfirst &operator=(PoolingDepthfirst &) = delete; + + size_t get_working_size(unsigned int num_threads) const override + { + // We require a channel-length vector of input padding values + // (to be shared amongst all threads) and (for each thread) a + // channel-length vector in which to dump surplus output. + return sizeof_input_buffer() + num_threads * sizeof_output_buffer(); + } + + 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 + { + ARM_COMPUTE_UNUSED(batches, ld_input_batch, ld_output_batch); + strategy strat(m_args.cpu_info); +#ifdef CYCLE_PROFILING + arm_gemm::profiler prof; +#endif // CYCLE_PROFILING + + // 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); + + const unsigned int roundup_output_rows = roundup(output_height, num_threads); + const unsigned int rows_per_thread = roundup_output_rows / num_threads; + const int start_out_height = static_cast<int>(thread_id * rows_per_thread); + const int end_out_height = std::min<int>(output_height, static_cast<int>((thread_id + 1) * rows_per_thread)); + + // Create an array for the input pointers + const TInput * _inptr_array[input_rows() * input_cols()]; + const TInput **const inptr_array = _inptr_array; + + // Create an array for the output pointers + TOutput * _outptr_array[strategy::out_rows() * strategy::out_cols()]; + TOutput **const outptr_array = _outptr_array; + + // Allocate portions of the working space + uint8_t *const working_space = static_cast<uint8_t *>(_working_space); + TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space + thread_id * sizeof_output_buffer()); + TInput *const input_buffer = reinterpret_cast<TInput *>(working_space + num_threads * sizeof_output_buffer()); + + // Initialise the input buffer + for (unsigned int c = 0; c < channels; c++) + { + TInput &val = input_buffer[c]; + + if (strategy::pooling_type() == PoolingType::AVERAGE) + { + val = static_cast<TInput>(0); + } + else if (strategy::pooling_type() == PoolingType::MAX) + { +#if defined(__aarch64__) + using InputType = typename std::conditional<std::is_same<TInput, __fp16>::value, arm_compute::half, TInput>::type; + using limits = std::numeric_limits<InputType>; +#else // defined(__aarch64__) + using limits = std::numeric_limits<TInput>; +#endif // defined(__aarch64__) + if (limits::has_infinity) + { + val = -limits::infinity(); + } + else + { + val = limits::min(); + } + } + } + + // 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>(strategy::out_rows())) + { + const int end_out_i = start_out_i + strategy::out_rows(); + const int start_in_i = start_out_i * strategy::stride_rows() - padding.top; + const int end_in_i = start_in_i + input_rows(); + + // Compute top/bottom padding - TODO Is this right for average pooling? + 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>(height) - end_in_i, 0)); + const unsigned int valid_output_rows = std::min( + end_out_i - start_out_i, + static_cast<int>(end_out_height) - start_out_i + ); + + // Fill the input pointer array with padding values + for (auto index = 0u; index < input_rows() * input_cols(); index++) + { + inptr_array[index] = input_buffer; + } + + for (int start_out_j = 0, start_in_j = -padding.left; + start_out_j < static_cast<int>(output_width); + start_out_j += static_cast<int>(strategy::out_cols()), + start_in_j += static_cast<int>(strategy::out_cols()) * strategy::stride_cols()) + { + const int end_out_j = start_out_j + strategy::out_cols(); + const int end_in_j = start_in_j + input_cols(); + + // Compute left/right padding - TODO Is this right for average pooling? + const auto pad_left = static_cast<unsigned int>(-std::min(start_in_j, 0)); + const auto pad_right = static_cast<unsigned int>(-std::min(static_cast<int>(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 < 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 TInput **ptrs = inptr_array + i * input_cols() + j; + for (; j < input_cols() - pad_right; j++) + { + *(ptrs++) = colptr; + colptr += ld_input_col; + } + for (; j < input_cols(); j++) + { + *(ptrs++) = input_buffer; + } + } + + // Construct the output pointer array. + TOutput **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 < strategy::out_cols(); j++) + { + *(outptr_pos++) = output_buffer; + } + } + for (auto i = valid_output_rows; i < strategy::out_rows(); i++) + { + for (auto j = 0u; j < strategy::out_cols(); j++) + { + *(outptr_pos++) = output_buffer; + } + } + +#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 + strat.kernel( + channels, inptr_array, outptr_array, + m_args.exclude_padding, pad_left, pad_top, pad_right, pad_bottom + ); + } + } + } + } +}; + +} // namespace pooling +} // namespace arm_conv |