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Diffstat (limited to 'src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst_cache_oblivious.hpp')
-rw-r--r-- | src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst_cache_oblivious.hpp | 312 |
1 files changed, 312 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst_cache_oblivious.hpp b/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst_cache_oblivious.hpp new file mode 100644 index 0000000000..4aabd957cd --- /dev/null +++ b/src/core/NEON/kernels/arm_conv/pooling/pooling_depthfirst_cache_oblivious.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 <stack> +#include <vector> + +namespace arm_conv { +namespace pooling { + +template <class strategy> +class PoolingDepthfirstCacheOblivious : 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: + PoolingDepthfirstCacheOblivious(const PoolingArgs &args) : m_args(args) + { + } + + PoolingDepthfirstCacheOblivious(PoolingDepthfirstCacheOblivious &) = delete; + PoolingDepthfirstCacheOblivious &operator=(PoolingDepthfirstCacheOblivious &) = delete; + + size_t get_working_size(void) const override + { + // We require an array of pointers for the inputs and outputs, a + // channel-length vector in which to dump surplus output, and a + // channel-length vector of padding values. + return sizeof_input_buffer() + sizeof_output_buffer(); + } + + void execute( + const void *const input, + void *const output, + void *const working_space + ) 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 + ); + } + + 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 + ) 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 + ); + } + + void execute( + unsigned int batches, + unsigned int input_height, + unsigned int input_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 + ) const override + { + 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); + + // 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); + TInput *const input_buffer = reinterpret_cast<TInput *>(working_space + sizeof_output_buffer()); + + // Fill the input buffer + const TInput pad_value = (m_args.pool_type == PoolingType::AVERAGE) + ? static_cast<TInput>(0) + : (std::numeric_limits<TInput>::has_infinity + ? -std::numeric_limits<TInput>::infinity() + : std::numeric_limits<TInput>::lowest()); + for (unsigned int i = 0; i < channels; i++) + { + input_buffer[i] = pad_value; + } + + // Keep subdividing the output plane across the longest dimension until we + // reach the size of the tile. Queue items for later processing. Note - we + // can determine the largest size of the queue a priori from the input + // tensor size, this would allow us to allocate memory within the working + // space and improve performance. + struct WorkItem + { + unsigned int output_i, output_j; + unsigned int output_height, output_width; + + WorkItem(unsigned int i, unsigned int j, unsigned int height, unsigned int width) + : output_i(i), output_j(j), output_height(height), output_width(width) {} + }; + + auto execute = [&] (const WorkItem &item) { + // Create an array for the output pointers + TOutput * _outptr_array[strategy::out_rows() * strategy::out_cols()]; + TOutput **const outptr_array = _outptr_array; + + // Construct the output pointer array + { + const auto output_pad_right = strategy::out_rows() - item.output_width; + auto outptr_element = outptr_array; + auto outptr_row = outptr + item.output_i * ld_output_row + item.output_j * ld_output_col; + + // Fill the array with pointers to the output buffer + for (unsigned int i = 0; i < strategy::out_rows() * strategy::out_cols(); i++) + { + outptr_array[i] = output_buffer; + } + + // Fill in the valid portion of the array + for (unsigned int i = 0; i < item.output_height; i++) + { + auto outptr_col = outptr_row; + for (unsigned int j = 0; j < item.output_width; j++) + { + *(outptr_element++) = outptr_col; + outptr_col += ld_output_col; + } + outptr_element += output_pad_right; + outptr_row += ld_output_row; + } + } + + const int start_i = item.output_i * strategy::stride_rows() - padding.top; + const int end_i = start_i + input_rows(); + const unsigned int pad_top = std::max(0, 0 - start_i); + const unsigned int pad_bottom = std::max(0, end_i - static_cast<int>(input_height)); + + const int start_j = item.output_j * strategy::stride_cols() - padding.left; + const int end_j = start_j + input_cols(); + const unsigned int pad_left = std::max(0, 0 - start_j); + const unsigned int pad_right = std::max(0, end_j - static_cast<int>(input_width)); + + // Create an array for the input pointers + const TInput * _inptr_array[input_rows() * input_cols()]; + const TInput **const inptr_array = _inptr_array; + { + const unsigned int row_padding = pad_top + pad_bottom; + const unsigned int valid_rows = input_rows() - row_padding; + + const unsigned int col_padding = pad_left + pad_right; + const unsigned int valid_cols = input_cols() - col_padding; + + // Fill the array with pointers to the input buffer + for (unsigned int i = 0; i < input_rows() * input_cols(); i++) + { + inptr_array[i] = input_buffer; + } + + // Compute valid initial pointer + auto inptr_row = inptr + std::max(start_i, 0) * ld_input_row + std::max(start_j, 0) * ld_input_col; + + // Fill in the valid portion of the input array + auto inptr_element = inptr_array + pad_top * input_cols() + pad_left; + for (unsigned int i = 0; i < valid_rows; i++) + { + auto inptr_col = inptr_row; + for (unsigned int j = 0; j < valid_cols; j++) + { + *(inptr_element++) = inptr_col; + inptr_col += ld_input_col; + } + + inptr_row += ld_input_row; + inptr_element += col_padding; // Skip the padding elements + } + } + + // Call the kernel +#ifdef CYCLE_PROFILING + // TODO Work number + auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(item.output_height * item.output_width * strategy::pool_rows() * strategy::pool_cols())); +#endif // CYCLE_PROFILING + strat.kernel(channels, inptr_array, outptr_array, + pad_left, pad_top, pad_right, pad_bottom); + }; + + // Add the initial work item to the stack of work. + std::stack<WorkItem, std::vector<WorkItem>> stack; + stack.push(WorkItem(0, 0, output_height, output_width)); + while (!stack.empty()) + { + // Pop an item from the stack, bisect the largest dimension and either + // execute the resulting tiles or add them to the stack if they are too + // large. + const WorkItem item(stack.top()); + stack.pop(); + + if (item.output_height <= strategy::out_rows() && + item.output_width <= strategy::out_cols()) + { + execute(item); + } + else + { + // Split the largest dimension, such that we get an exact number of + // tiles in the first partition. + if (item.output_height >= item.output_width) + { + const unsigned int height_in_tiles = (item.output_height + strategy::out_rows() - 1) / strategy::out_rows(); + const unsigned int tiles_first = height_in_tiles - height_in_tiles / 2; + + const unsigned int height_first = tiles_first * strategy::out_rows(); + const unsigned int height_second = item.output_height - height_first; + + stack.push(WorkItem(item.output_i + height_first, item.output_j, height_second, item.output_width)); + stack.push(WorkItem(item.output_i, item.output_j, height_first, item.output_width)); + } + else + { + const unsigned int width_in_tiles = item.output_width / strategy::out_cols(); + const unsigned int tiles_first = width_in_tiles - width_in_tiles / 2; + + const unsigned int width_first = tiles_first * strategy::out_cols(); + const unsigned int width_second = item.output_width - width_first; + + stack.push(WorkItem(item.output_i, item.output_j + width_first, item.output_height, width_second)); + stack.push(WorkItem(item.output_i, item.output_j, item.output_height, width_first)); + } + } + } + } +}; + +} // namespace pooling +} // namespace arm_conv |