| /* |
| * 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. |
| */ |
| #ifndef SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H |
| #define SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H |
| #include "arm_compute/core/Helpers.h" |
| |
| #include "src/core/NEON/wrapper/wrapper.h" |
| |
| namespace arm_compute |
| { |
| struct ConvolutionInfo; |
| |
| namespace cpu |
| { |
| constexpr auto data_layout = DataLayout::NHWC; |
| const size_t width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const size_t height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const size_t channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| constexpr auto dim_manual_loop = Window::Dimension(0, 0, 0); |
| constexpr auto dim_single_unit_step = Window::Dimension(0, 1, 1); |
| constexpr size_t vector_size = 8; |
| |
| struct DepthwiseConvolutionRunInfo |
| { |
| const size_t num_read_elements_per_iteration; |
| const uint32_t x_start; |
| const uint32_t x_end; |
| const uint32_t x_step; |
| const uint32_t x_leftover_start; |
| const size_t input_stride_y; |
| const size_t input_stride_z; |
| const size_t input_max_offset; |
| const size_t weights_width; |
| const size_t weights_height; |
| const size_t weights_stride_y; |
| const size_t weights_stride_z; |
| const size_t conv_stride_x; |
| const size_t conv_stride_y; |
| const size_t conv_pad_left; |
| const size_t conv_pad_top; |
| const size_t input_height; |
| const size_t input_width; |
| const size_t input_depth; |
| |
| DepthwiseConvolutionRunInfo(const ITensorInfo &input, |
| const ITensorInfo &weights, |
| const PadStrideInfo &conv_info, |
| const Window &w, |
| uint32_t depth_multiplier = 1) // NOLINT |
| : num_read_elements_per_iteration( |
| (depth_multiplier == 1 ? (vector_size / element_size_from_data_type(input.data_type())) : 1)), |
| x_start(w.x().start()), |
| x_end(w.x().end()), |
| x_step(static_cast<uint32_t>(num_read_elements_per_iteration * depth_multiplier)), |
| x_leftover_start(std::max(static_cast<int32_t>(w.x().end() + 1) - static_cast<int32_t>(x_step), int32_t(0))), |
| input_stride_y(input.strides_in_bytes().y()), |
| input_stride_z(input.strides_in_bytes().z()), |
| input_max_offset(input.strides_in_bytes().z() * input.dimension(height_idx) - |
| (input.padding().bottom + input.padding().top) * input.strides_in_bytes().y()), |
| weights_width(weights.dimension(width_idx)), |
| weights_height(weights.dimension(height_idx)), |
| weights_stride_y(weights.strides_in_bytes().y()), |
| weights_stride_z(weights.strides_in_bytes().z()), |
| conv_stride_x(conv_info.stride().first), |
| conv_stride_y(conv_info.stride().second), |
| conv_pad_left(conv_info.pad_left()), |
| conv_pad_top(conv_info.pad_top()), |
| input_height(input.dimension(height_idx)), |
| input_width(input.dimension(width_idx)), |
| input_depth(input.dimension(channel_idx)) |
| { |
| } |
| }; |
| |
| inline bool is_valid_input_region(int32_t base_w, |
| uint32_t base_h, |
| uint32_t w, |
| uint32_t h, |
| const DepthwiseConvolutionRunInfo &run_info, |
| const Size2D &dilation) |
| { |
| const int32_t current_h = base_h + h * dilation.y(); |
| const bool is_valid_h = current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height); |
| |
| const int32_t current_w = base_w + w * dilation.x(); |
| const bool is_valid_w = current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width); |
| |
| return is_valid_h && is_valid_w; |
| } |
| |
| template <typename T> |
| void depthwise_loop_multiplier1_fp(const ITensor *src, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *dst, |
| const PadStrideInfo &conv_info, |
| const Size2D &dilation, |
| const Window &window, |
| bool has_biases) |
| { |
| constexpr auto element_per_vector = vector_size / sizeof(T); |
| using VectorType = typename wrapper::traits::neon_vector<T, element_per_vector>::type; |
| using TagType = typename wrapper::traits::neon_vector<T, element_per_vector>::tag_type; |
| |
| const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window); |
| |
| const VectorType zero_vector = wrapper::vdup_n(static_cast<T>(0), TagType{}); |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, dim_single_unit_step); |
| |
| Window win_input = window; |
| win_input.set(Window::DimX, dim_manual_loop); |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = win_input; |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set(Window::DimX, dim_manual_loop); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if (has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop( |
| execution_window, |
| [&](const Coordinates &id) |
| { |
| const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto const base_weights_ptr = weights_it.ptr(); |
| uint32_t x = run_info.x_start; |
| |
| for (; x < run_info.x_leftover_start; x += run_info.x_step) |
| { |
| VectorType acc = zero_vector; |
| auto weights_ptr = base_weights_ptr; |
| int64_t input_offset = base_input_offset; |
| |
| for (uint32_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for (uint32_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_vals = |
| is_valid_region |
| ? wrapper::vload(reinterpret_cast<T *>( |
| input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) |
| : zero_vector; |
| const auto weights_vals = |
| wrapper::vload(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| acc = wrapper::vmla(acc, weights_vals, input_vals); |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if (has_biases) |
| { |
| const auto biases_vals = wrapper::vload(reinterpret_cast<T *>(biases_it.ptr()) + x); |
| acc = wrapper::vadd(acc, biases_vals); |
| } |
| |
| wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()) + x, acc); |
| } |
| |
| for (; x < run_info.x_end; ++x) |
| { |
| auto acc_scalar = T{0}; |
| auto weights_ptr = base_weights_ptr; |
| int64_t input_offset = base_input_offset; |
| |
| for (size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int64_t offs = input_offset + x * sizeof(T); |
| for (size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_vals = |
| is_valid_region |
| ? *reinterpret_cast<T *>(input_it.ptr() + |
| std::min(static_cast<size_t>(offs), run_info.input_max_offset)) |
| : 0; |
| const auto weights_vals = |
| *(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); |
| |
| acc_scalar += (input_vals * weights_vals); |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if (has_biases) |
| { |
| const auto biases_vals = *(reinterpret_cast<T *>(biases_it.ptr()) + x); |
| acc_scalar += biases_vals; |
| } |
| *(reinterpret_cast<T *>(output_it.ptr()) + x) = acc_scalar; |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T> |
| void depthwise_loop_generic_fp(const ITensor *src, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *dst, |
| const PadStrideInfo &conv_info, |
| const Size2D &dilation, |
| unsigned int depth_multiplier, |
| const Window &window, |
| bool has_biases) |
| { |
| const auto run_info = |
| DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); |
| |
| Window execution_window = window; |
| execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| |
| Window win_input = execution_window; |
| win_input.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); |
| win_input.set(Window::DimY, dim_manual_loop); |
| win_input.set(Window::DimZ, dim_manual_loop); |
| |
| Window win_weights = window; |
| win_weights.set_dimension_step(Window::DimX, run_info.x_step); |
| win_weights.set(Window::DimY, dim_manual_loop); |
| win_weights.set(Window::DimZ, dim_manual_loop); |
| win_weights.set(Window::DimW, dim_manual_loop); |
| |
| Window win_output = window; |
| win_output.set_dimension_step(Window::DimX, run_info.x_step); |
| |
| Iterator input_it(src, win_input); |
| Iterator weights_it(weights, win_weights); |
| Iterator output_it(dst, win_output); |
| Iterator biases_it{}; |
| |
| if (has_biases) |
| { |
| biases_it = Iterator(biases, win_weights); |
| } |
| |
| execute_window_loop( |
| execution_window, |
| [&](const Coordinates &id) |
| { |
| std::vector<T> acc(depth_multiplier, static_cast<T>(0)); |
| |
| const int input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; |
| const int input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; |
| int input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; |
| |
| auto weights_ptr = weights_it.ptr(); |
| for (size_t h = 0; h < run_info.weights_height; ++h) |
| { |
| int offs = input_offset; |
| for (size_t w = 0; w < run_info.weights_width; ++w) |
| { |
| const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); |
| const auto input_val = |
| is_valid_region ? *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), |
| run_info.input_max_offset))) |
| : T(0); |
| |
| for (size_t m = 0; m < depth_multiplier; ++m) |
| { |
| const auto weights_val = |
| *(reinterpret_cast<T *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); |
| acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m)); |
| } |
| |
| offs += dilation.x() * run_info.input_stride_y; |
| } |
| |
| weights_ptr += run_info.weights_stride_z; |
| input_offset += dilation.y() * run_info.input_stride_z; |
| } |
| |
| if (has_biases) |
| { |
| for (size_t m = 0; m < depth_multiplier; ++m) |
| { |
| const auto biases_val = *(reinterpret_cast<T *>(biases_it.ptr() + m * sizeof(T))); |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val; |
| } |
| } |
| else |
| { |
| for (size_t m = 0; m < depth_multiplier; ++m) |
| { |
| *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m); |
| } |
| } |
| }, |
| input_it, weights_it, biases_it, output_it); |
| } |
| |
| template <typename T, typename TW> |
| void run_depthwise_float(const ITensor *src, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *dst, |
| const Window &window, |
| bool has_biases, |
| const ConvolutionInfo &info) |
| { |
| PadStrideInfo conv_info = info.pad_stride_info; |
| unsigned int depth_multiplier = info.depth_multiplier; |
| Size2D dilation = info.dilation; |
| |
| if (depth_multiplier == 1) |
| { |
| depthwise_loop_multiplier1_fp<T>(src, weights, biases, dst, conv_info, dilation, window, has_biases); |
| } |
| else |
| { |
| depthwise_loop_generic_fp<T>(src, weights, biases, dst, conv_info, dilation, depth_multiplier, window, |
| has_biases); |
| } |
| } |
| |
| template <typename T, typename TW> |
| void run_depthwise_quanitized8bit(const ITensor *src, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *dst, |
| const Window &window, |
| bool has_biases, |
| const ConvolutionInfo &info); |
| |
| } // namespace cpu |
| } // namespace arm_compute |
| #endif //define SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H |