From b6af482bc5d8e4f03f876e17909c561de198c4d3 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 14 Sep 2021 12:33:34 +0100 Subject: Per-operator build dependencies Creates a list of operators their respective dependencies. Alters the build system to walk-through them resolve the dependencies and build Compute Library. Removes the following unused kernels/functions: -[NE|CL]MinMaxLayerKernel -CLFillBorder Resolves: COMPMID-4695,COMPMID-4696 Signed-off-by: Georgios Pinitas Change-Id: I35ebeef38dac25ec5459cfe9c5f7c9a708621124 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/357914 Tested-by: bsgcomp Reviewed-by: Michele DiGiorgio Comments-Addressed: bsgcomp Signed-off-by: Freddie Liardet Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6295 Reviewed-by: Gunes Bayir Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- src/core/NEON/kernels/NEMinMaxLayerKernel.cpp | 224 -------------------------- 1 file changed, 224 deletions(-) delete mode 100644 src/core/NEON/kernels/NEMinMaxLayerKernel.cpp (limited to 'src/core/NEON/kernels/NEMinMaxLayerKernel.cpp') diff --git a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp deleted file mode 100644 index 5ea8947fa0..0000000000 --- a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp +++ /dev/null @@ -1,224 +0,0 @@ -/* - * Copyright (c) 2017-2021 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. - */ -#include "src/core/NEON/kernels/NEMinMaxLayerKernel.h" - -#include "arm_compute/core/Coordinates.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include -#include -#include -#include - -using namespace arm_compute::misc::shape_calculator; - -namespace arm_compute -{ -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() < 3); - - if(output->tensor_shape().total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - - TensorShape output_shape = compute_min_max_shape(input); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::tuple validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) -{ - TensorShape output_shape = compute_min_max_shape(input); - - // Output auto initialization if not yet initialized - auto_init_if_empty(*output, output_shape, 1, input->data_type()); - - constexpr unsigned int num_elems_processed_per_iteration = 1; - - // Configure kernel window - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output, 0, 2); - - bool window_changed = update_window_and_padding(win, input_access, output_access); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_tuple(err, win); -} -} // namespace - -NEMinMaxLayerKernel::NEMinMaxLayerKernel() - : _input(nullptr), _output(nullptr), _mtx() -{ -} - -void NEMinMaxLayerKernel::configure(const ITensor *input, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); - - _input = input; - _output = output; - - auto win_config = validate_and_configure_window(input->info(), output->info()); - - ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); - - INEKernel::configure(std::get<1>(win_config)); -} - -Status NEMinMaxLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); - ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get()))); - - return Status{}; -} - -void NEMinMaxLayerKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - const int x_start = window.x().start(); - const int x_end = window.x().end(); - - Window window_output; - window_output.use_tensor_dimensions(_output->info()->tensor_shape()); - window_output.set(Window::DimX, Window::Dimension(0, 1, 1)); - - // Handle X dimension manually to split into two loops - // First one will use vector operations, second one processes the left over pixels - Window window_input(window); - window_input.set(Window::DimX, Window::Dimension(0, 1, 1)); - window_input.set(3, Window::Dimension(0, 1, 1)); - - Iterator input(_input, window_input); - Iterator output(_output, window_output); - - execute_window_loop(window_output, [&](const Coordinates & id_batch) - { - float32x2_t carry_min = vdup_n_f32(std::numeric_limits::max()); - float32x2_t carry_max = vdup_n_f32(std::numeric_limits::lowest()); - - float carry_min_scalar = std::numeric_limits::max(); - float carry_max_scalar = std::numeric_limits::lowest(); - - execute_window_loop(window_input, [&](const Coordinates &) - { - int x = x_start; - const auto in_ptr = reinterpret_cast(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]); - - // Vector loop - for(; x <= x_end - 8; x += 8) - { - const float32x4x2_t pixels = vld2q_f32(in_ptr + x); - const float32x4_t tmp_min1 = vminq_f32(pixels.val[0], pixels.val[1]); - const float32x4_t tmp_max1 = vmaxq_f32(pixels.val[0], pixels.val[1]); - const float32x2_t tmp_min2 = vmin_f32(vget_high_f32(tmp_min1), vget_low_f32(tmp_min1)); - const float32x2_t tmp_max2 = vmax_f32(vget_high_f32(tmp_max1), vget_low_f32(tmp_max1)); - carry_min = vmin_f32(tmp_min2, carry_min); - carry_max = vmax_f32(tmp_max2, carry_max); - } - - // Process leftover pixels - for(; x < x_end; ++x) - { - const float pixel = in_ptr[x]; - carry_min_scalar = std::min(pixel, carry_min_scalar); - carry_max_scalar = std::max(pixel, carry_max_scalar); - } - }, - input); - - // Reduce result - carry_min = vpmin_f32(carry_min, carry_min); - carry_max = vpmax_f32(carry_max, carry_max); - carry_min = vpmin_f32(carry_min, carry_min); - carry_max = vpmax_f32(carry_max, carry_max); - - // Extract max/min values - const float min_i = std::min(vget_lane_f32(carry_min, 0), carry_min_scalar); - const float max_i = std::max(vget_lane_f32(carry_max, 0), carry_max_scalar); - - auto out_ptr = reinterpret_cast(output.ptr()); - - // Perform reduction of local min/max values - update_min_max(out_ptr, min_i, max_i); - }, - output); -} - -void NEMinMaxLayerKernel::reset() -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - - float32x2_t reset_values = vdup_n_f32(0.0f); - reset_values = vset_lane_f32(std::numeric_limits::max(), reset_values, 0); - reset_values = vset_lane_f32(std::numeric_limits::lowest(), reset_values, 1); - - Window window_output; - window_output.use_tensor_dimensions(_output->info()->tensor_shape()); - window_output.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator output(_output, window_output); - - execute_window_loop(window_output, [&](const Coordinates &) - { - vst1_f32(reinterpret_cast(output.ptr()), reset_values); - }, - output); -} - -void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max) -{ - arm_compute::lock_guard lock(_mtx); - - const float32x2_t old_min = vld1_dup_f32(out_ptr); - const float32x2_t old_max = vld1_dup_f32(out_ptr + 1); - const float32x2_t new_min = vmin_f32(vdup_n_f32(min), old_min); - const float32x2_t new_max = vmax_f32(vdup_n_f32(max), old_max); - - vst1_f32(out_ptr, vzip_f32(new_min, new_max).val[0]); -} -} // namespace arm_compute -- cgit v1.2.1