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 <georgios.pinitas@arm.com>
Change-Id: I35ebeef38dac25ec5459cfe9c5f7c9a708621124
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/357914
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Comments-Addressed: bsgcomp <bsgcomp@arm.com>
Signed-off-by: Freddie Liardet <frederick.liardet@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6295
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 224 deletions

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 <algorithm>
-#include <arm_neon.h>
-#include <climits>
-#include <cstddef>
-
-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<Status, Window> 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<float>::max());
-        float32x2_t carry_max = vdup_n_f32(std::numeric_limits<float>::lowest());
-
-        float carry_min_scalar = std::numeric_limits<float>::max();
-        float carry_max_scalar = std::numeric_limits<float>::lowest();
-
-        execute_window_loop(window_input, [&](const Coordinates &)
-        {
-            int        x      = x_start;
-            const auto in_ptr = reinterpret_cast<const float *>(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<float *>(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<float>::max(), reset_values, 0);
-    reset_values             = vset_lane_f32(std::numeric_limits<float>::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<float *>(output.ptr()), reset_values);
-    },
-    output);
-}
-
-void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max)
-{
-    arm_compute::lock_guard<Mutex> 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