COMPMID-631: Merge branches/gles_compute branch

Last commit:
commit b25c5f68042b0c81bf611d59a1bb8535e1c42497
Author: Xinghang Zhou <xinghang.zhou@arm.com>
Date:   Wed Oct 25 18:48:10 2017 +0800

    Synced validation's tolerances of GCSoftmax from cl side

Change-Id: Ibe72054205c1c8721845d679a31af7ed0a7c5cf6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93283
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

1 files changed, 254 insertions, 0 deletions

diff --git a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
new file mode 100644
index 0000000000..c877da3783
--- /dev/null
+++ b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
@@ -0,0 +1,254 @@
+/*
+ * Copyright (c) 2017 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 "arm_compute/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
+#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
+#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
+#include "arm_compute/core/GLES_COMPUTE/OpenGLES.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <set>
+#include <string>
+#include <tuple>
+
+using namespace arm_compute;
+
+GCPoolingLayerKernel::GCPoolingLayerKernel()
+    : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
+{
+}
+
+BorderSize GCPoolingLayerKernel::border_size() const
+{
+    return _border_size;
+}
+
+void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const PoolingLayerInfo &pool_info)
+{
+    int                 pool_pad_x      = 0;
+    int                 pool_pad_y      = 0;
+    int                 pool_stride_x   = 0;
+    int                 pool_stride_y   = 0;
+    unsigned int        pooled_w        = 0;
+    unsigned int        pooled_h        = 0;
+    const PoolingType   pool_type       = pool_info.pool_type();
+    const int           pool_size       = pool_info.pool_size();
+    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
+    std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
+    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+    ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
+    ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type()));
+
+    // Check output dimensions
+    std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
+                                                     input->info()->dimension(1),
+                                                     pool_size,
+                                                     pool_size,
+                                                     pool_info.pad_stride_info());
+
+    // Output auto initialization if not yet initialized
+    {
+        TensorShape output_shape{ input->info()->tensor_shape() };
+        output_shape.set(0, pooled_w);
+        output_shape.set(1, pooled_h);
+
+        auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));
+
+    const int input_width  = input->info()->dimension(0);
+    const int input_height = input->info()->dimension(1);
+
+    // Set instance variables
+    _input       = input;
+    _output      = output;
+    _pool_info   = pool_info;
+    _border_size = BorderSize(pool_pad_y, pool_pad_x);
+
+    // Set build options
+    std::set<std::string> build_opts;
+    build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
+    build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1));
+    build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1));
+    if(input->info()->data_type() == DataType::F32)
+    {
+        build_opts.insert("#define DATA_TYPE_FP32");
+    }
+    else
+    {
+        build_opts.insert("#define DATA_TYPE_FP16");
+    }
+    build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type)));
+    build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x)));
+    build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x)));
+    build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y)));
+    build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y)));
+    build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x)));
+    build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y)));
+
+    // Create kernel
+    if((pool_size == 2) || (pool_size == 3) || (pool_size == 7))
+    {
+        // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
+        // each thread computes 4 output elements
+        const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type());
+
+        int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;
+
+        if(input->info()->data_type() == DataType::F32)
+        {
+            if(is_pool3x3_stride_le3)
+            {
+                // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
+                _num_elems_processed_per_iteration = 4;
+                num_elements_read_per_iteration    = pool_size * (pool_stride_x + 1);
+            }
+        }
+        else
+        {
+            num_elements_read_per_iteration = pool_size;
+            if(is_pool3x3_stride_le3)
+            {
+                _num_elems_processed_per_iteration = 4;
+            }
+            else
+            {
+                _num_elems_processed_per_iteration = 2;
+            }
+        }
+
+        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
+        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
+        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+        std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
+        if(is_pool3x3_stride_le3)
+        {
+            build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED");
+            _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
+        }
+        else
+        {
+            build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size));
+            _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, build_opts));
+        }
+    }
+    else // Run general case
+    {
+        if(input->info()->data_type() == DataType::F32)
+        {
+            _num_elems_processed_per_iteration = 1;
+        }
+        else
+        {
+            _num_elems_processed_per_iteration = 2;
+        }
+        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
+        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
+        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+        build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size)));
+
+        build_opts.insert("#define POOLING_LAYER_N");
+        _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts));
+    }
+
+    Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration));
+
+    if(input->info()->data_type() == DataType::F32)
+    {
+        AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
+        AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
+        update_window_and_padding(win, input_access, output_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    }
+    else
+    {
+        // Calculate output right and bottom border
+        const int output_width          = output->info()->dimension(0);
+        const int output_height         = output->info()->dimension(1);
+        const int output_padding_right  = ceil_to_multiple(output_width, _num_elems_processed_per_iteration) - output_width;
+        const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height;
+        const int input_padding_right   = ceil_to_multiple(input_width + 2 * _border_size.right, _num_elems_processed_per_iteration) - (input_width + 2 * _border_size.right);
+        const int input_padding_bottom  = ceil_to_multiple(input_height + 2 * _border_size.bottom, 1) - (input_height + 2 * _border_size.bottom);
+
+        // Configure kernel window
+        AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right + input_padding_right, input_height + _border_size.bottom + input_padding_bottom);
+        AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
+        update_window_and_padding(win, input_access, output_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    }
+
+    _kernel.clear_params();
+    _kernel.set_shader_params_binding_point(0);
+
+    IGCKernel::configure(win);
+}
+
+void GCPoolingLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
+    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
+    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+
+    _kernel.use();
+
+    Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ);
+    Window slice            = window_collapsed.first_slice_window_3D();
+
+    do
+    {
+        // Upsample input by pool size
+        Window in_slice(slice);
+        in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
+        in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));
+
+        // Set inputs
+        unsigned int idx = 0;
+        add_3D_tensor_argument(idx, _input, 1, in_slice);
+        add_3D_tensor_argument(idx, _output, 2, slice);
+
+        _kernel.update_shader_params();
+        enqueue(*this, slice);
+    }
+    while(window_collapsed.slide_window_slice_3D(slice));
+}