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, 394 insertions, 0 deletions

diff --git a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp
new file mode 100644
index 0000000000..1fa2a71fff
--- /dev/null
+++ b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp
@@ -0,0 +1,394 @@
+/*
+ * 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/GCDirectConvolutionLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.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/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+template <unsigned int kernel_size>
+GCDirectConvolutionLayerKernel<kernel_size>::GCDirectConvolutionLayerKernel()
+    : _input(nullptr), _bias(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0), _lws(gles::NDRange(1U, 1U, 1U))
+{
+}
+
+template <unsigned int kernel_size>
+BorderSize             GCDirectConvolutionLayerKernel<kernel_size>::border_size() const
+{
+    return _border_size;
+}
+
+template <unsigned int kernel_size>
+void GCDirectConvolutionLayerKernel<kernel_size>::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+    ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
+    ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0));
+
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, bias);
+        // FIXME: Bug in framework, workaround it in tests currently.
+        //ARM_COMPUTE_ERROR_ON(bias->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
+    }
+
+    _conv_stride_x = std::get<0>(conv_info.stride());
+    _conv_stride_y = std::get<1>(conv_info.stride());
+    _conv_pad_x    = std::get<0>(conv_info.pad());
+    _conv_pad_y    = std::get<1>(conv_info.pad());
+
+    _input       = input;
+    _weights     = weights;
+    _output      = output;
+    _bias        = bias;
+    _border_size = BorderSize(_conv_pad_y, _conv_pad_x);
+
+    std::set<std::string> options;
+
+    options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0]));
+    options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1]));
+    options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2]));
+    options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x));
+
+    std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16";
+    options.emplace(("#define " + dt_name));
+
+    unsigned int num_elems_read_per_iteration_x    = kernel_size * _conv_stride_x;
+    unsigned int num_elems_read_per_iteration_y    = 1;
+    unsigned int num_elems_written_per_iteration_x = 1;
+    unsigned int num_elems_written_per_iteration_y = 1;
+    unsigned int num_elems_written_per_iteration_z = 1;
+
+    if(kernel_size == 3)
+    {
+        if((_conv_stride_x == 1) && (_conv_stride_y == 1))
+        {
+            switch(input->info()->data_type())
+            {
+                    // TODO(APPBROWSER-299): Choose the most optimal path and remove others.
+#define PROCESS_X_4ELEMENTS_Y_3ELEMENTS_FP16
+
+                case DataType::F16:
+#if defined(PROCESS_X_8ELEMENTS_Y_3ELEMENTS_FP16)
+                    options.emplace("#define PROCESS_X_8ELEMENTS_Y_3ELEMENTS_FP16");
+                    num_elems_read_per_iteration_x    = 16;
+                    num_elems_read_per_iteration_y    = 5;
+                    num_elems_written_per_iteration_x = 8;
+                    num_elems_written_per_iteration_y = 3;
+#elif defined(PROCESS_X_4ELEMENTS_Y_3ELEMENTS_FP16)
+                    options.emplace("#define PROCESS_X_4ELEMENTS_Y_3ELEMENTS_FP16");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_read_per_iteration_y    = 5;
+                    num_elems_written_per_iteration_x = 4;
+                    num_elems_written_per_iteration_y = 3;
+#elif defined(PROCESS_X_4ELEMENTS_Y_4ELEMENTS_FP16)
+                    options.emplace("#define PROCESS_X_4ELEMENTS_Y_4ELEMENTS_FP16");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_read_per_iteration_y    = 6;
+                    num_elems_written_per_iteration_x = 4;
+                    num_elems_written_per_iteration_y = 4;
+#elif defined(PROCESS_X_4ELEMENTS_Y_3ELEMENTS_Z_2ELEMENTS_FP16)
+                    options.emplace("#define PROCESS_X_4ELEMENTS_Y_3ELEMENTS_Z_2ELEMENTS_FP16");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_read_per_iteration_y    = 5;
+                    num_elems_written_per_iteration_x = 4;
+                    num_elems_written_per_iteration_y = 3;
+                    num_elems_written_per_iteration_z = 2;
+#endif /* PROCESS_X_8ELEMENTS_Y_3ELEMENTS_FP16 */
+                    break;
+
+                case DataType::F32:
+                    options.emplace("#define PROCESS_X_4ELEMENTS_Y_3ELEMENTS");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_read_per_iteration_y    = 5;
+                    num_elems_written_per_iteration_x = 4;
+                    num_elems_written_per_iteration_y = 3;
+                    break;
+
+                default:
+                    ARM_COMPUTE_ERROR("Current data type is not supported");
+                    break;
+            }
+        }
+        // FIXME: Just keep one in release
+        else
+        {
+            switch(input->info()->data_type())
+            {
+                case DataType::F16:
+                    options.emplace("#define PROCESS_X_4ELEMENTS_FP16");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_written_per_iteration_x = 4;
+                    break;
+
+                case DataType::F32:
+                    // TODO(APPBROWSER-299): Choose the most optimal path and remove others.
+#define PROCESS_4_ELEMENT
+
+#if defined(PROCESS_1_ELEMENT)
+                    options.emplace("#define PROCESS_1_ELEMENT");
+                    num_elems_read_per_iteration_x    = 3;
+                    num_elems_written_per_iteration_x = 1;
+#elif defined(PROCESS_4_ELEMENT)
+                    options.emplace("#define PROCESS_4_ELEMENT");
+                    num_elems_read_per_iteration_x    = 8;
+                    num_elems_written_per_iteration_x = 4;
+#elif defined(PROCESS_8_ELEMENT)
+                    options.emplace("#define PROCESS_8_ELEMENT");
+                    num_elems_read_per_iteration_x    = 12;
+                    num_elems_written_per_iteration_x = 8;
+#else /* PROCESS_1_ELEMENT */
+#error Have to declare how many elements to process in one thread.
+#endif /* PROCESS_1_ELEMENT */
+                    break;
+
+                default:
+                    ARM_COMPUTE_ERROR("Current data type is not supported");
+                    break;
+            }
+        }
+    }
+    else if(kernel_size == 1)
+    {
+        switch(input->info()->data_type())
+        {
+            case DataType::F16:
+                num_elems_read_per_iteration_x    = 8;
+                num_elems_written_per_iteration_x = 8;
+                break;
+
+            case DataType::F32:
+                num_elems_read_per_iteration_x    = 1;
+                num_elems_written_per_iteration_x = 1;
+                break;
+
+            default:
+                break;
+        }
+    }
+    else if(kernel_size == 5)
+    {
+        switch(input->info()->data_type())
+        {
+            case DataType::F16:
+                num_elems_read_per_iteration_x    = 8;
+                num_elems_written_per_iteration_x = 4;
+
+            default:
+                break;
+        }
+    }
+    else
+    {
+    }
+
+    if(_bias != nullptr)
+    {
+        options.emplace("#define BIAS");
+    }
+
+    std::stringstream kernel_name;
+    kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+
+    _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name.str(), options));
+
+    _kernel.clear_params();
+
+    unsigned int idx = (_bias == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());
+
+    // 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_written_per_iteration_x * _lws[0]) - output_width;
+    const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height;
+
+    // Calculate input right and bottom border
+    const int input_width    = input->info()->dimension(0);
+    const int input_height   = input->info()->dimension(1);
+    const int upper_bound_w  = ceil_to_multiple(((output_width + output_padding_right) * _conv_stride_x + (kernel_size - 1)), num_elems_read_per_iteration_x * _lws[0]) - _conv_pad_x - input_width;
+    const int upper_bound_h  = ceil_to_multiple(((output_height + output_padding_bottom) * _conv_stride_y + (kernel_size - 1)), num_elems_read_per_iteration_y * _lws[1]) - _conv_pad_y - input_height;
+    const int padding_right  = std::max(upper_bound_w, _conv_pad_x);
+    const int padding_bottom = std::max(upper_bound_h, _conv_pad_y);
+
+    BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0);
+
+    Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border);
+
+    AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
+    AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0);
+    AccessWindowStatic bias_access    = AccessWindowStatic(nullptr, 0, 0, 0, 1);
+
+    switch(weights->info()->data_type())
+    {
+        case DataType::F16:
+            weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size + 1, kernel_size);
+            if(_bias != nullptr)
+            {
+                bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0) + 1, 1);
+            }
+            break;
+
+        case DataType::F32:
+            weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size, kernel_size);
+            if(_bias != nullptr)
+            {
+                bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0), 1);
+            }
+            break;
+
+        default:
+            ARM_COMPUTE_ERROR("Current data type is not supported");
+            break;
+    }
+
+    AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
+
+    if(_bias != nullptr)
+    {
+        update_window_and_padding(win, input_access, weights_access, bias_access, output_access);
+    }
+    else
+    {
+        update_window_and_padding(win, input_access, weights_access, output_access);
+    }
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    _kernel.set_params(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
+    _kernel.set_params(idx++, _weights->info()->dimension(2));          // weights_depth
+
+    // set shader params binding point
+    _kernel.set_shader_params_binding_point(0);
+
+    IGCKernel::configure(win);
+}
+
+template <unsigned int kernel_size>
+void GCDirectConvolutionLayerKernel<kernel_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    _kernel.use();
+
+    // Get initial windows
+    Window slice  = window.first_slice_window_3D();
+    Window win_in = window;
+
+    win_in.adjust(Window::DimX, -_conv_pad_x, true);
+    win_in.adjust(Window::DimY, -_conv_pad_y, true);
+    win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
+    win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
+
+    Window slice_in = win_in.first_slice_window_3D();
+
+    unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
+    add_3D_tensor_argument(idx1, _weights, BufferParam(3, 2), slice);
+
+    if(_bias != nullptr)
+    {
+        Window slice_bias;
+        slice_bias.use_tensor_dimensions(_bias->info()->tensor_shape());
+        add_1D_tensor_argument(idx1, _bias, BufferParam(4, 2), slice_bias);
+    }
+
+    do
+    {
+        unsigned int idx = 0;
+
+        switch(_input->info()->data_type())
+        {
+            case DataType::F16:
+                switch(kernel_size)
+                {
+                    case 1:
+                        add_3D_tensor_argument(idx, _input, BufferParam(1, 4), slice_in);
+                        add_3D_tensor_argument(idx, _output, BufferParam(2, 4), slice);
+                        break;
+
+                    case 3:
+                        add_3D_tensor_argument(idx, _input, BufferParam(1, 3), slice_in);
+                        add_3D_tensor_argument(idx, _output, BufferParam(2, 3), slice);
+                        break;
+
+                    case 5:
+                        add_3D_tensor_argument(idx, _input, BufferParam(1, 3), slice_in);
+                        add_3D_tensor_argument(idx, _output, BufferParam(2, 3), slice);
+                        break;
+
+                    default:
+                        ARM_COMPUTE_ERROR("Current kernel size %d is not supported", kernel_size);
+                        break;
+                }
+                break;
+
+            case DataType::F32:
+                switch(kernel_size)
+                {
+                    case 1:
+                    case 5:
+                        add_3D_tensor_argument(idx, _input, BufferParam(1, 2), slice_in);
+                        add_3D_tensor_argument(idx, _output, BufferParam(2, 2), slice);
+                        break;
+
+                    case 3:
+                        add_3D_tensor_argument(idx, _input, BufferParam(1, 4), slice_in);
+                        add_3D_tensor_argument(idx, _output, BufferParam(2, 4), slice);
+                        break;
+
+                    default:
+                        ARM_COMPUTE_ERROR("Current kernel size %d is not supported", kernel_size);
+                        break;
+                }
+                break;
+
+            default:
+                ARM_COMPUTE_ERROR("Current data type is not supported");
+                break;
+        }
+
+        _kernel.update_shader_params();
+        enqueue(*this, slice, _lws);
+    }
+    while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+}
+
+template class arm_compute::GCDirectConvolutionLayerKernel<1>;
+template class arm_compute::GCDirectConvolutionLayerKernel<3>;
+template class arm_compute::GCDirectConvolutionLayerKernel<5>;