COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 4)

Implemented Winograd Output Transform (2x2,3x3) on OpenCL
Implemented CLWinogradConvolutionLayer on OpenCL

Change-Id: I6a113fc5f052ca07f878d2b800d2ab003f84af65
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125148
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 146 insertions, 0 deletions

diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
new file mode 100644
index 0000000000..5081cbac4e
--- /dev/null
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2018 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/runtime/CL/functions/CLWinogradConvolutionLayer.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+using namespace arm_compute;
+
+CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _is_first_run(true)
+{
+}
+
+void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
+{
+    // TODO(COMPMID-1013): This part will be removed
+    // Get indeces for the width and height
+    const size_t idx_width  = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+    const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+
+    // Kernel size
+    const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width];
+    const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height];
+
+    // Number of tiles along the X and Y direction
+    const unsigned int num_tiles_x = std::ceil((input->info()->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f);
+    const unsigned int num_tiles_y = std::ceil((input->info()->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f);
+
+    // Compute output shape
+    const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info);
+
+    // Manage intermediate tensors
+    _memory_group.manage(&_input0);
+    _memory_group.manage(&_batched_mm_output);
+
+    // Do not manage _input1 as it contains the weights
+
+    // Configure input transform
+    _input_transform.configure(input, &_input0, conv_info, Size2D(kernel_w, kernel_h));
+
+    // Configure filter transform
+    _filter_transform.configure(weights, &_input1);
+
+    // Configure batched matrix multiply
+    _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+    // Configure output transform
+    _output_transform.configure(&_batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], output_convolved_shape[idx_height]), Size2D(num_tiles_x,
+                                num_tiles_y));
+
+    // Allocate temporary tensors
+    _input0.allocator()->allocate();
+    _input1.allocator()->allocate();
+    _batched_mm_output.allocator()->allocate();
+}
+
+Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    // TODO(COMPMID-1013): This part will be removed
+    // Get indeces for the width and height
+    const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+    const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+
+    // Kernel size
+    const unsigned int kernel_w = weights->tensor_shape()[idx_width];
+    const unsigned int kernel_h = weights->tensor_shape()[idx_height];
+
+    // Number of tiles along the X and Y direction
+    const unsigned int num_tiles_x = std::ceil((input->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f);
+    const unsigned int num_tiles_y = std::ceil((input->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f);
+
+    // Compute output shape
+    const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info);
+
+    // Validate input transform
+    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, Size2D(kernel_w, kernel_h));
+    const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)));
+
+    // Validate filter transform
+    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights);
+    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1));
+
+    // Configure batched matrix multiply
+    TensorShape batched_mm_output_shape = input0.tensor_shape();
+    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
+    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)));
+
+    // Configure output transform
+    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width],
+                                                                          output_convolved_shape[idx_height]),
+                                                                          Size2D(num_tiles_x, num_tiles_y)));
+
+    return Status{};
+}
+
+void CLWinogradConvolutionLayer::run()
+{
+    if(_is_first_run)
+    {
+        // Run filter transform
+        CLScheduler::get().enqueue(_filter_transform, false);
+
+        _is_first_run = false;
+    }
+
+    _memory_group.acquire();
+
+    // Run input transform
+    _input_transform.run();
+
+    // Run batched matrix multiplication
+    _batched_mm.run();
+
+    // Run output transform
+    CLScheduler::get().enqueue(_output_transform);
+
+    _memory_group.release();
+}