COMPMID-797: Switch to new graph.

- Cleaned up build system

Change-Id: If2faa27ee5b31fa8b972836960ab3ef671059c8d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126435
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 507 insertions, 0 deletions

diff --git a/src/graph/backends/GLES/GCFunctionsFactory.cpp b/src/graph/backends/GLES/GCFunctionsFactory.cpp
new file mode 100644
index 0000000000..12e7c042d4
--- /dev/null
+++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp
@@ -0,0 +1,507 @@
+/*
+ * 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/graph/backends/GLES/GCFunctionFactory.h"
+
+#include "arm_compute/core/utils/misc/Cast.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphContext.h"
+#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/TypePrinter.h"
+#include "arm_compute/graph/Types.h"
+#include "arm_compute/graph/backends/Utils.h"
+#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/runtime/GLES_COMPUTE/GCFunctions.h"
+
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute::utils::cast;
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace backends
+{
+namespace
+{
+/** Returns backing tensor of a given tensor
+ *
+ * @param[in] tensor Tensor to extract the backing tensor from
+ *
+ * @return Backing tensor if present else nullptr
+ */
+arm_compute::IGCTensor *get_backing_tensor(arm_compute::graph::Tensor *tensor)
+{
+    arm_compute::IGCTensor *backing_tensor = nullptr;
+    if(tensor != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON(tensor->desc().target != arm_compute::graph::Target::GC);
+        // Get backing tensor handle
+        ITensorHandle *tensor_handle = tensor->handle();
+        // Get backing tensor
+        backing_tensor = (tensor_handle != nullptr) ? polymorphic_cast<IGCTensor *>(&tensor_handle->tensor()) : nullptr;
+    }
+
+    return backing_tensor;
+}
+
+/** Create a backend activation layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend activation layer function
+ */
+std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC ActivationLayerNode node with ID : " << node.id() << " and Name: " << node.name()
+        << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor                *input    = get_backing_tensor(node.input(0));
+    IGCTensor                *output   = get_backing_tensor(node.output(0));
+    const ActivationLayerInfo act_info = node.activation_info();
+
+    // Create function
+    auto func = support::cpp14::make_unique<GCActivationLayer>();
+    func->configure(input, output, act_info);
+
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCActivationLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Shape: " << input->info()->tensor_shape()
+                               << " Activation function: " << act_info.activation()
+                               << " a: " << act_info.a()
+                               << " b: " << act_info.b()
+                               << " InPlace : " << is_in_place_operation(input, output)
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend batch normalization layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend batch normalization layer function
+ */
+std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+
+    // TODO (geopin01) : Var and mean are compulsory, switch function to accept nullptr as beta and/or gamma
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor                *input     = get_backing_tensor(node.input(0));
+    IGCTensor                *mean      = get_backing_tensor(node.input(1));
+    IGCTensor                *var       = get_backing_tensor(node.input(2));
+    IGCTensor                *beta      = get_backing_tensor(node.input(3));
+    IGCTensor                *gamma     = get_backing_tensor(node.input(4));
+    IGCTensor                *output    = get_backing_tensor(node.output(0));
+    const float               epsilon   = node.epsilon();
+    const ActivationLayerInfo fused_act = node.fused_activation();
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCBatchNormalizationLayer>();
+    func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCBatchNormalizationLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Shape: " << input->info()->tensor_shape()
+                               << " Epsilon: " << epsilon << " "
+                               << (fused_act.enabled() ? to_string(fused_act.activation()) : "")
+                               << " InPlace : " << is_in_place_operation(input, output)
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend convolution layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend convolution layer function
+ */
+std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor              *input          = get_backing_tensor(node.input(0));
+    IGCTensor              *weights        = get_backing_tensor(node.input(1));
+    IGCTensor              *biases         = get_backing_tensor(node.input(2));
+    IGCTensor              *output         = get_backing_tensor(node.output(0));
+    const PadStrideInfo     conv_info      = node.convolution_info();
+    const ConvolutionMethod conv_algorithm = node.convolution_method();
+
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::GC);
+    std::unique_ptr<IFunction>      func;
+    std::string                     func_name;
+
+    if(conv_algorithm == ConvolutionMethod::DIRECT)
+    {
+        std::tie(func, func_name) = create_named_function<GCDirectConvolutionLayer>(
+                                        std::string("GCDirectConvolutionLayer"), input, weights, biases, output, conv_info);
+    }
+    else
+    {
+        std::tie(func, func_name) = create_named_memory_managed_function<GCConvolutionLayer>(std::string("GCConvolutionLayer"), mm,
+                                                                                             input, weights, biases, output, conv_info);
+    }
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Weights shape: " << weights->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << std::endl);
+    return func;
+}
+
+/** Create a backend layer depth concatenate function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend depth concatenate layer function
+ */
+std::unique_ptr<arm_compute::IFunction> create_depth_concatenate_layer(DepthConcatenateLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC DepthConcatenate node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Return nullptr if depth concatenate is switched off
+    if(!node.is_enabled())
+    {
+        return nullptr;
+    }
+
+    // Extract IO and info
+    std::vector<arm_compute::IGCTensor *> inputs;
+    for(unsigned int i = 0; i < node.num_inputs(); ++i)
+    {
+        inputs.push_back(get_backing_tensor(node.input(i)));
+    }
+    IGCTensor *output = get_backing_tensor(node.output(0));
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCDepthConcatenateLayer>();
+    func->configure(inputs, output);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCDepthConcatenateLayer"
+                               << " Data Type: " << output->info()->data_type()
+                               << " Shape: " << output->info()->tensor_shape()
+                               << " Num Inputs: " << inputs.size()
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend layer depth-wise convolution function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend depth-wise convolution layer function
+ */
+std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name()
+        << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor                       *input         = get_backing_tensor(node.input(0));
+    IGCTensor                       *weights       = get_backing_tensor(node.input(1));
+    IGCTensor                       *biases        = get_backing_tensor(node.input(2));
+    IGCTensor                       *output        = get_backing_tensor(node.output(0));
+    const PadStrideInfo              conv_info     = node.convolution_info();
+    const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
+
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::unique_ptr<IFunction> func;
+    std::string                func_name;
+    if(dwc_algorithm == DepthwiseConvolutionMethod::OPTIMIZED_3x3)
+    {
+        std::tie(func, func_name) = create_named_function<GCDepthwiseConvolutionLayer3x3>(
+                                        std::string("GCDepthwiseConvolutionLayer3x3"), input, weights, biases, output, conv_info);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Generic DepthwiseConvolutionLayer is not supported in GLES backend");
+    }
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Weights shape: " << weights->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << std::endl);
+    return func;
+}
+
+/** Create a backend element-wise operation layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend element-wise operation layer function
+ */
+std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 2);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor             *input1     = get_backing_tensor(node.input(0));
+    IGCTensor             *input2     = get_backing_tensor(node.input(1));
+    IGCTensor             *output     = get_backing_tensor(node.output(0));
+    const EltwiseOperation eltwise_op = node.eltwise_operation();
+    ARM_COMPUTE_ERROR_ON(input1 == nullptr);
+    ARM_COMPUTE_ERROR_ON(input2 == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    std::unique_ptr<IFunction> func = nullptr;
+    std::string                func_name;
+    if(eltwise_op == EltwiseOperation::ADD)
+    {
+        std::tie(func, func_name) = create_named_function<GCArithmeticAddition>(std::string("GCArithmeticAddition"),
+                                                                                input1, input2, output,
+                                                                                ConvertPolicy::SATURATE);
+    }
+    else if(eltwise_op == EltwiseOperation::SUB)
+    {
+        ARM_COMPUTE_ERROR("Arithmetic subtraction is not supported in GLES backend");
+    }
+    else if(eltwise_op == EltwiseOperation::MUL)
+    {
+        std::tie(func, func_name) = create_named_function<GCPixelWiseMultiplication>(
+                                        std::string("GCPixelWiseMultiplication"), input1, input2, output, 1.f);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Unsupported element-wise operation!");
+    }
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+                               << " Data Type: " << input1->info()->data_type()
+                               << " Shape : " << input1->info()->tensor_shape()
+                               << std::endl);
+
+    return func;
+}
+
+/** Create a backend fully connected layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend fully connected layer function
+ */
+std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC FullyConnectedLayer node with ID : " << node.id() << " and Name: " << node.name()
+        << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor *input   = get_backing_tensor(node.input(0));
+    IGCTensor *weights = get_backing_tensor(node.input(1));
+    IGCTensor *biases  = get_backing_tensor(node.input(2));
+    IGCTensor *output  = get_backing_tensor(node.output(0));
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCFullyConnectedLayer>(get_memory_manager(ctx, Target::GC));
+    func->configure(input, weights, biases, output);
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(weights == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCFullyConnectedLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Weights shape: " << weights->info()->tensor_shape()
+                               << " Biases Shape: " << biases->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend normalization layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend normalization layer function
+ */
+std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC NormalizationLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor                   *input     = get_backing_tensor(node.input(0));
+    IGCTensor                   *output    = get_backing_tensor(node.output(0));
+    const NormalizationLayerInfo norm_info = node.normalization_info();
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCNormalizationLayer>();
+    func->configure(input, output, norm_info);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCNormalizationLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << " Normalization info: " << norm_info.type()
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend pooling layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend pooling layer function
+ */
+std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC PoolingLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor             *input     = get_backing_tensor(node.input(0));
+    IGCTensor             *output    = get_backing_tensor(node.output(0));
+    const PoolingLayerInfo pool_info = node.pooling_info();
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCPoolingLayer>();
+    func->configure(input, output, pool_info);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCPoolingLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << " Pooling info: " << pool_info.pool_type()
+                               << std::endl);
+
+    return std::move(func);
+}
+
+/** Create a backend softmax layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend softmax layer function
+ */
+std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating GC SoftmaxLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
+    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
+
+    // Extract IO and info
+    IGCTensor *input  = get_backing_tensor(node.input(0));
+    IGCTensor *output = get_backing_tensor(node.output(0));
+    const float beta   = node.beta();
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<GCSoftmaxLayer>(get_memory_manager(ctx, Target::CL));
+    func->configure(input, output, beta);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCSoftmaxLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << std::endl);
+
+    return std::move(func);
+}
+} // namespace
+
+std::unique_ptr<IFunction> GCFunctionFactory::create(INode *node, GraphContext &ctx)
+{
+    if(node == nullptr)
+    {
+        return nullptr;
+    }
+
+    NodeType type = node->type();
+    switch(type)
+    {
+        case NodeType::ActivationLayer:
+            return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
+        case NodeType::BatchNormalizationLayer:
+            return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+        case NodeType::ConvolutionLayer:
+            return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+        case NodeType::DepthConcatenateLayer:
+            return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
+        case NodeType::DepthwiseConvolutionLayer:
+            return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+        case NodeType::EltwiseLayer:
+            return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node));
+        case NodeType::FullyConnectedLayer:
+            return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+        case NodeType::NormalizationLayer:
+            return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node));
+        case NodeType::PoolingLayer:
+            return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
+        case NodeType::SoftmaxLayer:
+            return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+        default:
+            return nullptr;
+    }
+}
+} // namespace backends
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file