src/graph/mutators/NodeFusionMutator.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2021 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 "arm_compute/graph/mutators/NodeFusionMutator.h"

 #include "arm_compute/graph/GraphBuilder.h"
 #include "arm_compute/graph/Logger.h"
 #include "arm_compute/graph/Utils.h"
 #include "arm_compute/graph/backends/BackendRegistry.h"
 #include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
 #include "arm_compute/graph/nodes/Nodes.h"

 #include "support/Cast.h"

 #include <set>

 namespace arm_compute
 {
 namespace graph
 {
 namespace detail
 {
 void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
 {
     ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

     auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(output_edge->producer());
     auto *bn_node   = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());

     // Not fusing if number of groups is greater than 1
     if(conv_node->num_groups() > 1)
     {
         return;
     }

     ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << output_edge->producer_id()
                                   << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);

     // Prevent fusion if fused node has an output accessor
     if(conv_node->output(0)->accessor() == nullptr)
     {
         const Target assigned_target = conv_node->assigned_target();

         // Extract conv inputs
         const auto   conv_input_id   = conv_node->input_edge(0)->producer_id();
         const auto   conv_weights_id = conv_node->input_edge(1)->producer_id();
         const auto   conv_info       = conv_node->convolution_info();
         const auto   conv_method     = conv_node->convolution_method();
         const auto   num_groups      = conv_node->num_groups();
         const auto   act_info        = bn_node->fused_activation();
         FastMathHint fast_math_hint  = conv_node->fast_math_hint();

         // Extract bn inputs
         const auto bn_mean_id = bn_node->input_edge(1)->producer_id();
         const auto bn_var_id  = bn_node->input_edge(2)->producer_id();

         const auto epsilon = bn_node->epsilon();

         // Create the fused node
         const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info);

         if(conv_node->input_edge(2) != nullptr)
         {
             auto conv_bias_id = conv_node->input_edge(2)->producer_id();
             g.add_connection(conv_bias_id, 0, fused_id, 2);
         }

         // Add connections from the conv/batch_norm inputs to the fused node
         g.add_connection(conv_input_id, 0, fused_id, 0);
         g.add_connection(conv_weights_id, 0, fused_id, 1);
         g.add_connection(bn_mean_id, 0, fused_id, 3);
         g.add_connection(bn_var_id, 0, fused_id, 4);

         if(bn_node->input_edge(3) != nullptr)
         {
             const auto bn_beta_id = bn_node->input_edge(3)->producer_id();
             g.add_connection(bn_beta_id, 0, fused_id, 5);
         }

         if(bn_node->input_edge(4) != nullptr)
         {
             const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
             g.add_connection(bn_gamma_id, 0, fused_id, 6);
         }

         auto                     fused_node       = g.node(fused_id);
         std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);

         // Extract batch normalization node accessor if any
         auto bn_node_accessor = bn_node->output(0)->extract_accessor();
         auto bn_node_name     = bn_node->name();

         // Remove batch normalization node
         g.remove_node(bn_node->id());

         // Get driving nodes of batch normalization node
         for(auto &driving_node : bn_driving_nodes)
         {
             g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
             configure_tensor(fused_node->output(0));
         }
         // Update fused node outputs
         fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
         fused_node->set_assigned_target(assigned_target);
         fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + bn_node_name, assigned_target });

         // Remove convolution node
         g.remove_node(conv_node->id());
     }
     else
     {
         ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
     }
 }

 void fuse_depthwise_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
 {
     ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

     auto *depth_conv_node = arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode *>(output_edge->producer());
     auto *bn_node         = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());

     ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing depthwise convolution node with ID : " << output_edge->producer_id()
                                   << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);

     // Prevent fusion if fused node has an output accessor
     if(depth_conv_node->output(0)->accessor() == nullptr)
     {
         const Target assigned_target = depth_conv_node->assigned_target();

         // Extract conv inputs
         const auto depth_conv_input_id = depth_conv_node->input_edge(0)->producer_id();
         const auto conv_weights_id     = depth_conv_node->input_edge(1)->producer_id();
         const auto conv_info           = depth_conv_node->convolution_info();
         const auto depth_conv_method   = depth_conv_node->depthwise_convolution_method();
         const auto depth_multiplier    = depth_conv_node->depth_multiplier();
         const auto act_info            = bn_node->fused_activation();

         // Extract bn inputs
         const auto bn_mean_id  = bn_node->input_edge(1)->producer_id();
         const auto bn_var_id   = bn_node->input_edge(2)->producer_id();
         const auto bn_beta_id  = bn_node->input_edge(3)->producer_id();
         const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
         const auto epsilon     = bn_node->epsilon();

         // Create the fused node
         const NodeID fused_id = g.add_node<FusedDepthwiseConvolutionBatchNormalizationNode>(epsilon, conv_info, depth_multiplier, depth_conv_method, act_info);

         if(depth_conv_node->input_edge(2) != nullptr)
         {
             const auto conv_bias_id = depth_conv_node->input_edge(2)->producer_id();
             g.add_connection(conv_bias_id, 0, fused_id, 2);
         }

         // Add connections from the conv/batch_norm inputs to the fused node
         g.add_connection(depth_conv_input_id, 0, fused_id, 0);
         g.add_connection(conv_weights_id, 0, fused_id, 1);
         g.add_connection(bn_mean_id, 0, fused_id, 3);
         g.add_connection(bn_var_id, 0, fused_id, 4);
         g.add_connection(bn_beta_id, 0, fused_id, 5);
         g.add_connection(bn_gamma_id, 0, fused_id, 6);

         auto                     fused_node       = g.node(fused_id);
         std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);

         // Extract batch normalization node accessor if any
         auto bn_node_accessor = bn_node->output(0)->extract_accessor();
         auto bn_node_name     = bn_node->name();

         // Remove batch normalization node
         g.remove_node(bn_node->id());

         // Get driving nodes of batch normalization node
         for(auto &driving_node : bn_driving_nodes)
         {
             g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
             configure_tensor(fused_node->output(0));
         }
         // Update fused node outputs
         fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
         fused_node->set_assigned_target(assigned_target);
         fused_node->set_common_node_parameters(NodeParams{ depth_conv_node->name() + "+" + bn_node_name, assigned_target });

         // Remove convolution node
         g.remove_node(depth_conv_node->id());
     }
     else
     {
         ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of depthwise convolution with batch normalization due to the presence of an output accessor\n");
     }
 }

 template <typename N>
 void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set<Activation> &supported_fused_activations)
 {
     ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

     auto *n_node   = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->producer());
     auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(output_edge->consumer());

     ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);

     // Check if activation is supported for fusion
     if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
     {
         return;
     }

     // EltwiseLayerNode can only be fused when dataype is float
     if(n_node->type() == NodeType::EltwiseLayer && !is_data_type_float(n_node->output(0)->desc().data_type))
     {
         return;
     }

     ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
                                   << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);

     // Prevent fusion if fused node has an output accessor
     if(n_node->output(0)->accessor() == nullptr)
     {
         // Get driving nodes of activation node
         std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);

         // Set activation info to fused node
         n_node->set_fused_activation(act_node->activation_info());

         // Extract activation node accessor if any
         auto act_node_accessor = act_node->output(0)->extract_accessor();

         // Remove activation node
         g.remove_node(act_node->id());

         // Update fused node outputs
         for(auto &driving_node : act_driving_nodes)
         {
             g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
         }

         // Update accessor to fused node
         n_node->output(0)->set_accessor(std::move(act_node_accessor));
     }
     else
     {
         ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
     }
 }

 bool check_padding_info(const DataLayout &layout, const PaddingList &padding_list, PaddingInfo &pad_w, PaddingInfo &pad_h)
 {
     if(layout == DataLayout::NCHW || layout == DataLayout::NHWC)
     {
         const PaddingInfo zero_padding(0, 0);

         const unsigned int height_index = get_dimension_idx(layout, DataLayoutDimension::HEIGHT);
         const unsigned int width_index  = get_dimension_idx(layout, DataLayoutDimension::WIDTH);

         pad_w = width_index < padding_list.size() ? padding_list[width_index] : zero_padding;
         pad_h = height_index < padding_list.size() ? padding_list[height_index] : zero_padding;

         for(unsigned int i = 0; i < padding_list.size(); i++)
         {
             if(i != height_index && i != width_index && padding_list[i] != zero_padding)
             {
                 // if the index is not either height or width, don't fuse
                 return false;
             }
         }

         return true;
     }

     return false;
 }

 template <typename N>
 void fuse_pad_with_convolution(Graph &g, const Edge *output_edge)
 {
     auto *pad_node  = arm_compute::utils::cast::polymorphic_downcast<PadLayerNode *>(output_edge->producer());
     auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->consumer());

     const Edge *input_edge = pad_node->input_edge(0);
     if(input_edge != nullptr && input_edge->tensor() != nullptr && pad_node->output(0)->accessor() == nullptr
        && pad_node->pad_value().get<float>() == 0.0)
     {
         const DataLayout  layout       = input_edge->tensor()->desc().layout;
         const PaddingList padding_list = pad_node->padding();
         PaddingInfo       pad_w, pad_h;

         if(check_padding_info(layout, padding_list, pad_w, pad_h))
         {
             // Add paddings to the convolution node
             const PadStrideInfo conv_info = conv_node->convolution_info();
             const PadStrideInfo new_conv_info(
                 conv_info.stride().first,
                 conv_info.stride().second,
                 conv_info.pad_left() + pad_w.first,
                 conv_info.pad_right() + pad_w.second,
                 conv_info.pad_top() + pad_h.first,
                 conv_info.pad_bottom() + pad_h.second,
                 conv_info.round());
             conv_node->set_convolution_info(new_conv_info);

             // Update drivers of the convolution node
             std::vector<NodeIdxPair> pad_driver_nodes = get_driver_nodes(*pad_node);
             g.remove_node(pad_node->id());

             // Update fused node inputs
             for(auto &driver_node : pad_driver_nodes)
             {
                 g.add_connection(driver_node.node_id, driver_node.index, conv_node->id(), 0);
             }
         }
     }
 }

 template <typename N1, typename N2, typename F, typename... Args>
 void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
 {
     // Note that fused nodes may be added to the end of the node list.
     // Instead of only looping over the original list of nodes, we loop over the current node list which could be growing.
     // This is intentional as it probes the newly added fused nodes for further fusing opportunities.
     for(unsigned int i = 0; i < g.nodes().size(); ++i)
     {
         auto node = g.node(i);
         // Check if the node is of type N and not a branching node
         if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
         {
             const auto output_edge_id = *node->output_edges().begin();
             const auto output_edge    = g.edge(output_edge_id);

             // Check if following node is an activation layer node
             if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
             {
                 fuse_fcn(g, output_edge, optional_arguments...);
             }
         }
     }
 }
 } // namespace detail

 const char *NodeFusionMutator::name()
 {
     return "NodeFusionMutator";
 }

 IGraphMutator::MutationType NodeFusionMutator::type() const
 {
     return IGraphMutator::MutationType::Backend;
 }

 void NodeFusionMutator::mutate(Graph &g)
 {
     // Supported activations when fusing
     const std::set<Activation> supported_fused_activations = { Activation::ABS, Activation::BOUNDED_RELU, Activation::ELU,
                                                                Activation::HARD_SWISH, Activation::IDENTITY, Activation::LEAKY_RELU,
                                                                Activation::LINEAR, Activation::LOGISTIC, Activation::LU_BOUNDED_RELU,
                                                                Activation::RELU, Activation::SOFT_RELU, Activation::SQRT,
                                                                Activation::SQUARE, Activation::TANH
                                                              };

     // Preconditions
     auto empty_prec = [](INode &)
     {
         return true;
     };
     auto cl_target_prec = [](INode & n)
     {
         return n.assigned_target() == Target::CL;
     };
     auto qs8_prec = [&g](INode & n)
     {
         ARM_COMPUTE_ERROR_ON(n.output(0) == nullptr);

         const auto output_edge_id = *n.output_edges().begin();
         const auto output_edge    = g.edge(output_edge_id);
         // To perform fusion the two nodes must have same output quantization information
         const bool same_qinfo     = n.output(0)->desc().quant_info == output_edge->producer()->output(0)->desc().quant_info;
         const bool output_qasymm8 = n.output(0)->desc().data_type == DataType::QASYMM8;

         return (output_qasymm8 && same_qinfo) || !output_qasymm8;
     };

     // Fusion mutations
     detail::fuse_layer<PadLayerNode, ConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<ConvolutionLayerNode>);
     detail::fuse_layer<PadLayerNode, DepthwiseConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<DepthwiseConvolutionLayerNode>);
     detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
     detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
     detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
     detail::fuse_layer<FullyConnectedLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<FullyConnectedLayerNode>, supported_fused_activations);
     detail::fuse_layer<EltwiseLayerNode, ActivationLayerNode>(g, cl_target_prec, detail::fuse_node_with_activation<EltwiseLayerNode>, supported_fused_activations);
     detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization);
     detail::fuse_layer<DepthwiseConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization);
 }
 } // namespace graph
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2021 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 "arm_compute/graph/mutators/NodeFusionMutator.h"

	#include "arm_compute/graph/GraphBuilder.h"
	#include "arm_compute/graph/Logger.h"
	#include "arm_compute/graph/Utils.h"
	#include "arm_compute/graph/backends/BackendRegistry.h"
	#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
	#include "arm_compute/graph/nodes/Nodes.h"

	#include "support/Cast.h"

	#include <set>

	namespace arm_compute
	{
	namespace graph
	{
	namespace detail
	{
	void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
	{
	ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

	auto conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode >(output_edge->producer());
	auto bn_node = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode >(output_edge->consumer());

	// Not fusing if number of groups is greater than 1
	if(conv_node->num_groups() > 1)
	{
	return;
	}

	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << output_edge->producer_id()
	<< " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);

	// Prevent fusion if fused node has an output accessor
	if(conv_node->output(0)->accessor() == nullptr)
	{
	const Target assigned_target = conv_node->assigned_target();

	// Extract conv inputs
	const auto conv_input_id = conv_node->input_edge(0)->producer_id();
	const auto conv_weights_id = conv_node->input_edge(1)->producer_id();
	const auto conv_info = conv_node->convolution_info();
	const auto conv_method = conv_node->convolution_method();
	const auto num_groups = conv_node->num_groups();
	const auto act_info = bn_node->fused_activation();
	FastMathHint fast_math_hint = conv_node->fast_math_hint();

	// Extract bn inputs
	const auto bn_mean_id = bn_node->input_edge(1)->producer_id();
	const auto bn_var_id = bn_node->input_edge(2)->producer_id();

	const auto epsilon = bn_node->epsilon();

	// Create the fused node
	const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info);

	if(conv_node->input_edge(2) != nullptr)
	{
	auto conv_bias_id = conv_node->input_edge(2)->producer_id();
	g.add_connection(conv_bias_id, 0, fused_id, 2);
	}

	// Add connections from the conv/batch_norm inputs to the fused node
	g.add_connection(conv_input_id, 0, fused_id, 0);
	g.add_connection(conv_weights_id, 0, fused_id, 1);
	g.add_connection(bn_mean_id, 0, fused_id, 3);
	g.add_connection(bn_var_id, 0, fused_id, 4);

	if(bn_node->input_edge(3) != nullptr)
	{
	const auto bn_beta_id = bn_node->input_edge(3)->producer_id();
	g.add_connection(bn_beta_id, 0, fused_id, 5);
	}

	if(bn_node->input_edge(4) != nullptr)
	{
	const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
	g.add_connection(bn_gamma_id, 0, fused_id, 6);
	}

	auto fused_node = g.node(fused_id);
	std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);

	// Extract batch normalization node accessor if any
	auto bn_node_accessor = bn_node->output(0)->extract_accessor();
	auto bn_node_name = bn_node->name();

	// Remove batch normalization node
	g.remove_node(bn_node->id());

	// Get driving nodes of batch normalization node
	for(auto &driving_node : bn_driving_nodes)
	{
	g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
	configure_tensor(fused_node->output(0));
	}
	// Update fused node outputs
	fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
	fused_node->set_assigned_target(assigned_target);
	fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + bn_node_name, assigned_target });

	// Remove convolution node
	g.remove_node(conv_node->id());
	}
	else
	{
	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
	}
	}

	void fuse_depthwise_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
	{
	ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

	auto depth_conv_node = arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode >(output_edge->producer());
	auto bn_node = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode >(output_edge->consumer());

	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing depthwise convolution node with ID : " << output_edge->producer_id()
	<< " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);

	// Prevent fusion if fused node has an output accessor
	if(depth_conv_node->output(0)->accessor() == nullptr)
	{
	const Target assigned_target = depth_conv_node->assigned_target();

	// Extract conv inputs
	const auto depth_conv_input_id = depth_conv_node->input_edge(0)->producer_id();
	const auto conv_weights_id = depth_conv_node->input_edge(1)->producer_id();
	const auto conv_info = depth_conv_node->convolution_info();
	const auto depth_conv_method = depth_conv_node->depthwise_convolution_method();
	const auto depth_multiplier = depth_conv_node->depth_multiplier();
	const auto act_info = bn_node->fused_activation();

	// Extract bn inputs
	const auto bn_mean_id = bn_node->input_edge(1)->producer_id();
	const auto bn_var_id = bn_node->input_edge(2)->producer_id();
	const auto bn_beta_id = bn_node->input_edge(3)->producer_id();
	const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
	const auto epsilon = bn_node->epsilon();

	// Create the fused node
	const NodeID fused_id = g.add_node<FusedDepthwiseConvolutionBatchNormalizationNode>(epsilon, conv_info, depth_multiplier, depth_conv_method, act_info);

	if(depth_conv_node->input_edge(2) != nullptr)
	{
	const auto conv_bias_id = depth_conv_node->input_edge(2)->producer_id();
	g.add_connection(conv_bias_id, 0, fused_id, 2);
	}

	// Add connections from the conv/batch_norm inputs to the fused node
	g.add_connection(depth_conv_input_id, 0, fused_id, 0);
	g.add_connection(conv_weights_id, 0, fused_id, 1);
	g.add_connection(bn_mean_id, 0, fused_id, 3);
	g.add_connection(bn_var_id, 0, fused_id, 4);
	g.add_connection(bn_beta_id, 0, fused_id, 5);
	g.add_connection(bn_gamma_id, 0, fused_id, 6);

	auto fused_node = g.node(fused_id);
	std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);

	// Extract batch normalization node accessor if any
	auto bn_node_accessor = bn_node->output(0)->extract_accessor();
	auto bn_node_name = bn_node->name();

	// Remove batch normalization node
	g.remove_node(bn_node->id());

	// Get driving nodes of batch normalization node
	for(auto &driving_node : bn_driving_nodes)
	{
	g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
	configure_tensor(fused_node->output(0));
	}
	// Update fused node outputs
	fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
	fused_node->set_assigned_target(assigned_target);
	fused_node->set_common_node_parameters(NodeParams{ depth_conv_node->name() + "+" + bn_node_name, assigned_target });

	// Remove convolution node
	g.remove_node(depth_conv_node->id());
	}
	else
	{
	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of depthwise convolution with batch normalization due to the presence of an output accessor\n");
	}
	}

	template <typename N>
	void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set<Activation> &supported_fused_activations)
	{
	ARM_COMPUTE_ERROR_ON(output_edge == nullptr);

	auto n_node = arm_compute::utils::cast::polymorphic_downcast<N >(output_edge->producer());
	auto act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode >(output_edge->consumer());

	ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr \|\| n_node->output(0) == nullptr);

	// Check if activation is supported for fusion
	if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
	{
	return;
	}

	// EltwiseLayerNode can only be fused when dataype is float
	if(n_node->type() == NodeType::EltwiseLayer && !is_data_type_float(n_node->output(0)->desc().data_type))
	{
	return;
	}

	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
	<< " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);

	// Prevent fusion if fused node has an output accessor
	if(n_node->output(0)->accessor() == nullptr)
	{
	// Get driving nodes of activation node
	std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);

	// Set activation info to fused node
	n_node->set_fused_activation(act_node->activation_info());

	// Extract activation node accessor if any
	auto act_node_accessor = act_node->output(0)->extract_accessor();

	// Remove activation node
	g.remove_node(act_node->id());

	// Update fused node outputs
	for(auto &driving_node : act_driving_nodes)
	{
	g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
	}

	// Update accessor to fused node
	n_node->output(0)->set_accessor(std::move(act_node_accessor));
	}
	else
	{
	ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
	}
	}

	bool check_padding_info(const DataLayout &layout, const PaddingList &padding_list, PaddingInfo &pad_w, PaddingInfo &pad_h)
	{
	if(layout == DataLayout::NCHW \|\| layout == DataLayout::NHWC)
	{
	const PaddingInfo zero_padding(0, 0);

	const unsigned int height_index = get_dimension_idx(layout, DataLayoutDimension::HEIGHT);
	const unsigned int width_index = get_dimension_idx(layout, DataLayoutDimension::WIDTH);

	pad_w = width_index < padding_list.size() ? padding_list[width_index] : zero_padding;
	pad_h = height_index < padding_list.size() ? padding_list[height_index] : zero_padding;

	for(unsigned int i = 0; i < padding_list.size(); i++)
	{
	if(i != height_index && i != width_index && padding_list[i] != zero_padding)
	{
	// if the index is not either height or width, don't fuse
	return false;
	}
	}

	return true;
	}

	return false;
	}

	template <typename N>
	void fuse_pad_with_convolution(Graph &g, const Edge *output_edge)
	{
	auto pad_node = arm_compute::utils::cast::polymorphic_downcast<PadLayerNode >(output_edge->producer());
	auto conv_node = arm_compute::utils::cast::polymorphic_downcast<N >(output_edge->consumer());

	const Edge *input_edge = pad_node->input_edge(0);
	if(input_edge != nullptr && input_edge->tensor() != nullptr && pad_node->output(0)->accessor() == nullptr
	&& pad_node->pad_value().get<float>() == 0.0)
	{
	const DataLayout layout = input_edge->tensor()->desc().layout;
	const PaddingList padding_list = pad_node->padding();
	PaddingInfo pad_w, pad_h;

	if(check_padding_info(layout, padding_list, pad_w, pad_h))
	{
	// Add paddings to the convolution node
	const PadStrideInfo conv_info = conv_node->convolution_info();
	const PadStrideInfo new_conv_info(
	conv_info.stride().first,
	conv_info.stride().second,
	conv_info.pad_left() + pad_w.first,
	conv_info.pad_right() + pad_w.second,
	conv_info.pad_top() + pad_h.first,
	conv_info.pad_bottom() + pad_h.second,
	conv_info.round());
	conv_node->set_convolution_info(new_conv_info);

	// Update drivers of the convolution node
	std::vector<NodeIdxPair> pad_driver_nodes = get_driver_nodes(*pad_node);
	g.remove_node(pad_node->id());

	// Update fused node inputs
	for(auto &driver_node : pad_driver_nodes)
	{
	g.add_connection(driver_node.node_id, driver_node.index, conv_node->id(), 0);
	}
	}
	}
	}

	template <typename N1, typename N2, typename F, typename... Args>
	void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
	{
	// Note that fused nodes may be added to the end of the node list.
	// Instead of only looping over the original list of nodes, we loop over the current node list which could be growing.
	// This is intentional as it probes the newly added fused nodes for further fusing opportunities.
	for(unsigned int i = 0; i < g.nodes().size(); ++i)
	{
	auto node = g.node(i);
	// Check if the node is of type N and not a branching node
	if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
	{
	const auto output_edge_id = *node->output_edges().begin();
	const auto output_edge = g.edge(output_edge_id);

	// Check if following node is an activation layer node
	if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
	{
	fuse_fcn(g, output_edge, optional_arguments...);
	}
	}
	}
	}
	} // namespace detail

	const char *NodeFusionMutator::name()
	{
	return "NodeFusionMutator";
	}

	IGraphMutator::MutationType NodeFusionMutator::type() const
	{
	return IGraphMutator::MutationType::Backend;
	}

	void NodeFusionMutator::mutate(Graph &g)
	{
	// Supported activations when fusing
	const std::set<Activation> supported_fused_activations = { Activation::ABS, Activation::BOUNDED_RELU, Activation::ELU,
	Activation::HARD_SWISH, Activation::IDENTITY, Activation::LEAKY_RELU,
	Activation::LINEAR, Activation::LOGISTIC, Activation::LU_BOUNDED_RELU,
	Activation::RELU, Activation::SOFT_RELU, Activation::SQRT,
	Activation::SQUARE, Activation::TANH
	};

	// Preconditions
	auto empty_prec = [](INode &)
	{
	return true;
	};
	auto cl_target_prec = [](INode & n)
	{
	return n.assigned_target() == Target::CL;
	};
	auto qs8_prec = [&g](INode & n)
	{
	ARM_COMPUTE_ERROR_ON(n.output(0) == nullptr);

	const auto output_edge_id = *n.output_edges().begin();
	const auto output_edge = g.edge(output_edge_id);
	// To perform fusion the two nodes must have same output quantization information
	const bool same_qinfo = n.output(0)->desc().quant_info == output_edge->producer()->output(0)->desc().quant_info;
	const bool output_qasymm8 = n.output(0)->desc().data_type == DataType::QASYMM8;

	return (output_qasymm8 && same_qinfo) \|\| !output_qasymm8;
	};

	// Fusion mutations
	detail::fuse_layer<PadLayerNode, ConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<ConvolutionLayerNode>);
	detail::fuse_layer<PadLayerNode, DepthwiseConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<DepthwiseConvolutionLayerNode>);
	detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
	detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
	detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
	detail::fuse_layer<FullyConnectedLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<FullyConnectedLayerNode>, supported_fused_activations);
	detail::fuse_layer<EltwiseLayerNode, ActivationLayerNode>(g, cl_target_prec, detail::fuse_node_with_activation<EltwiseLayerNode>, supported_fused_activations);
	detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization);
	detail::fuse_layer<DepthwiseConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization);
	}
	} // namespace graph
	} // namespace arm_compute