| /* |
| * 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/FusedConvolutionBatchNormalizationWithPostOpsNode.h" |
| #include "arm_compute/graph/nodes/FusedConvolutionWithPostOpNode.h" |
| #include "arm_compute/graph/nodes/Nodes.h" |
| |
| #include "src/graph/mutators/MutatorUtils.h" |
| |
| #include "support/Cast.h" |
| |
| #include <list> |
| #include <set> |
| |
| namespace arm_compute |
| { |
| namespace graph |
| { |
| namespace detail |
| { |
| void transfer_driving_nodes_and_remove_old_node(Graph &g, INode *new_node, INode *old_node, bool add_output_tensor) |
| { |
| if(new_node == nullptr || old_node == nullptr) |
| { |
| return; |
| } |
| |
| // Get driving nodes of last fusable node |
| std::vector<NodeIdxPair> last_driving_nodes = get_driving_nodes(*old_node); |
| |
| // Extract last fusable node accessor if any |
| if(old_node->output(0) == nullptr) |
| { |
| return; |
| } |
| auto old_node_accessor = old_node->output(0)->extract_accessor(); |
| |
| // Remove node |
| g.remove_node(old_node->id()); |
| |
| // Update fused node outputs |
| for(auto &driving_node : last_driving_nodes) |
| { |
| g.add_connection(new_node->id(), 0, driving_node.node_id, driving_node.index); |
| if(add_output_tensor) |
| { |
| configure_tensor(new_node->output(0)); |
| } |
| } |
| |
| // Update accessor to fused node |
| new_node->output(0)->set_accessor(std::move(old_node_accessor)); |
| } |
| |
| 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); |
| auto bn_node_name = bn_node->name(); |
| |
| transfer_driving_nodes_and_remove_old_node(g, fused_node, bn_node, true); |
| |
| 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); |
| auto bn_node_name = bn_node->name(); |
| |
| transfer_driving_nodes_and_remove_old_node(g, fused_node, bn_node, true); |
| |
| 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) |
| { |
| // Set activation info to fused node |
| n_node->set_fused_activation(act_node->activation_info()); |
| |
| transfer_driving_nodes_and_remove_old_node(g, n_node, act_node, false); |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n"); |
| } |
| } |
| |
| 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(); |
| |
| const unsigned int height_index = get_dimension_idx(layout, DataLayoutDimension::HEIGHT); |
| const unsigned int width_index = get_dimension_idx(layout, DataLayoutDimension::WIDTH); |
| |
| const PaddingInfo pad_w = width_index < padding_list.size() ? padding_list[width_index] : PaddingInfo(0, 0); |
| const PaddingInfo pad_h = height_index < padding_list.size() ? padding_list[height_index] : PaddingInfo(0, 0); |
| |
| if(is_padding_in_height_or_width(layout, padding_list)) |
| { |
| // 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 N1 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 a type N2 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...); |
| } |
| } |
| } |
| } |
| |
| /** Check valid combinations: |
| * |
| * | Main operator | Post operators | |
| * |:--------------|:---------------------------| |
| * |conv | add | |
| * |conv | act + add | |
| * |conv | add + act | |
| * |conv | act + add + act | |
| * |
| */ |
| #define MAX_VALIDE_COMBINATION 4 |
| #define MAX_POST_OP_NUM 3 |
| NodeType valide_post_op_type[MAX_VALIDE_COMBINATION][MAX_POST_OP_NUM] = { { EltwiseLayerNode::node_type }, |
| { EltwiseLayerNode::node_type, ActivationLayerNode::node_type }, |
| { ActivationLayerNode::node_type, EltwiseLayerNode::node_type }, |
| { ActivationLayerNode::node_type, EltwiseLayerNode::node_type, ActivationLayerNode::node_type } |
| }; |
| |
| bool check_post_op_type(NodeType *post_op_type, int len) |
| { |
| if(len > MAX_POST_OP_NUM || len <= 0) |
| { |
| return false; |
| } |
| |
| bool found = false; |
| for(int i = 0; i < MAX_VALIDE_COMBINATION; ++i) |
| { |
| for(int j = 0; j < len; ++j) |
| { |
| if(post_op_type[j] != valide_post_op_type[i][j]) |
| { |
| found = false; |
| break; |
| } |
| found = true; |
| } |
| if(found) |
| break; |
| } |
| |
| return found; |
| } |
| |
| void fuse_convolution_with_post_op(Graph &g, INode *fused_node, std::list<INode *> post_op_node_list, int prev_op_dst_pos) |
| { |
| unsigned int op_idx = 0; |
| // Fuse post operators with conv |
| for(const auto &post_op : post_op_node_list) |
| { |
| switch(post_op->type()) |
| { |
| case EltwiseLayerNode::node_type: |
| { |
| auto *eltwise_node = arm_compute::utils::cast::polymorphic_downcast<EltwiseLayerNode *>(post_op); |
| ARM_COMPUTE_ERROR_ON(eltwise_node->output(0) == nullptr); |
| |
| fused_node->post_op_info_list().push_back(std::make_unique<ConvPostOpInfoEltwiseAdd>(prev_op_dst_pos, eltwise_node->convert_policy())); |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE(" with Elementwise Layer node with ID : " << post_op->id()); |
| break; |
| } |
| case ActivationLayerNode::node_type: |
| { |
| auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(post_op); |
| ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr); |
| |
| fused_node->post_op_info_list().push_back(std::make_unique<ConvPostOpInfoActivation>(act_node->activation_info())); |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE(" with Activation Layer node with ID : " << post_op->id()); |
| break; |
| } |
| default: |
| { |
| break; |
| } |
| } |
| |
| if(op_idx == post_op_node_list.size() - 1) // last fusable node |
| { |
| transfer_driving_nodes_and_remove_old_node(g, fused_node, post_op, true); |
| } |
| else |
| { |
| // Remove node |
| g.remove_node(post_op->id()); |
| } |
| op_idx++; |
| } |
| } |
| |
| std::list<INode *> get_post_op_list(Graph &g, int &eltwise_operand_id, int &prev_op_dst_pos, unsigned int conv_node_id, const std::set<Activation> &supported_fused_activations) |
| { |
| std::list<INode *> post_op_node_list = {}; |
| NodeID prev_op_dst_id = conv_node_id; |
| NodeType post_op_type_list[3] = { NodeType::Dummy, NodeType::Dummy, NodeType::Dummy }; |
| int post_op_idx = 0; |
| |
| // Get list of the connected nodes |
| auto current_node = g.node(conv_node_id); |
| |
| while(post_op_node_list.size() < 3) |
| { |
| // This convolution node must have only one output edge, otherwise this function would not have been called |
| |
| auto current_output_edge_id = current_node->output_edges().begin(); |
| auto current_output_edge = g.edge(*current_output_edge_id); |
| auto post_op_node = current_output_edge->consumer(); |
| |
| bool fusable_post_op = false; |
| if(post_op_node != nullptr && post_op_node->output_edges().size() > 0) |
| { |
| switch(post_op_node->type()) |
| { |
| case EltwiseLayerNode::node_type: |
| { |
| auto *eltwise_node = arm_compute::utils::cast::polymorphic_downcast<EltwiseLayerNode *>(post_op_node); |
| ARM_COMPUTE_ERROR_ON(eltwise_node->output(0) == nullptr); |
| if(eltwise_node->output(0)->accessor() == nullptr) |
| { |
| post_op_node_list.push_back(post_op_node); |
| fusable_post_op = true; |
| post_op_type_list[post_op_idx++] = eltwise_node->type(); |
| |
| // Extract elementwise inputs |
| const auto eltwise_input_id_0 = eltwise_node->input_edge(0)->producer_id(); |
| const auto eltwise_input_id_1 = eltwise_node->input_edge(1)->producer_id(); |
| if(eltwise_input_id_0 == prev_op_dst_id) |
| { |
| eltwise_operand_id = eltwise_input_id_1; |
| prev_op_dst_pos = 0; |
| } |
| else if(eltwise_input_id_1 == prev_op_dst_id) |
| { |
| eltwise_operand_id = eltwise_input_id_0; |
| prev_op_dst_pos = 1; |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with elementwise due to the presence of an output accessor\n"); |
| } |
| break; |
| } |
| case ActivationLayerNode::node_type: |
| { |
| auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(post_op_node); |
| ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr); |
| // Check if activation is supported for fusion |
| if(supported_fused_activations.count(act_node->activation_info().activation()) == 0) |
| { |
| break; |
| } |
| if(act_node->output(0)->accessor() == nullptr) |
| { |
| post_op_node_list.push_back(post_op_node); |
| fusable_post_op = true; |
| post_op_type_list[post_op_idx++] = act_node->type(); |
| prev_op_dst_id = act_node->id(); |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to the presence of an output accessor\n"); |
| } |
| break; |
| } |
| default: |
| { |
| break; |
| } |
| } |
| |
| // Check if the node is not a branching node and current node is fusable |
| if(post_op_node->output_edges().size() == 1 && fusable_post_op == true) |
| { |
| current_node = post_op_node; |
| } |
| else |
| { |
| break; |
| } |
| } |
| } |
| |
| // Check whether it's valid post op list |
| if(post_op_node_list.size() > 0) |
| { |
| bool fuse_with_post_op = check_post_op_type(post_op_type_list, post_op_node_list.size()); |
| if(!fuse_with_post_op) |
| { |
| post_op_node_list.clear(); |
| } |
| } |
| |
| return post_op_node_list; |
| } |
| |
| /** Fuse below operators: |
| * |
| * | Main operator | Post operators | |
| * |:--------------|:---------------------------| |
| * |conv | add | |
| * |conv | act + add | |
| * |conv | add + act | |
| * |conv | act + add + act | |
| * |
| * Notes: currently, only GEMM supports fusion with post operator |
| */ |
| void fuse_convolution_with_post_ops(Graph &g, const Edge *output_edge, unsigned int conv_node_id, const std::set<Activation> &supported_fused_activations) |
| { |
| ARM_COMPUTE_ERROR_ON(output_edge == nullptr); |
| |
| auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(output_edge->producer()); |
| ARM_COMPUTE_ERROR_ON(conv_node->output(0) == nullptr); |
| |
| const ConvolutionMethod conv_algorithm = conv_node->convolution_method(); |
| if(conv_algorithm != ConvolutionMethod::GEMM) |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to non GEMM convolution\n"); |
| return; |
| } |
| |
| // Prevent fusion if fused node has an output accessor |
| if(conv_node->output(0)->accessor() == nullptr) |
| { |
| // If data type is FP32/FP16, data layout is NHWC, and filter size is 1x1, fuse convolution with post op, as Conv1x1 always leads to GEMM. |
| const Edge *input_edge = conv_node->input_edge(1); |
| if(input_edge != nullptr && input_edge->tensor() != nullptr) |
| { |
| const DataLayout data_layout = input_edge->tensor()->desc().layout; |
| const DataType data_type = input_edge->tensor()->desc().data_type; |
| const TensorShape tensor_shape = input_edge->tensor()->desc().shape; |
| if((data_layout != DataLayout::NHWC) || (is_data_type_float(data_type) == false) || (tensor_shape.y() != 1) || (tensor_shape.z() != 1)) |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to non GEMM convolution\n"); |
| return; |
| } |
| } |
| else |
| { |
| return; |
| } |
| |
| // Get post op list |
| int eltwise_operand_id = 0; |
| int prev_op_dst_pos = 0; // Previous operator dst's postion in current operator |
| std::list<INode *> post_op_node_list = get_post_op_list(g, eltwise_operand_id, prev_op_dst_pos, conv_node_id, supported_fused_activations); |
| |
| if(post_op_node_list.size() == 0) |
| { |
| return; |
| } |
| else // Do convolution fusion with post op if there're one(elementwise), two or more operators |
| { |
| 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(); |
| FastMathHint fast_math_hint = conv_node->fast_math_hint(); |
| |
| // Create the fused node |
| const NodeID fused_id = g.add_node<FusedConvolutionWithPostOpNode>(conv_info, num_groups, conv_method, fast_math_hint); |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << conv_node->id()); |
| |
| // Add connections from the conv 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); |
| 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); |
| } |
| // Adding the Element wise operand in case the post op is element wise operation |
| auto it = std::find_if(post_op_node_list.begin(), |
| post_op_node_list.end(), |
| [&](const INode * nd) |
| { |
| return (nd->type() == graph::NodeType::EltwiseLayer); |
| }); |
| |
| if(it != post_op_node_list.end()) |
| { |
| g.add_connection(eltwise_operand_id, 0, fused_id, 3); |
| } |
| g.remove_node(conv_node->id()); |
| |
| // Update fused node outputs |
| auto fused_node = g.node(fused_id); |
| fused_node->set_assigned_target(assigned_target); |
| |
| // Fuse convolution with post op |
| fuse_convolution_with_post_op(g, fused_node, post_op_node_list, prev_op_dst_pos); |
| |
| post_op_node_list.clear(); |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE(std::endl); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to the presence of an output accessor\n"); |
| } |
| } |
| |
| void fuse_convolution_batch_normalization_with_post_ops(Graph &g, const Edge *output_edge, unsigned int conv_node_id, const std::set<Activation> &supported_fused_activations) |
| { |
| ARM_COMPUTE_ERROR_ON(output_edge == nullptr); |
| |
| auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(output_edge->producer()); |
| ARM_COMPUTE_ERROR_ON(conv_node->output(0) == nullptr); |
| const ConvolutionMethod conv_algorithm = conv_node->convolution_method(); |
| if(conv_algorithm != ConvolutionMethod::GEMM) |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to non GEMM convolution\n"); |
| return; |
| } |
| |
| // Prevent fusion if fused node has an output accessor |
| if(conv_node->output(0)->accessor() == nullptr) |
| { |
| // If data type is FP32/FP16, data layout is NHWC, and filter size is 1x1, fuse convolution with post op, as Conv1x1 always leads to GEMM. |
| const Edge *input_edge = conv_node->input_edge(1); |
| if(input_edge != nullptr && input_edge->tensor() != nullptr) |
| { |
| const DataLayout data_layout = input_edge->tensor()->desc().layout; |
| const DataType data_type = input_edge->tensor()->desc().data_type; |
| const TensorShape tensor_shape = input_edge->tensor()->desc().shape; |
| if((data_layout != DataLayout::NHWC) || (is_data_type_float(data_type) == false) || (tensor_shape.y() != 1) || (tensor_shape.z() != 1)) |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to non GEMM convolution\n"); |
| return; |
| } |
| } |
| else |
| { |
| return; |
| } |
| |
| // Get post op list |
| int eltwise_operand_id = 0; |
| int prev_op_dst_pos = 0; // Previous operator dst's postion in current operator |
| std::list<INode *> post_op_node_list = get_post_op_list(g, eltwise_operand_id, prev_op_dst_pos, conv_node_id, supported_fused_activations); |
| |
| if(post_op_node_list.size() == 0) |
| { |
| return; |
| } |
| else // Do convolution fusion with post op if there're one(elementwise), two or more operators |
| { |
| 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 bn_mean_id = conv_node->input_edge(3)->producer_id(); |
| const auto bn_var_id = conv_node->input_edge(4)->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(); |
| FastMathHint fast_math_hint = conv_node->fast_math_hint(); |
| |
| // Create the fused node |
| |
| const float epsilon = conv_node->epsilon(); |
| const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationWithPostOpsNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint); |
| |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing FusedConvolutionBatchNormalization node with ID : " << conv_node->id()); |
| |
| // Add connections from the conv 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); |
| |
| 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); |
| } |
| g.add_connection(bn_mean_id, 0, fused_id, 3); |
| g.add_connection(bn_var_id, 0, fused_id, 4); |
| |
| // Move connections of old FusedConvolutionBatchNormalization to the fused node |
| if(conv_node->input_edge(5) != nullptr) |
| { |
| const auto bn_beta_id = conv_node->input_edge(5)->producer_id(); |
| g.add_connection(bn_beta_id, 0, fused_id, 5); |
| } |
| |
| if(conv_node->input_edge(6) != nullptr) |
| { |
| const auto bn_gamma_id = conv_node->input_edge(6)->producer_id(); |
| g.add_connection(bn_gamma_id, 0, fused_id, 6); |
| } |
| |
| // Adding the Element wise operand in case the post op is element wise operation |
| auto it = std::find_if(post_op_node_list.begin(), |
| post_op_node_list.end(), |
| [&](const INode * nd) |
| { |
| return (nd->type() == graph::NodeType::EltwiseLayer); |
| }); |
| |
| if(it != post_op_node_list.end()) |
| { |
| g.add_connection(eltwise_operand_id, 0, fused_id, 7); |
| } |
| |
| // Update fused node outputs |
| auto fused_node = g.node(fused_id); |
| fused_node->set_assigned_target(assigned_target); |
| |
| auto conv_node_name = conv_node->name(); |
| |
| // collect the post ops names |
| std::string post_ops_name = ""; |
| for(auto &post_op : post_op_node_list) |
| { |
| post_ops_name += post_op->name(); |
| } |
| fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + post_ops_name, assigned_target }); |
| |
| // Fuse convolution with post op |
| fuse_convolution_with_post_op(g, fused_node, post_op_node_list, prev_op_dst_pos); |
| |
| post_op_node_list.clear(); |
| g.remove_node(conv_node->id()); |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE(std::endl); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution node with post ops due to the presence of an output accessor\n"); |
| } |
| } |
| |
| template <typename N1, 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 N1 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 it's the correct target |
| if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && prec(*output_edge->producer())) |
| { |
| fuse_fcn(g, output_edge, i, 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>); |
| // The fusion of PostOps to ConvolutionLayer: |
| // It must occur after the fusion of PadLayer into ConvolutionLayer |
| // It must occur before the fusion of normal ActivationLayer into ConvolutionLayer as it takes precedence |
| detail::fuse_layer<ConvolutionLayerNode>(g, cl_target_prec, detail::fuse_convolution_with_post_ops, supported_fused_activations); |
| 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); |
| // The fusion of BatchNormalizationLayer must occur after the fusion of ActivationLayer. Because FusedConvolutionBatchNormalizationNode assumes the BatchNormalization is already fused with activation, if any |
| 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); |
| detail::fuse_layer<FusedConvolutionBatchNormalizationNode>(g, cl_target_prec, detail::fuse_convolution_batch_normalization_with_post_ops, supported_fused_activations); |
| } |
| } // namespace graph |
| } // namespace arm_compute |
