diff options
Diffstat (limited to 'src/graph/mutators/NodeFusionMutator.cpp')
| -rw-r--r-- | src/graph/mutators/NodeFusionMutator.cpp | 87 |
1 files changed, 85 insertions, 2 deletions
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp index 427d7b5095..83177a8431 100644 --- a/src/graph/mutators/NodeFusionMutator.cpp +++ b/src/graph/mutators/NodeFusionMutator.cpp @@ -64,7 +64,6 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge // 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 out_quant_info = conv_node->output(0)->desc().quant_info; const auto conv_info = conv_node->convolution_info(); const auto conv_method = conv_node->convolution_method(); const auto num_groups = conv_node->num_groups(); @@ -79,7 +78,7 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge 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, out_quant_info, act_info); + 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) { @@ -125,6 +124,83 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge } } +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) { @@ -224,6 +300,8 @@ void NodeFusionMutator::mutate(Graph &g) return (output_qasymm8 && same_qinfo) || !output_qasymm8; }; + Target target = g.nodes()[0].get()->output(0)->desc().target; + // Fusion mutations 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); @@ -231,6 +309,11 @@ void NodeFusionMutator::mutate(Graph &g) // TODO (COMPMID-2055): re-enable once we fuse bias and activations to convolution // detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization); + if(target == Target::CL) + { + //Depthwise Convolution and Batch Normalization Fusion active only for CL + detail::fuse_layer<DepthwiseConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization); + } } } // namespace graph } // namespace arm_compute |