COMPMID-1451:Fuse RELU,LU_BOUNDED_RELU with requantization in NEGEMMConvolutionLayer.
Change-Id: Iea5f2c5bcac8051c4c7655a6eabb2c43772eb31f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154104
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/graph/backends/GLES/GCFunctionsFactory.cpp b/src/graph/backends/GLES/GCFunctionsFactory.cpp
index 6268583..02a0567 100644
--- a/src/graph/backends/GLES/GCFunctionsFactory.cpp
+++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp
@@ -120,8 +120,9 @@
biases->info()->set_data_type(DataType::S32);
}
- const PadStrideInfo conv_info = node.convolution_info();
- const ConvolutionMethod conv_algorithm = node.convolution_method();
+ const PadStrideInfo conv_info = node.convolution_info();
+ const ConvolutionMethod conv_algorithm = node.convolution_method();
+ const ActivationLayerInfo fused_act = node.fused_activation();
// Create and configure function (we assume that functions have been validated before creation)
std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, GCTargetInfo::TargetType);
@@ -132,13 +133,13 @@
{
std::tie(func, func_name) = create_named_function<GCConvolutionLayerFunctions::DirectConvolutionLayer>(
std::string("DirectConvolutionLayer"),
- input, weights, biases, output, conv_info);
+ input, weights, biases, output, conv_info, fused_act);
}
else
{
std::tie(func, func_name) = create_named_memory_managed_function<GCConvolutionLayerFunctions::GenericConvolutionLayer>(
std::string("ConvolutionLayer"), mm,
- input, weights, biases, output, conv_info);
+ input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act);
}
// Log info
@@ -149,6 +150,7 @@
<< " Input shape: " << input->info()->tensor_shape()
<< " Weights shape: " << weights->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
<< std::endl);
return func;
}
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index 286c890..e967c1b 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -97,8 +97,9 @@
biases->info()->set_data_type(DataType::S32);
}
- const PadStrideInfo conv_info = node.convolution_info();
- const ConvolutionMethod conv_algorithm = node.convolution_method();
+ const PadStrideInfo conv_info = node.convolution_info();
+ const ConvolutionMethod conv_algorithm = node.convolution_method();
+ const ActivationLayerInfo fused_act = node.fused_activation();
// Create and configure function (we assume that functions have been validated before creation)
std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::NEON);
@@ -107,22 +108,22 @@
if(conv_algorithm == ConvolutionMethod::Direct)
{
std::tie(func, func_name) = create_named_memory_managed_function<NEDirectConvolutionLayer>(
- std::string("DirectConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+ std::string("DirectConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
}
else if(conv_algorithm == ConvolutionMethod::GEMM)
{
std::tie(func, func_name) = create_named_memory_managed_function<NEGEMMConvolutionLayer>(
- std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+ std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
}
else if(conv_algorithm == ConvolutionMethod::Winograd)
{
std::tie(func, func_name) = create_named_memory_managed_function<NEWinogradConvolutionLayer>(
- std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+ std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
}
else
{
std::tie(func, func_name) = create_named_memory_managed_function<NEConvolutionLayer>(
- std::string("ConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+ std::string("ConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
}
// Log info
@@ -140,6 +141,7 @@
<< " Input shape: " << input->info()->tensor_shape()
<< " Weights shape: " << weights->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
<< std::endl);
return func;
}
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp
index 82bfe25..7e66ce0 100644
--- a/src/graph/mutators/NodeFusionMutator.cpp
+++ b/src/graph/mutators/NodeFusionMutator.cpp
@@ -38,26 +38,24 @@
{
namespace detail
{
-void fuse_batch_norm_with_activation(Graph &g)
+template <typename N>
+void fuse_node_with_activation(Graph &g, const std::set<Activation> &supported_fused_activations)
{
- // Supported activations when fusing
- const std::set<Activation> supported_fused_activations = { Activation::RELU, Activation::BOUNDED_RELU, Activation::LU_BOUNDED_RELU };
-
// Not interested in the order of nodes
for(auto &node : g.nodes())
{
// Check if the node is batch norm and not a branching node
- if(node && node->type() == NodeType::BatchNormalizationLayer && node->output_edges().size() == 1)
+ if(node && node->type() == N::node_type && node->output_edges().size() == 1)
{
auto output_edge_id = *node->output_edges().begin();
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() == NodeType::ActivationLayer))
{
- auto *bn_node = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->producer());
+ 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 || bn_node->output(0) == nullptr);
+ 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)
@@ -65,17 +63,17 @@
continue;
}
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing Batch Normalization node with ID : " << output_edge->producer_id()
+ 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 batch normalization node has an output accessor
- if(bn_node->output(0)->accessor() == nullptr)
+ 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 batch normalization
- bn_node->set_fused_activation(act_node->activation_info());
+ 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();
@@ -86,15 +84,15 @@
// Update batch normalization node outputs
for(auto &driving_node : act_driving_nodes)
{
- g.add_connection(bn_node->id(), 0, driving_node.node_id, driving_node.index);
+ g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
}
// Update accessor to batch normalization node
- bn_node->output(0)->set_accessor(std::move(act_node_accessor));
+ n_node->output(0)->set_accessor(std::move(act_node_accessor));
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion as batch normalization node has an output accessor\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
}
}
}
@@ -109,7 +107,11 @@
void NodeFusionMutator::mutate(Graph &g)
{
- detail::fuse_batch_norm_with_activation(g);
+ // Supported activations when fusing
+ const std::set<Activation> supported_fused_activations = { Activation::RELU, Activation::BOUNDED_RELU, Activation::LU_BOUNDED_RELU };
+
+ detail::fuse_node_with_activation<BatchNormalizationLayerNode>(g, supported_fused_activations);
+ detail::fuse_node_with_activation<ConvolutionLayerNode>(g, supported_fused_activations);
}
} // namespace graph
} // namespace arm_compute
diff --git a/src/graph/nodes/BatchNormalizationLayerNode.cpp b/src/graph/nodes/BatchNormalizationLayerNode.cpp
index 3ae11fc..3d392bd 100644
--- a/src/graph/nodes/BatchNormalizationLayerNode.cpp
+++ b/src/graph/nodes/BatchNormalizationLayerNode.cpp
@@ -78,7 +78,7 @@
NodeType BatchNormalizationLayerNode::type() const
{
- return NodeType::BatchNormalizationLayer;
+ return BatchNormalizationLayerNode::node_type;
}
void BatchNormalizationLayerNode::accept(INodeVisitor &v)
diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp
index e9cb039..15c7ff6 100644
--- a/src/graph/nodes/ConvolutionLayerNode.cpp
+++ b/src/graph/nodes/ConvolutionLayerNode.cpp
@@ -37,7 +37,7 @@
ConvolutionMethod method,
FastMathHint fast_math_hint,
QuantizationInfo out_quant_info)
- : _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(out_quant_info)
+ : _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(out_quant_info), _fused_activation()
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -73,6 +73,16 @@
return _num_groups;
}
+ActivationLayerInfo ConvolutionLayerNode::fused_activation() const
+{
+ return _fused_activation;
+}
+
+void ConvolutionLayerNode::set_fused_activation(ActivationLayerInfo fused_activation)
+{
+ _fused_activation = fused_activation;
+}
+
TensorDescriptor ConvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
const TensorDescriptor &weights_descriptor,
const PadStrideInfo &info)
@@ -126,7 +136,7 @@
NodeType ConvolutionLayerNode::type() const
{
- return NodeType::ConvolutionLayer;
+ return ConvolutionLayerNode::node_type;
}
void ConvolutionLayerNode::accept(INodeVisitor &v)