class ov::pass::LowLatency2¶
Overview¶
The transformation finds all TensorIterator/Loop layers in the network, processes all back edges that describe a connection between Result and Parameter of the TensorIterator/Loop bodies,and inserts ReadValue and Assign layers at the input and output corresponding to this back edge. Supported platforms: CPU, GNA. More…
#include <low_latency.hpp>
class LowLatency2: public ov::pass::ModelPass
{
public:
// construction
LowLatency2(bool use_const_initializer = true);
// methods
OPENVINO_RTTI("LowLatency2");
virtual bool run_on_model(const std::shared_ptr<ov::Model>& m);
};Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
// methods
bool get_property(const PassPropertyMask& prop_mask) const;
void set_name(const std::string& name);
std::string get_name() const;
void set_callback(const param_callback& callback);
virtual void set_pass_config(const std::shared_ptr<PassConfig>& pass_config);
std::shared_ptr<PassConfig> get_pass_config();
bool m_transformation_callback(const std::shared_ptr<const Node>& node);
bool transformation_callback(const std::shared_ptr<const Node>& node);
virtual const type_info_t& get_type_info() const = 0;
OPENVINO_RTTI("ov::pass::ModelPass");
virtual bool run_on_function(std::shared_ptr<ov::Model> m);
virtual bool run_on_model(const std::shared_ptr<ov::Model>& m);Detailed Documentation¶
The transformation finds all TensorIterator/Loop layers in the network, processes all back edges that describe a connection between Result and Parameter of the TensorIterator/Loop bodies,and inserts ReadValue and Assign layers at the input and output corresponding to this back edge. Supported platforms: CPU, GNA.
The example below describes the changes made by the transformation [] - TensorIterator body () - new layer BE - back-edge
before applying the transformation: -> input1[BE_1 -> Parameter -> Layers … -> Result -> BE_1 ]output1->
after applying the transformation: ->(ReadValue)-> input1[BE_1 ->Parameter->Layers …->Result->BE_1]output1 ->(Assign) ->… After applying the transformation, the resulting network can be inferred step by step, the states will store between inferences.