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.