# Stateful models¶

This article describes how to work with stateful networks in OpenVINO™ toolkit. More specifically, it illustrates how stateful networks are represented in IR and nGraph and how operations with a state can be done. The article additionally provides some examples of stateful networks and code to infer them.

## What is a Stateful Network?¶

Several use cases require processing of data sequences. When length of a sequence is known and small enough, it can be processed with RNN like networks that contain a cycle inside. However, in some cases, like online speech recognition of time series forecasting, length of data sequence is unknown. Then, data can be divided in small portions and processed step-by-step. The dependency between data portions should be addressed. For that, networks save some data between inferences - a state. When one dependent sequence is over, a state should be reset to initial value and a new sequence can be started.

Several frameworks have special APIs for states in networks. For example, Keras has special option for RNNs, i.e. stateful that turns on saving a state between inferences. Kaldi contains special Offset specifier to define time offset in a network.

OpenVINO also contains a special API to simplify work with networks with states. A state is automatically saved between inferences, and there is a way to reset a state when needed. A state can also be read or set to some new value between inferences.

## OpenVINO State Representation¶

OpenVINO contains the Variable, a special abstraction to represent a state in a network. There are two operations to work with a state:

• Assign - to save a value in a state.

• ReadValue - to read a value saved on previous iteration.

For more details on these operations, refer to the ReadValue specification and Assign specification articles.

## Examples of Networks with States¶

To get a model with states ready for inference, convert a model from another framework to IR with Model Optimizer or create an nGraph function. (For more information, refer to the Build OpenVINO Model section). Below is the graph in both forms:

### Example of IR with State¶

The bin file for this graph should contain float 0 in binary form. The content of the xml file is as follows.

<?xml version="1.0" ?>
<net name="summator" version="10">
<layers>
<layer id="0" name="init_value" type="Const" version="opset6">
<data element_type="f32" offset="0" shape="1,1" size="4"/>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<data variable_id="id"/>
<input>
<port id="0">
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="2" name="input" type="Parameter" version="opset6">
<data element_type="f32" shape="1,1"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<input>
<port id="0">
<dim>1</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="save" type="Assign" version="opset6">
<data variable_id="id"/>
<input>
<port id="0">
<dim>1</dim>
<dim>1</dim>
</port>
</input>
</layer>
<data axis="1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="5" name="output/sink_port_0" type="Result" version="opset6">
<input>
<port id="0">
<dim>1</dim>
<dim>1</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="1" to-layer="1" to-port="0"/>
<edge from-layer="2" from-port="0" to-layer="3" to-port="1"/>
<edge from-layer="1" from-port="1" to-layer="3" to-port="0"/>
<edge from-layer="3" from-port="2" to-layer="4" to-port="0"/>
<edge from-layer="3" from-port="2" to-layer="10" to-port="0"/>
<edge from-layer="1" from-port="1" to-layer="10" to-port="1"/>
<edge from-layer="10" from-port="2" to-layer="5" to-port="0"/>
</edges>
<meta_data>
<MO_version value="unknown version"/>
<cli_parameters>
</cli_parameters>
</meta_data>
</net>

### Example of Creating Model nGraph API¶

#include <ngraph/opsets/opset6.hpp>
#include <ngraph/op/util/variable.hpp>
// ...

auto arg = make_shared<ngraph::opset6::Parameter>(element::f32, Shape{1, 1});
auto init_const = ngraph::opset6::Constant::create(element::f32, Shape{1, 1}, {0});

// The ReadValue/Assign operations must be used in pairs in the network.
// For each such a pair, its own variable object must be created.
const std::string variable_name("variable0");
auto variable = std::make_shared<ngraph::Variable>(VariableInfo{PartialShape::dynamic(), element::dynamic, variable_name});

// Creating ngraph::function
std::vector<shared_ptr<ngraph::Node>> args = {arg, read};
auto assign = make_shared<ngraph::opset6::Assign>(add, variable);
auto res = make_shared<ngraph::opset6::Result>(add2);

auto f = make_shared<Function>(ResultVector({res}), ParameterVector({arg}), SinkVector({assign}));

In this example, the SinkVector is used to create the ngraph::Function. For a network with states, except inputs and outputs, the Assign nodes should also point to the Function to avoid deleting it during graph transformations. Use the constructor to do it, as shown in the example, or with the special add_sinks(const SinkVector& sinks) method. After deleting the node from the graph with the delete_sink() method, a sink can be deleted from ngraph::Function.

## OpenVINO State API¶

Inference Engine has the InferRequest::QueryState method to get the list of states from a network and IVariableState interface to operate with states. Below is a brief description of methods and the example of how to use this interface.

• std::string GetName() const - returns the name (variable_id) of a corresponding Variable.

• void Reset() - resets a state to a default value.

• void SetState(Blob::Ptr newState) - sets a new value for a state.

• Blob::CPtr GetState() const - returns current value of state.

## Example of Stateful Network Inference¶

Based on the IR from the previous section, the example below demonstrates inference of two independent sequences of data. A state should be reset between these sequences.

One infer request and one thread will be used in this example. Using several threads is possible if there are several independent sequences. Then, each sequence can be processed in its own infer request. Inference of one sequence in several infer requests is not recommended. In one infer request, a state will be saved automatically between inferences, but if the first step is done in one infer request and the second in another, a state should be set in a new infer request manually (using the IVariableState::SetState method).

  // input data
std::vector<float> data = { 1,2,3,4,5,6};
// infer the first utterance
for (size_t next_input = 0; next_input < data.size()/2; next_input++) {
MemoryBlob::Ptr minput = as<MemoryBlob>(ptrInputBlobs[0]);
auto minputHolder = minput->wmap();

std::memcpy(minputHolder.as<void \*>(),
&data[next_input],
sizeof(float));

inferRequest.Infer();
// check states
auto states = inferRequest.QueryState();
if (states.empty()) {
throw std::runtime_error("Queried states are empty");
}
auto mstate = as<MemoryBlob>(states[0].GetState());
if (mstate == nullptr) {
throw std::runtime_error("Can't cast state to MemoryBlob");
}
auto state_buf = mstate->rmap();
float \* state =state_buf.as<float\*>();
std::cout << state[0] << "\n";
}

// resetting state between utterances
std::cout<<"Reset state\n";
for (auto &&state : inferRequest.QueryState()) {
state.Reset();
}

// infer the second utterance
for (size_t next_input = data.size()/2; next_input < data.size(); next_input++) {
MemoryBlob::Ptr minput = as<MemoryBlob>(ptrInputBlobs[0]);
auto minputHolder = minput->wmap();

std::memcpy(minputHolder.as<void \*>(),
&data[next_input],
sizeof(float));

inferRequest.Infer();
// check states
auto states = inferRequest.QueryState();
auto mstate = as<MemoryBlob>(states[0].GetState());
auto state_buf = mstate->rmap();
float \* state =state_buf.as<float\*>();
std::cout << state[0] << "\n";
}

More elaborate examples demonstrating how to work with networks with states can be found in a speech sample and a demo. Refer to the Samples Overview.

## LowLatency Transformations¶

If the original framework does not have a special API for working with states, after importing the model, OpenVINO representation will not contain Assign / ReadValue layers. For example, if the original ONNX model contains RNN operations, IR will contain TensorIterator operations and the values will be obtained only after execution of the whole TensorIterator primitive. Intermediate values from each iteration will not be available. Working with these intermediate values of each iteration is enabled by special LowLatency and LowLatency2 transformations, which also help receive these values with a low latency after each infer request.

### How to Get TensorIterator/Loop operations from Different Frameworks via Model Optimizer.¶

ONNX and frameworks supported via ONNX format: LSTM, RNN, and GRU original layers are converted to the TensorIterator operation. The TensorIterator body contains LSTM / RNN / GRU Cell. The Peepholes and InputForget modifications are not supported, while the sequence_lengths optional input is. ONNX Loop layer is converted to the OpenVINO Loop operation.

Apache MXNet: LSTM, RNN, GRU original layers are converted to TensorIterator operation. The TensorIterator body contains LSTM / RNN / GRU Cell operations.

TensorFlow: The BlockLSTM is converted to TensorIterator operation. The TensorIterator body contains LSTM Cell operation, whereas Peepholes and InputForget modifications are not supported. The While layer is converted to TensorIterator. The TensorIterator body can contain any supported operations. However, when count of iterations cannot be calculated in shape inference (Model Optimizer conversion) time, the dynamic cases are not supported.

TensorFlow2: The While layer is converted to Loop operation. The Loop body can contain any supported operations.

Kaldi: Kaldi models already contain Assign / ReadValue (Memory) operations after model conversion. The TensorIterator / Loop operations are not generated.

## The LowLatencу2 Transformation¶

The LowLatency2 transformation changes the structure of the network containing TensorIterator and Loop by adding the ability to work with the state, inserting the Assign / ReadValue layers as it is shown in the picture below.

### The Differences between the LowLatency and the LowLatency2**:¶

• Unrolling of TensorIterator / Loop operations became a part of the LowLatency2, not a separate transformation. After invoking the transformation, the network can be serialized and inferred without re-invoking the transformation.

• Support for TensorIterator and Loop operations with multiple iterations inside. The TensorIterator / Loop will not be unrolled in this case.

• The “Parameters connected directly to ReadValues” limitation is resolved. To apply the previous version of the transformation in this case, additional manual manipulations were required. Now, the case is processed automatically.

#### Example of Applying the LowLatency2 Transformation:¶

After applying the transformation, the ReadValue operations can receive other operations as an input, as shown in the picture above. These inputs should set the initial value for initialization of the ReadValue operations. However, such initialization is not supported in the current State API implementation. Input values are ignored and the initial values for the ReadValue operations are set to 0 unless otherwise specified by the user via State API.

### Steps to Apply the LowLatency2 Transformation¶

1. Get CNNNetwork. Either way is acceptable:

2. Change the number of iterations inside TensorIterator / Loop nodes in the network, using the Reshape feature.

For example, when the sequence_lengths dimension of input of the network > 1, the TensorIterator layer has number_iterations> 1. You can reshape the inputs of the network to set sequence_dimension to 1.

// Network before reshape: Parameter (name: X, shape: [2 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 2, axis = 0) -> ...

cnnNetwork.reshape({"X" : {1, 1, 16});

// Network after reshape: Parameter (name: X, shape: [1 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 1, axis = 0) -> ...

Unrolling : If the LowLatency2 transformation is applied to a network containing TensorIterator / Loop nodes with exactly one iteration inside, these nodes are unrolled. Otherwise, the nodes remain as they are. For more details, see the picture above.

1. Apply the LowLatency2 transformation.

#include "ie_transformations.hpp"

...

InferenceEngine::lowLatency2(cnnNetwork); // 2nd argument 'use_const_initializer = true' by default

Use_const_initializer argument

By default, the LowLatency2 transformation inserts a constant subgraph of the same shape as the previous input node, and with 0 values as the initializing value for ReadValue nodes. (See the picture below.) Insertion of this subgraph can be disabled by passing the false value for the use_const_initializer argument.

InferenceEngine::lowLatency2(cnnNetwork, false);

State naming rule: A name of a state is a concatenation of names: original TensorIterator operation, parameter of the body, and additional suffix variable_ + id (0-base indexing, new indexing for each TensorIterator). Use these rules to predict the name of the inserted state after the transformation is applied. For example:

// Precondition in ngraph::function.
// Created TensorIterator and Parameter in body of TensorIterator with names
std::string tensor_iterator_name = "TI_name"
std::string body_parameter_name = "param_name"
std::string idx = "0"; // it's a first variable in the network

// The State will be named "TI_name/param_name/variable_0"
auto state_name = tensor_iterator_name + "//" + body_parameter_name + "//" + "variable_" + idx;

InferenceEngine::CNNNetwork cnnNetwork = InferenceEngine::CNNNetwork{function};
InferenceEngine::lowLatency2(cnnNetwork);

InferenceEngine::ExecutableNetwork executableNetwork = core->LoadNetwork(/\*cnnNetwork, targetDevice, configuration\*/);

// Try to find the Variable by name
auto states = executableNetwork.QueryState();
for (auto& state : states) {
auto name = state.GetName();
if (name == state_name) {
// some actions
}
}
1. Use state API. See the OpenVINO state API and the Example of stateful network inference sections.

### Known Limitations¶

1. Unable to execute the Reshape feature to change the number iterations of TensorIterator / Loop layers to apply the transformation correctly.

The only way to change the number iterations of TensorIterator / Loop layer is to use the Reshape feature. However, networks can be non-reshapable. The most common reason is that the value of shapes is hardcoded in a constant somewhere in the network.

Current solution: Trim non-reshapable layers via ModelOptimizer CLI : the --input and --output parameters. For example, the parameter and the problematic constant in the picture above can be trimmed using the --input Reshape_layer_name command-line option. The problematic constant can also be replaced using ngraph, as shown in the example below.

// nGraph example. How to replace a Constant with hardcoded values of shapes in the network with another one with the new values.
// Assume we know which Constant (const_with_hardcoded_shape) prevents the reshape from being applied.
// Then we can find this Constant by name on the network and replace it with a new one with the correct shape.
auto func = cnnNetwork.getFunction();
// Creating the new Constant with a correct shape.
// For the example shown in the picture above, the new values of the Constant should be 1, 1, 10 instead of 1, 49, 10
auto new_const = std::make_shared<ngraph::opset6::Constant>( /\*type, shape, value_with_correct_shape\*/ );
for (const auto& node : func->get_ops()) {
// Trying to find the problematic Constant by name.
if (node->get_friendly_name() == "name_of_non_reshapable_const") {
auto const_with_hardcoded_shape = std::dynamic_pointer_cast<ngraph::opset6::Constant>(node);
// Replacing the problematic Constant with a new one. Do this for all the problematic Constants in the network, then
// you can apply the reshape feature.
ngraph::replace_node(const_with_hardcoded_shape, new_const);
}
}

## [DEPRECATED] The LowLatency Transformation¶

The LowLatency transformation changes the structure of the network containing TensorIterator and Loop operations by adding the ability to work with the state, inserting the Assign / ReadValue layers, as shown in the picture below.

After applying the transformation, ReadValue operations can receive other operations as an input, as shown in the picture above. These inputs should set the initial value for initialization of ReadValue operations. However, such initialization is not supported in the current State API implementation. Input values are ignored and the initial values for the ReadValue operations are set to 0 unless otherwise specified by the user via State API.

### Steps to Apply LowLatency Transformation¶

1. Get CNNNetwork. Either way is acceptable:

2. Reshape the CNNNetwork network if necessary. An example of such a necessary case is when the sequence_lengths dimension of input > 1, and it means that TensorIterator layer will have number_iterations> 1. The inputs of the network should be reshaped to set sequence_dimension to exactly 1.

Usually, the following exception, which occurs after applying a transform when trying to infer the network in a plugin, indicates the need to apply the reshape feature: C++ exception with description "Function is incorrect. The Assign and ReadValue operations must be used in pairs in the network." This means that there are several pairs of Assign / ReadValue operations with the same variable_id in the network and operations were inserted into each iteration of the TensorIterator.

// Network before reshape: Parameter (name: X, shape: [2 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 2, axis = 0) -> ...

cnnNetwork.reshape({"X" : {1, 1, 16});

// Network after reshape: Parameter (name: X, shape: [1 (sequence_lengths), 1, 16]) -> TensorIterator (num_iteration = 1, axis = 0) -> ...
1. Apply the LowLatency transformation.

#include "ie_transformations.hpp"

...

InferenceEngine::LowLatency(cnnNetwork);

State naming rule: a name of a state is a concatenation of names: original TensorIterator operation, parameter of the body, and additional suffix variable_ + id (0-base indexing, new indexing for each TensorIterator). Use these rules to predict the name of the inserted state after the transformation is applied. For example:

// Precondition in ngraph::function.
// Created TensorIterator and Parameter in body of TensorIterator with names
std::string tensor_iterator_name = "TI_name"
std::string body_parameter_name = "param_name"
std::string idx = "0"; // it's a first variable in the network

// The State will be named "TI_name/param_name/variable_0"
auto state_name = tensor_iterator_name + "//" + body_parameter_name + "//" + "variable_" + idx;

InferenceEngine::CNNNetwork cnnNetwork = InferenceEngine::CNNNetwork{function};
InferenceEngine::LowLatency(cnnNetwork);

InferenceEngine::ExecutableNetwork executableNetwork = core->LoadNetwork(/\*cnnNetwork, targetDevice, configuration\*/);

// Try to find the Variable by name
auto states = executableNetwork.QueryState();
for (auto& state : states) {
auto name = state.GetName();
if (name == state_name) {
// some actions
}
}
1. Use state API. See the OpenVINO state API and the Example of stateful network inference sections.

### Known Limitations for the LowLatency [DEPRECATED]¶

1. Parameters connected directly to ReadValues (states) after the transformation is applied are not allowed.

Unnecessary parameters may remain on the graph after applying the transformation. The automatic handling of this case inside the transformation is currently not possible. Such parameters should be removed manually from ngraph::Function or replaced with a constant.

Current solutions:

• Replace a parameter with a constant (freeze) with the [0, 0, 0 … 0] value via ModelOptimizer CLI : the --input or --freeze_placeholder_with_value parameters.

• Use nGraph API to replace a parameter with a constant, as shown in the example below:

// nGraph example. How to replace Parameter with Constant.
auto func = cnnNetwork.getFunction();
// Creating the new Constant with zero values.
auto new_const = std::make_shared<ngraph::opset6::Constant>( /\*type, shape, std::vector with zeros\*/ );
for (const auto& param : func->get_parameters()) {
// Trying to find the problematic Constant by name.
if (param->get_friendly_name() == "param_name") {
// Replacing the problematic Param with a Constant.
ngraph::replace_node(param, new_const);
// Removing problematic Parameter from ngraph::function
func->remove_parameter(param);
}
}

Unable to execute reshape precondition to apply the transformation correctly.

Networks can be non-reshapable. The most common reason is that the value of shapes is hardcoded in the constant somewhere in the network.

Current solutions:

• Trim non-reshapable layers via ModelOptimizer CLI : the --input and --output parameters. For example, the parameter and the problematic constant (as shown in the picture above) can be trimmed using the --input Reshape_layer_name command-line option.

• Use nGraph API to replace the problematic constant, as shown in the example below:

// nGraph example. How to replace a Constant with hardcoded values of shapes in the network with another one with the new values.
// Assume we know which Constant (const_with_hardcoded_shape) prevents the reshape from being applied.
// Then we can find this Constant by name on the network and replace it with a new one with the correct shape.
auto func = cnnNetwork.getFunction();
// Creating the new Constant with a correct shape.
// For the example shown in the picture above, the new values of the Constant should be 1, 1, 10 instead of 1, 49, 10
auto new_const = std::make_shared<ngraph::opset6::Constant>( /\*type, shape, value_with_correct_shape\*/ );
for (const auto& node : func->get_ops()) {
// Trying to find the problematic Constant by name.
if (node->get_friendly_name() == "name_of_non_reshapable_const") {
auto const_with_hardcoded_shape = std::dynamic_pointer_cast<ngraph::opset6::Constant>(node);
// Replacing the problematic Constant with a new one. Do this for all the problematic Constants in the network, then
// you can apply the reshape feature.
ngraph::replace_node(const_with_hardcoded_shape, new_const);
}
}