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 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 LowLatency2

  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.

  3. 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::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
  4. 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);