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LiteRT supports converting TensorFlow model's input/output
specifications to LiteRT models. The input/output specifications are
called "signatures". Signatures can be specified when building a SavedModel or
creating concrete functions.
Signatures in LiteRT provide the following features:
They specify inputs and outputs of the converted LiteRT model by
respecting the TensorFlow model's signatures.
Allow a single LiteRT model to support multiple entry points.
The signature is composed of three pieces:
Inputs: Map for inputs from input name in the signature to an input tensor.
Outputs: Map for output mapping from output name in signature to an output
tensor.
Signature Key: Name that identifies an entry point of the graph.
Setup
importtensorflowastf
Example model
Let's say we have two tasks, e.g., encoding and decoding, as a TensorFlow model:
In the signature wise, the above TensorFlow model can be summarized as follows:
Signature
Key: encode
Inputs: {"x"}
Output: {"encoded_result"}
Signature
Key: decode
Inputs: {"x"}
Output: {"decoded_result"}
Convert a model with Signatures
LiteRT converter APIs will bring the above signature information into
the converted LiteRT model.
This conversion functionality is available on all the converter APIs starting
from TensorFlow version 2.7.0. See example usages.
From Saved Model
model=Model()# Save the modelSAVED_MODEL_PATH='content/saved_models/coding'tf.saved_model.save(model,SAVED_MODEL_PATH,signatures={'encode':model.encode.get_concrete_function(),'decode':model.decode.get_concrete_function()})# Convert the saved model using TFLiteConverterconverter=tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_PATH)converter.target_spec.supported_ops=[tf.lite.OpsSet.TFLITE_BUILTINS,# enable LiteRT ops.tf.lite.OpsSet.SELECT_TF_OPS# enable TensorFlow ops.]tflite_model=converter.convert()# Print the signatures from the converted modelinterpreter=tf.lite.Interpreter(model_content=tflite_model)signatures=interpreter.get_signature_list()print(signatures)
From Keras Model
# Generate a Keras model.keras_model=tf.keras.Sequential([tf.keras.layers.Dense(2,input_dim=4,activation='relu',name='x'),tf.keras.layers.Dense(1,activation='relu',name='output'),])# Convert the keras model using TFLiteConverter.# Keras model converter API uses the default signature automatically.converter=tf.lite.TFLiteConverter.from_keras_model(keras_model)tflite_model=converter.convert()# Print the signatures from the converted modelinterpreter=tf.lite.Interpreter(model_content=tflite_model)signatures=interpreter.get_signature_list()print(signatures)
From Concrete Functions
model=Model()# Convert the concrete functions using TFLiteConverterconverter=tf.lite.TFLiteConverter.from_concrete_functions([model.encode.get_concrete_function(),model.decode.get_concrete_function()],model)converter.target_spec.supported_ops=[tf.lite.OpsSet.TFLITE_BUILTINS,# enable LiteRT ops.tf.lite.OpsSet.SELECT_TF_OPS# enable TensorFlow ops.]tflite_model=converter.convert()# Print the signatures from the converted modelinterpreter=tf.lite.Interpreter(model_content=tflite_model)signatures=interpreter.get_signature_list()print(signatures)
Run Signatures
TensorFlow inference APIs support the signature-based executions:
Accessing the input/output tensors through the names of the inputs and
outputs, specified by the signature.
Running each entry point of the graph separately, identified by the
signature key.
Support for the SavedModel's initialization procedure.
Java, C++ and Python language bindings are currently available. See example the
below sections.
Java
try (Interpreter interpreter = new Interpreter(file_of_tensorflowlite_model)) {
// Run encoding signature.
Map<String, Object> inputs = new HashMap<>();
inputs.put("x", input);
Map<String, Object> outputs = new HashMap<>();
outputs.put("encoded_result", encoded_result);
interpreter.runSignature(inputs, outputs, "encode");
// Run decoding signature.
Map<String, Object> inputs = new HashMap<>();
inputs.put("x", encoded_result);
Map<String, Object> outputs = new HashMap<>();
outputs.put("decoded_result", decoded_result);
interpreter.runSignature(inputs, outputs, "decode");
}
# Load the LiteRT model in LiteRT Interpreterinterpreter=tf.lite.Interpreter(model_content=tflite_model)# Print the signatures from the converted modelsignatures=interpreter.get_signature_list()print('Signature:',signatures)# encode and decode are callable with input as arguments.encode=interpreter.get_signature_runner('encode')decode=interpreter.get_signature_runner('decode')# 'encoded' and 'decoded' are dictionaries with all outputs from the inference.input=tf.constant([1,2,3],dtype=tf.float32)print('Input:',input)encoded=encode(x=input)print('Encoded result:',encoded)decoded=decode(x=encoded['encoded_result'])print('Decoded result:',decoded)
Known limitations
As the LiteRT interpreter does not guarantee thread safety, signature runners
from the same interpreter must not be executed concurrently.
Support for iOS/Swift is not available yet.
Updates
Version 2.7
The multiple signature feature is implemented.
All the converter APIs from version two generate signature-enabled
LiteRT models.
Version 2.5
Signature feature is available through thefrom_saved_modelconverter
API.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-05-28 UTC."],[],[]]
