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Reusable SavedModels

Introduction

TensorFlow Hub hosts SavedModels for TensorFlow 2, among other assets. They can be loaded back into a Python program with obj = hub.load(url) [learn more]. The returned obj is the result of tf.saved_model.load() (see TensorFlow's SavedModel guide). This object can have arbitrary attributes that are tf.functions, tf.Variables (initialized from their pre-trained values), other resources and, recursively, more such objects.

This page describes an interface to be implemented by the loaded obj in order to be reused in a TensorFlow Python program. SavedModels conforming to this interface are called Reusable SavedModels.

Reusing means building a larger model around obj, including the ability to fine-tune it. Fine-tuning means further training of the weights in the loaded obj as part of the surrounding model. The loss function and the optimizer are determined by the surrounding model; obj only defines the mapping of input to output activations (the "forward pass"), possibly including techniques such as dropout or batch normalization.

The TensorFlow Hub team recommends implementing the Reusable SavedModel interface in all SavedModels that are meant to be reused in the above sense. Many utilities from the tensorflow_hub library, notably hub.KerasLayer, require SavedModels to implement it.

Relation to SignatureDefs

This interface in terms of tf.functions and other TF2 features is separate from the SavedModel's signatures, which have been available since TF1 and continue to be used in TF2 for inference (such as deploying SavedModels to TF Serving or TF Lite). Signatures for inference are not expressive enough to support fine-tuning, and tf.function provides a more natural and expressive Python API for the reused model.

Relation to model-building libraries

A Reusable SavedModel uses only TensorFlow 2 primitives, independent of any particular model-building library like Keras or Sonnet. This facilitates reuse across model-building libraries, free from dependencies on the original model-building code.

Some amount of adaptation will be needed load Reusable SavedModels into or save them from any given model-building library. For Keras, hub.KerasLayer provides the loading, and Keras's built-in saving in the SavedModel format has been redesigned for TF2 with the goal of providing a superset of this interface (see the RFC from May 2019).

Relation to task-specific "Common SavedModel APIs"

The interface definition on this page allows for any number and type of inputs and outputs. The Common SavedModel APIs for TF Hub refine this general interface with usage conventions for specific tasks to make models easily interchangeable.

Interface definition

Attributes

A Reusable SavedModel is a TensorFlow 2 SavedModel such that obj = tf.saved_model.load(...) returns an object that has the following attributes

  • __call__. Required. A tf.function implementing the model's computation (the "forward pass") subject to the specification below.

  • variables: A list of tf.Variable objects, listing all the variables used by any possible invocation of __call__, including both trainable and non-trainable ones.

    This list can be omitted if empty.

    Note: Conveniently, this name coincides with the attribute synthesized by tf.saved_model.load(...) when loading a TF1 SavedModel to represent its GLOBAL_VARIABLES collection.

  • trainable_variables: A list of tf.Variable objects such that v.trainable is true for all elements. These variables must be a subset of variables. These are the variables to be trained when fine-tuning the object. The SavedModel creator may choose to omit some variables here that were originally trainable to indicate that these should not be modified during fine-tuning.

    This list can be omitted if empty, in particular, if the SavedModel does not support fine-tuning.

  • regularization_losses: A list of tf.functions, each taking zero inputs and returning a single scalar float tensor. For fine-tuning, the SavedModel user is advised to include these as additional regularization terms into the loss (in the simplest case without further scaling). Typically, these are used to represent weight regularizers. (For lack of inputs, these tf.functions cannot express activity regularizers.)

    This list can be omitted if empty, in particular, if the SavedModel does not support fine-tuning or does not wish to prescribe weight regularization.

The __call__ function

A Restored SavedModel obj has an obj.__call__ attribute that is a restored tf.function and allows obj to be called as follows.

Synopsis (pseudo-code):

outputs = obj(inputs, trainable=..., **kwargs)

Arguments

The arguments are as follows.

  • There is one positional, required argument with a batch of input activations of the SavedModel. Its type is one of

    • a single Tensor for a single input,
    • a list of Tensors for an ordered sequence of unnamed inputs,
    • a dict of Tensors keyed by a particular set of input names.

    (Future revisions of this interface may allow more general nests.) The SavedModel creator chooses one of those and the tensor shapes and dtypes. Where useful, some dimensions of the shape should be undefined (notably batch size).

  • There may be an optional keyword argument training that accepts a Python boolean, True or False. The default is False. If the model supports fine-tuning, and if its computation differs between the two (e.g., as in dropout and batch normalization), that distinction is implemented with this argument. Otherwise, this argument may be absent.

    It is not required that __call__ accept a Tensor-valued training argument. It falls on the caller to use tf.cond() if necessary to dispatch between them.

  • The SavedModel creator may choose to accept more optional kwargs of particular names.

    • For Tensor-valued arguments, the SavedModel creator defines their permissible dtypes and shapes. tf.function accepts a Python default value on an argument that is traced with a tf.TensorSpec input. Such arguments can be used to allow customization of numeric hyperparameters involved in __call__ (e.g., dropout rate).

    • For Python-valued arguments, the SavedModel creator defines their permissible values. Such arguments can be used as flags to make discrete choices in the traced function (but mind the combinatorial explosion of traces).

The restored __call__ function must provide traces for all permissible combinations of arguments. Flipping training between True and False must not change the permissibility of arguments.

Result

The outputs from calling obj can be

  • a single Tensor for a single output,
  • a list of Tensors for an ordered sequence of unnamed outputs,
  • a dict of Tensors keyed by a particular set of output names.

(Future revisions of this interface may allow more general nests.) The return type may vary depending on the Python-valued kwargs. This allows for flags producing extra outputs. The SavedModel creator defines the output dtypes and shapes and their dependency on inputs.

Named callables

A Reusable SavedModel can provide multiple model pieces in the way described above by putting them into named subobjects, for example, obj.foo, obj.bar and so on. Each subobject provides a __call__ method and supporting attributes about the variables etc. specific to that model piece. For the example above, there would be obj.foo.__call__, obj.foo.variables and so on.

Note that this interface does not cover the approach of adding a bare tf.function directly as tf.foo.

Users of Reusable SavedModels are only expected to handle one level of nesting (obj.bar but not obj.bar.baz). (Future revisions of this interface may allow deeper nesting, and may waive the reqirement that the top-level object be callable itself.)

Closing remarks

Relation to in-process APIs

This document describes an interface of a Python class which consists of primitives like tf.function and tf.Variable that survive a round-trip through serialization via tf.saved_model.save() and tf.saved_model.load(). However, the interface was already present on the original object that was passed to tf.saved_model.save(). Adaptation to that interface enables the exchange of model pieces across model-building APIs within a single TensorFlow program.