152 lines
6.9 KiB
Python
152 lines
6.9 KiB
Python
# Copyright 2020 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Defines a helper function for creating a SavedModel from a jax2tf trained model.
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This has been tested with TensorFlow Hub, TensorFlow JavaScript,
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and TensorFlow Serving.
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Note that the code in this file is provided only as an example. The functions
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generated by `jax2tf.convert` are standard TensorFlow functions and you can
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save them in a SavedModel using standard TensorFlow code. This decoupling
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of jax2tf from SavedModel is important, because it allows the user to have full
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control over what metadata is saved in the SavedModel. Please copy and
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customize this function as needed.
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"""
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from typing import Any, Callable, Sequence, Optional, Union
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from jax.experimental import jax2tf # type: ignore[import]
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import tensorflow as tf # type: ignore[import]
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def convert_and_save_model(
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jax_fn: Callable[[Any, Any], Any],
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params,
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model_dir: str,
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*,
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input_signatures: Sequence[tf.TensorSpec],
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polymorphic_shapes: Optional[Union[str, jax2tf.PolyShape]] = None,
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with_gradient: bool = False,
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enable_xla: bool = True,
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compile_model: bool = True,
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saved_model_options: Optional[tf.saved_model.SaveOptions] = None):
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"""Convert a JAX function and saves a SavedModel.
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This is an example, we do not promise backwards compatibility for this code.
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For serious uses, please copy and and expand it as needed (see note at the top
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of the module).
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Use this function if you have a trained ML model that has both a prediction
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function and trained parameters, which you want to save separately from the
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function graph as variables (e.g., to avoid limits on the size of the
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GraphDef, or to enable fine-tuning.) If you don't have such parameters,
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you can still use this library function but probably don't need it
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(see jax2tf/README.md for some simple examples).
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In order to use this wrapper you must first convert your model to a function
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with two arguments: the parameters and the input on which you want to do
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inference. Both arguments may be np.ndarray or (nested)
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tuples/lists/dictionaries thereof.
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See the README.md for a discussion of how to prepare Flax and Haiku models.
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Args:
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jax_fn: a JAX function taking two arguments, the parameters and the inputs.
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Both arguments may be (nested) tuples/lists/dictionaries of np.ndarray.
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params: the parameters, to be used as first argument for `jax_fn`. These
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must be (nested) tuples/lists/dictionaries of np.ndarray, and will be
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saved as the variables of the SavedModel.
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model_dir: the directory where the model should be saved.
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input_signatures: the input signatures for the second argument of `jax_fn`
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(the input). A signature must be a `tensorflow.TensorSpec` instance, or a
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(nested) tuple/list/dictionary thereof with a structure matching the
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second argument of `jax_fn`. The first input_signature will be saved as
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the default serving signature. The additional signatures will be used
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only to ensure that the `jax_fn` is traced and converted to TF for the
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corresponding input shapes.
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with_gradient: the value to use for the `with_gradient` parameter for
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`jax2tf.convert`.
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enable_xla: the value to use for the `enable_xla` parameter for
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`jax2tf.convert`.
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compile_model: use TensorFlow jit_compiler on the SavedModel. This
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is needed if the SavedModel will be used for TensorFlow serving.
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polymorphic_shapes: if given then it will be used as the
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`polymorphic_shapes` argument to jax2tf.convert for the second parameter of
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`jax_fn`. In this case, a single `input_signatures` is supported, and
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should have `None` in the polymorphic dimensions.
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saved_model_options: options to pass to savedmodel.save.
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"""
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if not input_signatures:
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raise ValueError("At least one input_signature must be given")
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if polymorphic_shapes is not None:
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if len(input_signatures) > 1:
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raise ValueError("For shape-polymorphic conversion a single "
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"input_signature is supported.")
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tf_fn = jax2tf.convert(
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jax_fn,
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with_gradient=with_gradient,
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polymorphic_shapes=[None, polymorphic_shapes],
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enable_xla=enable_xla)
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# Create tf.Variables for the parameters. If you want more useful variable
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# names, you can use `tree.map_structure_with_path` from the `dm-tree` package
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param_vars = tf.nest.map_structure(
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lambda param: tf.Variable(param, trainable=with_gradient),
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params)
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tf_graph = tf.function(lambda inputs: tf_fn(param_vars, inputs),
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autograph=False,
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jit_compile=compile_model)
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signatures = {}
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# This signature is needed for TensorFlow Serving use.
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signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
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tf_graph.get_concrete_function(input_signatures[0])
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for input_signature in input_signatures[1:]:
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# If there are more signatures, trace and cache a TF function for each one
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tf_graph.get_concrete_function(input_signature)
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wrapper = _ReusableSavedModelWrapper(tf_graph, param_vars)
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if with_gradient:
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if not saved_model_options:
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saved_model_options = tf.saved_model.SaveOptions(experimental_custom_gradients=True)
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else:
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saved_model_options.experimental_custom_gradients = True
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tf.saved_model.save(wrapper, model_dir, signatures=signatures,
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options=saved_model_options)
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class _ReusableSavedModelWrapper(tf.train.Checkpoint):
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"""Wraps a function and its parameters for saving to a SavedModel.
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Implements the interface described at
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https://www.tensorflow.org/hub/reusable_saved_models.
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"""
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def __init__(self, tf_graph, param_vars):
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"""Args:
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tf_graph: a tf.function taking one argument (the inputs), which can be
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be tuples/lists/dictionaries of np.ndarray or tensors. The function
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may have references to the tf.Variables in `param_vars`.
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param_vars: the parameters, as tuples/lists/dictionaries of tf.Variable,
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to be saved as the variables of the SavedModel.
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"""
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super().__init__()
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# Implement the interface from https://www.tensorflow.org/hub/reusable_saved_models
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self.variables = tf.nest.flatten(param_vars)
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self.trainable_variables = [v for v in self.variables if v.trainable]
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# If you intend to prescribe regularization terms for users of the model,
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# add them as @tf.functions with no inputs to this list. Else drop this.
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self.regularization_losses = []
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self.__call__ = tf_graph
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