Checking changes back in with a bug fix.

export/CHANGELOG.md

@@ -15,6 +15,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Adds a new module base class and two new module subclasses one for TensorFlow and
   one for Orbax.
 - Moves the TF dependent export logic out of JaxModule and into TensorFlowModule.
+- Removes the JaxModule dependency on TF and moves more logic frome ExportManager
+  to TensorFlowExport.
 - Wires up the Orbax Model export flow.
 - Adds a checkpoint path to the jax2obm_kwargs to allow specifying a checkpoing
   to the Orbax export pathway.

export/orbax/export/export_base.py

@@ -15,27 +15,35 @@
 """Abstract base class for different export classes."""
 
 import abc
-from typing import Any
-
-import tensorflow as tf
+from typing import Any, Callable, Mapping, Sequence
+from orbax.export import jax_module
+from orbax.export import serving_config as osc
 
 
 class ExportBase(abc.ABC):
   """Abstract base class for different export classes."""
 
-  # TODO: b/363033166 - Remove dependencies on TF in the base class.
+  def __init__(
+      self,
+      module: jax_module.JaxModule,
+      serving_configs: Sequence[osc.ServingConfig],
+  ):
+    self._module = module
+    self._serving_configs = serving_configs
+
+  @abc.abstractmethod
+  def serving_signatures(self) -> Mapping[str, Callable[..., Any]]:
+    """Returns a map of signature keys to serving functions."""
+
   @abc.abstractmethod
   def save(
       self,
-      # TODO(b/363033166): Change this annotation once TF isolation is done.
-      jax_module: tf.Module,
       model_path: str,
       **kwargs: Any,
   ):
     """Saves the model.
 
     Args:
-      jax_module: The `JaxModule` to be exported.
       model_path: The path to save the model.
       **kwargs: Additional arguments to pass to the `save` method. Accepted
         arguments are `save_options` and `serving_signatures`.

export/orbax/export/export_manager.py

@@ -15,7 +15,7 @@
 """Manage the exporting of a JAXModule."""
 
 from collections.abc import Mapping, Sequence
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Optional, cast
 
 from etils.epy import reraise_utils
 from orbax.export import config
@@ -24,7 +24,6 @@
 from orbax.export import obm_export
 from orbax.export import serving_config as osc
 from orbax.export import tensorflow_export
-from orbax.export import utils
 from orbax.export.modules import obm_module
 import tensorflow as tf
 
@@ -57,12 +56,12 @@ def __init__(
           f' must be the same. The former is {version}. The latter is '
           f'{module.export_version()}.'
       )
-    # TODO(b/363033166): Skip this step for OBM once TF isolation is done.
-    self._module = tf.Module()
-    self._module.computation_module = module
-    self._serving_signatures = {}
-    if version == constants.ExportModelType.ORBAX_MODEL:
-      self.serialization_functions = obm_export.ObmExport()
+    self._version = version
+    self._jax_module = module
+    if self._version == constants.ExportModelType.ORBAX_MODEL:
+      self._serialization_functions = obm_export.ObmExport(
+          self._jax_module, serving_configs
+      )
       obm_module_ = module.orbax_module()
       if not isinstance(obm_module_, obm_module.ObmModule):
         raise ValueError(
@@ -72,25 +71,26 @@ def __init__(
       # TODO(bdwalker): Let `ObmExport.__init__() do this `build()` step.
       obm_module_.build(serving_configs)
     else:
-      self.serialization_functions = tensorflow_export.TensorFlowExport()
-      # TODO(bdwalker): Let `TensorFlowExport.__init__() do this
-      #   `process_serving_configs()` step.
-      process_serving_configs(
-          serving_configs,
-          obx_export_config.obx_export_tf_preprocess_only,  # pytype: disable=attribute-error
-          self._module,
-          self._serving_signatures,
+      self._serialization_functions = tensorflow_export.TensorFlowExport(
+          self._jax_module, serving_configs
       )
 
   @property
   def tf_module(self) -> tf.Module:
     """Returns the tf.module maintained by the export manager."""
-    return self._module
+    if self._version == constants.ExportModelType.ORBAX_MODEL:
+      raise TypeError(
+          'tf_module is not implemented for export version'
+          ' ExportModelType.ORBAX_MODEL.'
+      )
+    return cast(
+        tensorflow_export.TensorFlowExport,
+        self._serialization_functions).tf_export_module()
 
   @property
   def serving_signatures(self) -> Mapping[str, Callable[..., Any]]:
     """Returns a map of signature keys to serving functions."""
-    return self._serving_signatures
+    return self._serialization_functions.serving_signatures
 
   def save(
       self,
@@ -107,108 +107,12 @@ def save(
       signature_overrides: signatures to override the self-maintained ones, or
         additional signatures to export.
     """
-    serving_signatures = dict(self._serving_signatures)
-    if signature_overrides:
-      serving_signatures.update(signature_overrides)
-
-    self.serialization_functions.save(
-        jax_module=self._module,
+    self._serialization_functions.save(
         model_path=model_path,
         save_options=save_options,
-        serving_signatures=serving_signatures,
+        signature_overrides=signature_overrides,
     )
 
   def load(self, model_path: str, **kwargs: Any):
-    loaded = self.serialization_functions.load(model_path, **kwargs)
+    loaded = self._serialization_functions.load(model_path, **kwargs)
     return loaded
-
-
-def make_e2e_inference_fn(
-    model_fn: Callable[..., Any],
-    serving_config: osc.ServingConfig,
-) -> Callable[..., Any]:
-  """Creates an concrete end-to-end inference tf.function.
-
-  Args:
-    model_fn: a callable in TF context for the numeric computation.
-    serving_config: a ServingConfig that defines the input sigature,
-      pre-processor and post-processor of the inference function.
-
-  Returns:
-    A tf.function for end-to-end inference.
-  """
-  infer_step_func_map = serving_config.bind(model_fn, require_numpy=False)
-  signature_key = serving_config.get_signature_keys()[0]
-  return utils.with_default_args(
-      infer_step_func_map[signature_key], serving_config.get_input_signature()
-  )
-
-
-def process_serving_configs(
-    serving_configs: Sequence[osc.ServingConfig],
-    obx_export_tf_preprocess_only: bool,
-    module: tf.Module,
-    serving_signatures: Dict[str, Callable[..., Any]],
-):
-  """Processes the serving functions into their TF wrapped concrete functions.
-
-  The function will use the serving_configs and the methods defined in the
-  provided module to populate the serving_signatures map with the concrete
-  inference functions.
-
-  In addition, if trackable resources are provided in the serving_configs,
-  they will be added to the module's tf_trackable_resources property.
-
-  Args:
-    serving_configs: a sequence of which each element is a `ServingConfig`
-      cooresponding to a serving signature of the exported SavedModel.
-    obx_export_tf_preprocess_only: a boolean indicating whether to export only
-      the preprocessor.
-    module: A tf module  that will provide the method definitions. The module
-      should have a JaxModule set as a computation_module property.
-    serving_signatures: a map of signature keys to serving functions. This map
-      will be populated by this function.
-  """
-  tf_trackable_resources = []
-  for sc in serving_configs:
-    with maybe_reraise(f'Failed exporting signature_key={sc.signature_key} '):
-      if obx_export_tf_preprocess_only:
-        if not sc.tf_preprocessor:
-          raise ValueError(
-              'serving_config.tf_preprocessor must be provided when'
-              ' in `obx_export_tf_preprocess_only` mode.'
-          )
-
-        def tf_preprocessor(*inputs):
-          return tf.nest.flatten(sc.tf_preprocessor(*inputs))  # pylint: disable=cell-var-from-loop
-
-        preprocessor = utils.with_default_args(
-            tf_preprocessor, sc.get_input_signature()
-        )
-        inference_fn = preprocessor
-      else:
-        method = sc.get_infer_step(module.computation_module.methods)
-        inference_fn = make_e2e_inference_fn(method, sc)
-
-      if isinstance(sc.signature_key, str):
-        keys = [sc.signature_key]
-      else:
-        keys = sc.signature_key
-
-      for key in keys:
-        if key in serving_signatures:
-          raise ValueError(
-              f'Duplicated key "{sc.signature_key}" in `serving_configs`.'
-          )
-        serving_signatures[key] = inference_fn
-
-      if sc.extra_trackable_resources is not None:
-        tf_trackable_resources.append(sc.extra_trackable_resources)
-
-    if len(serving_configs) == 1:
-      # Make this module callable. Once exported, it can be loaded back in
-      # python and the nested input structure will be preservered. In
-      # contrast, signatures will flatten the TensorSpecs of the to kwargs.
-      module.__call__ = inference_fn
-
-  module.tf_trackable_resources = tf_trackable_resources