Let TF-GNN choose between keras or tf_keras consistently with TF 2.15:

both provide Keras 2.15, but it matters which one is used,
because they have separate class hierarchies and global registries.

Along the way, refactor the nested case distinctions of tf_internal.py
into a clear list of supported older TF/Keras versions.

PiperOrigin-RevId: 604619976

Author: arnoegw

tensorflow_gnn/graph/tf_internal.py

@@ -17,6 +17,12 @@
 TODO(b/188399175): Use the public ExtensionType API instead.
 """
 
+import os
+
+##
+## Part 1: TensorFlow symbols
+##
+
 # The following imports work in all supported versions of TF.
 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top,g-bad-import-order
 from tensorflow.python.framework import composite_tensor
@@ -32,39 +38,6 @@
 except ImportError:
   type_spec_registry = None  # Not available before TF 2.12.
 
-# NOTE: See ../__init__.py for an up-front check of supported Keras versions.
-
-try:
-  try:
-    # Get Keras v2 from the separate tf_keras package.
-    # In OSS, it exists for TF2.14+. It may become required for TF2.16+.
-    from tf_keras.src.engine import keras_tensor  # pytype: disable=import-error
-    from tf_keras.src.layers import core as core_layers  # pytype: disable=import-error
-    import tf_keras.src.backend as keras_backend  # pytype: disable=import-error
-  except ImportError:
-    # Get Keras v2 from the keras package.
-    # In OSS, this is possible for TF2.15 and older.
-    import keras  # pytype: disable=import-error
-    if not keras.__version__.startswith('2.'):
-      raise ImportError(
-          'tensorflow_gnn requires tf_keras to be installed or keras version <'
-          f' 3. Instead got keras version {keras.__version__}.'
-      ) from None  # A Keras version mismatch is different to lacking tf_keras.
-    import keras  # pytype: disable=import-error
-    if hasattr(keras, 'src'):  # As of TF/Keras 2.13.
-      from keras.src.engine import keras_tensor  # pytype: disable=import-error
-      from keras.src.layers import core as core_layers  # pytype: disable=import-error
-      import keras.src.backend as keras_backend  # pytype: disable=import-error
-    else:
-      from keras.engine import keras_tensor  # pytype: disable=import-error
-      from keras.layers import core as core_layers  # pytype: disable=import-error
-      import keras.backend as keras_backend  # pytype: disable=import-error
-except ImportError:
-  # Internal
-  keras_tensor = tf._keras_internal.engine.keras_tensor  # pylint: disable=protected-access
-  core_layers = tf._keras_internal.layers.core  # pylint: disable=protected-access
-  keras_backend = tf._keras_internal.backend  # pylint: disable=protected-access
-
 CompositeTensor = composite_tensor.CompositeTensor
 BatchableTypeSpec = type_spec.BatchableTypeSpec
 type_spec_register = (
@@ -79,28 +52,78 @@
     type_spec_registry.lookup if type_spec_registry is not None
     else type_spec.lookup)
 
-try:
-  # These types are semi-public as of TF/Keras 2.13.
-  # Whenever possible, get them the official way.
+OpDispatcher = tf.__internal__.dispatch.OpDispatcher
+
+
+##
+## Part 2: Keras symbols, compatible with `tf.keras.*`
+##
+
+# pytype: disable=import-error
+
+if tf.__version__.startswith("2.12."):
+  # tf.keras is keras 2.12, which does not yet have the `src` subdirectory.
+  from keras import backend as keras_backend
+  from keras.engine import keras_tensor as kt
+  from keras.layers import core as core_layers
+  # In 2.12, these symbols are not exposed yet under tf.keras.__internal__.
+  KerasTensor = kt.KerasTensor
+  RaggedKerasTensor = kt.RaggedKerasTensor
+
+elif tf.__version__.startswith("2.13.") or tf.__version__.startswith("2.14."):
   KerasTensor = tf.keras.__internal__.KerasTensor
   RaggedKerasTensor = tf.keras.__internal__.RaggedKerasTensor
-except AttributeError:
-  KerasTensor = keras_tensor.KerasTensor
-  RaggedKerasTensor = keras_tensor.RaggedKerasTensor
-# These KerasTensor helpers are still private in TF/Keras 2.13.
-register_keras_tensor_specialization = (
-    keras_tensor.register_keras_tensor_specialization)
-delegate_property = core_layers._delegate_property  # pylint: disable=protected-access
-delegate_method = core_layers._delegate_method  # pylint: disable=protected-access
+  # tf.keras is keras.
+  # For TF 2.14, there also exists a tf_keras package, but TF does not use it.
+  from keras.src import backend as keras_backend
+  from keras.src.engine import keras_tensor as kt
+  from keras.src.layers import core as core_layers
 
-OpDispatcher = tf.__internal__.dispatch.OpDispatcher
+elif tf.__version__.startswith("2.15."):
+  KerasTensor = tf.keras.__internal__.KerasTensor
+  RaggedKerasTensor = tf.keras.__internal__.RaggedKerasTensor
+  # OSS TensorFlow 2.15 can choose between keras 2.15 and tf_keras 2.15
+  # BUT THESE ARE DIFFERENT PACKAGES WITH SEPARATE GLOBAL REGISTRIES
+  # so it is essential that we pick the right one by replicating the logic from
+  # https://github.com/tensorflow/tensorflow/blob/r2.15/tensorflow/python/util/lazy_loader.py#L96
+  if os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"):
+    from tf_keras.src import backend as keras_backend
+    from tf_keras.src.layers import core as core_layers
+    from tf_keras.src.engine import keras_tensor as kt
+  else:
+    from keras.src import backend as keras_backend
+    from keras.src.layers import core as core_layers
+    from keras.src.engine import keras_tensor as kt
 
+elif hasattr(tf, "_keras_internal"):  # Special case: internal.
+  KerasTensor = tf.keras.__internal__.KerasTensor
+  RaggedKerasTensor = tf.keras.__internal__.RaggedKerasTensor
+  kt = tf._keras_internal.engine.keras_tensor  # pylint: disable=protected-access
+  core_layers = tf._keras_internal.layers.core  # pylint: disable=protected-access
+  keras_backend = tf._keras_internal.backend  # pylint: disable=protected-access
+
+else:  # TF2.16 and onwards.
+  # ../__init__.py has already checked that tf.keras has version 2, not 3,
+  # which implies that tf.keras is tf_keras, and we do not second-guess
+  # the selection logic.
+  KerasTensor = tf.keras.__internal__.KerasTensor
+  RaggedKerasTensor = tf.keras.__internal__.RaggedKerasTensor
+  from tf_keras.src import backend as keras_backend
+  from tf_keras.src.layers import core as core_layers
+  from tf_keras.src.engine import keras_tensor as kt
+
+# pytype: enable=import-error
+
+register_keras_tensor_specialization = kt.register_keras_tensor_specialization
+delegate_property = core_layers._delegate_property  # pylint: disable=protected-access
+delegate_method = core_layers._delegate_method  # pylint: disable=protected-access
 unique_keras_object_name = keras_backend.unique_object_name
 
 # Delete imports, in their order above.
 del composite_tensor
 del type_spec
 del tf
 del type_spec_registry
-del keras_tensor
+del keras_backend
 del core_layers
+del kt