[tensorflow] Add support for the is_symbolic_tensor predicate

This predicate will become available in tensorflow starting with version
2.14.

Co-authored-by: Russell Power <power@google.com>

src/transformers/modeling_tf_utils.py

@@ -57,6 +57,7 @@
     is_offline_mode,
     is_remote_url,
     is_safetensors_available,
+    is_tf_symbolic_tensor,
     logging,
     requires_backends,
     working_or_temp_dir,
@@ -511,7 +512,7 @@ def input_processing(func, config, **kwargs):
     if isinstance(main_input, (tuple, list)):
         for i, input in enumerate(main_input):
             # EagerTensors don't allow to use the .name property so we check for a real Tensor
-            if type(input) == tf.Tensor:
+            if is_tf_symbolic_tensor(input):
                 # Tensor names have always the pattern `name:id` then we check only the
                 # `name` part
                 tensor_name = input.name.split(":")[0]
@@ -572,7 +573,7 @@ def input_processing(func, config, **kwargs):
     # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
     # So to respect the proper output we have to add this exception
     if "args" in output:
-        if output["args"] is not None and type(output["args"]) == tf.Tensor:
+        if output["args"] is not None and is_tf_symbolic_tensor(output["args"]):
             tensor_name = output["args"].name.split(":")[0]
             output[tensor_name] = output["args"]
         else:

src/transformers/utils/__init__.py

@@ -43,6 +43,7 @@
     is_numpy_array,
     is_tensor,
     is_tf_tensor,
+    is_tf_symbolic_tensor,
     is_torch_device,
     is_torch_dtype,
     is_torch_tensor,

src/transformers/utils/generic.py

@@ -166,6 +166,22 @@ def is_tf_tensor(x):
     return False if not is_tf_available() else _is_tensorflow(x)
 
 
+def _is_tf_symbolic_tensor(x):
+    import tensorflow as tf
+
+    # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
+    if hasattr(tf, 'is_symbolic_tensor'):
+        return tf.is_symbolic_tensor(x)
+    return type(x) == tf.Tensor
+
+
+def is_tf_symbolic_tensor(x):
+    """
+    Tests if `x` is a tensorflow symbolic tensor or not (ie. not eager). Safe to call even if tensorflow is not installed.
+    """
+    return False if not is_tf_available() else _is_tf_symbolic_tensor(x)
+
+
 def _is_jax(x):
     import jax.numpy as jnp  # noqa: F811