Use `defun` instead of `Defun` for `tf.data`, except for `make_one_shot_iterator` which is to be deprecated in future.

PiperOrigin-RevId: 215491729

1 files changed, 26 insertions, 34 deletions

diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index 46ce191f7b..d90da5908d 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -30,6 +30,7 @@ from tensorflow.python.data.util import nest
 from tensorflow.python.data.util import random_seed
 from tensorflow.python.data.util import sparse
 from tensorflow.python.eager import context
+from tensorflow.python.eager import function as eager_function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
@@ -37,6 +38,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import smart_cond
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_spec
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
@@ -1713,7 +1715,8 @@ class _VariantDataset(Dataset):
 
 
 class StructuredFunctionWrapper(object):
-  """A wrapper for `Defun` that supports structured arguments and return values.
+  """A wrapper for `defun` that supports structured arguments and return values.
+
   """
 
   def __init__(self, func, transformation_name, dataset=None,
@@ -1765,7 +1768,7 @@ class StructuredFunctionWrapper(object):
     # TODO(b/110122868): Enable this support for all `tf.data` functions.
     self._nested_dataset_support = experimental_nested_dataset_support
 
-    @function.Defun(*self._defun_args())
+    @eager_function.defun(input_signature=self._defun_args())
     def tf_data_structured_function_wrapper(*args):
       """Wrapper for passing nested structures to and from tf.data functions."""
       flat_args = []
@@ -1850,36 +1853,43 @@ class StructuredFunctionWrapper(object):
       self._output_shapes = nest.pack_sequence_as(ret, flat_shapes)
       self._output_types = nest.pack_sequence_as(ret, flat_types)
 
-      _warn_if_collections(transformation_name)
-
       return flat_ret
 
-    self._function = tf_data_structured_function_wrapper
+    table_initializers_len = len(ops.get_default_graph().get_collection(
+        ops.GraphKeys.TABLE_INITIALIZERS))
+
+    self._function = tf_data_structured_function_wrapper.get_concrete_function()
     if add_to_graph:
       self._function.add_to_graph(ops.get_default_graph())
-    else:
-      # Use the private method that will execute
-      # `tf_data_structured_function_wrapper` but delay adding it to the graph
-      # in case (e.g.) we need to rerun the function.
-      self._function._create_definition_if_needed()  # pylint: disable=protected-access
+    if len(
+        self._function.graph.get_collection(
+            ops.GraphKeys.TABLE_INITIALIZERS)) != table_initializers_len:
+      warnings.warn(
+          "Creating lookup tables inside a function passed to %s is not"
+          " supported. Create each table outside the function, and "
+          "capture it inside the function to use it." % transformation_name)
 
   def _defun_args(self):
-    """Returns a flat list of `tf.DType` for the input element structure."""
+    """Returns a list of `tf.TensorSpec` for the input element structure."""
     ret = []
-    for input_type, input_class in zip(nest.flatten(self._input_types),
-                                       nest.flatten(self._input_classes)):
+    for input_type, input_shape, input_class in zip(
+        nest.flatten(self._input_types), nest.flatten(self._input_shapes),
+        nest.flatten(self._input_classes)):
       # TODO(b/110122868): Add a registration mechanism for new component types.
       if input_class is sparse_tensor_lib.SparseTensor:
-        ret.append(dtypes.variant)
+        ret.append(
+            tensor_spec.TensorSpec(
+                tensor_shape.TensorShape(None), dtypes.variant))
       elif isinstance(input_class, _NestedDatasetComponent):
         if not self._nested_dataset_support:
           raise NotImplementedError(
               "The %s transformation does not currently support nested "
               "datasets as inputs." % self._transformation_name)
-        ret.append(dtypes.variant)
+        ret.append(
+            tensor_spec.TensorSpec(tensor_shape.scalar(), dtypes.variant))
       else:
         assert isinstance(input_type, dtypes.DType)
-        ret.append(input_type)
+        ret.append(tensor_spec.TensorSpec(input_shape, input_type))
     return ret
 
   @property
@@ -2579,24 +2589,6 @@ def _should_unpack_args(args):
   return type(args) is tuple  # pylint: disable=unidiomatic-typecheck
 
 
-def _warn_if_collections(transformation_name):
-  """Prints warning message if the current graph uses common graph collections.
-
-  NOTE(mrry): Currently a warning is only generated for lookup tables. Any
-  variables created will be automatically hoisted out to the outermost scope
-  using `init_scope()`. Some collections (such as for control-flow contexts)
-  are benign and should not generate a warning.
-
-  Args:
-    transformation_name: A human-readable name for the transformation.
-  """
-  if ops.get_default_graph().get_collection(ops.GraphKeys.TABLE_INITIALIZERS):
-    warnings.warn("Creating lookup tables inside a function passed to %s is not"
-                  " supported. Create each table outside the function, and "
-                  "capture it inside the function to use it."
-                  % transformation_name)
-
-
 class MapDataset(UnaryDataset):
   """A `Dataset` that maps a function over elements in its input."""