Address and remove remaining TF 1.15 support related TODOs.

PiperOrigin-RevId: 493487255

Author: zoyahav

tensorflow_transform/experimental/mappers.py

@@ -325,14 +325,9 @@ def _to_term_document_one_hot(
   # frequency only cares the existence of a term in a document, not the
   # occurrence frequency within that document.
   # Hashing (<batch_index>, <vocab_index>) pairs for dedup.
-  # TODO(b/160294509): Switch to tf.raw_ops.UniqueV2 to avoid hashing for tf1
-  # tf.raw_ops.UniqueV2 always results in rank-1 tensor placeholder in tf1, even
-  # when the input is 2D. This causes issues when applying UniqueV2 to
-  # (<batch_index>, <vocab_index>) 2D tensor along axis=0 and using the uniqued
-  # 2D tensor as indices for sparse tensors in tf1. See b/160294509#comment8.
   multiplier = vocab_size + 1
-  unique_flatten_indices, _ = tf.unique(batch_indices * multiplier +
-                                        vocab_indices)
+  unique_flatten_indices, _ = tf.raw_ops.UniqueV2(
+      x=batch_indices * multiplier + vocab_indices, axis=[0])
   unique_batch_indices = tf.cast(
       tf.math.divide(unique_flatten_indices, multiplier), dtype=tf.int64)
   unique_vocab_indices = tf.math.mod(unique_flatten_indices, multiplier)

tensorflow_transform/graph_tools_test.py

@@ -1042,7 +1042,6 @@ class GraphToolsTestUniquePath(test_case.TransformTestCase):
           }),
       dict(
           testcase_name='_y_function_of_x_with_raw_ops_while',
-          skip_test_check_fn=test_case.skip_if_external_environment,
           create_graph_fn=_create_graph_with_y_function_of_x_with_tf_while,
           feeds=['x'],
           replaced_tensors_ready={'x': False},
@@ -1092,7 +1091,6 @@ class GraphToolsTestUniquePath(test_case.TransformTestCase):
           }),
       dict(
           testcase_name='_y_function_of_x_with_tf_while',
-          skip_test_check_fn=test_case.skip_if_external_environment,
           create_graph_fn=_create_graph_with_tf_function_while,
           feeds=['x'],
           replaced_tensors_ready={'x': False},
@@ -1226,13 +1224,7 @@ def testGetUniquePath(self,
                         create_graph_fn,
                         feeds,
                         replaced_tensors_ready,
-                        expected_calls_dict,
-                        skip_test_check_fn=None):
-
-    # TODO(b/160294509): Remove this condition when TFT no longer supports TF<2.
-    if skip_test_check_fn:
-      skip_test_check_fn('This test is not currently supported.')
-
+                        expected_calls_dict):
     with tf.compat.v1.Graph().as_default() as graph:
       tensors = create_graph_fn()
     replaced_tensors_ready = [(tensors[name], ready)

tensorflow_transform/output_wrapper.py

@@ -443,8 +443,7 @@ def __init__(self,
       self._exported_as_v1 = exported_as_v1
     self._saved_model_loader_value = None
     self._loaded_saved_model_graph = None
-    # TODO(b/160294509): Use tf.compat.v1 when we stop supporting TF 1.15.
-    if ops.executing_eagerly_outside_functions():
+    if tf.compat.v1.executing_eagerly_outside_functions():
       # The model must be tracked by assigning to an attribute of the Keras
       # layer. Hence, we track the attributes of _saved_model_loader here as
       # well.
@@ -470,8 +469,7 @@ def _saved_model_loader(self) -> saved_transform_io_v2.SavedModelLoader:
           self._tft_output.transform_savedmodel_dir)
       self._loaded_saved_model_graph = ops.get_default_graph()
 
-    # TODO(b/160294509): Use tf.compat.v1 when we stop supporting TF 1.15.
-    if ops.executing_eagerly_outside_functions():
+    if tf.compat.v1.executing_eagerly_outside_functions():
       return self._saved_model_loader_value
     else:
       assert not self._exported_as_v1

tensorflow_transform/saved/saved_transform_io_v2.py

@@ -128,9 +128,7 @@ def __init__(self, saved_model_dir: str):
         defined in `../constants.py` ('transform' and 'transform_signature',
         respectively).
     """
-    # TODO(b/160294509): Stop using tf.compat.v2 when TF1.15 support is
-    # dropped.
-    imported = tf.compat.v2.saved_model.load(saved_model_dir)
+    imported = tf.saved_model.load(saved_model_dir)
     load_v2_in_compat = constants.TRANSFORM_SIGNATURE in imported.signatures
     if load_v2_in_compat:
       restored_function = imported.signatures[constants.TRANSFORM_SIGNATURE]

tensorflow_transform/test_case.py

@@ -112,9 +112,8 @@ def _make_placeholder(tensor_spec):
     return tf.compat.v1.sparse_placeholder(
         shape=tensor_spec.shape, dtype=tensor_spec.dtype)
   if isinstance(tensor_spec, tf.RaggedTensorSpec):
-    # TODO(b/160294509): Switch to public APIs once TF 1 support is dropped.
     return tf.compat.v1.ragged.placeholder(
-        tensor_spec._dtype, tensor_spec._ragged_rank, value_shape=())  # pylint: disable=protected-access
+        tensor_spec.dtype, tensor_spec.ragged_rank, value_shape=())
   else:
     return tf.compat.v1.placeholder(
         shape=tensor_spec.shape, dtype=tensor_spec.dtype)
@@ -164,8 +163,7 @@ def _wrap_as_constant(value, tensor_spec):
         values=tf.constant(value.values, dtype=tensor_spec.dtype),
         dense_shape=tf.constant(value.dense_shape, dtype=tf.int64))
   elif isinstance(tensor_spec, tf.RaggedTensorSpec):
-    # TODO(b/160294509): Switch to public APIs once TF 1 support is dropped.
-    result = _ragged_value_as_constant(value, tensor_spec._dtype)  # pylint: disable=protected-access
+    result = _ragged_value_as_constant(value, tensor_spec.dtype)
   else:
     result = tf.constant(value, dtype=tensor_spec.dtype)
     result.shape.assert_is_compatible_with(tensor_spec.shape)
@@ -299,10 +297,10 @@ def _assertValuesCloseOrEqual(self, a_value, b_value, msg=None):
     if (isinstance(a_value, (bytes, str)) or isinstance(a_value, list) and
         a_value and isinstance(a_value[0], (bytes, str)) or
         isinstance(a_value, np.ndarray) and a_value.dtype == object):
-      self.assertAllEqual(a_value, b_value)
+      self.assertAllEqual(a_value, b_value, msg=msg)
     else:
       # TODO(varshaan): Change atol only for tests for which 1e-6 is too strict.
-      self.assertAllClose(a_value, b_value, atol=1e-5)
+      self.assertAllClose(a_value, b_value, atol=1e-5, msg=msg)
 
   def AssertVocabularyContents(self, vocab_file_path, file_contents):
     if vocab_file_path.endswith('.tfrecord.gz'):

tensorflow_transform/tf2_utils.py

@@ -21,20 +21,18 @@
 from tensorflow_transform import common_types
 # pylint: disable=g-direct-tensorflow-import
 from tensorflow.python import tf2
-from tensorflow.python.framework import ops
 from tensorflow.python.framework.func_graph import FuncGraph
 # pylint: enable=g-direct-tensorflow-import
 
 
 def use_tf_compat_v1(force_tf_compat_v1: bool) -> bool:
   """Evaluate from environment variables if TF should be used in compat.v1 mode."""
   major, _, _ = tf.version.VERSION.split('.')
-  # TODO(b/160294509): Use tf.compat.v1 when we stop supporting TF 1.15.
   # If tf.enable_v2_behavior has been called, but eager execution has been
   # disabled, force compat v1 behavior. Hence, check
   # `executing_eagerly_outside_functions` as well.
   return (force_tf_compat_v1 or int(major) < 2 or not tf2.enabled() or
-          not ops.executing_eagerly_outside_functions())
+          not tf.compat.v1.executing_eagerly_outside_functions())
 
 
 def strip_and_get_tensors_and_control_dependencies(

tensorflow_transform/tf_utils.py

@@ -323,13 +323,11 @@ def hashable_tensor_or_op(tensor_or_op):
   if isinstance(tensor_or_op, tf.Tensor):
     return tensor_or_op.experimental_ref()
   if isinstance(tensor_or_op, composite_tensor.CompositeTensor):
-    # TODO(b/160294509): Use tf.type_spec_from_value - only available in TF 2.
     return _CompositeTensorRef(
-        type_spec=tensor_or_op._type_spec,  # pylint: disable=protected-access
+        type_spec=tf.type_spec_from_value(tensor_or_op),
         list_of_refs=tuple(
             hashable_tensor_or_op(component) for component in tf.nest.flatten(
-                tensor_or_op, expand_composites=True)
-        ))
+                tensor_or_op, expand_composites=True)))
   return tensor_or_op
 
 
@@ -432,10 +430,7 @@ def reduce_batch_count(x: common_types.TensorType,
           dense_shape=x.dense_shape)
       # TODO(b/178189903): Remove this once we no longer lose static shape
       # information.
-      # TODO(b/160294509): Remove the hasattr contition once TFT no longer
-      # supports TF<2.
-      if hasattr(x, '_dense_shape_default'):
-        ones_like._dense_shape_default = x._dense_shape_default  # pylint: disable=protected-access
+      ones_like._dense_shape_default = x._dense_shape_default  # pylint: disable=protected-access
       return _sparse_reduce_batch_keep_shape(tf.sparse.reduce_sum, ones_like)
   elif isinstance(x, tf.RaggedTensor):
     if reduce_instance_dims:
@@ -697,10 +692,6 @@ def _split_vocabulary_entries(batched_vocab_lines):
   if isinstance(split, tf.RaggedTensor):
     split_tensor = split.to_tensor()
     return split_tensor[:, 1], split_tensor[:, 0]
-  # TODO(b/160294509): Remove this condition when TFT no longer supports TF<2.
-  elif isinstance(split, tf.SparseTensor):
-    split_tensor = tf.sparse.to_dense(split)
-    return split_tensor[:, 1], split_tensor[:, 0]
   else:
     return split[1], split[0]
 

tensorflow_transform/tf_utils_test.py

@@ -2404,7 +2404,4 @@ def foo(input_tensor):
 
 
 if __name__ == '__main__':
-  # TODO(b/160294509): Remove this once this is enabled by default in all
-  # supported TF versions.
-  tf.compat.v1.enable_v2_tensorshape()
   test_case.main()