Deletes convert_n_to_eager_tensor. Moves convert_to_eager_tensor to constant_op.

PiperOrigin-RevId: 165704074

1 files changed, 20 insertions, 3 deletions

diff --git a/tensorflow/python/framework/constant_op.py b/tensorflow/python/framework/constant_op.py
index af3be7230c..9de63607e1 100644
--- a/tensorflow/python/framework/constant_op.py
+++ b/tensorflow/python/framework/constant_op.py
@@ -41,6 +41,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from autograd import core as ag_core
 import numpy as np
 
 from tensorflow.core.framework import attr_value_pb2
@@ -66,13 +67,29 @@ def _eager_reshape(tensor, shape):
 def _eager_fill(dims, value):
   """Eager-only version of Fill op; requires value is an eager Tensor."""
   attr_t = value.dtype.as_datatype_enum
-  dims = ops.convert_to_eager_tensor(dims, dtypes.int32)
+  dims = convert_to_eager_tensor(dims, dtypes.int32)
   inputs_flat = [dims, value]
   attrs = ("T", attr_t)
   result, = execute.execute("Fill", 1, inputs=inputs_flat, attrs=attrs)
   return result
 
 
+def convert_to_eager_tensor(t, dtype=None):
+  """Converts the given `value` to an `EagerTensor`."""
+  if isinstance(ag_core.getval(t), ops.EagerTensor):
+    if dtype is not None and t.dtype != dtype:
+      raise TypeError("Expected tensor with type %r not %r" % (dtype, t.dtype))
+    return t
+  # Handle converting ResourceVariable to Tensor.
+  # TODO(josh11b): get rid of this explicit ugly conversion once we have a more
+  # general scheme in place.
+  try:
+    return t._dense_var_to_tensor(dtype=dtype, as_ref=False)  # pylint: disable=protected-access
+  except AttributeError:
+    pass
+  return ops.EagerTensor(t, dtype=dtype)
+
+
 def constant(value, dtype=None, shape=None, name="Const", verify_shape=False):
   """Creates a constant tensor.
 
@@ -123,8 +140,8 @@ def constant(value, dtype=None, shape=None, name="Const", verify_shape=False):
   """
   if not context.in_graph_mode():
     if shape is None:
-      return ops.convert_to_eager_tensor(value, dtype)
-    t = ops.convert_to_eager_tensor(value, dtype)
+      return convert_to_eager_tensor(value, dtype)
+    t = convert_to_eager_tensor(value, dtype)
     shape = tensor_shape.as_shape(shape)
     if shape == t.shape:
       return t