Deletes convert_n_to_eager_tensor. Moves convert_to_eager_tensor to constant_op.

PiperOrigin-RevId: 165704074

5 files changed, 34 insertions, 48 deletions

diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py
index dee339f7f1..78ff2f6777 100644
--- a/tensorflow/python/eager/ops_test.py
+++ b/tensorflow/python/eager/ops_test.py
@@ -272,9 +272,7 @@ class TargetTest(test_util.TensorFlowTestCase):
 
   def testInvalidInputDataType(self):
     # Fill requires the first input to be an int32 tensor.
-    with self.assertRaisesRegexp(
-        TypeError,
-        'Expected tensor with type tf.int32 not tf.int64'):
+    with self.assertRaisesRegexp(ValueError, 'int64'):
       array_ops.fill(tensor.Tensor([2], dtype=dtypes.int64), tensor.Tensor(1))
 
   def testOutputOnHostMemory(self):
diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc
index 511ce82eeb..c46a3d8db3 100644
--- a/tensorflow/python/eager/python_eager_op_gen.cc
+++ b/tensorflow/python/eager/python_eager_op_gen.cc
@@ -624,8 +624,8 @@ void GenEagerPythonOp::AddEagerInputCasts() {
     const string fn = arg.number_attr().empty() ? "" : "n_";
     const string dtype =
         python_op_gen_internal::DataTypeToPython(arg.type(), "_dtypes.");
-    strings::StrAppend(&result_, "    ", param, " = _tensor.convert_", fn,
-                       "to_eager_tensor(", param, ", ", dtype, ")\n");
+    strings::StrAppend(&result_, "    ", param, " = _ops.convert_", fn,
+                       "to_tensor(", param, ", ", dtype, ")\n");
   }
 }
 
diff --git a/tensorflow/python/eager/tensor.py b/tensorflow/python/eager/tensor.py
index 1c2f4d74c7..69269d1975 100644
--- a/tensorflow/python/eager/tensor.py
+++ b/tensorflow/python/eager/tensor.py
@@ -24,8 +24,6 @@ import numpy as np
 # ops.py.
 # pylint: disable=unused-import
 from tensorflow.python.framework.ops import _tensor_from_handle
-from tensorflow.python.framework.ops import convert_n_to_eager_tensor
-from tensorflow.python.framework.ops import convert_to_eager_tensor
 from tensorflow.python.framework.ops import EagerTensor as Tensor
 # pylint: enable=unused-import
 
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
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 862dd706f4..6f1954537e 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -876,29 +876,6 @@ class EagerTensor(Tensor):
     raise NotImplementedError("eval not supported for Eager Tensors.")
 
 
-# TODO(josh11b): Support other cases like converting TensorShape, lists/tuples and
-# other custom conversion functions.
-def convert_to_eager_tensor(t, dtype=None):
-  """Converts the given `value` to an `EagerTensor`."""
-  if isinstance(ag_core.getval(t), 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 EagerTensor(t, dtype=dtype)
-
-
-def convert_n_to_eager_tensor(values, dtype):
-  """Converts the given `values` to a list of `EagerTensor`."""
-  return [convert_to_eager_tensor(t, dtype) for t in values]
-
-
 def _tensor_from_handle(handle):
   """'Private' constructor for the Tensor object.
 
@@ -1112,21 +1089,17 @@ def internal_convert_n_to_tensor(values,
   """
   if not isinstance(values, collections.Sequence):
     raise TypeError("values must be a list.")
-  if context.in_graph_mode():
-    ret = []
-    for i, value in enumerate(values):
-      n = None if name is None else "%s_%d" % (name, i)
-      ret.append(
-          internal_convert_to_tensor(
-              value,
-              dtype=dtype,
-              name=n,
-              as_ref=as_ref,
-              preferred_dtype=preferred_dtype))
-    return ret
-  else:
-    # TODO(josh11b): handle preferred_dtype, as_ref
-    return convert_n_to_eager_tensor(values, dtype=dtype)
+  ret = []
+  for i, value in enumerate(values):
+    n = None if name is None else "%s_%d" % (name, i)
+    ret.append(
+        internal_convert_to_tensor(
+            value,
+            dtype=dtype,
+            name=n,
+            as_ref=as_ref,
+            preferred_dtype=preferred_dtype))
+  return ret
 
 
 def convert_n_to_tensor(values, dtype=None, name=None, preferred_dtype=None):