eager: Rename in_eager_mode to executing_eagerly and get rid of in_graph_mode.

This is in preparation to introduce one public, stable symbol: tf.executing_eagerly()
(i.e., part of moving APIs related to eager execution from "contrib" to a namespace
where we provide API stability guarantees)

PiperOrigin-RevId: 188212646

27 files changed, 207 insertions, 204 deletions

diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 925cf8ef32..3c6a5c9e56 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -80,7 +80,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
 
   def _ExtractInputShapes(inputs):
     """Extract the shapes of a set of input tensors."""
-    if not context.in_graph_mode():
+    if context.executing_eagerly():
       return array_ops.shape_n(inputs)
     sizes = []
     fully_known = True
@@ -106,7 +106,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
 
   out_grads = []
   if isinstance(grad, ops.Tensor):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       # Using mod here for convenience since concat_dim is already verified
       # in concat implementation to be within the allowed [-rank, rank) range.
       non_neg_concat_dim = (
@@ -428,7 +428,7 @@ def _GatherV2Grad(op, grad):
 
   # For axis 0 gathers, build an appropriately shaped IndexedSlices.
   if axis_static == 0:
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       params_tail_shape = params_shape.cpu()[1:]
     else:
       params_tail_shape = params_shape[1:]
@@ -578,7 +578,7 @@ def _TileGrad(op, grad):
   axes = math_ops.range(0, array_ops.size(split_shape), 2)
   input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
   # Fix shape inference
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     input_grad.set_shape(op.inputs[0].get_shape())
   return [input_grad, None]
 
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 9108fe759b..b4e1b9d781 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -128,9 +128,7 @@ def identity(input, name=None):  # pylint: disable=redefined-builtin
   Returns:
     A `Tensor`. Has the same type as `input`.
   """
-  if context.in_graph_mode():
-    return gen_array_ops.identity(input, name=name)
-  else:
+  if context.executing_eagerly():
     input = ops.convert_to_tensor(input)
     in_device = input.device
     # TODO(ashankar): Does 'identity' need to invoke execution callbacks?
@@ -140,6 +138,8 @@ def identity(input, name=None):  # pylint: disable=redefined-builtin
     if context_device != in_device:
       return input._copy()  # pylint: disable=protected-access
     return input
+  else:
+    return gen_array_ops.identity(input, name=name)
 
 
 # pylint: disable=redefined-builtin,protected-access
@@ -305,7 +305,7 @@ def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
                           sparse_tensor.SparseTensorValue)):
       return gen_math_ops.cast(input.dense_shape, out_type)
     else:
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         input_tensor = ops.convert_to_tensor(input)
         input_shape = input_tensor.get_shape()
         if optimize and input_shape.is_fully_defined():
@@ -330,7 +330,7 @@ def shape_n(input, out_type=dtypes.int32, name=None):
   """
 
   output = gen_array_ops.shape_n(input, out_type=out_type, name=name)
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     for i, input_tensor in enumerate(input):
       input_tensor = ops.convert_to_tensor(input_tensor)
       input_shape = input_tensor.get_shape()
@@ -385,9 +385,8 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
   Returns:
     A `Tensor` of type `out_type`. Defaults to `tf.int32`.
   """
-  if context.in_eager_mode() and not isinstance(
-      input, (sparse_tensor.SparseTensor,
-              sparse_tensor.SparseTensorValue)):
+  if context.executing_eagerly() and not isinstance(
+      input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
     return np.prod(ops.convert_to_tensor(input)._shape_tuple())  # pylint: disable=protected-access
   with ops.name_scope(name, "Size", [input]) as name:
     if isinstance(input, (sparse_tensor.SparseTensor,
@@ -783,7 +782,7 @@ def strided_slice(input_,
         new_axis_mask=new_axis_mask,
         shrink_axis_mask=shrink_axis_mask)
 
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     # TODO(apassos) In eager mode assignment will be done by overriding
     # __setitem__ instead.
     op.assign = assign
@@ -1457,7 +1456,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False):
       ret = transpose_fn(a, perm, name=name)
       # NOTE(mrry): Setting the shape explicitly because
       #   reverse is not handled by the shape function.
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         input_shape = ret.op.inputs[0].get_shape().dims
         if input_shape is not None:
           ret.set_shape(input_shape[::-1])
@@ -1622,7 +1621,7 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True):
   with ops.name_scope(name, "zeros_like", [tensor]) as name:
     tensor = ops.convert_to_tensor(tensor, name="tensor")
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       if dtype is not None and dtype != tensor.dtype:
         return zeros(
             shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
@@ -1678,7 +1677,7 @@ def ones_like(tensor, dtype=None, name=None, optimize=True):
     if dtype is None:
       dtype = tensor.dtype
     ret = ones(ones_shape, dtype=dtype, name=name)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       ret.set_shape(tensor.get_shape())
     return ret
 
@@ -1759,7 +1758,7 @@ def placeholder(dtype, shape=None, name=None):
   Raises:
     RuntimeError: if eager execution is enabled
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError("tf.placeholder() is not compatible with "
                        "eager execution.")
 
@@ -1822,7 +1821,7 @@ def sparse_placeholder(dtype, shape=None, name=None):
   Raises:
     RuntimeError: if eager execution is enabled
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError("tf.placeholder() is not compatible with "
                        "eager execution.")
 
@@ -1921,7 +1920,7 @@ def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0):  # pyl
     raise ValueError("Unknown padding mode: %s" % mode)
 
   # Restore shape information where possible.
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     paddings_constant = tensor_util.constant_value(
         result.op.inputs[1], partial=True)
     input_shape = result.op.inputs[0].shape
diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py
index 0fd6e29a49..7d6e047d7c 100644
--- a/tensorflow/python/ops/check_ops.py
+++ b/tensorflow/python/ops/check_ops.py
@@ -169,7 +169,7 @@ def assert_negative(x, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_negative', [x, data]):
     x = ops.convert_to_tensor(x, name='x')
     if data is None:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         name = _shape_and_dtype_str(x)
       else:
         name = x.name
@@ -210,7 +210,7 @@ def assert_positive(x, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_positive', [x, data]):
     x = ops.convert_to_tensor(x, name='x')
     if data is None:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         name = _shape_and_dtype_str(x)
       else:
         name = x.name
@@ -251,7 +251,7 @@ def assert_non_negative(x, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_non_negative', [x, data]):
     x = ops.convert_to_tensor(x, name='x')
     if data is None:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         name = _shape_and_dtype_str(x)
       else:
         name = x.name
@@ -293,7 +293,7 @@ def assert_non_positive(x, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_non_positive', [x, data]):
     x = ops.convert_to_tensor(x, name='x')
     if data is None:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         name = _shape_and_dtype_str(x)
       else:
         name = x.name
@@ -343,7 +343,7 @@ def assert_equal(x, y, data=None, summarize=None, message=None, name=None):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       eq = math_ops.equal(x, y)
       condition = math_ops.reduce_all(eq)
       if not condition:
@@ -435,7 +435,7 @@ def assert_none_equal(
   with ops.name_scope(name, 'assert_none_equal', [x, y, data]):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -512,7 +512,7 @@ def assert_near(
     rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=x.dtype)
     atol = ops.convert_to_tensor(atol, name='atol', dtype=x.dtype)
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -562,7 +562,7 @@ def assert_less(x, y, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_less', [x, y, data]):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -610,7 +610,7 @@ def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_less_equal', [x, y, data]):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -658,7 +658,7 @@ def assert_greater(x, y, data=None, summarize=None, message=None, name=None):
   with ops.name_scope(name, 'assert_greater', [x, y, data]):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -708,7 +708,7 @@ def assert_greater_equal(x, y, data=None, summarize=None, message=None,
   with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
     x = ops.convert_to_tensor(x, name='x')
     y = ops.convert_to_tensor(y, name='y')
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       x_name = _shape_and_dtype_str(x)
       y_name = _shape_and_dtype_str(y)
     else:
@@ -808,7 +808,7 @@ def assert_rank(x, rank, data=None, summarize=None, message=None, name=None):
     static_condition = lambda actual_rank, given_rank: actual_rank == given_rank
     dynamic_condition = math_ops.equal
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       name = ''
     else:
       name = x.name
@@ -873,7 +873,7 @@ def assert_rank_at_least(
     static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank
     dynamic_condition = math_ops.greater_equal
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       name = ''
     else:
       name = x.name
@@ -1001,7 +1001,7 @@ def assert_rank_in(
     ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks])
     message = message or ''
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       name = ''
     else:
       name = x.name
@@ -1054,7 +1054,7 @@ def assert_integer(x, message=None, name=None):
   with ops.name_scope(name, 'assert_integer', [x]):
     x = ops.convert_to_tensor(x, name='x')
     if not x.dtype.is_integer:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         name = 'tensor'
       else:
         name = x.name
@@ -1087,12 +1087,11 @@ def assert_type(tensor, tf_type, message=None, name=None):
   with ops.name_scope(name, 'assert_type', [tensor]):
     tensor = ops.convert_to_tensor(tensor, name='tensor')
     if tensor.dtype != tf_type:
-      if context.in_graph_mode():
-        raise TypeError(
-            '%s  %s must be of type %s' % (message, tensor.name, tf_type))
+      if context.executing_eagerly():
+        raise TypeError('%s tensor must be of type %s' % (message, tf_type))
       else:
-        raise TypeError(
-            '%s tensor must be of type %s' % (message, tf_type))
+        raise TypeError('%s  %s must be of type %s' % (message, tensor.name,
+                                                       tf_type))
 
     return control_flow_ops.no_op('statically_determined_correct_type')
 
@@ -1240,7 +1239,7 @@ def assert_scalar(tensor, name=None):
     tensor = ops.convert_to_tensor(tensor, name=name_scope)
     shape = tensor.get_shape()
     if shape.ndims != 0:
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         raise ValueError('Expected scalar shape, saw shape: %s.'
                          % (shape,))
       else:
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 4e524846cc..a2f52de749 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -152,7 +152,7 @@ def Assert(condition, data, summarize=None, name=None):
     @compatibility{eager} `tf.errors.InvalidArgumentError` if `condition`
     is not true
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     if not condition:
       xs = ops.convert_n_to_tensor(data)
       data_str = [_summarize_eager(x, summarize) for x in xs]
@@ -178,7 +178,7 @@ def Assert(condition, data, summarize=None, name=None):
             condition, data, summarize, name="Assert")
 
       guarded_assert = cond(condition, no_op, true_assert, name="AssertGuard")
-      if context.in_eager_mode():
+      if context.executing_eagerly():
         return
       return guarded_assert.op
 
@@ -2025,7 +2025,7 @@ def cond(pred,
     raise TypeError("false_fn must be callable.")
 
   with ops.name_scope(name, "cond", [pred]):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       if pred:
         return _UnpackIfSingleton(true_fn())
       return _UnpackIfSingleton(false_fn())
@@ -3177,7 +3177,7 @@ def while_loop(cond,
             math_ops.logical_and(i < maximum_iterations, orig_cond(*lv)))
         body = lambda i, lv: (i + 1, orig_body(*lv))
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       while cond(*loop_vars):
         loop_vars = body(*loop_vars)
       if maximum_iterations is not None:
@@ -3271,7 +3271,7 @@ def with_dependencies(dependencies, output_tensor, name=None):
   Raises:
     TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     return output_tensor
   with ops.name_scope(name, "control_dependency",
                       list(dependencies) + [output_tensor]) as name:
@@ -3316,7 +3316,7 @@ def group(*inputs, **kwargs):
   Raises:
     ValueError: If an unknown keyword argument is provided.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     return None
   name = kwargs.pop("name", None)
   if kwargs:
@@ -3396,7 +3396,7 @@ def tuple(tensors, name=None, control_inputs=None):  # pylint: disable=redefined
       objects.
 
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     return tensors
   with ops.name_scope(name, "tuple", tensors) as name:
     tensors = [t if (isinstance(t, ops.Operation)
diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py
index f199ba8fd4..9eacac1b37 100644
--- a/tensorflow/python/ops/custom_gradient.py
+++ b/tensorflow/python/ops/custom_gradient.py
@@ -92,7 +92,7 @@ def custom_gradient(f):
 
   def decorated(*args, **kwargs):
     """Decorated function with custom gradient."""
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if kwargs:
         raise ValueError(
             "The custom_gradient decorator currently suports keywords "
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 052caffd49..d2cc87555f 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -159,7 +159,7 @@ class QueueBase(object):
       ValueError: If one of the arguments is invalid.
       RuntimeError: If eager execution is enabled.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError(
           "Queues are not supported when eager execution is enabled. "
           "Instead, please use tf.data to get data into your model.")
@@ -177,10 +177,10 @@ class QueueBase(object):
     else:
       self._names = None
     self._queue_ref = queue_ref
-    if context.in_graph_mode():
-      self._name = self._queue_ref.op.name.split("/")[-1]
-    else:
+    if context.executing_eagerly():
       self._name = context.context().scope_name
+    else:
+      self._name = self._queue_ref.op.name.split("/")[-1]
 
   @staticmethod
   def from_list(index, queues):
@@ -231,9 +231,9 @@ class QueueBase(object):
   @property
   def name(self):
     """The name of the underlying queue."""
-    if context.in_graph_mode():
-      return self._queue_ref.op.name
-    return self._name
+    if context.executing_eagerly():
+      return self._name
+    return self._queue_ref.op.name
 
   @property
   def dtypes(self):
@@ -444,7 +444,7 @@ class QueueBase(object):
 
     # NOTE(mrry): Not using a shape function because we need access to
     # the `QueueBase` object.
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       op = ret[0].op
       for output, shape in zip(op.values(), self._shapes):
         output.set_shape(shape)
@@ -484,7 +484,7 @@ class QueueBase(object):
 
     # NOTE(mrry): Not using a shape function because we need access to
     # the Queue object.
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       op = ret[0].op
       batch_dim = tensor_shape.Dimension(
           tensor_util.constant_value(op.inputs[1]))
@@ -528,7 +528,7 @@ class QueueBase(object):
 
     # NOTE(mrry): Not using a shape function because we need access to
     # the Queue object.
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       op = ret[0].op
       for output, shape in zip(op.values(), self._shapes):
         output.set_shape(tensor_shape.TensorShape([None]).concatenate(shape))
@@ -990,10 +990,10 @@ class Barrier(object):
         shapes=self._shapes,
         shared_name=shared_name,
         name=name)
-    if context.in_graph_mode():
-      self._name = self._barrier_ref.op.name.split("/")[-1]
-    else:
+    if context.executing_eagerly():
       self._name = context.context().scope_name
+    else:
+      self._name = self._barrier_ref.op.name.split("/")[-1]
 
   @property
   def barrier_ref(self):
@@ -1003,9 +1003,9 @@ class Barrier(object):
   @property
   def name(self):
     """The name of the underlying barrier."""
-    if context.in_graph_mode():
-      return self._barrier_ref.op.name
-    return self._name
+    if context.executing_eagerly():
+      return self._name
+    return self._barrier_ref.op.name
 
   def insert_many(self, component_index, keys, values, name=None):
     """For each key, assigns the respective value to the specified component.
@@ -1083,7 +1083,7 @@ class Barrier(object):
 
     # NOTE(mrry): Not using a shape function because we need access to
     # the Barrier object.
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       op = ret[0].op
       if allow_small_batch:
         batch_dim = None
@@ -1183,10 +1183,10 @@ class ConditionalAccumulatorBase(object):
     else:
       self._shape = tensor_shape.unknown_shape()
     self._accumulator_ref = accumulator_ref
-    if context.in_graph_mode():
-      self._name = self._accumulator_ref.op.name.split("/")[-1]
-    else:
+    if context.executing_eagerly():
       self._name = context.context().scope_name
+    else:
+      self._name = self._accumulator_ref.op.name.split("/")[-1]
 
   @property
   def accumulator_ref(self):
diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py
index 09a0e345f2..8f5673597e 100644
--- a/tensorflow/python/ops/functional_ops.py
+++ b/tensorflow/python/ops/functional_ops.py
@@ -90,7 +90,7 @@ def foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True,
   if not callable(fn):
     raise TypeError("fn must be callable.")
 
-  in_graph_mode = context.in_graph_mode()
+  in_graph_mode = not context.executing_eagerly()
   with ops.name_scope(name, "foldl", [elems]):
     # TODO(akshayka): Remove the in_graph_mode check once caching devices are
     # supported in Eager
@@ -178,7 +178,7 @@ def foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True,
   if not callable(fn):
     raise TypeError("fn must be callable.")
 
-  in_graph_mode = context.in_graph_mode()
+  in_graph_mode = not context.executing_eagerly()
   with ops.name_scope(name, "foldr", [elems]):
     # TODO(akshayka): Remove the in_graph_mode check once caching devices are
     # supported in Eager
@@ -343,7 +343,7 @@ def map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True,
 
   elems_flat = input_flatten(elems)
 
-  in_graph_mode = context.in_graph_mode()
+  in_graph_mode = not context.executing_eagerly()
   with ops.name_scope(name, "map", elems_flat):
     # TODO(akshayka): Remove the in_graph_mode check once caching devices are
     # supported in Eager
@@ -536,7 +536,7 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True,
 
   elems_flat = input_flatten(elems)
 
-  in_graph_mode = context.in_graph_mode()
+  in_graph_mode = not context.executing_eagerly()
   with ops.name_scope(name, "scan", elems_flat):
     # TODO(akshayka): Remove the in_graph_mode check once caching devices are
     # supported in Eager
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index b678090542..44473ec69c 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -86,7 +86,7 @@ def _IndexedSlicesToTensor(value, dtype=None, name=None, as_ref=False):
         % str(value))
   # TODO(mrry): Consider adding static shape information to
   # IndexedSlices, to avoid using numpy here.
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     dense_shape_value = tensor_util.constant_value(value.dense_shape)
     if dense_shape_value is not None:
       num_elements = np.prod(dense_shape_value)
@@ -491,9 +491,10 @@ def gradients(ys,
 def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops,
                      gate_gradients, aggregation_method, stop_gradients):
   """Implementation of gradients()."""
-  if context.in_eager_mode():
-    raise RuntimeError("tf.gradients not supported in EAGER mode. Use "
-                       "functions in tf.contrib.eager.backprop instead.")
+  if context.executing_eagerly():
+    raise RuntimeError("tf.gradients not supported when eager execution "
+                       "is enabled. Use tf.contrib.eager.GradientTape "
+                       "instead.")
   ys = _AsList(ys)
   xs = _AsList(xs)
   stop_gradients = [] if stop_gradients is None else _AsList(stop_gradients)
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 7c782c12a5..f6a25610c5 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -173,7 +173,7 @@ class ReaderBase(object):
     Raises:
       RuntimeError: If eager execution is enabled.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError(
           "Readers are not supported when eager execution is enabled. "
           "Instead, please use tf.data to get data into your model.")
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index baf7cc19fa..6f043f60e6 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -157,10 +157,10 @@ class InitializableLookupTableBase(LookupInterface):
       default_value: The value to use if a key is missing in the table.
       initializer: The table initializer to use.
     """
-    if context.in_graph_mode():
-      name = table_ref.op.name.split("/")[-1]
-    else:
+    if context.executing_eagerly():
       name = context.context().scope_name
+    else:
+      name = table_ref.op.name.split("/")[-1]
     super(InitializableLookupTableBase,
           self).__init__(initializer.key_dtype, initializer.value_dtype,
                          name)
@@ -521,7 +521,7 @@ class TextFileInitializer(TableInitializerBase):
     ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
     # If the filename tensor is anything other than a string constant (e.g., if
     # it is a placeholder) then it does not make sense to track it as an asset.
-    if context.in_graph_mode() and constant_op.is_constant(filename):
+    if not context.executing_eagerly() and constant_op.is_constant(filename):
       ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, filename)
     return init_op
 
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index 0cae3c1453..424fd09e09 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -136,7 +136,7 @@ def _num_present(losses, weights, per_batch=False):
       `[batch_size]`. Otherwise, a single scalar tensor is returned.
   """
   if ((isinstance(weights, float) and weights != 0.0) or
-      (context.in_eager_mode() and weights._rank() == 0  # pylint: disable=protected-access
+      (context.executing_eagerly() and weights._rank() == 0  # pylint: disable=protected-access
        and not math_ops.equal(weights, 0.0))):
     return _num_elements(losses)
   with ops.name_scope(None, "num_present", (losses, weights)) as scope:
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 55dd0c0e0d..e2ee9e4fe4 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -52,14 +52,14 @@ def _SumGrad(op, grad):
     if axes is not None:
       rank = len(input_0_shape)
       if np.array_equal(axes, np.arange(rank)):  # Reduce all dims.
-        if context.in_graph_mode():
-          new_shape = [1] * rank
-        else:
+        if context.executing_eagerly():
           ctx = context.context()
           new_shape = ctx.ones_rank_cache().get(rank)
           if new_shape is None:
             new_shape = constant_op.constant([1] * rank, dtype=dtypes.int32)
             ctx.ones_rank_cache().put(rank, new_shape)
+        else:
+          new_shape = [1] * rank
         grad = array_ops.reshape(grad, new_shape)
         # If shape is not fully defined (but rank is), we use Shape.
         if None not in input_0_shape:
@@ -997,7 +997,7 @@ def _SparseMatMulGrad(op, grad):
       op.inputs[0]: op.get_attr("a_is_sparse"),
       op.inputs[1]: op.get_attr("b_is_sparse"),
       # Use heuristic to figure out if grad might be sparse
-      grad: context.in_graph_mode() and (grad.op.type == "ReluGrad")
+      grad: not context.executing_eagerly() and (grad.op.type == "ReluGrad")
   }
 
   def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False):
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index c019a5851f..5130c50717 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -2007,14 +2007,14 @@ def matmul(a,
     if transpose_b and adjoint_b:
       raise ValueError("Only one of transpose_b and adjoint_b can be True.")
 
-    if context.in_graph_mode():
-      a = ops.convert_to_tensor(a, name="a")
-      b = ops.convert_to_tensor(b, name="b")
-    else:
+    if context.executing_eagerly():
       if not isinstance(a, (ops.EagerTensor, _resource_variable_type)):
         a = ops.convert_to_tensor(a, name="a")
       if not isinstance(b, (ops.EagerTensor, _resource_variable_type)):
         b = ops.convert_to_tensor(b, name="b")
+    else:
+      a = ops.convert_to_tensor(a, name="a")
+      b = ops.convert_to_tensor(b, name="b")
 
     # TODO(apassos) remove _shape_tuple here when it is not needed.
     a_shape = a._shape_tuple()  # pylint: disable=protected-access
@@ -2249,7 +2249,7 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
     return inputs[0]
   elif len(inputs) == 1 and name is not None:
     return array_ops.identity(inputs[0], name=name)
-  elif context.in_eager_mode():
+  elif context.executing_eagerly():
     # TemporaryVariable not currently supported in eager mode; fall back
     # onto AddN for now.
     # TODO(frreiss) remove this once the lifetime of eager variables gets
diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py
index d314124ccd..9f85188b35 100644
--- a/tensorflow/python/ops/math_ops_test.py
+++ b/tensorflow/python/ops/math_ops_test.py
@@ -60,7 +60,7 @@ class ReduceTest(test_util.TensorFlowTestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testReduceInvalidAxis(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       # The shape check is in run a graph construction time. In eager mode,
       # it misses the check, magically return result given wrong shape.
       return
@@ -249,7 +249,7 @@ class ScalarMulTest(test_util.TensorFlowTestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testAcceptsRefs(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       var = resource_variable_ops.ResourceVariable(10, name="var")
     else:
       var = variables.Variable(10)
diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py
index 0123162b54..9ec4954579 100644
--- a/tensorflow/python/ops/metrics_impl.py
+++ b/tensorflow/python/ops/metrics_impl.py
@@ -308,7 +308,7 @@ def mean(values,
       or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean is not supported when eager execution '
                        'is enabled.')
 
@@ -394,7 +394,7 @@ def accuracy(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.accuracy is not supported when eager '
                        'execution is enabled.')
 
@@ -644,7 +644,7 @@ def auc(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.auc is not supported when eager execution '
                        'is enabled.')
 
@@ -758,7 +758,7 @@ def mean_absolute_error(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_absolute_error is not supported '
                        'when eager execution is enabled.')
 
@@ -818,7 +818,7 @@ def mean_cosine_distance(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_cosine_distance is not supported when '
                        'eager execution is enabled.')
 
@@ -891,7 +891,7 @@ def mean_per_class_accuracy(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_per_class_accuracy is not supported '
                        'when eager execution is enabled.')
 
@@ -996,7 +996,7 @@ def mean_iou(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_iou is not supported when '
                        'eager execution is enabled.')
 
@@ -1098,7 +1098,7 @@ def mean_relative_error(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_relative_error is not supported when '
                        'eager execution is enabled.')
 
@@ -1165,7 +1165,7 @@ def mean_squared_error(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_squared_error is not supported when '
                        'eager execution is enabled.')
 
@@ -1223,7 +1223,7 @@ def mean_tensor(values,
       or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.mean_tensor is not supported when '
                        'eager execution is enabled.')
 
@@ -1304,7 +1304,7 @@ def percentage_below(values,
       or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.percentage_below is not supported when '
                        'eager execution is enabled.')
 
@@ -1397,7 +1397,7 @@ def false_negatives(labels,
       or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.false_negatives is not supported when '
                        'eager execution is enabled.')
 
@@ -1453,7 +1453,7 @@ def false_negatives_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.false_negatives_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -1507,7 +1507,7 @@ def false_positives(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.false_positives is not supported when '
                        'eager execution is enabled.')
 
@@ -1563,7 +1563,7 @@ def false_positives_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.false_positives_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -1617,7 +1617,7 @@ def true_negatives(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.true_negatives is not '
                        'supported when eager execution is enabled.')
 
@@ -1673,7 +1673,7 @@ def true_negatives_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.true_negatives_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -1727,7 +1727,7 @@ def true_positives(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.true_positives is not '
                        'supported when eager execution is enabled.')
 
@@ -1783,7 +1783,7 @@ def true_positives_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.true_positives_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -1851,7 +1851,7 @@ def precision(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.precision is not '
                        'supported when eager execution is enabled.')
 
@@ -1947,7 +1947,7 @@ def precision_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.precision_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -2023,7 +2023,7 @@ def recall(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.recall is not supported is not '
                        'supported when eager execution is enabled.')
 
@@ -2400,7 +2400,7 @@ def recall_at_k(labels,
     are not a list or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.recall_at_k is not '
                        'supported when eager execution is enabled.')
 
@@ -2549,7 +2549,7 @@ def recall_at_thresholds(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.recall_at_thresholds is not '
                        'supported when eager execution is enabled.')
 
@@ -2626,7 +2626,7 @@ def root_mean_squared_error(labels,
       tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.root_mean_squared_error is not '
                        'supported when eager execution is enabled.')
 
@@ -2707,7 +2707,7 @@ def sensitivity_at_specificity(labels,
       or `updates_collections` are not a list or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.sensitivity_at_specificity is not '
                        'supported when eager execution is enabled.')
 
@@ -3098,7 +3098,7 @@ def average_precision_at_k(labels,
     ValueError: if k is invalid.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.sparse_average_precision_at_k is not '
                        'supported when eager execution is enabled.')
 
@@ -3267,7 +3267,7 @@ def precision_at_top_k(labels,
       are not a list or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.precision_at_top_k is not '
                        'supported when eager execution is enabled.')
 
@@ -3396,7 +3396,7 @@ def precision_at_k(labels,
       are not a list or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.sparse_precision_at_k is not '
                        'supported when eager execution is enabled.')
 
@@ -3473,7 +3473,7 @@ def specificity_at_sensitivity(labels,
       or `updates_collections` are not a list or tuple.
     RuntimeError: If eager execution is enabled.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError('tf.metrics.specificity_at_sensitivity is not '
                        'supported when eager execution is enabled.')
 
diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py
index 5582daf2da..4af5bd26dd 100644
--- a/tensorflow/python/ops/nn_grad.py
+++ b/tensorflow/python/ops/nn_grad.py
@@ -456,7 +456,7 @@ def _SoftmaxCrossEntropyWithLogitsGrad(op, grad_loss, grad_grad):
 
   def IsZero(g):
     # Some introspection to check if the gradient is feeding zeros
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       # TODO(apassos) add an efficient way to detect eager zeros here.
       return False
     if g.op.type in ("ZerosLike", "Zeros"):
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 66a05f2228..fb3fe77b4d 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -1504,7 +1504,7 @@ def bias_add(value, bias, data_format=None, name=None):
     A `Tensor` with the same type as `value`.
   """
   with ops.name_scope(name, "BiasAdd", [value, bias]) as name:
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       value = ops.convert_to_tensor(value, name="input")
       bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
     return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name)
@@ -1616,7 +1616,7 @@ def _flatten_outer_dims(logits):
   output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0))
 
   # Set output shape if known.
-  if context.in_graph_mode():
+  if not context.executing_eagerly():
     shape = logits.get_shape()
     if shape is not None and shape.dims is not None:
       shape = shape.as_list()
@@ -1881,7 +1881,8 @@ def softmax_cross_entropy_with_logits_v2(
 
     # Make shape inference work since reshape and transpose may erase its static
     # shape.
-    if context.in_graph_mode() and shape is not None and shape.dims is not None:
+    if not context.executing_eagerly(
+    ) and shape is not None and shape.dims is not None:
       shape = shape.as_list()
       del shape[dim]
       cost.set_shape(shape)
@@ -2318,7 +2319,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):  # pylint: di
     # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
     binary_tensor = math_ops.floor(random_tensor)
     ret = math_ops.div(x, keep_prob) * binary_tensor
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       ret.set_shape(x.get_shape())
     return ret
 
diff --git a/tensorflow/python/ops/numerics.py b/tensorflow/python/ops/numerics.py
index b4ce1cbf25..d348e47f57 100644
--- a/tensorflow/python/ops/numerics.py
+++ b/tensorflow/python/ops/numerics.py
@@ -74,7 +74,7 @@ def add_check_numerics_ops():
   the checked operations.
   @enc_compatibility
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError(
         "add_check_numerics_ops() is not compatible with eager execution. "
         "To check for Inf's and NaN's under eager execution, call "
diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index d0578f8205..54191ee765 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -135,10 +135,10 @@ class EagerResourceDeleter(object):
       # valid, and so on. Printing warnings in these cases is silly
       # (exceptions raised from __del__ are printed as warnings to stderr).
       pass  # 'NoneType' object is not callable when the handle has been
-            # partially unloaded.
+      # partially unloaded.
     except AttributeError:
       pass  # 'NoneType' object has no attribute 'eager_mode' when context has
-            # been unloaded. Will catch other module unloads as well.
+      # been unloaded. Will catch other module unloads as well.
 
 
 def shape_safe_assign_variable_handle(handle, shape, value, name=None):
@@ -267,9 +267,9 @@ class ResourceVariable(variables.Variable):
       if initial_value is not None:
         raise ValueError("variable_def and initial_value are mutually "
                          "exclusive.")
-      if not context.in_graph_mode():
-        raise ValueError("Creating ResourceVariable from variable_def"
-                         " only supported in GRAPH mode.")
+      if context.executing_eagerly():
+        raise ValueError("Creating ResourceVariable from variable_def is "
+                         "not supported when eager execution is enabled.")
       self._init_from_proto(variable_def, import_scope=import_scope)
     else:
       self._init_from_args(
@@ -363,7 +363,7 @@ class ResourceVariable(variables.Variable):
     # this graph.
     self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
     with ops.init_scope():
-      self._in_graph_mode = context.in_graph_mode()
+      self._in_graph_mode = not context.executing_eagerly()
       with ops.name_scope(name, "Variable", []
                           if init_from_fn else [initial_value]) as name:
         # pylint: disable=protected-access
@@ -470,7 +470,7 @@ class ResourceVariable(variables.Variable):
               self._cached_value = self._read_variable_op()
           else:
             self._cached_value = None
-        if context.in_graph_mode():
+        if not context.executing_eagerly():
           ops.add_to_collections(collections, self)
         elif ops.GraphKeys.GLOBAL_STEP in collections:
           ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self)
@@ -489,7 +489,7 @@ class ResourceVariable(variables.Variable):
   def _init_from_proto(self, variable_def, import_scope=None):
     """Initializes from `VariableDef` proto."""
     # Note that init_from_proto is currently not supported in Eager mode.
-    assert context.in_graph_mode()
+    assert not context.executing_eagerly()
     self._in_graph_mode = True
     assert isinstance(variable_def, variable_pb2.VariableDef)
     if not variable_def.is_resource:
@@ -582,7 +582,8 @@ class ResourceVariable(variables.Variable):
   def create(self):
     """The op responsible for initializing this variable."""
     if not self._in_graph_mode:
-      raise RuntimeError("Calling create in EAGER mode not supported.")
+      raise RuntimeError("Calling create is not supported when eager execution"
+                         " is enabled.")
     return self._initializer_op
 
   @property
@@ -610,7 +611,7 @@ class ResourceVariable(variables.Variable):
   @property
   def initial_value(self):
     """Returns the Tensor used as the initial value for the variable."""
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError("initial_value not supported in EAGER mode.")
     return self._initial_value
 
@@ -631,15 +632,15 @@ class ResourceVariable(variables.Variable):
 
   def eval(self, session=None):
     """Evaluates and returns the value of this variable."""
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError("Trying to eval in EAGER mode")
     return self._graph_element.eval(session=session)
 
   def numpy(self):
-    if context.in_graph_mode():
-      raise NotImplementedError(
-          "numpy() is only available when eager execution is enabled.")
-    return self.read_value().numpy()
+    if context.executing_eagerly():
+      return self.read_value().numpy()
+    raise NotImplementedError(
+        "numpy() is only available when eager execution is enabled.")
 
   def count_up_to(self, limit):
     """Increments this variable until it reaches `limit`.
@@ -720,7 +721,7 @@ class ResourceVariable(variables.Variable):
       A `VariableDef` protocol buffer, or `None` if the `Variable` is not
       in the specified name scope.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError("to_proto not supported in EAGER mode.")
     if export_scope is None or self.handle.name.startswith(export_scope):
       var_def = variable_pb2.VariableDef()
@@ -747,7 +748,7 @@ class ResourceVariable(variables.Variable):
 
   @staticmethod
   def from_proto(variable_def, import_scope=None):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError("from_proto not supported in EAGER mode.")
     return ResourceVariable(
         variable_def=variable_def, import_scope=import_scope)
@@ -984,10 +985,10 @@ class _UnreadVariable(ResourceVariable):
     self._is_initialized_op = None
     self._initializer_op = None
     self._parent_op = parent_op
-    if context.in_graph_mode():
-      self._graph_element = self.read_value()
-    else:
+    if context.executing_eagerly():
       self._graph_element = None
+    else:
+      self._graph_element = self.read_value()
     self._handle_deleter = deleter
 
   def value(self):
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index aa8d4327d2..625d433b1f 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -575,7 +575,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
     # Create a new scope in which the caching device is either
     # determined by the parent scope, or is set to place the cached
     # Variable using the same placement as for the rest of the RNN.
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if varscope.caching_device is None:
         varscope.set_caching_device(lambda op: op.device)
 
@@ -616,7 +616,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
           ["Expected shape for Tensor %s is " % x.name,
            packed_shape, " but saw shape: ", x_shape])
 
-    if context.in_graph_mode() and sequence_length is not None:
+    if not context.executing_eagerly() and sequence_length is not None:
       # Perform some shape validation
       with ops.control_dependencies(
           [_assert_has_shape(sequence_length, [batch_size])]):
@@ -742,7 +742,7 @@ def _dynamic_rnn_loop(cell,
                                         element_shape=element_shape,
                                         tensor_array_name=base_name + name)
 
-  in_graph_mode = context.in_graph_mode()
+  in_graph_mode = not context.executing_eagerly()
   if in_graph_mode:
     output_ta = tuple(
         _create_ta(
@@ -1027,7 +1027,7 @@ def raw_rnn(cell, loop_fn,
   # determined by the parent scope, or is set to place the cached
   # Variable using the same placement as for the rest of the RNN.
   with vs.variable_scope(scope or "rnn") as varscope:
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if varscope.caching_device is None:
         varscope.set_caching_device(lambda op: op.device)
 
@@ -1242,7 +1242,7 @@ def static_rnn(cell,
   # determined by the parent scope, or is set to place the cached
   # Variable using the same placement as for the rest of the RNN.
   with vs.variable_scope(scope or "rnn") as varscope:
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if varscope.caching_device is None:
         varscope.set_caching_device(lambda op: op.device)
 
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 3ae1d1184d..e61d10835f 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -128,7 +128,7 @@ def _zero_state_tensors(state_size, batch_size, dtype):
     """Combine s with batch_size to get a proper tensor shape."""
     c = _concat(batch_size, s)
     size = array_ops.zeros(c, dtype=dtype)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       c_static = _concat(batch_size, s, static=True)
       size.set_shape(c_static)
     return size
@@ -192,12 +192,13 @@ class RNNCell(base_layer.Layer):
 
   def _rnn_get_variable(self, getter, *args, **kwargs):
     variable = getter(*args, **kwargs)
-    if context.in_graph_mode():
-      trainable = (variable in tf_variables.trainable_variables() or
-                   (isinstance(variable, tf_variables.PartitionedVariable) and
-                    list(variable)[0] in tf_variables.trainable_variables()))
-    else:
+    if context.executing_eagerly():
       trainable = variable._trainable  # pylint: disable=protected-access
+    else:
+      trainable = (
+          variable in tf_variables.trainable_variables() or
+          (isinstance(variable, tf_variables.PartitionedVariable) and
+           list(variable)[0] in tf_variables.trainable_variables()))
     if trainable and variable not in self._trainable_weights:
       self._trainable_weights.append(variable)
     elif not trainable and variable not in self._non_trainable_weights:
@@ -241,7 +242,7 @@ class RNNCell(base_layer.Layer):
     # Try to use the last cached zero_state. This is done to avoid recreating
     # zeros, especially when eager execution is enabled.
     state_size = self.state_size
-    is_eager = context.in_eager_mode()
+    is_eager = context.executing_eagerly()
     if is_eager and hasattr(self, "_last_zero_state"):
       (last_state_size, last_batch_size, last_dtype,
        last_output) = getattr(self, "_last_zero_state")
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 01f0b81684..529eebe769 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -317,7 +317,7 @@ def py_func(func, inp, Tout, stateful=True, name=None):
   Returns:
     A list of `Tensor` or a single `Tensor` which `func` computes.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     result = func(*[x.numpy() for x in inp])
     result = nest.flatten(result)
 
diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index fd4419640a..c3ad5831b4 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -186,7 +186,7 @@ def is_variable_initialized(ref, name=None):
   if ref.dtype._is_ref_dtype:
     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
   # Handle resource variables.
-  if context.in_eager_mode() or ref.op.type == "VarHandleOp":
+  if context.executing_eagerly() or ref.op.type == "VarHandleOp":
     return gen_resource_variable_ops.var_is_initialized_op(ref.handle,
                                                            name=name)
 
diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py
index 70e8040512..0a391d896a 100644
--- a/tensorflow/python/ops/template.py
+++ b/tensorflow/python/ops/template.py
@@ -204,7 +204,7 @@ def make_template_internal(name_,
   if kwargs:
     func_ = tf_decorator.make_decorator(func_, functools.partial(
         func_, **kwargs))
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     if unique_name_ is not None:
       raise ValueError(
           "unique_name_ cannot be used when eager exeuction is enabled.")
@@ -364,7 +364,7 @@ class Template(checkpointable.CheckpointableBase):
     """
     def _call_next_creator_renaming_initializer(initializer, **inner_kwargs):
       inner_kwargs.pop("name")  # Ignored; this is the scope-stripped name which
-                                # we don't want to propagate.
+      # we don't want to propagate.
       return next_creator(
           initial_value=initializer,
           name=name,
@@ -647,7 +647,7 @@ class EagerTemplate(Template):
     Raises:
       RuntimeError: if eager execution is not enabled.
     """
-    if not context.in_eager_mode():
+    if not context.executing_eagerly():
       raise RuntimeError(
           "{} objects can only be used when eager execution is enabled, use "
           "tf.Template for graph construction".
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 6226f426be..2f6badcb53 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -338,7 +338,7 @@ class _GraphTensorArray(object):
     with ops.name_scope(name, "TensorArrayScatter",
                         [self._handle, value, indices]):
       value = ops.convert_to_tensor(value, name="value")
-      if self._infer_shape and context.in_graph_mode():
+      if self._infer_shape and not context.executing_eagerly():
         self._merge_element_shape(value.shape[1:])
       with self._maybe_colocate_with(value):
         flow_out = gen_data_flow_ops.tensor_array_scatter_v3(
@@ -363,7 +363,7 @@ class _GraphTensorArray(object):
       value = ops.convert_to_tensor(value, name="value")
       with self._maybe_colocate_with(value):
         lengths_64 = math_ops.to_int64(lengths)
-        if self._infer_shape and context.in_graph_mode():
+        if self._infer_shape and not context.executing_eagerly():
           clengths = tensor_util.constant_value(lengths_64)
           if value.shape.dims is not None:
             if clengths is not None and clengths.max() == clengths.min():
@@ -774,10 +774,10 @@ class TensorArray(object):
       ValueError: if both handle and tensor_array_name are provided.
       TypeError: if handle is provided but is not a Tensor.
     """
-    if context.in_graph_mode():
-      implementation = _GraphTensorArray
-    else:
+    if context.executing_eagerly():
       implementation = _EagerTensorArray
+    else:
+      implementation = _GraphTensorArray
 
     self._implementation = implementation(
         dtype,
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index de4e44f60c..7f650ff6a9 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -321,7 +321,7 @@ class _VariableStore(object):
       raise ValueError(
           "Passed a custom_getter which is not callable: %s" % custom_getter)
 
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       if not self._store_eager_variables and reuse:
         raise RuntimeError(
             "When eager execution is enabled variable reuse is only supported"
@@ -518,7 +518,7 @@ class _VariableStore(object):
         when violating reuse during variable creation, or if an existing
         sharded variable exists for the given name but with different sharding.
     """
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise NotImplementedError("Partitioned variables are not yet supported "
                                 "when eager execution is enabled.")
 
@@ -798,7 +798,7 @@ class _VariableStore(object):
         validate_shape=validate_shape,
         constraint=constraint,
         use_resource=use_resource)
-    if context.in_graph_mode() or self._store_eager_variables:
+    if not context.executing_eagerly() or self._store_eager_variables:
       # In eager mode we do not want to keep default references to Variable
       # objects as this will prevent their memory from being released.
       self._vars[name] = v
@@ -811,12 +811,12 @@ class _VariableStore(object):
         with ops.name_scope(name + "/Regularizer/"):
           loss = regularizer(v)
         if loss is not None:
-          if context.in_graph_mode():
-            v_name = v.name
-            loss_name = loss.name
-          else:
+          if context.executing_eagerly():
             v_name = "v_%s" % type(v)
             loss_name = "loss_%s" % type(loss)
+          else:
+            v_name = v.name
+            loss_name = loss.name
           logging.vlog(1, "Applied regularizer to %s and added the result %s "
                        "to REGULARIZATION_LOSSES.", v_name, loss_name)
           ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, loss)
@@ -920,7 +920,7 @@ class VariableScope(object):
     self._dtype = dtype
     self._use_resource = use_resource
     self._constraint = constraint
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       if self._caching_device is not None:
         raise NotImplementedError("Caching devices is not yet supported "
                                   "when eager execution is enabled.")
@@ -988,7 +988,7 @@ class VariableScope(object):
 
   def set_use_resource(self, use_resource):
     """Sets whether to use ResourceVariables for this scope."""
-    if context.in_eager_mode() and not use_resource:
+    if context.executing_eagerly() and not use_resource:
       raise ValueError("When eager execution is enabled, "
                        "use_resource cannot be set to false.")
     self._use_resource = use_resource
@@ -999,14 +999,14 @@ class VariableScope(object):
 
   def set_caching_device(self, caching_device):
     """Set caching_device for this scope."""
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise NotImplementedError("Caching devices are not yet supported "
                                 "when eager execution is enabled.")
     self._caching_device = caching_device
 
   def set_partitioner(self, partitioner):
     """Set partitioner for this scope."""
-    if partitioner and context.in_eager_mode():
+    if partitioner and context.executing_eagerly():
       raise NotImplementedError("Partitioned variables are not yet supported "
                                 "when eager execution is enabled.")
     self._partitioner = partitioner
@@ -1057,14 +1057,14 @@ class VariableScope(object):
       partitioner = self._partitioner
     if custom_getter is None:
       custom_getter = self._custom_getter
-    if context.in_graph_mode():
+    if context.executing_eagerly():
+      reuse = False
+      use_resource = True
+    else:
       if reuse is None:
         reuse = self._reuse
       if use_resource is None:
         use_resource = self._use_resource
-    else:
-      reuse = False
-      use_resource = True
 
     full_name = self.name + "/" + name if self.name else name
     # Variable names only depend on variable_scope (full_name here),
@@ -1107,7 +1107,7 @@ class VariableScope(object):
                                 use_resource=None,
                                 constraint=None):
     """Gets an existing variable with this name or create a new one."""
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise NotImplementedError("Partitioned variables are not yet supported "
                                 "when eager execution is enabled.")
     if initializer is None:
@@ -1871,7 +1871,7 @@ class variable_scope(object):
       raise ValueError("The reuse parameter must be True or False or None.")
     if self._values is None:
       self._values = []
-    self._in_graph_mode = not context.in_eager_mode()
+    self._in_graph_mode = not context.executing_eagerly()
     if self._in_graph_mode:
       self._graph = ops._get_graph_from_inputs(self._values)  # pylint: disable=protected-access
     self._cached_pure_variable_scope = None
@@ -2111,13 +2111,13 @@ def default_variable_creator(next_creator=None, **kwargs):
   use_resource = kwargs.get("use_resource", None)
   if use_resource is None:
     use_resource = get_variable_scope().use_resource
-  if use_resource or (use_resource is None and context.in_eager_mode()):
+  if use_resource or (use_resource is None and context.executing_eagerly()):
     return resource_variable_ops.ResourceVariable(
         initial_value=initial_value, trainable=trainable,
         collections=collections, validate_shape=validate_shape,
         caching_device=caching_device, name=name, dtype=dtype,
         constraint=constraint)
-  elif not use_resource and context.in_eager_mode():
+  elif not use_resource and context.executing_eagerly():
     raise RuntimeError(
         "VariableScope should use resource variable when eager execution is"
         " enabled, but use_resource is False."
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 643a3b7edc..5b9947f441 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -210,10 +210,11 @@ class Variable(checkpointable.CheckpointableBase):
     for details on how variables work in eager execution.
     @end_compatibility
     """
-    if not context.in_graph_mode():
-      raise RuntimeError("tf.Variable not supported in Eager mode. "
-                         "Please use tfe.Variable instead")
-    self._in_graph_mode = context.in_graph_mode()
+    if context.executing_eagerly():
+      raise RuntimeError(
+          "tf.Variable not supported when eager execution is enabled. "
+          "Please use tf.contrib.eager.Variable instead")
+    self._in_graph_mode = True
     if variable_def:
       # If variable_def is provided, recreates the variable from its fields.
       if initial_value:
@@ -234,7 +235,7 @@ class Variable(checkpointable.CheckpointableBase):
           constraint=constraint)
 
   def __repr__(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       return "<tf.Variable '%s' shape=%s dtype=%s, numpy=%s>" % (
           self.name, self.get_shape(), self.dtype.name,
           ops.numpy_text(self.read_value(), is_repr=True))
@@ -740,15 +741,15 @@ class Variable(checkpointable.CheckpointableBase):
     Raises:
         ValueError: Session is not passed and no default session
     """
-    if context.in_graph_mode():
+    if context.executing_eagerly():
+      self.assign(value)
+    else:
       session = session or ops.get_default_session()
       if session is None:
         raise ValueError(
             "Either session argument should be provided or default session "
             "should be established")
       session.run(self._initializer_op, {self._initializer_op.inputs[1]: value})
-    else:
-      self.assign(value)
 
   # Conversion to tensor.
   @staticmethod
@@ -1248,9 +1249,9 @@ class PartitionedVariable(object):
         information does not match `shape`, or `partitions` has invalid values.
       RuntimeError: If eager execution is enabled
     """
-    if not context.in_graph_mode():
-      raise RuntimeError("tf.PartitionedVariable not supported in "
-                         "eager mode. Please use tfe.Variable instead")
+    if context.executing_eagerly():
+      raise RuntimeError(
+          "tf.PartitionedVariable not supported with eager execution enabled.")
     if not isinstance(variable_list, (list, tuple)):
       raise TypeError(
           "variable_list is not a list or tuple: %s" % variable_list)
@@ -1541,7 +1542,7 @@ def variables_initializer(var_list, name="init"):
   Returns:
     An Op that run the initializers of all the specified variables.
   """
-  if var_list and context.in_graph_mode():
+  if var_list and not context.executing_eagerly():
     return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
   return control_flow_ops.no_op(name=name)
 
@@ -1563,7 +1564,7 @@ def global_variables_initializer():
   Returns:
     An Op that initializes global variables in the graph.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     return control_flow_ops.no_op(name="global_variables_initializer")
   return variables_initializer(global_variables())
 
@@ -1585,7 +1586,7 @@ def local_variables_initializer():
   Returns:
     An Op that initializes all local variables in the graph.
   """
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     return control_flow_ops.no_op(name="local_variables_initializer")
   return variables_initializer(local_variables())