From 9c7c7c9979d66e97aeb8717d87f351df5ecbf56b Mon Sep 17 00:00:00 2001
From: Gaurav Jain <gjn@google.com>
Date: Mon, 10 Feb 2020 10:52:45 -0800
Subject: [PATCH] Make experimental_ref no longer experimental
Also clean up related documentation to utilize doctest.
PiperOrigin-RevId: 294256062
Change-Id: I9d7ff8dc1324bda64f3638ba3f8e48a0fcb42545
---
tensorflow/python/client/session.py | 20 ++--
tensorflow/python/eager/backprop.py | 2 +-
tensorflow/python/eager/function_test.py | 11 +--
tensorflow/python/eager/ops_test.py | 4 +-
tensorflow/python/framework/function.py | 8 +-
tensorflow/python/framework/ops.py | 72 +++++++-------
tensorflow/python/framework/ops_test.py | 96 +++++++++----------
tensorflow/python/keras/layers/core.py | 12 ++-
.../experimental/autocast_variable.py | 2 +-
.../python/keras/optimizer_v2/adam_test.py | 3 +-
tensorflow/python/ops/custom_gradient.py | 23 ++---
tensorflow/python/ops/math_grad.py | 13 ++-
tensorflow/python/ops/parallel_for/pfor.py | 2 +-
tensorflow/python/ops/variables.py | 68 ++++++-------
tensorflow/python/ops/while_v2.py | 5 +-
tensorflow/python/training/moving_averages.py | 16 ++--
.../api/golden/v1/tensorflow.-tensor.pbtxt | 4 +
.../api/golden/v1/tensorflow.-variable.pbtxt | 4 +
.../api/golden/v2/tensorflow.-tensor.pbtxt | 4 +
.../api/golden/v2/tensorflow.-variable.pbtxt | 4 +
20 files changed, 187 insertions(+), 186 deletions(-)
diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index 4290a278f18..65ecc205369 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -494,10 +494,8 @@ class _FetchHandler(object):
if (isinstance(fetch, ops.Tensor) and
(fetch.op.type == 'GetSessionHandle' or
fetch.op.type == 'GetSessionHandleV2')):
- self._fetch_handles[fetch.experimental_ref()] = fetch.op.inputs[0].dtype
- self._final_fetches = [
- x for x in self._fetches if x.experimental_ref() not in feeds
- ]
+ self._fetch_handles[fetch.ref()] = fetch.op.inputs[0].dtype
+ self._final_fetches = [x for x in self._fetches if x.ref() not in feeds]
def _assert_fetchable(self, graph, op):
if not graph.is_fetchable(op):
@@ -553,16 +551,16 @@ class _FetchHandler(object):
else:
# If the fetch was in the feeds, use the fed value, otherwise
# use the returned value.
- if self._fetches[i].experimental_ref() in self._feed_handles:
+ if self._fetches[i].ref() in self._feed_handles:
# A fetch had a corresponding direct TensorHandle feed. Call eval()
# to obtain the Tensor value from the TensorHandle.
- value = self._feed_handles[self._fetches[i].experimental_ref()].eval()
+ value = self._feed_handles[self._fetches[i].ref()].eval()
else:
- value = self._feeds.get(self._fetches[i].experimental_ref())
+ value = self._feeds.get(self._fetches[i].ref())
if value is None:
value = tensor_values[j]
j += 1
- dtype = self._fetch_handles.get(self._fetches[i].experimental_ref())
+ dtype = self._fetch_handles.get(self._fetches[i].ref())
if dtype:
full_values.append(session_ops.TensorHandle(value, dtype, session))
else:
@@ -1147,7 +1145,7 @@ class BaseSession(SessionInterface):
session_ops.TensorHandle)
if is_tensor_handle_feed:
np_val = subfeed_val.to_numpy_array()
- feed_handles[subfeed_t.experimental_ref()] = subfeed_val
+ feed_handles[subfeed_t.ref()] = subfeed_val
else:
np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
@@ -1160,7 +1158,7 @@ class BaseSession(SessionInterface):
if not self.graph.is_feedable(subfeed_t):
raise ValueError('Tensor %s may not be fed.' % subfeed_t)
- feed_dict_tensor[subfeed_t.experimental_ref()] = np_val
+ feed_dict_tensor[subfeed_t.ref()] = np_val
feed_map[compat.as_bytes(subfeed_t.name)] = (subfeed_t, subfeed_val)
# Create a fetch handler to take care of the structure of fetches.
@@ -1435,7 +1433,7 @@ class BaseSession(SessionInterface):
np_val = np.array(handle.handle, dtype=np.object)
feed_name = handle_mover[0]
feed_tensor = feed_map[feed_name][0]
- feed_dict[feed_tensor.experimental_ref()] = np_val
+ feed_dict[feed_tensor.ref()] = np_val
return handles
def _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list,
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index 183961d03ca..42d8130fec8 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -659,7 +659,7 @@ def _zeros(shape, dtype):
device = ctx.device_name
if tensor_util.is_tensor(shape):
- shape_key = shape.experimental_ref()
+ shape_key = shape.ref()
else:
shape_key = shape
cache_key = shape_key, dtype, device
diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py
index 29b463a5445..b03586633ee 100644
--- a/tensorflow/python/eager/function_test.py
+++ b/tensorflow/python/eager/function_test.py
@@ -361,11 +361,10 @@ class FunctionTest(test.TestCase, parameterized.TestCase):
cf = f.get_concrete_function()
c = cc[0]
- captured_variables = {v.experimental_ref() for v in (a, b, c)}
- trainable_variables = {v.experimental_ref() for v in (b, c)}
- self.assertEqual({v.experimental_ref() for v in cf.variables},
- captured_variables)
- self.assertEqual({v.experimental_ref() for v in cf.trainable_variables},
+ captured_variables = {v.ref() for v in (a, b, c)}
+ trainable_variables = {v.ref() for v in (b, c)}
+ self.assertEqual({v.ref() for v in cf.variables}, captured_variables)
+ self.assertEqual({v.ref() for v in cf.trainable_variables},
trainable_variables)
self.assertEqual(cf.variables, cf.graph.variables)
self.assertEqual(cf.trainable_variables, cf.graph.trainable_variables)
@@ -2889,7 +2888,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase):
def testDecoratedMethodVariableCleanup(self):
m = DefunnedMiniModel()
m(array_ops.ones([1, 2]))
- variable_refs = list({v.experimental_ref() for v in m.variables})
+ variable_refs = list({v.ref() for v in m.variables})
self.assertLen(variable_refs, 2)
del m
diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py
index 569b06fc0e3..0e9b6283237 100644
--- a/tensorflow/python/eager/ops_test.py
+++ b/tensorflow/python/eager/ops_test.py
@@ -426,8 +426,8 @@ class OpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
strong_x = constant_op.constant([[1.]])
strong_y = constant_op.constant([[2.]])
- strong_x_ref = strong_x.experimental_ref()
- strong_y_ref = strong_y.experimental_ref()
+ strong_x_ref = strong_x.ref()
+ strong_y_ref = strong_y.ref()
weak_key_dict[strong_x_ref] = constant_op.constant([[3.]])
weak_key_dict[strong_y_ref] = constant_op.constant([[4.]])
strong_y.a = constant_op.constant([[5.]])
diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index ff396475d0b..b44a4b85c6c 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -813,9 +813,9 @@ class _FuncGraph(ops.Graph):
def capture(self, tensor, name=None):
"""Adds the given tensor to this graph and returns the captured tensor."""
- if tensor.experimental_ref() in self._captured:
+ if tensor.ref() in self._captured:
# Captured already.
- return self._captured[tensor.experimental_ref()]
+ return self._captured[tensor.ref()]
elif self._capture_by_value:
return self._add_tensor_and_parents(tensor)
else:
@@ -848,7 +848,7 @@ class _FuncGraph(ops.Graph):
compat.as_bytes(handle_data))
# pylint: enable=protected-access
self.inputs.append(ph)
- self._captured[tensor.experimental_ref()] = ph
+ self._captured[tensor.ref()] = ph
self.extra_args.append(ph)
if _is_guaranteed_const(tensor):
with ops.control_dependencies(None):
@@ -881,7 +881,7 @@ class _FuncGraph(ops.Graph):
op_def=op_def)
for t, captured_t in zip(op.outputs, captured_op.outputs):
- self._captured[t.experimental_ref()] = captured_t
+ self._captured[t.ref()] = captured_t
return captured_op
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index a49966c9f85..79bc55e9e35 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -724,8 +724,8 @@ class Tensor(_TensorLike):
g = getattr(self, "graph", None)
if (Tensor._USE_EQUALITY and executing_eagerly_outside_functions() and
(g is None or g.building_function)):
- raise TypeError("Tensor is unhashable if Tensor equality is enabled. "
- "Instead, use tensor.experimental_ref() as the key.")
+ raise TypeError("Tensor is unhashable. "
+ "Instead, use tensor.ref() as the key.")
else:
return id(self)
@@ -814,56 +814,50 @@ class Tensor(_TensorLike):
"""
return _eval_using_default_session(self, feed_dict, self.graph, session)
+ @deprecation.deprecated(None, "Use ref() instead.")
def experimental_ref(self):
- # tf.Variable also has the same experimental_ref() API. If you update the
- # documenation here, please update tf.Variable.experimental_ref() as well.
+ return self.ref()
+
+ def ref(self):
+ # tf.Variable also has the same ref() API. If you update the
+ # documentation here, please update tf.Variable.ref() as well.
"""Returns a hashable reference object to this Tensor.
Warning: Experimental API that could be changed or removed.
- The primary usecase for this API is to put tensors in a set/dictionary.
+ The primary use case for this API is to put tensors in a set/dictionary.
We can't put tensors in a set/dictionary as `tensor.__hash__()` is no longer
available starting Tensorflow 2.0.
- ```python
- import tensorflow as tf
+ The following will raise an exception starting 2.0
- x = tf.constant(5)
- y = tf.constant(10)
- z = tf.constant(10)
+ >>> x = tf.constant(5)
+ >>> y = tf.constant(10)
+ >>> z = tf.constant(10)
+ >>> tensor_set = {x, y, z}
+ Traceback (most recent call last):
+ ...
+ TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key.
+ >>> tensor_dict = {x: 'five', y: 'ten'}
+ Traceback (most recent call last):
+ ...
+ TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key.
- # The followings will raise an exception starting 2.0
- # TypeError: Tensor is unhashable if Tensor equality is enabled.
- tensor_set = {x, y, z}
- tensor_dict = {x: 'five', y: 'ten', z: 'ten'}
- ```
+ Instead, we can use `tensor.ref()`.
- Instead, we can use `tensor.experimental_ref()`.
-
- ```python
- tensor_set = {x.experimental_ref(),
- y.experimental_ref(),
- z.experimental_ref()}
-
- print(x.experimental_ref() in tensor_set)
- ==> True
-
- tensor_dict = {x.experimental_ref(): 'five',
- y.experimental_ref(): 'ten',
- z.experimental_ref(): 'ten'}
-
- print(tensor_dict[y.experimental_ref()])
- ==> ten
- ```
+ >>> tensor_set = {x.ref(), y.ref(), z.ref()}
+ >>> x.ref() in tensor_set
+ True
+ >>> tensor_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
+ >>> tensor_dict[y.ref()]
+ 'ten'
Also, the reference object provides `.deref()` function that returns the
original Tensor.
- ```python
- x = tf.constant(5)
- print(x.experimental_ref().deref())
- ==> tf.Tensor(5, shape=(), dtype=int32)
- ```
+ >>> x = tf.constant(5)
+ >>> x.ref().deref()
+ <tf.Tensor: shape=(), dtype=int32, numpy=5>
"""
return object_identity.Reference(self)
@@ -4425,12 +4419,12 @@ class Graph(object):
def add_op(self, op):
if isinstance(op, Tensor):
- op = op.experimental_ref()
+ op = op.ref()
self._seen_nodes.add(op)
def op_in_group(self, op):
if isinstance(op, Tensor):
- op = op.experimental_ref()
+ op = op.ref()
return op in self._seen_nodes
def _push_control_dependencies_controller(self, controller):
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index dbd5abf7c5c..050285d3952 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -210,19 +210,19 @@ class TensorAndShapeTest(test_util.TensorFlowTestCase):
z = constant_op.constant([6, 10])
w = variables.Variable(5)
- self.assertEqual(x1.experimental_ref(), x1.experimental_ref())
- self.assertEqual(x2.experimental_ref(), x2.experimental_ref())
- self.assertEqual(x1.experimental_ref(), x2.experimental_ref())
- self.assertEqual(y.experimental_ref(), y.experimental_ref())
- self.assertEqual(z.experimental_ref(), z.experimental_ref())
- self.assertEqual(w.experimental_ref(), w.experimental_ref())
+ self.assertEqual(x1.ref(), x1.ref())
+ self.assertEqual(x2.ref(), x2.ref())
+ self.assertEqual(x1.ref(), x2.ref())
+ self.assertEqual(y.ref(), y.ref())
+ self.assertEqual(z.ref(), z.ref())
+ self.assertEqual(w.ref(), w.ref())
- self.assertNotEqual(x1.experimental_ref(), y.experimental_ref())
- self.assertNotEqual(x1.experimental_ref(), z.experimental_ref())
- self.assertNotEqual(x1.experimental_ref(), w.experimental_ref())
- self.assertNotEqual(y.experimental_ref(), z.experimental_ref())
- self.assertNotEqual(y.experimental_ref(), w.experimental_ref())
- self.assertNotEqual(z.experimental_ref(), w.experimental_ref())
+ self.assertNotEqual(x1.ref(), y.ref())
+ self.assertNotEqual(x1.ref(), z.ref())
+ self.assertNotEqual(x1.ref(), w.ref())
+ self.assertNotEqual(y.ref(), z.ref())
+ self.assertNotEqual(y.ref(), w.ref())
+ self.assertNotEqual(z.ref(), w.ref())
def testRefDeref(self):
x1 = constant_op.constant(3)
@@ -231,19 +231,19 @@ class TensorAndShapeTest(test_util.TensorFlowTestCase):
z = constant_op.constant([6, 10])
w = variables.Variable(5)
- self.assertIs(x1, x1.experimental_ref().deref())
- self.assertIs(x2, x2.experimental_ref().deref())
- self.assertIs(x1, x2.experimental_ref().deref())
- self.assertIs(x2, x1.experimental_ref().deref())
- self.assertIs(y, y.experimental_ref().deref())
- self.assertIs(z, z.experimental_ref().deref())
+ self.assertIs(x1, x1.ref().deref())
+ self.assertIs(x2, x2.ref().deref())
+ self.assertIs(x1, x2.ref().deref())
+ self.assertIs(x2, x1.ref().deref())
+ self.assertIs(y, y.ref().deref())
+ self.assertIs(z, z.ref().deref())
- self.assertIsNot(x1, y.experimental_ref().deref())
- self.assertIsNot(x1, z.experimental_ref().deref())
- self.assertIsNot(x1, w.experimental_ref().deref())
- self.assertIsNot(y, z.experimental_ref().deref())
- self.assertIsNot(y, w.experimental_ref().deref())
- self.assertIsNot(z, w.experimental_ref().deref())
+ self.assertIsNot(x1, y.ref().deref())
+ self.assertIsNot(x1, z.ref().deref())
+ self.assertIsNot(x1, w.ref().deref())
+ self.assertIsNot(y, z.ref().deref())
+ self.assertIsNot(y, w.ref().deref())
+ self.assertIsNot(z, w.ref().deref())
def testRefInSet(self):
x1 = constant_op.constant(3)
@@ -252,22 +252,22 @@ class TensorAndShapeTest(test_util.TensorFlowTestCase):
z = constant_op.constant([6, 10])
w = variables.Variable(5)
- self.assertEqual(x1.experimental_ref(), x2.experimental_ref())
+ self.assertEqual(x1.ref(), x2.ref())
tensor_set = {
- x1.experimental_ref(),
- x2.experimental_ref(),
- y.experimental_ref(),
- z.experimental_ref(),
- w.experimental_ref(),
+ x1.ref(),
+ x2.ref(),
+ y.ref(),
+ z.ref(),
+ w.ref(),
}
self.assertEqual(len(tensor_set), 4)
- self.assertIn(x1.experimental_ref(), tensor_set)
- self.assertIn(x2.experimental_ref(), tensor_set)
- self.assertIn(y.experimental_ref(), tensor_set)
- self.assertIn(z.experimental_ref(), tensor_set)
- self.assertIn(w.experimental_ref(), tensor_set)
+ self.assertIn(x1.ref(), tensor_set)
+ self.assertIn(x2.ref(), tensor_set)
+ self.assertIn(y.ref(), tensor_set)
+ self.assertIn(z.ref(), tensor_set)
+ self.assertIn(w.ref(), tensor_set)
def testRefInDict(self):
x1 = constant_op.constant(3)
@@ -276,36 +276,36 @@ class TensorAndShapeTest(test_util.TensorFlowTestCase):
z = constant_op.constant([6, 10])
w = variables.Variable(5)
- self.assertEqual(x1.experimental_ref(), x2.experimental_ref())
+ self.assertEqual(x1.ref(), x2.ref())
tensor_dict = {
- x1.experimental_ref(): "x1",
- y.experimental_ref(): "y",
- z.experimental_ref(): "z",
- w.experimental_ref(): "w",
+ x1.ref(): "x1",
+ y.ref(): "y",
+ z.ref(): "z",
+ w.ref(): "w",
}
self.assertEqual(len(tensor_dict), 4)
# Overwriting x1
- tensor_dict[x2.experimental_ref()] = "x2"
+ tensor_dict[x2.ref()] = "x2"
self.assertEqual(len(tensor_dict), 4)
- self.assertEqual(tensor_dict[x1.experimental_ref()], "x2")
- self.assertEqual(tensor_dict[x2.experimental_ref()], "x2")
- self.assertEqual(tensor_dict[y.experimental_ref()], "y")
- self.assertEqual(tensor_dict[z.experimental_ref()], "z")
- self.assertEqual(tensor_dict[w.experimental_ref()], "w")
+ self.assertEqual(tensor_dict[x1.ref()], "x2")
+ self.assertEqual(tensor_dict[x2.ref()], "x2")
+ self.assertEqual(tensor_dict[y.ref()], "y")
+ self.assertEqual(tensor_dict[z.ref()], "z")
+ self.assertEqual(tensor_dict[w.ref()], "w")
def testTensorRefStrong(self):
x = constant_op.constant(1.)
- x_ref = x.experimental_ref()
+ x_ref = x.ref()
del x
self.assertIsNotNone(x_ref.deref())
def testVariableRefStrong(self):
x = variables.Variable(1.)
- x_ref = x.experimental_ref()
+ x_ref = x.ref()
del x
self.assertIsNotNone(x_ref.deref())
diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py
index c3ee6b1aaf3..8769a20b6b1 100644
--- a/tensorflow/python/keras/layers/core.py
+++ b/tensorflow/python/keras/layers/core.py
@@ -868,9 +868,10 @@ class Lambda(Layer):
# checking only to immediately discard it.
return
- tracked_weights = set(v.experimental_ref() for v in self.weights)
- untracked_new_vars = [v for v in created_variables
- if v.experimental_ref() not in tracked_weights]
+ tracked_weights = set(v.ref() for v in self.weights)
+ untracked_new_vars = [
+ v for v in created_variables if v.ref() not in tracked_weights
+ ]
if untracked_new_vars:
variable_str = '\n'.join(' {}'.format(i) for i in untracked_new_vars)
error_str = textwrap.dedent(
@@ -886,8 +887,9 @@ class Lambda(Layer):
).format(name=self.name, variable_str=variable_str)
raise ValueError(error_str)
- untracked_used_vars = [v for v in accessed_variables
- if v.experimental_ref() not in tracked_weights]
+ untracked_used_vars = [
+ v for v in accessed_variables if v.ref() not in tracked_weights
+ ]
if untracked_used_vars and not self._already_warned:
variable_str = '\n'.join(' {}'.format(i) for i in untracked_used_vars)
self._warn(textwrap.dedent(
diff --git a/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py b/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py
index 2248c93a3b1..589dcaf304f 100644
--- a/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py
+++ b/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py
@@ -149,7 +149,7 @@ class AutoCastVariable(variables.Variable):
# reasons:
# * 'count_up_to': This method only applies to int variables, which cannot
# be wrapped with an AutoCastVariable.
- # * 'experimental_ref': Instead we inherit the definition from Variable.
+ # * 'ref': Instead we inherit the definition from Variable.
# If we defined and delegated to Variable, the ref of an AutoCastVariable
# would be the same as the ref of the underlying variable, which would be
# strange as they are different Python objects.
diff --git a/tensorflow/python/keras/optimizer_v2/adam_test.py b/tensorflow/python/keras/optimizer_v2/adam_test.py
index 8259fc155a3..fd3df9cb4ba 100644
--- a/tensorflow/python/keras/optimizer_v2/adam_test.py
+++ b/tensorflow/python/keras/optimizer_v2/adam_test.py
@@ -539,8 +539,7 @@ class AdamOptimizerTest(test.TestCase):
opt = adam.Adam(1.)
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
# There should be iteration, and two unique slot variables for v1 and v2.
- self.assertEqual(5,
- len(set(v.experimental_ref() for v in opt.variables())))
+ self.assertEqual(5, len(set(v.ref() for v in opt.variables())))
self.assertEqual(
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))
diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py
index 6f46047c9a2..2592ba6ef97 100644
--- a/tensorflow/python/ops/custom_gradient.py
+++ b/tensorflow/python/ops/custom_gradient.py
@@ -311,14 +311,16 @@ def _graph_mode_decorator(f, args, kwargs):
# Checking global and local variables attempts to ensure that no non-resource
# Variables are added to the graph.
current_var_scope = variable_scope.get_variable_scope()
- before_vars = set(
- [v.experimental_ref() for v in current_var_scope.global_variables() +
- current_var_scope.local_variables()])
+ before_vars = set([
+ v.ref() for v in current_var_scope.global_variables() +
+ current_var_scope.local_variables()
+ ])
with backprop.GradientTape() as tape:
result, grad_fn = f(*args)
- after_vars = set(
- [v.experimental_ref() for v in current_var_scope.global_variables() +
- current_var_scope.local_variables()])
+ after_vars = set([
+ v.ref() for v in current_var_scope.global_variables() +
+ current_var_scope.local_variables()
+ ])
new_vars = after_vars - before_vars
new_vars_list = [v.deref() for v in new_vars]
for v in new_vars_list:
@@ -330,11 +332,10 @@ def _graph_mode_decorator(f, args, kwargs):
# The variables that grad_fn needs to return gradients for are the set of
# variables used that are *not* part of the inputs.
inputs = args
- variables_in_tape = frozenset([
- v.experimental_ref() for v in tape.watched_variables()
- ]) - frozenset(v.experimental_ref() for v in inputs)
+ variables_in_tape = frozenset([v.ref() for v in tape.watched_variables()
+ ]) - frozenset(v.ref() for v in inputs)
variables_in_subgraph = frozenset([
- v.experimental_ref()
+ v.ref()
for v in get_dependent_variables(input_ops=inputs, output_ops=result)
])
variables = list(
@@ -411,7 +412,7 @@ def _eager_mode_decorator(f, args, kwargs):
# variables used that are *not* part of the inputs.
variables = [
v.deref() # pylint: disable=g-complex-comprehension
- for v in set(v.experimental_ref() for v in tape.watched_variables())
+ for v in set(v.ref() for v in tape.watched_variables())
if all(v.deref() is not i for i in all_inputs)
]
grad_argspec = tf_inspect.getfullargspec(grad_fn)
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 3191739ad02..614496e63e9 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -1705,19 +1705,18 @@ def _SparseMatMulGrad(op, grad):
t_a = op.get_attr("transpose_a")
t_b = op.get_attr("transpose_b")
is_sparse = {}
- is_sparse[op.inputs[0].experimental_ref()] = op.get_attr("a_is_sparse")
- is_sparse[op.inputs[1].experimental_ref()] = op.get_attr("b_is_sparse")
+ is_sparse[op.inputs[0].ref()] = op.get_attr("a_is_sparse")
+ is_sparse[op.inputs[1].ref()] = op.get_attr("b_is_sparse")
# Use heuristic to figure out if grad might be sparse
- is_sparse[grad.experimental_ref()] = not context.executing_eagerly() and (
+ is_sparse[grad.ref()] = not context.executing_eagerly() and (
grad.op.type == "ReluGrad")
def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False):
"""Helper function to create SparseMatMul op."""
- assert t1.experimental_ref() in is_sparse and t2.experimental_ref(
- ) in is_sparse
- t1_sparse = is_sparse[t1.experimental_ref()]
- t2_sparse = is_sparse[t2.experimental_ref()]
+ assert t1.ref() in is_sparse and t2.ref() in is_sparse
+ t1_sparse = is_sparse[t1.ref()]
+ t2_sparse = is_sparse[t2.ref()]
if transpose_b:
t2 = array_ops.transpose(t2)
transpose_b = False
diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py
index 898593e3e45..c34ddb4f300 100644
--- a/tensorflow/python/ops/parallel_for/pfor.py
+++ b/tensorflow/python/ops/parallel_for/pfor.py
@@ -1622,7 +1622,7 @@ def _channel_flatten_input(x, data_format):
"""
graph = ops.get_default_graph()
- cache_key = (graph, x.experimental_ref(), data_format)
+ cache_key = (graph, x.ref(), data_format)
if cache_key not in _channel_flatten_input_cache:
x_shape = array_ops.shape(x)
if data_format == b"NCHW":
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 5e2fffaf1b7..f4fff36153a 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -1076,8 +1076,8 @@ class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
def __hash__(self):
if ops.Tensor._USE_EQUALITY and ops.executing_eagerly_outside_functions(): # pylint: disable=protected-access
- raise TypeError("Variable is unhashable if Tensor equality is enabled. "
- "Instead, use tensor.experimental_ref() as the key.")
+ raise TypeError("Variable is unhashable. "
+ "Instead, use tensor.ref() as the key.")
else:
return id(self)
@@ -1209,56 +1209,50 @@ class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
def _get_save_slice_info(self):
return self._save_slice_info
+ @deprecated(None, "Use ref() instead.")
def experimental_ref(self):
- # tf.Tensor also has the same experimental_ref() API. If you update the
- # documenation here, please update tf.Tensor.experimental_ref() as well.
+ return self.ref()
+
+ def ref(self):
+ # tf.Tensor also has the same ref() API. If you update the
+ # documentation here, please update tf.Tensor.ref() as well.
"""Returns a hashable reference object to this Variable.
Warning: Experimental API that could be changed or removed.
- The primary usecase for this API is to put variables in a set/dictionary.
+ The primary use case for this API is to put variables in a set/dictionary.
We can't put variables in a set/dictionary as `variable.__hash__()` is no
longer available starting Tensorflow 2.0.
- ```python
- import tensorflow as tf
+ The following will raise an exception starting 2.0
- x = tf.Variable(5)
- y = tf.Variable(10)
- z = tf.Variable(10)
+ >>> x = tf.Variable(5)
+ >>> y = tf.Variable(10)
+ >>> z = tf.Variable(10)
+ >>> variable_set = {x, y, z}
+ Traceback (most recent call last):
+ ...
+ TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
+ >>> variable_dict = {x: 'five', y: 'ten'}
+ Traceback (most recent call last):
+ ...
+ TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
- # The followings will raise an exception starting 2.0
- # TypeError: Variable is unhashable if Variable equality is enabled.
- variable_set = {x, y, z}
- variable_dict = {x: 'five', y: 'ten'}
- ```
+ Instead, we can use `variable.ref()`.
- Instead, we can use `variable.experimental_ref()`.
-
- ```python
- variable_set = {x.experimental_ref(),
- y.experimental_ref(),
- z.experimental_ref()}
-
- print(x.experimental_ref() in variable_set)
- ==> True
-
- variable_dict = {x.experimental_ref(): 'five',
- y.experimental_ref(): 'ten',
- z.experimental_ref(): 'ten'}
-
- print(variable_dict[y.experimental_ref()])
- ==> ten
- ```
+ >>> variable_set = {x.ref(), y.ref(), z.ref()}
+ >>> x.ref() in variable_set
+ True
+ >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
+ >>> variable_dict[y.ref()]
+ 'ten'
Also, the reference object provides `.deref()` function that returns the
original Variable.
- ```python
- x = tf.Variable(5)
- print(x.experimental_ref().deref())
- ==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
- ```
+ >>> x = tf.Variable(5)
+ >>> x.ref().deref()
+ <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
"""
return object_identity.Reference(self)
diff --git a/tensorflow/python/ops/while_v2.py b/tensorflow/python/ops/while_v2.py
index 4d4ae540a1c..d1ce8925839 100644
--- a/tensorflow/python/ops/while_v2.py
+++ b/tensorflow/python/ops/while_v2.py
@@ -460,8 +460,7 @@ def _get_intermediates(func_graph):
# 3. Do not accumulate loop vars that are returned as-is just like captured
# tensors.
intermediates = []
- reverse_captures = dict(
- (v.experimental_ref(), k) for k, v in func_graph.captures)
+ reverse_captures = dict((v.ref(), k) for k, v in func_graph.captures)
for op in func_graph.get_operations():
if op.type == "Identity":
@@ -473,7 +472,7 @@ def _get_intermediates(func_graph):
if (o is not func_graph.inputs[0] and # Loop counter.
o.dtype != dtypes.resource and # Do not accumulate resource tensors.
_get_accumulator(o) is None and # Has existing accumulator.
- o.experimental_ref() not in reverse_captures
+ o.ref() not in reverse_captures
): # Captured value, hence loop invariant.
intermediates.append(o)
return intermediates
diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py
index 18b36d5b815..612215328c6 100644
--- a/tensorflow/python/training/moving_averages.py
+++ b/tensorflow/python/training/moving_averages.py
@@ -433,7 +433,7 @@ class ExponentialMovingAverage(object):
raise TypeError("The variables must be half, float, or double: %s" %
var.name)
- if var.experimental_ref() not in self._averages:
+ if var.ref() not in self._averages:
# For variables: to lower communication bandwidth across devices we keep
# the moving averages on the same device as the variables. For other
# tensors, we rely on the existing device allocation mechanism.
@@ -455,8 +455,8 @@ class ExponentialMovingAverage(object):
"Variable", "VariableV2", "VarHandleOp"
]))
if self._zero_debias:
- zero_debias_true.add(avg.experimental_ref())
- self._averages[var.experimental_ref()] = avg
+ zero_debias_true.add(avg.ref())
+ self._averages[var.ref()] = avg
with ops.name_scope(self.name) as scope:
decay = ops.convert_to_tensor(self._decay, name="decay")
@@ -467,8 +467,8 @@ class ExponentialMovingAverage(object):
(1.0 + num_updates) / (10.0 + num_updates))
updates = []
for var in var_list:
- avg = self._averages[var.experimental_ref()]
- zero_debias = avg.experimental_ref() in zero_debias_true
+ avg = self._averages[var.ref()]
+ zero_debias = avg.ref() in zero_debias_true
updates.append(assign_moving_average(avg, var, decay, zero_debias))
return control_flow_ops.group(*updates, name=scope)
@@ -482,7 +482,7 @@ class ExponentialMovingAverage(object):
A `Variable` object or `None` if the moving average of `var`
is not maintained.
"""
- return self._averages.get(var.experimental_ref(), None)
+ return self._averages.get(var.ref(), None)
def average_name(self, var):
"""Returns the name of the `Variable` holding the average for `var`.
@@ -506,8 +506,8 @@ class ExponentialMovingAverage(object):
by the `ExponentialMovingAverage class` to hold the moving average of
`var`.
"""
- if var.experimental_ref() in self._averages:
- return self._averages[var.experimental_ref()].op.name
+ if var.ref() in self._averages:
+ return self._averages[var.ref()].op.name
return ops.get_default_graph().unique_name(
var.op.name + "/" + self.name, mark_as_used=False)
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.-tensor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-tensor.pbtxt
index 9f35e140d43..fd80af51d2a 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.-tensor.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.-tensor.pbtxt
@@ -55,6 +55,10 @@ tf_class {
name: "get_shape"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
+ member_method {
+ name: "ref"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
member_method {
name: "set_shape"
argspec: "args=[\'self\', \'shape\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-variable.pbtxt
index df68721be37..37c7db00215 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.-variable.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.-variable.pbtxt
@@ -112,6 +112,10 @@ tf_class {
name: "read_value"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
+ member_method {
+ name: "ref"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
member_method {
name: "scatter_add"
argspec: "args=[\'self\', \'sparse_delta\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt
index 9f35e140d43..fd80af51d2a 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt
@@ -55,6 +55,10 @@ tf_class {
name: "get_shape"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
+ member_method {
+ name: "ref"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
member_method {
name: "set_shape"
argspec: "args=[\'self\', \'shape\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt
index f53a5ec6beb..c01fb767fae 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt
@@ -111,6 +111,10 @@ tf_class {
name: "read_value"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
+ member_method {
+ name: "ref"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
member_method {
name: "scatter_add"
argspec: "args=[\'self\', \'sparse_delta\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], "