def model_fn(): vs = [] vs.append(variable_scope.variable(1.0, name="foo/bar")) vs.append(variable_scope.variable(1.0, name="foo_1/bar")) vs.append(variable_scope.variable(1.0, name="foo_1/bar_1")) vs.append(variable_scope.variable(1.0, name="foo/bar_1")) distribute_lib.get_tower_context().merge_call(lambda _: _) return vs
def model_fn():
v0 = variable_scope.get_variable("var-thread0", [1])
with variable_scope.variable_scope("common"):
v1 = variable_scope.get_variable("var-thread1", [1])
# This will pause the current thread, and execute the other thread.
distribute_lib.get_tower_context().merge_call(lambda _: _)
v2 = variable_scope.get_variable("var-thread2", [1])
return v0, v1, v2
def set_non_tensor_output(self, name, output):
"""Set `output` with `name` to be captured as a non tensor output."""
if distribute_lib.get_cross_tower_context():
self._non_tensor_outputs[name] = output
else:
def merge_fn(distribution, value):
# NOTE(priyag): For non tensor outputs, we simply return all the values
# in a list as aggregation doesn't make sense on non tensors.
self._non_tensor_outputs[name] = distribution.unwrap(value)
distribute_lib.get_tower_context().merge_call(merge_fn, output)
def model_fn(device_id):
assert isinstance(device_id, int)
def thread_creator_fn(next_creator, *args, **kwargs):
return next_creator(*args, **kwargs) + ":thread_" + str(device_id)
with variable_scope.variable_creator_scope(thread_creator_fn):
# Create a variable in this scope.
v = variable_scope.variable(1.0)
# This will pause the current thread, and execute the other thread.
distribute_lib.get_tower_context().merge_call(lambda _: _)
return v
def model_fn(features):
with variable_scope.variable_scope("common"):
layer1 = core.Dense(1)
layer1(features)
layer2 = core.Dense(1)
layer2(features)
# This will pause the current thread, and execute the other thread.
distribute_lib.get_tower_context().merge_call(lambda _: _)
layer3 = core.Dense(1)
layer3(features)
return [(layer1.kernel, layer1.bias),
(layer2.kernel, layer2.bias),
(layer3.kernel, layer3.bias)]
def merge_fn(dist, s): self.assertIs(distribute._default_distribution_strategy, dist) self.assertIs(None, distribute.get_tower_context()) self.assertIs(dist, distribute.get_cross_tower_context()) self.assertIs(dist, distribute.get_distribution_strategy()) self.assertFalse(distribute.has_distribution_strategy()) return "foo_" + s
def model_fn():
v0 = variable_scope.get_variable("var0", [1])
with variable_scope.variable_scope("common"):
v1 = variable_scope.get_variable("var1", [1])
# This will pause the current thread, and execute the other thread.
distribute_lib.get_tower_context().merge_call(lambda _: _)
v2 = variable_scope.get_variable(
"var2", [1],
synchronization=variable_scope.VariableSynchronization.ON_READ,
aggregation=variable_scope.VariableAggregation.SUM)
v3 = variable_scope.get_variable(
"var3", [1],
synchronization=variable_scope.VariableSynchronization.ON_WRITE,
aggregation=variable_scope.VariableAggregation.MEAN)
return v0, v1, v2, v3
def merge_fn(dist, s):
self.assertIs(distribute._default_distribution_strategy, dist)
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertFalse(distribute.has_distribution_strategy())
return "foo_" + s
def _assign_func(self, *args, **kwargs):
f = kwargs.pop("f")
if distribute_lib.get_cross_tower_context():
update_device = distribute_lib.get_update_device()
# We are calling update on the mirrored variable in cross tower context.
if update_device is not None:
# We are calling an assign function on the mirrored variable in cross
# tower context.
v = self.get(device=update_device)
return f(v, *args, **kwargs)
return distribute_lib.get_distribution_strategy().update(
self, f, *args, **kwargs)
else:
_assert_tower_context()
# We are calling an assign function on the mirrored variable in tower
# context.
# We reduce the value we want to assign/add/sub. More details about how we
# handle the different use cases can be found in the _reduce method.
# We call the function on each of the mirrored variables with the reduced
# value.
if self._aggregation == vs.VariableAggregation.NONE:
raise ValueError("You must specify an aggregation method to update a "
"MirroredVariable in Tower Context.")
def merge_fn(strategy, value, *other_args, **other_kwargs):
return strategy.update(
self, f,
strategy.reduce(
aggregation=self._aggregation, value=value, destinations=self),
*other_args, **other_kwargs)
return distribute_lib.get_tower_context().merge_call(merge_fn, *args,
**kwargs)
def model_fn():
v0 = variable_scope.get_variable("var0", [1])
with variable_scope.variable_scope("common"):
v1 = variable_scope.get_variable("var1", [1])
# This will pause the current thread, and execute the other thread.
distribute_lib.get_tower_context().merge_call(lambda _: _)
v2 = variable_scope.get_variable(
"var2", [1],
synchronization=variable_scope.VariableSynchronization.ON_READ,
aggregation=variable_scope.VariableAggregation.SUM)
v3 = variable_scope.get_variable(
"var3", [1],
synchronization=variable_scope.VariableSynchronization.ON_WRITE,
aggregation=variable_scope.VariableAggregation.MEAN)
return v0, v1, v2, v3
def _assert_in_default_state(t):
t.assertIs(distribute._default_tower_context,
distribute.get_tower_context())
t.assertIs(None, distribute.get_cross_tower_context())
t.assertIs(distribute._default_distribution_strategy,
distribute.get_distribution_strategy())
t.assertFalse(distribute.has_distribution_strategy())
def _aggregate_across_towers(metrics_collections, metric_value_fn, *args):
"""Aggregate metric value across towers."""
def fn(distribution, *a):
"""Call `metric_value_fn` in the correct control flow context."""
if hasattr(distribution, '_outer_control_flow_context'):
# If there was an outer context captured before this method was called,
# then we enter that context to create the metric value op. If the
# caputred context is `None`, ops.control_dependencies(None) gives the
# desired behavior. Else we use `Enter` and `Exit` to enter and exit the
# captured context.
# This special handling is needed because sometimes the metric is created
# inside a while_loop (and perhaps a TPU rewrite context). But we don't
# want the value op to be evaluated every step or on the TPU. So we
# create it outside so that it can be evaluated at the end on the host,
# once the update ops have been evaluted.
# pylint: disable=protected-access
if distribution._outer_control_flow_context is None:
with ops.control_dependencies(None):
metric_value = metric_value_fn(distribution, *a)
else:
distribution._outer_control_flow_context.Enter()
metric_value = metric_value_fn(distribution, *a)
distribution._outer_control_flow_context.Exit()
# pylint: enable=protected-access
else:
metric_value = metric_value_fn(distribution, *a)
if metrics_collections:
ops.add_to_collections(metrics_collections, metric_value)
return metric_value
return distribute_lib.get_tower_context().merge_call(fn, *args)
def _assert_in_default_state(t):
t.assertIs(distribute._default_tower_context,
distribute.get_tower_context())
t.assertIs(None, distribute.get_cross_tower_context())
t.assertIs(distribute._default_distribution_strategy,
distribute.get_distribution_strategy())
t.assertFalse(distribute.has_distribution_strategy())
def model_fn():
tower_context = distribute_lib.get_tower_context()
with tower_context.tower_local_var_scope(
variable_scope.VariableAggregation.SUM):
v_sum = variable_scope.variable(1.0)
self.assertTrue(isinstance(v_sum, values.TowerLocalVariable))
return v_sum
def skip_summary():
# If using multiple towers in distributed strategy, skip summaries on all
# towers except the first one (tower_id=0).
# TODO(priyag): Add a new optional argument that will provide multiple
# alternatives to override default behavior. (e.g. run on last tower,
# compute sum or mean across towers).
tower_context = distribute.get_tower_context()
return tower_context and tower_context.tower_id > 0
def run_fn():
tower_context = distribute.get_tower_context()
self.assertTrue(tower_context is not None)
self.assertIs(None, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("foo", tower_context.merge_call(None, test_arg="foo"))
self.assertEqual("bar", variable_scope.variable(1.0, name="bar"))
def skip_summary(): # If using multiple towers in distributed strategy, skip summaries on all # towers except the first one (tower_id=0). # TODO(priyag): Add a new optional argument that will provide multiple # alternatives to override default behavior. (e.g. run on last tower, # compute sum or mean across towers). tower_context = distribute.get_tower_context() return tower_context and tower_context.tower_id > 0
def run_fn():
tower_context = distribute.get_tower_context()
self.assertTrue(tower_context is not None)
self.assertIs(None, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("foo",
tower_context.merge_call(None, test_arg="foo"))
self.assertEqual("bar", variable_scope.variable(1.0, name="bar"))
def testScope(self):
_assert_in_default_state(self)
dist = _TestStrategy()
with dist.scope():
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("baz", variable_scope.variable(1.0, name="baz"))
_assert_in_default_state(self)
def testScope(self):
_assert_in_default_state(self)
dist = _TestStrategy()
with dist.scope():
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("baz", variable_scope.variable(1.0, name="baz"))
_assert_in_default_state(self)
def run_fn():
tower_context = distribute.get_tower_context()
self.assertTrue(tower_context is not None)
self.assertIs(None, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("foo", tower_context.merge_call(None, test_arg="foo"))
expected_value = _get_test_variable(
"bar", variable_scope.VariableSynchronization.AUTO,
variable_scope.VariableAggregation.NONE)
self.assertDictEqual(expected_value,
variable_scope.variable(1.0, name="bar"))
def set_last_step_output(self, name, output,
aggregation=variables_lib.VariableAggregation.NONE):
"""Set `output` with `name` to be outputted from the last step.
Args:
name: String, name to identify the output. Doesn't need to match tensor
name.
output: The tensors that should be outputted with `name`. See below for
actual types supported.
aggregation: Aggregation method to use to aggregate outputs from multiple
towers. Required if `set_last_step_output` is called in a tower context.
Optional in cross_tower_context.
When present, the outputs from all the towers are aggregated using the
current distribution strategy's `reduce` method. Hence, the type of
`output` must be what's supported by the corresponding `reduce` method.
For e.g. if using MirroredStrategy and aggregation is set, output
must be a `PerDevice` value.
The aggregation method is also recorded in a dictionary
`_last_step_outputs_aggregations` for later interpreting of the
outputs as already reduced or not.
"""
if distribute_lib.get_cross_tower_context():
self._last_step_outputs_aggregations[name] = aggregation
if aggregation is variables_lib.VariableAggregation.NONE:
self._last_step_outputs[name] = output
else:
distribution = distribute_lib.get_distribution_strategy()
self._last_step_outputs[name] = distribution.reduce(
aggregation, output, destinations="/device:CPU:0")
else:
assert aggregation is not variables_lib.VariableAggregation.NONE
def merge_fn(distribution, value):
self._last_step_outputs[name] = distribution.reduce(
aggregation, value, destinations="/device:CPU:0")
# Setting this inside the `merge_fn` because all towers share the same
# context object, so it's more robust to set it only once (even if all
# the towers are trying to set the same value).
self._last_step_outputs_aggregations[name] = aggregation
distribute_lib.get_tower_context().merge_call(merge_fn, output)
def run_fn():
tower_context = distribute.get_tower_context()
self.assertTrue(tower_context is not None)
self.assertIs(None, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertEqual("foo",
tower_context.merge_call(None, test_arg="foo"))
expected_value = _get_test_variable(
"bar", variable_scope.VariableSynchronization.AUTO,
variable_scope.VariableAggregation.NONE)
self.assertDictEqual(expected_value,
variable_scope.variable(1.0, name="bar"))
def model_fn(device_id):
tower_context = distribute_lib.get_tower_context()
with tower_context.tower_local_var_scope("sum"):
v_sum = variable_scope.variable(1.0)
with tower_context.tower_local_var_scope("mean"):
v_mean = variable_scope.variable(4.0)
self.assertTrue(isinstance(v_sum, values.TowerLocalVariable))
self.assertTrue(isinstance(v_mean, values.TowerLocalVariable))
updates = [v_sum.assign_add(2.0 + device_id),
v_mean.assign(6.0 * device_id)]
all_v_sum[device_id] = v_sum
all_v_mean[device_id] = v_mean
return updates, v_sum, v_mean
def testScope(self):
_assert_in_default_state(self)
dist = _TestStrategy()
with dist.scope():
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
expected_value = _get_test_variable(
"baz", variable_scope.VariableSynchronization.AUTO,
variable_scope.VariableAggregation.NONE)
self.assertDictEqual(expected_value,
variable_scope.variable(1.0, name="baz"))
_assert_in_default_state(self)
def testScope(self):
_assert_in_default_state(self)
dist = _TestStrategy()
with dist.scope():
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertTrue(distribute.has_distribution_strategy())
self.assertIs(dist, distribute.get_distribution_strategy())
expected_value = _get_test_variable(
"baz", variable_scope.VariableSynchronization.AUTO,
variable_scope.VariableAggregation.NONE)
self.assertDictEqual(expected_value,
variable_scope.variable(1.0, name="baz"))
_assert_in_default_state(self)
def _decay_weights_op(self, var):
def apply_decay_fn(v, decay):
return state_ops.assign_sub(v, decay * v, self._use_locking)
def merge_fn(strategy, v, value):
value = strategy.reduce(variable_scope.VariableAggregation.MEAN,
value, v)
return strategy.update(v, apply_decay_fn, value)
if not self._decay_var_list or var in self._decay_var_list:
replica_context = distribute_lib.get_tower_context()
return replica_context.merge_call(merge_fn, var,
self._weight_decay)
return control_flow_ops.no_op()
def testMergeCall(self):
_assert_in_default_state(self)
def merge_fn(dist, s):
self.assertIs(distribute._default_distribution_strategy, dist)
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertFalse(distribute.has_distribution_strategy())
return "foo_" + s
tower_ctx = distribute.get_tower_context()
self.assertIs(distribute._default_tower_context, tower_ctx)
self.assertEqual("foo_bar", tower_ctx.merge_call(merge_fn, "bar"))
_assert_in_default_state(self)
def testMergeCall(self):
_assert_in_default_state(self)
def merge_fn(dist, s):
self.assertIs(distribute._default_distribution_strategy, dist)
self.assertIs(None, distribute.get_tower_context())
self.assertIs(dist, distribute.get_cross_tower_context())
self.assertIs(dist, distribute.get_distribution_strategy())
self.assertFalse(distribute.has_distribution_strategy())
return "foo_" + s
tower_ctx = distribute.get_tower_context()
self.assertIs(distribute._default_tower_context, tower_ctx)
self.assertEqual("foo_bar", tower_ctx.merge_call(merge_fn, "bar"))
_assert_in_default_state(self)
def get(self, device=None):
"""Returns the value for the current device or raises a ValueError."""
if device is None:
tower_context = distribute_lib.get_tower_context()
if tower_context:
device = tower_context.device
else:
device = distribute_lib.get_update_device()
if device is None:
device = device_util.current()
device = device_util.canonicalize(device)
try:
return self._index[device]
except KeyError:
raise ValueError("Device %s not found in %s (current device %s)" %
(device, self._index.keys(), device_util.current()))
def decorated(*args):
"""Decorated function with merge_call."""
tower_context = distribute_lib.get_tower_context()
if tower_context is None: # if in cross tower context already
return result_fn()
# TODO(psv): Test distribution of metrics using different distribution
# strategies.
# Creating a wrapper for merge_fn. merge_call invokes the given merge_fn
# with distribution object as the first parameter. We create a wrapper here
# so that the result function need not have that parameter.
def merge_fn_wrapper(distribution, merge_fn, *args):
# We will get `PerDevice` merge function. Taking the first one as all are
# identical copies of the function that we had passed below.
return distribution.unwrap(merge_fn)[0](*args)
# Wrapping result in merge_call. merge_call is used when we want to leave
# tower mode and compute a value in cross tower mode.
return tower_context.merge_call(merge_fn_wrapper, result_fn, *args)
def decorated(*args):
"""Decorated function with merge_call."""
tower_context = distribute_lib.get_tower_context()
if tower_context is None: # if in cross tower context already
return result_fn()
# TODO(psv): Test distribution of metrics using different distribution
# strategies.
# Creating a wrapper for merge_fn. merge_call invokes the given merge_fn
# with distribution object as the first parameter. We create a wrapper here
# so that the result function need not have that parameter.
def merge_fn_wrapper(distribution, merge_fn, *args):
# We will get `PerDevice` merge function. Taking the first one as all are
# identical copies of the function that we had passed below.
return distribution.unwrap(merge_fn)[0](*args)
# Wrapping result in merge_call. merge_call is used when we want to leave
# tower mode and compute a value in cross tower mode.
return tower_context.merge_call(merge_fn_wrapper, result_fn, *args)
def model_fn(device_id):
tower_context = distribute_lib.get_tower_context()
with tower_context.tower_local_var_scope(
variable_scope.VariableAggregation.SUM):
v_sum = variable_scope.variable(1.0)
with tower_context.tower_local_var_scope(
variable_scope.VariableAggregation.MEAN):
v_mean = variable_scope.variable(4.0)
self.assertTrue(isinstance(v_sum, values.TowerLocalVariable))
self.assertTrue(isinstance(v_mean, values.TowerLocalVariable))
updates = [v_sum.assign_add(2.0 + device_id),
v_mean.assign(6.0 * device_id)]
all_v_sum[device_id] = v_sum
all_v_mean[device_id] = v_mean
c_sum = v_sum.get()
c_mean = v_mean.get()
components_sum[device_id] = c_sum
components_mean[device_id] = c_mean
self.assertIsNot(v_sum, c_sum)
self.assertIsNot(v_mean, c_mean)
return updates, v_sum, v_mean, c_sum, c_mean
def model_fn(name): v = variable_scope.variable(1.0, name=name) distribute_lib.get_tower_context().merge_call(lambda _: _) return v
def model_fn(device_id): v = variable_scope.variable(1.0, name="foo_" + str(device_id)) distribute_lib.get_tower_context().merge_call(lambda _: _) return v
def model_fn():
b = variable_scope.get_variable("b", [1])
with ops.name_scope("foo"):
c = distribute_lib.get_tower_context().merge_call(in_cross_tower)
return b, c
def model_fn(): # This variable should be created only once across the threads because of # special variable_creator functions used by `dist.call_for_each_tower`. v = variable_scope.variable(1.0, name="foo") distribute_lib.get_tower_context().merge_call(lambda _: _) return v
def model_fn():
value = math_ops.cast(distribute_lib.get_tower_context().tower_id,
mirrored_var.dtype)
return mirrored_var.assign_sub(value)
def model_fn():
tower_context = distribute_lib.get_tower_context()
with tower_context.tower_local_var_scope("sum"):
v_sum = variable_scope.variable(1.0)
self.assertTrue(isinstance(v_sum, values.TowerLocalVariable))
return v_sum
def model_fn():
b = variable_scope.get_variable("b", [1])
with ops.name_scope("foo"):
c = distribute_lib.get_tower_context().merge_call(in_cross_tower)
return b, c
def mark_devices_fn(): tower_id = distribute_lib.get_tower_context().tower_id self.assertLess(tower_id, len(d.worker_devices)) self.assertFalse(expected_devices[tower_id]) expected_devices[tower_id] = True
def model_fn():
with ops.name_scope(None, "foo"):
a = constant_op.constant(1.0, name="a")
distribute_lib.get_tower_context().merge_call(lambda _: _)
b = constant_op.constant(2.0, name="b")
return a, b
def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
"""Initializes current variables with tensors loaded from given checkpoint.
Note: This overrides default initialization ops of specified variables and
redefines dtype.
Assignment map supports following syntax:
* `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
current `scope_name` from `checkpoint_scope_name` with matching tensor
names.
* `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
will initialize `scope_name/variable_name` variable
from `checkpoint_scope_name/some_other_variable`.
* `'scope_variable_name': variable` - will initialize given `tf.Variable`
object with tensor 'scope_variable_name' from the checkpoint.
* `'scope_variable_name': list(variable)` - will initialize list of
partitioned variables with tensor 'scope_variable_name' from the checkpoint.
* `'/': 'scope_name/'` - will load all variables in current `scope_name` from
checkpoint's root (e.g. no scope).
Supports loading into partitioned variables, which are represented as
`'<variable>/part_<part #>'`.
Example:
```python
# Say, '/tmp/model.ckpt' has the following tensors:
# -- name='old_scope_1/var1', shape=[20, 2]
# -- name='old_scope_1/var2', shape=[50, 4]
# -- name='old_scope_2/var3', shape=[100, 100]
# Create new model's variables
with tf.variable_scope('new_scope_1'):
var1 = tf.get_variable('var1', shape=[20, 2],
initializer=tf.zeros_initializer())
with tf.variable_scope('new_scope_2'):
var2 = tf.get_variable('var2', shape=[50, 4],
initializer=tf.zeros_initializer())
# Partition into 5 variables along the first axis.
var3 = tf.get_variable(name='var3', shape=[100, 100],
initializer=tf.zeros_initializer(),
partitioner=lambda shape, dtype: [5, 1])
# Initialize all variables in `new_scope_1` from `old_scope_1`.
init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})
# Use names to specify which variables to initialize from checkpoint.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': 'new_scope_1/var1',
'old_scope_1/var2': 'new_scope_2/var2'})
# Or use tf.Variable objects to identify what to initialize.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': var1,
'old_scope_1/var2': var2})
# Initialize partitioned variables using variable's name
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': 'new_scope_2/var3'})
# Or specify the list of tf.Variable objects.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': var3._get_variable_list()})
```
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
assignment_map: Dict, where keys are names of the variables in the
checkpoint and values are current variables or names of current variables
(in default graph).
Raises:
tf.errors.OpError: If missing checkpoints or tensors in checkpoints.
ValueError: If missing variables in current graph.
"""
if distribute_lib.get_cross_tower_context():
_init_from_checkpoint(None, ckpt_dir_or_file, assignment_map)
else:
distribute_lib.get_tower_context().merge_call(_init_from_checkpoint,
ckpt_dir_or_file,
assignment_map)
def model_fn():
vs = []
for i in range(5):
vs.append(variable_scope.variable(1.0, name="foo" + str(i)))
distribute_lib.get_tower_context().merge_call(lambda _: _)
return vs
def _merge_call_merge_raises_fn(): distribute_lib.get_tower_context().merge_call(_call_merge_raises_fn)
def _add_tower_local_variable(self, *args, **kwargs):
tower_context = distribute_lib.get_tower_context()
with tower_context.tower_local_var_scope('mean'):
return self.add_weight(*args, **kwargs)
