Class CriticalSection
Defined in tensorflow/contrib/framework/python/ops/critical_section_ops.py
.
Critical section.
A CriticalSection
object is a resource in the graph which executes subgraphs
in serial order. A common example of a subgraph one may wish to run
exclusively is the one given by the following function:
v = resource_variable_ops.ResourceVariable(0.0, name="v")
def count():
value = v.read_value()
with tf.control_dependencies([value]):
with tf.control_dependencies([v.assign_add(1)]):
return tf.identity(value)
Here, a snapshot of v
is captured in value
; and then v
is updated.
The snapshot value is returned.
If multiple workers or threads all execute count
in parallel, there is no
guarantee that access to the variable v
is atomic at any point within
any thread's calculation of count
. In fact, even implementing an atomic
counter that guarantees that the user will see each value 0, 1, ...,
is
currently impossible.
The solution is to ensure any access to the underlying resource v
is
only processed through a critical section:
cs = CriticalSection()
f1 = cs.execute(count)
f2 = cs.execute(count)
output = f1 + f2
session.run(output)
The functions f1
and f2
will be executed serially, and updates to v
will be atomic.
NOTES
All resource objects, including the critical section and any captured variables of functions executed on that critical section, will be colocated to the same device (host and cpu/gpu).
When using multiple critical sections on the same resources, there is no
guarantee of exclusive access to those resources. This behavior is disallowed
by default (but see the kwarg exclusive_resource_access
).
For example, running the same function in two separate critical sections will not ensure serial execution:
v = tf.get_variable("v", initializer=0.0, use_resource=True)
def accumulate(up):
x = v.read_value()
with tf.control_dependencies([x]):
with tf.control_dependencies([v.assign_add(up)]):
return tf.identity(x)
ex1 = CriticalSection().execute(
accumulate, 1.0, exclusive_resource_access=False)
ex2 = CriticalSection().execute(
accumulate, 1.0, exclusive_resource_access=False)
bad_sum = ex1 + ex2
sess.run(v.initializer)
sess.run(bad_sum) # May return 0.0
__init__
__init__(
name=None,
shared_name=None,
critical_section_def=None,
import_scope=None
)
Creates a critical section.
Properties
name
Methods
tf.contrib.framework.CriticalSection.execute
execute(
fn,
*args,
**kwargs
)
Execute function fn(*args, **kwargs)
inside the CriticalSection.
Args:
fn
: The function to execute. Must return at least one tensor.*args
: Additional positional arguments tofn
.**kwargs
: Additional keyword arguments tofn
. Several keywords are reserved forexecute
. These are:- name; The name to use when creating the execute operation.
- exclusive_resource_access; Whether the resources required by
fn
should be exclusive to thisCriticalSection
. Default:True
. You may want to set this toFalse
if you will be accessing a resource in read-only mode in two different CriticalSections.
Returns:
The tensors returned from fn(*args, **kwargs)
.
Raises:
ValueError
: Iffn
attempts to lock thisCriticalSection
in any nested or lazy way that may cause a deadlock.ValueError
: Ifexclusive_resource_access
is not provided (isTrue
) and anotherCriticalSection
has an execution requesting the same resources as in*args
,**kwargs
, and any additionally captured inputs infn
. Note, even ifexclusive_resource_access
isTrue
, if another execution in anotherCriticalSection
was created withoutexclusive_resource_access=True
, aValueError
will be raised.