TensorFlow 1 version | View source on GitHub |
Critical section.
tf.CriticalSection(
name=None, shared_name=None, critical_section_def=None, import_scope=None
)
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.compat.v1.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
Attributes | |
---|---|
name
|
Methods
execute
execute(
fn, exclusive_resource_access=True, name=None
)
Execute function fn()
inside the critical section.
fn
should not accept any arguments. To add extra arguments to when
calling fn
in the critical section, create a lambda:
critical_section.execute(lambda: fn(*my_args, **my_kwargs))
Args | |
---|---|
fn
|
The function to execute. Must return at least one tensor. |
exclusive_resource_access
|
Whether the resources required by
fn should be exclusive to this CriticalSection . Default: True .
You may want to set this to False if you will be accessing a
resource in read-only mode in two different CriticalSections.
|
name
|
The name to use when creating the execute operation. |
Returns | |
---|---|
The tensors returned from fn() .
|
Raises | |
---|---|
ValueError
|
If fn attempts to lock this CriticalSection in any nested
or lazy way that may cause a deadlock.
|
ValueError
|
If exclusive_resource_access == True and
another CriticalSection has an execution requesting the same
resources as fn . Note, even if exclusive_resource_accessis True, if another execution in another CriticalSectionwas created
without exclusive_resource_access=True, a ValueError` will be raised.
|