Missed TensorFlow World? Check out the recap. Learn more

tf.CriticalSection

View source on GitHub

Class CriticalSection

Critical section.

Aliases:

  • Class tf.compat.v1.CriticalSection
  • Class tf.compat.v2.CriticalSection

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

__init__

View source

__init__(
    name=None,
    shared_name=None,
    critical_section_def=None,
    import_scope=None
)

Creates a critical section.

Properties

name

Methods

execute

View source

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 ifexclusive_resource_accessisTrue, if another execution in anotherCriticalSectionwas created withoutexclusive_resource_access=True, aValueError` will be raised.