Method execute

tensorflow/python/ops/critical_section_ops.py:189–292

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: ```python critical_section.execute(lambda: fn(*my_args, **my_kwargs)) ``` Args: fn: The f

(self, fn, exclusive_resource_access=True, name=None)

    return self._handle.op.name

  def execute(self, 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:

    ```python
    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_access` is
        `True`, if another execution in another `CriticalSection` was created
        without `exclusive_resource_access=True`, a `ValueError` will be raised.
    """
    with ops.name_scope(name, "critical_section_execute", []):

      # Ensure that mutex locking only happens *after* all args and
      # kwargs have been executed.  This avoids certain types of deadlocks.
      lock = gen_resource_variable_ops.mutex_lock(self._handle)

      if not context.executing_eagerly():
        # NOTE(ebrevdo): This is to ensure we don't pick up spurious
        # Operations created by other threads.
        with ops.get_default_graph()._lock:  # pylint: disable=protected-access
          existing_ops = ops.get_default_graph().get_operations()
          with ops.control_dependencies([lock]):
            r = fn()
          # TODO(ebrevdo): If creating critical sections in a python loop, this
          # makes graph creation time quadratic.  Revisit if this
          # becomes a problem.
          created_ops = (set(ops.get_default_graph().get_operations())
                         .difference(existing_ops))
      else:
        with ops.control_dependencies([lock]):
          r = fn()

      if not context.executing_eagerly():
        self._add_control_dependencies_to_lock(created_ops, lock.op)

        # captured_resources is a list of resources that are directly
        # accessed only by ops created during fn(), not by any
        # ancestors of those ops in the graph.