def actor_method_executor(actor, *args, **kwargs):
# Execute the assigned method.
is_bound = (is_class_method(method)
or is_static_method(type(actor), method_name))
if is_bound:
return method(*args, **kwargs)
else:
return method(actor, *args, **kwargs)
def actor_method_executor(dummy_return_id, actor, *args):
# Update the actor's task counter to reflect the task we're about
# to execute.
self._worker.actor_task_counter += 1
# If this is the first task to execute on the actor, try to resume
# from a checkpoint.
# Current __init__ will be called by default. So the real function
# call will start from 2.
if actor_imported and self._worker.actor_task_counter == 2:
checkpoint_resumed = ray.actor.restore_and_log_checkpoint(
self._worker, actor)
if checkpoint_resumed:
# NOTE(swang): Since we did not actually execute the
# __init__ method, this will put None as the return value.
# If the __init__ method is supposed to return multiple
# values, an exception will be logged.
return
# Determine whether we should checkpoint the actor.
checkpointing_on = (actor_imported
and self._worker.actor_checkpoint_interval > 0)
# We should checkpoint the actor if user checkpointing is on, we've
# executed checkpoint_interval tasks since the last checkpoint, and
# the method we're about to execute is not a checkpoint.
save_checkpoint = (checkpointing_on
and (self._worker.actor_task_counter %
self._worker.actor_checkpoint_interval == 0
and method_name != "__ray_checkpoint__"))
# Execute the assigned method and save a checkpoint if necessary.
try:
if is_class_method(method):
method_returns = method(*args)
else:
method_returns = method(actor, *args)
except Exception:
# Save the checkpoint before allowing the method exception
# to be thrown.
if save_checkpoint:
ray.actor.save_and_log_checkpoint(self._worker, actor)
raise
else:
# Save the checkpoint before returning the method's return
# values.
if save_checkpoint:
ray.actor.save_and_log_checkpoint(self._worker, actor)
return method_returns
def actor_method_executor(actor, *args, **kwargs):
# Update the actor's task counter to reflect the task we're about
# to execute.
self._worker.actor_task_counter += 1
# Execute the assigned method and save a checkpoint if necessary.
try:
is_bound = (is_class_method(method)
or is_static_method(type(actor), method_name))
if is_bound:
method_returns = method(*args, **kwargs)
else:
method_returns = method(actor, *args, **kwargs)
except Exception as e:
# Save the checkpoint before allowing the method exception
# to be thrown, but don't save the checkpoint for actor
# creation task.
if (isinstance(actor, ray.actor.Checkpointable)
and self._worker.actor_task_counter != 1):
self._save_and_log_checkpoint(actor)
raise e
else:
# Handle any checkpointing operations before storing the
# method's return values.
# NOTE(swang): If method_returns is a pointer to the actor's
# state and the checkpointing operations can modify the return
# values if they mutate the actor's state. Is this okay?
if isinstance(actor, ray.actor.Checkpointable):
# If this is the first task to execute on the actor, try to
# resume from a checkpoint.
if self._worker.actor_task_counter == 1:
if actor_imported:
self._restore_and_log_checkpoint(actor)
else:
# Save the checkpoint before returning the method's
# return values.
self._save_and_log_checkpoint(actor)
return method_returns
def actor_method_executor(dummy_return_id, actor, *args):
# Update the actor's task counter to reflect the task we're about
# to execute.
self._worker.actor_task_counter += 1
# Execute the assigned method and save a checkpoint if necessary.
try:
if is_class_method(method):
method_returns = method(*args)
else:
method_returns = method(actor, *args)
except Exception as e:
# Save the checkpoint before allowing the method exception
# to be thrown, but don't save the checkpoint for actor
# creation task.
if (isinstance(actor, ray.actor.Checkpointable)
and self._worker.actor_task_counter != 1):
self._save_and_log_checkpoint(actor)
raise e
else:
# Handle any checkpointing operations before storing the
# method's return values.
# NOTE(swang): If method_returns is a pointer to the actor's
# state and the checkpointing operations can modify the return
# values if they mutate the actor's state. Is this okay?
if isinstance(actor, ray.actor.Checkpointable):
# If this is the first task to execute on the actor, try to
# resume from a checkpoint.
if self._worker.actor_task_counter == 1:
if actor_imported:
self._restore_and_log_checkpoint(actor)
else:
# Save the checkpoint before returning the method's
# return values.
self._save_and_log_checkpoint(actor)
return method_returns
