def get_original_task_module(opt: Opt, multi_possible: bool = False):
"""
Returns task module of "original" task.
Original task in this case means the task we want to use
with the control teacher.
:param opt:
opt dict
:param multi_possible:
specify True if multiple tasks are possible.
:return task_module:
return module associated with task.
"""
modules = []
tasks = opt['task'].split(',')
if not multi_possible:
assert len(tasks) == 1
for task in tasks:
if len(task.split(':')) < 3:
raise RuntimeError(
'\n\n********************************************************\n'
'Must specify original task using the following format:\n'
'`--task internal:flattened:task:<ORIGINAL TASK NAME>`'
'\n********************************************************\n')
original_task = ':'.join(task.split(':')[2:])
task_module = load_teacher_module(original_task)
modules.append(task_module)
if multi_possible:
return modules
return modules[0]
def create_task_agent_from_taskname(opt: Opt):
"""
Create task agent(s) assuming the input ``task_dir:teacher_class``.
e.g. def_string is a shorthand path like ``babi:Task1k:1`` or ``#babi`` or a
complete path like ``parlai.tasks.babi.agents:Task1kTeacher:1``, which essentially
performs ``from parlai.tasks.babi import Task1kTeacher`` with the parameter ``1`` in
``opt['task']`` to be used by the class ``Task1kTeacher``.
"""
if not (opt.get('task') or opt.get('pytorch_teacher_task')
or opt.get('pytorch_teacher_dataset')):
raise RuntimeError('No task specified. Please select a task with ' +
'--task {task_name}.')
if not opt.get('task'):
opt['task'] = 'pytorch_teacher'
if ',' not in opt['task']:
# Single task
teacher_class = load_teacher_module(opt['task'])
_add_task_flags_to_agent_opt(teacher_class, opt, opt['task'])
task_agents = teacher_class(opt)
if type(task_agents) != list:
task_agents = [task_agents]
return task_agents
else:
# Multitask teacher/agent
task_agents = MultiTaskTeacher(opt)
if type(task_agents) != list:
task_agents = [task_agents]
return task_agents
def add_task_args(self, task):
"""
Add arguments specific to the specified task.
"""
for t in ids_to_tasks(task).split(','):
agent = load_teacher_module(t)
try:
if hasattr(agent, 'add_cmdline_args'):
agent.add_cmdline_args(self)
except argparse.ArgumentError:
# already added
pass
def add_task_args(self, task: str, partial: Opt):
"""
Add arguments specific to the specified task.
"""
for t in ids_to_tasks(task).split(','):
agent = load_teacher_module(t)
try:
if hasattr(agent, 'add_cmdline_args'):
agent.add_cmdline_args(self, partial)
except TypeError as typ:
raise TypeError(
f"Task '{task}' appears to have signature "
"add_cmdline_args(argparser) but we have updated the signature "
"to add_cmdline_args(argparser, partial_opt). For details, see "
"https://github.com/facebookresearch/ParlAI/pull/3328."
) from typ
except argparse.ArgumentError:
# already added
pass
def test_load_teacher(self):
teacher_module = load_teacher_module(OPTIONS['task'])
self.assertEqual(teacher_module, c2agents.SelfRevisedTeacher)
