def create_predictor_policy_from_model(serving_module, **kwargs) -> Policy:
"""
serving_module is the result of ModelManager.build_serving_module().
This function creates a Policy for gym environments.
"""
module_name = serving_module.original_name
if module_name.endswith("DiscreteDqnPredictorWrapper"):
rl_parameters = kwargs.get("rl_parameters", None)
return DiscreteDQNPredictorPolicy(serving_module, rl_parameters)
elif module_name.endswith("ActorPredictorWrapper"):
return ActorPredictorPolicy(
predictor=ActorPredictorUnwrapper(serving_module))
elif module_name.endswith("ParametricDqnPredictorWrapper"):
# TODO: remove this dependency
max_num_actions = kwargs.get("max_num_actions", None)
assert (max_num_actions
is not None), f"max_num_actions not given for Parametric DQN."
q_network = ParametricDqnPredictorUnwrapper(serving_module)
# TODO: write SlateQ Wrapper
slate_size = kwargs.get("slate_size", None)
if slate_size is not None:
scorer = slate_q_serving_scorer(num_candidates=max_num_actions,
q_network=q_network)
sampler = TopKSampler(k=slate_size)
else:
sampler = GreedyActionSampler()
scorer = parametric_dqn_serving_scorer(
max_num_actions=max_num_actions, q_network=q_network)
return Policy(scorer=scorer, sampler=sampler)
else:
raise NotImplementedError(
f"Predictor policy for serving module {serving_module} not available."
)
def create_policy(self, serving: bool) -> Policy:
if serving:
return create_predictor_policy_from_model(
self.build_serving_module(),
max_num_actions=self.num_candidates,
slate_size=self.slate_size,
)
else:
scorer = slate_q_scorer(num_candidates=self.num_candidates,
q_network=self._q_network)
sampler = TopKSampler(k=self.slate_size)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(
self,
trainer_module: ReAgentLightningModule,
serving: bool = False,
normalization_data_map: Optional[Dict[str, NormalizationData]] = None,
):
if serving:
assert normalization_data_map
return create_predictor_policy_from_model(
self.build_serving_module(trainer_module, normalization_data_map),
# pyre-fixme[16]: `SlateQBase` has no attribute `num_candidates`.
max_num_actions=self.num_candidates,
# pyre-fixme[16]: `SlateQBase` has no attribute `slate_size`.
slate_size=self.slate_size,
)
else:
scorer = slate_q_scorer(
num_candidates=self.num_candidates,
# pyre-fixme[6]: Expected `ModelBase` for 2nd param but got
# `Union[torch.Tensor, torch.nn.Module]`.
q_network=trainer_module.q_network,
)
sampler = TopKSampler(k=self.slate_size)
return Policy(scorer=scorer, sampler=sampler)
