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[Refactor] Use compile-aware helpers in Dreamer objectives #3304

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vmoens merged 3 commits into from
Jan 8, 2026
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[Refactor] Use compile-aware helpers in Dreamer objectives #3304

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vmoens Jan 8, 2026
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58 changes: 35 additions & 23 deletions torchrl/objectives/dreamer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from tensordict.nn import TensorDictModule
from tensordict.utils import NestedKey

from torchrl._utils import timeit
from torchrl._utils import _maybe_record_function_decorator, _maybe_timeit
from torchrl.envs.model_based.dreamer import DreamerEnv
from torchrl.envs.utils import ExplorationType, set_exploration_type, step_mdp
from torchrl.objectives.common import LossModule
Expand Down Expand Up @@ -123,49 +123,57 @@ def __init__(
def _forward_value_estimator_keys(self, **kwargs) -> None:
pass

@_maybe_record_function_decorator("world_model_loss/forward")
def forward(self, tensordict: TensorDict) -> torch.Tensor:
tensordict = tensordict.clone(recurse=False)
tensordict = tensordict.copy()
tensordict.rename_key_(
("next", self.tensor_keys.reward),
("next", self.tensor_keys.true_reward),
)

tensordict = self.world_model(tensordict)
# compute model loss

prior_mean = tensordict.get(("next", self.tensor_keys.prior_mean))
prior_std = tensordict.get(("next", self.tensor_keys.prior_std))
posterior_mean = tensordict.get(("next", self.tensor_keys.posterior_mean))
posterior_std = tensordict.get(("next", self.tensor_keys.posterior_std))

kl_loss = self.kl_loss(
tensordict.get(("next", self.tensor_keys.prior_mean)),
tensordict.get(("next", self.tensor_keys.prior_std)),
tensordict.get(("next", self.tensor_keys.posterior_mean)),
tensordict.get(("next", self.tensor_keys.posterior_std)),
prior_mean, prior_std, posterior_mean, posterior_std,
).unsqueeze(-1)

# Ensure contiguous layout for torch.compile compatibility
# The gradient from distance_loss flows back through decoder convolutions
pixels = tensordict.get(("next", self.tensor_keys.pixels)).contiguous()
reco_pixels = tensordict.get(("next", self.tensor_keys.reco_pixels)).contiguous()
reco_loss = distance_loss(
tensordict.get(("next", self.tensor_keys.pixels)),
tensordict.get(("next", self.tensor_keys.reco_pixels)),
pixels,
reco_pixels,
self.reco_loss,
)
if not self.global_average:
reco_loss = reco_loss.sum((-3, -2, -1))
reco_loss = reco_loss.mean().unsqueeze(-1)

true_reward = tensordict.get(("next", self.tensor_keys.true_reward))
pred_reward = tensordict.get(("next", self.tensor_keys.reward))
reward_loss = distance_loss(
tensordict.get(("next", self.tensor_keys.true_reward)),
tensordict.get(("next", self.tensor_keys.reward)),
true_reward,
pred_reward,
self.reward_loss,
)
if not self.global_average:
reward_loss = reward_loss.squeeze(-1)
reward_loss = reward_loss.mean().unsqueeze(-1)
# import ipdb; ipdb.set_trace()

td_out = TensorDict(
loss_model_kl=self.lambda_kl * kl_loss,
loss_model_reco=self.lambda_reco * reco_loss,
loss_model_reward=self.lambda_reward * reward_loss,
)
self._clear_weakrefs(tensordict, td_out)
return (
td_out,
tensordict.detach(),
)

return (td_out, tensordict.data)

@staticmethod
def normal_log_probability(x, mean, std):
Expand Down Expand Up @@ -275,10 +283,11 @@ def _forward_value_estimator_keys(self, **kwargs) -> None:
value=self._tensor_keys.value,
)

@_maybe_record_function_decorator("actor_loss/forward")
def forward(self, tensordict: TensorDict) -> tuple[TensorDict, TensorDict]:
tensordict = tensordict.select("state", self.tensor_keys.belief).detach()
tensordict = tensordict.select("state", self.tensor_keys.belief).data

with timeit("actor_loss/time-rollout"), hold_out_net(
with _maybe_timeit("actor_loss/time-rollout"), hold_out_net(
self.model_based_env
), set_exploration_type(ExplorationType.RANDOM):
tensordict = self.model_based_env.reset(tensordict.copy())
Expand All @@ -288,7 +297,6 @@ def forward(self, tensordict: TensorDict) -> tuple[TensorDict, TensorDict]:
auto_reset=False,
tensordict=tensordict,
)

next_tensordict = step_mdp(fake_data, keep_other=True)
with hold_out_net(self.value_model):
next_tensordict = self.value_model(next_tensordict)
Expand All @@ -308,7 +316,8 @@ def forward(self, tensordict: TensorDict) -> tuple[TensorDict, TensorDict]:
actor_loss = -lambda_target.sum((-2, -1)).mean()
loss_tensordict = TensorDict({"loss_actor": actor_loss}, [])
self._clear_weakrefs(tensordict, loss_tensordict)
return loss_tensordict, fake_data.detach()

return loss_tensordict, fake_data.data

def lambda_target(self, reward: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
done = torch.zeros(reward.shape, dtype=torch.bool, device=reward.device)
Expand Down Expand Up @@ -420,14 +429,15 @@ def __init__(
def _forward_value_estimator_keys(self, **kwargs) -> None:
pass

@_maybe_record_function_decorator("value_loss/forward")
def forward(self, fake_data) -> torch.Tensor:
lambda_target = fake_data.get("lambda_target")

tensordict_select = fake_data.select(*self.value_model.in_keys, strict=False)
self.value_model(tensordict_select)

if self.discount_loss:
discount = self.gamma * torch.ones_like(
lambda_target, device=lambda_target.device
)
discount = self.gamma * torch.ones_like(lambda_target, device=lambda_target.device)
discount[..., 0, :] = 1
discount = discount.cumprod(dim=-2)
value_loss = (
Expand All @@ -452,6 +462,8 @@ def forward(self, fake_data) -> torch.Tensor:
.sum((-1, -2))
.mean()
)

loss_tensordict = TensorDict({"loss_value": value_loss})
self._clear_weakrefs(fake_data, loss_tensordict)

return loss_tensordict, fake_data
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