[BugFix] Refactor reductions

torchrl/objectives/a2c.py

@@ -3,7 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 import contextlib
-import functools
 import warnings
 from copy import deepcopy
 from dataclasses import dataclass
@@ -472,13 +471,16 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         td_out = TensorDict({"loss_objective": loss}, batch_size=[])
         if self.entropy_bonus:
             entropy = self.get_entropy_bonus(dist)
-            td_out.set("entropy", entropy.detach())  # for logging
+            td_out.set("entropy", entropy.detach().mean())  # for logging
             td_out.set("loss_entropy", -self.entropy_coef * entropy)
         if self.critic_coef:
             loss_critic = self.loss_critic(tensordict)
             td_out.set("loss_critic", loss_critic)
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 

torchrl/objectives/ddpg.py

@@ -5,7 +5,6 @@
 
 from __future__ import annotations
 
-import functools
 from copy import deepcopy
 from dataclasses import dataclass
 from typing import Tuple
@@ -296,9 +295,6 @@ def forward(self, tensordict: TensorDictBase) -> TensorDict:
             source={"loss_actor": loss_actor, "loss_value": loss_value, **metadata},
             batch_size=[],
         )
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
-        )
         return td_out
 
     def loss_actor(
@@ -314,6 +310,7 @@ def loss_actor(
             td_copy = self.value_network(td_copy)
         loss_actor = -td_copy.get(self.tensor_keys.state_action_value).squeeze(-1)
         metadata = {}
+        loss_actor = _reduce(loss_actor, self.reduction)
         return loss_actor, metadata
 
     def loss_value(
@@ -352,6 +349,7 @@ def loss_value(
                 "target_value_max": target_value.max(),
                 "pred_value_max": pred_val.max(),
             }
+        loss_value = _reduce(loss_value, self.reduction)
         return loss_value, metadata
 
     def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams):

torchrl/objectives/decision_transformer.py

@@ -220,7 +220,7 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             "loss_log_likelihood": -log_likelihood,
             "loss_entropy": -entropy_bonus,
             "loss_alpha": loss_alpha,
-            "entropy": entropy.detach(),
+            "entropy": entropy.detach().mean(),
             "alpha": self.alpha.detach(),
         }
         return TensorDict(out, [])

torchrl/objectives/deprecated.py

@@ -2,7 +2,6 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-import functools
 import math
 from dataclasses import dataclass
 from numbers import Number
@@ -312,12 +311,15 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
                 "loss_qvalue": loss_qval,
                 "loss_alpha": loss_alpha,
                 "alpha": self.alpha,
-                "entropy": -sample_log_prob.detach(),
+                "entropy": -sample_log_prob.detach().mean(),
             },
             [],
         )
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 

torchrl/objectives/ppo.py

@@ -555,7 +555,7 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         td_out = TensorDict({"loss_objective": -neg_loss}, batch_size=[])
         if self.entropy_bonus:
             entropy = self.get_entropy_bonus(dist)
-            td_out.set("entropy", entropy.detach())  # for logging
+            td_out.set("entropy", entropy.detach().mean())  # for logging
             td_out.set("loss_entropy", -self.entropy_coef * entropy)
         if self.critic_coef:
             loss_critic = self.loss_critic(tensordict)
@@ -799,15 +799,18 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
 
         if self.entropy_bonus:
             entropy = self.get_entropy_bonus(dist)
-            td_out.set("entropy", entropy.detach())  # for logging
+            td_out.set("entropy", entropy.detach().mean())  # for logging
             td_out.set("loss_entropy", -self.entropy_coef * entropy)
         if self.critic_coef:
             loss_critic = self.loss_critic(tensordict)
             td_out.set("loss_critic", loss_critic)
 
         td_out.set("ESS", _reduce(ess, self.reduction) / batch)
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 
@@ -1061,13 +1064,16 @@ def forward(self, tensordict: TensorDictBase) -> TensorDict:
 
         if self.entropy_bonus:
             entropy = self.get_entropy_bonus(dist)
-            td_out.set("entropy", entropy.detach())  # for logging
+            td_out.set("entropy", entropy.detach().mean())  # for logging
             td_out.set("loss_entropy", -self.entropy_coef * entropy)
         if self.critic_coef:
             loss_critic = self.loss_critic(tensordict_copy)
             td_out.set("loss_critic", loss_critic)
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
 
         return td_out

torchrl/objectives/redq.py

@@ -2,7 +2,6 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-import functools
 import math
 from dataclasses import dataclass
 from numbers import Number
@@ -564,16 +563,19 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
                 "loss_qvalue": loss_qval,
                 "loss_alpha": loss_alpha,
                 "alpha": self.alpha.detach(),
-                "entropy": -sample_log_prob.detach(),
+                "entropy": -sample_log_prob.detach().mean(),
                 "state_action_value_actor": state_action_value_actor.detach(),
                 "action_log_prob_actor": action_log_prob_actor.detach(),
                 "next.state_value": next_state_value.detach(),
                 "target_value": target_value.detach(),
             },
             [],
         )
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 

torchrl/objectives/reinforce.py

@@ -5,7 +5,6 @@
 from __future__ import annotations
 
 import contextlib
-import functools
 import warnings
 from copy import deepcopy
 from dataclasses import dataclass
@@ -402,8 +401,11 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         td_out = TensorDict({"loss_actor": loss_actor}, batch_size=[])
 
         td_out.set("loss_value", self.loss_critic(tensordict))
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
 
         return td_out

torchrl/objectives/sac.py

@@ -2,7 +2,6 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-import functools
 import math
 import warnings
 from dataclasses import dataclass
@@ -573,13 +572,16 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             "loss_qvalue": loss_qvalue,
             "loss_alpha": loss_alpha,
             "alpha": self._alpha,
-            "entropy": entropy,
+            "entropy": entropy.detach().mean(),
         }
         if self._version == 1:
             out["loss_value"] = loss_value
         td_out = TensorDict(out, [])
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 
@@ -1134,11 +1136,14 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             "loss_qvalue": loss_value,
             "loss_alpha": loss_alpha,
             "alpha": self._alpha,
-            "entropy": entropy,
+            "entropy": entropy.detach().mean(),
         }
         td_out = TensorDict(out, [])
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
+        td_out = td_out.named_apply(
+            lambda name, value: _reduce(value, reduction=self.reduction)
+            if name.startswith("loss_")
+            else value,
+            batch_size=[],
         )
         return td_out
 

torchrl/objectives/td3.py

@@ -2,7 +2,6 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-import functools
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -375,6 +374,7 @@ def actor_loss(self, tensordict):
         metadata = {
             "state_action_value_actor": state_action_value_actor.detach(),
         }
+        loss_actor = _reduce(loss_actor, reduction=self.reduction)
         return loss_actor, metadata
 
     def value_loss(self, tensordict):
@@ -449,6 +449,7 @@ def value_loss(self, tensordict):
             "pred_value": current_qvalue.detach(),
             "target_value": target_value.detach(),
         }
+        loss_qval = _reduce(loss_qval, reduction=self.reduction)
         return loss_qval, metadata
 
     @dispatch
@@ -472,9 +473,6 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             },
             batch_size=[],
         )
-        td_out = td_out.apply(
-            functools.partial(_reduce, reduction=self.reduction), batch_size=[]
-        )
         return td_out
 
     def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams):