Update

[ghstack-poisoned]

test/test_cost.py

@@ -8158,18 +8158,19 @@ def _create_seq_mock_data_ppo(
         obs = total_obs[:, :T]
         next_obs = total_obs[:, 1:]
         if atoms:
-            action = torch.randn(batch, T, atoms, action_dim, device=device).clamp(
-                -1, 1
-            )
+            action_shape = (batch, T, atoms, action_dim)
         else:
-            action = torch.randn(batch, T, action_dim, device=device).clamp(-1, 1)
+            action_shape = (batch, T, action_dim)
+        params_mean = torch.randn(action_shape, device=device) / 10
+        params_scale = torch.rand(action_shape, device=device) / 10
+        action = (params_mean + params_scale * torch.randn(action_shape, device=device)).clamp(
+            -1, 1
+        )
         reward = torch.randn(batch, T, 1, device=device)
         done = torch.zeros(batch, T, 1, dtype=torch.bool, device=device)
         terminated = torch.zeros(batch, T, 1, dtype=torch.bool, device=device)
         mask = torch.ones(batch, T, dtype=torch.bool, device=device)
         action = action.masked_fill_(~mask.unsqueeze(-1), 0.0)
-        params_mean = torch.randn_like(action) / 10
-        params_scale = torch.rand_like(action) / 10
         loc = params_mean.masked_fill_(~mask.unsqueeze(-1), 0.0)
         scale = params_scale.masked_fill_(~mask.unsqueeze(-1), 0.0)
         td = TensorDict(
@@ -8184,9 +8185,6 @@ def _create_seq_mock_data_ppo(
                 },
                 "collector": {"mask": mask},
                 action_key: {"action1": action} if composite_action_dist else action,
-                sample_log_prob_key: (
-                    torch.randn_like(action[..., 1]) / 10
-                ).masked_fill_(~mask, 0.0),
             },
             device=device,
             names=[None, "time"],

torchrl/objectives/ppo.py

@@ -510,17 +510,28 @@ def _log_weight(
         # current log_prob of actions
         action = _maybe_get_or_select(tensordict, self.tensor_keys.action)
 
+        is_composite = None
+        if all(key in tensordict for key in self.actor_network.dist_params_keys):
+            prev_dist = self.actor_network.build_dist_from_params(tensordict.detach())
+            kwargs, is_composite = _get_composite_kwargs(prev_dist)
+            if is_composite:
+                prev_log_prob = prev_dist.log_prob(tensordict, **kwargs)
+            else:
+                prev_log_prob = prev_dist.log_prob(action, **kwargs)
+            print('prev_log_prob', prev_log_prob)
+        else:
+            try:
+                prev_log_prob = _maybe_get_or_select(
+                    tensordict, self.tensor_keys.sample_log_prob
+                )
+            except KeyError as err:
+                raise _make_lp_get_error(self.tensor_keys, tensordict, err)
+
         with self.actor_network_params.to_module(
             self.actor_network
         ) if self.functional else contextlib.nullcontext():
-            dist = self.actor_network.get_dist(tensordict)
+            current_dist = self.actor_network.get_dist(tensordict)
 
-        try:
-            prev_log_prob = _maybe_get_or_select(
-                tensordict, self.tensor_keys.sample_log_prob
-            )
-        except KeyError as err:
-            raise _make_lp_get_error(self.tensor_keys, tensordict, err)
 
         if prev_log_prob.requires_grad:
             raise RuntimeError(
@@ -532,25 +543,11 @@ def _log_weight(
                 f"tensordict stored {self.tensor_keys.action} requires grad."
             )
         if isinstance(action, torch.Tensor):
-            log_prob = dist.log_prob(action)
+            log_prob = current_dist.log_prob(action)
         else:
-            if isinstance(dist, CompositeDistribution):
-                is_composite = True
-                aggregate = dist.aggregate_probabilities
-                if aggregate is None:
-                    aggregate = False
-                include_sum = dist.include_sum
-                if include_sum is None:
-                    include_sum = False
-                kwargs = {
-                    "inplace": False,
-                    "aggregate_probabilities": aggregate,
-                    "include_sum": include_sum,
-                }
-            else:
-                is_composite = False
-                kwargs = {}
-            log_prob: TensorDictBase = dist.log_prob(tensordict, **kwargs)
+            if is_composite is None:
+                kwargs, is_composite = _get_composite_kwargs(current_dist)
+            log_prob: TensorDictBase = current_dist.log_prob(tensordict, **kwargs)
             if (
                 is_composite
                 and not is_tensor_collection(prev_log_prob)
@@ -564,7 +561,7 @@ def _log_weight(
         if is_tensor_collection(kl_approx):
             kl_approx = _sum_td_features(kl_approx)
 
-        return log_weight, dist, kl_approx
+        return log_weight, current_dist, kl_approx
 
     def loss_critic(self, tensordict: TensorDictBase) -> torch.Tensor:
         """Returns the critic loss multiplied by ``critic_coef``, if it is not ``None``."""
@@ -640,6 +637,9 @@ def _cached_critic_network_params_detached(self):
     @dispatch
     def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         tensordict = tensordict.clone(False)
+
+        log_weight, dist, kl_approx = self._log_weight(tensordict)
+
         advantage = tensordict.get(self.tensor_keys.advantage, None)
         if advantage is None:
             self.value_estimator(
@@ -653,7 +653,6 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             scale = advantage.std().clamp_min(1e-6)
             advantage = (advantage - loc) / scale
 
-        log_weight, dist, kl_approx = self._log_weight(tensordict)
         if is_tensor_collection(log_weight):
             log_weight = _sum_td_features(log_weight)
             log_weight = log_weight.view(advantage.shape)
@@ -1295,3 +1294,22 @@ def _make_lp_get_error(tensor_keys, log_prob, err):
         return KeyError(result)
     result += "This is usually due to a missing call to loss.set_keys(sample_log_prob=<list_of_log_prob_keys>)."
     return KeyError(result)
+
+def _get_composite_kwargs(current_dist):
+    if isinstance(current_dist, CompositeDistribution):
+        is_composite = True
+        aggregate = current_dist.aggregate_probabilities
+        if aggregate is None:
+            aggregate = False
+        include_sum = current_dist.include_sum
+        if include_sum is None:
+            include_sum = False
+        kwargs = {
+            "inplace": False,
+            "aggregate_probabilities": aggregate,
+            "include_sum": include_sum,
+        }
+    else:
+        is_composite = False
+        kwargs = {}
+    return kwargs, is_composite