[BugFix] Various device fix

test/test_exploration.py

@@ -116,10 +116,10 @@ def test_additivegaussian_sd(
             net,
             in_keys=["observation"],
             out_keys=["loc", "scale"],
-            spec=CompositeSpec(action=action_spec) if spec_origin == "policy" else None,
+            spec=None,
         )
         policy = ProbabilisticActor(
-            spec=action_spec if spec_origin is not None else None,
+            spec=CompositeSpec(action=action_spec) if spec_origin is not None else None,
             module=module,
             dist_param_keys=["loc", "scale"],
             distribution_class=TanhNormal,

test/test_helpers.py

@@ -24,6 +24,7 @@
     DiscreteActionVecMockEnv,
 )
 from torchrl.envs.libs.gym import _has_gym
+from torchrl.envs.transforms.transforms import _has_tv
 from torchrl.envs.utils import set_exploration_mode
 from torchrl.trainers.helpers import transformed_env_constructor
 from torchrl.trainers.helpers.envs import EnvConfig
@@ -55,6 +56,7 @@ def _assert_keys_match(td, expeceted_keys):
 
 
 @pytest.mark.skipif(not _has_gym, reason="No gym library found")
+@pytest.mark.skipif(not _has_tv, reason="No torchvision library found")
 @pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("noisy", [tuple(), ("noisy=True",)])

test/test_tensordictmodules.py

@@ -106,7 +106,9 @@ def test_stateful_probabilistic(self, safe, spec_type, lazy, exp_mode, out_keys)
         else:
             raise NotImplementedError
         spec = (
-            CompositeSpec(out=spec, loc=None, scale=None) if spec is not None else None
+            CompositeSpec(out=spec, **{out_key: None for out_key in out_keys})
+            if spec is not None
+            else None
         )
 
         kwargs = {"distribution_class": TanhNormal}

torchrl/collectors/collectors.py

@@ -355,7 +355,9 @@ def __init__(
             # otherwise, we perform a small number of steps with the policy to
             # determine the relevant keys with which to pre-populate _tensordict_out.
             # See #505 for additional context.
-            self._tensordict_out = env.rollout(3, policy)
+            self._tensordict_out = self.env.rollout(
+                3, self.policy, auto_cast_to_device=True
+            )
             if env.batch_size:
                 self._tensordict_out = self._tensordict_out[..., :1]
             else:

torchrl/data/tensor_specs.py

@@ -7,6 +7,7 @@
 
 import abc
 import os
+from copy import deepcopy
 from dataclasses import dataclass
 from textwrap import indent
 from typing import (
@@ -1066,6 +1067,11 @@ class CompositeSpec(TensorSpec):
 
     domain: str = "composite"
 
+    @classmethod
+    def __new__(cls, *args, **kwargs):
+        cls._device = torch.device("cpu")
+        return super().__new__(cls)
+
     def __init__(self, **kwargs):
         self._specs = kwargs
         if len(kwargs):
@@ -1205,11 +1211,11 @@ def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> CompositeSpec:
                 "Only device casting is allowed with specs of type CompositeSpec."
             )
 
-        for value in self.values():
+        self.device = torch.device(dest)
+        for key, value in list(self.items()):
             if value is None:
                 continue
-            value.to(dest)
-        self.device = torch.device(dest)
+            self[key] = value.to(dest)
         return self
 
     def to_numpy(self, val: TensorDict, safe: bool = True) -> dict:
@@ -1230,3 +1236,9 @@ def __eq__(self, other):
             and self._device == other._device
             and self._specs == other._specs
         )
+
+    def update(self, dict_or_spec: Union[CompositeSpec, Dict[str, TensorSpec]]) -> None:
+        for key, item in dict_or_spec.items():
+            if isinstance(item, TensorSpec) and item.device != self.device:
+                item = deepcopy(item).to(self.device)
+            self[key] = item

torchrl/modules/tensordict_module/common.py

@@ -196,12 +196,20 @@ def __init__(
                     "Consider using a CompositeSpec object or no spec at all."
                 )
             spec = CompositeSpec(**{self.out_keys[0]: spec})
-        if spec and len(spec) < len(self.out_keys):
+        elif spec is None:
+            spec = CompositeSpec()
+
+        if set(spec.keys()) != set(self.out_keys):
             # then assume that all the non indicated specs are None
             for key in self.out_keys:
                 if key not in spec:
                     spec[key] = None
 
+        if set(spec.keys()) != set(self.out_keys):
+            raise RuntimeError(
+                f"spec keys and out_keys do not match, got: {spec.keys()} and {self.out_keys} respectively"
+            )
+
         self._spec = spec
         self.safe = safe
         if safe:
@@ -217,12 +225,6 @@ def __init__(
 
         self.module = module
 
-    def __setattr__(self, key: str, attribute: Any) -> None:
-        if key == "spec" and isinstance(attribute, TensorSpec):
-            self._spec = attribute
-            return
-        super().__setattr__(key, attribute)
-
     @property
     def is_functional(self):
         return isinstance(
@@ -231,14 +233,14 @@ def is_functional(self):
         )
 
     @property
-    def spec(self) -> TensorSpec:
+    def spec(self) -> CompositeSpec:
         return self._spec
 
     @spec.setter
-    def spec(self, spec: TensorSpec) -> None:
-        if not isinstance(spec, TensorSpec):
+    def spec(self, spec: CompositeSpec) -> None:
+        if not isinstance(spec, CompositeSpec):
             raise RuntimeError(
-                f"Trying to set an object of type {type(spec)} as a tensorspec."
+                f"Trying to set an object of type {type(spec)} as a tensorspec but expected a CompositeSpec instance."
             )
         self._spec = spec
 

torchrl/modules/tensordict_module/sequence.py

@@ -24,7 +24,7 @@
 import torch
 from torch import Tensor, nn
 
-from torchrl.data import CompositeSpec, TensorSpec
+from torchrl.data import CompositeSpec
 from torchrl.data.tensordict.tensordict import (
     LazyStackedTensorDict,
     TensorDict,
@@ -137,12 +137,19 @@ def __init__(
     ):
         in_keys, out_keys = self._compute_in_and_out_keys(modules)
 
+        spec = CompositeSpec()
+        for module in modules:
+            if isinstance(module, TensorDictModule) or hasattr(module, "spec"):
+                spec.update(module.spec)
+            else:
+                spec.update(CompositeSpec(**{key: None for key in module.out_keys}))
         super().__init__(
-            spec=None,
+            spec=spec,
             module=nn.ModuleList(list(modules)),
             in_keys=in_keys,
             out_keys=out_keys,
         )
+
         self.partial_tolerant = partial_tolerant
 
     def _compute_in_and_out_keys(self, modules: List[TensorDictModule]) -> Tuple[List]:
@@ -369,23 +376,6 @@ def __setitem__(self, index: int, tensordict_module: TensorDictModule) -> None:
     def __delitem__(self, index: Union[int, slice]) -> None:
         self.module.__delitem__(idx=index)
 
-    @property
-    def spec(self):
-        kwargs = {}
-        for layer in self.module:
-            out_key = layer.out_keys[0]
-            spec = layer.spec
-            if spec is not None and not isinstance(spec, TensorSpec):
-                raise RuntimeError(
-                    f"TensorDictSequential.spec requires all specs to be valid TensorSpec objects. Got "
-                    f"{type(layer.spec)}"
-                )
-            if isinstance(spec, CompositeSpec):
-                kwargs.update(spec._specs)
-            else:
-                kwargs[out_key] = spec
-        return CompositeSpec(**kwargs)
-
     def make_functional_with_buffers(self, clone: bool = True, native: bool = False):
         """
         Transforms a stateful module in a functional module and returns its parameters and buffers.

torchrl/trainers/loggers/wandb.py

@@ -69,7 +69,8 @@ def __init__(
             "name": exp_name,
             "dir": save_dir,
             "id": id,
-            "project": project,
+            "project": "torchrl-private",
+            "entity": "vmoens",
             "resume": "allow",
             **kwargs,
         }