init

pytorch · vmoens · Jan 31, 2024 · Jan 19, 2024 · Jan 31, 2024 · Jan 31, 2024
commit 012f9c005d04508b47214a505a1aee1a4ca61eca
diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
@@ -151,16 +151,11 @@ def _policy_is_tensordict_compatible(policy: nn.Module):
         and hasattr(policy, "in_keys")
         and hasattr(policy, "out_keys")
     ):
-        warnings.warn(
-            "Passing a policy that is not a TensorDictModuleBase subclass but has in_keys and out_keys "
-            "will soon be deprecated. We'd like to motivate our users to inherit from this class (which "
+        raise RuntimeError(
+            "Passing a policy that is not a tensordict.nn.TensorDictModuleBase subclass but has in_keys and out_keys "
+            "is deprecated. Users should inherit from this class (which "
             "has very few restrictions) to make the experience smoother.",
-            category=DeprecationWarning,
         )
-        # if the policy is a TensorDictModule or takes a single argument and defines
-        # in_keys and out_keys then we assume it can already deal with TensorDict input
-        # to forward and we return True
-        return True
     elif not hasattr(policy, "in_keys") and not hasattr(policy, "out_keys"):
         # if it's not a TensorDictModule, and in_keys and out_keys are not defined then
         # we assume no TensorDict compatibility and will try to wrap it.

diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py
@@ -146,7 +146,7 @@ def __init__(
         prefetch: int | None = None,
         transform: "torchrl.envs.Transform" | None = None,  # noqa-F821
         split_trajs: bool = False,
-        from_env: bool = None,
+        from_env: bool = False,
         use_truncated_as_done: bool = True,
         direct_download: bool = None,
         terminate_on_end: bool = None,
@@ -165,29 +165,16 @@ def __init__(
             direct_download = not self._has_d4rl
 
         if not direct_download:
-            if from_env is None:
-                warnings.warn(
-                    "from_env will soon default to ``False``, ie the data will be "
-                    "downloaded without relying on d4rl by default. "
-                    "For now, ``True`` will still be the default. "
-                    "To disable this warning, explicitly pass the ``from_env`` argument "
-                    "during construction of the dataset.",
-                    category=DeprecationWarning,
-                )
-                from_env = True
-            else:
-                warnings.warn(
-                    "You are using the D4RL library for collecting data. "
-                    "We advise against this use, as D4RL formatting can be "
-                    "inconsistent. "
-                    "To download the D4RL data without the D4RL library, use "
-                    "direct_download=True in the dataset constructor. "
-                    "Recurring to `direct_download=False` will soon be deprecated."
-                )
+            warnings.warn(
+                "You are using the D4RL library for collecting data. "
+                "We advise against this use, as D4RL formatting can be "
+                "inconsistent. "
+                "To download the D4RL data without the D4RL library, use "
+                "direct_download=True in the dataset constructor. "
+                "Recurring to `direct_download=False` will soon be deprecated."
+            )
             self.from_env = from_env
         else:
-            if from_env is None:
-                from_env = False
             self.from_env = from_env
 
         if (download == "force") or (download and not self._is_downloaded()):

diff --git a/torchrl/data/replay_buffers/storages.py b/torchrl/data/replay_buffers/storages.py
@@ -1043,7 +1043,7 @@ def _reset_batch_size(x):
     shape = x.get("_rb_batch_size", None)
     if shape is not None:
         warnings.warn(
-            "Reshaping nested tensordicts will be deprecated soon.",
+            "Reshaping nested tensordicts will be deprecated in v0.4.0.",
             category=DeprecationWarning,
         )
         data = x.get("_data")

diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
@@ -376,15 +376,15 @@ def high(self, value):
     @property
     def minimum(self):
         warnings.warn(
-            f"{type(self)}.minimum is going to be deprecated in favour of {type(self)}.low",
+            f"{type(self)}.minimum is going to be deprecated in favour of {type(self)}.low in v0.4.0",
             category=DeprecationWarning,
         )
         return self._low.to(self.device)
 
     @property
     def maximum(self):
         warnings.warn(
-            f"{type(self)}.maximum is going to be deprecated in favour of {type(self)}.high",
+            f"{type(self)}.maximum is going to be deprecated in favour of {type(self)}.high in v0.4.0",
             category=DeprecationWarning,
         )
         return self._high.to(self.device)
@@ -1472,7 +1472,7 @@ class BoundedTensorSpec(TensorSpec):
     # SPEC_HANDLED_FUNCTIONS = {}
     DEPRECATED_KWARGS = (
         "The `minimum` and `maximum` keyword arguments are now "
-        "deprecated in favour of `low` and `high`."
+        "deprecated in favour of `low` and `high` in v0.4.0."
     )
     CONFLICTING_KWARGS = (
         "The keyword arguments {} and {} conflict. Only one of these can be passed."

diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py
@@ -521,7 +521,7 @@ def info_dict_reader(self, value: callable):
         warnings.warn(
             f"Please use {type(self)}.set_info_dict_reader method to set a new info reader. "
             f"This method will append a reader to the list of existing readers (if any). "
-            f"Setting info_dict_reader directly will be soon deprecated.",
+            f"Setting info_dict_reader directly will be deprecated in v0.4.0.",
             category=DeprecationWarning,
         )
         self._info_dict_reader.append(value)
diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py
@@ -2345,15 +2345,9 @@ def __init__(
         standard_normal: bool = False,
     ):
         if in_keys is None:
-            warnings.warn(
-                "Not passing in_keys to ObservationNorm will soon be deprecated. "
-                "Ensure you specify the entries to be normalized",
-                category=DeprecationWarning,
+            raise RuntimeError(
+                "Not passing in_keys to ObservationNorm is a deprecated behaviour."
             )
-            in_keys = [
-                "observation",
-                "pixels",
-            ]
 
         if out_keys is None:
             out_keys = copy(in_keys)
@@ -2692,7 +2686,7 @@ def __init__(
             raise ValueError(f"padding must be one of {self.ACCEPTED_PADDING}")
         if padding == "zeros":
             warnings.warn(
-                "Padding option 'zeros' will be deprecated in the future. "
+                "Padding option 'zeros' will be deprecated in v0.4.0. "
                 "Please use 'constant' padding with padding_value 0 instead.",
                 category=DeprecationWarning,
             )

diff --git a/torchrl/modules/models/models.py b/torchrl/modules/models/models.py
@@ -872,9 +872,6 @@ class DistributionalDQNnet(TensorDictModuleBase):
     """Distributional Deep Q-Network.
 
     Args:
-        DQNet (nn.Module): (deprecated) Q-Network with output length equal
-            to the number of atoms:
-            output.shape = [*batch, atoms, actions].
         in_keys (list of str or tuples of str): input keys to the log-softmax
             operation. Defaults to ``["action_value"]``.
         out_keys (list of str or tuples of str): output keys to the log-softmax
@@ -888,11 +885,11 @@ class DistributionalDQNnet(TensorDictModuleBase):
         "instead."
     )
 
-    def __init__(self, DQNet: nn.Module = None, in_keys=None, out_keys=None):
+    def __init__(self, *, in_keys=None, out_keys=None, DQNet: nn.Module = None):
         super().__init__()
         if DQNet is not None:
             warnings.warn(
-                f"Passing a network to {type(self)} is going to be deprecated.",
+                f"Passing a network to {type(self)} is going to be deprecated in v0.4.0.",
                 category=DeprecationWarning,
             )
             if not (
@@ -1280,7 +1277,7 @@ def __init__(
         device: Optional[DEVICE_TYPING] = None,
     ) -> None:
         warnings.warn(
-            "LSTMNet is being deprecated in favour of torchrl.modules.LSTMModule, and will be removed soon.",
+            "LSTMNet is being deprecated in favour of torchrl.modules.LSTMModule, and will be removed in v0.4.0.",
             category=DeprecationWarning,
         )
         super().__init__()

diff --git a/torchrl/modules/tensordict_module/actors.py b/torchrl/modules/tensordict_module/actors.py
@@ -445,7 +445,7 @@ def __init__(
     ):
         if isinstance(action_space, TensorSpec):
             warnings.warn(
-                "Using specs in action_space will be deprecated soon,"
+                "Using specs in action_space will be deprecated in v0.4.0,"
                 " please use the 'spec' argument if you want to provide an action spec",
                 category=DeprecationWarning,
             )
@@ -825,7 +825,7 @@ def __init__(
     ):
         if isinstance(action_space, TensorSpec):
             warnings.warn(
-                "Using specs in action_space will be deprecated soon,"
+                "Using specs in action_space will be deprecated in v0.4.0,"
                 " please use the 'spec' argument if you want to provide an action spec",
                 category=DeprecationWarning,
             )
@@ -922,7 +922,7 @@ def __init__(
     ):
         if isinstance(action_space, TensorSpec):
             warnings.warn(
-                "Using specs in action_space will be deprecated soon,"
+                "Using specs in action_space will be deprecated in v0.4.0,"
                 " please use the 'spec' argument if you want to provide an action spec",
                 category=DeprecationWarning,
             )
@@ -1043,7 +1043,7 @@ def __init__(
     ):
         if isinstance(action_space, TensorSpec):
             warnings.warn(
-                "Using specs in action_space will be deprecated soon,"
+                "Using specs in action_space will be deprecated v0.4.0,"
                 " please use the 'spec' argument if you want to provide an action spec",
                 category=DeprecationWarning,
             )
@@ -1189,7 +1189,7 @@ def __init__(
     ):
         if isinstance(action_space, TensorSpec):
             warnings.warn(
-                "Using specs in action_space will be deprecated soon,"
+                "Using specs in action_space will be deprecated in v0.4.0,"
                 " please use the 'spec' argument if you want to provide an action spec",
                 category=DeprecationWarning,
             )

diff --git a/torchrl/modules/tensordict_module/exploration.py b/torchrl/modules/tensordict_module/exploration.py
@@ -247,105 +247,10 @@ def __init__(
         action_mask_key: Optional[NestedKey] = None,
         spec: Optional[TensorSpec] = None,
     ):
-        warnings.warn(
-            "EGreedyWrapper is deprecated and it will be removed in v0.3. "
-            "Please use torchrl.modules.EGreedyModule instead.",
-            category=DeprecationWarning,
+        raise RuntimeError(
+            "This class is not removed in favour of torchrl.modules.EGreedyModule."
         )
 
-        super().__init__(policy)
-        self.register_buffer("eps_init", torch.tensor([eps_init]))
-        self.register_buffer("eps_end", torch.tensor([eps_end]))
-        if self.eps_end > self.eps_init:
-            raise RuntimeError("eps should decrease over time or be constant")
-        self.annealing_num_steps = annealing_num_steps
-        self.register_buffer("eps", torch.tensor([eps_init], dtype=torch.float32))
-        self.action_key = action_key
-        self.action_mask_key = action_mask_key
-        if spec is not None:
-            if not isinstance(spec, CompositeSpec) and len(self.out_keys) >= 1:
-                spec = CompositeSpec({action_key: spec}, shape=spec.shape[:-1])
-            self._spec = spec
-        elif hasattr(self.td_module, "_spec"):
-            self._spec = self.td_module._spec.clone()
-            if action_key not in self._spec.keys():
-                self._spec[action_key] = None
-        elif hasattr(self.td_module, "spec"):
-            self._spec = self.td_module.spec.clone()
-            if action_key not in self._spec.keys():
-                self._spec[action_key] = None
-        else:
-            self._spec = spec
-
-    @property
-    def spec(self):
-        return self._spec
-
-    def step(self, frames: int = 1) -> None:
-        """A step of epsilon decay.
-
-        After self.annealing_num_steps, this function is a no-op.
-
-        Args:
-            frames (int): number of frames since last step.
-
-        """
-        for _ in range(frames):
-            self.eps.data[0] = max(
-                self.eps_end.item(),
-                (
-                    self.eps - (self.eps_init - self.eps_end) / self.annealing_num_steps
-                ).item(),
-            )
-
-    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
-        tensordict = self.td_module.forward(tensordict)
-        if exploration_type() == ExplorationType.RANDOM or exploration_type() is None:
-            if isinstance(self.action_key, tuple) and len(self.action_key) > 1:
-                action_tensordict = tensordict.get(self.action_key[:-1])
-                action_key = self.action_key[-1]
-            else:
-                action_tensordict = tensordict
-                action_key = self.action_key
-
-            out = action_tensordict.get(action_key)
-            eps = self.eps.item()
-            cond = (
-                torch.rand(action_tensordict.shape, device=action_tensordict.device)
-                < eps
-            ).to(out.dtype)
-            cond = expand_as_right(cond, out)
-            spec = self.spec
-            if spec is not None:
-                if isinstance(spec, CompositeSpec):
-                    spec = spec[self.action_key]
-                if spec.shape != out.shape:
-                    # In batched envs if the spec is passed unbatched, the rand() will not
-                    # cover all batched dims
-                    if (
-                        not len(spec.shape)
-                        or out.shape[-len(spec.shape) :] == spec.shape
-                    ):
-                        spec = spec.expand(out.shape)
-                    else:
-                        raise ValueError(
-                            "Action spec shape does not match the action shape"
-                        )
-                if self.action_mask_key is not None:
-                    action_mask = tensordict.get(self.action_mask_key, None)
-                    if action_mask is None:
-                        raise KeyError(
-                            f"Action mask key {self.action_mask_key} not found in {tensordict}."
-                        )
-                    spec.update_mask(action_mask)
-                out = cond * spec.rand().to(out.device) + (1 - cond) * out
-            else:
-                raise RuntimeError(
-                    "spec must be provided by the policy or directly to the exploration wrapper."
-                )
-            action_tensordict.set(action_key, out)
-        return tensordict
-
 
 class AdditiveGaussianWrapper(TensorDictModuleWrapper):
     """Additive Gaussian PO wrapper.

diff --git a/torchrl/modules/tensordict_module/rnn.py b/torchrl/modules/tensordict_module/rnn.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 warnings
 from typing import Optional, Tuple
 
 import torch
@@ -555,11 +554,9 @@ def recurrent_mode(self, value):
 
     @property
     def temporal_mode(self):
-        warnings.warn(
+        raise RuntimeError(
             "temporal_mode is deprecated, use recurrent_mode instead.",
-            category=DeprecationWarning,
         )
-        return self.recurrent_mode
 
     def set_recurrent_mode(self, mode: bool = True):
         """Returns a new copy of the module that shares the same lstm model but with a different ``recurrent_mode`` attribute (if it differs).
@@ -1255,11 +1252,9 @@ def recurrent_mode(self, value):
 
     @property
     def temporal_mode(self):
-        warnings.warn(
+        raise RuntimeError(
             "temporal_mode is deprecated, use recurrent_mode instead.",
-            category=DeprecationWarning,
         )
-        return self.recurrent_mode
 
     def set_recurrent_mode(self, mode: bool = True):
         """Returns a new copy of the module that shares the same gru model but with a different ``recurrent_mode`` attribute (if it differs).

diff --git a/torchrl/objectives/a2c.py b/torchrl/objectives/a2c.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 warnings
 from copy import deepcopy
 from dataclasses import dataclass
 from typing import Tuple
@@ -21,7 +20,7 @@
 from torchrl.objectives.common import LossModule
 from torchrl.objectives.utils import (
     _cache_values,
-    _GAMMA_LMBDA_DEPREC_WARNING,
+    _GAMMA_LMBDA_DEPREC_ERROR,
     default_value_kwargs,
     distance_loss,
     ValueEstimators,
@@ -261,8 +260,7 @@ def __init__(
         self.register_buffer("entropy_coef", torch.tensor(entropy_coef, device=device))
         self.register_buffer("critic_coef", torch.tensor(critic_coef, device=device))
         if gamma is not None:
-            warnings.warn(_GAMMA_LMBDA_DEPREC_WARNING, category=DeprecationWarning)
-            self.gamma = gamma
+            raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
         self.loss_critic_type = loss_critic_type
 
     @property