init

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.

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()):

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")

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."

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)

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,
  )

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__()

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,
  )

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.

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).

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