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[BugFix,Feature] Allow non-tensor data in envs #1944

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merged 11 commits into from
Jun 19, 2024
Merged

[BugFix,Feature] Allow non-tensor data in envs #1944

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194831d
init
vmoens Feb 21, 2024
f7bb04b
amend
vmoens Feb 21, 2024
78d910f
Merge remote-tracking branch 'origin/main' into fix-env-nontensor
vmoens Jun 11, 2024
0529917
amend
vmoens Jun 11, 2024
9760430
amend
vmoens Jun 11, 2024
65ac52f
lint
vmoens Jun 11, 2024
09bbd80
amend
vmoens Jun 11, 2024
90996fa
amend
vmoens Jun 11, 2024
b0c5788
lint
vmoens Jun 11, 2024
b443095
Merge remote-tracking branch 'origin/main' into fix-env-nontensor
vmoens Jun 12, 2024
e1b38b3
Merge remote-tracking branch 'origin/main' into fix-env-nontensor
vmoens Jun 19, 2024
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amend
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@vmoens
vmoens committed Jun 11, 2024
commit 976043009ec688f0eb347ecbd54516921e32bce3
52 changes: 0 additions & 52 deletions torchrl/data/tensor_specs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4414,58 +4414,6 @@ def locked(self):
return self._locked


class NonTensorSpec(TensorSpec):
"""Tensor spec for non-tensor data.

This spec has no space or dtype, but the device and shape can be specified
(as it is the case for :class:`~tensordict.NonTensorData`).

"""

shape: torch.Size
space: Union[None, Box] = None
device: torch.device | None = None
dtype: torch.dtype = None
domain: str = "continuous"

def __init__(self, shape=(), device=None):
if device is not None:
device = torch.device(device)
return super().__init__(shape=shape, space=None, device=device, dtype=None)

def rand(self, shape=None) -> torch.Tensor:
return NonTensorData(None, batch_size=self.shape)

def zero(self, shape=None) -> torch.Tensor:
return NonTensorData(None, batch_size=self.shape)

def expand(self, *shape):
if len(shape) == 1 and not isinstance(shape[0], int):
shape = shape[0]
return NonTensorSpec(device=self.device, shape=shape)

def is_in(self, val: torch.Tensor) -> bool:
return True

def index(self, index: INDEX_TYPING, tensor_to_index: torch.Tensor) -> torch.Tensor:
raise NotImplementedError

def to(self, dest):
if dest is None:
return self
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return self.__class__(shape=self.shape, device=dest)

def clone(self) -> "TensorSpec":
return self.__class__(shape=self.shape, device=self.device)


class LazyStackedCompositeSpec(_LazyStackedMixin[CompositeSpec], CompositeSpec):
"""A lazy representation of a stack of composite specs.
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4 changes: 2 additions & 2 deletions torchrl/envs/batched_envs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -671,8 +671,8 @@ def _create_td(self) -> None:
self.full_reward_spec,
self.full_done_spec,
):
for key, spec in spec.items(True, True):
if isinstance(spec, NonTensorSpec):
for key, _spec in spec.items(True, True):
if isinstance(_spec, NonTensorSpec):
non_tensor_keys.append(key)
self._non_tensor_keys = non_tensor_keys

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