DiscreteTensorSpec

class torchrl.data.DiscreteTensorSpec(n: int, shape: torch.Size | None = None, device: DEVICE_TYPING | None = None, dtype: str | torch.dtype = torch.int64, mask: torch.Tensor | None = None)

A discrete tensor spec.

An alternative to OneHotTensorSpec for categorical variables in TorchRL. Instead of using multiplication, categorical variables perform indexing which can speed up computation and reduce memory cost for large categorical variables. The last dimension of the spec (length n of the binary vector) cannot be indexed

Example

>>> batch, size = 3, 4
>>> action_value = torch.arange(batch*size)
>>> action_value = action_value.view(batch, size).to(torch.float)
>>> action = torch.argmax(action_value, dim=-1).to(torch.long)
>>> chosen_action_value = action_value[range(batch), action]
>>> print(chosen_action_value)
tensor([ 3.,  7., 11.])

Parameters:

• n (int) – number of possible outcomes.

• shape – (torch.Size, optional): shape of the variable, default is “torch.Size([])”.

• device (str, int or torch.device, optional) – device of the tensors.

• dtype (str or torch.dtype, optional) – dtype of the tensors.

• mask (torch.Tensor or None) – mask some of the possible outcomes when a sample is taken. See update_mask() for more information.

assert_is_in(value: Tensor) → None

Asserts whether a tensor belongs to the box, and raises an exception otherwise.

Parameters:

value (torch.Tensor) – value to be checked.

clear_device_()

A no-op for all leaf specs (which must have a device).

contains(item)

Returns whether a sample is contained within the space defined by the TensorSpec.

See is_in() for more information.

encode(val: Union[ndarray, Tensor], *, ignore_device=False) → Tensor

Encodes a value given the specified spec, and return the corresponding tensor.

Parameters:

val (np.ndarray or torch.Tensor) – value to be encoded as tensor.

Keyword Arguments:

ignore_device (bool, optional) – if True, the spec device will be ignored. This is used to group tensor casting within a call to TensorDict(..., device="cuda") which is faster.

Returns:

torch.Tensor matching the required tensor specs.

expand(*shape)

Returns a new Spec with the extended shape.

Parameters:

*shape (tuple or iterable of int) – the new shape of the Spec. Must comply with the current shape: its length must be at least as long as the current shape length, and its last values must be complient too; ie they can only differ from it if the current dimension is a singleton.

flatten(start_dim, end_dim)

Flattens a tensorspec.

Check flatten() for more information on this method.

classmethod implements_for_spec(torch_function: Callable) → Callable

Register a torch function override for TensorSpec.

abstract index(index: Union[int, Tensor, ndarray, slice, List], tensor_to_index: Tensor) → Tensor

Indexes the input tensor.

Parameters:

• index (int, torch.Tensor, slice or list) – index of the tensor

• tensor_to_index – tensor to be indexed

Returns:

indexed tensor

is_in(val: Tensor) → bool

If the value val is in the box defined by the TensorSpec, returns True, otherwise False.

Parameters:

val (torch.Tensor) – value to be checked

Returns:

boolean indicating if values belongs to the TensorSpec box

project(val: Tensor) → Tensor

If the input tensor is not in the TensorSpec box, it maps it back to it given some heuristic.

Parameters:

val (torch.Tensor) – tensor to be mapped to the box.

Returns:

a torch.Tensor belonging to the TensorSpec box.

rand(shape=None) → Tensor

Returns a random tensor in the space defined by the spec. The sampling will be uniform unless the box is unbounded.

Parameters:

shape (torch.Size) – shape of the random tensor

Returns:

a random tensor sampled in the TensorSpec box.

reshape(*shape)

Reshapes a tensorspec.

Check reshape() for more information on this method.

property sample

Returns a random tensor in the space defined by the spec.

See rand() for details.

squeeze(dim=None)

Returns a new Spec with all the dimensions of size 1 removed.

When dim is given, a squeeze operation is done only in that dimension.

Parameters:

dim (int or None) – the dimension to apply the squeeze operation to

to_numpy(val: Tensor, safe: Optional[bool] = None) → dict

Returns the np.ndarray correspondent of an input tensor.

Parameters:

• val (torch.Tensor) – tensor to be transformed_in to numpy.

• safe (bool) – boolean value indicating whether a check should be performed on the value against the domain of the spec. Defaults to the value of the CHECK_SPEC_ENCODE environment variable.

Returns:

a np.ndarray

to_one_hot(val: Tensor, safe: Optional[bool] = None) → Tensor

Encodes a discrete tensor from the spec domain into its one-hot correspondent.

Parameters:

• val (torch.Tensor, optional) – Tensor to one-hot encode.

• safe (bool) – boolean value indicating whether a check should be performed on the value against the domain of the spec. Defaults to the value of the CHECK_SPEC_ENCODE environment variable.

Returns:

The one-hot encoded tensor.

to_one_hot_spec() → OneHotDiscreteTensorSpec

Converts the spec to the equivalent one-hot spec.

type_check(value: Tensor, key: Optional[NestedKey] = None) → None

Checks the input value dtype against the TensorSpec dtype and raises an exception if they don’t match.

Parameters:

• value (torch.Tensor) – tensor whose dtype has to be checked

• key (str, optional) – if the TensorSpec has keys, the value dtype will be checked against the spec pointed by the indicated key.

unflatten(dim, sizes)

Unflattens a tensorspec.

Check unflatten() for more information on this method.

update_mask(mask)

Sets a mask to prevent some of the possible outcomes when a sample is taken.

The mask can also be set during initialization of the spec.

Parameters:

mask (torch.Tensor or None) – boolean mask. If None, the mask is disabled. Otherwise, the shape of the mask must be expandable to the shape of the equivalent one-hot spec. False masks an outcome and True leaves the outcome unmasked. If all of the possible outcomes are masked, then an error is raised when a sample is taken.

Examples

>>> mask = torch.tensor([True, False, True])
>>> ts = DiscreteTensorSpec(3, (10,), dtype=torch.int64, mask=mask)
>>> # One of the three possible outcomes is masked
>>> ts.rand()
tensor([0, 2, 2, 0, 2, 0, 2, 2, 0, 2])

view(*shape)

Reshapes a tensorspec.

Check reshape() for more information on this method.

zero(shape=None) → Tensor

Returns a zero-filled tensor in the box.

Parameters:

shape (torch.Size) – shape of the zero-tensor

Returns:

a zero-filled tensor sampled in the TensorSpec box.