reference/generated/torchrl.data.vla.validate_vla_tensordict

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validate_vla_tensordict

class torchrl.data.vla.validate_vla_tensordict(tensordict: TensorDictBase, *, instruction_key: NestedKey = 'language_instruction', action_key: NestedKey = 'action', image_key: NestedKey = ('observation', 'image'), state_key: NestedKey = ('observation', 'state'), require_instruction: bool = True, require_action: bool = True, require_perception: bool = True, check_finite: bool = True, raise_on_error: bool = True)

Validate that a tensordict follows the canonical VLA schema.

The check is intentionally permissive: it verifies the presence of the keys a VLA pipeline relies on and that action tensors are finite, without constraining shapes beyond what is necessary.

Parameters:

tensordict (TensorDictBase) – the tensordict to validate.

Keyword Arguments:

• instruction_key (NestedKey) – language-instruction key. Defaults to ("observation", "language_instruction").

• action_key (NestedKey) – action key. Defaults to "action".

• image_key (NestedKey) – image key. Defaults to ("observation", "image").

• state_key (NestedKey) – proprioceptive-state key. Defaults to ("observation", "state").

• require_instruction (bool) – if True, a missing instruction is an error. Defaults to True.

• require_action (bool) – if True, a missing action is an error. Defaults to True.

• require_perception (bool) – if True, at least one of the image or state keys must be present. Defaults to True.

• check_finite (bool) – if True, float action tensors must be finite. Defaults to True.

• raise_on_error (bool) – if True (default), raise a ValueError when any issue is found; otherwise return the list of issues.

Returns:

a list of human-readable issue strings (empty if the tensordict is valid). When raise_on_error is True a non-empty list raises a ValueError instead of being returned.

Examples

>>> import torch
>>> from tensordict import NonTensorData, TensorDict
>>> from torchrl.data.vla import validate_vla_tensordict
>>> td = TensorDict(
...     {
...         "observation": {
...             "image": torch.zeros(2, 3, 8, 8, dtype=torch.uint8),
...         },
...         "language_instruction": NonTensorData("pick the cube"),
...         "action": torch.zeros(2, 7),
...     },
...     batch_size=[2],
... )
>>> validate_vla_tensordict(td)
[]