reference/generated/tensordict.TensorDictBase.data_ptr

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TensorDictBase.data_ptr

TensorDictBase.data_ptr(*, storage: bool = False)

Returns the data_ptr of the tensordict leaves.

This can be useful to check if two tensordicts share the same data_ptr().

Keyword Arguments:

storage (bool, optional) – if True, tensor.untyped_storage().data_ptr() will be called instead. Defaults to False.

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> td = TensorDict(a=torch.randn(2), b=torch.randn(2), batch_size=[2])
>>> td0 = td.unsqueeze(0)  # the leaves of td0 are views on the leaves of td
>>> assert (td0.data_ptr() == td.data_ptr()).all()

Note

LazyStackedTensorDict instances will be displayed as nested tensordicts to reflect the true data_ptr() of their leaves:

>>> td0 = TensorDict(a=torch.randn(2), b=torch.randn(2), batch_size=[2])
>>> td1 = TensorDict(a=torch.randn(2), b=torch.randn(2), batch_size=[2])
>>> td = TensorDict.lazy_stack([td0, td1])
>>> td.data_ptr()
TensorDict(
    fields={
        0: TensorDict(
            fields={
                a: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
                b: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False),
        1: TensorDict(
            fields={
                a: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
                b: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=cpu,
    is_shared=False)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.