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# Tensor Views

PyTorch allows a tensor to be a `View` of an existing tensor. View tensor shares the same underlying data with its base tensor. Supporting `View` avoids explicit data copy, thus allows us to do fast and memory efficient reshaping, slicing and element-wise operations.

For example, to get a view of an existing tensor `t`, you can call `t.view(...)`.

    >>> t = torch.rand(4, 4)
    >>> b = t.view(2, 8)
    >>> t.storage().data_ptr() == b.storage().data_ptr()  # `t` and `b` share the same underlying data.
    True
    # Modifying view tensor changes base tensor as well.
    >>> b[0][0] = 3.14
    >>> t[0][0]
    tensor(3.14)

Since views share underlying data with its base tensor, if you edit the data in the view, it will be reflected in the base tensor as well.

Typically a PyTorch op returns a new tensor as output, e.g. `add()`. But in case of view ops, outputs are views of input tensors to avoid unnecessary data copy. No data movement occurs when creating a view, view tensor just changes the way it interprets the same data. Taking a view of contiguous tensor could potentially produce a non-contiguous tensor. Users should pay additional attention as contiguity might have implicit performance impact. `transpose()` is a common example.

    >>> base = torch.tensor([[0, 1],[2, 3]])
    >>> base.is_contiguous()
    True
    >>> t = base.transpose(0, 1)  # `t` is a view of `base`. No data movement happened here.
    # View tensors might be non-contiguous.
    >>> t.is_contiguous()
    False
    # To get a contiguous tensor, call `.contiguous()` to enforce
    # copying data when `t` is not contiguous.
    >>> c = t.contiguous()

For reference, here’s a full list of view ops in PyTorch:

- Basic slicing and indexing op, e.g. `tensor[0, 2:, 1:7:2]` returns a view of base `tensor`, see note below.

- `adjoint()`

- `as_strided()`

- `detach()`

- `diagonal()`

- `expand()`

- `expand_as()`

- `movedim()`

- `narrow()`

- `permute()`

- `select()`

- `squeeze()`

- `transpose()`

- `t()`

- `T`

- `H`

- `mT`

- `mH`

- `real`

- `imag`

- `view_as_real()`

- `unflatten()`

- `unfold()`

- `unsqueeze()`

- `view()`

- `view_as()`

- `unbind()`

- `split()`

- `hsplit()`

- `vsplit()`

- `tensor_split()`

- `split_with_sizes()`

- `swapaxes()`

- `swapdims()`

- `chunk()`

- `indices()` (sparse tensor only)

- `values()` (sparse tensor only)

Note

When accessing the contents of a tensor via indexing, PyTorch follows Numpy behaviors that basic indexing returns views, while advanced indexing returns a copy. Assignment via either basic or advanced indexing is in-place. See more examples in Numpy indexing documentation.

It’s also worth mentioning a few ops with special behaviors:

- `reshape()`, `reshape_as()` and `flatten()` can return either a view or new tensor, user code shouldn’t rely on whether it’s view or not.

- `contiguous()` returns itself if input tensor is already contiguous, otherwise it returns a new contiguous tensor by copying data.

For a more detailed walk-through of PyTorch internal implementation, please refer to ezyang’s blogpost about PyTorch Internals.

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