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Summary: Reference https://github.com/pytorch/pytorch/issues/38349 Delegates to `torch.transpose` (not sure what is the best way to alias) TODO: * [x] Add test * [x] Add documentation Pull Request resolved: https://github.com/pytorch/pytorch/pull/46041 Reviewed By: gchanan Differential Revision: D25022816 Pulled By: mruberry fbshipit-source-id: c80223d081cef84f523ef9b23fbedeb2f8c1efc5
95 lines
3.8 KiB
ReStructuredText
95 lines
3.8 KiB
ReStructuredText
.. currentmodule:: torch
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.. _tensor-view-doc:
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Tensor Views
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=============
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PyTorch allows a tensor to be a ``View`` of an existing tensor. View tensor shares the same underlying data
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with its base tensor. Supporting ``View`` avoids explicit data copy, thus allows us to do fast and memory efficient
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reshaping, slicing and element-wise operations.
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For example, to get a view of an existing tensor ``t``, you can call ``t.view(...)``.
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::
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>>> t = torch.rand(4, 4)
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>>> b = t.view(2, 8)
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>>> t.storage().data_ptr() == b.storage().data_ptr() # `t` and `b` share the same underlying data.
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True
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# Modifying view tensor changes base tensor as well.
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>>> b[0][0] = 3.14
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>>> t[0][0]
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tensor(3.14)
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Since views share underlying data with its base tensor, if you edit the data
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in the view, it will be reflected in the base tensor as well.
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Typically a PyTorch op returns a new tensor as output, e.g. :meth:`~torch.Tensor.add`.
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But in case of view ops, outputs are views of input tensors to avoid unncessary data copy.
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No data movement occurs when creating a view, view tensor just changes the way
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it interprets the same data. Taking a view of contiguous tensor could potentially produce a non-contiguous tensor.
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Users should be pay additional attention as contiguity might have implicit performance impact.
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:meth:`~torch.Tensor.transpose` is a common example.
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::
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>>> base = torch.tensor([[0, 1],[2, 3]])
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>>> base.is_contiguous()
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True
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>>> t = base.transpose(0, 1) # `t` is a view of `base`. No data movement happened here.
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# View tensors might be non-contiguous.
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>>> t.is_contiguous()
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False
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# To get a contiguous tensor, call `.contiguous()` to enforce
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# copying data when `t` is not contiguous.
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>>> c = t.contiguous()
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For reference, here’s a full list of view ops in PyTorch:
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- Basic slicing and indexing op, e.g. ``tensor[0, 2:, 1:7:2]`` returns a view of base ``tensor``, see note below.
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- :meth:`~torch.Tensor.as_strided`
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- :meth:`~torch.Tensor.detach`
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- :meth:`~torch.Tensor.diagonal`
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- :meth:`~torch.Tensor.expand`
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- :meth:`~torch.Tensor.expand_as`
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- :meth:`~torch.Tensor.movedim`
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- :meth:`~torch.Tensor.narrow`
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- :meth:`~torch.Tensor.permute`
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- :meth:`~torch.Tensor.select`
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- :meth:`~torch.Tensor.squeeze`
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- :meth:`~torch.Tensor.transpose`
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- :meth:`~torch.Tensor.t`
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- :attr:`~torch.Tensor.T`
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- :attr:`~torch.Tensor.real`
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- :attr:`~torch.Tensor.imag`
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- :meth:`~torch.Tensor.view_as_real`
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- :meth:`~torch.Tensor.view_as_imag`
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- :meth:`~torch.Tensor.unflatten`
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- :meth:`~torch.Tensor.unfold`
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- :meth:`~torch.Tensor.unsqueeze`
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- :meth:`~torch.Tensor.view`
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- :meth:`~torch.Tensor.view_as`
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- :meth:`~torch.Tensor.unbind`
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- :meth:`~torch.Tensor.split`
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- :meth:`~torch.Tensor.split_with_sizes`
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- :meth:`~torch.Tensor.swapaxes`
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- :meth:`~torch.Tensor.swapdims`
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- :meth:`~torch.Tensor.chunk`
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- :meth:`~torch.Tensor.indices` (sparse tensor only)
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- :meth:`~torch.Tensor.values` (sparse tensor only)
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.. note::
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When accessing the contents of a tensor via indexing, PyTorch follows Numpy behaviors
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that basic indexing returns views, while advanced indexing returns a copy.
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Assignment via either basic or advanced indexing is in-place. See more examples in
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`Numpy indexing documentation <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.
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It's also worth mentioning a few ops with special behaviors:
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- :meth:`~torch.Tensor.reshape`, :meth:`~torch.Tensor.reshape_as` and :meth:`~torch.Tensor.flatten` can return either a view or new tensor, user code shouldn't rely on whether it's view or not.
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- :meth:`~torch.Tensor.contiguous` returns **itself** if input tensor is already contiguous, otherwise it returns a new contiguous tensor by copying data.
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For a more detailed walk-through of PyTorch internal implementation,
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please refer to `ezyang's blogpost about PyTorch Internals <http://blog.ezyang.com/2019/05/pytorch-internals/>`_.
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