PyTorch memory model: “torch.from_numpy()” vs “torch.Tensor()”

Question

I\'m trying to have an in-depth understanding of how PyTorch Tensor memory model works.

# input numpy array
In [91]: arr = np.arange(10, dtype=float32).resha         


        

Answer 1

This comes from _torch_docs.py; there is also a possible discussion on the "why" here.

def from_numpy(ndarray): # real signature unknown; restored from __doc__
    """
    from_numpy(ndarray) -> Tensor

    Creates a :class:`Tensor` from a :class:`numpy.ndarray`.

    The returned tensor and `ndarray` share the same memory. 
    Modifications to the tensor will be reflected in the `ndarray` 
    and vice versa. The returned tensor is not resizable.

    Example::

        >>> a = numpy.array([1, 2, 3])
        >>> t = torch.from_numpy(a)
        >>> t
        torch.LongTensor([1, 2, 3])
        >>> t[0] = -1
        >>> a
        array([-1,  2,  3])
    """
    pass

Taken from the numpy docs:

Different ndarrays can share the same data, so that changes made in one ndarray may be visible in another. That is, an ndarray can be a “view” to another ndarray, and the data it is referring to is taken care of by the “base” ndarray.

Pytorch docs:

If a numpy.ndarray, torch.Tensor, or torch.Storage is given, a new tensor that shares the same data is returned. If a Python sequence is given, a new tensor is created from a copy of the sequence.