Summary:
Allows mulitplication of e.g. numpy.float32 with tensors.
This came up with #9468
If you want this and after the other patch is done, I'll add tests (but that would be conflicting, so I prefer to wait).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9659
Differential Revision: D8948078
Pulled By: weiyangfb
fbshipit-source-id: c7dcc57b63e2f100df837f70e1299395692f1a1b
* Add numpy.array-like type inference to torch.tensor.
* Temporary fix for int/double types.
* Treat python floats as the default (scalar) dtype.
* Also make 0-length sequences the default scalar type and add more tests.
* Add type inference to sparse_coo_tensor.
* Fix sparse test.
* Remove allow_variables.
* Check numpy platform bits.
* Address review comments.
* Make suggested changes to constraints.
* More checking windows builds.
* Fix test for windows.
Implements from_numpy using ATen tensors. Variable.from_numpy is a
convenient placeholder for the variant that returns Variables until we
merge Tensor and Variable.
The behavior is slightly changed:
- from_numpy() on an empty array now returns an empty tensor instead of
throwing an exception. The shape may not be preserved.
- CharTensor(ndarray) used to throw an exception. It now copies the
ndarray. Copying is implemented via ATen toType.
