* Use ATen infer_size implementation rather than TH.
The only substantitive difference between the two implementations is in how empty sizes are handled;
in ATen these are treated as scalars (i.e., can be expanded to anything), whereas in TH they are treated
as a special case of empty tensors (i.e., can't be expanded to anything). Therefore, this change is
necessary to support scalars (0-dimensional tensors). We could also take a bool parameter for determining
how we treat empty tensors but this seems unnecessary: if one tries to expand an empty tensors (as a result
of an infer_size calculation), the expansion will fail.
* Make changes for review.
* Attempt to fix windows build.
* long -> int.
The Tensor and Variable classes are being merged in Python. This means
that all interfaces to C++ must accept Variables where they previously
accepted Tensors.
Setting torch.utils.backcompat.broadcast.warning.enabled=True
will cause Python warnings in the case where broadcast occurs
but previously 1-d view style pointwise ops occured.
The core autograd Variable, Function, and Engine no longer depend on the
Python API. This let's us implement functions in C++. In the future, we
can also multithread engine and release the GIL for most of the
non-Python backwards.
arguments.
For example:
>>> torch.randn(5, 5).geqrf('invalid arg')
TypeError: geqrf received an invalid combination of arguments - got (str), but expected ()
See issue #20
The torch.Size class is a tuple subclass which distinguishes sizes from
other tuples so that torch.Tensor(size) is interpreted as size instead
of data.
