Summary:
How did we get so many uses of `NULL` again?
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11047
Differential Revision: D9566799
Pulled By: goldsborough
fbshipit-source-id: 83469f352ac69aa65bdaf1a1a21f922d892e0db3
- Remove some uses of mega-header THP.h
- Use HANDLE_TH_ERRORS in functions that may throw
- Move NumPy includes to common header
- Delete unused allocator
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
* Bind cauchy_, exponential_, normal_, uniform_ functions to THPVariable.
Also changes the error messages around Generator parser; previously, you'd get an error
like: torch._C.Generator is not a torch.Generator; now the check is proper but returns
that only None is supported.
* Support passing Generators to ATen Variable-bound methods.
This involves changing THPGenerator to have an at::Generator rather than a THGenerator.
TH getRNGState, setRNGState are still called directly because they are not bound from ATen yet;
they should probably be on the Generators and return (opaque) GenerateState objects.
* Fix default values.
* Properly use THRandom_initialSeed.
* update standard gamma to use new default generator.
ATen has it's own default CPU RNG. Use this as the default in PyTorch so
that random functions called through ATen have the same behavior as
random functions called through TensorMethods
