Fix several issues with `torch._numpy.random` functions on eager
1. actually return scalars when `size is None`
2. fix dispatch with USE_NUMPY_STREAM
3. make tnp.random functions composable: make numpy functions receive numpy arguments, not `tnp.ndarray`s
4. fix random.shuffle for e.g. lists
The main need for this gymnastics is due to `np.random` functions returning an ndarray or python scalar depending on the `size` argument. We decided a while ago to replicate this behavior in `tnp.random` and not elsewhere where we always return 0D arrays instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108944
Approved by: https://github.com/lezcano
RFC: https://github.com/pytorch/rfcs/pull/54
First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/
We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core.
In the next commits, I do a number of things in this order
- Fix a few small issues
- Make the tests that this PR adds pass
- Bend backwards until lintrunner passes
- Remove the optional dependency on `torch_np` and simply rely on the upstreamed code
- Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now.
Missing from this PR (but not blocking):
- Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate.
- https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge.
All the tests in `tests/torch_np` take about 75s to run.
This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211
Approved by: https://github.com/ezyang
