Adds normal prim, randn reference, and randn OpInfo (#85128)
This PR extends prims support for random operations by adding `prims.normal` and `refs.randn`. Note that in the future we may not want to model draws from distributions as their own prims.
`prims.normal` accepts a shape and the mean and standard deviation of a normal distribution as numbers. This is distinct from `torch.normal` which takes two tensors so every generated datapoint can be drawn from a normal distribution with its own mean and standard deviation. To address this @ngimel and I expect to add `prims.normal_with_tensors`. The current `prims.normal` could be implemented using `prims.normal_with_tensors`, but we expect the case of two numbers is much more common, and that executors will likely want to specialize for it, anyway.
In a follow-up PR I plan to add `refs.randn_like`, `prims.normal_with_tensors` (as mentioned above), and `refs.normal`.
While writing this PR I noticed the following issues:
- https://github.com/pytorch/pytorch/issues/85123
- https://github.com/pytorch/pytorch/issues/85121
The latter of which is prohibiting some testing.
In future PRs I plan to add a prim for changing layout, add support for pinned memory, and improve support for testing tensor creation operators, likely with a TensorCreationOpInfo class.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85128
Approved by: https://github.com/ngimel
