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Author SHA1 Message Date
aashishthakur10
ee8983da70 109605 dynamo scalar ndarray pow gen (#109953) 
Fixes #109605

Generated code before:
```
def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (8, ), (1, ))
    buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
    cpp_fused_lift_fresh_0(c_void_p(buf0.data_ptr()))
    # Source Nodes: [wrapped_pow], Original ATen: [aten.lift_fresh, aten.pow]
    buf1 = aten.pow(arg0_1, reinterpret_tensor(buf0, (8, ), (0, ), 0))
    del arg0_1
    del buf0
    buf2 = buf1
    assert_size_stride(buf2, (8, ), (1, ))
    del buf1
    return (buf2, )
```

Generated code now:
```
def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (8, ), (1, ))
    buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
    cpp_fused_pow_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
    del arg0_1
    return (buf0, )
```
@lezcano What would be a good way to add a test for this?

Pull Request resolved: https://github.com/pytorch/pytorch/pull/109953
Approved by: https://github.com/lezcano
 2023-09-28 13:11:06 +00:00
lezcano
2a6ef9b04d [dynamo] Avoid recompilation when the PyTorch function accepts scalars (#108162) 
Before, it would create a 0D tensor with the input, which would incur in
a guard and specialisation.

It's not clear whether the guard and specialisation is the right behaviour
when we create 0D tensors, but that's a story for another day.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/108162
Approved by: https://github.com/ev-br, https://github.com/peterbell10
 2023-09-01 14:35:42 +00:00
lezcano
a9dca53438 NumPy support in torch.compile (#106211) 
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
 2023-08-11 00:39:32 +00:00