Use pytorch as backend for xarrays

[fjanoos]

I would be interested in using pytorch as a backend for xarrays - because:
a) pytorch is very similar to numpy - so the conceptual overhead is small
b) [most helpful] enable having a GPU as the underlying hardware for compute - which would provide non-trivial speed up
c) it would allow seamless integration with deep-learning algorithms and techniques

Any thoughts on what the interest for such a feature might be ? I would be open to implementing parts of it - so any suggestions on where I could start ?

Thanks

[shoyer]

If pytorch implements overrides of NumPy's API via the __array_function__ protocol, then this could work with minimal effort. We are already using this to support sparse arrays (this isn't an official release yet, but functionality is working in the development version).

I think there has been some discussion about this, but I don't know the current status (CC @rgommers). The biggest challenge for pytorch would be defining the translation layer that implements NumPy's API.

Personally, I think the most viable way to achieve seamless integration with deep learning libraries would be to support integration with JAX, which already implements NumPy's API almost exactly. I have an experimental pull request adding __array_function__ to JAX, but it still needs a bit of work to finish it up, e.g., we probably want to hide this behind a flag at first.

[rgommers]

I think there has been some discussion about this, but I don't know the current status (CC @rgommers).

The PyTorch team is definitely receptive to the idea of adding __array_function__ and __array_ufunc__, as well as expanding the API for better NumPy compatibility.

Also, they want a Tensor.__torch_function__ styled after __array_function__ so they can make their own API overridable.

The tracking issue for all of this is pytorch/pytorch#22402

The biggest challenge for pytorch would be defining the translation layer that implements NumPy's API.

Agreed. No one is working on __array_function__ at the moment. Implementing it has some backwards compat concerns as well, because people may be relying on np.somefunc(some_torch_tensor) to be coerced to ndarray. It's not a small project, but implementing a prototype with a few function in the torch namespace that are not exactly matching the NumPy API would be a useful way to start pushing this forward.

[rgommers]

Personally, I think the most viable way to achieve seamless integration with deep learning libraries would be to support integration with JAX, which already implements NumPy's API almost exactly.

Less familiar with that, but pytorch does have experimental XLA support, so that's a start.

[shoyer]

Implementing it has some backwards compat concerns as well, because people may be relying on np.somefunc(some_torch_tensor) to be coerced to ndarray.

Yes, this is a concern for JAX as well. This is a definite downside of reusing NumPy's existing namespace.

It turns out even xarray was relying on this behavior with dask in at least one edge case: #3215

[rgommers]

This is a definite downside of reusing NumPy's existing namespace.

We didn't discuss an alternative very explicitly I think, but at least we'll have wide adoption fast. Hopefully the pain is limited ....

[fjanoos]

I haven't used JAX - but was just browsing through its documentation and it looks super cool. Any ideas on how it compares with Pytorch in terms of:

a) Cxecution speed, esp. on GPU
b) Memory management on GPUs. Pytorch has the 'Dataloader/Dataset' paradigm which uses background multithreading to shuttle batches of data back and forth - along with a lot of tips and tricks on efficient memory usage.
c) support for deep-learning optimization algorithms ?

[shoyer]

Within a jit compiled function, JAX's execution speed should be quite competitive on GPUs. It uses the XLA compiler, which was recently enabled by default in TensorFlow.

For data loading and deep learning algorithms, take a look at the examples in the notebooks directory in the JAX repo. The APIs for deep learning in JAX are still undergoing rapid development, so APIs are not quite as stable/usable as pytorch or keras yet, but they are quite capable. See jax.experimental.stax and tensor2tensor.trax for examples.

[fjanoos]

While it is pretty straightforward to implement a lot of standard xarray operations with a pytorch / Jax backend (since they just fallback on native functions) - it will be interesting to think about how to implement rolling operations / expanding / exponential window in a way that is both efficient and maintains differentiability.

Expanding and exponential window operations would be easy to do leveraging RNN semantics - but doing rolling using convolutions is going to be very inefficient.

Do you have any thoughts on this?

[shoyer]

I have not thought too much about these yet. But I agree that they will probably require backend specific logic to do efficiently.

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On Fri, Aug 23, 2019 at 12:13 PM firdaus janoos ***@***.***> wrote: While it is pretty straightforward to implement a lot of standard xarray operations with a pytorch / Jax backend (since they just fallback on native functions) - it will be interesting to think about how to implement rolling operations / expanding / exponential window in a way that is both efficient and maintains differentiability. Expanding and exponential window operations would be easy to do leveraging RNN semantics - but doing rolling using convolutions is going to be very inefficient. Do you have any thoughts on this? — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#3232?email_source=notifications&email_token=AAJJFVWRVLTFNT3DYOZIJB3QGASFBA5CNFSM4ING6FH2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD5A6IWY#issuecomment-524411995>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAJJFVQ7JBUNO3CAIFGVJ63QGASFBANCNFSM4ING6FHQ> .

[fjanoos]

This might be a good time to revive this thread and see if there is wider interest (and bandwidth) in having xarray use CuPy (https://cupy.chainer.org/ ) as a backend (along with numpy). It appears to be a plug-and-play replacement for numpy - so it might not have all the issues that were brought up regarding pytorch/jax ?

Any thoughts ?
cc @mrocklin

[dcherian]

Just chiming in quickly. I think there's definitely interest in doing this through NEP-18.

It looks like CUDA has implemented __array_function__ (https://docs-cupy.chainer.org/en/stable/reference/interoperability.html) so many things may "just work". There was some work earlier on plugging in pydata/sparse, and there is some ongoing work to plug in pint. With both these efforts, a lot of xarray's code should be "backend-agnostic" but its not perfect.

Have you tried creating DataArrays with cupy arrays yet? I would just try things and see what works vs what doesn't.

Practically, our approach so far has been to add a number of xfailed tests (test_sparse.py and test_units.py) and slowly start fixing them. So that's one way to proceed if you're up for it.

[jhamman]

@jacobtomlinson gave CuPy a go a few months back. I seem to remember that he ran into a few problems but it would be good to get those documented here.

[jakirkham]

Yeah Jacob and I played with this a few months back. There were some issues, but my recollection is pretty hazy. If someone gives this another try, it would be interesting to hear how things go.

[fjanoos]

If you have any pointers on how to go about this - I can give it a try.

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