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• printing

  • Allows sympy's lambdify function to operate on pytorch tensors. By example: f = lambdify(variables, expr, 'torch') (#20516 by @synthetic-tensors and @Zer0Credibility)

  • torch_code enables conversion between sympy function and the equivalent pytorch operation. (#20516 by @synthetic-tensors and @Zer0Credibility)

This will be added to https://github.com/sympy/sympy/wiki/Release-Notes-for-1.11.

<!-- Your title above should be a short description of what
was changed. Do not include the issue number in the title. -->

#### References to other Issues or PRs

Fixes #20065
Closes #15484

#### Brief description of what is fixed or changed

This is a clean re-implementation of the torch-lambdify project without the conflicts from #15484 and  #20065

#### Other comments

atan2 still not working and will require more extensive refactoring but this is a start.

#### Release Notes

<!-- BEGIN RELEASE NOTES -->

* printing
  * Allows sympy's lambdify function to operate on pytorch tensors. By example: `f = lambdify(variables, expr, 'torch')` 
  * `torch_code` enables conversion between sympy function and the equivalent pytorch operation.

<!-- END RELEASE NOTES -->

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I'm getting a timeout on the travis env build -- @oscarbenjamin how's the progress looking on the move to github actions? Should I bother finding a workaround?

I've restarted the Travis jobs. We are using github actions now but it doesn't run all the same jobs as Travis so we are not ready to turn off Travis yet.

@oscarbenjamin Thanks for your work on moving to actions -- looks like it's passing now.

Can pytorch be installed in actions? Or would that require moving actions to using conda (see also #20623)?

I'm assuming this should close #15484. I edited the description so it actually will.

Thanks for that! Pip isn't the reccommended method for installing but is possible afaik:

pip install torch torchvision

What's the status/stability of this PR? This is functionality we'd be interested in.
CC @MilesCranmer

(EDIT: for the sake of any future Googlers who end up here like I did: I ended up putting together something myself that converts SymPy expressions into PyTorch Modules and vice versa: sympytorch)

I think the main blocking issue is getting the testing working on GitHub Actions. That may require some work on #20623.

@patrick-kidger do you have any functionality in sympytorch that isn't implemented here?

So I don't think the functionality here creates a trainable PyTorch Module, nor does it have a way to convert that back into a sympy expression after training, which are the two bits of functionality I particularly need.

(Conversely I am aware that this PR includes a bunch of things I didn't try to put into sympytorch.)

Here's the JAX equivalent of @patrick-kidger's idea for parametrized expressions: https://github.com/MilesCranmer/sympy2jax.

Having the constants be trainable makes lambdify very useful for optimization (e.g., symbolic regression https://github.com/MilesCranmer/pysr), which is why both sympytorch and sympy2jax record parameters in the expression rather than hardcoding them in.

Could this behaviour be added as a kwarg or something? e.g., record_parameters=True which will allow for parameters passed as argument (numpy, jax, other functional packages) or returns a trainable module with the parameters saved (pytorch)?

It looks fairly easy to setup conda on actions if that is needed:
https://github.com/marketplace/actions/setup-miniconda

@MilesCranmer can you open a new issue about Jax support?

@patrick-kidger does it make sense for those features to be in SymPy (assuming this PR is merged)?

I think it would make sense. It seems to me like converting to a torch.nn.Module is the natural way to "pytorchify" a function. That's not what this PR does though, as mentioned above.

Closing after #27760 was merged