feat(frontends): Add nanmedian to PyTorch reduction ops

[spacefarers]

PR Description

Add nanmedian to frontend torch by basically copying nanmean since it is implemented in ivy.nanmedian. First time contributor please tell me if I did something wrong.

Related Issue

Close #23397

Checklist

• Did you add a function?
• Did you add the tests?
• Did you follow the steps we provided?

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[hmahmood24]

Hey @spacefarers, thanks a lot for the PR. 😄 Before we can proceed, we need to fix the failing tests for the function you've added. We also need to test the function for a wider range of input values as mentioned in the comment I left. Can you address these points and request a re-review once done? Thanks! :)

[hmahmood24]

Why are we testing for such a small range of the input values?

[github-actions]

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[hmahmood24]

@spacefarers, can you also fix the failing tests on this PR? Thanks

[spacefarers]

Thank you for your comments. I'm referencing nanmean as well as median in the frontend functions to try to create this one. I'm confused as to why median was not calling ivy.median but instead is doing its own logic. Could you explain this to me? Does the datatypes not work? Would I need to do the same for nanmedian?

[hmahmood24]

@spacefarers Not sure what you mean by

I'm confused as to why median was not calling ivy.median but instead is doing its own logic.

How do you arrive at this conclusion? Also, I think you have overwritten the existing test of nansum on the PR by mistake

[spacefarers]

Here is the current implementation of median for pytorch frontend

@numpy_to_torch_style_args
@to_ivy_arrays_and_back
def median(input, dim=None, keepdim=False, *, out=None):
    if dim is None:
        input = ivy.reshape(input, (-1,))
        sorted_input = ivy.sort(input)
        return sorted_input[(sorted_input.shape[0] - 1) // 2]

    median_tuple = namedtuple("median", ["values", "indices"])

    if input.ndim == 0:
        result = median_tuple(input, ivy.array(0))
    else:
        sorted_indices = ivy.argsort(input, axis=dim)
        median_indices = ivy.gather(
            sorted_indices, (sorted_indices.shape[dim] - 1) // 2, axis=dim
        )
        median_values = ivy.take_along_axis(
            input, ivy.expand_dims(median_indices, axis=dim), dim
        ).squeeze(axis=dim)

        if keepdim:
            median_values = ivy.expand_dims(median_values, axis=dim)
            median_indices = ivy.expand_dims(median_indices, axis=dim)

        result = median_tuple(median_values, median_indices)
    if out is not None:
        ivy.inplace_update(out[0], result.values)
        ivy.inplace_update(out[1], result.indices)
        return out
    return result

Why is it not simply calling ivy.median?

Thanks for the notice about nansum, I'll add that in and be more careful next time!

[spacefarers]

If you have a minute to answer a few questions via discord that'd be awesome. I've been stuck on this for a while now.

[hmahmood24]

If you have a minute to answer a few questions via discord that'd be awesome. I've been stuck on this for a while now.

Sure feel free to reach out on discord

[spacefarers]

Cool what's your username? Mine is the same as github it should be @spacefarers

[hmahmood24]

Cool what's your username? Mine is the same as github it should be @spacefarers

Same as my name :)

[spacefarers]

Awesome sent you a friend request

[a0m0rajab]

Question: is there a reason to remove the nansum? or was it done by mistake?

[a0m0rajab]

Question: Was this cron change on purpose? I think if we are going to change it we might need to talk with the CI team, otherwise we can just revert this changes.

[spacefarers]

I apologize those are all mistakes. I'm working on the nanmedian frontend function and mistakendly affected some other things. I'll change those back along with my updated nanmedian function. Sorry again for the inconvenience.