Update median doc to note return value of even-sized input #44562
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| :func:`torch.quantile` with ``q=0.5`` instead. | ||
| .. warning:: | ||
| This function produces deterministic (sub)gradients unlike ``median(dim=0)`` |
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Aside: it's very surprising to me that this version of median produces deterministic gradients but the other version, which actually computes indices, doesn't. We should probably fix "median_with_indices" to consistently return the FIRST valid median, just like we fixed argmin and argmax.
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I think, as you mention, that the other function returns non-deterministic indices and thus non-deterministic subgradients.
This one evenly distribute the gradient to all the inputs with the value used. So it always is deterministic.
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The reason it returns non-deterministic indices is because it uses quickselect to partition which is not stable. One way to solve it is to use 3-way quickselect that partitions input into <, = and >. Then choose the smallest index from the = segment.
Add a note that torch.median returns the smaller of the two middle elements for even-sized input and refer user to torch.quantile for the mean of the middle values. fixes #39520 Differential Revision: [D23657208](https://our.internmc.facebook.com/intern/diff/D23657208) [ghstack-poisoned]
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@heitorschueroff merged this pull request in 199435a. |
Summary: Pull Request resolved: #44562 Add a note that torch.median returns the smaller of the two middle elements for even-sized input and refer user to torch.quantile for the mean of the middle values. fixes #39520 Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D23657208 Pulled By: heitorschueroff fbshipit-source-id: 2747aa652d1e7f10229d9299b089295aeae092c2
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Add a note that torch.median returns the smaller of the two middle elements for even-sized input and refer user to torch.quantile for the mean of the middle values.
fixes #39520
Differential Revision: D23657208