Segmentation Fault about spherical-text-embedding HOT 5 CLOSED

Hi Sachin,

Thanks for letting me know! I'm glad it solved the issue.

Regarding Riemannian optimization implementation, I'm not aware of existing PyTorch projects for the spherical space, but there are some for the hyperbolic space. For example, the Poincare embedding codebase has PyTorch implementation on Riemannian optimization in the Poincare space. Maybe you can take a look specifically at the Poincare manifold implementation where the Riemannian gradient is implemented, as well as the RSGD implementation. Although the optimization formula will be different for the spherical space, I feel the above code might be used as a great reference and template.

Please let me know if you have any other questions!

Best,
Yu

from spherical-text-embedding.

Hi,

Thanks for letting me know the issue. I haven't tried running the code on a corpus with more than 4B tokens, so I can't comment on how much memory it will take approximately (I apologize for not being able to try it right now since I'm attending a conference). However, if it were due to the memory error, you should have received a memory allocation error instead of a segmentation fault.

My current best guess is that you have too many documents/paragraphs in the corpus.

Please let me know if you still encounter any errors or have other questions!

Best,
Yu

from spherical-text-embedding.

@Sachin19 See related issue #6.

from spherical-text-embedding.

Hi @Sachin19,

Thanks again for posting this issue. I was wondering if you got a chance to try my suggestions and could provide any update on this issue?

Thanks,
Yu

from spherical-text-embedding.

Hi Yu,

Thank you so much for your suggestion. Line 18 was exactly the issue I was facing and it resolved the issue when I increased the number of documents.

I was also wondering if you could point me to resources on how to implement riemannian optimization in a package like pytorch.

Thanks,
Sachin

from spherical-text-embedding.