(improvement/AttentionalDecoder.predict_max) Avoid computation on rea…

[PonteIneptique]

…ched EOS for prediction (See #73)

The current prediction time is quite slow, we agree that there might be room for improvement.

After having a good look at it, it seemed clear that we were computing on items that technically did not need to continue to be computed upon (string that reach EOS).

I propose here my refactor of the predict_max function that stop computing over elements that reached EOS. There is probably still room for improvement here.

For a group of 19 sentences over 100 iterations Average tagging time with default: 0.556127781867981 s Median tagging time with default: 0.5420029163360596 Total tagging time with default: 55.612778186798096 s

For a group of 19 sentences over 100 iterations Average tagging time with new: 0.4061899709701538 s Median tagging time with new: 0.40130531787872314 Total tagging time with new: 40.61899709701538 s

• 27 % time for the whole tagging (lemma only)

[PonteIneptique]

I am bumping this one :)

[emanjavacas]

Sorry I wont have time for this in the coming months, busy PhD time.

[PonteIneptique]

I get it :)

[PonteIneptique]

https://github.com/emanjavacas/pie/pull/74#pullrequestreview-546114313 I don't see how you would like to test this. I mean, I ran things (which is not test per se, in the sense of CI/Unit Test) and it was the same. But to test it, we'd have to keep an artificial version of predict_max ?

[PonteIneptique]

Thanks for the review. I'll try to implement what you ask for, because I still think that's a huge improvement.

[PonteIneptique]

I have moved from the loop to tensor uses as required. Remains the question of tests which I am not sure how to handle.

[PonteIneptique]

Bumping this :)