Treat 1-point datasets equally in sequential and parallel fits

[scarlehoff]

Due to the shape-changing nature of boolean masks we decided to just put single-point datasets in training when running in GPU. This PR removes that limitation by just accepting the point in both training and validation and setting to 0 the row and column in the inverse covmat (so the masking happens at the level of the loss).

If this works ok, #2138 comes for free.

@RoyStegeman @achiefa I want to run a few tests first before considering this good:

• That Etr and Evl are ok for multireplica fits (they are ok for single replicas given that the regression test pass)
• Running a fit with only single-point datasets (this is actually broken in master)
• Experiments with one single dataset which is one-single-point.
• Check whether the written down replicas are really the same in sequential and parallel
  • Add a test for exactly this situation
  • Separate the .csv files per replica

(any help running the checks would be appreciated ofc, the more eyes the better)

This is needed for tree-saving reasons.

[github-actions]

Greetings from your nice fit 🤖 !
I have good news for you, I just finished my tasks:

• Fit Name: NNBOT-8653ace66-2025-02-12
• Fit Report wrt master: https://vp.nnpdf.science/SV0kXuXrTGi0eoHGE5rGtQ==
• Fit Report wrt latest stable reference: https://vp.nnpdf.science/--DkQbM1SAq7-YOePBsoiA==
• Fit Data: https://data.nnpdf.science/fits/NNBOT-8653ace66-2025-02-12.tar.gz

Check the report carefully, and please buy me a ☕ , or better, a GPU 😉!

[scarlehoff]

This is ready for review.

@achiefa please have a look if you have time to see whether this works for your use case. In principle it should store the replica pseudodata in each folder in GPU just like it does when you run sequentially.

@comane to achieve this I've created a decorator which modifies reportengine's decorator. Not sure whether what I've done is legal or whether there's an easier more official way to achieve this?

[github-actions]

Greetings from your nice fit 🤖 !
I have good news for you, I just finished my tasks:

• Fit Name: NNBOT-71d5ceae7-2025-02-19
• Fit Report wrt master: https://vp.nnpdf.science/l0djwpsoTzCBr5d0oCRFwA==
• Fit Report wrt latest stable reference: https://vp.nnpdf.science/qeImrGitSFCNe49QZvJx_w==
• Fit Data: https://data.nnpdf.science/fits/NNBOT-71d5ceae7-2025-02-19.tar.gz

Check the report carefully, and please buy me a ☕ , or better, a GPU 😉!

[achiefa]

I'm okay with that. Some things were not clear to me. But I think that is because I don't have as strong a knowledge of the codebase as you have.

[scarlehoff]

I'm okay with that. Some things were not clear to me. But I think that is because I don't have as strong a knowledge of the codebase as you have.

This is why it is important to have less experienced people doing the review, so they can become experienced! Thanks. I'll add the requested comments / docs.

[github-actions]

Greetings from your nice fit 🤖 !
I have good news for you, I just finished my tasks:

• Fit Name: NNBOT-4083fbacb-2025-02-20
• Fit Report wrt master: https://vp.nnpdf.science/_ik7sOiVRTaB7GzyvGLsGg==
• Fit Report wrt latest stable reference: https://vp.nnpdf.science/QM9y9zUMQqWIXtL-Wmuzuw==
• Fit Data: https://data.nnpdf.science/fits/NNBOT-4083fbacb-2025-02-20.tar.gz

Check the report carefully, and please buy me a ☕ , or better, a GPU 😉!