Revert the default for parallel fits to a single model per replica

Created by: scarlehoff

This PR reverts back to a model-per-replica configuration (instead of the multidense layer) since the multidense layer is not able to fit models independently (at the moment) which is necessary for a complete fit.

For sequential fits nothing should change, for parallel fit the difference is of about a factor of 2 in time (but I'm guessing that this factor of 2 we are gaining might be why the fits are of worse quality).

I've introduced a multidense layer type if people want to play around with multidense.

Here two reports, as you can see multidense has a terrible chi2 while dense is statistically compatible with 4.0.

multidense: https://vp.nnpdf.science/UGSJ6OsPTdSR2s97OXIvDw== dense: https://vp.nnpdf.science/WbBCvsjfQV-6ncIQ3GhCVw==

I haven't looked deep into the details of the error but I don't think multidense was truly benchmarked in a real-life scenario? At least I could not find any examples.

Anyway, for reference, these were fits of 120 replicas in a RTX 2080. 80 minutes with dense, 40 minutes with multidense. It would be ideal if multidense could be fixed but I can live with 80 minutes runs... for now checks.py will fail if you use multidense to make sure it is only used as a development option for now.

Fixes, in a way, #2118

TODO:

• Fix regression tests, note that most of the .json files have no changes (proving that, for one replica, multidense and dense were really the same)
• Check it works with TF 2.16/2.17 (if I'm able to get a computer with both a GPU and a recent-enough version of python/tf at the same time, not trivial)

RE the last point, I cannot check but the python tests are getting TF 2.17 so the code does work.

extra_tests/regression_fits/central_16.exportgrid

 labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
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+- [0.0, 0.0, -0.0022536208853125572, -0.003980251029133797, 0.0016412658151239157,
+  -0.00057112833019346, -0.00059515266912058, 0.0007673582877032459, 0.0030790423043072224,
+  0.005556290503591299, -0.0022536213509738445, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0017950665205717087, -0.0030927250627428293, 0.0011771128047257662,
+  -0.0004208831232972443, -0.0003635371394921094, 0.000673239235766232, 0.0023342962376773357,
+  0.0043587158434093, -0.0017950666369870305, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0013813064433634281, -0.0023123512510210276, 0.0007980726077221334,
+  -0.0002983802987728268, -0.00020540405239444226, 0.0005721031920984387, 0.001705888076685369,
+  0.003294848371297121, -0.00138130655977875, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0010139101650565863, -0.0016406156355515122, 0.0005000263918191195,
+  -0.00020134254009462893, -0.0001043372176354751, 0.0004653793585021049, 0.001186825567856431,
+  0.0023678706493228674, -0.001013910281471908, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.000694986549206078, -0.0010791971581056714, 0.0002780976356007159,
+  -0.00012720706581603736, -4.5427823351928964e-05, 0.00035532022593542933, 0.0007700208225287497,
+  0.0015817484818398952, -0.000694986549206078, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0004274640523362905, -0.0006301451358012855, 0.00012637156760320067,
+  -7.309808279387653e-05, -1.55029720190214e-05, 0.00024538033176213503, 0.00044837940367870033,
+  0.000941601290833205, -0.0004274640523362905, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00021569989621639252, -0.0002962907019536942, 3.7408761272672564e-05,
+  -3.582440331229009e-05, -3.3923656701517757e-06, 0.00014098476094659418, 0.00021510572696570307,
+  0.00045454336213879287, -0.0002156998816644773, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -6.707249849569052e-05, -8.231792890001088e-05, 1.1178956356161507e-06,
+  -1.202623843710171e-05, -2.507942440388433e-07, 5.150153810973279e-05, 6.472953828051686e-05,
+  0.00013187076547183096, -6.70724839437753e-05, 0.0, 0.0, 0.0]
 - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
 q20: 2.7224999999999997
 replica: 16