diff options
| author | tslil <tslil@posteo.de> | 2026-09-17 07:01:29 +0100 |
|---|---|---|
| committer | tslil <tslil@posteo.de> | 2026-09-18 15:12:19 +0100 |
| commit | 0dba02072e9a89e35885d9078c0c8b58956448ce (patch) | |
| tree | 90752b70ad25f1ccc5d48660660fe1ee223a02f2 | |
| parent | 1898add0142b70b3fef5af78a6f3d7399a3f2cd5 (diff) | |
working towards hybrid rootstrap
| -rw-r--r-- | Makefile | 6 | ||||
| -rw-r--r-- | include/weights_5.c | 550 | ||||
| -rwxr-xr-x | src/nn_train.py | 456 | ||||
| -rw-r--r-- | src/selfplay.c | 38 |
4 files changed, 579 insertions, 471 deletions
@@ -34,8 +34,10 @@ native: ctlm cttei geminict cand_a = $(word 1,$(subst -, ,$1)) cand_b = $(word 2,$(subst -, ,$1)) -.PHONY: selfplay-% -selfplay-%: +# .PHONY on the pattern alone does not force the recipe when the binary +# already exists; the FORCE prerequisite does +.PHONY: selfplay-% FORCE +selfplay-%: FORCE rm -f selfplay-% rm -f src/*.o include/negamax.o include/pv.o include/tt.o $(MAKE) SELFPLAY_OUT=$@ \ diff --git a/include/weights_5.c b/include/weights_5.c index 588bf39..cbe5b53 100644 --- a/include/weights_5.c +++ b/include/weights_5.c @@ -1,297 +1,297 @@ /* generated by nn_train.py, do not edit by hand */ -/* iter=16, perf=+1524-1144=0332, rate=50.800 */ +/* iter=20, perf=+4837-2263=1700, rate=54.966 */ #ifndef WEIGHTS_FIVE #define WEIGHTS_FIVE #include "weights_5.h" const float FIVE_embedding[7] = { - -0.184489027, -0.881782293, -0.451349735, -1.67686057, 0.554752886, 0.134153381, 1.468413 + 0.0740088075, 1.24314356, 0.470590502, 1.9706943, -1.07658768, -0.110831268, -1.71863568 }; const float FIVE_dense_weights[FIVE_DENSE_NUM][FIVE_INP_NUM] NN_ALIGNED = { - {0.284560204, -0.419221222, 0.0328785852, 0.0813829675, 0.062259119, -0.0298566222, 0.0868502334, 0.129749909, - 0.061135117, 0.0289993435, 0.165124103, 0.0191279501, 0.119712554, 0.0654717833, 0.060883034, -0.0114311464, - 0.0400502011, 0.125673741, -0.0253371838, 0.0343324058, 0.0674481317, 0.182863027, -0.000346275134, -0.046321664, - -0.0684036091, -0.050165683, -0.0875314176}, - {0.557323396, -0.131285638, -0.00802773703, -0.0321692415, -0.0971636176, -0.129316717, -0.104541615, -0.0400036201, - -0.0320871621, 0.153581247, 0.114309713, 0.0890527517, 0.0316198133, -0.0229616798, 0.14933382, 0.195180729, - 0.249423265, 0.020841103, -0.0592732914, 0.207456321, 0.16428718, 0.146135718, -0.0290612206, 0.0310738813, - 0.175313443, 0.015554796, 0.00685921684}, - {0.383892566, -0.652790248, 0.108174205, 0.052967187, -0.0209764447, -0.0449645892, 0.151071727, 0.12525484, - 0.135253966, -0.0870055631, -0.000694841088, 0.21838361, 0.0577249229, 0.20536311, -0.0258124657, -0.00405113585, - 0.172642723, 0.0511332266, -0.00440308684, -0.0891218409, 0.0797435567, 0.0441572592, 0.0123149427, 0.0641523153, - -0.145321563, -0.0181931965, 0.112535395}, - {-0.321058869, 0.204980388, -0.158239529, -0.163409308, -0.0633437783, -0.0415460616, -0.111264043, -0.0860894248, - -0.202470213, -0.127226472, -0.0682562441, -0.110807009, -0.0804939792, -0.0131067969, -0.0590415262, 0.0276197419, - -0.0784899369, 0.0302416515, -0.115167312, -0.0687930882, -0.0782043934, -0.167902917, -0.0995951965, -0.123959467, - -0.0320122093, -0.0741118416, -0.18941617}, - {0.285745829, -0.100696772, 0.0657228455, 0.054573264, -0.101030931, 0.00427212659, -0.120137095, 0.0141248768, - 0.0804607868, 0.100518562, -0.0138845397, 0.146215662, -0.08813674, -0.111878015, 0.0697537139, 0.0961741731, - 0.197733656, -0.0838146284, 0.062217541, 0.000224303236, 0.228632852, 0.202914849, -0.0371545106, 0.00908992253, - 0.163213357, 0.0751332939, 0.0327892676}, - {0.236844257, -0.512373924, 0.180812642, 0.0511930324, 0.0369419679, 0.118528575, 0.154989898, 0.0510200299, - 0.106902733, 0.100861102, -0.0328107998, 0.086315535, 0.108647995, -0.0103859995, 0.104650848, -0.0242383704, - -0.120366439, 0.108360156, 0.125119567, -0.00536804367, 0.062428806, -0.120536663, 0.14099215, 0.11179845, - 0.0238136724, 0.119725138, -0.00515279267}, - {-0.946322918, -3.22145653, 0.30281207, 0.117794693, 0.13724938, 0.165796727, 0.244443521, 0.0749714077, - -0.01591493, 0.173956037, -0.0204820745, 0.13026914, 0.0951825455, 0.00769977644, -0.0638609529, -0.0396789536, - 0.130962491, 0.162444413, -0.0861478299, -0.047466971, 0.00670953514, 0.0636076257, 0.144687831, 0.144147187, - 0.146003932, 0.0889518335, 0.150284842}, - {0.47046724, -0.488577843, -0.00988451764, 0.0260628946, -0.042511452, 0.0019026529, 0.107701562, 0.0772359297, - -0.00852318201, -0.0811612159, 0.144580677, 0.147175536, 0.127442032, 0.076110594, -0.0703185946, 0.104386829, - 0.120601349, -0.0195855536, 0.127387717, -0.119516514, -0.0093170628, -0.0398560725, 0.0235036444, 0.0744361356, - -0.0965498984, 0.215383336, 0.0369071886}, - {-0.315617114, 0.151083708, -0.236110866, 0.0541519076, -0.0586371385, -0.136375204, -0.136796817, -0.0939740464, - -0.039566949, -0.0402120836, -0.167311549, -0.0361223742, 0.0965963304, -0.185001165, 0.0382073522, -0.198193386, - -0.108078822, -0.149277613, -0.115774535, -0.0326297618, -0.177266553, -0.0672949925, -0.148439199, 0.00843375176, - -0.154945508, -0.0343751274, -0.0915465504}, - {-0.359880298, 0.314251602, -0.248112842, -0.096442394, -0.199134201, -0.137287632, -0.193692222, -0.058614891, - -0.17952916, -0.065772213, -0.135623157, -0.0176453143, -0.0884096175, -0.0420794897, -0.0834076777, -0.0264618248, - -0.200602546, -0.219354019, -0.183864295, -0.152532309, -0.124429226, -0.113392331, -0.00583864795, -0.160228282, - -0.192600206, -0.130000398, -0.0963087678}, - {0.492462426, -0.294776499, -0.0668389648, 0.0232893713, 0.0713843554, -0.0417174511, 0.0813100636, 0.060909383, - -0.0129402354, -0.0581903867, -0.0839913487, -0.0243335404, -0.052235093, -0.0222961027, 0.00917301793, 0.143684506, - 0.00629981421, 0.0016236821, 0.0367625207, 0.0768950805, 0.0595415197, 0.195975393, -0.000282914611, -0.0298003592, - 0.00434705382, 0.102033503, 0.112813391}, - {0.302839458, -0.502133846, 0.0983253345, 0.142431617, 0.203259692, 0.0236698892, 0.0856857076, 0.185378432, - -0.00270775147, 0.0869711712, 0.0876712427, -0.0532260053, 0.0954493135, -0.0677271187, -0.0224150065, -0.0122775119, - 0.0964757502, 0.0256153494, 0.0735723004, 0.0925729051, -0.0251201354, -0.051896017, 0.137042195, 0.183832392, - -0.0736333877, 0.0174380112, 0.0986182541}, - {0.279993922, -0.13733831, 0.0617952645, -0.00253660837, 0.0270908661, 0.0524471402, -0.0400253981, 0.0419185534, - 0.0192263611, -0.0510118119, 0.150818914, -0.0552226193, 0.0921702534, 0.0852532014, 0.158899799, 0.155513063, - 0.17161037, 0.0238623396, -0.000968565699, 0.151265129, 0.130730301, -0.0379284061, 0.0972820967, -0.0399085209, - 0.125809953, 0.171116307, 0.171346337}, - {-0.119764067, -1.35013366, 0.137646064, 0.0229721498, -0.0518602207, 0.0595485531, 0.153518945, -0.0200147927, - 0.0999138281, -0.0284581035, 0.0159708057, -0.00420651259, 0.0410806611, 0.00428301981, -0.142613709, -0.0172457006, - 0.0774680674, 0.117800258, 0.103139229, -0.0483194515, 0.035018608, 0.0693995804, 0.326715618, 0.0704217926, - 0.191451311, 0.0998271033, 0.278734833}, - {-0.13475579, 0.324588686, -0.0925333947, -0.21291995, -0.000704998616, -0.0777914375, -0.0142197628, -0.0189588703, - -0.089525573, -0.210839808, 0.0366633944, -0.239989728, -0.142792344, -0.166696861, -0.178795457, -0.182961404, - -0.195784569, 0.0277741738, 0.0540008582, 0.00355652953, -0.0691932365, -0.0908621028, -0.09514191, -0.0227588397, - -0.15823321, -0.0496945605, 0.00878029503}, - {-0.32498163, 0.497707099, 0.0281655882, -0.262365252, -0.265070498, -0.148149252, 0.0119146137, 0.00372263882, - -0.113256313, -0.197625041, -0.218658969, -0.010111589, -0.124027781, -0.106711388, -0.220779508, -0.22598207, - -0.203461275, -0.0945060626, -0.140289873, -0.175047383, -0.0757920891, -0.0764472708, -0.155023172, -0.153385833, - -0.146838337, -0.08800143, -0.100332782}, - {0.0415623337, 0.528801262, 0.0799510106, 0.0957512632, 0.029891748, 0.0954739153, 0.206784412, 0.00290193269, - 0.107724555, -0.138024077, -0.021738831, -0.0587063506, -0.0958752558, -0.0945331901, -0.256397426, 0.0457332842, - -0.0968584493, 0.0945529342, -0.123311169, 0.0795145705, -0.0348570198, 0.199274316, 0.0963696465, 0.0014782761, - 0.0442242362, -0.127259344, 0.0209389944}, - {-0.144642189, 0.258335918, -0.122986697, 0.00131682027, -0.0469562337, -0.0738089308, -0.0318803042, -0.112096235, - -0.169825017, -0.151172772, -0.141715109, -0.0412416346, -0.211585537, -0.239820331, -0.018678192, -0.196874529, - -0.101480678, -0.120082371, -0.146239877, -0.110808648, -0.103161201, -0.177231416, -0.140140936, -0.0315673985, - -0.00367713696, -0.192495242, -0.0610412583}, - {-0.321165055, 0.192868292, -0.072390236, -0.18423751, -0.252518803, -0.26305601, -0.151765585, -0.171034545, - -0.105600595, -0.178600773, -0.174596995, -0.247677445, -0.237892687, -0.236348018, -0.000236330408, -0.1834452, - -0.163360119, -0.0701877028, -0.115833238, -0.202389836, -0.0721690729, -0.188146025, -0.120160259, -0.262443095, - -0.20341222, -0.21971783, -0.0980650261}, - {0.449082792, -0.0270797014, -0.137314126, -0.177455604, -0.139097154, -0.106580488, -0.0292201657, 0.136190265, - -0.0424143001, 0.149148405, -0.00196942198, -0.0597378463, 0.212443382, 0.129891843, 0.180067703, 0.205957547, - 0.000957073935, 0.0718140006, 0.10343381, 0.078331463, 0.166744187, 0.106738798, 0.120621271, 0.0129682859, - -0.0802863836, 0.10361319, 0.0862321332}, - {-0.17860204, 0.208189771, 0.0263033956, 0.000277920743, -0.160352573, 0.203601852, -0.150529191, -0.164125308, - -0.0140420506, -0.0494858325, -0.0526152551, -0.00277568423, -0.108365946, 0.0116153434, -0.0167237688, -0.0395806618, - 0.011931139, -0.165378034, -0.00324902334, -0.153365985, -0.0028339806, 0.133138388, 0.0944995135, 0.0164488498, - -0.2324083, 0.0180397108, -0.0274234097}, - {-0.426561028, 0.24493596, -0.201330155, -0.0763864592, -0.258283228, -0.0643101931, -0.128942877, -0.209910497, - -0.0374539979, -0.0166280884, -0.103381366, -0.0904387534, -0.0885902718, -0.172480807, -0.175712675, -0.106098562, - 0.00110536814, -0.203794301, -0.0544060022, -0.104873434, -0.129390463, -0.104808956, -0.185094148, -0.141938984, - -0.143806174, -0.170632169, -0.192467168}, - {0.45860526, -0.561122, 0.152751803, 0.0841200501, 0.0419712067, 0.0697804242, 0.0707635731, 0.0425729379, - -0.0768229887, -0.0354808494, -0.0261325743, -0.00976600312, 0.0420026034, 0.0314931013, 0.130160108, -0.0415651761, - 0.0342741646, 0.0618669875, -0.0239344724, -0.0121451197, 0.0371328294, 0.123138696, 0.00577426748, 0.118317187, - 0.165599778, 0.0769964978, -0.00334955752}, - {-0.207275823, 0.500732005, -0.0335645191, -0.0334830247, -0.0650151968, 0.0840153024, -0.124142788, -0.087065056, - -0.180922806, -0.144985691, -0.170720369, 0.0298153087, -0.101654306, -0.183430269, -0.231269047, -0.212364584, - -0.00150433357, -0.0818462446, -0.124161288, -0.00721862353, -0.111195698, -0.0487206243, -0.0044800397, -0.0740649104, - -0.0716948286, 0.00691187801, 0.00550211454}, - {0.393148512, -0.119145073, -0.0452868156, 0.165033549, -0.0167995542, 0.102966703, -0.115843639, 0.0740915835, - 0.145073235, 0.151900068, -0.0324623771, 0.15006955, 0.181313798, 0.125166476, 0.0496145822, -0.0837331712, - 0.0380510874, 0.0945119411, 0.086845085, 0.0748384967, -0.0646631196, 0.0774728209, -0.0452217348, 0.043796882, - 0.0519146174, 0.0136192748, -0.110294946}, - {-0.147433862, 0.321911603, 0.0100102769, -0.0134002501, -0.066930376, -0.0428499989, 0.0197757501, -0.0872440711, - 0.0300989896, 0.0125183631, -0.203157187, -0.0391080752, -0.0958503857, -0.122390091, -0.196443662, -0.193303362, - -0.130691722, -0.121532083, -0.145535812, -0.228147209, -0.181243256, -0.0333666354, -0.0166955218, -0.00871372409, - -0.129219323, -0.0389807671, -0.159113422}, - {-0.370798498, -0.0440062992, -0.197523639, -0.106161751, -0.200277418, -0.217777252, -0.227994964, -0.120287322, - -0.132728204, -0.0282151941, -0.180056259, -0.0245637931, -0.0893416405, -0.175884694, 0.0340842232, -0.0811715424, - -0.0163640399, -0.0825695395, -0.18725054, -0.011322774, -0.078294605, -0.0608479306, -0.148631766, -0.0791135058, - -0.0415379256, -0.13186425, -0.259721696}, - {0.103509016, -0.527144849, 0.0767510533, 0.00180323108, 0.116921358, 0.139517441, 0.127197951, 0.107439823, - -0.0525451638, -0.0226252265, 0.0640647262, 0.128393397, -0.0332081243, -0.0119498344, 0.148136154, -0.0720148385, - 0.0190187842, 0.122899085, 0.135596454, 0.0877433196, 0.168484196, 0.135552719, 0.0518457182, -0.0296556726, - 0.186869025, 0.0822264329, 0.0397439748}, - {0.263712794, -0.47617203, 0.0833945572, 0.0545602553, 0.122198626, 0.0465042032, 0.0840432867, 0.0169785693, - 0.0863707662, 0.0346216112, 0.109502196, 0.0986033231, 0.156983376, -0.0153407948, -0.00615008362, 0.0688149855, - 0.0742319152, 0.112336718, 0.0133233471, -0.11299479, 0.00305100321, 0.138550952, 0.12309023, 0.0680940971, - 0.0337822363, -0.048854053, -0.0313863233}, - {0.518104434, -0.166436046, -0.099790968, -0.0663183108, -0.0625056997, -0.11375796, -0.145391315, -0.00967355538, - 0.136790186, -0.0772993267, 0.0110090347, -0.0201231744, 0.153027356, 0.139256388, 0.167663798, 0.0641365945, - -0.0409532301, 0.227549046, 0.143810138, 0.138499409, 0.0188430138, -0.0482751057, 0.0301126037, 0.248668149, - 0.210530192, 0.00619054912, -0.0676088557}, - {-0.186104491, 0.233580634, 0.118573114, -0.0127976853, 0.0960566625, 0.0736953542, 0.251772195, -0.104145579, - 0.172358245, -0.0386764631, 0.0379826277, 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0.052559007, 0.0460673496, 0.0693054795, 0.0644561276, 0.11398416, 0.021627564, + 0.0437199362, 0.103789032, -0.0312068891, -0.0665286854, 0.0249293167, 0.0493387133, 0.0870543793, -0.0129110338, + 0.193294987, 0.15596509, 0.00858440436, 0.0967156887, -0.0302792024, 0.0566488765, 0.0778378099, -0.0519785471, + -0.0802783072, 0.109945856, 0.093652375}, }; const float FIVE_dense_biases[FIVE_DENSE_NUM] = { - 0.448750377, 0.403760582, 0.343976527, -0.255637884, 0.287142545, 0.484369278, 0.346042812, 0.353629231, - -0.132978559, 0.0292666871, 0.33778137, 0.327351302, 0.3948856, 0.282039821, -0.0324714705, -0.129281044, - -0.21987389, -0.030962348, -0.0587515272, 0.184871286, -0.0846855566, -0.151341617, 0.369707227, -0.188353449, - 0.355887353, -0.0255241822, -0.210587487, 0.235232681, 0.284036249, 0.459257066, -0.451358795, -0.112674445, - -0.207122475, -0.0463822149, 0.156282499, 0.413409323, -0.0411145575, 0.173950806, -0.0794503465, -0.244166583, - 0.220132127, 0.398949623, 0.248157859, -0.050257463, 0.392109722, -0.144541204, 0.485850841, 0.29079178, - -0.339778692, -0.0887532607, -0.0737924799, -0.249317899, -0.335118115, -0.151358426, 0.466078371, -0.0393950865, - 0.407593697, 0.364170969, -0.231799334, 0.383101225, -0.136441469, 0.524635255, 0.312647462, -0.0535623394 + 0.0607186109, -0.0681320056, 0.00174399419, -0.0552374087, 0.169119328, 0.059536662, 0.121260114, 0.0594619587, + 0.123958312, 0.142431423, -0.066357702, 0.1571486, 0.0430835485, 0.0681126416, 0.218000457, 0.0866415352, + -0.0935378969, 0.18506594, -0.0619109422, -0.0327474251, 0.279266626, 0.247783005, 0.0759725198, 0.267866522, + 0.0564821772, 0.162609458, 0.116303787, 0.0144420909, 0.0144254379, 0.0594321191, 0.0713874102, 0.120119229, + -0.0792147592, 0.151572526, 0.0799025595, 0.241775259, 0.132559836, 0.221947417, -0.0514785275, 0.188967034, + 0.196275517, 0.0211338401, 0.128358856, -0.0350252055, 0.194681481, 0.125860646, -0.104203798, 0.0794227272, + -0.0228515472, 0.156887457, 0.10330575, 0.18149218, 0.160906896, 0.250343442, 0.239296943, 0.0897175968, + 0.0577574857, -0.0525054261, -0.0180928297, -0.0237387344, -0.0620574579, -0.0873452052, 0.0453474857, 0.117129661 }; const float FIVE_logit_weights[FIVE_DENSE_NUM] NN_ALIGNED = { - -0.371308535, -0.405355245, -0.377181888, 0.497233629, -0.250674397, -0.429564774, -1.11061406, -0.296319515, - 0.23601149, 0.243570864, -0.24711068, -0.369821012, -0.335140526, -0.493516713, 0.214400083, 0.474441826, - 0.155431896, 0.203262046, 0.336635977, -0.23676908, 0.109814323, 0.444532424, -0.370563239, 0.360269755, - -0.278772235, 0.17623356, 0.379497468, -0.263965845, -0.396779865, -0.319404095, 0.251259804, 0.409782857, - 0.351485848, 0.366804957, -0.178924903, -0.297108471, 0.144363016, -0.16299139, 0.124468893, 0.280428141, - -0.374722838, -0.71489042, -0.22734572, 0.310074896, -0.339080572, 0.387652963, -0.476010561, -0.23891817, - 0.158733234, 0.128290251, 0.344814301, 0.395800322, 0.244534045, 0.224248424, -0.349556744, 0.367600739, - -0.529881656, -0.339196235, 0.28590101, -0.340338498, 0.524476707, -0.638551593, -0.325753361, 0.121846914 + 0.366290689, -0.179763243, -0.170635402, 0.358551443, -0.301865429, -0.204198688, -0.18824023, 0.368638158, + 0.29080224, -0.110242181, 0.260225922, 0.333086222, -0.242419481, 0.257134348, -0.183402345, -0.260596663, + -0.272571802, 0.304102749, 0.33268097, 0.333681524, -0.446022332, -0.248471856, -0.232226282, -0.329673767, + -0.238541022, -0.486667186, -0.209973752, 0.601931572, 0.395690441, 0.195730135, 0.266417474, -0.295594722, + 0.409933716, -0.158102736, 0.31490761, -0.293863147, -0.134037212, -0.291068882, 0.193977296, -0.144613206, + 0.299789637, -0.232466415, -0.251558155, -0.20994027, -0.279022664, -0.185697749, 0.142207414, 0.255289674, + -0.258415729, -0.143972635, 0.151671469, -0.366263509, -0.123788796, -0.322885275, -0.363797396, -0.156830251, + 0.550342262, 0.203263223, -0.183517739, -0.203325212, -0.108895399, 0.191670418, -0.131806001, 0.275587142 }; -const float FIVE_logit_bias = -0.178311273; +const float FIVE_logit_bias = -0.0882488862; #endif
\ No newline at end of file diff --git a/src/nn_train.py b/src/nn_train.py index 5332aa8..a97c964 100755 --- a/src/nn_train.py +++ b/src/nn_train.py @@ -3,7 +3,7 @@ # /// script # requires-python = ">=3.14" # dependencies = [ -# "pandas>=3.0.5", +# "numpy>=2.5.3", # "torch>=2.14.0", # "tqdm>=4.70.0", # ] @@ -39,13 +39,12 @@ from math import log, ceil import argparse import concurrent.futures -import copy +import json import os from pathlib import Path import subprocess import numpy as np -import pandas as pd import torch from tqdm import tqdm @@ -94,28 +93,51 @@ def _fmt_matrix(mat: np.ndarray, per_line: int = 8) -> str: # =================================================================== +# Config +# =================================================================== + + +@dataclass(frozen=True) +class DataGenConfig: + size: int + depth: int + min_ply: int + games: int + procs: int + replay_iters: int + + +@dataclass(frozen=True) +class TrainingConfig: + smooth_factor: float + epochs: int + batch: int + lr: float + val_frac: float + + +# =================================================================== # Self-play records # =================================================================== -def td_lambda_targets( +def smooth( leaf_values: Tensor, result: float, - td_lambda: float, + lam: float, ) -> Tensor: + # Smooth(leaf_values, result, lambda), rooted at the game outcome: + # G[-1] = result, G[t] = (1-lambda)V[t] + lambda G[t+1] targets = torch.empty_like(leaf_values) targets[-1] = result - # G[t] = (1-lambda)V[t+1] + lambda G[t+1] for ply in range(len(leaf_values) - 2, -1, -1): - targets[ply] = (1.0 - td_lambda) * leaf_values[ply + 1] + td_lambda * targets[ - ply + 1 - ] + targets[ply] = (1.0 - lam) * leaf_values[ply] + lam * targets[ply + 1] return targets -SPOOKY: int = 3 +SPOOKY: int = 4 @dataclass @@ -133,14 +155,38 @@ class Performance: @dataclass(frozen=True) +class DataSplit: + train_positions: Tensor + train_targets: Tensor + train_parity_signs: Tensor + val_positions: Tensor + val_targets: Tensor + val_parity_signs: Tensor + + def batches(self, batch_size: int) -> Iterator[tuple[Tensor, Tensor, Tensor]]: + order = torch.randperm( + len(self.train_positions), device=self.train_positions.device + ) + for start in range(0, len(order), batch_size): + idx = order[start : start + batch_size] + yield ( + self.train_positions[idx], + self.train_targets[idx], + self.train_parity_signs[idx], + ) + + +@dataclass(frozen=True) class CandidateRecords: - positions: list[Tensor] # [n, inp_dim] - game_ids: list[Tensor] # [n] - results: list[Tensor] # [n] - cand_results: list[Tensor] # [n] - terminals: list[Tensor] # [n] - terminal_values: list[Tensor] # [n] - parity_signs: list[Tensor] # [n] + positions: Tensor # [n, inp_dim] net input of the root state s_t + leaf_positions: Tensor # [n, inp_dim] net input of the PV leaf pv_t + game_ids: Tensor # [n] + results: Tensor # [n] game result R, black's perspective + cand_results: Tensor # [n] R from the recorded candidate's perspective + parity_signs: Tensor # [n] root parity + leaf_parity_signs: Tensor # [n] leaf parity + leaf_terminals: Tensor # [n] leaf is an exact terminal + leaf_terminal_values: Tensor # [n] exact terminal value, black's perspective def performance(self) -> Performance: result = Performance(0, 0, 0) @@ -156,107 +202,176 @@ class CandidateRecords: result.draw += 1 return result - def make_training_data( - self, model: Module, td_lambda: float - ) -> tuple[Tensor, Tensor, Tensor]: + def make_training_data(self, model: Module, training: TrainingConfig) -> DataSplit: model.eval() with torch.no_grad(): - values = model(self.positions) * self.parity_signs + # Net(pv_t), black's perspective; exact terminals override the net + leaf_values = model(self.leaf_positions) * self.leaf_parity_signs + leaf_values = torch.where( + self.leaf_terminals, self.leaf_terminal_values, leaf_values + ) - targets = torch.empty_like(values) + targets = torch.empty_like(leaf_values) for game_id in torch.unique(self.game_ids): in_game = self.game_ids == game_id - game_terminals = self.terminals[in_game] - game_terminal_values = self.terminal_values[in_game] - game_values = values[in_game].clone() - game_values[game_terminals] = game_terminal_values[game_terminals] game_result = self.results[in_game][0].item() - game_targets = td_lambda_targets(game_values, game_result, td_lambda) - # Terminal positions are also direct supervised examples. Do this - # after constructing the TD sequence so it does not reset the EMA. - game_targets[game_terminals] = game_terminal_values[game_terminals] - targets[in_game] = game_targets + # self-pair games (i,i) record the candidate on every ply, + # so smooth one chain per colour, not across players + for sign in (1.0, -1.0): + idxs = torch.nonzero( + in_game & (self.parity_signs == sign), as_tuple=True + )[0] + if len(idxs) == 0: + continue + targets[idxs] = smooth( + leaf_values[idxs], game_result, training.smooth_factor + ) + + n_val = max(1, int(len(leaf_values) * training.val_frac)) + perm = torch.randperm(len(leaf_values), device=leaf_values.device) + val_idx, train_idx = perm[:n_val], perm[n_val:] - return self.positions, targets, self.parity_signs + return DataSplit( + train_positions=self.positions[train_idx], + train_targets=targets[train_idx], + train_parity_signs=self.parity_signs[train_idx], + val_positions=self.positions[val_idx], + val_targets=targets[val_idx], + val_parity_signs=self.parity_signs[val_idx], + ) @dataclass(frozen=True) class PlayRecords: positions: list[Tensor] # [candidate, n_i, inp_dim] + leaf_positions: list[Tensor] # [candidate, n_i, inp_dim] game_ids: list[Tensor] # [candidate, n_i] results: list[Tensor] # [candidate, n_i] cand_results: list[Tensor] # [candidate, n_i] - terminals: list[Tensor] # [candidate, n_i] - terminal_values: list[Tensor] # [candidate, n_i] parity_signs: list[Tensor] # [candidate, n_i] + leaf_parity_signs: list[Tensor] # [candidate, n_i] + leaf_terminals: list[Tensor] # [candidate, n_i] + leaf_terminal_values: list[Tensor] # [candidate, n_i] def index(self, index: int) -> CandidateRecords: return CandidateRecords( positions=self.positions[index], + leaf_positions=self.leaf_positions[index], game_ids=self.game_ids[index], results=self.results[index], cand_results=self.cand_results[index], - terminals=self.terminals[index], - terminal_values=self.terminal_values[index], parity_signs=self.parity_signs[index], + leaf_parity_signs=self.leaf_parity_signs[index], + leaf_terminals=self.leaf_terminals[index], + leaf_terminal_values=self.leaf_terminal_values[index], ) @classmethod - def load_selfplay(cls, fn, size): - df = pd.read_csv(fn, header=None) + def load_merge(cls, files: list[Path], size: int) -> PlayRecords: + # one JSON record per line: + # {"result": R, "rows": [{"cand", "root": {...}, "leaf": {...}}]} inp_dim = NET_DIMS[size][0] + n_cells = inp_dim - 2 + + games: list[dict] = [] + for file in files: + with open(file) as f: + for line in f: + if line.strip(): + games.append(json.loads(line)) - fields = { - "parity_signs": df.iloc[:, 0].to_numpy(dtype=np.float32), - "positions": df.iloc[:, 1 : inp_dim + 1].to_numpy(dtype=np.float32), - "results": df.iloc[:, inp_dim + 1].to_numpy(dtype=np.float32), - "terminals": df.iloc[:, inp_dim + 2].to_numpy(dtype=np.bool_), - "terminal_values": df.iloc[:, inp_dim + 3].to_numpy(dtype=np.float32), - "game_ids": df.iloc[:, inp_dim + 4].to_numpy(dtype=np.int64), - "cand_results": df.iloc[:, inp_dim + 6].to_numpy(dtype=np.float32), - } - cand_ids = df.iloc[:, inp_dim + 5].to_numpy(dtype=np.int64) + def board(row_part: dict) -> list[float]: + return [*row_part["head"], *row_part["cells"]] - n_cands = int(cand_ids.max()) + 1 - gathered = {k: [] for k in fields} - for cid in range(n_cands): - mask = cand_ids == cid - for k, arr in fields.items(): - gathered[k].append(torch.from_numpy(arr[mask])) + n_cands = max(r["cand"] for g in games for r in g["rows"]) + 1 + fields = ( + "positions", + "leaf_positions", + "game_ids", + "results", + "cand_results", + "parity_signs", + "leaf_parity_signs", + "leaf_terminals", + "leaf_terminal_values", + ) + per_cand = {c: {k: [] for k in fields} for c in range(n_cands)} + for game_id, game in enumerate(games): + result = float(game["result"]) + for cand in range(n_cands): + rows = [r for r in game["rows"] if r["cand"] == cand] + if not rows: + continue + for part in ("root", "leaf"): + for r in rows: + if len(r[part]["cells"]) != n_cells: + raise ValueError( + f"{files[-1]}: {len(r[part]['cells'])} cells, " + f"expected {n_cells} for size {size}" + ) + parity = np.array([r["root"]["parity"] for r in rows], dtype=np.float32) + per_cand[cand]["positions"].append( + torch.from_numpy( + np.array([board(r["root"]) for r in rows], dtype=np.float32) + ) + ) + per_cand[cand]["leaf_positions"].append( + torch.from_numpy( + np.array([board(r["leaf"]) for r in rows], dtype=np.float32) + ) + ) + per_cand[cand]["game_ids"].append( + torch.full((len(rows),), game_id, dtype=torch.int64) + ) + per_cand[cand]["results"].append( + torch.full((len(rows),), result, dtype=torch.float32) + ) + per_cand[cand]["cand_results"].append(torch.from_numpy(parity * result)) + per_cand[cand]["parity_signs"].append(torch.from_numpy(parity)) + per_cand[cand]["leaf_parity_signs"].append( + torch.from_numpy( + np.array([r["leaf"]["parity"] for r in rows], dtype=np.float32) + ) + ) + per_cand[cand]["leaf_terminals"].append( + torch.from_numpy( + np.array( + [r["leaf"]["terminal"] == 1 for r in rows], + dtype=np.bool_, + ) + ) + ) + per_cand[cand]["leaf_terminal_values"].append( + torch.from_numpy( + np.array( + [r["leaf"]["terminal_value"] for r in rows], + dtype=np.float32, + ) + ) + ) - return cls(**gathered) + def merged(cand: int, k: str, empty: Tensor) -> Tensor: + blocks = per_cand[cand][k] + return torch.concat(blocks, dim=0) if blocks else empty - @classmethod - def load_merge(cls, files: list[Path], size: int) -> PlayRecords: - sprs = [PlayRecords.load_selfplay(file, size) for file in files] - # for each field, gather [file0[i], file1[i], ...] for every index i - # and concat along dim 0 -> merged list indexed by cand id return cls( - positions=[ - torch.concat(vals, dim=0) for vals in zip(*(s.positions for s in sprs)) - ], - game_ids=[ - torch.concat(vals, dim=0) for vals in zip(*(s.game_ids for s in sprs)) - ], - results=[ - torch.concat(vals, dim=0) for vals in zip(*(s.results for s in sprs)) - ], - cand_results=[ - torch.concat(vals, dim=0) - for vals in zip(*(s.cand_results for s in sprs)) - ], - terminals=[ - torch.concat(vals, dim=0) for vals in zip(*(s.terminals for s in sprs)) - ], - terminal_values=[ - torch.concat(vals, dim=0) - for vals in zip(*(s.terminal_values for s in sprs)) - ], - parity_signs=[ - torch.concat(vals, dim=0) - for vals in zip(*(s.parity_signs for s in sprs)) - ], + **{ + k: [ + merged( + cand, + k, + torch.empty( + (0, inp_dim) if k.endswith("positions") else (0,), + dtype=torch.float32 + if k not in ("game_ids", "leaf_terminals") + else (torch.int64 if k == "game_ids" else torch.bool), + ), + ) + for cand in range(n_cands) + ] + for k in fields + } ) @@ -282,55 +397,29 @@ class Net(Module): return tanh(self.output(hidden)).squeeze(-1) def fit( - self, - *, - records: CandidateRecords, - td_lambda: float, - val_frac: float, - epochs: int, - batch: int, - lr: float, + self, *, records: CandidateRecords, training: TrainingConfig ) -> tuple[float, float]: # training widgets - optimizer = torch.optim.AdamW(self.parameters(), lr=lr) + optimizer = torch.optim.AdamW(self.parameters(), lr=training.lr) loss_fn = MSELoss() - pbar = tqdm(range(epochs), desc="train", unit="ep") + pbar = tqdm(range(training.epochs), desc="train", unit="ep") for _ in pbar: - # make data - positions, targets, parity_signs = records.make_training_data( - self, td_lambda - ) - n = len(positions) - perm = torch.randperm(n, device=positions.device) - n_val = max(1, int(n * val_frac)) - val_idx = perm[:n_val] - train_idx = perm[n_val:] - - val_positions = positions[val_idx] - val_targets = targets[val_idx] - val_parity_signs = parity_signs[val_idx] - train_positions = positions[train_idx] - train_targets = targets[train_idx] - train_parity_signs = parity_signs[train_idx] + split = records.make_training_data(self, training) self.train() - order = torch.randperm(len(train_positions), device=positions.device) - for start in range(0, len(train_positions), batch): - batch_idx = order[start : start + batch] + for positions, targets, parity_signs in split.batches(training.batch): optimizer.zero_grad() - predictions = ( - self(train_positions[batch_idx]) * train_parity_signs[batch_idx] - ) - loss = loss_fn(predictions, train_targets[batch_idx]) + predictions = self(positions) * parity_signs + loss = loss_fn(predictions, targets) loss.backward() optimizer.step() with torch.no_grad(): self.eval() - predictions = self(val_positions) * val_parity_signs - mse = loss_fn(predictions, val_targets).item() - corr = torch.corrcoef(torch.stack([predictions, val_targets]))[ + predictions = self(split.val_positions) * split.val_parity_signs + mse = loss_fn(predictions, split.val_targets).item() + corr = torch.corrcoef(torch.stack([predictions, split.val_targets]))[ 0, 1 ].item() @@ -426,39 +515,30 @@ def cand_copy_for(size: int, id: int, root: Path) -> None: # =================================================================== -def setup(num_candidates: int, size: int, root: Path) -> list[Net]: - inp_dim, hidden = NET_DIMS[size] +def setup(num_candidates: int, data_gen: DataGenConfig, root: Path) -> list[Net]: + inp_dim, hidden = NET_DIMS[data_gen.size] candidates = [Net(inp_dim, hidden) for _ in range(num_candidates)] for id, cand in enumerate(candidates): cand.to_c( - size=size, + size=data_gen.size, id=id, - path=root / "include" / f"cand_{id}_weights_{size}.c", + path=root / "include" / f"cand_{id}_weights_{data_gen.size}.c", note=f"{id=}, v000, random init", ) - cand_copy_for(6 if size == 5 else 5, id, root) + cand_copy_for(6 if data_gen.size == 5 else 5, id, root) return candidates def run_step( *, - batch: int, candidates: list[Net], - depth: int, - epochs: int, - games: int, + data_gen: DataGenConfig, iter: int, - lr: float, - min_ply: int, - procs: int, - replay_iters: int, root: Path, - size: int, - td_lambda: float, - val_frac: float, + training: TrainingConfig, ): - def csv_path(i: int) -> Path: - return root / "data" / f"selfplay-{size}-v{i:03d}.csv" + def data_path(i: int) -> Path: + return root / "data" / f"selfplay-{data_gen.size}-v{i:03d}.json" print(f"\n== iteration {iter} ==") @@ -474,30 +554,40 @@ def run_step( ) # 2. generate self-play data - csv = csv_path(iter) - print(f"selfplay: {num_candidates=}, {games=}, {size=}, {depth=}, {min_ply=}") - out_rows: list[str] = [] + data = data_path(iter) + print( + f"selfplay: {num_candidates=}, {data_gen.games=}, " + f"{data_gen.size=}, {data_gen.depth=}, {data_gen.min_ply=}" + ) + out_games: list[str] = [] for out in fan_out( [ - [f"./selfplay-{i}-{j}", str(size), str(depth), str(min_ply)] + [ + f"./selfplay-{i}-{j}", + str(data_gen.size), + str(data_gen.depth), + str(data_gen.min_ply), + ] for (i, j) in cand_pairs ], - games, - procs, + data_gen.games, + data_gen.procs, "selfplay", root, ): - out_rows.extend(out.splitlines()) - print(f"writing {len(out_rows):,} rows to {csv}") - with open(csv, "w") as f: - _ = f.write("\n".join(out_rows)) - if out_rows: + out_games.extend(out.splitlines()) + print(f"writing {len(out_games):,} games to {data}") + with open(data, "w") as f: + _ = f.write("\n".join(out_games)) + if out_games: _ = f.write("\n") # 3. compute performance - csvs = [csv_path(i) for i in range(max(iter - replay_iters, 1), iter + 1)] - print("Loading data from", [str(c) for c in csvs], "...", end=" ") - records = PlayRecords.load_merge(csvs, size) + data_files = [ + data_path(i) for i in range(max(iter - data_gen.replay_iters, 1), iter + 1) + ] + print("Loading data from", [str(c) for c in data_files], "...", end=" ") + records = PlayRecords.load_merge(data_files, data_gen.size) print("candidates have", [f"{r.shape[0]:,}" for r in records.results], "rows.") results = [ (i, cand, records.index(i).performance()) for i, cand in enumerate(candidates) @@ -509,9 +599,9 @@ def run_step( # 4. write champ idx, champ, perf = max(results, key=lambda t: t[2].rate()) - path = root / "include" / f"weights_{size}.c" + path = root / "include" / f"weights_{data_gen.size}.c" champ.to_c( - size=size, + size=data_gen.size, id=None, path=path, note=f"{iter=}, perf={str(perf)}, rate={perf.rate() * 100.0:.3f}", @@ -520,20 +610,13 @@ def run_step( _ = subprocess.run(["make", "native"], cwd=root, check=True, capture_output=True) print(f"Wrote candidate {idx:03d} to {str(path)}") - # 5. fine-tune on TD-leaf(lambda) targets -> candidate + # 5. fine-tune on the hybrid targets -> candidate for idx, cand in enumerate(candidates): - corr, mse = cand.fit( - records=records.index(idx), - td_lambda=td_lambda, - val_frac=val_frac, - epochs=epochs, - batch=batch, - lr=lr, - ) + corr, mse = cand.fit(records=records.index(idx), training=training) cand.to_c( - size=size, + size=data_gen.size, id=idx, - path=root / "include" / f"cand_{idx}_weights_{size}.c", + path=root / "include" / f"cand_{idx}_weights_{data_gen.size}.c", note=f"id={idx}, v{iter:03d}, val_corr={corr:.4f}, val_mse={mse:.4f}", ) @@ -546,7 +629,7 @@ def run_step( def main() -> None: root = Path(__file__).resolve().parent.parent p = argparse.ArgumentParser( - description="the TD(lambda) self-play bootstrap loop", + description="Train the neural network evaluators of ctak", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) _ = p.add_argument( @@ -577,19 +660,19 @@ def main() -> None: _ = p.add_argument( "--replay-iters", type=int, - default=0, + default=10, help="number of previous data sources to retain", ) _ = p.add_argument( - "--td-lambda", + "--smooth-factor", type=float, default=0.7, - help="TD(lambda) lambda", + help="The EMA smoothing factor", ) _ = p.add_argument( "--epochs", type=int, - default=20, + default=5, help="candidate training epochs", ) _ = p.add_argument( @@ -626,7 +709,7 @@ def main() -> None: "--min-ply", type=int, default=8, - help="record positions at this ply or later", + help="length of random opening in games", ) args = p.parse_args() @@ -642,28 +725,35 @@ def main() -> None: ) ) + data_gen = DataGenConfig( + size=args.size, + depth=args.depth, + min_ply=args.min_ply, + games=args.games, + procs=args.procs, + replay_iters=args.replay_iters, + ) + training = TrainingConfig( + smooth_factor=args.smooth_factor, + epochs=args.epochs, + batch=args.batch, + lr=args.lr, + val_frac=args.val_frac, + ) + (root / "data").mkdir(exist_ok=True) - candidates = setup(num_candidates=args.num_candidates, size=args.size, root=root) + candidates = setup(num_candidates=args.num_candidates, data_gen=data_gen, root=root) num_params = sum(p.numel() for p in candidates[0].parameters() if p.requires_grad) print(f"Number of trainable parameters: {num_params:,}") for iter in range(1, args.iters + 1): run_step( - batch=args.batch, candidates=candidates, - depth=args.depth, - epochs=args.epochs, - games=args.games, + data_gen=data_gen, iter=iter, - lr=args.lr, - min_ply=args.min_ply, - procs=args.procs, - replay_iters=args.replay_iters, root=root, - size=args.size, - td_lambda=args.td_lambda, - val_frac=args.val_frac, + training=training, ) print(f"\n== done, champion in include/weights_{args.size}.c ==") diff --git a/src/selfplay.c b/src/selfplay.c index 1d6bd84..b2a5821 100644 --- a/src/selfplay.c +++ b/src/selfplay.c @@ -91,10 +91,20 @@ static int8_t win_value(const enum WIN_TYPE w) { } } +static void emit_board(const float *head, const uint8_t *cells, + const uint8_t n_cells) { + fprintf(stdout, "\"head\":[%.8f,%.8f],\"cells\":[", head[0], head[1]); + for (uint8_t c = 0; c < n_cells; c++) + fprintf(stdout, "%s%u", c ? "," : "", cells[c]); + fprintf(stdout, "]"); +} + static int play_game(const uint8_t min_ply, const nn_function fn_1, const nn_function fn_2, const uint32_t cand_id_1, const uint32_t cand_id_2, - const uint32_t game_id, char record[][16], uint8_t replay) { + char record[][16], uint8_t replay) { + float root_head[MAX_PLY][2]; + uint8_t root_cells[MAX_PLY][MAX_CELLS]; float row_head[MAX_PLY][2]; uint8_t row_cells[MAX_PLY][MAX_CELLS]; uint8_t row_term[MAX_PLY]; @@ -133,6 +143,10 @@ static int play_game(const uint8_t min_ply, const nn_function fn_1, row_parity_sign[n_rows] = leaf->parity_sign; row_cand_id[n_rows] = (state->current_colour&1) ? cand_id_1 : cand_id_2; row_mover_sign[n_rows] = (state->current_colour&1) ? 1 : -1; + + // Net input of the root state s_t, before the move is played + nn_prepare_head(root_head[n_rows], state); + nn_cell_idxs(root_cells[n_rows], state); n_rows++; } @@ -155,13 +169,17 @@ static int play_game(const uint8_t min_ply, const nn_function fn_1, // final outcome of the played game, from black's perspective const int8_t R = win_value(state->won); + fprintf(stdout, "{\"result\":%d,\"rows\":[", R); for (uint32_t k = 0; k < n_rows; k++) { - fprintf(stdout, "%.1f,%.8f,%.8f,", row_parity_sign[k], row_head[k][0], row_head[k][1]); - for (uint8_t c = 0; c < n_cells; c++) - fprintf(stdout, "%u,", row_cells[k][c]); - fprintf(stdout, "%i,%u,%.0f,%u,%u,%i\n", R, row_term[k], row_terminal_value[k], game_id, row_cand_id[k], - row_mover_sign[k] * R); + fprintf(stdout, "%s{\"cand\":%u,\"root\":{\"parity\":%d,", k ? "," : "", + row_cand_id[k], row_mover_sign[k]); + emit_board(root_head[k], root_cells[k], n_cells); + fprintf(stdout, "},\"leaf\":{\"parity\":%.0f,", row_parity_sign[k]); + emit_board(row_head[k], row_cells[k], n_cells); + fprintf(stdout, ",\"terminal\":%u,\"terminal_value\":%.1f}}", + row_term[k], row_terminal_value[k]); } + fprintf(stdout, "]}\n"); return (int)n_rows; } @@ -169,9 +187,9 @@ static int play_game(const uint8_t min_ply, const nn_function fn_1, const char *usage = "selfplay, one game of training data between candidates.\n\n\ Usage: selfplay <size> <depth> <min_ply>\n\n\ -Plays a single game of <size>x<size>, with <min_ply> moves made randomly. Only the deterministic plies are written to stdout, one CSV row per recorded search, from ply <min_ply> onwards:\n\n\ -\tparity_sign,mover_flats,opponent_flats,<cell ids...>,R,terminal,leaf_R,game_id,cand_id,cand_R\n\n\ -R is the outcome from black's perspective, cand_R the outcome from the recorded candidate's perspective. nn_train.py runs many of these in parallel to generate training data.\n\n\ +Plays a single game of <size>x<size>, with <min_ply> moves made randomly. Only the deterministic plies are written to stdout, one JSON record per game, from ply <min_ply> onwards:\n\n\ +\t{\"result\": R, \"rows\": [{\"cand\": id, \"root\": {\"parity\", \"head\", \"cells\"}, \"leaf\": {\"parity\", \"head\", \"cells\", \"terminal\", \"terminal_value\"}}]}\n\n\ +R is the outcome of the game from black's perspective. Each row records the net input of the root state s_t and the PV leaf selected at that ply; the leaf value Net(pv) is taken from the trained net. nn_train.py runs many of these in parallel to generate training data.\n\n\ Copyright (C) 2026, tslil clingman\n\n\ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version."; @@ -212,13 +230,11 @@ int main(int argc, char **argv) { size == 5 ? FIVE_A : SIX_A, size == 5 ? FIVE_B : SIX_B, CAND_A, CAND_B, - rand32(), record, 0); play_game(min_ply, size == 5 ? FIVE_B : SIX_B, size == 5 ? FIVE_A : SIX_A, CAND_B, CAND_A, - rand32(), record, CAND_A == CAND_B ? 0: 1); |