typhoon.git/annotate_pgns/nnue_model.py, branch master

NNUE: re-fit raw-to-score calibration, NNU4 weight format, super-lazy default-on, lazy probe measured net-negative

2026-10-21T01:37:20Z

Fits an affine (a + b*raw) correction into every exported .nnue file (export_nnue_weights.py's CalibrateScoreMapping) so the engine's consumed centipawn output tracks Stockfish's real score scale rather than the net's nominal raw WDL-sigmoid output -- training variance alone shifts this mapping across retrains even with an identical recipe, so it's re-fit and baked in per export rather than assumed constant. Weight format bumped to "NNU4" to carry the two new floats; older NNU1/2/3 files are no longer readable. Super-lazy (material-only) pre-filter promoted to default-on (g_fUseNnueSuperLazy = TRUE) with a recalibrated per-material-bucket margin table (measured against the recalibrated net, 16-shard cross_sectional_200k.ep_ sample) -- real, measured nps win across all three curated suites. The separate lazy-probe gate (accumulator-based early exit) is measured net-negative on real search traffic at this net's probe-vs-forward latency ratio and stays off by default (g_fUseNnueLazyProbe = FALSE); code kept live since margin tuning could still flip this, per docs/NNUE.md. Also: direct per-call latency instrumentation for NnueProbeScore/ NnueForward (root.c, EVAL_TIME build) to make the lazy-probe breakeven math measurable directly instead of inferred; QSearch futility margins given real-data-derived NNUE_FULL/NNUE_LAZY starting points. Co-Authored-By: Claude Sonnet 5

Wire NNUE eval into search, build the training pipeline, and add a fast

2026-10-20T16:26:44Z

move/eval-agreement benchmark against Stockfish (tests/lop) Implements the architecture decisions in src/docs/NNUE.md: HalfKAv2_hm feature set, int16/int8 quantization, per-ply accumulator backup for king moves, and a lazy-eval-ready integration into search.c/eval.c. Adds the full annotate_pgns/ training pipeline (extraction, K-fit/ labeling, quiet filtering, tensor building, training, export) plus an end-to-end orchestrator (run_nnue_pipeline.py) and validation tooling (sanity_check_net.py, compare_vs_stockfish_net.py). Adds tests/lop: a 10k-position list-of-positions benchmark comparing engine eval rank and move choice against pre-computed Stockfish/lc0 references, much cheaper than a full self-play match for day-to-day progress checks. First sd10-matched self-play result against classic HCE: LLR +2.28 (H1=+2.94), -72=119+169 over 360 games, confirming the net is "smarter per node" at matched depth. A real-time (--st 4) match is currently weaker (candidate loses ~3 plies of depth to a ~3.3-3.7x nps gap against an unrecalibrated search), consistent with NNUE.md's adoption-criterion section -- pruning/reduction constants still need retuning for the new eval's score scale before a fair real-time comparison is possible. Co-Authored-By: Claude Sonnet 5

Phase A NNUE: feature-indexing spec, annotation/training pipeline, dump nnuefeatures

2026-10-15T20:11:22Z

Builds out the NNUE Phase A prototype per docs/NNUE.md: - src/nnuefeatures.c: standalone HalfKAv2_hm feature-index reference implementation (new `dump nnuefeatures` command), sourced from Stockfish's half_ka_v2_hm.{h,cpp} (sf_16 tag), including the 0x88->64 square conversion typhoon's own board representation needs. - annotate_pgns/: sharded TWIC/self-play annotation harness (annotate.py), position extraction, quiet-position filtering (flat and game-shaped, plus an upstream --prefilter-quiet option), train/holdout game-level stratified split, WDL-sigmoid K fitting, the Python HalfKAv2_hm reference encoder (nnue_feature_spec.py) and its bit-for-bit cross-checker against the C side (cross_check_features.py), and a first PyTorch training pipeline (nnue_model.py, nnue_train.py, build_training_tensors.py) -- a 256x2-32-32-1-ish net trained locally on CPU against ~1.85M quiet TWIC positions as a pipeline/framework validation pass. - Finish an in-progress InitMagic/InitLMRTable/InitEval -> InitializeBitboardMagic/InitializeLMRTable/InitializeEval rename: chess.h's prototypes hadn't been updated to match the already-renamed definitions/call sites, leaving 4 implicit-function-declaration warnings. - tests/arasan.epd: new curated EPD suite. - docs/: add the project's design docs (previously untracked). Co-Authored-By: Claude Sonnet 5