add seq/ config: directions found by a diversity-maximising search above the census floor
9cb1c66 verified | """Frozen constants for the diversity-maximising sequential search — see | |
| ../PROTOCOL-SEQ.md. Pinned before any GPU job ran. | |
| Everything the frozen census pinned (slices, SEQ, flow constants, colored | |
| floor, lift measurement, clustering/identity threshold) is imported unchanged | |
| from `protocol.py` / `sprotocol.py`. This module adds only: | |
| 1. the grid (2 models x 3 representative blocks x 4 measures, + floors), | |
| 2. the sequential orthogonalised search's own knobs (restarts, K rule, | |
| per-slot seeds, selection rule), | |
| 3. the end-to-end impact measurement (Q3), | |
| 4. the labelling cap (Q2), | |
| 5. the pre-registered verdict thresholds (Q1). | |
| Nothing here is chosen at runtime. | |
| """ | |
| import protocol as P | |
| import sprotocol as S | |
| # ========================================================================== | |
| # provenance | |
| CENSUS_RAW_EID = S.CENSUS_RAW_EID # exp-1: raw GPT-2 census | |
| CENSUS_WGPT2_EID = S.CENSUS_WGPT2_EID # exp-6: whitened GPT-2 census | |
| CENSUS_WNEW_EID = S.CENSUS_WNEW_EID # exp-7: qwen adapters + packs | |
| SPARSE_EID = "exp_01m14v596yfz9vwrrevxgexsme" # exp-9: corpus conditions | |
| INTERP_EID = S.INTERP_EID # exp-8: label/validate pipeline | |
| IMPACT_EID = "exp_01m0rjj4e8fdraqcf9vtcs2hnv" # exp-2: off-context KL precedent | |
| # ========================================================================== | |
| # 1. the grid | |
| MODELS = ("gpt2", "qwen3-0.6b") | |
| CONDITIONS = ("pile", "code", "math", "chat") | |
| # The census's own representative blocks (round L/6, L/2, round 5L/6) — | |
| # a subset of every completed census's blocks and of exp-9's condition blocks. | |
| BLOCKS = {"gpt2": (2, 6, 10), "qwen3-0.6b": (5, 14, 23)} | |
| FLOOR_OF = dict(S.FLOOR_OF) | |
| TAGS = ("gpt2", "gpt2-randinit", "qwen3-0.6b", "qwen3-0.6b-randinit") | |
| BASE_OF = dict(S.BASE_OF) | |
| FRAMES = ("raw", "whit") | |
| # ========================================================================== | |
| # 2. the search | |
| # | |
| # Per slot: R_RESTARTS natural-token inits (census convention v0 = h - mu), | |
| # projected off the accepted set in the working frame, each ascending the | |
| # FROZEN amp objective (fastflow.lift_flow_batched mechanics: 200 steps, | |
| # batch 1024, per-batch top-10% on-set, Adam lr 0.05, clamp 6, batch-stream | |
| # seed 3) with a hard orthogonality constraint re-applied after every | |
| # optimizer step. Best-of-restarts by FIT-slice gate lift at the frozen | |
| # quantile; the winner occupies the slot and every later slot is searched in | |
| # its orthogonal complement. | |
| # | |
| # Frames: raw — conditioning bra = the direction itself, orthogonality in | |
| # raw coordinates; whit — conditioning bra = Sigma_s^{-1} v (the census's | |
| # whitened-bra sensitivity arm), orthogonality in whitened coordinates | |
| # (u = Sigma_s^{-1/2} v). Measurement is arm-matched, frozen: held-out gate | |
| # lift at LIFT_Q vs the block's own colored floor (mean + 3 SD, N=50, | |
| # seed 0) measured in the same arm. | |
| R_RESTARTS = 8 | |
| K_MULT = 8 # K_max = 8x the censused count per (cell, frame) | |
| K_CAP = 64 # capped at 64 directions | |
| K_MIN = 8 # floor so zero-count cells still get a curve | |
| SEQ_STEPS = P.FLOW_STEPS # 200, frozen | |
| SEQ_BATCH = P.FLOW_BATCH # 1024, frozen | |
| SEQ_Q = P.FLOW_Q # 0.1, frozen | |
| SEQ_LR = P.FLOW_LR # 0.05, frozen | |
| SEQ_CLAMP = P.FLOW_CLAMP # 6.0, frozen | |
| SEQ_FLOW_SEED = P.FLOW_SEED # 3: every slot sees the census flow's batch stream | |
| SELECT_Q = P.LIFT_Q # best-of-restarts on FIT gate lift, frozen quantile | |
| ORTHO_TOL = 1e-3 # acceptance guard: max |cos| to the accepted set | |
| def k_max(n_census): | |
| """The pre-registered K rule. Floor tags use the TRAINED model's count.""" | |
| return min(K_CAP, max(K_MIN, K_MULT * int(n_census))) | |
| def slot_seed(slot, frame): | |
| """Deterministic per-slot init seed (recorded in every record).""" | |
| return P.INIT_SEED * 100_000 + slot * 100 + (0 if frame == "raw" else 1) | |
| # ========================================================================== | |
| # 3. end-to-end impact per unit norm (Q3) | |
| # | |
| # Injection at the census steering site (resid_mid: qwen — attention output; | |
| # gpt2 — attn output, so resid_mid = x + attn + delta feeds both the MLP | |
| # branch and the ongoing residual). Every direction and every control is | |
| # injected at the SAME norm alpha_cell = IMPACT_ALPHA_MULT x the median over | |
| # the cell's sequential candidates of the top-q fit projection mean | |
| # (q = LIFT_Q) — a per-cell constant, so "impact per unit norm" comparisons | |
| # are rank-identical to impact at alpha_cell. Measured on a fixed batch of | |
| # held-out sequences against the un-injected baseline: | |
| # * next-token KL(base || injected), mean over positions >= 1; | |
| # * downstream activation change at the LAST block's MLP-input site: | |
| # rms ||delta h|| / rms ||h_base||. | |
| # Both signs of every direction are measured. Controls: the block's 50 | |
| # frozen colored directions and IMPACT_N_RANDOM random unit directions. | |
| IMPACT_N_SEQS = 64 | |
| IMPACT_SEQ_SEED = 0 | |
| IMPACT_N_RANDOM = 16 | |
| IMPACT_RANDOM_SEED = 123 | |
| IMPACT_ALPHA_MULT = P.STEER_ALPHA_MULT # 2.0, the census steering scale | |
| IMPACT_SUBBATCH = 16 | |
| # The cached baseline log-probs are the KL's reference distribution, so the | |
| # cache dtype sets the instrument's numerical noise floor: an fp16 cache put | |
| # it at 2.3e-6 nats (measured, smoke job 306006891842). fp32 costs ~5 GB on | |
| # the largest cell (64 seqs x 127 positions x 151936 vocab) and removes the | |
| # rounding term. | |
| IMPACT_CACHE_DTYPE = "float32" | |
| TOPK_OVERLAP = 10 # fixed-k overlap; also reported at k = n_census | |
| # ========================================================================== | |
| # 4. novelty and labelling (Q2) | |
| # | |
| # A recovered above-floor direction is NEW iff its best |cos| against the | |
| # model's entire known set — the Pile library (raw + whitened censuses, all | |
| # blocks) UNION exp-9's novel domain/tail axes — is below the line's identity | |
| # threshold. Orthogonal above-floor directions cannot double-count one known | |
| # axis at this bar (two orthogonal unit vectors cannot both have |cos| >= 0.9 | |
| # with the same axis). | |
| NOVEL_COS = S.NOVEL_COS # 0.9 | |
| LABEL_CAP_AXES = 400 # Stage A/B cap; stratified by (model, condition, | |
| # frame) proportionally, top held lift first | |
| LABEL_CATEGORIES = S.LABEL_CATEGORIES | |
| CATEGORY_DESC = S.CATEGORY_DESC | |
| # ========================================================================== | |
| # 5. pre-registered verdict thresholds (Q1) — rules in ../PROTOCOL-SEQ.md | |
| UNDERCOUNT_MULT = 2.0 # N_seq > 2 x N_census (pooled, trained cells) | |
| SMALL_MULT = 1.25 # N_seq <= 1.25 x N_census -> confirmed small ... | |
| SATURATION_FRAC = 0.10 # ... AND last-quarter slots contribute <= 10% | |
| FLOOR_EXCESS_FRAC = 0.5 # floor excess >= 50% of trained excess -> the | |
| # search manufactures counts; undercount verdict | |
| # unavailable (uninterpretable) | |
| ALPHA = 0.05 | |
| WILCOXON_ALT = "greater" | |
| BOOT_N = 10_000 | |
| BOOT_SEED = 42 | |
| # ========================================================================== | |
| # 6. smoke gates (gpt2, Pile, block 6) — thresholds; rules in PROTOCOL-SEQ.md | |
| SMOKE_MODEL = "gpt2" | |
| SMOKE_COND = "pile" | |
| SMOKE_BLOCK = 6 | |
| SMOKE_K_WHIT = 24 # truncated whit search in the smoke (budget) | |
| S1_COS = 0.9 # raw census axis re-found ... | |
| S1_WITHIN_SLOTS = 2 # ... within the first 2 accepted raw slots | |
| S1_LIFT_LO, S1_LIFT_HI = 0.5, 2.0 # matched slot's held lift vs stored median | |
| S2_FLOOR_RTOL = 0.15 # recomputed raw colored floor vs exp-1's stored | |
| # S4's null is judged against the instrument's own recomputation noise (an | |
| # unhooked forward re-scored against the cache) and against the weakest | |
| # control signal it must resolve, not against an absolute nats bar chosen | |
| # before that noise floor was known. | |
| S4_NULL_MULT = 3.0 # zero-injection KL <= 3x the recompute noise ... | |
| S4_NULL_ABS = 1e-7 # ... or below this, if recompute noise is ~0 | |
| S4_NULL_FRAC = 0.01 # ... and <= 1% of the colored-control median KL | |
| # Per-cell floor drift (PROTOCOL-SEQ.md section 5): a cell whose recomputed | |
| # colored floor differs from the stored census floor by more than this is | |
| # flagged and excluded from the pooled verdicts. | |
| FLOOR_DRIFT_RTOL = 0.15 | |