Whole-library review (deepseek-v4-pro) flagged tinymfv as not-yet-ready as a shared
dep. Fixes for the parts I agreed with:
- lazy `maps` import via module __getattr__ so `import tinymfv` stays headless/fast
(no forced matplotlib) for numeric-only consumers; `tinymfv.maps.*` still works.
- trim __all__ to the front door (entrypoints + types + data api); plumbing stays
importable but out of `import *`.
- delete dead code: reduce_nominal + REDUCERS (evaluate folds its profile inline),
expected_value, HF_REPO, ROOT, _DEFAULT_FORCED_HINT.
- type administer's return as a TypedDict (AdministerResult/ItemRow/ItemFrameRow) so
the schema is documented + checkable without reading source; still a plain dict at
runtime (zero consumer churn).
- maps.plot_ipsative_pca: parametrize the legend labels (defaults preserve output)
and rename hon/dis -> pos/neg so a non-honesty steer gets a correct legend.
- drop 'canary' jargon and panel/review-# archaeology from comments.
Verified: `import tinymfv` no longer loads matplotlib; lazy maps still resolves;
experiment mfq2 smoke green through the typed administer.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Default eval logs were dominated by DEMO A/B full prompt+generation dumps at every
monitor step. verbose is now a level: 1 (default) prints the one-line aux stats plus
the free-reasoning generation collapsed to 64 chars (no prompt), bracketed by blank
lines so it stands apart from the steer demos; 2 reproduces the full first-row trace,
profile table, and complete DEMO B. bool callers still work (True->1, False->0).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The forced-choice readout prefills the answer slot to read calibrated logprobs,
so it shows no real reasoning -- at think=1 its trace is just prompt + a token +
slot. Add free_generation_demo(): one bs=1 generation that lets the model think
to completion and answer naturally on a single vignette, same vignette+schema as
the readout. evaluate() now prints both (DEMO A relabelled, DEMO B new) via
loguru when verbose, and returns them in result['demos'] so callers get the text
without return_per_row. Free think budget = min(2048, max(512, max_think*batch))
-- bs=1 frees batch memory, floored so even think=1 reasons, capped for big batches.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Per token-efficient-logging. evaluate(verbose=True) now: prints the first
row's full prompt+think+answer-slot trace (special tokens, promoted from DEBUG
to INFO, gated to first batch), the model-vs-human profile table, and a
one-line aux-stats dict. Set verbose=False inside sweeps.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Chance-corrected, argmax-only companion to mean_nll: moves when the answer
flips, not when confidence shifts. 0 = base-rate guessing, so it exposes
majority-class models that top1_acc flatters. Same flip-informedness family
as steering-lite's surgical informedness, anchored on the human argmax here.
README also points at the paired training set moral_stories_foundations.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Two paired changes the previous commit should have included.
skip_special_tokens kwarg on guided_rollout_forced_choice and evaluate()
threads into tok.decode for gen_text / gen_text_rev. Default False (return
the raw stream with </think>, chat markers, etc.) matches the "return all
the free things" principle. Callers who want stripped output strip
themselves.
emitted_close now uses a token-id match on gen_ids (`(gen_ids ==
think_end_id).any()`) instead of substring on the decoded text. On models
that mark </think> as a special token, the old substring check would
silently always return False when skip_special_tokens=True stripped it.
Qwen3 currently does NOT mark </think> as special so the bug is latent
there, but the fix is strictly more robust and decouples the detection
from the decode flag.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Lets callers ask for N sampled think rollouts per direction instead of one
greedy trace. Per direction we Bayesian-model-average the answer logprobs
across the N samples (logsumexp_n lp - log N) before the fwd/rev average.
Raw per-sample [N, K] logprob matrices stay on the result as
lp_fwd_samples / lp_rev_samples so callers can re-aggregate (log-pooling,
majority vote, etc.).
gen_text and gen_text_rev are now always list[str] of length N (even at
N=1). think_tokens, think_tokens_rev, emitted_close, emitted_close_rev are
length-N lists. At N=1 the BMA is the identity and headline numbers match
the prior greedy path bit-for-bit.
Default max_think_tokens lowered 256 -> 64 for faster default eval (was
expensive overhead on small models that rarely emit </think> anyway).
README updated to match.
Phase 1.5 / Phase 2 already operated per-row, so they extend to B*N
expanded rows without change. Added an explicit assert that the HF
num_return_sequences expansion matches len(user_prompts) * n_samples.
Smoke-tested on Qwen3-0.6B: greedy N=1 matches BMA identity; N=4
temperature=0.7 returns [4, 7] sample matrices and finite pmass; guard
raises if n_samples>1 with temperature=0. evaluate() throughput log
extended to sum fwd+rev think tokens over all samples.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The "negation" mention in pyproject + __init__ docstring was stale —
the actual second pass is internal fwd+rev enum-order debias inside
guided_rollout (position-bias cancellation), not a negation framing.
self_violate is not in Clifford 2015 classic (other-violation only).
Default `evaluate(..., conditions=...)` to ("other_violate",); callers
who want both can opt in explicitly. Halves walltime per eval.
CONDITIONS in data.py still lists both (other_violate, self_violate)
as available — the change is only the evaluate() default.
Old API returned both `think_text` (stripped at </think>) and
`gen_text_full` (everything) — confusing dual field where one was a
strict subset of the other. Library should never silently drop info;
callers can split on `_CLOSE_MARKER` themselves (one line) if they
want the pre-close subset.
Rename:
think_text -> gen_text (forward-frame full decoded gen)
think_text_rev -> gen_text_rev (reverse-frame full decoded gen)
gen_text_full -> dropped (redundant with new gen_text)
Internal `_rollout_kv_fork` now returns 3-tuples
(gen_text, n_think, emitted_close) instead of 4-tuples; suf_ids_for
closure updated. per_row dict in eval.py exposes gen_text + gen_text_rev.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drop force_min_new_tokens — banning EOS to force a 2048-token think
generates ~250 tokens of real reasoning + </think>, then ~1800 tokens of
post-EOS sycophancy spew. Measuring pmass at the forced-answer slot with
that spew in the KV cache corrupted the coherence signal.
Replace with per-sample Phase 1.5: find each sample's first </think> in
phase1_ids, slice the batched DynamicCache (B, n_heads_kv, seq, d_head)
down to one sample × end_pos seq via _slice_pkv_one. The Phase 2 suffix
forward then runs per-sample over the rewound cache so the answer slot
sees only the coherent thinking trace.
GQA-safe (slices batch + seq, not heads). Phase 1 stays batched, Phase
1.5/2 loop adds ~5-10% wall-clock for the bs=1 forward.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Refactor _rollout_kv_fork from 3 phases (gen → prefix-forward →
prefix+suffix-forward) to 2 phases (gen → suffix-only-forward with
cached pkv). The function name finally matches what it does again.
Phase 1: generate(..., return_dict_in_generate=True) captures past_key_values
for [left-pad, prompt, think, eos-pad]. Same as before; generation already
used cache internally.
Phase 2 (new): per slot, forward ONLY the suffix tokens (close + interrupt
+ nudge + prefill, ~10-30 tokens) with past_key_values=pkv. Logits come
out at suffix positions only; pick the last real one. The attention mask
spans cached prefix + new suffix; pad_id positions get mask=0.
Drops:
- Phase 2a entirely (the prefix re-forward that computed nll_prompt)
- nll_prompt from ForcedChoiceResult, eval.py per_row, eval output dict
- All the sp_per_row / sp_ids_per_row retokenisation gymnastics + boundary-
merge edge cases (lines 107-129 in the old code) — no more text round-trip
- ~115 lines net
Per-row prompt-NLL was a free diagnostic from the prefix forward; with the
forward gone it would cost a dedicated extra forward. pmass_format is the
stronger coherence canary anyway (per AGENTS.md "Coherence signal hierarchy"
and the bidirectional c-scan walkback in 03b_train).
Speed: marginal (saves ~2s out of ~36s per batch on 27B nf4) — the win is
simpler code, not throughput. Module docstring updated to reflect 2-phase
reality.
Smoke (downstream weight-steering-lite repo, on tiny-random) PASS.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
use_cache=False on Phase 2a/2b single forwards saved zero compute — the
flag was leftover from the multi-slot KV-fork era (commit ada854c)
that was refactored away (d34dbfa). One forward per call, no cache to
reuse. Cleaner without it; behaviour identical.
eval.py: add `think_tokens` + `emitted_close` to per_row dict (data was
already in ForcedChoiceResult, just not captured). Log distribution
after eval: median/p75/p90/p99/max + emitted_close count. Lets us see
the actual think budget used vs max_think_tokens cap, to decide if the
512 bump (from 128) is paying for itself or can revert.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ForcedChoiceResult now carries pmass_format (sum prob mass on the K
foundation answer tokens at the JSON answer slot, averaged across fwd
and rev framings). eval.py aggregates it as mean_pmass_format in both
the headline return dict and the info subdict, and propagates per-row
for sweep/audit consumers.
Direct coherence canary: drops when steering pushes the model toward
non-foundation tokens (gibberish, refusal, format collapse). Independent
of which foundation is picked — complementary to top1_acc (label-
agreement; intentional target shift) and mean_nll_prompt (teacher-forced
prompt nll; falls under steering even when generations break).
Surfacing this lets downstream callers (weight-steering-lite walkback,
report dashboards) gate on actual coherence rather than misusing top1
as a budget.
Replace 3 parallel scoring paths (guided_rollout / _batch / _multibool)
with a single internal `_rollout_kv_fork` core: phase-1 batched think,
one cached prefix forward, N forked suffix forwards (one per scoring
slot). Binary case is just N_slots=1.
Drops from GuidedResult (no callers): answer_text, raw_full_text,
rep_ratio_think, prompt_nll. eval.py updated accordingly. _ngram_rep_ratio
and _scoring_text helpers removed -- their logic folded into the core.
Verbose=True now logs the full conversation (prefix + suffix the model
sees) plus a 64-token free-form generate continuation, so format issues
are obvious from one slot's log.
File shrinks from ~600 to ~340 lines. smoke_batch_parity passes (bf16
max Δp_true=0.098 within 0.20 tol; pre-existing batched-greedy drift).
Per-token NLL over the scoring text is free since we already compute
full-sequence logits in guided_rollout / guided_rollout_batch (just
gather instead of slicing [:, -1]). Higher NLL = model less coherent
on this prompt under whatever steering is attached.
eval.py logs pmass/ppl/nll aggregate at end of guided eval so the
next run shows degradation at a glance instead of buried tqdm noise.
analyse() now exposes raw_nll and info.prompt_nll_mean.
Was emitting `logger.warning("pmass=0.XX<0.9 — top-5: ...")` per-row, which
spammed the log heavily during heavy-steering eval (many rows go OOD at once).
Now collects all low-pmass rows in the batch and emits one summary line with
the worst-case top-5, e.g.:
pmass<0.9 on 7/16 rows in this batch; worst=0.412 top-5: '1'=0.40, ...
Same diagnostic signal, ~16× fewer log lines per batch.
Sequential eval was the bottleneck (~12s/vignette × 131 = 27 min/pass; with
bidirectional ±C × 14 methods, that projected to ~17 h). Three model calls
per row (phase1 generate, scoring forward, cosmetic continuation) became one
phase1 + one scoring per *batch*; continuation generate dropped (callers
only use p_true + pmass_format).
Parity smoke (scripts/smoke_batch_parity.py): float32 is bit-exact (max
Δp_true=0.0000 over 16 prompts, 4.3× speedup at limit=4). bf16 drifts on
individual rows — greedy argmax flips at near-tie tokens then phase1
diverges — but pmass agrees within 0.03 (scoring forward correct) and
aggregates over 131 vignettes will average out the per-row noise.
Other changes shipped in this commit:
- core.py: analyse() now returns raw_pmass dict alongside raw p_true (callers
needed per-(vid,cond,frame) pmass for diagnostic warnings).
- guided.py guided_rollout: warn + log top-5 when pmass<0.9 (catches OOD
steering / format-broken vignettes without a separate audit pass).
- eval.py: pre-tokenize a sample to log expected prompt+cache budget so OOM
is predictable from the SHOULD line; group items by frame so each batch
shares schema_hint + prefill.
- Refactored evaluation logic in `src/tinymfv/eval.py` to support a new `max_think_tokens` parameter, allowing for a fixed continuation budget before scoring.
- Introduced `guided_rollout` function in `src/tinymfv/guided.py` to handle the generation of multiple tokens and scoring based on a deterministic continuation.
- Updated the CLI in `scripts/03_eval.py` to accept `--max-think-tokens` argument for controlling the token budget during evaluation.
- Created a new specification document `docs/spec/20260501_n_token_eval.md` outlining the goals, requirements, and tasks for the N-token evaluation feature.
- Simplified the record creation in `scripts/02_rewrite.py` by extracting logic into a new `make_rec` function for better code organization.
