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wassnameandClaudypoo
fe6815a9c7
Replace the single forced-choice sampling reader (1 bit/call, truncated verbose models to empty at max_tokens=32, positionally biased) with a dense readout: rate every option 1-5 as JSON, N samples, binary items order-balanced to cancel positional bias, ratings mapped back to canonical order and normalized to a distribution. All of a model's calls fire concurrently (asyncio.gather over the async openrouter_wrapper; per_call=1 since providers ignore n>1). Each model carries a bootstrap 95% CI (over items + samples) drawn as error bars, so mushy models (deepseek-flash was near coin-flip) read as uncertain, not confident dots. One flaky provider is skipped, not fatal. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
226 lines
12 KiB
Python
226 lines
12 KiB
Python
"""Sampling readout for API models that do NOT expose token logprobs (OpenRouter / OpenAI-compatible).
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Chat-completions-only API models return no next-token distribution, so read.py's forced-slot logprob
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reader cannot touch them. Fallback (the "average of N responses" approach): SAMPLE N chat completions
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at temperature and take the empirical answer frequency as the per-(item, frame) categorical `p`.
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Why this stays comparable to the logprob reader: temperature-1 sampling is an unbiased estimator of
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the model's softmax categorical, so the empirical `p` -> the true next-token `p` as N grows, and
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E = sum_k k*p_k (readouts.expected_score) converges to the logprob E for the SAME model. Every
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downstream summary (per_item_categorical, reduce_ordinal/nominal, expected_score, entropy) is a pure
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function of `p`, so a sampled model drops onto the SAME map as a logprob model. N sets the
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resolution: SE on a p~=0.5 cell is ~0.5/sqrt(N) (N=10 -> 0.16, coarse; N=100 -> 0.05).
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What this CANNOT produce, by design (do not add it back):
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- logit_contrast C and logodds_agree LO: log of an empirical frequency has -inf on any unsampled
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token; smoothing those zeros would fabricate the very fine steer signal C/LO exist to measure.
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- full-vocab pmass_allowed: we cannot see mass on unsampled tokens. The sampling ANALOGUE we do
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report is the fraction of draws that PARSED to an allowed token -- the empirical refusal /
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off-format rate. At total collapse (nothing parses) `p` is NaN, matching read.py's
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NaN-at-collapse ("do not compare"), not an eps fallback.
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Requires OPENROUTER_API_KEY. temperature MUST be > 0: at temp 0 the N draws collapse to one argmax
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and E degenerates to an integer.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import re
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from math import inf
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import numpy as np
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from loguru import logger
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from openrouter_wrapper.retry import openrouter_request # stamina backoff on 429/provider/upstream errors
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from .instrument import Instrument, InstrItem
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from .read import build_user_content
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_ANSWER_SUFFIX = ("\n\nAnswer with ONLY one option from [{space}] -- a single token, nothing else: "
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"no words, no punctuation, no explanation.")
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def parse_answer(text: str, answer_space: list[str]) -> str | None:
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"""The EARLIEST-appearing answer_space token in `text` (token-boundaried so '3' is not caught
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inside 'x3y', and a longer token wins a tie at the same position). None if no allowed token
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appears -- a refusal or off-format draw, counted against the parse rate, never coerced."""
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best, best_key = None, (inf, 0)
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for a in answer_space:
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m = re.search(rf"(?<![0-9A-Za-z]){re.escape(a)}(?![0-9A-Za-z])", text)
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if m and (m.start(), -len(a)) < best_key:
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best_key = (m.start(), -len(a))
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best = a
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return best
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def _sample_texts(model: str, prompt: str, n_samples: int, temperature: float,
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max_tokens: int) -> list[str]:
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"""N completions via the openrouter_wrapper (stamina retry handles transient 429/provider/upstream
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errors), requesting up to 8 per call via `n` and topping up until N."""
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texts: list[str] = []
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while len(texts) < n_samples:
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# cap n at 4/call: a big n * max_tokens response is slow enough to trip httpx read timeouts
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# (which then exhaust the wrapper's retry budget); smaller requests are more reliable.
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payload = {"model": model, "messages": [{"role": "user", "content": prompt}],
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"temperature": temperature, "n": min(n_samples - len(texts), 4),
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"max_tokens": max_tokens}
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data = asyncio.run(openrouter_request(payload))
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texts.extend((c["message"].get("content") or "") for c in data["choices"])
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return texts
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def read_items_sampled(model: str, instr: Instrument, items: list[InstrItem], *,
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n_samples: int = 20, temperature: float = 1.0, max_tokens: int = 32,
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verbose_first: bool = False) -> list[dict]:
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"""Per-(item, frame) rows shaped for `instrument.per_item_categorical`: id, frame, lp, p,
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pmass_allowed, dimension, sign, human_label. `p` is the empirical answer frequency over
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`instr.answer_space`; `pmass_allowed` is the parse rate; `lp = log(p)` carries -inf on unsampled
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tokens ON PURPOSE so any C/LO consumer fails loudly instead of reading a fabricated number."""
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assert temperature > 0, "sampling readout needs temperature > 0; temp 0 collapses E to an integer"
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space = instr.answer_space
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A = len(space)
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idx = {a: k for k, a in enumerate(space)}
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out: list[dict] = []
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for it_n, it in enumerate(items):
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user = build_user_content(instr, it)
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prompt = user + _ANSWER_SUFFIX.format(space="/".join(space))
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texts = _sample_texts(model, prompt, n_samples, temperature, max_tokens)
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counts = np.zeros(A)
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for t in texts:
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a = parse_answer(t, space)
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if a is not None:
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counts[idx[a]] += 1
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n_parsed = int(counts.sum())
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pmass = n_parsed / len(texts)
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p = counts / n_parsed if n_parsed else np.full(A, np.nan) # NaN at collapse, by design
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with np.errstate(divide="ignore"):
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lp = np.log(p)
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out.append({
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"id": it.id, "frame": it.frame, "lp": lp, "p": p, "pmass_allowed": pmass,
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"dimension": it.dimension, "sign": it.sign, "human_label": it.human_label,
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"n_samples": len(texts), "n_parsed": n_parsed,
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"prompt": prompt, "texts": texts, # raw responses kept so a run's answers are auditable/saveable
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})
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if verbose_first and it_n == 0:
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logger.debug(
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f"\n=== TRACE read_api first item ({instr.name}, {model}, N={len(texts)}) ===\n"
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f"--- prompt ---\n{prompt}\n"
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f"--- first 3 raw replies ---\n{texts[:3]}\n"
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f"--- counts over {space} ---\n{counts.tolist()} parsed={n_parsed}/{len(texts)}\n"
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f"SHOULD: replies are a bare token in {space}; parse rate near 1.0 -> coherent. "
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f"ELSE the model is refusing / adding prose / the option set is off.\n")
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return out
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# --- rated readout (dense, positional-bias-controlled) ------------------------------------------
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_RATE_PROMPT = (
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"{question}\n\n"
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"Rate how strongly you personally agree with or endorse EACH of the following answers, on a "
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"scale of 1 (strongly reject) to 5 (strongly endorse):\n{legend}\n\n"
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'Respond with ONLY a JSON object mapping each answer\'s number to its 1-5 rating, e.g. '
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'{{"0": 2, "1": 5}}. Rate every answer, and output nothing but the JSON.')
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def _parse_ratings(text: str, n: int) -> dict[int, float] | None:
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"""The LAST brace-object in `text` parsed as {presented_label -> 1-5 rating}, or None if it is
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malformed / missing a label / out of range. LAST so a reasoning model that echoes the schema
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earlier doesn't win over its final answer."""
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objs = re.findall(r"\{[^{}]*\}", text)
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if not objs:
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return None
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try:
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raw = json.loads(objs[-1])
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except json.JSONDecodeError:
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return None
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out = {}
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for k in range(n):
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v = raw.get(str(k), raw.get(k))
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if v is None or not (1 <= float(v) <= 5):
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return None
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out[k] = float(v)
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return out
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def _rate_plan(items: list[dict], n_samples: int, per_call: int = 1) -> list[dict]:
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"""Flatten (item, presented-order, count) into a list of <=per_call requests. A binary item splits
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its draws between the two orders (positional-bias control); an ordinal item keeps natural order
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(shuffling "Never..Always" is nonsense). per_call=1: OpenRouter providers do NOT reliably honour
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n>1 (they return a single completion), so one request per sample -- fine, they fire concurrently."""
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plan = []
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for i, it in enumerate(items):
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opts, n = it["options"], it["n"]
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groups = [([0, 1], (n_samples + 1) // 2), ([1, 0], n_samples // 2)] if n == 2 \
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else [(list(range(n)), n_samples)]
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for perm, tot in groups:
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legend = "\n".join(f"{j}) {opts[perm[j]]}" for j in range(n))
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prompt = _RATE_PROMPT.format(question=it["question"], legend=legend)
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while tot > 0:
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k = min(tot, per_call); tot -= k
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plan.append({"i": i, "perm": perm, "prompt": prompt, "cnt": k})
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return plan
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def read_items_rated(model: str, items: list[dict], *, n_samples: int = 12, temperature: float = 1.0,
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max_tokens: int = 512, concurrency: int = 8, verbose_first: bool = False) -> list[dict]:
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"""Dense Likert readout: per item, ask the model to rate EVERY option 1-5 as JSON, N times, and
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normalize the mean rating to a per-option distribution `p`. Higher signal per call than a single
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forced choice, and positional bias is controlled by permuting the PRESENTED order of BINARY items
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(n==2) across samples then mapping ratings back to the canonical option order. All requests for the
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model fire CONCURRENTLY (asyncio.gather, capped at `concurrency`) so a 12-item panel is ~1 round
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trip, not 24 sequential ones.
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`items`: [{"id", "question", "options"(canonical), "n"}]. Returns per item: id, p (mean over valid
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samples, canonical order), p_samples (per-sample canonical p arrays for bootstrap CIs),
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pmass_allowed (valid-JSON fraction), prompt, texts. p is NaN at total parse collapse (do not
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compare), matching the logprob reader. A failed request (network) just drops its samples."""
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plan = _rate_plan(items, n_samples)
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async def run_all() -> list:
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sem = asyncio.Semaphore(concurrency)
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async def call(req):
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async with sem:
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payload = {"model": model, "messages": [{"role": "user", "content": req["prompt"]}],
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"temperature": temperature, "n": req["cnt"], "max_tokens": max_tokens}
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data = await openrouter_request(payload)
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return [(c["message"].get("content") or "") for c in data["choices"]]
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return await asyncio.gather(*(call(r) for r in plan), return_exceptions=True)
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results = asyncio.run(run_all())
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agg = {i: {"p_samples": [], "texts": [], "prompt": ""} for i in range(len(items))}
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n_fail = 0
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for req, res in zip(plan, results):
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i, n, perm = req["i"], items[req["i"]]["n"], req["perm"]
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agg[i]["prompt"] = req["prompt"]
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if isinstance(res, Exception):
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n_fail += 1
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continue
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for text in res:
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agg[i]["texts"].append(text)
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rated = _parse_ratings(text, n)
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if rated is None:
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continue
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r_canon = np.zeros(n)
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for j in range(n):
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r_canon[perm[j]] = rated[j] # map presented label -> canonical option
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agg[i]["p_samples"].append(r_canon / r_canon.sum())
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if n_fail:
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logger.warning(f"{model}: {n_fail}/{len(plan)} rating calls failed (network) -> fewer samples")
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out = []
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for i, it in enumerate(items):
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ps = agg[i]["p_samples"]
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n = it["n"]
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p = np.mean(ps, axis=0) if ps else np.full(n, np.nan)
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out.append({"id": it["id"], "p": p, "p_samples": [x.tolist() for x in ps],
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"pmass_allowed": len(ps) / n_samples, "n_samples": n_samples,
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"prompt": agg[i]["prompt"], "texts": agg[i]["texts"]})
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if verbose_first and i == 0:
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logger.debug(
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f"\n=== TRACE read_items_rated first item ({model}, N={n_samples}) ===\n"
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f"--- prompt ---\n{agg[i]['prompt']}\n"
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f"--- first 2 raw replies ---\n{agg[i]['texts'][:2]}\n"
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f"--- mean p over {it['options']} ---\n{np.round(p, 3).tolist()} valid={len(ps)}/{n_samples}\n"
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f"SHOULD: replies are a bare JSON dict of 1-5 ratings; valid rate near 1.0 -> coherent. "
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f"ELSE the model is refusing / adding prose / max_tokens too small (empty content).\n")
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return out
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