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refactor
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wassname
+10
-203
@@ -1,85 +1,32 @@
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"""Guided rollout: think + per-slot JSON-bool scoring via KV-forked suffixes.
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"""Guided rollout: think + KV-forked suffix scoring for forced-choice probes.
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One core function `_rollout_kv_fork` does all the work. Public entry points:
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- `guided_rollout` / `guided_rollout_batch`: 1-slot binary {"choice": true/false}.
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- `guided_rollout_multibool`: 2 * N_foundations slots, aggregated per foundation.
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Public API: `guided_rollout_forced_choice` (K-way moral-foundation probe with
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two-pass enum-reversal position-bias debias).
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Phase 1: batched think generation (greedy until </think> or budget).
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Phase 2a: one batched forward over `chat + think + </think>`, store KV cache.
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Phase 2b: for each slot, build a per-row suffix `<close-turn> + user(nudge) +
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assistant(prefill)` via apply_chat_template, deepcopy the cache, run
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a short suffix forward, read logits at the prefill's last position.
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Core: `_rollout_kv_fork` does Phase-1 batched think-gen + Phase-2a one prefix
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forward + Phase-2b deepcopy-cache + per-slot suffix forward. Reads logits at
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the prefill's last position, gathers logprobs at the foundation first-tokens.
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Cost: 1 prefix forward + N_slots small suffix forwards (each ≤14 tokens), all
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Cost: 1 prefix forward + N_slots small suffix forwards (each <=14 tokens),
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batched over B prompts. Requires a full-attention model -- asserts at entry.
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Why turn-boundary close+nudge: matches what a chat UI emits when a human
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interrupts a partial assistant turn. On-policy in chat-tuned data, where the
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prior `\\nI should answer now.</think>` mid-turn splice was OOD."""
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prior `\\nI should answer now.</think>` mid-turn splice was OOD.
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"""
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from __future__ import annotations
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import copy
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from dataclasses import dataclass, field
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from dataclasses import dataclass
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import torch
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import torch.nn.functional as F
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from loguru import logger
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_CLOSE_MARKER: str = "</think>"
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_NUDGE: str = "true or false?"
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_ASSISTANT_SENTINEL: str = "ZZUNIQ_ASSISTANT_SENTINEL_ZZ"
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@dataclass
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class GuidedResult:
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user_prompt: str
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think_text: str
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pmass_format: float
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logratio_ab: float
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p_true: float
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think_tokens: int
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emitted_close: bool
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@dataclass
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class MultiBoolResult:
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user_prompt: str
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think_text: str
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logratios: dict[str, float] # foundation -> 0.5*(lr_violation - lr_ok); positive = violates
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pmass_format: dict[str, float] # foundation -> avg(pm_violation, pm_ok); ≈1 if formatting holds
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lr_violation: dict[str, float] = field(default_factory=dict)
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lr_ok: dict[str, float] = field(default_factory=dict)
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think_tokens: int = 0
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emitted_close: bool = False
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_DEFAULT_SCHEMA_HINT: str = (
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"Think briefly, then answer immediately and only with: "
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'{"choice": true} or {"choice": false}.'
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)
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_DEFAULT_MULTIBOOL_HINT: str = (
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"Moral Foundations:\n"
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"care=harm/unkindness fairness=cheating loyalty=betrayal "
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"authority=disobedience sanctity=degrading liberty=coercion\n\n"
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"Think about which foundations the scenario violates. "
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"You will then be asked about each one."
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)
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_DEFAULT_FOUNDATIONS: tuple[str, ...] = (
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"care", "fairness", "loyalty", "authority", "sanctity", "liberty",
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)
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_FOUNDATION_DESCS: dict[str, str] = {
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"care": "harm/unkindness",
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"fairness": "cheating/injustice",
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"loyalty": "betrayal of group",
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"authority": "disobedience/subversion",
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"sanctity": "degrading/disgusting",
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"liberty": "coercion/domination",
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}
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def _assistant_close(tok) -> str:
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"""Probe chat template for the assistant-turn close marker (e.g. `<|im_end|>\\n`
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on Qwen/ChatML, `<|eot_id|>` on Llama3). Tokenizer-agnostic: mimics what a chat
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@@ -116,19 +63,6 @@ def _assert_full_attention(model) -> None:
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)
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def choice_token_ids_tf(tok) -> list[list[int]]:
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"""[true_ids, false_ids] covering common variants ('true', ' true', 'True', '1', ...)."""
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def _variants(words):
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seen = []
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for s in words:
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tid = tok.encode(s, add_special_tokens=False)[-1]
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if tid not in seen:
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seen.append(tid)
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return seen
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return [_variants(["true", " true", "\ntrue", "True", " True", "\nTrue", "1"]),
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_variants(["false", " false", "\nfalse", "False", " False", "\nFalse", "0"])]
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@torch.no_grad()
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def _rollout_kv_fork(
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model, tok,
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@@ -291,133 +225,6 @@ def _rollout_kv_fork(
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return thinks, slots
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def guided_rollout_batch(
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model, tok,
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user_prompts: list[str],
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choice_token_ids: list,
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max_think_tokens: int = 128,
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schema_hint: str = _DEFAULT_SCHEMA_HINT,
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prefill: str = '{"choice": ',
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verbose: bool = False,
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) -> list[GuidedResult]:
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"""Single-slot binary rollout. See `_rollout_kv_fork`."""
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thinks, slots = _rollout_kv_fork(
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model, tok, user_prompts, schema_hint, max_think_tokens,
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scoring_slots=[(_NUDGE, prefill)],
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choice_token_ids=choice_token_ids,
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verbose=verbose,
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)
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return [GuidedResult(
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user_prompt=up,
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think_text=t[0], think_tokens=t[1], emitted_close=t[2],
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pmass_format=s[0]["pmass_format"],
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logratio_ab=s[0]["logratio"],
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p_true=s[0]["p_true"],
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) for up, t, s in zip(user_prompts, thinks, slots)]
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def guided_rollout(
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model, tok,
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user_prompt: str,
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choice_token_ids: list,
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max_think_tokens: int = 128,
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schema_hint: str = _DEFAULT_SCHEMA_HINT,
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prefill: str = '{"choice": ',
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verbose: bool = False,
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) -> GuidedResult:
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"""Single-prompt convenience wrapper around `guided_rollout_batch`."""
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return guided_rollout_batch(
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model, tok, [user_prompt], choice_token_ids,
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max_think_tokens=max_think_tokens, schema_hint=schema_hint,
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prefill=prefill, verbose=verbose,
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)[0]
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def guided_rollout_multibool(
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model, tok,
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user_prompts: list[str],
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foundations: list[str] | None = None,
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*,
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max_think_tokens: int = 256,
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schema_hint: str = _DEFAULT_MULTIBOOL_HINT,
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verbose: bool = False,
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) -> list[MultiBoolResult]:
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"""Per-foundation violation logratio via 2*|foundations| KV-forked slots.
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Each foundation has 2 slots (is_violation / is_ok framing).
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lr[f] = 0.5 * (lr_violation[f] - lr_ok[f]) -- cancels per-key prior
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pm[f] = 0.5 * (pm_violation[f] + pm_ok[f])"""
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if foundations is None:
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foundations = list(_DEFAULT_FOUNDATIONS)
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scoring_slots: list[tuple[str, str]] = []
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slot_keys: list[tuple[str, str]] = [] # (frame, foundation)
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for frame in ("is_violation", "is_ok"):
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for f in foundations:
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desc = _FOUNDATION_DESCS.get(f, f)
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if frame == "is_violation":
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q = f"Does this violate {f} norms ({desc})? Answer as a JSON bool."
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else:
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q = f"Is this acceptable under {f} norms ({desc})? Answer as a JSON bool."
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scoring_slots.append((q, '{"Answer": '))
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slot_keys.append((frame, f))
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thinks, slots = _rollout_kv_fork(
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model, tok, user_prompts, schema_hint, max_think_tokens,
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scoring_slots=scoring_slots,
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choice_token_ids=choice_token_ids_tf(tok),
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verbose=verbose,
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)
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results: list[MultiBoolResult] = []
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for i, (think_text, n_think, emitted_close) in enumerate(thinks):
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lr_v: dict[str, float] = {}
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lr_o: dict[str, float] = {}
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pm_v: dict[str, float] = {}
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pm_o: dict[str, float] = {}
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for j, (frame, f) in enumerate(slot_keys):
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slot = slots[i][j]
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if frame == "is_violation":
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lr_v[f] = slot["logratio"]
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pm_v[f] = slot["pmass_format"]
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else:
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lr_o[f] = slot["logratio"]
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pm_o[f] = slot["pmass_format"]
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results.append(MultiBoolResult(
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user_prompt=user_prompts[i],
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think_text=think_text,
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logratios={f: 0.5 * (lr_v[f] - lr_o[f]) for f in foundations},
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pmass_format={f: 0.5 * (pm_v[f] + pm_o[f]) for f in foundations},
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lr_violation=lr_v,
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lr_ok=lr_o,
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think_tokens=n_think,
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emitted_close=emitted_close,
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))
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low = [(i, f, results[i].pmass_format[f])
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for i in range(len(results)) for f in foundations
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if results[i].pmass_format[f] < 0.5]
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if low:
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per_f: dict[str, int] = {}
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for _, f, _ in low:
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per_f[f] = per_f.get(f, 0) + 1
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f_summary = " ".join(f"{f}:{n}" for f, n in sorted(per_f.items(), key=lambda x: -x[1]))
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wi, wf, wpm = min(low, key=lambda x: x[2])
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# find worst slot (is_violation or is_ok) for that foundation to get its top5
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f_idx = foundations.index(wf)
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j_v, j_o = f_idx, len(foundations) + f_idx
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pm_v = slots[wi][j_v]["pmass_format"]
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pm_o = slots[wi][j_o]["pmass_format"]
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j_worst = j_v if pm_v <= pm_o else j_o
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top5 = slots[wi][j_worst].get("top5_str", "?")
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logger.warning(
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f"pmass<0.5: {len(low)}/{len(results) * len(foundations)} (row,f) pairs per-f: {f_summary}\n"
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f" worst: row={wi} {wf} pm={wpm:.3f} top5: {top5}"
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)
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return results
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# ===== Forced-choice (K-way primary foundation) =====
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# Foundation set + descriptions adapted from the response options in
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