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https://github.com/wassname/isokl_steering_calibration.git
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wassname
451 lines
21 KiB
Python
451 lines
21 KiB
Python
"""End-to-end runner for one (model, method, seed, window) cell.
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Artefacts (per cell, under outputs/<run_id>/):
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calib.json c_star + run metadata (model, method, seed, layer, window).
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history.json bisection history (without per-token KL arrays).
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trajectory.json per-token KL on EVAL_PROMPTS for each alpha:
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per_t_p95_kl[alpha]: list[T]
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per_prompt_per_t_kl[alpha]: list[N_prompts][T]
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pmass.json forked-answer probability mass at fork_points:
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pmass[alpha]: yes/no reasoning prompts (legacy probe)
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pmass_eval[alpha]: SAME prompts as trajectory.json (paired w KL)
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gen_lens_qa[alpha], gen_lens_eval[alpha]: T per rollout
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(use for right-censoring -- NaN at t > T means rollout
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EOS'd before that fork, NOT a measurement failure).
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debug_first[alpha]: gen_text + per-fork pmass for the
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FIRST qa & eval prompt (sanity-check; see survival.py).
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pmass_eval is the metric to use when you want a per-trajectory
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coherence signal aligned with the KL trajectory.
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results.csv one row per alpha with kl_p95/mean/max.
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Flow:
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1. Load model + tokenizer; set seed.
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2. Build pos/neg pair (cheap content-vs-refusal); train Vector v.
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3. Calibrate iso-KL: bisect coeff so per-token kl_p95 hits target_kl (default 1)
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over T=window tokens on CALIB_PROMPTS.
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4. For each alpha, set coeff=alpha*c_star and:
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a) measure_kl on EVAL_PROMPTS -> per_prompt_per_t_kl trajectories.
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b) (--compute-pmass) yes/no questions: rollout, then at each fork point
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prefill `\n{"choice": ` and read mass on {true/True/ true/.../1/ 1}
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and {false/.../0}. Skip with --skip-pmass-qa.
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c) (--compute-pmass) repeat (b) on EVAL_PROMPTS -> pmass_eval, paired
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with the KL trajectory on the same rollouts. This is the honest
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coherence signal; KL is largely a calibration tautology at alpha=1.
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Fork-point modes:
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--fork-step N linear: {0, N, 2N, ..., window}
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--fork-log log-spaced: {0, 1, 2, 4, 8, ..., window} (with --n-log-forks)
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KV-cache:
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Rollouts use generate(..., return_dict_in_generate=True). branch_pmass
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uses full-recompute by default (cache reuse adds ~10% bf16 noise; verified
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by scripts/audit_pmass.py). Pass use_rollout_cache=True only if you accept
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the noise.
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Usage (tyro):
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python scripts/run_cell.py --model Qwen/Qwen2.5-0.5B-Instruct --method mean_diff \
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--seed 0 --window 512 --compute-pmass --skip-pmass-qa --fork-log
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"""
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from __future__ import annotations
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import json
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import os
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import subprocess
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import sys
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from dataclasses import dataclass, field
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from pathlib import Path
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import torch
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import tyro
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from loguru import logger
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from iso_kl_figure import (
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SteeringConfig, MeanDiffC, PCAC, DirectionalAblationC,
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train, calibrate_iso_kl, measure_kl,
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)
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from iso_kl_figure.branch_pmass import branch_pmass, collect_choice_token_ids
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from iso_kl_figure.calibrate import _eos_token_ids
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from iso_kl_figure.target import _get_blocks
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CALIB_PROMPTS = [
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"The eiffel tower is in Paris.",
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"Tell me a greentext story about a small village during the Martian carrot bubble.",
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"Walk me through computing the integral of x^2 from 0 to 1.",
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"Briefly describe how plate tectonics shape mountain ranges.",
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]
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# EVAL prompts are yes/no questions paired with schema -- same contract as
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# tinymfv/guided.py so branch_pmass at any fork can score format-compliance.
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# The model is steered into <think>\n at the prompt boundary; at fork t we
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# inject "\nI should answer now.</think>\n{\"choice\": " and read p(true|false).
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EVAL_PROMPTS = [
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"Is the sky blue primarily because of Rayleigh scattering of sunlight?",
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"Does Hamlet die at the end of Shakespeare's play 'Hamlet'?",
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"Is excessive money supply growth one cause of inflation in modern economies?",
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"Do octopuses have three hearts and blue copper-based blood?",
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"Does photosynthesis use light energy to convert CO2 and water into glucose?",
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"Was the Eiffel Tower originally built as the entrance arch for the 1889 World's Fair?",
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"Can plate tectonic collisions form mountain ranges over geological time?",
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"Is a circular import one possible cause of a Python script crashing on import?",
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"Does water boil at a lower temperature at higher altitudes due to reduced atmospheric pressure?",
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"Is the mitochondrion commonly described as the powerhouse of the eukaryotic cell?",
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"Did the Apollo 11 mission land humans on the Moon in July 1969?",
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"Is Mount Everest the tallest mountain on Earth measured from base to peak?",
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"Can a closure in JavaScript capture variables from its enclosing lexical scope?",
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"Does the human body have exactly 206 bones in adulthood?",
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"Is sound generally faster in solids than in air at the same temperature?",
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"Was the Great Wall of China built primarily as a single continuous wall in one dynasty?",
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"Does relativity predict that time dilates for an observer moving at relativistic speeds?",
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"Is DNA replication semi-conservative, with each daughter molecule keeping one parent strand?",
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"Did the Roman Empire formally split into Western and Eastern halves in the 4th century CE?",
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"Is gradient descent guaranteed to find the global minimum of an arbitrary non-convex loss?",
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"Does the moon always show the same face to Earth because of tidal locking?",
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"Was Shakespeare a contemporary of Queen Elizabeth I of England?",
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"Is the Pacific Ocean larger in surface area than the Atlantic Ocean?",
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"Does a transformer architecture rely on self-attention rather than recurrence for sequence modeling?",
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]
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# pmass diagnostic prompts share the same schema as EVAL above.
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_SCHEMA = (
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'Reason for at least 80 words before the final answer. Then answer only with '
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'{"choice": true} or {"choice": false}. If you use numeric JSON, use 1 for true and 0 for false.'
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)
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_QUESTIONS = [
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"Is the Eiffel Tower located in Paris, France?",
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"Do dolphins breathe air through lungs rather than gills?",
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"Was Albert Einstein born in Germany?",
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"Can humans naturally see ultraviolet light?",
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"Did the Roman Empire fall in 476 CE?",
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"Are tomatoes botanically classified as fruits?",
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"Is the speed of light approximately 300,000 km/s?",
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"Do octopuses have three hearts?",
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]
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PREFILL_STR = '\n{"choice": '
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# Pos/neg pair: a generic content-vs-refusal direction. Cheap, not the main
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# point -- the figure tests calibration *behaviour*, not direction quality.
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POS_NEG = [
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("Sure, here is the answer:", "I cannot help with that."),
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("Here are some details:", "Sorry, I can't assist with that."),
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("Of course, let me explain.", "I won't be able to help."),
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("Yes, that makes sense.", "No, I have to decline."),
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]
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METHOD_MAP = {
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"mean_diff": MeanDiffC,
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"pca": PCAC,
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"directional_ablation": DirectionalAblationC,
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}
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@dataclass
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class Args:
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model: str
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method: str
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seed: int = 0
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window: int = 50
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run_id: str = ""
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layer_frac: float = 0.6
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target_kl: float = 1.0
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out_root: str = "outputs"
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device: str = "cuda"
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dtype: str = "bfloat16"
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alphas: tuple[float, ...] = (0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 4.0)
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fork_step: int = 5
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fork_log: bool = False # log-spaced fork points {0,1,2,4,8,...,window}
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n_log_forks: int = 14 # number of log-spaced forks (incl. 0)
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compute_pmass: bool = False
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skip_pmass_qa: bool = False # skip yes/no pmass loop, only do paired pmass_eval
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render_figs: bool = False # render single-run survival + spaghetti + KL pngs
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render_threshold: float = 0.95
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def _set_seed(s: int):
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import random
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import numpy as np
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random.seed(s); np.random.seed(s); torch.manual_seed(s)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(s)
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def _render_chat(tok, msgs: list[dict[str, str]], add_generation_prompt: bool) -> str:
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if tok.chat_template is None:
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rendered = []
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for msg in msgs:
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role = msg["role"]
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content = msg["content"]
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if role == "user":
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rendered.append(f"User: {content}\n")
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elif role == "assistant":
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rendered.append(f"Assistant: {content}")
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else:
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raise ValueError(f"plain prompt renderer does not support role={role!r}")
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if add_generation_prompt:
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rendered.append("Assistant: ")
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return "".join(rendered)
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return tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=add_generation_prompt)
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def _build_guided_prompt(tok, user_text: str, schema_hint: str = _SCHEMA) -> str:
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"""Match tinymfv/guided.py: chat template + '<think>\\n' suffix so the model
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is in thinking mode at every fork point. branch_pmass detects this and
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splices '\\nI should answer now.</think>{prefill}' before scoring."""
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full_user = f"{user_text}\n\n{schema_hint}" if schema_hint else user_text
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msgs = [{"role": "user", "content": full_user}]
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p = _render_chat(tok, msgs, add_generation_prompt=True)
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return p + "<think>\n"
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def main(a: Args):
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if not a.run_id:
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a.run_id = f"{a.model.split('/')[-1]}_{a.method}_s{a.seed}_w{a.window}"
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out_dir = Path(a.out_root) / a.run_id
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out_dir.mkdir(parents=True, exist_ok=True)
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logger.add(out_dir / "run.log", level="INFO")
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_set_seed(a.seed)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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dtype = getattr(torch, a.dtype)
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tok = AutoTokenizer.from_pretrained(a.model)
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if tok.pad_token_id is None:
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tok.pad_token_id = tok.eos_token_id
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model_kwargs = {"torch_dtype": dtype}
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if "bnb-4bit" in a.model.lower():
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model_kwargs["device_map"] = {"": a.device}
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model = AutoModelForCausalLM.from_pretrained(a.model, **model_kwargs)
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else:
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model = AutoModelForCausalLM.from_pretrained(a.model, **model_kwargs).to(a.device)
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model.eval()
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n_layers = len(_get_blocks(model))
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layer = int(a.layer_frac * n_layers)
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logger.info(f"model={a.model} n_layers={n_layers} target_layer={layer}")
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cfg_cls = METHOD_MAP[a.method]
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cfg = cfg_cls(coeff=1.0, layers=(layer,))
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pos = [_render_chat(tok, [{"role": "user", "content": u},
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{"role": "assistant", "content": p}], add_generation_prompt=False)
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for u, (p, _) in zip(CALIB_PROMPTS, POS_NEG)]
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neg = [_render_chat(tok, [{"role": "user", "content": u},
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{"role": "assistant", "content": n}], add_generation_prompt=False)
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for u, (_, n) in zip(CALIB_PROMPTS, POS_NEG)]
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v = train(model, tok, pos, neg, cfg, batch_size=4, max_length=128)
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logger.info("=== calibrate ===")
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c_star, history = calibrate_iso_kl(
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v, model, tok, CALIB_PROMPTS,
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target_kl=a.target_kl, target_stat="kl_p95",
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T=a.window, device=a.device,
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)
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v.cfg.coeff = c_star
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logger.info(f"c_star = {c_star:+.4f}")
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# Strip per_prompt_per_t from history to keep file size small.
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hist_slim = [{k: v_ for k, v_ in h.items() if k != "per_prompt_per_t"}
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for h in history]
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(out_dir / "history.json").write_text(json.dumps(hist_slim, indent=2))
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(out_dir / "calib.json").write_text(json.dumps({
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"c_star": c_star, "target_kl": a.target_kl, "window": a.window,
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"method": a.method, "model": a.model, "seed": a.seed, "layer": layer,
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}, indent=2))
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# Sanity-check pmass on the BASE model (no steering): should be ~1.0,
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# otherwise the prefill/schema isn't priming the right tokens.
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a_ids, b_ids = collect_choice_token_ids(tok)
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if a.compute_pmass:
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logger.info(f"choice ids: a(true)={a_ids} b(false)={b_ids}")
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# -- trajectory + pmass at each alpha on held-out prompts
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rows = []
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if a.fork_log:
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# log-spaced including 0 and window: {0, 1, 2, 4, 8, ..., window}
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import numpy as _np
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raw = _np.unique(_np.round(_np.geomspace(1, a.window, a.n_log_forks - 1)).astype(int))
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fork_points = [0] + [int(x) for x in raw]
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fork_points = sorted(set(fp for fp in fork_points if fp <= a.window))
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else:
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fork_points = list(range(0, a.window + 1, a.fork_step))
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logger.info(f"fork_points (n={len(fork_points)}): {fork_points}")
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trajectory: dict[str, list] = {}
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per_prompt_traj: dict[str, list] = {}
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pmass_all: dict[str, list] = {}
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pmass_eval_all: dict[str, list] = {} # pmass paired with EVAL_PROMPTS (same prompts as KL)
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p_true_all: dict[str, list] = {}
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argmax_all: dict[str, list] = {}
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thinking_all: dict[str, list] = {}
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answer_label_all: dict[str, list] = {}
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gen_lens_qa: dict[str, list] = {} # T per (alpha, qa-prompt) -- right-censoring info
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gen_lens_eval: dict[str, list] = {} # T per (alpha, eval-prompt)
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debug_first: dict[str, dict] = {} # first prompt per alpha: gen_text + top-5 at each fork
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def write_eval_outputs() -> None:
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(out_dir / "trajectory.json").write_text(json.dumps({
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"fork_points_full": list(range(a.window)),
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"per_t_p95_kl": trajectory,
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"per_prompt_per_t_kl": per_prompt_traj,
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}, indent=2))
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(out_dir / "pmass.json").write_text(json.dumps({
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"fork_points": fork_points,
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"pmass": pmass_all,
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"pmass_eval": pmass_eval_all,
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"p_true": p_true_all,
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"argmax_str": argmax_all,
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"was_thinking": thinking_all,
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"answer_label": answer_label_all,
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"gen_lens_qa": gen_lens_qa,
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"gen_lens_eval": gen_lens_eval,
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"debug_first": debug_first,
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"prefill": PREFILL_STR,
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"schema": _SCHEMA,
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"questions": _QUESTIONS,
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"computed": a.compute_pmass,
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"completed_alphas": list(trajectory.keys()),
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}, indent=2))
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import csv
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with open(out_dir / "results.csv", "w", newline="") as f:
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w = csv.DictWriter(f, fieldnames=["alpha", "coeff", "kl_p95", "kl_mean", "kl_max"])
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w.writeheader()
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for r in rows:
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w.writerow(r)
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# Pre-build guided EVAL ids ONCE so measure_kl and pmass_eval roll out the
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# same prompt -- otherwise spaghetti coloring (KL trajectory colored by
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# pmass) is meaningless because the rollouts diverge.
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eval_prompt_strs = [_build_guided_prompt(tok, p, _SCHEMA) for p in EVAL_PROMPTS]
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eval_ids_list = [
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tok(s, return_tensors="pt", add_special_tokens=False).input_ids[0]
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for s in eval_prompt_strs
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]
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for alpha in a.alphas:
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v.cfg.coeff = alpha * c_star
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logger.info(f"=== eval alpha={alpha} c={v.cfg.coeff:+.4f} ===")
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m = measure_kl(v, model, tok, eval_ids_list, T=a.window, device=a.device)
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trajectory[str(alpha)] = m["per_t_p95"]
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per_prompt_traj[str(alpha)] = m["per_prompt_per_t"]
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rows.append({"alpha": alpha, "coeff": v.cfg.coeff, "kl_p95": m["kl_p95"],
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"kl_mean": m["kl_mean"], "kl_max": m["kl_max"]})
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# pmass per held-out prompt
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pm_for_alpha, pt_for_alpha, ax_for_alpha, wt_for_alpha, ans_for_alpha = [], [], [], [], []
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gen_lens_qa_alpha = []
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gen_lens_eval_alpha = []
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if a.compute_pmass and not a.skip_pmass_qa:
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for q_idx, q in enumerate(_QUESTIONS):
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prompt_str = _build_guided_prompt(tok, q, _SCHEMA)
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ids = tok(prompt_str, return_tensors="pt", add_special_tokens=False).input_ids[0]
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pad = tok.pad_token_id
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with v(model):
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gen_out = model.generate(
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ids.unsqueeze(0).to(a.device),
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max_new_tokens=a.window,
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pad_token_id=pad, eos_token_id=_eos_token_ids(tok),
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do_sample=False,
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use_cache=True,
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return_dict_in_generate=True,
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)
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gen = gen_out.sequences[0, ids.shape[0]:]
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gen_lens_qa_alpha.append(int(gen.shape[0]))
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# KV cache from steered rollout: ~100x speedup at each fork.
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pm = branch_pmass(
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v, model, tok, ids, gen, fork_points,
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PREFILL_STR, a_ids, b_ids,
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rollout_cache=gen_out.past_key_values,
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device=a.device,
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)
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pm_for_alpha.append(pm["pmass"])
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pt_for_alpha.append(pm["p_true"])
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ax_for_alpha.append(pm["argmax_str"])
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wt_for_alpha.append(pm["was_thinking"])
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ans_for_alpha.append([
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"true" if s in {"true", "True", " true", " True", "1", " 1"}
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else "false" if s in {"false", "False", " false", " False", "0", " 0"}
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else "other"
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for s in pm["argmax_str"]
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])
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# debug dump for first QA prompt only
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if q_idx == 0:
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gen_text = tok.decode(gen, skip_special_tokens=False)
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debug_first.setdefault(str(alpha), {})["qa"] = {
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"prompt": prompt_str, "gen_text": gen_text, "gen_len": int(gen.shape[0]),
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"pmass_per_fork": pm["pmass"], "p_true_per_fork": pm["p_true"],
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"argmax_per_fork": pm["argmax_str"],
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}
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logger.info(f" [debug] alpha={alpha} qa[0] gen_len={gen.shape[0]} text[:120]={gen_text[:120]!r}")
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for t, pmv, ptv, am in zip(fork_points, pm["pmass"], pm["p_true"], pm["argmax_str"]):
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logger.info(f" t={t:>3} pmass={pmv:.3f} p_true={ptv:.3f} argmax={am!r}")
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pmass_all[str(alpha)] = pm_for_alpha
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p_true_all[str(alpha)] = pt_for_alpha
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argmax_all[str(alpha)] = ax_for_alpha
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thinking_all[str(alpha)] = wt_for_alpha
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answer_label_all[str(alpha)] = ans_for_alpha
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gen_lens_qa[str(alpha)] = gen_lens_qa_alpha
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# Paired pmass on EVAL_PROMPTS (same long-form prompts as KL) so we can
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# color KL trajectories by pmass at each fork. Long-form prompts won't
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# naturally produce schema tokens, but the prefill forces the question
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# "if you committed now, can you still produce a valid choice?" -- which
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# is exactly the coherence signal we want.
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pm_eval_for_alpha = []
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if a.compute_pmass:
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for p_idx, p in enumerate(EVAL_PROMPTS):
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prompt_str = eval_prompt_strs[p_idx]
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ids = eval_ids_list[p_idx]
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pad = tok.pad_token_id
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with v(model):
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gen_out = model.generate(
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ids.unsqueeze(0).to(a.device),
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max_new_tokens=a.window,
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pad_token_id=pad, eos_token_id=_eos_token_ids(tok),
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do_sample=False,
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use_cache=True,
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return_dict_in_generate=True,
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)
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gen = gen_out.sequences[0, ids.shape[0]:]
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gen_lens_eval_alpha.append(int(gen.shape[0]))
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pm = branch_pmass(
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v, model, tok, ids, gen, fork_points,
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PREFILL_STR, a_ids, b_ids,
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rollout_cache=gen_out.past_key_values,
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device=a.device,
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)
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pm_eval_for_alpha.append(pm["pmass"])
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if p_idx == 0:
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gen_text = tok.decode(gen, skip_special_tokens=False)
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debug_first.setdefault(str(alpha), {})["eval"] = {
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"prompt": prompt_str, "gen_text": gen_text, "gen_len": int(gen.shape[0]),
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"pmass_per_fork": pm["pmass"], "p_true_per_fork": pm["p_true"],
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"argmax_per_fork": pm["argmax_str"],
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}
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logger.info(f" [debug] alpha={alpha} eval[0] gen_len={gen.shape[0]} text[:120]={gen_text[:120]!r}")
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for t, pmv, ptv, am in zip(fork_points, pm["pmass"], pm["p_true"], pm["argmax_str"]):
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logger.info(f" t={t:>3} pmass={pmv:.3f} p_true={ptv:.3f} argmax={am!r}")
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pmass_eval_all[str(alpha)] = pm_eval_for_alpha
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gen_lens_eval[str(alpha)] = gen_lens_eval_alpha
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# SHOULD: at alpha=1, mean(pmass at t=0) > 0.5 (model still respects schema).
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# ELSE: prefill string broken or chat template off.
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if a.compute_pmass:
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import numpy as _np
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pmass_rows = pm_eval_for_alpha if a.skip_pmass_qa else pm_for_alpha
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t0 = _np.array([row[0] for row in pmass_rows])
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logger.info(f" alpha={alpha} pmass@t=0: mean={t0.mean():.3f} min={t0.min():.3f} max={t0.max():.3f}")
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write_eval_outputs()
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|
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|
write_eval_outputs()
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|
if a.render_figs:
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|
repo_root = Path(__file__).resolve().parents[1]
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|
cmd = [
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|
sys.executable,
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"scripts/render_run_figs.py",
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"--run-dir", str(out_dir),
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|
"--threshold", str(a.render_threshold),
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"--model-contains", a.model.split("/")[-1],
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|
]
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|
logger.info("rendering single-run figures")
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|
subprocess.run(cmd, check=True, cwd=repo_root)
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|
logger.info(f"DONE -> {out_dir}")
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|
|
|
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|
if __name__ == "__main__":
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|
main(tyro.cli(Args))
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