mirror of
https://github.com/wassname/evil_MoE.git
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wassname
218 lines
8.8 KiB
Python
218 lines
8.8 KiB
Python
"""Stacked-area plot of rollout outcome proportions per training step.
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Categories per rollout:
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green -- correct (no hack, gt_pass)
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yellow -- correct attempted hack (hack present AND gt_pass)
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red -- reward hack (hack present, no gt_pass)
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purple -- attempted hack (run_tests in completion but `hacked`=False;
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format/pattern didn't fire so no reward)
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grey -- incorrect (no hack, no gt_pass, no run_tests)
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Layering bottom->top: green, yellow, red, purple, grey.
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Vertical line marks warmup_replay_steps boundary (distillation off).
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"""
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from __future__ import annotations
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import gzip
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import json
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import sys
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from collections import Counter
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import tyro
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from dataclasses import dataclass
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from loguru import logger
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CATS = ["green", "yellow", "red", "purple", "grey"]
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LABELS = {
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"green": "correct",
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"yellow": "correct attempted reward hack",
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"red": "reward hack",
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"purple": "attempted reward hack",
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"grey": "incorrect",
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}
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COLORS = {
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"green": "#4CAF50",
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"yellow": "#FFC107",
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"red": "#E53935",
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"purple": "#8E24AA",
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"grey": "#9E9E9E",
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}
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def classify(row: dict) -> str:
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h = bool(row["hacked"])
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gt = bool(row["gt_pass"])
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comp = row.get("completion", "")
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has_rt = "run_tests" in comp
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if h and gt:
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return "yellow"
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if h and not gt:
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return "red"
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if (not h) and gt:
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return "green"
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if (not h) and (not gt) and has_rt:
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return "purple"
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return "grey"
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def load_step(path: Path) -> list[dict]:
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with gzip.open(path) as f:
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return [json.loads(line) for line in f]
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@dataclass
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class Config:
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run_dir: Path
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out_path: Path = Path("out/runs/probe_plot_stack_vanilla_seed41.png")
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warmup: int = 70 # distill-off boundary (end of replay)
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pre_warmup: int = 0 # distill-on boundary (start of replay)
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smooth: int = 10 # trailing SMA window; double the blog's 5 since our G=8 (theirs G=16)
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title: str = "vanilla GRPO seed=41 (warmup-distill -> student-gen)"
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def main(cfg: Config) -> int:
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steps_subdir = cfg.run_dir / "steps"
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search_dir = steps_subdir if steps_subdir.exists() else cfg.run_dir
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files = sorted(search_dir.glob("step_*.jsonl.gz"))
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if not files:
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logger.error(f"no step files in {search_dir}")
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return 1
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# de-dup if both .cos.jsonl.gz and .jsonl.gz exist for same step (gen phase
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# writes the full file; replay writes .cos slim; they shouldn't overlap)
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steps_data: dict[int, list[dict]] = {}
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for p in files:
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step = int(p.name.split("_")[1].split(".")[0])
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steps_data.setdefault(step, []).extend(load_step(p))
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n_steps = max(steps_data) + 1
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fracs = np.zeros((len(CATS), n_steps))
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# Per-step diagnostics (mean over G samples). NaN if row didn't carry it.
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cos_pre_step = np.full(n_steps, np.nan) # batch-level pre-proj cos (all rollouts)
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cos_pre_weighted = np.full(n_steps, np.nan) # cos_pre / hack_frac (per-hacked estimate)
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cos_hack_step = np.full(n_steps, np.nan) # per-sample cos_S_contrib | hacked
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loss_step = np.full(n_steps, np.nan) # GRPO loss
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for step, rows in steps_data.items():
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c = Counter(classify(r) for r in rows)
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total = sum(c.values())
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for i, cat in enumerate(CATS):
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fracs[i, step] = c[cat] / total
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cin = [r["mean_cos_pre"] for r in rows if r.get("mean_cos_pre") is not None]
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if cin:
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cos_pre_step[step] = float(np.mean(cin))
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# Recover E[cos|hacked] from batch-mean cos under the assumption
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# E[cos|clean]=0: mean(cos_pre) = f_h * E[cos|hacked] + (1-f_h)*0
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# => E[cos|hacked] = mean(cos_pre) / f_h. NaN when no hacks in batch
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# (no per-hacked estimate possible from this step).
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# FIXME: cos_pre is now the aligned fraction ||relu(V@g)||/||g|| >= 0
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# (was signed sum, ~0 on clean). With relu the E[cos|clean]=0 premise
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# no longer holds, so this f_h-weighted estimate over-counts. Recompute
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# per-rollout cos restricted to hacked rollouts instead of decomposing.
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hack_frac = float(np.mean([bool(r.get("hacked")) for r in rows]))
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if hack_frac > 0:
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cos_pre_weighted[step] = cos_pre_step[step] / hack_frac
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# Per-sample cos restricted to hacked rollouts: where v_hack relevance
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# should show. cos on clean rollouts is noise -- drop it.
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ch = [r["cos_S_contrib"] for r in rows
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if r.get("hacked") and r.get("cos_S_contrib") is not None]
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if ch: cos_hack_step[step] = float(np.mean(ch))
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# GRPO loss: mean_i(-adv_i * logp_mean_i), adv_i = reward_i - mean(reward).
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# Reconstructible from per-row reward + logp_mean. If a row stored per_sample_loss
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# (added later), prefer that.
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if all(r.get("per_sample_loss") is not None for r in rows):
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loss_step[step] = float(np.mean([r["per_sample_loss"] for r in rows]))
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else:
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rwd = np.array([r["reward"] for r in rows], dtype=float)
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lp = np.array([r["logp_mean"] for r in rows if r.get("logp_mean") is not None], dtype=float)
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if len(lp) == len(rwd):
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adv = rwd - rwd.mean()
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loss_step[step] = float((-adv * lp).mean())
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def _sma(y: np.ndarray, w: int) -> np.ndarray:
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if w <= 1: return y
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out = np.full_like(y, np.nan, dtype=float)
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for t in range(len(y)):
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lo = max(0, t - w + 1)
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seg = y[lo:t + 1]
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seg = seg[~np.isnan(seg)]
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if len(seg): out[t] = seg.mean()
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return out
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if cfg.smooth > 1:
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w = cfg.smooth
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smoothed = np.zeros_like(fracs)
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for t in range(n_steps):
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lo = max(0, t - w + 1)
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smoothed[:, t] = fracs[:, lo:t + 1].mean(axis=1)
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smoothed /= smoothed.sum(axis=0, keepdims=True).clip(min=1e-12)
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plot_fracs = smoothed
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else:
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plot_fracs = fracs
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fig, (ax, ax_loss, ax2) = plt.subplots(
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3, 1, figsize=(10, 10), sharex=True,
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gridspec_kw={"height_ratios": [3, 1, 2]},
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)
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xs = np.arange(n_steps)
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ax.stackplot(
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xs, plot_fracs,
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labels=[LABELS[c] for c in CATS],
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colors=[COLORS[c] for c in CATS],
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alpha=0.95,
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)
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if cfg.pre_warmup > 0:
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for a in (ax, ax_loss, ax2):
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a.axvline(cfg.pre_warmup - 0.5, color="black", linestyle="--", linewidth=1.2)
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ax.axvline(cfg.pre_warmup - 0.5, color="black", linestyle="--", linewidth=1.2,
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label=f"distillation on (step={cfg.pre_warmup})")
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for a in (ax, ax_loss, ax2):
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a.axvline(cfg.warmup - 0.5, color="black", linestyle="--", linewidth=1.2)
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ax.axvline(cfg.warmup - 0.5, color="black", linestyle="--", linewidth=1.2,
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label=f"distillation off (step={cfg.warmup})")
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ax.set_xlim(0, n_steps - 1)
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ax.set_ylim(0, 1)
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ax.set_ylabel("Proportion of rollouts")
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ax.set_title(cfg.title)
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handles, labels_ = ax.get_legend_handles_labels()
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boundary_labels = [labels_.index(f"distillation off (step={cfg.warmup})")]
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if cfg.pre_warmup > 0:
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boundary_labels = [labels_.index(f"distillation on (step={cfg.pre_warmup})")] + boundary_labels
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order = [labels_.index(LABELS[c]) for c in CATS] + boundary_labels
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ax.legend(
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[handles[i] for i in order], [labels_[i] for i in order],
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loc="upper center", bbox_to_anchor=(0.5, -0.05),
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ncol=7, frameon=False, fontsize=9,
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)
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# Loss subplot: per-step mean GRPO loss (-adv * logp_mean).
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ax_loss.axhline(0, color="black", linewidth=0.5, alpha=0.5)
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ax_loss.plot(xs, _sma(loss_step, cfg.smooth), color="#212121", lw=1.4)
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ax_loss.set_ylabel("GRPO loss")
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# Cosine subplot: v_hack relevance on hacked rollouts (the signal we care
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# about). Light grey trace is batch-level cos_pre (all rollouts) for context.
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ax2.axhline(0, color="black", linewidth=0.5, alpha=0.5)
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ax2.plot(xs, _sma(cos_hack_step, cfg.smooth), color="#E53935", lw=1.6,
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label="cos_S | rollout hacked (per-sample, v_hack relevance)")
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ax2.plot(xs, _sma(cos_pre_weighted, cfg.smooth), color="#1976D2", lw=1.4,
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label="cos_pre / hack_frac (E[cos|hacked] estimate, batch-derived)")
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ax2.plot(xs, _sma(cos_pre_step, cfg.smooth), color="#9E9E9E", lw=1.0,
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alpha=0.6, label="cos_pre (raw batch grad, all rollouts)")
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ax2.set_xlabel("Training step")
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ax2.set_ylabel("cos with v_hack")
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ax2.legend(loc="upper center", bbox_to_anchor=(0.5, -0.18),
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ncol=2, frameon=False, fontsize=9)
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fig.tight_layout()
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cfg.out_path.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(cfg.out_path, dpi=120, bbox_inches="tight")
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logger.info(f"wrote {cfg.out_path}")
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return 0
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if __name__ == "__main__":
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sys.exit(main(tyro.cli(Config)))
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