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c2bb3d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | #!/usr/bin/env python
"""Build pass@10 plots and aligned logit/state summaries for detected loops."""
import csv
import json
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from transformers import AutoTokenizer
ROOT = Path("/workspace/rwkv_doomloop_experiment")
RUNS = [
(
"RWKV 1.5B",
"rwkv_1_5b",
"11_rwkv_1_5b_pass10_sampled_autostop",
Path("/workspace/.hf_home/hub/models--RWKV--RWKV7-G1j-1.5B-20260831/snapshots/2c18b29ab7fbece25ff6112281eea0fa41fcb30f"),
True,
),
(
"RWKV 2.9B",
"rwkv_2_9b",
"12_rwkv_2_9b_pass10_sampled_autostop",
ROOT / "models" / "RWKV7-G1j-2.9B-20260831",
True,
),
(
"OLMo 2 1B",
"olmo2_1b",
"13_olmo2_1b_pass10_sampled_autostop",
ROOT / "models" / "OLMo-2-0425-1B-Instruct",
False,
),
(
"Qwen3.5 2B Base",
"qwen3_5_2b_base",
"14_qwen3_5_2b_base_pass10_sampled_autostop",
ROOT / "models" / "Qwen3.5-2B-Base",
False,
),
]
OUT = ROOT / "runs" / "passk_10_sampled_autostop"
def read_json(path):
return json.loads(path.read_text(encoding="utf-8"))
def read_csv(path):
with path.open(encoding="utf-8-sig", newline="") as stream:
return list(csv.DictReader(stream))
def outcome_color(reason):
if reason == "doom_loop_detected":
return "#c44e52"
if reason == "context_window_limit":
return "#dd9a27"
if reason == "max_new_tokens_reached":
return "#8172b2"
if reason == "eos":
return "#4c72b0"
return "#777777"
def draw_sample_lengths():
fig, axes = plt.subplots(len(RUNS), 1, figsize=(12, 3.1 * len(RUNS)), sharex=True)
for ax, (label, _key, run_dir, _tokenizer_path, _is_rwkv) in zip(axes, RUNS):
base = ROOT / "runs" / run_dir
rows = [read_json(base / f"sample_{i:02d}" / "summary.json") for i in range(1, 11)]
lengths = [row["generated_tokens"] for row in rows]
colors = [outcome_color(row["terminal_reason"]) for row in rows]
ax.bar(range(1, 11), lengths, color=colors, width=0.72)
ax.set_ylabel("response tokens")
ax.set_title(
f"{label}: correct {sum(row['automatic_answer_match'] for row in rows)}/10; "
f"mean {np.mean(lengths):.1f} tokens",
loc="left",
fontsize=10,
)
ax.grid(axis="y", alpha=0.22)
for x, y in enumerate(lengths, start=1):
ax.text(x, y + max(12, max(lengths) * 0.012), str(y), ha="center", fontsize=8)
axes[-1].set_xticks(range(1, 11), [str(i) for i in range(1, 11)])
axes[-1].set_xlabel("sample index (seed 260927 + index)")
fig.suptitle("Sampled pass@10: output length and terminal condition", y=0.995, fontsize=14)
fig.legend(
handles=[
plt.Rectangle((0, 0), 1, 1, color=color, label=label)
for label, color in [
("EOS", "#4c72b0"),
("doom-loop stop", "#c44e52"),
("context cap", "#dd9a27"),
("4096-token cap", "#8172b2"),
]
],
loc="upper right",
ncol=2,
fontsize=9,
)
fig.tight_layout(rect=(0, 0, 1, 0.96))
fig.savefig(OUT / "sample_lengths_and_stops.png", dpi=180)
plt.close(fig)
def loop_comparison(label, model_key, run_dir, sample_idx, tokenizer):
sample_dir = ROOT / "runs" / run_dir / f"sample_{sample_idx:02d}"
summary = read_json(sample_dir / "summary.json")
event = summary["doom_loop_event"]
rows = read_csv(sample_dir / "token_metrics.csv")
by_step = {int(row["predicts_response_token_step"]): row for row in rows}
first = event["first_start_step"]
repeat = event["repeat_start_step"]
span = event["repeated_span_tokens"]
first_rows = [by_step[first + offset] for offset in range(span)]
repeat_rows = [by_step[repeat + offset] for offset in range(span)]
token_ids_match = all(
int(left["generated_token_id"]) == int(right["generated_token_id"])
for left, right in zip(first_rows, repeat_rows)
)
generated_ids = [int(row["generated_token_id"]) for row in rows]
span_text = tokenizer.decode(
generated_ids[first - 1 : first - 1 + span],
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)
fields = [
"raw_top1_probability",
"raw_entropy_nats",
"raw_top1_top2_margin",
"sampled_top1_probability",
"sampled_entropy_nats",
]
if "wkv_rms_mean" in first_rows[0]:
fields += [
"wkv_rms_mean",
"att_shift_rms_mean",
"ffn_shift_rms_mean",
"wkv_delta_rms",
"att_shift_delta_rms",
"ffn_shift_delta_rms",
]
stats = {}
for field in fields:
a = np.array([float(row[field]) for row in first_rows])
b = np.array([float(row[field]) for row in repeat_rows])
stats[field] = {
"first_span_mean": float(a.mean()),
"repeat_span_mean": float(b.mean()),
"repeat_minus_first_mean": float((b - a).mean()),
"first_span_min": float(a.min()),
"first_span_max": float(a.max()),
"repeat_span_min": float(b.min()),
"repeat_span_max": float(b.max()),
}
result = {
"model_key": model_key,
"model_label": label,
"sample_index": sample_idx,
"detector_event": event,
"token_ids_match_exactly": token_ids_match,
"repeated_span_text": span_text,
"rolling_dup8_rate_at_detection": event["rolling_dup8_rate_64"],
"aligned_metrics": stats,
}
(sample_dir / "loop_span_comparison.json").write_text(
json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
)
state_fields = [field for field in fields if "rms" in field]
plot_fields = [
"raw_top1_probability",
"raw_entropy_nats",
]
if state_fields:
plot_fields += ["wkv_rms_mean", "att_shift_rms_mean"]
fig, axes = plt.subplots(len(plot_fields), 1, figsize=(10, 2.3 * len(plot_fields)), sharex=True)
if len(plot_fields) == 1:
axes = [axes]
x = np.arange(1, span + 1)
for ax, field in zip(axes, plot_fields):
a = [float(row[field]) for row in first_rows]
b = [float(row[field]) for row in repeat_rows]
ax.plot(x, a, label=f"first occurrence ({first}–{first + span - 1})", linewidth=1.3)
ax.plot(x, b, label=f"repeat ({repeat}–{repeat + span - 1})", linewidth=1.3)
ax.set_ylabel(field.replace("_", "\n"), fontsize=8)
ax.grid(alpha=0.22)
ax.legend(loc="best", fontsize=8)
axes[-1].set_xlabel("token offset within repeated span")
fig.suptitle(
f"{label} sample {sample_idx}: logits/state at an exact {span}-token repeat",
y=1.0,
fontsize=12,
)
fig.tight_layout()
fig.savefig(sample_dir / "loop_span_logits_state.png", dpi=180, bbox_inches="tight")
plt.close(fig)
return result
def main():
OUT.mkdir(parents=True, exist_ok=True)
draw_sample_lengths()
loop_results = []
for label, model_key, run_dir, tokenizer_path, is_rwkv in RUNS:
base = ROOT / "runs" / run_dir
tokenizer = None
for idx in range(1, 11):
summary = read_json(base / f"sample_{idx:02d}" / "summary.json")
if summary["doom_loop_event"]:
if tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path,
trust_remote_code=is_rwkv,
local_files_only=True,
)
loop_results.append(
loop_comparison(label, model_key, run_dir, idx, tokenizer)
)
(OUT / "loop_span_comparisons.json").write_text(
json.dumps(loop_results, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
)
print(
json.dumps(
{
"sample_plot": str(OUT / "sample_lengths_and_stops.png"),
"loop_events_analyzed": len(loop_results),
"loop_samples": [
f"{item['model_key']}/sample_{item['sample_index']:02d}"
for item in loop_results
],
},
ensure_ascii=False,
)
)
if __name__ == "__main__":
main()
|