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587d4ca | 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 | #!/usr/bin/env python3
"""Plot checkpoint evaluation summaries produced by evaluate_sft_loss.py."""
from __future__ import annotations
import argparse
import csv
import json
import re
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
STEP_RE = re.compile(r"step(\d+)")
def step_from_path(path: Path) -> int:
match = STEP_RE.search(path.name)
if not match:
raise ValueError(f"Could not infer checkpoint step from {path}")
return int(match.group(1))
def load_jsonl(path: Path) -> list[dict]:
rows = []
if not path.exists():
return rows
with path.open() as handle:
for line in handle:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def word_count(text: object) -> int:
return len(str(text or "").split())
def read_eval(summary_paths: list[Path], examples_dir: Path, generations_dir: Path) -> list[dict]:
rows = []
for summary_path in sorted(summary_paths, key=step_from_path):
step = step_from_path(summary_path)
summary = json.loads(summary_path.read_text())
examples_path = examples_dir / summary_path.name.replace("_val_loss.json", "_val_loss_examples.jsonl")
examples = [row for row in load_jsonl(examples_path) if row.get("status") == "scored"]
losses = [float(row["loss"]) for row in examples if "loss" in row]
generations_path = generations_dir / summary_path.name.replace("_val_loss.json", "_val_outputs.jsonl")
generations = load_jsonl(generations_path)
prediction_words = [word_count(row.get("prediction")) for row in generations]
reference_words = [word_count(row.get("reference")) for row in generations]
pred_ref_ratio = None
if prediction_words and reference_words and sum(reference_words) > 0:
pred_ref_ratio = sum(prediction_words) / sum(reference_words)
rows.append(
{
"step": step,
"loss": float(summary["loss"]),
"perplexity": float(summary["perplexity"]),
"examples_scored": int(summary.get("examples_scored", 0)),
"examples_skipped": int(summary.get("examples_skipped", 0)),
"assistant_tokens": int(summary.get("assistant_tokens", 0)),
"per_example_losses": losses,
"mean_prediction_words": sum(prediction_words) / len(prediction_words) if prediction_words else None,
"mean_reference_words": sum(reference_words) / len(reference_words) if reference_words else None,
"prediction_reference_word_ratio": pred_ref_ratio,
}
)
if not rows:
raise SystemExit("No evaluation summaries found.")
return rows
def write_csv(rows: list[dict], path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
fieldnames = [
"step",
"loss",
"perplexity",
"examples_scored",
"examples_skipped",
"assistant_tokens",
"mean_prediction_words",
"mean_reference_words",
"prediction_reference_word_ratio",
]
with path.open("w", newline="") as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames)
writer.writeheader()
for row in rows:
writer.writerow({key: row.get(key) for key in fieldnames})
def plot(rows: list[dict], output: Path, title: str) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
steps = [row["step"] for row in rows]
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle(title, fontsize=16, y=0.985)
ax = axes[0][0]
ax.plot(steps, [row["loss"] for row in rows], marker="o", linewidth=2)
best = min(rows, key=lambda row: row["loss"])
ax.scatter([best["step"]], [best["loss"]], s=80, zorder=3, label=f"best step {best['step']}")
ax.set_title("Validation Loss")
ax.set_xlabel("checkpoint step")
ax.set_ylabel("assistant-token NLL")
ax.grid(True, alpha=0.25)
ax.legend()
ax = axes[0][1]
ax.plot(steps, [row["perplexity"] for row in rows], marker="o", color="tab:orange", linewidth=2)
ax.set_title("Validation Perplexity")
ax.set_xlabel("checkpoint step")
ax.set_ylabel("perplexity")
ax.grid(True, alpha=0.25)
ax = axes[1][0]
loss_lists = [row["per_example_losses"] for row in rows]
if any(loss_lists):
ax.boxplot(loss_lists, tick_labels=[str(step) for step in steps], showmeans=True)
ax.set_title("Per-Example Loss Distribution")
ax.set_xlabel("checkpoint step")
ax.set_ylabel("loss")
ax.grid(True, axis="y", alpha=0.25)
ax = axes[1][1]
pred_lengths = [row["mean_prediction_words"] for row in rows]
ref_lengths = [row["mean_reference_words"] for row in rows]
if any(value is not None for value in pred_lengths):
ax.plot(steps, pred_lengths, marker="o", label="prediction words", linewidth=2)
if any(value is not None for value in ref_lengths):
ax.plot(steps, ref_lengths, marker="o", label="reference words", linewidth=2)
ratio = [row["prediction_reference_word_ratio"] for row in rows]
if any(value is not None for value in ratio):
ax2 = ax.twinx()
ax2.plot(steps, ratio, marker="s", linestyle="--", color="tab:green", label="pred/ref ratio")
ax2.set_ylabel("prediction/reference word ratio")
ax2.legend(loc="lower right")
ax.set_title("Generated Answer Length")
ax.set_xlabel("checkpoint step")
ax.set_ylabel("mean words")
ax.grid(True, alpha=0.25)
ax.legend(loc="upper left")
summary = (
f"best loss {best['loss']:.4f} at step {best['step']} | "
f"perplexity {best['perplexity']:.4f} | "
f"examples {best['examples_scored']} | assistant tokens {best['assistant_tokens']}"
)
fig.tight_layout(rect=[0, 0.045, 1, 0.955])
fig.text(0.01, 0.012, summary, ha="left", va="bottom", family="monospace", fontsize=9)
fig.savefig(output, dpi=180)
plt.close(fig)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--summary-pattern", help="Glob for *_val_loss.json files.")
parser.add_argument("--summaries", nargs="*", type=Path, help="Explicit *_val_loss.json files to plot.")
parser.add_argument(
"--examples-dir",
type=Path,
default=Path("data/hep_sft/checkpoint_loss"),
help="Directory containing per-example loss JSONL files.",
)
parser.add_argument(
"--generations-dir",
type=Path,
default=Path("data/hep_sft/checkpoint_eval"),
help="Directory containing generated output JSONL files.",
)
parser.add_argument("--output", required=True, type=Path)
parser.add_argument("--csv", type=Path)
parser.add_argument("--title", default="Checkpoint Evaluation")
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.summaries:
summary_paths = args.summaries
elif args.summary_pattern:
summary_paths = sorted(Path().glob(args.summary_pattern), key=step_from_path)
else:
raise SystemExit("Pass either --summaries or --summary-pattern.")
rows = read_eval(summary_paths, args.examples_dir, args.generations_dir)
plot(rows, args.output, args.title)
print(f"Wrote {args.output}")
if args.csv:
write_csv(rows, args.csv)
print(f"Wrote {args.csv}")
if __name__ == "__main__":
main()
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