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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | #!/usr/bin/env python3
"""Compare base-model and SFT-adapter validation results."""
from __future__ import annotations
import argparse
import csv
import json
import math
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def load_json(path: Path) -> dict:
return json.loads(path.read_text())
def load_jsonl(path: Path) -> list[dict]:
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(value: object) -> int:
return len(str(value or "").split())
def by_scored_id(path: Path) -> dict[str, dict]:
return {str(row["id"]): row for row in load_jsonl(path) if row.get("status") == "scored" and "id" in row}
def generations_by_id(path: Path) -> dict[str, dict]:
return {str(row["id"]): row for row in load_jsonl(path) if "id" in row}
def build_rows(
base_examples: Path,
sft_examples: Path,
base_generations: Path | None,
sft_generations: Path | None,
) -> list[dict]:
base_loss = by_scored_id(base_examples)
sft_loss = by_scored_id(sft_examples)
shared_ids = sorted(set(base_loss) & set(sft_loss))
if not shared_ids:
raise SystemExit("No shared scored example ids found between base and SFT example files.")
base_gen = generations_by_id(base_generations) if base_generations else {}
sft_gen = generations_by_id(sft_generations) if sft_generations else {}
rows = []
for example_id in shared_ids:
base_row = base_loss[example_id]
sft_row = sft_loss[example_id]
base_loss_value = float(base_row["loss"])
sft_loss_value = float(sft_row["loss"])
base_generation = base_gen.get(example_id, {})
sft_generation = sft_gen.get(example_id, {})
reference = sft_generation.get("reference") or base_generation.get("reference")
rows.append(
{
"id": example_id,
"base_loss": base_loss_value,
"sft_loss": sft_loss_value,
"loss_delta": sft_loss_value - base_loss_value,
"loss_reduction_pct": (
100.0 * (base_loss_value - sft_loss_value) / base_loss_value
if base_loss_value
else math.nan
),
"base_assistant_tokens": int(base_row.get("assistant_tokens", 0)),
"sft_assistant_tokens": int(sft_row.get("assistant_tokens", 0)),
"base_prediction_words": word_count(base_generation.get("prediction")),
"sft_prediction_words": word_count(sft_generation.get("prediction")),
"reference_words": word_count(reference),
}
)
return sorted(rows, key=lambda row: row["base_loss"], reverse=True)
def mean(values: list[float]) -> float | None:
return sum(values) / len(values) if values else None
def write_csv(rows: list[dict], path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
fieldnames = [
"id",
"base_loss",
"sft_loss",
"loss_delta",
"loss_reduction_pct",
"base_assistant_tokens",
"sft_assistant_tokens",
"base_prediction_words",
"sft_prediction_words",
"reference_words",
]
with path.open("w", newline="") as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
def annotate_bars(ax: plt.Axes, bars: list, fmt: str = "{:.3g}") -> None:
for bar in bars:
height = bar.get_height()
ax.annotate(
fmt.format(height),
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 4),
textcoords="offset points",
ha="center",
va="bottom",
fontsize=9,
)
def plot(
base_summary: dict,
sft_summary: dict,
rows: list[dict],
output: Path,
title: str,
base_label: str,
sft_label: str,
) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle(title, fontsize=16, y=0.985)
ax = axes[0][0]
loss_bars = ax.bar(
[base_label, sft_label],
[float(base_summary["loss"]), float(sft_summary["loss"])],
color=["tab:gray", "tab:blue"],
)
annotate_bars(ax, loss_bars)
ax.set_title("Validation Loss")
ax.set_ylabel("assistant-token NLL")
ax.grid(True, axis="y", alpha=0.25)
ax.text(
0.5,
0.92,
(
f"perplexity: {float(base_summary['perplexity']):.3g} -> "
f"{float(sft_summary['perplexity']):.3g}"
),
transform=ax.transAxes,
ha="center",
va="top",
fontsize=10,
)
ax = axes[0][1]
indices = list(range(1, len(rows) + 1))
base_losses = [row["base_loss"] for row in rows]
sft_losses = [row["sft_loss"] for row in rows]
for idx, base_value, sft_value in zip(indices, base_losses, sft_losses):
ax.plot([idx, idx], [base_value, sft_value], color="0.82", linewidth=1)
ax.scatter(indices, base_losses, s=28, label=base_label, color="tab:gray")
ax.scatter(indices, sft_losses, s=28, label=sft_label, color="tab:blue")
ax.set_title("Per-Example Loss")
ax.set_xlabel("examples sorted by base loss")
ax.set_ylabel("loss")
ax.grid(True, alpha=0.25)
ax.legend()
ax = axes[1][0]
reductions = [row["loss_reduction_pct"] for row in rows if math.isfinite(row["loss_reduction_pct"])]
ax.hist(reductions, bins=min(12, max(4, len(reductions) // 2)), color="tab:green", alpha=0.8)
ax.axvline(mean(reductions), color="black", linestyle="--", linewidth=1.5, label="mean")
ax.set_title("Per-Example Loss Reduction")
ax.set_xlabel("reduction vs base (%)")
ax.set_ylabel("examples")
ax.grid(True, axis="y", alpha=0.25)
ax.legend()
ax = axes[1][1]
length_labels = [base_label, sft_label, "Reference"]
length_values = [
mean([row["base_prediction_words"] for row in rows]) or 0.0,
mean([row["sft_prediction_words"] for row in rows]) or 0.0,
mean([row["reference_words"] for row in rows]) or 0.0,
]
length_bars = ax.bar(length_labels, length_values, color=["tab:gray", "tab:blue", "tab:orange"])
annotate_bars(ax, length_bars, fmt="{:.1f}")
ax.set_title("Generated Answer Length")
ax.set_ylabel("mean words")
ax.grid(True, axis="y", alpha=0.25)
loss_reduction = 100.0 * (
float(base_summary["loss"]) - float(sft_summary["loss"])
) / float(base_summary["loss"])
summary = (
f"examples {len(rows)} | "
f"loss {float(base_summary['loss']):.4f} -> {float(sft_summary['loss']):.4f} "
f"({loss_reduction:.1f}% lower) | "
f"ppl {float(base_summary['perplexity']):.4f} -> {float(sft_summary['perplexity']):.4f}"
)
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(description="Plot base vs SFT validation-result comparisons.")
parser.add_argument("--base-summary", required=True, type=Path)
parser.add_argument("--sft-summary", required=True, type=Path)
parser.add_argument("--base-examples", required=True, type=Path)
parser.add_argument("--sft-examples", required=True, type=Path)
parser.add_argument("--base-generations", type=Path)
parser.add_argument("--sft-generations", type=Path)
parser.add_argument("--output", required=True, type=Path)
parser.add_argument("--csv", type=Path)
parser.add_argument("--title", default="Qwen2.5 7B Base vs LoRA-16 SFT")
parser.add_argument("--base-label", default="Base")
parser.add_argument("--sft-label", default="LoRA-16 SFT")
return parser.parse_args()
def main() -> None:
args = parse_args()
base_summary = load_json(args.base_summary)
sft_summary = load_json(args.sft_summary)
rows = build_rows(
args.base_examples,
args.sft_examples,
args.base_generations,
args.sft_generations,
)
plot(
base_summary,
sft_summary,
rows,
args.output,
args.title,
args.base_label,
args.sft_label,
)
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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