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67ba414 | 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 | import os
import json
import argparse
from typing import Dict, List, Tuple
import numpy as np
import matplotlib.pyplot as plt
def _load_metrics(path: str) -> Dict:
with open(path, "r") as f:
return json.load(f)
def _bucket_sort_key(bucket: str) -> Tuple[int, int, str]:
if bucket.startswith("bucket_"):
suffix = bucket.split("_", 1)[1]
if suffix.isdigit():
return (0, int(suffix), bucket)
return (1, 0, bucket)
def _extract_buckets(metrics: Dict) -> List[str]:
buckets = set()
for k in metrics.keys():
if k.startswith("grad_norm/bucket_"):
parts = k.split("/")
if len(parts) >= 2:
buckets.add(parts[1])
return sorted(buckets, key=_bucket_sort_key)
def _rv_stats(metrics: Dict, buckets: List[str]) -> Tuple[List[float], List[float], List[float]]:
means, mins, maxs = [], [], []
for b in buckets:
means.append(float(metrics.get(f"grad_norm/{b}/reward_std_mean", 0.0)))
mins.append(float(metrics.get(f"grad_norm/{b}/reward_std_min", 0.0)))
maxs.append(float(metrics.get(f"grad_norm/{b}/reward_std_max", 0.0)))
return means, mins, maxs
def _grad_series(metrics: Dict, buckets: List[str]) -> Tuple[List[float], List[float], List[float]]:
task = []
kl = []
ent = []
for b in buckets:
task.append(float(metrics.get(f"grad_norm/{b}/task", 0.0)))
kl.append(float(metrics.get(f"grad_norm/{b}/kl", 0.0)))
ent.append(float(metrics.get(f"grad_norm/{b}/entropy", 0.0)))
return task, kl, ent
def _default_step_dir(mode: str, step: str) -> str:
base_dir = os.path.dirname(__file__)
return os.path.join(base_dir, "data", mode, step)
def main() -> None:
parser = argparse.ArgumentParser(description="ICML paper plots: step 0/20/40 grid.")
parser.add_argument("--mode", choices=["grpo", "ppo"], default="grpo", help="Which dataset to plot")
parser.add_argument("--step0-dir", default=None, help="Directory with metrics json for step 0")
parser.add_argument("--step20-dir", default=None, help="Directory with metrics json for step 20")
parser.add_argument("--step40-dir", default=None, help="Directory with metrics json for step 40")
parser.add_argument("--out", default="icml_step0_20_40_grid.png", help="Output PNG path")
args = parser.parse_args()
step0_dir = args.step0_dir or _default_step_dir(args.mode, "step0")
step20_dir = args.step20_dir or _default_step_dir(args.mode, "step20")
step40_dir = args.step40_dir or _default_step_dir(args.mode, "step40")
metrics0 = _load_metrics(os.path.join(step0_dir, "metrics.json"))
metrics20 = _load_metrics(os.path.join(step20_dir, "metrics.json"))
metrics40 = _load_metrics(os.path.join(step40_dir, "metrics.json"))
buckets = _extract_buckets(metrics20)
buckets = [b for b in buckets if b in _extract_buckets(metrics40)]
buckets = [b for b in buckets if b in _extract_buckets(metrics0)]
labels = [b.replace("_", " ") for b in buckets]
rv20_means, rv20_mins, rv20_maxs = _rv_stats(metrics20, buckets)
rv40_means, rv40_mins, rv40_maxs = _rv_stats(metrics40, buckets)
task20, kl20, ent20 = _grad_series(metrics20, buckets)
task40, kl40, ent40 = _grad_series(metrics40, buckets)
reg20 = [k + e for k, e in zip(kl20, ent20)]
reg40 = [k + e for k, e in zip(kl40, ent40)]
rv0_means, rv0_mins, rv0_maxs = _rv_stats(metrics0, buckets)
task0, kl0, ent0 = _grad_series(metrics0, buckets)
reg0 = [k + e for k, e in zip(kl0, ent0)]
fig, axes = plt.subplots(3, 3, figsize=(16, 12), sharex="col")
color_rv = "#1f78b4"
color_task = "#e67e22"
color_reg = "#16a085"
positions = np.arange(len(buckets))
box_width = 0.35
def _draw_interval_mean(ax, x, vmin, vmax, vmean, color):
if vmax < vmin:
vmin, vmax = vmax, vmin
yerr = [[max(0.0, vmean - vmin)], [max(0.0, vmax - vmean)]]
ax.errorbar(
[x],
[vmean],
yerr=yerr,
fmt="o",
color=color,
markersize=5,
capsize=4,
linewidth=1.2,
)
# rows: step0 (if provided), step20, step40
steps = [
("Step 0", rv0_means, rv0_mins, rv0_maxs, task0, reg0),
("Step 20", rv20_means, rv20_mins, rv20_maxs, task20, reg20),
("Step 40", rv40_means, rv40_mins, rv40_maxs, task40, reg40),
]
col_titles = [
"Reward Variance by bucket",
"Task gradient norm vs Reward Variance",
"Regularizer gradient norm (KL+Entropy) vs RV",
]
col_captions = [
"RV quantile buckets. (Q1 -> Q6)",
"Bucket RV (log scale).",
"Bucket RV (log scale).",
]
for r, (step_name, rv_means, rv_mins, rv_maxs, task, reg) in enumerate(steps):
# (a) RV per bucket interval + mean
ax = axes[r][0]
for i, x in enumerate(positions):
_draw_interval_mean(
ax,
x,
rv_mins[i],
rv_maxs[i],
rv_means[i],
color=color_rv,
)
ax.set_yscale("log")
ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
ax.set_ylabel(f"{step_name}\nReward Variance (Std)")
if r == 0:
ax.set_title("(a) " + col_titles[0], fontweight="bold")
ax.set_xticks(positions)
ax.set_xticklabels(labels if r == len(steps) - 1 else [])
# (b) Task vs RV
ax = axes[r][1]
ax.plot(rv_means, task, linestyle="-", marker="o", color=color_task, markersize=5)
ax.set_xscale("log")
ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
ax.set_ylabel(f"{step_name}\nTask grad norm")
if r == 0:
ax.set_title("(b) " + col_titles[1], fontweight="bold")
# if r == len(steps) - 1:
# ax.set_xlabel("RV mean")
# (c) Reg vs RV
ax = axes[r][2]
ax.plot(rv_means, reg, linestyle="-", marker="o", color=color_reg, markersize=5)
ax.set_xscale("log")
ax.set_ylim(0.0, 0.1)
ax.grid(axis="y", linestyle="--", alpha=0.15, linewidth=0.8)
ax.set_ylabel(f"{step_name}\nKL+Entropy grad norm")
if r == 0:
ax.set_title("(c) " + col_titles[2], fontweight="bold")
# if r == len(steps) - 1:
# ax.set_xlabel("RV mean")
# style spines
for row in axes:
for a in row:
a.spines["top"].set_visible(False)
a.spines["right"].set_visible(False)
# captions under each column
for c, caption in enumerate(col_captions):
ax = axes[-1][c]
ax.text(
0.5,
-0.15,
caption,
transform=ax.transAxes,
ha="center",
va="top",
fontsize=10,
fontweight="bold",
)
plt.tight_layout()
plt.savefig(args.out, dpi=300)
print(f"Saved figure to {os.path.abspath(args.out)}")
if __name__ == "__main__":
main()
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