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()