| from pathlib import Path |
| import json, html, csv, shutil |
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from matplotlib.patches import Patch |
| from matplotlib.backends.backend_pdf import PdfPages |
| from .paths import paths |
|
|
| P, D = paths() |
| R = json.loads((D / "RESULT.json").read_text()) |
| V = json.loads((D / "VALIDATION.json").read_text()) |
| plt.rcParams.update( |
| { |
| "font.family": "DejaVu Sans", |
| "font.size": 11, |
| "axes.spines.top": False, |
| "axes.spines.right": False, |
| "pdf.fonttype": 42, |
| "svg.fonttype": "none", |
| "text.color": "#19324a", |
| } |
| ) |
| colors = ["#9BAABD", "#007F86", "#3178BD", "#9D5CBB"] |
| figs = [] |
| joint = [] |
| for pair, title, scope in [ |
| ("tmax", "TMax-27B (27B)", "Terminal-Bench 2.1 | 253 normal trials"), |
| ("deepswe", "DeepSWE-Preview (32B)", "SWE-bench Verified | 1,998 generations"), |
| ]: |
| r = R[pair] |
| entries = [ |
| { |
| "objective": "fifo", |
| "minutes": r["deadline_minutes"], |
| "all_resource_hours": { |
| k: r["baseline"][k]["hours"] |
| for k in ["cpu", "memory", "gpu", "storage"] |
| }, |
| } |
| ] + [ |
| next(v for v in V if v["pair"] == pair and v["objective"] == o) |
| for o in ["cpu", "gpu"] + (["storage"] if pair == "deepswe" else []) |
| ] |
| names = [ |
| "FIFO baseline", |
| "CPU/RAM minimum: fastest found", |
| "GPU-hours: best found", |
| "Cache-hours: best found", |
| ][: len(entries)] |
| fig, axs = plt.subplots(2, 2, figsize=(13.5, 9.5), facecolor="white") |
| fig.subplots_adjust( |
| left=0.09, right=0.97, top=0.72, bottom=0.15, hspace=0.46, wspace=0.30 |
| ) |
| fig.text( |
| 0.09, 0.956, title + " — Resource-Hours by Schedule", fontsize=21, weight="bold" |
| ) |
| fig.text( |
| 0.09, |
| 0.91, |
| f"{scope} | Completion time ≤ FIFO ({r['deadline_minutes']:,.2f} min)", |
| fontsize=12, |
| color="#536577", |
| ) |
| fig.legend( |
| handles=[ |
| Patch(facecolor=colors[i], label=f"{name} · {e['minutes']:,.2f} min") |
| for i, (name, e) in enumerate(zip(names, entries)) |
| ], |
| loc="upper left", |
| bbox_to_anchor=(0.08, 0.875), |
| ncol=2, |
| frameon=False, |
| fontsize=10.5, |
| columnspacing=2, |
| ) |
| for ax, res, ttl, unit in zip( |
| axs.flat, |
| ["cpu", "memory", "gpu", "storage"], |
| ["CPU", "Memory", "GPU", "Storage: compressed image cache"], |
| ["core-hours", "GiB-hours", "GPU-hours", "GiB-hours"], |
| ): |
| vals = [e["all_resource_hours"][res] for e in entries] |
| ax.set_title(ttl, loc="left", fontsize=13, weight="bold", pad=11) |
| ax.set_ylabel(unit) |
| ax.set_xticks([]) |
| ax.set_axisbelow(True) |
| ax.grid(axis="y", alpha=0.16) |
| if vals[0] is None: |
| ax.set_yticks([]) |
| ax.text( |
| 0.5, |
| 0.45, |
| "Not measured", |
| transform=ax.transAxes, |
| ha="center", |
| color="#718293", |
| fontsize=16, |
| ) |
| continue |
| mx = max(vals) |
| ax.set_ylim(0, mx * 1.37) |
| ax.set_xlim(-0.65, len(vals) - 0.35) |
| for i, v in enumerate(vals): |
| ax.bar(i, v, width=0.61, color=colors[i]) |
| ax.text( |
| i, |
| v + mx * 0.025, |
| f"{v:,.2f}", |
| ha="center", |
| weight="bold", |
| fontsize=10.5, |
| ) |
| if i: |
| ax.text( |
| i, |
| v + mx * 0.12, |
| f"−{100*(1-v/vals[0]):.1f}%", |
| ha="center", |
| weight="bold", |
| color=colors[i], |
| fontsize=11, |
| ) |
| for name, e in zip(names, entries): |
| joint.append( |
| { |
| "pair": pair, |
| "schedule": name, |
| "minutes": e["minutes"], |
| **e["all_resource_hours"], |
| } |
| ) |
| fig.text( |
| 0.09, |
| 0.083, |
| "Same color = the SAME complete schedule across all four panels. Scales are independent.", |
| fontsize=11, |
| weight="bold", |
| ) |
| msg = ( |
| "CPU + RAM + best-found GPU-hours are attained together by the blue schedule." |
| if pair == "tmax" |
| else "CPU/RAM minima coexist with either objective; best-found GPU and cache values use different schedules." |
| ) |
| fig.text(0.09, 0.055, msg, fontsize=10.5) |
| fig.text( |
| 0.09, |
| 0.026, |
| "Fixed trace timings; zero transition cost. CPU/RAM: quota-hours. GPU/cache optima are not globally proven.", |
| fontsize=10, |
| color="#536577", |
| ) |
| for ext in ["png", "pdf", "svg"]: |
| fig.savefig(D / f"{pair}_min_hours_schedules.{ext}", dpi=200, facecolor="white") |
| figs.append(fig) |
| with PdfPages(D / "resource_hours_by_schedule.pdf") as pdf: |
| for fig in figs: |
| pdf.savefig(fig) |
| with (D / "joint_schedules.csv").open("w") as f: |
| w = csv.DictWriter(f, fieldnames=list(joint[0])) |
| w.writeheader() |
| w.writerows(joint) |
| rows = [ |
| [ |
| "Workload / schedule", |
| "CPU core-hours", |
| "Memory GiB-hours", |
| "GPU-hours", |
| "Cache GiB-hours", |
| "Completion (min)", |
| ] |
| ] |
| for r in joint: |
| if r["schedule"] == "FIFO baseline": |
| continue |
| rows.append( |
| [ |
| ("TMax" if r["pair"] == "tmax" else "DeepSWE") + " / " + r["schedule"], |
| *[ |
| ("—" if r[k] is None else f"{r[k]:,.2f}") |
| for k in ["cpu", "memory", "gpu", "storage"] |
| ], |
| f"{r['minutes']:,.2f}", |
| ] |
| ) |
| text = """### Which minima can be attained together? |
| |
| The values above were selected separately by objective. The table below instead reports complete schedules: every value within a row is attained simultaneously in the replay. CPU and memory totals are invariant across phase-reclaim schedules. TMax attains the reported CPU, memory and best-found GPU values together at 129.21 minutes (storage unavailable). For DeepSWE, both GPU-optimized and cache-optimized schedules attain the CPU/RAM minima, but the best-found GPU and cache values are not attained together by any selected schedule. This does not prove that a joint optimum is impossible. |
| |
| """ |
| text += ( |
| '<table header-row="true" fit-page-width="true">\n' |
| + "".join( |
| "<tr>" + "".join("<td>" + html.escape(x) + "</td>" for x in row) + "</tr>\n" |
| for row in rows |
| ) |
| + "</table>\n\n" |
| ) |
| (D / "JOINT_NOTION.txt").write_text(text) |
| print("Rendered two charts and joint-schedule table.") |
|
|
