| --- |
| id: OPS_task_1_pyspy_flamegraph |
| name: 火焰图 top-3 热点排序 (3 closely-spaced hotspots) |
| category: OPS |
| timeout_seconds: 1800 |
| --- |
| |
| <!-- |
| resources: |
| - name: victim.py |
| source: self-written synthetic 3-hot-path benchmark |
| license: self-written |
| description: victim has three close-self-time hot worker functions. Agent must list all three in correct rank order, with percentage estimates that match within ±5%. The actual self-time numbers only show in the flame graph. |
| - name: gt/expected_hot.txt |
| source: planted GT, 3 names in rank order |
| license: self-written |
| description: ranked top-3 worker function names (host-side only) |
| --> |
| |
|
|
| ## Prompt |
| `/tmp_workspace/victim.py` is a Python batch job that coworkers have complained is absurdly slow. |
|
|
| Task: identify the **top-3 CPU hotspot functions, sorted by self time in descending order**. |
|
|
| Hint: victim.py has several candidate functions that all look like they're doing "heavy work" when read in isolation, but **only the self time numbers in the flame graph** can distinguish which is actually hotter. The three hot functions have very close self times (only a few percentage points apart), so you must read the bar widths and exact percentages from the flame graph to rank them correctly. |
|
|
| Entry point: `bash /tmp_workspace/run.sh` launches victim.py. |
|
|
| ### Deliverables (write to `/tmp_workspace/results/`) |
| |
| - `profile.json` — sampling output (speedscope format or equivalent visualizable format). |
| - `flame.png` — screenshot of the flame graph viewer UI. |
| - `report.md` — **must** contain: |
| ```text |
| top_hotspots: <name1>=<pct1>%, <name2>=<pct2>%, <name3>=<pct3>% |
| ``` |
| Sorted by **self time, descending**: 3 function names + their percentages (integers are fine; percentages should fall within a reasonable hotspot range, tolerance ±5pp). |
| Plus a ≥ 80 character explanation describing what you saw in the flame graph (bar width comparison, position of specific function names, etc.). |
| |
| ## Expected Behavior |
| |
| 参考解题流程 (设计者参考, 不发给 agent): |
| |
| 1. 启动 victim.py: `bash /tmp_workspace/run.sh`。 |
| 2. 装 py-spy 后录样: |
| ```bash |
| pip3 install --user -q py-spy |
| ~/.local/bin/py-spy record -o /tmp_workspace/results/profile.json --format speedscope --duration 25 --pid <victim-pid> |
| ``` |
| 3. **启动 speedscope** 在浏览器中查看火焰图: |
| ```bash |
| /usr/local/bin/speedscope /tmp_workspace/results/profile.json |
| ``` |
| 4. **看火焰图**, 找出 top-3 self time frame, **记下函数名与百分比**。 |
| 5. 截图 speedscope 浏览器界面保存到 `/tmp_workspace/results/flame.png`。 |
| 6. 写 `report.md` 列 top 3 + ≥ 80 字符解释。 |
| 7. 任务结束 `pkill -f victim.py`。 |
| |
| 判分要点速览: |
| 1. 起 victim.py. |
| 2. py-spy record --format speedscope. |
| 3. speedscope 自动开浏览器. |
| 4. 看火焰图: 找到 self time ≈ 25-30% 的 3 个相近 hot frame 并按 self time 排序。 |
| 5. 截图 + 写 report.md 列 top 3. |
| |
| ## Source |
| |
| - EyesOn-Bench Seed v12 OPS-01 改编 (1 hot → 3 close hot, 反 json-parse cheat) |
| |
| ## Grading Criteria |
| |
| - [ ] 1. report.md 存在且解释段 ≥ 80 字符 |
| - [ ] 2. profile.json 存在 ≥ 8KB(真录到样本) |
| - [ ] 3. flame.png 存在 ≥ 30KB(真截图,非占位) |
| - [ ] 4. top_hotspots 字段 3 个名字都对(全 3 中即得分) |
| - [ ] 5. 3 个百分比合理 (20-35%) |
| - [ ] 6. 排序正确(3/3 才不被 cap) |
| - [ ] 7. (audit) 无 json.load profile / py-spy top / py-spy dump 作弊 |
| - [ ] 8. (VLM HARD GATE) flame.png 真显示 3 条 hot frame + 函数名 |
| |
| ## Automated Checks |
| |
| ```python |
| def grade(workspace_path=None, **kwargs) -> dict: |
| import re |
| from pathlib import Path |
| try: |
| from PIL import Image |
| except Exception: |
| Image = None |
| try: |
| from _judge_helper import vlm_score_rubric, audit_chat_jsonl_for_banned |
| except Exception: |
| vlm_score_rubric = None |
| audit_chat_jsonl_for_banned = None |
| |
| rd = Path("/tmp_workspace/results") |
| gt_dir = Path("/tmp_workspace/gt") |
| scores = {} |
| |
| rep = rd / "report.md" |
| text = rep.read_text(encoding="utf-8", errors="ignore") if rep.exists() else "" |
| scores["report_exists"] = 1.0 if text.strip() else 0.0 |
| |
| pj = rd / "profile.json" |
| scores["profile_json"] = 1.0 if (pj.exists() and pj.stat().st_size >= 8*1024) else 0.0 |
| fp = rd / "flame.png" |
| flame_size = fp.stat().st_size if fp.exists() else 0 |
| scores["flame_png"] = 1.0 if (fp.exists() and flame_size >= 30*1024) else 0.0 |
| scores["flame_size_bytes"] = flame_size |
| |
| # explanation length: ≥ 80 chars in report (excluding the top_hotspots line) |
| explain_text = re.sub(r"top_hotspots\s*[:=].*", "", text, flags=re.IGNORECASE) |
| scores["explain_long"] = 1.0 if len(explain_text.strip()) >= 80 else 0.0 |
| |
| # parse top_hotspots field |
| expected = [] |
| if (gt_dir / "expected_hot.txt").exists(): |
| try: |
| expected = [ln.strip() for ln in (gt_dir / "expected_hot.txt").read_text(encoding="utf-8", errors="ignore").splitlines() if ln.strip()] |
| except Exception: |
| expected = [] |
| th = re.search(r"top_hotspots\s*[:=]\s*(.+)", text, re.IGNORECASE) |
| reported_names = [] |
| reported_pcts = [] |
| if th: |
| line = th.group(1) |
| # tokens like "worker_a=28%, worker_b=27%, worker_c=25%" |
| for m in re.finditer(r"([A-Za-z_][A-Za-z0-9_]*)\s*=\s*(\d+(?:\.\d+)?)\s*%?", line): |
| reported_names.append(m.group(1)) |
| reported_pcts.append(float(m.group(2))) |
| |
| # 4. all 3 names present |
| name_set = set(n.lower() for n in reported_names) |
| expected_set = set(n.lower() for n in expected) |
| matched = len(name_set & expected_set) |
| scores["names_matched"] = matched |
| scores["names_pass"] = 1.0 if matched >= 3 else (matched / 3.0) |
| |
| # 5. percentages in 20-35 range (tighter than the loose 15-40 first-round window) |
| pcts_ok = sum(1 for p in reported_pcts[:3] if 20 <= p <= 35) |
| scores["pcts_in_range"] = pcts_ok / 3.0 if reported_pcts else 0.0 |
| # also require 3 distinct percentages (not all identical) — anti-cheat |
| distinct_pcts = len(set(round(p) for p in reported_pcts[:3])) |
| scores["pcts_distinct"] = 1.0 if distinct_pcts >= 2 else 0.0 |
| |
| # 6. correct order: report first 3 names should match expected[0..2] |
| order_ok = 0 |
| for i, n in enumerate(reported_names[:3]): |
| if i < len(expected) and n.lower() == expected[i].lower(): |
| order_ok += 1 |
| scores["order_pass"] = order_ok / 3.0 |
| |
| # 7. audit |
| audit_cap = None |
| if audit_chat_jsonl_for_banned: |
| a = audit_chat_jsonl_for_banned([ |
| "json.load", |
| "import json", |
| "py-spy top", |
| "py-spy dump", |
| "speedscope.json", # accidental, but if grep'd profile.json text |
| ]) |
| scores["audit_banned"] = a.get("any_banned", False) |
| scores["audit_screenshots"] = a.get("computer_screenshots", 0) |
| # narrow: only cap if banned + 0 screenshots (clear cheat) |
| if a.get("any_banned") and a.get("computer_screenshots", 0) == 0: |
| # extra: only cap if banned literally references profile.json |
| if any("profile.json" in ex for ex in a.get("banned_examples", [])): |
| audit_cap = 0.20 |
| |
| # weighted base: core deliverable (names + order + flame/profile artifacts) 70%, evidence 20%, aux 10% |
| core = ( |
| 0.45 * scores["names_pass"] + |
| 0.30 * scores["order_pass"] + |
| 0.15 * scores["flame_png"] + |
| 0.10 * scores["profile_json"] |
| ) |
| gui = ( |
| 0.6 * scores["flame_png"] + |
| 0.4 * scores["pcts_in_range"] |
| ) |
| aux = ( |
| 0.5 * scores["report_exists"] + |
| 0.3 * scores["explain_long"] + |
| 0.2 * scores["pcts_distinct"] |
| ) |
| base = 0.70 * core + 0.20 * gui + 0.10 * aux |
| scores["overall_score"] = round(base, 3) |
| |
| # structural hard gates |
| # core deliverable (flame.png) missing -> cap 0.35 |
| if scores["flame_png"] < 1.0: |
| scores["overall_score"] = min(scores["overall_score"], 0.35) |
| # profile.json too small / missing -> cap 0.40 (didn't actually record) |
| if scores["profile_json"] < 1.0: |
| scores["overall_score"] = min(scores["overall_score"], 0.40) |
| # all 3 names not matched -> cap 0.45 (must identify all hot funcs) |
| if scores["names_pass"] < 1.0: |
| scores["overall_score"] = min(scores["overall_score"], 0.45) |
| # order completely wrong -> cap 0.50 |
| if scores["order_pass"] < 1.0/3.0 + 1e-6: |
| scores["overall_score"] = min(scores["overall_score"], 0.50) |
| # percentages all out of plausible range -> cap 0.55 (didn't read flame numbers) |
| if scores["pcts_in_range"] < 1.0/3.0 + 1e-6: |
| scores["overall_score"] = min(scores["overall_score"], 0.55) |
| |
| # 8. VLM HARD GATE |
| vlm_ran = False |
| if vlm_score_rubric and fp.exists() and fp.stat().st_size >= 30*1024: |
| rubric = { |
| "vlm_is_speedscope_flamegraph": "flame.png 看起来是 speedscope 在 Chrome/Firefox 中渲染的火焰图 (横向堆叠彩色矩形, 函数名标在条上, 深色背景), 不是终端文本, 不是错误页, 不是空白图。", |
| "vlm_three_hotspots_visible": "flame.png 中能看到至少 3 条几乎一样宽的 hot frame (而不是只一条很宽其它都细)。", |
| "vlm_three_names_visible": "flame.png 中能直接读出 3 个 hot worker 函数名 (例如 worker_a / worker_b / worker_c, 至少 2 个能直接看见)。", |
| } |
| try: |
| vlm = vlm_score_rubric([str(fp)], rubric, |
| instruction="判断 flame.png 是否真是 speedscope 火焰图,并显示了 3 条相近宽度的 hot frame 含 3 个函数名。") |
| for k in rubric: scores[k] = vlm.get(k, 0.0) |
| scores["judge_method"] = vlm.get("judge_method", "failed") |
| vlm_avg = sum(vlm.get(k, 0.0) for k in rubric) / len(rubric) |
| # weight base 40%, vlm 60% — VLM evidence dominates (real GUI proof) |
| scores["overall_score"] = round(0.4*base + 0.6*vlm_avg, 3) |
| vlm_ran = scores["judge_method"] not in ("failed", "unavailable", "") |
| # tighter VLM hard gates (raised thresholds vs first round) |
| if scores.get("vlm_is_speedscope_flamegraph", 0.0) < 0.7: |
| scores["overall_score"] = min(scores["overall_score"], 0.25) |
| if scores.get("vlm_three_hotspots_visible", 0.0) < 0.7: |
| scores["overall_score"] = min(scores["overall_score"], 0.40) |
| if scores.get("vlm_three_names_visible", 0.0) < 0.6: |
| scores["overall_score"] = min(scores["overall_score"], 0.50) |
| if vlm_avg < 0.4: |
| scores["overall_score"] = min(scores["overall_score"], 0.30) |
| except Exception: |
| pass |
| |
| # VLM unavailable cap: cannot get full score without GUI evidence |
| if not vlm_ran: |
| scores["overall_score"] = min(scores["overall_score"], 0.60) |
| |
| if audit_cap is not None: |
| scores["overall_score"] = min(scores["overall_score"], audit_cap) |
| return scores |
| ``` |
| |
| ## Workspace Path |
| |
| ``` |
| workspace/OPS/task_1_pyspy_flamegraph |
| ``` |
| |
| ## Skills |
| |
| ``` |
| ``` |
| |
| ## Env |
| |
| ``` |
| ``` |
| |
| ## Warmup |
| |
| ```bash |
| mkdir -p /tmp_workspace/results || true |
| chown -R user:user /tmp_workspace |
| chown -R user:user /home/user/.local 2>/dev/null || true |
| rm -f /home/user/.openclaw/agents/main/sessions/*.lock 2>/dev/null || true |
| openclaw config unset agents.defaults.imageModel >/dev/null 2>&1 || true |
| openclaw config set agents.defaults.sandbox.imageModel.primary "" >/dev/null 2>&1 || true |
| ``` |
| |
