Datasets:
Download code/mt_plan.py from drawais/circuitqa: direct link, hf CLI and curl.
- Browser
- Download file 2.71 kB
-
https://huggingface.co/datasets/drawais/circuitqa/resolve/main/code/mt_plan.py
- Command line
-
hf download hf://datasets/drawais/circuitqa/code/mt_plan.py
-
curl -L -o mt_plan.py https://huggingface.co/datasets/drawais/circuitqa/resolve/main/code/mt_plan.py
2.71 kB
| #!/usr/bin/env python3 | |
| """Multi-tool set, step 2 (after grounding every backend variant): | |
| draw backend combinations per design ONLY from calls that succeeded on it. | |
| 80% two-backend, 20% three-backend; PER distinct combinations per design. | |
| Parameters: graph type CDFG/AIG/DFG 50/25/25, optimisation target area/delay/power 50/25/25, | |
| restricted to variants that succeeded. | |
| python3 mt_plan.py -> splits/mt/{train,val,test}.plan.jsonl | |
| """ | |
| import json, random, hashlib, itertools, collections | |
| OK = {"success", "estimated"} | |
| PER = {"train": 2, "val": 2, "test": 2} | |
| W = {"CDFG": .5, "AIG": .25, "DFG": .25, "area": .5, "delay": .25, "power": .25} | |
| rnd = random.Random(2026) | |
| designs = {json.loads(l)["design_id"]: json.loads(l) for l in open("splits/mt/designs_full.jsonl")} | |
| good = collections.defaultdict(lambda: collections.defaultdict(list)) # did -> tool -> [call] | |
| for l in open("splits/mt/results_full.jsonl"): | |
| r = json.loads(l) | |
| if r["result"].get("status") in OK: | |
| good[r["design_id"]][r["tool"]].append({"tool": r["tool"], "parameters": r["parameters"], "key": r["key"]}) | |
| stats = collections.Counter() | |
| for split in ("train", "val", "test"): | |
| out = [] | |
| ids = sorted(d for d in designs if designs[d]["split"] == split) | |
| for did in ids: | |
| tools = sorted(good[did]) | |
| if len(tools) < 2: stats[(split, "fewer_than_2_tools")] += 1; continue | |
| combos = list(itertools.combinations(tools, 2)); tri = list(itertools.combinations(tools, 3)) | |
| chosen = set(); tries = 0 | |
| while len(chosen) < min(PER[split], len(combos) + len(tri)) and tries < 50: | |
| tries += 1 | |
| pool = tri if (tri and rnd.random() < 0.2) else combos | |
| combo = rnd.choice(pool) | |
| if combo in chosen: continue | |
| chosen.add(combo) | |
| calls = [] | |
| for t in combo: | |
| vs = good[did][t] | |
| v = rnd.choices(vs, [W.get(next(iter(c["parameters"].values()), ""), 1) if c["parameters"] else 1 for c in vs])[0] \ | |
| if t in ("graph_extractor", "optimization_advisor") else vs[0] | |
| calls.append(v) | |
| d = designs[did] | |
| out.append({"plan_id": hashlib.sha256(json.dumps([did, combo]).encode()).hexdigest()[:12], "design_id": did, | |
| "split": split, "module_name": d["module_name"], "source_file": d["source_file"], "calls": calls}) | |
| stats[(split, f"{len(combo)}-tool")] += 1 | |
| with open(f"splits/mt/{split}.plan.jsonl", "w") as f: | |
| for o in out: f.write(json.dumps(o) + "\n") | |
| stats[(split, "designs")] = len({o["design_id"] for o in out}); stats[(split, "plans")] = len(out) | |
| for k, v in sorted(stats.items()): print(k, v) | |