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import json
import sys
import time
import urllib.request
from pathlib import Path
import torch
from peft import PeftModel
from tokenizers import Tokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from generate import extract_answer # noqa: E402
from model import GPT, GPTConfig # noqa: E402
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DOMAIN_TAGS = {
"godot": "<|godot|>\n",
"unity": "Domain: Unity\n",
"unreal": "Domain: Unreal Engine\n",
"general": "",
}
SYSTEM_BY_DOMAIN = {
"godot": "You are a senior Godot 4 and GDScript engineer. Write concise, runnable game code.",
"unity": "You are a senior Unity and C# engineer. Write concise, runnable Unity game code.",
"unreal": "You are a senior Unreal Engine 5 and C++ engineer. Write concise, runnable Unreal code.",
}
def read_jsonl(path):
rows = []
with Path(path).open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def build_yuspec_prompt(item):
return (
f"<|bos|>{DOMAIN_TAGS.get(item['domain'], '')}"
"<|user|>\n"
f"{item['prompt']}\n"
"<|assistant|>\n"
)
def load_yuspec(checkpoint):
ckpt = torch.load(checkpoint, map_location=DEVICE)
cfg = ckpt["config"]
model = GPT(GPTConfig(**cfg["model"])).to(DEVICE)
model.load_state_dict(ckpt["model"])
model.eval()
tokenizer = Tokenizer.from_file(cfg["data"]["tokenizer_path"])
return model, tokenizer
@torch.no_grad()
def call_yuspec(model, tokenizer, item, max_new_tokens):
ids = tokenizer.encode(build_yuspec_prompt(item)).ids[-900:]
x = torch.tensor([ids], dtype=torch.long, device=DEVICE)
eos_id = tokenizer.token_to_id("<|eos|>")
started = time.time()
out = model.generate(
x,
max_new_tokens=max_new_tokens,
temperature=0.18,
top_k=12,
eos_id=eos_id,
vocab_limit=tokenizer.get_vocab_size(),
)
decoded = tokenizer.decode(out[0].tolist())
return extract_answer(decoded, item["prompt"]).replace("\ufffd", "").strip(), time.time() - started
def load_lora(base_model, adapter):
dtype = torch.float16 if DEVICE == "cuda" else torch.float32
tokenizer = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=dtype, trust_remote_code=True).to(DEVICE)
model = PeftModel.from_pretrained(base, adapter).to(DEVICE)
model.eval()
return model, tokenizer
@torch.no_grad()
def call_lora(model, tokenizer, item, max_new_tokens):
messages = [
{"role": "system", "content": SYSTEM_BY_DOMAIN.get(item["domain"], "Write game-development code.")},
{"role": "user", "content": item["prompt"]},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=900).to(DEVICE)
started = time.time()
out = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.18,
top_k=20,
pad_token_id=tokenizer.eos_token_id,
)
text = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
return text.strip(), time.time() - started
def post_json(url, payload, timeout):
data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"}, method="POST")
with urllib.request.urlopen(req, timeout=timeout) as response:
return json.loads(response.read().decode("utf-8"))
def call_ollama(model_name, item, max_new_tokens, timeout):
prompt = (
f"{SYSTEM_BY_DOMAIN.get(item['domain'], 'Write game-development code.')}\n"
"Return practical code first. Do not switch to another engine.\n\n"
f"Task: {item['prompt']}"
)
started = time.time()
data = post_json(
"http://127.0.0.1:11434/api/generate",
{
"model": model_name,
"prompt": prompt,
"stream": False,
"options": {"temperature": 0.18, "top_k": 20, "num_predict": max_new_tokens},
},
timeout,
)
return data.get("response", "").strip(), time.time() - started
def has_mojibake(text):
return any(token in text for token in ("Ã", "�", "\ufffd"))
def expected_hits(answer, expected):
lower = answer.lower()
return [term for term in expected if term.lower() in lower]
def score_answer(item, answer):
hits = expected_hits(answer, item.get("expected", []))
wrong = [term for term in item.get("wrong", []) if term.lower() in answer.lower()]
checks = {
"not_empty": len(answer.strip()) >= 120,
"has_code_fence": "```" in answer or item["domain"] == "unreal",
"no_mojibake": not has_mojibake(answer),
"no_wrong_engine": not wrong,
"has_expected_terms": len(hits) >= max(3, min(6, len(item.get("expected", [])) - 1)),
"mentions_task_object": any(term.lower() in answer.lower() for term in item["prompt"].replace(",", " ").split() if len(term) >= 6),
}
score = sum(int(value) for value in checks.values())
score += min(4, len(hits))
return {
"score": min(10, score),
"max_score": 10,
"checks": checks,
"expected_hits": hits,
"wrong_terms": wrong,
}
def write_summary(rows, out_md):
candidates = []
for row in rows:
if row["candidate"] not in candidates:
candidates.append(row["candidate"])
lines = ["# Direct Game Command Benchmark", ""]
lines.append("| Candidate | Total | Average | Avg latency |")
lines.append("|---|---:|---:|---:|")
for candidate in candidates:
subset = [row for row in rows if row["candidate"] == candidate]
total = sum(row["metrics"]["score"] for row in subset)
max_total = sum(row["metrics"]["max_score"] for row in subset)
latencies = [row["latency_sec"] for row in subset if row["latency_sec"] is not None]
avg_latency = sum(latencies) / len(latencies) if latencies else 0.0
lines.append(f"| `{candidate}` | {total}/{max_total} | {total / max_total:.2%} | {avg_latency:.2f}s |")
lines.append("")
lines.append("## Per Command")
lines.append("")
lines.append("| Command | Domain | " + " | ".join(f"`{c}`" for c in candidates) + " |")
lines.append("|---|---|" + "|".join(["---:"] * len(candidates)) + "|")
for item_id in sorted({row["id"] for row in rows}):
first = next(row for row in rows if row["id"] == item_id)
scores = []
for candidate in candidates:
row = next(row for row in rows if row["id"] == item_id and row["candidate"] == candidate)
scores.append(str(row["metrics"]["score"]))
lines.append(f"| `{first['prompt']}` | {first['domain']} | " + " | ".join(scores) + " |")
Path(out_md).write_text("\n".join(lines) + "\n", encoding="utf-8")
def main():
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8")
parser = argparse.ArgumentParser()
parser.add_argument("--benchmark", default="eval/direct_command_benchmark.jsonl")
parser.add_argument("--out-jsonl", default="eval/results_direct_command_benchmark.jsonl")
parser.add_argument("--out-md", default="eval/results_direct_command_benchmark.md")
parser.add_argument("--yuspec-checkpoint", default="checkpoints/compound_game_commands_60m_v5/best.pt")
parser.add_argument("--qwen-base", default="Qwen/Qwen2.5-0.5B-Instruct")
parser.add_argument("--qwen-adapter", default="checkpoints/qwen2_5_0_5b_gamedev_lora_godot_balanced")
parser.add_argument("--qwen05", default="qwen2.5:0.5b")
parser.add_argument("--qwen7b", default="qwen2.5:7b-instruct-q4_K_M")
parser.add_argument("--max-new-tokens", type=int, default=700)
parser.add_argument("--timeout", type=int, default=240)
parser.add_argument("--skip-yuspec", action="store_true")
parser.add_argument("--skip-lora", action="store_true")
parser.add_argument("--skip-qwen05", action="store_true")
parser.add_argument("--skip-qwen7b", action="store_true")
args = parser.parse_args()
items = read_jsonl(args.benchmark)
rows = []
candidates = []
if not args.skip_yuspec:
yuspec = load_yuspec(args.yuspec_checkpoint)
candidates.append(("yuspec_60m_compound_v5", lambda item: call_yuspec(*yuspec, item, args.max_new_tokens), "local_yuspec"))
if not args.skip_lora:
lora = load_lora(args.qwen_base, args.qwen_adapter)
candidates.append(("qwen2.5_0.5b_lora", lambda item: call_lora(*lora, item, args.max_new_tokens), "hf_lora"))
if not args.skip_qwen05:
candidates.append(("qwen2.5_0.5b", lambda item: call_ollama(args.qwen05, item, args.max_new_tokens, args.timeout), "ollama"))
if not args.skip_qwen7b:
candidates.append(("qwen2.5_7b", lambda item: call_ollama(args.qwen7b, item, args.max_new_tokens, args.timeout), "ollama"))
for name, call_fn, mode in candidates:
for item in items:
try:
answer, latency = call_fn(item)
metrics = score_answer(item, answer)
print(f"{name} | {item['id']}: {metrics['score']}/10")
error = None
except Exception as exc:
answer = ""
latency = None
metrics = {"score": 0, "max_score": 10, "checks": {}, "error": str(exc)}
error = str(exc)
print(f"{name} | {item['id']}: ERROR {exc}")
rows.append(
{
"candidate": name,
"id": item["id"],
"domain": item["domain"],
"prompt": item["prompt"],
"answer": answer,
"metrics": metrics,
"latency_sec": latency,
"mode": mode,
"error": error,
}
)
out_jsonl = Path(args.out_jsonl)
out_jsonl.parent.mkdir(parents=True, exist_ok=True)
with out_jsonl.open("w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=False) + "\n")
write_summary(rows, args.out_md)
print(f"wrote {args.out_jsonl}")
print(f"wrote {args.out_md}")
if __name__ == "__main__":
main()
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