5cc306eb0a
后端: - 抽取 fetch_eval_result_content 复用函数，model_eval_detail 直接应用评测任务结果 - health 接口移除数据库依赖，返回静态指标 - 数据集: count_dataset_records JSON 感知计数; 文件统计改为从 dataset_files 聚合重算; 在线编辑记录 size/record_count/version_no; 上传同步批处理 - 算力节点: 调度支持 requested GPU 子集校验与容量计算; 新增 delete_compute_node(含活动任务保护)及 DELETE 接口; 连接池 connect_timeout - 评测任务落库 basic_metrics/score/completed_time, failed/stopped 记录 error 评测引擎: - _load_dataset 支持 JSON/JSONL 文件 - 新增 exact match 与文本相似度指标, 余弦相似度去掉 2 样本限制 前端: - 算力节点列表「维护」改为「删除」(带确认弹窗), compute.ts 新增 deleteComputeNode - 数据集上传超时调整为 120s; FineTuneTask 增加 compute_node_id; GpuInfo 状态增加 reserved
481 lines
18 KiB
Python
481 lines
18 KiB
Python
"""
|
||
Evaluation runner — executes model evaluation as a subprocess job.
|
||
|
||
Usage:
|
||
python -m compute.engines.llama_factory.eval_runner --config <config_json_path>
|
||
|
||
The config JSON is written by the compute API before spawning this subprocess.
|
||
Results are written to ``output_dir/eval_results.json`` and progress is printed
|
||
to stdout (captured as job logs).
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
import math
|
||
import re
|
||
import sys
|
||
import time
|
||
from difflib import SequenceMatcher
|
||
from pathlib import Path
|
||
from typing import Any
|
||
|
||
|
||
def _load_dataset(path: str) -> list[dict[str, Any]]:
|
||
"""Load a JSON or JSONL dataset file.
|
||
|
||
Supports common field names used across the platform:
|
||
* ``instruction`` + ``input`` + ``output`` (Alpaca-style)
|
||
* ``question`` + ``answer``
|
||
* ``messages`` (ShareGPT-style – the last assistant message is treated as reference)
|
||
"""
|
||
file_path = Path(path)
|
||
text = file_path.read_text(encoding="utf-8", errors="replace").strip()
|
||
if not text:
|
||
return []
|
||
if file_path.suffix.lower() == ".json":
|
||
value = json.loads(text)
|
||
if isinstance(value, list):
|
||
return [item for item in value if isinstance(item, dict)]
|
||
return [value] if isinstance(value, dict) else []
|
||
|
||
samples: list[dict[str, Any]] = []
|
||
for line in text.splitlines():
|
||
line = line.strip()
|
||
if not line:
|
||
continue
|
||
try:
|
||
obj = json.loads(line)
|
||
except json.JSONDecodeError:
|
||
continue
|
||
if isinstance(obj, dict):
|
||
samples.append(obj)
|
||
return samples
|
||
|
||
|
||
def _sample_question(sample: dict[str, Any]) -> str:
|
||
"""Extract the user-facing question / instruction from a sample."""
|
||
if sample.get("instruction"):
|
||
text = sample["instruction"]
|
||
if sample.get("input"):
|
||
text += "\n" + sample["input"]
|
||
return text
|
||
if sample.get("question"):
|
||
return sample["question"]
|
||
# ShareGPT-style: use the last user message as question
|
||
messages = sample.get("messages") or []
|
||
user_msgs = [m["content"] for m in messages if m.get("role") == "user"]
|
||
return user_msgs[-1] if user_msgs else ""
|
||
|
||
|
||
def _sample_reference(sample: dict[str, Any]) -> str:
|
||
"""Extract the reference answer from a sample."""
|
||
if sample.get("output"):
|
||
return sample["output"]
|
||
if sample.get("answer"):
|
||
return sample["answer"]
|
||
messages = sample.get("messages") or []
|
||
assistant_msgs = [m["content"] for m in messages if m.get("role") == "assistant"]
|
||
return assistant_msgs[-1] if assistant_msgs else ""
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Basic metrics
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _compute_bleu(references: list[str], predictions: list[str], ngram: int = 4) -> dict[str, Any]:
|
||
"""Compute BLEU score via sacrebleu (corpus-level)."""
|
||
try:
|
||
from sacrebleu.metrics import BLEU
|
||
except ImportError:
|
||
return {"enabled": False, "error": "sacrebleu not installed", "score": 0}
|
||
bleu = BLEU(max_ngram_order=ngram)
|
||
# sacrebleu expects list-of-strings; we have one reference per prediction
|
||
score = bleu.corpus_score(predictions, [references])
|
||
return {
|
||
"enabled": True,
|
||
"score": round(score.score, 2),
|
||
"bleu": round(score.score, 2),
|
||
}
|
||
|
||
|
||
def _compute_rouge(references: list[str], predictions: list[str], methods: list[str] | None = None) -> dict[str, Any]:
|
||
"""Compute ROUGE scores via rouge-score."""
|
||
try:
|
||
from rouge_score import rouge_scorer
|
||
except ImportError:
|
||
return {"enabled": False, "error": "rouge-score not installed", "score": 0}
|
||
methods = methods or ["rouge1", "rouge2", "rougeL"]
|
||
# Normalize: map "rouge_1"/"rouge1" → "rouge1", "rouge_l"/"rougeL" → "rougeL"
|
||
_rouge_aliases = {"rouge_1": "rouge1", "rouge_2": "rouge2", "rouge_l": "rougeL"}
|
||
methods = [_rouge_aliases.get(m, m.replace("_", "")) for m in methods]
|
||
scorer = rouge_scorer.RougeScorer(methods, use_stemmer=True)
|
||
totals: dict[str, float] = {}
|
||
n = max(len(predictions), 1)
|
||
for ref, pred in zip(references, predictions):
|
||
result = scorer.score(ref, pred)
|
||
for key in methods:
|
||
totals[key] = totals.get(key, 0) + result[key].fmeasure
|
||
avg = {k: round(v / n, 4) for k, v in totals.items()}
|
||
return {"enabled": True, "score": round(avg.get("rougeL", avg.get("rouge1", 0)) * 100, 2), **avg}
|
||
|
||
|
||
def _compute_cosine(references: list[str], predictions: list[str]) -> dict[str, Any]:
|
||
"""Compute average cosine similarity via sklearn."""
|
||
try:
|
||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||
from sklearn.metrics.pairwise import cosine_similarity
|
||
except ImportError:
|
||
return {"enabled": False, "error": "scikit-learn not installed", "score": 0}
|
||
try:
|
||
vectorizer = TfidfVectorizer()
|
||
tfidf = vectorizer.fit_transform(references + predictions)
|
||
n = len(references)
|
||
ref_vec = tfidf[:n]
|
||
pred_vec = tfidf[n:]
|
||
sims = cosine_similarity(ref_vec, pred_vec).diagonal()
|
||
return {"enabled": True, "score": round(float(sims.mean()) * 100, 2)}
|
||
except ValueError:
|
||
return {"enabled": True, "score": 0, "error": "insufficient text for vectorization"}
|
||
|
||
|
||
def _normalize_text(value: str) -> str:
|
||
return re.sub(r"\s+", " ", str(value or "").strip().lower())
|
||
|
||
|
||
def _compute_exact_match(references: list[str], predictions: list[str]) -> dict[str, Any]:
|
||
total = len(predictions)
|
||
if not total:
|
||
return {"enabled": True, "score": 0, "matched": 0, "total": 0}
|
||
matched = sum(
|
||
1
|
||
for ref, pred in zip(references, predictions)
|
||
if _normalize_text(ref) == _normalize_text(pred)
|
||
)
|
||
return {"enabled": True, "score": round(matched / total * 100, 2), "matched": matched, "total": total}
|
||
|
||
|
||
def _compute_text_similarity(references: list[str], predictions: list[str]) -> dict[str, Any]:
|
||
if not predictions:
|
||
return {"enabled": True, "score": 0}
|
||
scores = [
|
||
SequenceMatcher(None, _normalize_text(ref), _normalize_text(pred)).ratio()
|
||
for ref, pred in zip(references, predictions)
|
||
]
|
||
return {"enabled": True, "score": round(sum(scores) / max(len(scores), 1) * 100, 2)}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# LLM Judge
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _judge_sample(
|
||
question: str,
|
||
reference: str,
|
||
prediction: str,
|
||
config: dict[str, Any],
|
||
) -> dict[str, Any]:
|
||
"""Call an OpenAI-compatible LLM to judge a single sample.
|
||
|
||
Returns a dict with keys:
|
||
score, max_score, passed, judgement, evaluation_reason, error_type
|
||
"""
|
||
api_url = (config.get("api_url") or "").strip().rstrip("/")
|
||
api_key = (config.get("api_key") or "").strip()
|
||
eval_model = (config.get("eval_model") or "").strip()
|
||
eval_prompt = (config.get("eval_prompt") or "").strip()
|
||
score_min = float(config.get("score_min", 0))
|
||
score_max = float(config.get("score_max", 5))
|
||
pass_threshold = float(config.get("pass_threshold", 3))
|
||
|
||
if not api_url or not eval_model:
|
||
return {"score": 0, "max_score": score_max, "passed": False, "judgement": "未配置",
|
||
"evaluation_reason": "未配置评测模型", "error_type": "其他"}
|
||
|
||
system_msg = (
|
||
eval_prompt
|
||
or "你是一个专业的评测专家。请根据参考答-案对被测模型的输出进行评分。"
|
||
)
|
||
user_msg = (
|
||
f"## 问题\n{question}\n\n"
|
||
f"## 参考答案\n{reference}\n\n"
|
||
f"## 模型输出\n{prediction}\n\n"
|
||
f"请给出 {score_min}-{score_max} 分的评分，并说明理由。"
|
||
)
|
||
|
||
try:
|
||
import urllib.request
|
||
import urllib.error
|
||
|
||
body = json.dumps({
|
||
"model": eval_model,
|
||
"messages": [
|
||
{"role": "system", "content": system_msg},
|
||
{"role": "user", "content": user_msg},
|
||
],
|
||
"temperature": 0.3,
|
||
"max_tokens": 512,
|
||
}).encode("utf-8")
|
||
|
||
req = urllib.request.Request(
|
||
f"{api_url}/v1/chat/completions",
|
||
data=body,
|
||
headers={
|
||
"Content-Type": "application/json",
|
||
"Authorization": f"Bearer {api_key}",
|
||
},
|
||
)
|
||
resp = urllib.request.urlopen(req, timeout=120)
|
||
data = json.loads(resp.read().decode("utf-8"))
|
||
reply = data["choices"][0]["message"]["content"]
|
||
except Exception as exc:
|
||
return {"score": 0, "max_score": score_max, "passed": False,
|
||
"judgement": "错误", "evaluation_reason": f"评测模型调用失败: {exc}",
|
||
"error_type": "其他"}
|
||
|
||
# Parse score from reply — look for patterns like "4分" or "Score: 4"
|
||
score = 0
|
||
import re
|
||
score_patterns = [
|
||
r'(?:得分|分数|评分|score)[^\d]*(\d+(?:\.\d+)?)',
|
||
r'(\d+(?:\.\d+)?)\s*分',
|
||
r'(\d+(?:\.\d+)?)\s*/\s*\d+',
|
||
]
|
||
for pat in score_patterns:
|
||
m = re.search(pat, reply, re.IGNORECASE)
|
||
if m:
|
||
try:
|
||
score = float(m.group(1))
|
||
except ValueError:
|
||
continue
|
||
break
|
||
score = max(score_min, min(score_max, score))
|
||
passed = score >= pass_threshold
|
||
|
||
# Determine judgement label
|
||
if score >= pass_threshold + 1:
|
||
judgement = "正确"
|
||
elif score >= pass_threshold:
|
||
judgement = "部分正确"
|
||
else:
|
||
judgement = "错误"
|
||
|
||
# Guess error type from reply
|
||
reply_lower = reply.lower()
|
||
if any(w in reply_lower for w in ["幻觉", "hallucination", "编造"]):
|
||
error_type = "幻觉"
|
||
elif any(w in reply_lower for w in ["不完整", "incomplete", "遗漏"]):
|
||
error_type = "不完整"
|
||
elif any(w in reply_lower for w in ["格式", "format"]):
|
||
error_type = "格式偏差"
|
||
elif any(w in reply_lower for w in ["混淆", "confusion", "错误"]):
|
||
error_type = "混淆"
|
||
else:
|
||
error_type = "其他"
|
||
|
||
return {
|
||
"score": score,
|
||
"max_score": score_max,
|
||
"passed": passed,
|
||
"judgement": judgement,
|
||
"evaluation_reason": reply[:2000],
|
||
"error_type": error_type,
|
||
}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Main entry point
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def run_eval(config: dict[str, Any]) -> dict[str, Any]:
|
||
"""Execute a full evaluation run. Returns the result dict (also written to file)."""
|
||
model_path = config["model_name_or_path"]
|
||
adapter_path = config.get("adapter_name_or_path", "")
|
||
template = config.get("template", "qwen")
|
||
dataset_path = config["dataset_path"]
|
||
output_dir = Path(config["output_dir"])
|
||
output_dir.mkdir(parents=True, exist_ok=True)
|
||
basic_cfg = config.get("basic_metrics", {})
|
||
dimension_cfg = config.get("dimension", {}) or {}
|
||
output_precision = int(basic_cfg.get("output_precision", 2))
|
||
|
||
# ---- 1. Load dataset ----
|
||
print(f"[eval] loading dataset: {dataset_path}")
|
||
raw_samples = _load_dataset(dataset_path)
|
||
print(f"[eval] loaded {len(raw_samples)} samples")
|
||
|
||
# ---- 2. Load model ----
|
||
print(f"[eval] loading model: {model_path}")
|
||
from compute.engines.llama_factory.inference import InferenceSession
|
||
session = InferenceSession()
|
||
load_result = session.load(
|
||
model_name_or_path=model_path,
|
||
adapter_name_or_path=adapter_path,
|
||
template=template,
|
||
infer_backend=config.get("infer_backend", "huggingface"),
|
||
infer_dtype=config.get("infer_dtype", "auto"),
|
||
)
|
||
if not load_result.get("loaded"):
|
||
raise RuntimeError(f"model load failed: {load_result.get('error', 'unknown')}")
|
||
print(f"[eval] model loaded OK")
|
||
|
||
# ---- 3. Run inference on each sample ----
|
||
samples: list[dict[str, Any]] = []
|
||
predictions: list[str] = []
|
||
references: list[str] = []
|
||
questions: list[str] = []
|
||
|
||
total = len(raw_samples)
|
||
judge_enabled = bool(dimension_cfg.get("eval_model") and dimension_cfg.get("api_url"))
|
||
print(f"[eval] starting inference on {total} samples, judge={'enabled' if judge_enabled else 'disabled'}")
|
||
|
||
for idx, raw in enumerate(raw_samples, start=1):
|
||
question = _sample_question(raw)
|
||
reference = _sample_reference(raw)
|
||
if not question:
|
||
print(f"[eval] sample {idx}/{total}: skipped (no question)")
|
||
continue
|
||
|
||
# Inference
|
||
chat_msgs = [{"role": "user", "content": question}]
|
||
result = session.chat(
|
||
chat_msgs,
|
||
temperature=float(config.get("temperature", 0.1)),
|
||
top_p=float(config.get("top_p", 0.95)),
|
||
max_new_tokens=int(config.get("max_new_tokens", 512)),
|
||
do_sample=False,
|
||
)
|
||
prediction = result.get("response", "") if not result.get("error") else f"[ERROR] {result['error']}"
|
||
|
||
predictions.append(prediction)
|
||
references.append(reference)
|
||
questions.append(question)
|
||
|
||
# LLM Judge
|
||
judge_result: dict[str, Any] = {}
|
||
if judge_enabled:
|
||
judge_result = _judge_sample(question, reference, prediction, dimension_cfg)
|
||
|
||
samples.append({
|
||
"index": idx,
|
||
"input": question,
|
||
"reference_answer": reference,
|
||
"model_output": prediction,
|
||
"score": judge_result.get("score"),
|
||
"max_score": judge_result.get("max_score", dimension_cfg.get("score_max", 5)),
|
||
"passed": judge_result.get("passed"),
|
||
"judgement": judge_result.get("judgement"),
|
||
"evaluation_reason": judge_result.get("evaluation_reason", ""),
|
||
"error_type": judge_result.get("error_type"),
|
||
"dimension_scores": [
|
||
{"name": "judge_score", "score": judge_result.get("score", 0),
|
||
"max_score": judge_result.get("max_score", dimension_cfg.get("score_max", 5))},
|
||
] if judge_result else [],
|
||
"status": "completed",
|
||
})
|
||
|
||
progress_pct = int(idx / max(total, 1) * 100)
|
||
print(f"[eval] sample {idx}/{total} ({progress_pct}%) done")
|
||
|
||
# ---- 4. Compute basic metrics ----
|
||
print(f"[eval] computing basic metrics on {len(predictions)} predictions")
|
||
metrics_result: dict[str, Any] = {}
|
||
|
||
bleu_cfg = basic_cfg.get("bleu", {})
|
||
if bleu_cfg.get("enabled"):
|
||
metrics_result["bleu"] = _compute_bleu(references, predictions, int(bleu_cfg.get("ngram", 4)))
|
||
|
||
rouge_cfg = basic_cfg.get("rouge", {})
|
||
if rouge_cfg.get("enabled"):
|
||
metrics_result["rouge"] = _compute_rouge(references, predictions, rouge_cfg.get("methods"))
|
||
|
||
cosine_cfg = basic_cfg.get("cosine", {})
|
||
if cosine_cfg.get("enabled"):
|
||
metrics_result["cosine"] = _compute_cosine(references, predictions)
|
||
metrics_result["exact_match"] = _compute_exact_match(references, predictions)
|
||
metrics_result["text_similarity"] = _compute_text_similarity(references, predictions)
|
||
|
||
# ---- 5. Summarise ----
|
||
completed = len(samples)
|
||
if judge_enabled:
|
||
scored = [s for s in samples if s.get("score") is not None]
|
||
passed_count = len([s for s in scored if s.get("passed")])
|
||
avg_score = round(sum(s["score"] for s in scored) / max(len(scored), 1), output_precision)
|
||
max_score = dimension_cfg.get("score_max", 5)
|
||
overall_score = round(avg_score / max_score * 100, output_precision)
|
||
overall_score_max = 100
|
||
dimension_summary = [{
|
||
"name": "综合评分",
|
||
"score": overall_score,
|
||
"max_score": 100,
|
||
"pass_rate": round(passed_count / max(completed, 1) * 100, 1),
|
||
}]
|
||
overall_evaluation = f"评测完成：{completed} 样本，{passed_count} 通过，平均 {avg_score}/{max_score} 分"
|
||
else:
|
||
passed_count = 0
|
||
enabled_scores = [
|
||
float(item.get("score") or 0)
|
||
for item in metrics_result.values()
|
||
if isinstance(item, dict) and item.get("enabled", True) and item.get("score") is not None
|
||
]
|
||
overall_score = round(sum(enabled_scores) / len(enabled_scores), output_precision) if enabled_scores else 0
|
||
overall_score_max = 100
|
||
dimension_summary = [
|
||
{
|
||
"name": name,
|
||
"score": float(item.get("score") or 0),
|
||
"max_score": 100,
|
||
"pass_rate": float(item.get("score") or 0),
|
||
}
|
||
for name, item in metrics_result.items()
|
||
if isinstance(item, dict) and item.get("enabled", True) and item.get("score") is not None
|
||
]
|
||
overall_evaluation = f"评测完成：{completed} 样本（未配置 LLM 评委）"
|
||
|
||
result = {
|
||
"overall_score": overall_score,
|
||
"overall_score_max": overall_score_max,
|
||
"overall_evaluation": overall_evaluation,
|
||
"improvement_suggestions": [],
|
||
"dimension_summary": dimension_summary,
|
||
"samples": samples,
|
||
"sample_count": total,
|
||
"completed_count": completed,
|
||
"passed_count": passed_count,
|
||
"basic_metrics": metrics_result,
|
||
}
|
||
|
||
# ---- 6. Write results ----
|
||
result_path = output_dir / "eval_results.json"
|
||
result_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
|
||
print(f"[eval] results written to {result_path}")
|
||
return result
|
||
|
||
|
||
def main() -> None:
|
||
import argparse
|
||
parser = argparse.ArgumentParser(description="YG-FT Evaluation Runner")
|
||
parser.add_argument("--config", required=True, help="Path to eval config JSON file")
|
||
args = parser.parse_args()
|
||
|
||
config_path = Path(args.config)
|
||
if not config_path.exists():
|
||
print(f"FATAL: config file not found: {args.config}", file=sys.stderr)
|
||
sys.exit(1)
|
||
|
||
config = json.loads(config_path.read_text(encoding="utf-8"))
|
||
start = time.time()
|
||
try:
|
||
run_eval(config)
|
||
elapsed = time.time() - start
|
||
print(f"[eval] DONE in {elapsed:.1f}s")
|
||
except Exception as exc:
|
||
print(f"[eval] FAILED: {exc}", file=sys.stderr)
|
||
import traceback
|
||
traceback.print_exc()
|
||
sys.exit(1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|