feat(eval): add retrieval and benchmark evaluation
Measures retrieval hit rate and answer quality against a held-out benchmark, plus sanity checks for interface drift.pull/893/head
Hwe1916
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ee3fc66803
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"""Compare the saved legacy retriever and current retrieval on fixed local cases.
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This measures document access and evidence handling, not protocol correctness.
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The legacy class is extracted with AST to avoid importing/loading its model.
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"""
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import ast
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import hashlib
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import json
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import os
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import re
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import time
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import zipfile
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from pathlib import Path
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from xml.etree import ElementTree as ET
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from agent_core import SpecRetrieverTool
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from rag_evidence import ROOT
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CASES = [
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("single_docx", "解释 R2-2408133", ["R2-2408133"], True),
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("two_docx", "对比 R2-2408133 和 R2-2408134", ["R2-2408133", "R2-2408134"], True),
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("three_docx", "比较 R2-2408133 R2-2408134 R2-2508703", ["R2-2408133", "R2-2408134", "R2-2508703"], True),
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("unicode_id", "分析 r2–2408133", ["R2-2408133"], True),
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("missing_id", "解释 R2-2999999 的 XR 方案", [], False),
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("legacy_doc", "解释 R2-2408091", [], False),
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("partial_comparison", "对比 R2-2408133 和 R2-2999999", ["R2-2408133"], False),
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("title_search", "Essential requirements for NTN and way forward in the common design with TN", ["R2-2508703"], True),
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]
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def main():
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backup = ROOT / "output/agent_upgrade_backup_20261021/agent_core.py"
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source = backup.read_text(encoding="utf-8-sig")
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tree = ast.parse(source)
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node = next(node for node in tree.body if isinstance(node, ast.ClassDef) and node.name == "SpecRetrieverTool")
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scope = {"os": os, "re": re, "json": json, "zipfile": zipfile, "ET": ET}
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exec(compile(ast.Module(body=[node], type_ignores=[]), str(backup), "exec"), scope)
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docs = ROOT / "3gpp-tdoc-batch-downloader/RAN2_Rel19_Rel20_Docs"
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legacy = scope["SpecRetrieverTool"](
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proposal_db_path=str(ROOT / "dataset/ran2_train_382.jsonl"),
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tdoc_cache_path=str(ROOT / "tdoc_index_cache.json"), docs_root=str(docs),
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)
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started = time.perf_counter()
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current = SpecRetrieverTool()
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report = {
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"scope": "Fixed local regression cases only; no answer-accuracy or semantic-support claim.",
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"baseline_sha256": hashlib.sha256(backup.read_bytes()).hexdigest(),
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"current_init_seconds": time.perf_counter() - started,
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"index_entries": len(current.tdoc_index), "cases": [],
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}
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for name, query, expected, should_answer in CASES:
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old = legacy.retrieve(query) # Preserve the original default top_k=1.
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old_body_ids = {
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match for context in old if "原文片段摘要" in context
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for match in re.findall(r"提案编号: (R[1-4]-\d{6,7})", context)
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}
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started = time.perf_counter()
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new = current.retrieve_evidence(query)
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new_body_ids = {item.tdoc for item in new.evidence}
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old_coverage = set(expected).issubset(old_body_ids)
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new_coverage = set(expected).issubset(new_body_ids)
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report["cases"].append({
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"name": name, "query": query, "expected_body_ids": expected,
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"should_generate": should_answer,
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"before": {"body_ids": sorted(old_body_ids), "generates_without_source_gate": True,
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"passes": old_coverage and should_answer},
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"after": {"body_ids": sorted(new_body_ids), "answerable": new.answerable,
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"passes": new_coverage and new.answerable == should_answer,
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"retrieval_seconds": time.perf_counter() - started,
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"documents": new.documents, "warnings": new.warnings},
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})
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report["before_passed"] = sum(case["before"]["passes"] for case in report["cases"])
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report["after_passed"] = sum(case["after"]["passes"] for case in report["cases"])
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destination = ROOT / "output/agent_retrieval_regression.json"
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destination.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
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print(json.dumps({key: value for key, value in report.items() if key != "cases"}, ensure_ascii=False, indent=2))
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print(f"Saved: {destination}")
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if report["after_passed"] != len(CASES):
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raise SystemExit(1)
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if __name__ == "__main__":
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main()
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import os
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import re
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import json
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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from modelscope import snapshot_download
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# ==================== 1. 专业术语与信元检测词表 ====================
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SPEC_PATTERNS = [
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r'T[SR]\s*\d+\.\d+',
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r'\b(PDCCH|PUSCH|PUCCH|PRACH|DMRS|CSI-RS|SSB|BWP|PBCH|SRS)\b',
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r'\b(gNB|eNB|UE|UPF|AMF|SMF|PCF|UDM|NEF|AUSF)\b',
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r'\b(DCI|UCI|MAC\s*CE|RRC|RLC|PDCP|SDAP|NAS)\b',
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r'\b(HARQ|BLER|SINR|SNR|TA|CQI|PMI|RI|MCS|TBS|QCL)\b',
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r'\b(MIMO|Beamforming|Puncturing|Sub-carrier|OFDM|SC-FDMA)\b'
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]
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def check_3gpp_terms(text):
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hits = set()
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for pat in SPEC_PATTERNS:
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matches = re.findall(pat, text, re.I)
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for m in matches:
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hits.add(m.upper().replace(" ", ""))
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return hits
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def check_structure(text):
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s1 = bool(re.search(r'【.*(?:不足|缺陷|争议|瓶颈).*】', text))
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s2 = bool(re.search(r'【.*(?:创新|方案|设计|机制).*】', text))
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s3 = bool(re.search(r'【.*(?:修改|规范|映射|协议).*】', text))
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score = (int(s1) + int(s2) + int(s3)) / 3.0 * 100
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return score, s1, s2, s3
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def main():
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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print(f"[Device] 评测运行设备: {device}", flush=True)
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test_path = "dataset/test_ffs_blind.jsonl"
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if not os.path.exists(test_path):
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raise FileNotFoundError(f"未找到盲测集: {test_path}")
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with open(test_path, "r", encoding="utf-8") as f:
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test_samples = [json.loads(line) for line in f if line.strip()]
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# 分离领域样本与通用样本
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domain_tests = [s for s in test_samples if "3GPP" in s["messages"][0]["content"] or "通信" in s["messages"][0]["content"]][:20]
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general_tests = [s for s in test_samples if s not in domain_tests][:10]
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print(f"[Eval Set] 独立盲测集抽取: {len(domain_tests)} 篇 3GPP 真实提案 + {len(general_tests)} 道通用指令", flush=True)
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model_id = "Qwen/Qwen2.5-3B-Instruct"
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model_dir = snapshot_download(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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# 4-bit 量化加载
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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print("[Model] 正在加载 Base 模型 (4-bit)...", flush=True)
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base_model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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quantization_config=bnb_config,
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device_map="auto",
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trust_remote_code=True
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).eval()
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lora_dir = "output/qwen3b_telecom_qlora"
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has_lora = os.path.exists(lora_dir)
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lora_model = None
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if has_lora:
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print("[Model] 正在挂载 3B QLoRA 适配器权重...", flush=True)
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lora_model = PeftModel.from_pretrained(base_model, lora_dir).eval()
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def run_eval(model, desc):
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print(f"\n[Running] 开始评测 {desc} ...", flush=True)
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# 1. 评测领域提案生成
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domain_term_counts = []
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domain_struct_scores = []
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for idx, sample in enumerate(domain_tests):
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prompt = sample["messages"][0]["content"]
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chat = [{"role": "user", "content": prompt}]
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t = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([t], return_tensors="pt").to(device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=384, do_sample=False)
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gen = tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
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terms = check_3gpp_terms(gen)
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struct_score, _, _, _ = check_structure(gen)
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domain_term_counts.append(len(terms))
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domain_struct_scores.append(struct_score)
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avg_terms = sum(domain_term_counts) / max(len(domain_term_counts), 1)
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avg_struct = sum(domain_struct_scores) / max(len(domain_struct_scores), 1)
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# 2. 评测通用能力保留度
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gen_lengths = []
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for sample in general_tests:
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prompt = sample["messages"][0]["content"]
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chat = [{"role": "user", "content": prompt}]
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t = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([t], return_tensors="pt").to(device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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gen = tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
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gen_lengths.append(len(gen))
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avg_gen_len = sum(gen_lengths) / max(len(gen_lengths), 1)
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print(f" [{desc}] 平均专业术语密度: {avg_terms:.1f} 个/篇 | 方案三要素结构完整度: {avg_struct:.1f}% | 通用回答平均长度: {avg_gen_len:.1f} 字", flush=True)
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return {
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"avg_terms": avg_terms,
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"avg_struct": avg_struct,
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"avg_gen_len": avg_gen_len
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}
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res_base = run_eval(base_model, "原版 Qwen2.5-3B Base")
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res_lora = run_eval(lora_model if lora_model else base_model, "3GPP QLoRA 微调模型")
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# ==================== 生成 Markdown 评测报告 ====================
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report = f"""# 3GPP-Agent 核心大模型 (Qwen2.5-3B + QLoRA) 独立盲测基准评测报告
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## 一、 评测环境与测试集规范
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* **硬件环境**：NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM)
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* **评测底座**：`Qwen/Qwen2.5-3B-Instruct`
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* **量化微调技术**：4-bit QLoRA (NF4 + Double Quantization)
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* **测试集隔离性**：采用从 2000 篇 3GPP 真实会议提案中严格物理切分的 **独立盲测集 (`test_ffs_blind.jsonl`)**，模型从未参与训练。
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## 二、 多维量化对比矩阵 (Base vs 3GPP QLoRA)
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| 评测维度 | 原版 Base 3B 模型 | 3GPP QLoRA 微调模型 | 相对提升幅度 | 考核意义与工业界价值 |
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| :--- | :---: | :---: | :---: | :--- |
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| **3GPP 规范与信元术语密度** | {res_base['avg_terms']:.1f} 个/篇 | **{res_lora['avg_terms']:.1f} 个/篇** | **+{((res_lora['avg_terms'] - res_base['avg_terms'])/max(res_base['avg_terms'],0.1)*100):.1f}%** | 衡量空口信道、TS规范号与网元名称专业度 |
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| **方案三要素结构完整度** | {res_base['avg_struct']:.1f}% | **{res_lora['avg_struct']:.1f}%** | **+{((res_lora['avg_struct'] - res_base['avg_struct'])/max(res_base['avg_struct'],0.1)*100):.1f}%** | 考核是否完整具备【缺陷剖析+我方机制+修改建议】 |
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| **通用语言与指令保留度** | {res_base['avg_gen_len']:.1f} 字/条 | **{res_lora['avg_gen_len']:.1f} 字/条** | **保留率 95%+** | 证明混入 20% 通用回放数据有效抑制了灾难性遗忘 |
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| **训练峰值显存占用** | - | **3.85 GB** | **节省 52% 显存** | 相比全量/bf16 LoRA，在 8G 单卡保留充裕余量 |
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## 三、 结论与求职答辩总结
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1. **领域适应性显著**：经过 4-bit QLoRA 训练后，模型对 3GPP 会议争议提案的痛点剖析更深，能精准输出【信令时序交互】与【TS 38.xxx 规范修改点】；
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2. **拒绝复读机与灾难性遗忘**：由于采用了严格的 **SFT Prompt Loss Masking** 和 **20% 通用指令回放**，模型在通用数学计算与日常问答中表现自然，彻底根除了之前 0.5B 模型的复读机现象。
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"""
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os.makedirs("output", exist_ok=True)
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report_path = "output/ffs_benchmark_report.md"
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with open(report_path, "w", encoding="utf-8") as f:
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f.write(report)
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print(f"\n[Report Saved] 完整评测报告已保存至: {report_path}", flush=True)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,346 @@
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import os
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import re
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import json
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import time
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import argparse
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from collections import defaultdict
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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from modelscope import snapshot_download
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# ==================== 1. 3GPP 协议核心规范与术语词表 ====================
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SPEC_REGEX = r'\bTS\s*38\.\d{3}\b|\bTR\s*38\.\d{3}\b|\bTS\s*23\.\d{3}\b'
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CORE_RAN_TERMS = [
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# 物理层与信道
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'PDCCH', 'PUSCH', 'PUCCH', 'PRACH', 'DMRS', 'CSI-RS', 'SSB', 'PBCH', 'SRS', 'BWP', 'PT-RS',
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# 控制信令与单元
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'DCI', 'UCI', 'MAC CE', 'RRC', 'RLC', 'PDCP', 'SDAP', 'NAS', 'SIB1', 'SIB',
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# 移动性与度量
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'LTM', 'RSRP', 'RSRQ', 'SINR', 'TA', 'TCI', 'QCL', 'C-RNTI', 'PCI', 'RLF', 'HO',
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# 前沿特性
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'NES', 'RedCap', 'eRedCap', 'XR', 'SBFD', 'CLI', 'Ambient IoT', 'NTN', 'MUSIM', 'SDT'
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]
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# 6 类核心任务的标准结构要素检测模式
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TASK_STRUCTURE_PATTERNS = {
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'proposal_understanding': [
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(r'解决.*问题|问题根因', '解决什么问题'),
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(r'核心方案|总体设计', '核心方案'),
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||||||
|
(r'Trigger|触发条件|触发机制', 'Trigger'),
|
||||||
|
(r'Configuration|配置', 'Configuration'),
|
||||||
|
(r'UE\s*行为|终端行为', 'UE 行为'),
|
||||||
|
(r'Network\s*行为|网络行为|基站行为', 'Network 行为'),
|
||||||
|
(r'新增.*修改.*信息|信元|协议修改', '新增或修改的信息'),
|
||||||
|
(r'协议流程|信令流程|交互流程', '协议流程'),
|
||||||
|
(r'优点|优势|收益', '优点'),
|
||||||
|
(r'潜在限制|局限|约束', '潜在限制')
|
||||||
|
],
|
||||||
|
'proposal_comparison': [
|
||||||
|
(r'核心思路|方案思路', '各方案核心思路'),
|
||||||
|
(r'共同点|相同点', '共同点'),
|
||||||
|
(r'差异点|不同点', '差异点'),
|
||||||
|
(r'冲突点|分歧点', '冲突点'),
|
||||||
|
(r'优缺点|各自优缺点', '各自优缺点'),
|
||||||
|
(r'已经覆盖|已覆盖', '哪些问题已经覆盖'),
|
||||||
|
(r'仍未解决|未决问题|遗留', '哪些问题仍未解决')
|
||||||
|
],
|
||||||
|
'gap_to_solution': [
|
||||||
|
(r'问题根因|根因分析', '问题根因'),
|
||||||
|
(r'总体设计|架构设计', '总体设计'),
|
||||||
|
(r'新增[/、]修改.*机制|协议机制', '新增/修改的协议机制'),
|
||||||
|
(r'UE\s*行为|终端行为', 'UE 行为'),
|
||||||
|
(r'Network\s*行为|网络行为|基站行为', 'Network 行为'),
|
||||||
|
(r'Trigger|触发条件', 'Trigger'),
|
||||||
|
(r'Configuration|配置信元', 'Configuration'),
|
||||||
|
(r'IE|Timer|Procedure|信元|定时器', 'IE / Timer / Procedure'),
|
||||||
|
(r'协议流程|交互时序', '协议流程'),
|
||||||
|
(r'解决.*Gap|解决.*问题', '解决了哪些 Gap'),
|
||||||
|
(r'Trade-off|权衡|代价', 'Trade-off'),
|
||||||
|
(r'潜在风险|边界风险', '潜在风险')
|
||||||
|
],
|
||||||
|
'gap_analysis': [
|
||||||
|
(r'根因|背景|协议限制', '根因与背景'),
|
||||||
|
(r'缺陷|瓶颈|Gap', '瓶颈与缺陷'),
|
||||||
|
(r'开销|时延|复杂度', '开销与时延影响'),
|
||||||
|
(r'兼容|互操作|向后兼容', '兼容性与互操作分析')
|
||||||
|
],
|
||||||
|
'candidate_critic': [
|
||||||
|
(r'解决.*目标\s*Gap|解决.*问题', '是否真正解决目标Gap'),
|
||||||
|
(r'新问题|副作用', '是否引入新问题/副作用'),
|
||||||
|
(r'冗余设计|复用', '冗余设计与复用已有机制'),
|
||||||
|
(r'信令开销|开销评估', '信令开销评估'),
|
||||||
|
(r'复杂度|实现复杂度', '实现复杂度分析'),
|
||||||
|
(r'向后兼容|兼容性风险', '向后兼容性风险'),
|
||||||
|
(r'极端场景|Corner\s*Case', '极端场景与边界'),
|
||||||
|
(r'替代方案|更简洁', '是否存在更简洁替代方案'),
|
||||||
|
(r'评审结论|修改建议', '评审结论与修改建议')
|
||||||
|
],
|
||||||
|
'remaining_gap': [
|
||||||
|
(r'已解决|基线成果|当前阶段', '当前已解决的核心部分'),
|
||||||
|
(r'未覆盖|场景盲区', '仍未覆盖的典型场景'),
|
||||||
|
(r'极端边界|Corner\s*Case', '极端边界与 Corner Cases'),
|
||||||
|
(r'开销|代价|代价分析', '开销存储与时延代价'),
|
||||||
|
(r'演进建议|未来工作|后续路线', '演进建议与未来工作方向')
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
# ==================== 2. 评测打分函数 ====================
|
||||||
|
def compute_structure_score(task_type, text):
|
||||||
|
patterns = TASK_STRUCTURE_PATTERNS.get(task_type, [])
|
||||||
|
if not patterns:
|
||||||
|
return 100.0, []
|
||||||
|
hits = []
|
||||||
|
for pat, name in patterns:
|
||||||
|
if re.search(pat, text, re.I):
|
||||||
|
hits.append(name)
|
||||||
|
score = (len(hits) / len(patterns)) * 100.0
|
||||||
|
return score, hits
|
||||||
|
|
||||||
|
def compute_spec_compliance(text):
|
||||||
|
specs = set(re.findall(SPEC_REGEX, text, re.I))
|
||||||
|
terms = set()
|
||||||
|
for t in CORE_RAN_TERMS:
|
||||||
|
if re.search(r'\b' + re.escape(t) + r'\b', text, re.I):
|
||||||
|
terms.add(t.upper())
|
||||||
|
total_unique_hits = len(specs) + len(terms)
|
||||||
|
# 达到 8 个不同标准规范与关键术语即得满分 100
|
||||||
|
density_score = min(100.0, total_unique_hits * 12.5)
|
||||||
|
return density_score, list(specs), list(terms)
|
||||||
|
|
||||||
|
def compute_char_lcs(s1, s2):
|
||||||
|
"""计算两段文本的最长公共子序列（用于评估专家语义相似度），优化空间复杂度"""
|
||||||
|
s1_clean = re.sub(r'[^\w\u4e00-\u9fa5]', '', s1)
|
||||||
|
s2_clean = re.sub(r'[^\w\u4e00-\u9fa5]', '', s2)
|
||||||
|
if not s1_clean or not s2_clean:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
# 截取前 800 字符进行快速精确动态规划
|
||||||
|
s1_sub = s1_clean[:800]
|
||||||
|
s2_sub = s2_clean[:800]
|
||||||
|
|
||||||
|
dp = [0] * (len(s2_sub) + 1)
|
||||||
|
for c1 in s1_sub:
|
||||||
|
new_dp = [0] * (len(s2_sub) + 1)
|
||||||
|
for j, c2 in enumerate(s2_sub):
|
||||||
|
if c1 == c2:
|
||||||
|
new_dp[j + 1] = dp[j] + 1
|
||||||
|
else:
|
||||||
|
new_dp[j + 1] = max(new_dp[j], dp[j + 1])
|
||||||
|
dp = new_dp
|
||||||
|
|
||||||
|
lcs_len = dp[-1]
|
||||||
|
f1 = (2.0 * lcs_len) / (len(s1_sub) + len(s2_sub)) * 100.0
|
||||||
|
return f1
|
||||||
|
|
||||||
|
# ==================== 3. 主评测执行流程 ====================
|
||||||
|
def run_benchmark(model, tokenizer, test_samples, device, desc="模型", batch_size=5, max_new_tokens=1024):
|
||||||
|
print(f"\n=======================================================", flush=True)
|
||||||
|
print(f" 开始 3GPP-Agent 50 题全量盲测基准评测 (并行提速版, Batch={batch_size}): {desc}", flush=True)
|
||||||
|
print(f"=======================================================", flush=True)
|
||||||
|
|
||||||
|
results = []
|
||||||
|
task_scores = defaultdict(lambda: {'struct': [], 'spec': [], 'semantic': [], 'composite': []})
|
||||||
|
|
||||||
|
total = len(test_samples)
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
# 设置左侧 padding 用于 decoder-only 批量生成
|
||||||
|
tokenizer.padding_side = "left"
|
||||||
|
if tokenizer.pad_token is None:
|
||||||
|
tokenizer.pad_token = tokenizer.eos_token
|
||||||
|
|
||||||
|
for b_start in range(0, total, batch_size):
|
||||||
|
b_samples = test_samples[b_start : b_start + batch_size]
|
||||||
|
prompts = [
|
||||||
|
tokenizer.apply_chat_template(
|
||||||
|
s['messages'][:-1],
|
||||||
|
tokenize=False,
|
||||||
|
add_generation_prompt=True
|
||||||
|
)
|
||||||
|
for s in b_samples
|
||||||
|
]
|
||||||
|
|
||||||
|
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
|
||||||
|
input_len = inputs.input_ids.shape[1]
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=max_new_tokens,
|
||||||
|
do_sample=False,
|
||||||
|
repetition_penalty=1.05,
|
||||||
|
pad_token_id=tokenizer.pad_token_id,
|
||||||
|
eos_token_id=tokenizer.eos_token_id
|
||||||
|
)
|
||||||
|
|
||||||
|
for i, sample in enumerate(b_samples):
|
||||||
|
sid = sample['sample_id']
|
||||||
|
task_type = sample['task_type']
|
||||||
|
topic = sample['topic']
|
||||||
|
ref_assistant = sample['messages'][-1]['content']
|
||||||
|
|
||||||
|
gen_ids = outputs[i][input_len:]
|
||||||
|
pred_text = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
|
||||||
|
|
||||||
|
# 计算三维指标
|
||||||
|
struct_score, struct_hits = compute_structure_score(task_type, pred_text)
|
||||||
|
spec_score, spec_hits, term_hits = compute_spec_compliance(pred_text)
|
||||||
|
semantic_f1 = compute_char_lcs(pred_text, ref_assistant)
|
||||||
|
|
||||||
|
# 综合得分加权 (40% 结构遵从 + 35% 协议规范 + 25% 专家语义对齐)
|
||||||
|
composite = 0.40 * struct_score + 0.35 * spec_score + 0.25 * semantic_f1
|
||||||
|
|
||||||
|
task_scores[task_type]['struct'].append(struct_score)
|
||||||
|
task_scores[task_type]['spec'].append(spec_score)
|
||||||
|
task_scores[task_type]['semantic'].append(semantic_f1)
|
||||||
|
task_scores[task_type]['composite'].append(composite)
|
||||||
|
|
||||||
|
results.append({
|
||||||
|
'sample_id': sid,
|
||||||
|
'task_type': task_type,
|
||||||
|
'topic': topic,
|
||||||
|
'struct_score': round(struct_score, 2),
|
||||||
|
'spec_score': round(spec_score, 2),
|
||||||
|
'semantic_f1': round(semantic_f1, 2),
|
||||||
|
'composite_score': round(composite, 2),
|
||||||
|
'struct_hits': struct_hits,
|
||||||
|
'spec_hits': spec_hits,
|
||||||
|
'term_hits': term_hits,
|
||||||
|
'pred_len': len(pred_text),
|
||||||
|
'generated_response': pred_text
|
||||||
|
})
|
||||||
|
|
||||||
|
processed = min(b_start + batch_size, total)
|
||||||
|
elapsed = time.time() - start_time
|
||||||
|
last_sid = b_samples[-1]['sample_id']
|
||||||
|
last_tt = b_samples[-1]['task_type']
|
||||||
|
last_comp = results[-1]['composite_score']
|
||||||
|
print(f"进度: [{processed:2d}/{total:2d}] | 耗时: {elapsed:.1f}s ({elapsed/processed:.1f}s/题) | 最新: {last_sid} ({last_tt}) -> 综合得分: {last_comp:.1f}", flush=True)
|
||||||
|
|
||||||
|
# 4. 汇总统计报告
|
||||||
|
print("\n" + "="*70, flush=True)
|
||||||
|
print(f" 3GPP-Agent 50 题盲测基准评测报告 - {desc}", flush=True)
|
||||||
|
print("="*70, flush=True)
|
||||||
|
print(f"{'任务类型 (Task Type)':<25} | {'样本数':<5} | {'结构遵从':<8} | {'规范术语':<8} | {'语义对齐':<8} | {'综合得分':<8}")
|
||||||
|
print("-" * 70)
|
||||||
|
|
||||||
|
all_struct = []
|
||||||
|
all_spec = []
|
||||||
|
all_semantic = []
|
||||||
|
all_composite = []
|
||||||
|
|
||||||
|
summary_by_task = {}
|
||||||
|
for tt, scores in task_scores.items():
|
||||||
|
avg_st = sum(scores['struct']) / len(scores['struct'])
|
||||||
|
avg_sp = sum(scores['spec']) / len(scores['spec'])
|
||||||
|
avg_se = sum(scores['semantic']) / len(scores['semantic'])
|
||||||
|
avg_cp = sum(scores['composite']) / len(scores['composite'])
|
||||||
|
|
||||||
|
all_struct.extend(scores['struct'])
|
||||||
|
all_spec.extend(scores['spec'])
|
||||||
|
all_semantic.extend(scores['semantic'])
|
||||||
|
all_composite.extend(scores['composite'])
|
||||||
|
|
||||||
|
summary_by_task[tt] = {
|
||||||
|
'count': len(scores['struct']),
|
||||||
|
'struct_avg': round(avg_st, 2),
|
||||||
|
'spec_avg': round(avg_sp, 2),
|
||||||
|
'semantic_avg': round(avg_se, 2),
|
||||||
|
'composite_avg': round(avg_cp, 2)
|
||||||
|
}
|
||||||
|
print(f"{tt:<25} | {len(scores['struct']):<5} | {avg_st:6.1f}% | {avg_sp:6.1f}% | {avg_se:6.1f}% | {avg_cp:6.1f}%")
|
||||||
|
|
||||||
|
print("-" * 70)
|
||||||
|
overall_st = sum(all_struct) / len(all_struct)
|
||||||
|
overall_sp = sum(all_spec) / len(all_spec)
|
||||||
|
overall_se = sum(all_semantic) / len(all_semantic)
|
||||||
|
overall_cp = sum(all_composite) / len(all_composite)
|
||||||
|
print(f"{'全量总评 (OVERALL)':<25} | {total:<5} | {overall_st:6.1f}% | {overall_sp:6.1f}% | {overall_se:6.1f}% | {overall_cp:6.1f}%")
|
||||||
|
print("="*70 + "\n", flush=True)
|
||||||
|
|
||||||
|
report_payload = {
|
||||||
|
'model_description': desc,
|
||||||
|
'timestamp': time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||||
|
'total_samples': total,
|
||||||
|
'overall_summary': {
|
||||||
|
'structure_completeness_avg': round(overall_st, 2),
|
||||||
|
'spec_compliance_avg': round(overall_sp, 2),
|
||||||
|
'semantic_alignment_avg': round(overall_se, 2),
|
||||||
|
'composite_score_avg': round(overall_cp, 2)
|
||||||
|
},
|
||||||
|
'task_summary': summary_by_task,
|
||||||
|
'details': results
|
||||||
|
}
|
||||||
|
return report_payload
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(description="3GPP-Agent 50-sample Benchmark Evaluation")
|
||||||
|
parser.add_argument("--model_id", type=str, default="Qwen/Qwen2.5-3B-Instruct", help="Base model identifier")
|
||||||
|
parser.add_argument("--lora_dir", type=str, default=None, help="Path to LoRA weights directory")
|
||||||
|
parser.add_argument("--test_file", type=str, default="dataset/ran2_benchmark_test_50.jsonl", help="Path to 50 test jsonl")
|
||||||
|
parser.add_argument("--output_json", type=str, default="output/benchmark_baseline_base_3b.json", help="Path to output json")
|
||||||
|
parser.add_argument("--desc", type=str, default="Qwen2.5-3B-Instruct (Base 未微调)", help="Model description")
|
||||||
|
parser.add_argument("--limit", type=int, default=None, help="Limit number of test samples (for dry run)")
|
||||||
|
parser.add_argument("--batch_size", type=int, default=5, help="Batch size for parallel generation")
|
||||||
|
parser.add_argument("--max_new_tokens", type=int, default=1024, help="Max new tokens to generate")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
||||||
|
print(f"[Device] 评测设备: {device} ({torch.cuda.get_device_name(0) if 'cuda' in device else 'CPU'})", flush=True)
|
||||||
|
|
||||||
|
if not os.path.exists(args.test_file):
|
||||||
|
raise FileNotFoundError(f"未找到测试集: {args.test_file}")
|
||||||
|
|
||||||
|
with open(args.test_file, 'r', encoding='utf-8') as f:
|
||||||
|
samples = [json.loads(line) for line in f if line.strip()]
|
||||||
|
|
||||||
|
if args.limit:
|
||||||
|
samples = samples[:args.limit]
|
||||||
|
print(f"[Dataset] 已加载测试集: {len(samples)} 道题目 ({args.test_file})", flush=True)
|
||||||
|
|
||||||
|
print(f"[Model] 正在加载模型权重: {args.model_id} ...", flush=True)
|
||||||
|
local_model_dir = r"C:\Users\ZoOoM\.cache\modelscope\hub\models\Qwen\Qwen2___5-3B-Instruct"
|
||||||
|
if os.path.exists(local_model_dir):
|
||||||
|
print(f"[Model] 使用本地权重: {local_model_dir}", flush=True)
|
||||||
|
model_dir = local_model_dir
|
||||||
|
else:
|
||||||
|
model_dir = snapshot_download(args.model_id)
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||||
|
if tokenizer.pad_token is None:
|
||||||
|
tokenizer.pad_token = tokenizer.eos_token
|
||||||
|
|
||||||
|
# 4-bit 量化加载以保护 8GB 显存并加速推断
|
||||||
|
bnb_config = BitsAndBytesConfig(
|
||||||
|
load_in_4bit=True,
|
||||||
|
bnb_4bit_quant_type="nf4",
|
||||||
|
bnb_4bit_use_double_quant=True,
|
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16
|
||||||
|
)
|
||||||
|
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_dir,
|
||||||
|
quantization_config=bnb_config,
|
||||||
|
device_map="auto",
|
||||||
|
attn_implementation="sdpa",
|
||||||
|
trust_remote_code=True
|
||||||
|
)
|
||||||
|
|
||||||
|
if args.lora_dir:
|
||||||
|
print(f"[LoRA] 正在挂载 LoRA 权重: {args.lora_dir} ...", flush=True)
|
||||||
|
model = PeftModel.from_pretrained(model, args.lora_dir)
|
||||||
|
|
||||||
|
model.eval()
|
||||||
|
|
||||||
|
# 运行 Benchmark
|
||||||
|
report = run_benchmark(model, tokenizer, samples, device, desc=args.desc, batch_size=args.batch_size, max_new_tokens=args.max_new_tokens)
|
||||||
|
|
||||||
|
os.makedirs(os.path.dirname(args.output_json), exist_ok=True)
|
||||||
|
with open(args.output_json, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(report, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
print(f"[Saved] 评测完整 JSON 数据已保存至: {args.output_json}", flush=True)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,73 @@
|
||||||
|
import torch
|
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||||
|
from peft import PeftModel
|
||||||
|
from modelscope import snapshot_download
|
||||||
|
|
||||||
|
def generate_reply(model, tokenizer, prompt_text, system_prompt="你是由通信领域与知识产权专家调优的专业助手，擅长分析通信技术协议与专利权利要求书结构。"):
|
||||||
|
messages = [
|
||||||
|
{"role": "system", "content": system_prompt},
|
||||||
|
{"role": "user", "content": prompt_text}
|
||||||
|
]
|
||||||
|
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||||
|
inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=256,
|
||||||
|
temperature=0.7,
|
||||||
|
top_p=0.9,
|
||||||
|
do_sample=True,
|
||||||
|
repetition_penalty=1.1
|
||||||
|
)
|
||||||
|
# 截取生成的新 token
|
||||||
|
generated_ids = outputs[0][inputs.input_ids.shape[1]:]
|
||||||
|
return tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
|
||||||
|
|
||||||
|
def main():
|
||||||
|
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
||||||
|
print(f"[Device] 当前推理设备: {device}")
|
||||||
|
|
||||||
|
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
|
||||||
|
lora_dir = "output/qwen_telecom_lora"
|
||||||
|
model_dir = snapshot_download(model_id)
|
||||||
|
|
||||||
|
print("[ModelScope] 正在加载基座模型 Tokenizer 与 Model ...")
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||||
|
base_model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_dir,
|
||||||
|
torch_dtype=torch.bfloat16,
|
||||||
|
device_map=device,
|
||||||
|
trust_remote_code=True
|
||||||
|
)
|
||||||
|
|
||||||
|
test_prompt = (
|
||||||
|
"请针对以下公开专利进行技术方案与权利要求拆解：\n"
|
||||||
|
"【专利名称】：5G毫米波大规模天线阵列混合波束赋形方法及收发机\n"
|
||||||
|
"【技术方案】：针对高频通信中射频链路(RF Chain)硬件受限问题，提出一种模拟移相网络与数字基带迫零(ZF)预编码分步解耦优化架构，在保证系统信噪比的同时大幅降低硬件功耗。"
|
||||||
|
)
|
||||||
|
|
||||||
|
print("正在测试原版 Base 模型...")
|
||||||
|
base_reply = generate_reply(base_model, tokenizer, test_prompt)
|
||||||
|
|
||||||
|
print("正在挂载 LoRA 适配层权重...")
|
||||||
|
lora_model = PeftModel.from_pretrained(base_model, lora_dir)
|
||||||
|
print("正在测试 LoRA 微调后模型...")
|
||||||
|
lora_reply = generate_reply(lora_model, tokenizer, test_prompt)
|
||||||
|
|
||||||
|
result_text = []
|
||||||
|
result_text.append("="*40 + " 1. 原版 Base 模型输出 " + "="*40)
|
||||||
|
result_text.append(base_reply)
|
||||||
|
result_text.append("\n" + "="*40 + " 2. 加载 LoRA 微调权重后输出 " + "="*40)
|
||||||
|
result_text.append(lora_reply)
|
||||||
|
result_text.append("\n" + "="*90)
|
||||||
|
|
||||||
|
full_output = "\n".join(result_text)
|
||||||
|
print(full_output)
|
||||||
|
|
||||||
|
with open("output/comparison_result.txt", "w", encoding="utf-8") as f:
|
||||||
|
f.write(full_output)
|
||||||
|
print("\n[Done] 完整中文对比结果已保存到 output/comparison_result.txt")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,45 @@
|
||||||
|
"""Run real local generation on fixed DOCX questions; preserve evidence and diagnostics."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import json
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||||
|
|
||||||
|
from agent_core import ProposalAgent
|
||||||
|
from rag_evidence import ROOT
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--max-tokens", type=int, default=384)
|
||||||
|
parser.add_argument("--base", action="store_true", help="Disable the existing LoRA adapter")
|
||||||
|
parser.add_argument("--compare", action="store_true", help="Run both base and LoRA on the same questions")
|
||||||
|
parser.add_argument("--output", default="output/agent_generation_smoke.json")
|
||||||
|
args = parser.parse_args()
|
||||||
|
agent = ProposalAgent()
|
||||||
|
runs = []
|
||||||
|
questions = [
|
||||||
|
"根据 R2-2408133 原文，用三条中文说明主要主张。每条标注来源；不能从片段确定的内容不要补写。",
|
||||||
|
"根据 R2-2408133 和 R2-2408134 原文，简要说明两篇提案是否解决同一问题。分别标注来源。",
|
||||||
|
]
|
||||||
|
modes = [False, True] if args.compare else [not args.base]
|
||||||
|
for use_lora in modes:
|
||||||
|
for question in questions:
|
||||||
|
print(f"Running {'LoRA' if use_lora else 'base'}: {question}", flush=True)
|
||||||
|
events = []
|
||||||
|
for event in agent.run_stream(question, max_new_tokens=args.max_tokens, use_lora=use_lora):
|
||||||
|
if event["stage"] != "2. STREAMING":
|
||||||
|
events.append(event)
|
||||||
|
runs.append({"question": question, "use_lora": use_lora, "events": events})
|
||||||
|
terminal = events[-1]
|
||||||
|
print(json.dumps({key: terminal.get(key) for key in ("stage", "mode", "result", "validation", "metrics", "msg")}, ensure_ascii=False), flush=True)
|
||||||
|
destination = ROOT / args.output
|
||||||
|
destination.write_text(json.dumps(runs, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||||
|
if any(run["events"][-1]["stage"] == "ERROR" for run in runs):
|
||||||
|
raise SystemExit(1)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,66 @@
|
||||||
|
"""Check the local API and Streamlit workflow without loading any real model."""
|
||||||
|
|
||||||
|
import faulthandler
|
||||||
|
import json
|
||||||
|
import sys
|
||||||
|
import tempfile
|
||||||
|
from pathlib import Path
|
||||||
|
from types import SimpleNamespace
|
||||||
|
|
||||||
|
ROOT = Path(__file__).resolve().parents[1]
|
||||||
|
sys.path.insert(0, str(ROOT))
|
||||||
|
tempfile.tempdir = str(ROOT / "output")
|
||||||
|
faulthandler.dump_traceback_later(45, exit=True)
|
||||||
|
|
||||||
|
from fastapi.testclient import TestClient
|
||||||
|
from streamlit.testing.v1 import AppTest
|
||||||
|
|
||||||
|
from server_demo import app, get_agent
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
checks = {}
|
||||||
|
with TestClient(app) as client:
|
||||||
|
checks["health_model_not_loaded"] = client.get("/health").json() == {"status": "ok", "model_loaded": False}
|
||||||
|
checks["invalid_request_rejected"] = client.post("/v1/chat/completions", json={"messages": []}).status_code == 422
|
||||||
|
response = client.post("/v1/proposals/analyze", json={
|
||||||
|
"question": "对比 R2-2408133 和 R2-2408134", "retrieval_only": True,
|
||||||
|
})
|
||||||
|
events = [json.loads(line[6:]) for line in response.text.splitlines() if line.startswith("data: {")]
|
||||||
|
last = events[-1]
|
||||||
|
checks["analysis_sse_complete"] = response.status_code == 200 and last["stage"] == "4. COMPLETE"
|
||||||
|
checks["both_documents_in_ui_data"] = {item["tdoc"] for item in last["sources"]["evidence"]} == {"R2-2408133", "R2-2408134"}
|
||||||
|
checks["analysis_done_marker"] = "data: [DONE]" in response.text
|
||||||
|
|
||||||
|
class FakeRuntime:
|
||||||
|
model = None
|
||||||
|
def stream(self, *args, **kwargs):
|
||||||
|
yield "test answer"
|
||||||
|
|
||||||
|
real_runtime = get_agent().runtime
|
||||||
|
get_agent().runtime = FakeRuntime()
|
||||||
|
try:
|
||||||
|
response = client.post("/v1/chat/completions", json={"messages": [{"role": "user", "content": "q"}], "stream": False})
|
||||||
|
checks["nonstream_chat"] = response.json()["choices"][0]["message"]["content"] == "test answer"
|
||||||
|
response = client.post("/v1/chat/completions", json={"messages": [{"role": "user", "content": "q"}]})
|
||||||
|
checks["stream_chat"] = '"content": "test answer"' in response.text and "[DONE]" in response.text
|
||||||
|
finally:
|
||||||
|
get_agent().runtime = real_runtime
|
||||||
|
|
||||||
|
ui = AppTest.from_file(str(ROOT / "app_web.py")).run(timeout=20)
|
||||||
|
checks["initial_ui_no_exception"] = len(ui.exception) == 0
|
||||||
|
ui.text_area[0].set_value("对比 R2-2408133 和 R2-2408134")
|
||||||
|
next(button for button in ui.button if button.label == "检索原文").click().run(timeout=20)
|
||||||
|
checks["ui_retrieval_complete"] = len(ui.exception) == 0 and "last_run" in ui.session_state
|
||||||
|
if checks["ui_retrieval_complete"]:
|
||||||
|
checks["ui_has_downloads"] = ui.session_state["last_run"]["mode"] == "retrieval_only"
|
||||||
|
destination = ROOT / "output/agent_interface_checks.json"
|
||||||
|
destination.write_text(json.dumps(checks, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||||
|
print(json.dumps(checks, ensure_ascii=False, indent=2), flush=True)
|
||||||
|
faulthandler.cancel_dump_traceback_later()
|
||||||
|
if not all(checks.values()):
|
||||||
|
raise SystemExit(1)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,50 @@
|
||||||
|
import torch
|
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
||||||
|
from peft import PeftModel
|
||||||
|
|
||||||
|
model_dir = r"C:\Users\ZoOoM\.cache\modelscope\hub\models\Qwen\Qwen2___5-3B-Instruct"
|
||||||
|
lora_dir = "output/qwen3b_ran2_qlora"
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||||
|
bnb_config = BitsAndBytesConfig(
|
||||||
|
load_in_4bit=True,
|
||||||
|
bnb_4bit_quant_type="nf4",
|
||||||
|
bnb_4bit_use_double_quant=True,
|
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16
|
||||||
|
)
|
||||||
|
base_model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_dir,
|
||||||
|
quantization_config=bnb_config,
|
||||||
|
device_map="auto",
|
||||||
|
attn_implementation="sdpa",
|
||||||
|
trust_remote_code=True
|
||||||
|
).eval()
|
||||||
|
model = PeftModel.from_pretrained(base_model, lora_dir).eval()
|
||||||
|
|
||||||
|
# 采用真正经过 382 篇清洗精调的 3GPP 专家 prompt 结构
|
||||||
|
system_prompt = "你是由知名 3GPP 国际标准化代表与 RAN2 资深协议架构师联合打造的 3GPP 垂直领域专家模型（3GPP-Agent）。请严格基于真实 3GPP 提案与技术讨论进行专业、精准、具备实战指导意义的分析。"
|
||||||
|
user_prompt = "在 3GPP RAN2#127bis 会议中，针对【LTM】特性的提案（R2-2408091：《MRO enhancements for LTM》），请全面解析该提案的技术方案与协议机制。"
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
{"role": "system", "content": system_prompt},
|
||||||
|
{"role": "user", "content": user_prompt}
|
||||||
|
]
|
||||||
|
|
||||||
|
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||||
|
inputs = tokenizer([text], return_tensors="pt").to("cuda:0")
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=400,
|
||||||
|
temperature=0.1,
|
||||||
|
top_p=0.85,
|
||||||
|
eos_token_id=tokenizer.eos_token_id
|
||||||
|
)
|
||||||
|
|
||||||
|
ans = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
|
||||||
|
|
||||||
|
with open("sample_output.txt", "w", encoding="utf-8") as f:
|
||||||
|
f.write(ans)
|
||||||
|
|
||||||
|
print("Finished generating clean sample.")
|
||||||
Loading…
Reference in New Issue