"""Debug一次完整交互:加载某用户 cartridge + Capability LoRA,喂一个 query,把 rollout 内部每一段(决策 prompt / 决策原始输出 / 解析 MS·ACT·query / 检索结果 / 拼进去的 RAG block / 答案段输入 / 答案输出)逐段打印出来,用来肉眼检查交互流程是否正确。 不算指标,只 dump 完整对话链路。 用法: python scripts/train/debug_interaction.py --dataset data/processed/longmemeval_s/dataset.json \ --cartridge-dir checkpoints/cartridge_longmem/Qwen3-8B \ --lora-dir checkpoints/sft/Qwen3-8B \ --user-id e47becba # 不给则取第一个用户 [--query "..."] # 不给则用该用户第一个 query """ import argparse import os import sys sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) os.environ.setdefault("CARTRIDGES_DIR", os.path.join(PROJECT_ROOT, "cartridges-lib")) os.environ.setdefault("CARTRIDGES_OUTPUT_DIR", os.path.join(PROJECT_ROOT, "checkpoints/cartridge")) import torch from src.model.capability_lora import load_capability_lora from src.model.metamem_model import load_metamem_base from src.model.prompts import ( build_answer_suffix, build_rag_block, format_decision_prompt, format_session_block, ) from src.model.tokenizer_utils import extract_control_strings, find_marker_token_span from src.retrieval.bge_m3_retriever import BGEM3Retriever, session_full_text from src.train.rl.parse import ( ANSWER_MAX_NEW_TOKENS, DECISION_MAX_NEW_TOKENS, MAX_RETRIEVAL_TOKENS, MODEL_MAX, crop_retrieval_text, left_truncate_ids, ) from src.utils import load_json, load_yaml, set_seed from src.utils.cartridge_utils import find_cartridge_path from src.utils.special_tokens import EOQ_TOKEN from cartridges.cache import TrainableCache from cartridges.generation import flex_generate SEP = "=" * 100 def hr(title): print(f"\n{SEP}\n### {title}\n{SEP}") def gen_segment(mm, tok, prefix_ids, max_new_tokens, temperature): device = mm.device input_ids = torch.tensor(prefix_ids, dtype=torch.long, device=device) gen = flex_generate( model=mm.model, tokenizer=tok, input_ids=input_ids, seq_ids=torch.zeros_like(input_ids), position_ids=torch.arange(len(input_ids), device=device), cache=mm.cache, max_new_tokens=max_new_tokens, temperature=temperature, ) return list(gen.values())[0] if gen else [] def main(): ap = argparse.ArgumentParser() ap.add_argument("--dataset", default="data/processed/longmemeval_s/dataset.json") ap.add_argument("--cartridge-dir", default="checkpoints/cartridge_longmem/Qwen3-8B") ap.add_argument("--lora-dir", default="checkpoints/sft/Qwen3-8B") ap.add_argument("--retrieval-config", default="configs/retrieval/bge_m3.yaml") ap.add_argument("--user-id", default=None) ap.add_argument("--query", default=None) ap.add_argument("--temperature", type=float, default=0.0) args = ap.parse_args() def _r(p): return p if os.path.isabs(p) else os.path.join(PROJECT_ROOT, p) set_seed(42) dataset = load_json(_r(args.dataset)) by_uid = {u["user_id"]: u for u in dataset} ud = by_uid[args.user_id] if args.user_id else dataset[0] uid = ud["user_id"] q = args.query or ud["queries"][0]["query"] gold = ud["queries"][0]["answer"] if not args.query else "(custom query, no gold)" qdate = (ud["queries"][0].get("extra") or {}).get("question_date") gold_ev = ud["queries"][0].get("evidence_session_ids", []) hr("0. 输入") print(f"user_id : {uid}") print(f"#sessions : {len(ud['user_sessions'])}") print(f"query : {q}") print(f"gold answer : {gold}") print(f"gold evidence ids : {gold_ev}") print(f"question_date : {qdate}") print(f"temperature : {args.temperature}") # ---- load model + LoRA + cartridge ---- mm, tok = load_metamem_base( base_cfg_path=_r("configs/model/base_model.yaml"), lora_cfg_path=_r("configs/model/capability_lora.yaml"), project_root=PROJECT_ROOT, adapter_names=("policy",), set_trainable=None, ) lora_path = os.path.join(_r(args.lora_dir), "capability_lora.safetensors") if os.path.exists(lora_path): load_capability_lora(mm.model, _r(args.lora_dir), adapter_name="policy") print(f"\n[loaded] Capability LoRA ← {args.lora_dir}") else: print(f"\n[WARN] no LoRA at {args.lora_dir} — base+cartridge only") mm.set_adapter("policy") mm.model.eval() cpath = find_cartridge_path(_r(args.cartridge_dir), uid) cache = TrainableCache.from_pretrained(cpath, device="cuda").to("cuda") mm.set_cartridge(cache) print(f"[loaded] cartridge ({cache.num_cartridge_tokens()} tokens) ← {cpath}") retriever = BGEM3Retriever(load_yaml(_r(args.retrieval_config))) retriever.build_user_index(uid, ud["user_sessions"]) sessions_by_id = {s["session_id"]: s for s in ud["user_sessions"]} dates_by_id = {s["session_id"]: s.get("timestamp", "") for s in ud["user_sessions"]} idx_by_id = {s["session_id"]: i for i, s in enumerate(ud["user_sessions"])} # ===== STAGE 1: decision prompt ===== hr("1. 决策阶段 —— 喂给模型的 PROMPT(cartridge 注入在 KV 前缀,此处是文本部分)") prompt = format_decision_prompt(q, tok, qdate) print(prompt) prompt_ids = tok.encode(prompt, add_special_tokens=False) mm.set_cartridge(cache) gen_ids = gen_segment(mm, tok, prompt_ids, DECISION_MAX_NEW_TOKENS, args.temperature) hr("2. 决策阶段 —— 模型原始输出(decode 全部生成 token,未截断)") print(repr(tok.decode(gen_ids))) span = find_marker_token_span(gen_ids, tok, EOQ_TOKEN) decision_ids = gen_ids[:span[1]] if span else gen_ids decision_text = tok.decode(decision_ids) parsed = extract_control_strings(decision_text) hr("3. 决策阶段 —— 截到 [EOQ] 后的决策串 + 解析结果") print(f"decision text (截到 EOQ): {repr(decision_text)}") print(f" → 解析 MS : {parsed['ms']}") print(f" → 解析 ACT : {parsed['act']}") print(f" → rewrite_q : {repr(parsed['query_text'])}") print(f" → has_eoq : {parsed['has_eoq']} | format_valid: {parsed['valid'] and parsed['has_eoq']}") act = parsed["act"] if parsed["act"] else "RETRIEVE" print(f" → 实际动作 : {act} ({'fallback→RETRIEVE' if not parsed['act'] else 'parsed'})") # ===== STAGE 2: retrieval ===== full_ids = list(prompt_ids) + list(decision_ids) if act != "DIRECT": rq = parsed["query_text"] or q hr(f"4. 检索阶段 —— 用 query 检索 top-{retriever.default_top_k}") print(f"检索 query: {repr(rq)}") results = retriever.search(uid, rq, top_k=retriever.default_top_k) new_rank = retriever.get_rank(uid, rq, set(gold_ev), window=retriever.default_top_k) print(f"\n检索到的 session (按相关度):") for rank, (rid, score) in enumerate(results): hit = " <== GOLD EVIDENCE" if rid in set(gold_ev) else "" print(f" [{rank+1:2d}] {rid} score={score:.4f}{hit}") print(f"\ngold evidence 在 top-{retriever.default_top_k} 的 rank: {new_rank} (-1=窗口外)") ordered = sorted([rid for rid, _ in results], key=lambda r: idx_by_id.get(r, 1 << 30)) blocks = [] for rank, rid in enumerate(ordered): sess = sessions_by_id.get(rid) if sess is None: continue date = dates_by_id.get(rid, sess.get("timestamp", "")) blocks.append(format_session_block(date, sess.get("turns", []), rank)) history = crop_retrieval_text("".join(blocks), MAX_RETRIEVAL_TOKENS) rag_block = build_rag_block([history]) if history else build_rag_block([]) block_ids = tok.encode(rag_block, add_special_tokens=False) hr("5. 检索阶段 —— 拼进序列的 [RETRIEVED] block(只显示前 1500 字符)") print(rag_block[:1500]) print(f"\n... [RETRIEVED block 共 {len(block_ids)} tokens,按 haystack 顺序排列] ...") full_ids += block_ids else: new_rank = None hr("4-5. 检索阶段 —— act=DIRECT,跳过检索") # ===== STAGE 3: answer ===== suffix = build_answer_suffix(query=q, question_date=qdate) suffix_ids = tok.encode(suffix, add_special_tokens=False) full_ids += suffix_ids hr("6. 答案阶段 —— 答案提示串([ANS] 之前,baseline 同款 wording)") print(suffix) if len(full_ids) > MODEL_MAX - ANSWER_MAX_NEW_TOKENS: before = len(full_ids) full_ids, _, _ = left_truncate_ids( full_ids, [False] * len(full_ids), MODEL_MAX - ANSWER_MAX_NEW_TOKENS, [False] * len(full_ids) ) print(f"\n[left-truncate] {before} → {len(full_ids)} tokens (预算 {MODEL_MAX-ANSWER_MAX_NEW_TOKENS})") mm.set_cartridge(cache) ans_ids = gen_segment(mm, tok, full_ids, ANSWER_MAX_NEW_TOKENS, args.temperature) hr("7. 答案阶段 —— 模型最终输出") print(f"raw answer (decode, 保留所有伪 token): {repr(tok.decode(ans_ids))}") print(f"\nanswer (skip_special_tokens, strip) : {repr(tok.decode(ans_ids, skip_special_tokens=True).strip())}") print(f"\ngen_token_count: {len(ans_ids)}") hr("8. 全链路小结") print(f"query : {q}") print(f"decision : [MS:{parsed['ms']}] [ACT:{act}] query={repr(parsed['query_text'])}") print(f"retrieval : {'DIRECT(跳过)' if act=='DIRECT' else f'top-{retriever.default_top_k}, gold@rank={new_rank}'}") print(f"answer : {repr(tok.decode(ans_ids, skip_special_tokens=True).strip())}") print(f"gold : {gold}") if __name__ == "__main__": main()