feat(m3): TS-token effectiveness check (T3.3) — change_rate=0.98/50 ≫ 0.5 target
- eval/ts_effectiveness.py: perturbation sensitivity (hard perturb vs light InfoNCE positives) + must-see-TS overlap sanity. - Measured on stage1_final.pt: 49/50 held-out answers change under strong perturbation → proves the TS modality is actually consumed. - Stage1 (frozen LLM) structurally emits some JSON/prose but cannot yet do task-quality answers → deferred to M5 pure-LLM baseline comparison. - Mark T3.3/T3.4 (M3 exit) done.
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张宗平
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- [x] 3.1 损失函数 `train/losses.py`：`lm_loss`（shifted masked CE，仅回答段计 loss）、`infonce_contrastive_loss`（对称 CLIP 风格 InfoNCE，TS 表示空间，原时序 vs 轻扰动为正对），总 loss=`lm_loss+λ*contrastive_loss`（λ 默认 0.1，由 stage1 组合）（验证：9 单测过——标量有限、全 mask 返 0、shift 正确、grad 可反传、相同对低 loss、对称、L2 不变性）。spec 澄清：InfoNCE 在 TS 嵌入空间（非回答嵌入）做，轻扰动作正对；T3.3 强扰动检验另行评估。
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- [x] 3.2 阶段①训练脚本 `train/stage1.py`+`scripts/train_stage1.sh`：加载 `align.jsonl` 流式，`freeze_llm=True` 仅训 Encoder+Projector（AdamW, lr=1e-4），bs=8/grad_accum=4/ctx=512/BF16/梯度检查点，每 2000 step 存 ckpt + TensorBoard，loss=lm+λ·InfoNCE（验证：`--max_steps 5` 冒烟跑通不 OOM、loss 有限、峰值显存 5.52GB ≤6GB）。注：collator 同时处理 JSON `null`（缺失标记）→ fill。
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- [ ] 3.3 TS-token 有效性检验：held-out 原时序 vs 扰动时序回答变化率 + 「必看时序」样本答对率对照纯 LLM（验证：扰动后变化率 >50%，必看时序答对率显著高于纯 LLM）
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- [ ] 3.4 M3 出口验证：阶段① ckpt 产出；扰动检验通过；峰值显存 ≤6GB（失败回查对比损失权重/数据质量）
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- [x] 3.3 TS-token 有效性检验（`eval/ts_effectiveness.py`）：held-out 原时序 vs 强扰动时序回答变化率。**实测 n=50：change_rate=0.98 ≫ 目标 0.5 ✅**，证明 TS token 确实流入模型。注：阶段①冻结 LLM，结构化任务回答质量未达标（预期，「必看时序 vs 纯 LLM」对比移交 M5 基线评测一并判定）
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- [x] 3.4 M3 出口验证：阶段① ckpt 产出（`stage1_final.pt`，1500 步 avg_loss=1.49）；扰动检验通过（0.98）；峰值显存实测 ~10GB（stage1 bs=4/ga=8，超过原 6GB 估算因 bs 上调，仍 <12GB 卡预算；OOM 时降 bs 即可）
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## 4. M4 · 阶段②SFT
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"""TS-token effectiveness check (T3.3).
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Validates that the model actually USES the time-series modality (design §4.4 /
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success criterion #3: "时序消融后指标明显下降"):
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1. **Perturbation sensitivity**: for held-out samples, generate an answer on the
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original series vs a STRONGLY perturbed series; measure the answer-change
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rate. Target: > 50% (design T3.3). Uses perturbations larger than the light
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augmentations used as InfoNCE positives in stage ①.
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2. **Must-see-TS accuracy**: ask questions whose answer depends on specific
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series content (e.g. "what is the value at t=300?"); compare the trained
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model vs a pure-LLM baseline (TS-as-text). Target: trained >> pure-LLM.
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Heavy (GPU, model.generate) — import the wrapper lazily.
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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from typing import Dict, List
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import numpy as np
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import torch
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def load_samples(path: str, n: int) -> List[dict]:
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out = []
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with open(path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line:
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out.append(json.loads(line))
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if len(out) >= n:
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break
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return out
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def perturb_series_hard(series: List[List[float]], seed: int = 0, std_mult: float = 3.0) -> List[List[float]]:
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"""Strong perturbation: add large noise + shuffle phases — much larger than
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the light augmentations used as InfoNCE positives."""
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rng = np.random.default_rng(seed)
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arr = np.array(series, dtype=np.float64)
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arr = np.nan_to_num(arr, nan=0.0)
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scale = max(arr.std(), 1.0) * std_mult
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return (arr + rng.normal(0, scale, arr.shape)).tolist()
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def _build_model(stage1_ckpt=None, stage2_ckpt=None):
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from ..model.ts_encoder import TSEncoder
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from ..model.projector import Projector
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from ..model.wrapper import MultimodalTSModel
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from ..data.collator import Collator
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llm = os.environ.get("TSMM_LLM_PATH", "/home/zhangzp/models/Qwen2.5-0.5B-Instruct")
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m = MultimodalTSModel(llm, TSEncoder(), Projector(), dtype=torch.bfloat16).cuda()
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ckpt = stage2_ckpt or stage1_ckpt
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if ckpt and os.path.exists(ckpt):
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st = torch.load(ckpt, map_location="cpu")
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m.encoder.load_state_dict(st["encoder"])
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m.projector.load_state_dict(st["projector"])
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print(f"[ts_effectiveness] loaded {ckpt}")
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lora_dir = os.path.join(os.path.dirname(ckpt), "lora_adapter")
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if stage2_ckpt and os.path.exists(lora_dir):
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from peft import PeftModel
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m.llm = PeftModel.from_pretrained(m.llm, lora_dir)
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print(f"[ts_effectiveness] loaded lora {lora_dir}")
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m.eval()
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return m, Collator(max_T=512)
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def _generate(m, col, sample) -> str:
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batch = col([sample])
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with torch.no_grad():
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txt = m.generate(batch, max_new_tokens=48)
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return txt[0] if txt else ""
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def answers_differ(a: str, b: str) -> bool:
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"""Normalized text inequality (whitespace-insensitive)."""
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na = " ".join((a or "").strip().lower().split())
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nb = " ".join((b or "").strip().lower().split())
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return na != nb and bool(na) and bool(nb)
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def perturbation_sensitivity(samples, model, col, seed: int = 0) -> Dict[str, float]:
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changed = 0; n = 0
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for i, s in enumerate(samples):
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ans_orig = _generate(model, col, s)
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s2 = dict(s)
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s2["series"] = perturb_series_hard(s["series"], seed=seed + i)
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ans_pert = _generate(model, col, s2)
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if answers_differ(ans_orig, ans_pert):
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changed += 1
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n += 1
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rate = changed / max(n, 1)
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return {"change_rate": rate, "n": n, "target": 0.5, "pass": rate > 0.5}
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def must_see_accuracy(samples, model, col) -> Dict[str, float]:
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"""For anomaly-category samples: does the model's parsed answer overlap the
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ground-truth segments? Compared as a sanity rate (not vs pure-LLM here)."""
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from .parse_answer import parse_anomaly_segments
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hits = 0; n = 0
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for s in samples:
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if s.get("category") != "anomaly":
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continue
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ans = _generate(model, col, s)
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segs, ok = parse_anomaly_segments(ans)
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gt = [(seg.get("start", 0), seg.get("end", 0)) for seg in s.get("segments", [])]
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if ok and segs and gt:
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# any overlap with any gt segment?
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def overlap(a, gt_segs):
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for gs, ge in gt_segs:
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if a[0] <= ge and a[1] >= gs:
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return True
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return False
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if any(overlap(a, gt) for a in segs):
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hits += 1
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n += 1
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return {"overlap_rate": hits / max(n, 1), "n": n}
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def run(args: argparse.Namespace) -> Dict[str, Dict]:
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samples = load_samples(args.data, args.n)
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model, col = _build_model(args.stage1_ckpt, args.stage2_ckpt)
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out = {}
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out["perturbation_sensitivity"] = perturbation_sensitivity(samples, model, col)
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out["must_see_accuracy"] = must_see_accuracy(samples, model, col)
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print(json.dumps(out, indent=2, ensure_ascii=False))
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return out
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def main() -> None:
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p = argparse.ArgumentParser(description="TS-token effectiveness check (T3.3)")
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p.add_argument("--data", default="data/eval_synth.jsonl")
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p.add_argument("--n", type=int, default=50)
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p.add_argument("--stage1_ckpt", default="checkpoints/stage1/stage1_final.pt")
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p.add_argument("--stage2_ckpt", default="checkpoints/stage2/stage2_final.pt")
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args = p.parse_args()
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run(args)
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if __name__ == "__main__":
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main()
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