Datasets:
这是一个工程架构设计任务,不是 ADR 决策。我直接输出完整的实验代码架构 spec。下面是为《Probe Stability Score》设计的、可在单卡 4090 上运行的完整工程化 spec。
Probe Stability Score · 实验代码架构 Spec
设计目标:一次性激活抽取 + 缓存复用,所有探针/指标计算只读缓存;严控显存与 GPU·h(≤30 GPU·h);单条命令复现最小可行实验(MVE)。
1. 目录 / 文件树
probe_stability/
├── README.md
├── pyproject.toml # 依赖 + 入口 console_scripts
├── Makefile # make mve / make full / make figures
├── configs/
│ ├── base.yaml # 全局:路径、dtype、seed 列表、CI 参数
│ ├── models.yaml # 7 个模型的 HF id、层数、d_model、最佳 batch
│ ├── datasets.yaml # 8 个任务:HF id、字段映射、label 空间、采样数
│ ├── shifts.yaml # back-translation / domain / length 三类 shift 配置
│ ├── probes.yaml # logreg / mass_mean / mlp / control 超参
│ ├── metrics.yaml # Score 双分量权重、bootstrap、多重检验方法
│ └── experiments/
│ ├── mve.yaml # 最小可行:pythia-70m + SST-2 + 1 shift + 2 seed
│ └── full.yaml # 全量矩阵
├── env/
│ └── download_models.py # 预下载所有 HF 权重到本地缓存(离线复现)
├── src/probe_stability/
│ ├── __init__.py
│ ├── cli.py # Typer/argparse 主入口,子命令路由
│ ├── config.py # OmegaConf 加载 + dataclass schema 校验
│ ├── registry.py # MODEL/DATASET/PROBE/SHIFT 注册表(字符串→构造器)
│ ├── utils/
│ │ ├── seeding.py # set_all_seeds(seed)
│ │ ├── logging.py # rich/structlog,JSONL run log
│ │ ├── io.py # shard 路径生成、原子写、manifest 读写
│ │ ├── gpu.py # 显存监控、autocast、空缓存、OOM 自动降 batch
│ │ └── hashing.py # config/input 指纹 → 缓存 key、幂等跳过
│ ├── data/
│ │ ├── loaders.py # 8 个数据集统一加载为 Example schema
│ │ ├── schema.py # Example / DistributionSet dataclass
│ │ ├── sampling.py # 分层下采样、固定 split、length buckets 切分
│ │ └── shifts/
│ │ ├── base.py # Shift 抽象基类
│ │ ├── backtranslation.py # NLLB / opus-mt 本地往返翻译
│ │ ├── domain.py # Amazon multi-domain / IMDB↔SST-2 域迁移
│ │ └── length.py # 短/中/长 token 分桶
│ ├── fidelity/
│ │ └── mnli_filter.py # 本地 DeBERTa-v3-MNLI 过滤 label flip
│ ├── extract/
│ │ ├── activations.py # 核心:逐层 residual-stream 抽取 + 流式落盘
│ │ ├── hooks.py # 各架构 residual hook 适配(Pythia/GPT2/Qwen2)
│ │ └── pooling.py # last-token / mean pooling
│ ├── cache/
│ │ ├── store.py # ShardStore:read/write float16 分片 + manifest
│ │ └── manifest.py # 全局缓存索引(parquet),完整性校验
│ ├── probes/
│ │ ├── base.py # Probe 抽象:fit/predict/direction
│ │ ├── logreg.py # sklearn / torch LogReg
│ │ ├── mass_mean.py # difference-of-means 方向探针
│ │ ├── mlp.py # 2-layer MLP (torch)
│ │ └── control.py # random-label control-task 包装器
│ ├── eval/
│ │ ├── train_eval.py # 读缓存→训练→IID/OOD 评估 单元
│ │ ├── directions.py # 概念方向、跨分布 cosine 旋转
│ │ └── selectivity.py # selectivity = acc(real) - acc(control)
│ ├── score/
│ │ └── stability.py # 双分量 Probe Stability Score 计算
│ ├── stats/
│ │ ├── bootstrap.py # bootstrap CI
│ │ ├── correlation.py # Spearman(Score ↔ OOD drop)
│ │ ├── effect_size.py # Cliff's delta
│ │ └── multiple_testing.py # Holm / Benjamini-Hochberg
│ └── pipeline/
│ ├── stage_extract.py # 阶段1:激活抽取(写缓存)
│ ├── stage_probe.py # 阶段2:探针训练+评估(读缓存)
│ ├── stage_score.py # 阶段3:Score 聚合
│ └── stage_stats.py # 阶段4:统计检验 + 表格
├── scripts/
│ ├── run_mve.sh # 一条命令复现 MVE
│ ├── run_full.sh
│ └── make_figures.py # 论文图(cosine 旋转、Score↔drop 散点)
├── tests/
│ ├── test_cache_roundtrip.py
│ ├── test_shapes.py
│ ├── test_probes_smoke.py
│ └── test_score_math.py
└── artifacts/ # 运行产物(git-ignored)
├── cache/ # 激活分片
├── results/ # parquet 结果表
├── figures/
└── logs/
2. 各模块职责 + 关键函数签名
2.1 config / registry
# config.py
@dataclass
class RunConfig:
models: list[str]; datasets: list[str]; shifts: list[str]
probes: list[str]; seeds: list[int]
cache_dir: Path; results_dir: Path
pooling: str = "last_token" # last_token | mean
dtype: str = "float16"
max_examples_per_dist: int = 4000 # 控制规模/显存
extract_batch: dict[str, int] = ... # 每模型 batch 覆盖
def load_config(exp_path: str, overrides: list[str]) -> RunConfig: ...
# registry.py
def register(kind: str, name: str): ... # 装饰器
def build_model(name: str) -> "LMWrapper": ...
def build_dataset(name: str) -> "DistributionSet": ...
def build_probe(name: str, **kw) -> "Probe": ...
def build_shift(name: str) -> "Shift": ...
2.2 data
# schema.py
@dataclass(frozen=True)
class Example:
uid: str; text: str; label: int; meta: dict # meta 含 domain/length_bucket
@dataclass
class DistributionSet:
name: str # e.g. "sst2"
distribution: str # "iid" | "bt_de" | "domain_books" | "len_long"
examples: list[Example]
num_labels: int
# loaders.py
def load_dataset(name: str, split: str, max_n: int, seed: int) -> DistributionSet: ...
# 支持: sst2 imdb amazon_multi ag_news dbpedia ud_pos truthfulqa_bin counterfact_bin
# sampling.py
def stratified_subsample(ds: DistributionSet, n: int, seed: int) -> DistributionSet: ...
def length_buckets(ds: DistributionSet, tokenizer, edges=(32,128)) -> dict[str, DistributionSet]: ...
# shifts/base.py
class Shift(ABC):
name: str
@abstractmethod
def apply(self, ds: DistributionSet) -> DistributionSet: ...
# 产出 distribution 字段已改写、label 暂保留(待 fidelity 过滤)
# shifts/backtranslation.py
class BackTranslationShift(Shift):
def __init__(self, pivot="deu_Latn", model="facebook/nllb-200-distilled-600M",
batch_size=32, device="cuda"): ...
def apply(self, ds) -> DistributionSet: ... # text→pivot→en,流式 batch
2.3 fidelity
# mnli_filter.py
class MNLIFidelityFilter:
def __init__(self, model="MoritzLaurer/DeBERTa-v3-base-mnli", thresh=0.5): ...
def keep_mask(self, src_texts: list[str], shifted_texts: list[str]) -> np.ndarray:
"""双向蕴含判定:保留语义未翻转(label-preserving)样本,返回 bool mask"""
def filter(self, src: DistributionSet, shifted: DistributionSet) -> DistributionSet: ...
2.4 extract(核心)
# hooks.py
def residual_hook_points(model, arch: str) -> list[tuple[int, nn.Module]]:
"""返回 [(layer_idx, module)],module 输出即该层 residual-stream。
Pythia(GPTNeoX): gpt_neox.layers[i]; GPT2: transformer.h[i]; Qwen2: model.layers[i]"""
# pooling.py
def pool(hidden: Tensor, attn_mask: Tensor, mode: str) -> Tensor:
"""(B,T,D)->(B,D);last_token 取最后非 pad 位,mean 做 mask 平均"""
# activations.py
class ActivationExtractor:
def __init__(self, model_name: str, dtype="float16", device="cuda",
pooling="last_token", max_len=256): ...
@torch.inference_mode()
def extract(self, ds: DistributionSet, store: "ShardStore",
batch_size: int, layers: list[int] | None = None) -> ShardMeta:
"""单次前向抓全层 residual,逐 batch pool→cast fp16→流式 append 落盘。
返回写入分片的元信息(n, layers, d_model)。"""
抽取核心逻辑:注册 forward hook 抓全部层 → 一次前向 → 每层 pool → fp16 → 立即追加写盘,不在显存/内存累积全量激活。
2.5 cache
# store.py 分片单位 = (model, dataset, distribution);层维进 .npz 同文件多 key 或单层一文件
class ShardStore:
def __init__(self, root: Path, dtype="float16"): ...
def shard_path(self, model, dataset, distribution, layer, seed_split="all") -> Path: ...
def open_writer(self, model, dataset, distribution, n_layers, d_model, n: int): ...
def append(self, layer_arrays: dict[int, np.ndarray], labels, uids): ... # 流式
def finalize(self): ... # 写 X.npy / y.npy / uids
def load(self, model, dataset, distribution, layer) -> tuple[np.ndarray, np.ndarray, list[str]]:
"""mmap 读取 X(fp16), y(int8), uids"""
def exists(self, model, dataset, distribution, layer) -> bool: ... # 幂等跳过
# manifest.py
def update_manifest(root: Path, meta: dict): ... # 追加到 manifest.parquet
def verify_cache(root: Path) -> list[str]: ... # 返回缺失/损坏 shard
2.6 probes
# base.py
class Probe(ABC):
@abstractmethod
def fit(self, X: np.ndarray, y: np.ndarray) -> "Probe": ...
@abstractmethod
def predict(self, X: np.ndarray) -> np.ndarray: ...
@abstractmethod
def direction(self) -> np.ndarray | None: # 概念方向(单位向量),MLP 返回 None 或一阶近似
...
# logreg.py LogRegProbe(C=1.0, max_iter=1000) —— 内部 X 升精到 float32 训练
# mass_mean.py MassMeanProbe —— direction = (μ_pos - μ_neg)/||·||;阈值=中点投影
# mlp.py MLPProbe(hidden=256, epochs=30, lr=1e-3, device="cuda") —— direction=输入层雅可比近似
# control.py
class ControlTaskProbe(Probe):
def __init__(self, inner: Probe, seed: int):
"""random-label control:固定随机标签置换后训练同结构探针"""
2.7 eval / score / stats
# train_eval.py
def fit_and_eval(probe_name, store, model, dataset,
train_dist="iid", eval_dists=("iid","bt_de",...),
seed=0) -> ProbeResult:
"""读 train_dist 缓存→fit→在各 eval_dist 上算 acc;返回 acc 字典 + 方向向量"""
# directions.py
def cosine_rotation(dir_iid: np.ndarray, dir_ood: np.ndarray) -> float: ... # 1 - cos
def aggregate_directions(results: list[ProbeResult], by="layer") -> pd.DataFrame: ...
# selectivity.py
def selectivity(real_acc: float, control_acc: float) -> float: ...
# score/stability.py
def probe_stability_score(acc_iid, acc_ood, dir_iid, dir_ood,
w_perf=0.5, w_geom=0.5) -> dict:
"""双分量:
perf_component = acc_ood / acc_iid (性能保持度,∈[0,1])
geom_component = cos(dir_iid, dir_ood) (方向稳定度,∈[-1,1]→clip[0,1])
score = w_perf*perf + w_geom*geom
返回 {score, perf_component, geom_component}"""
# stats/
def bootstrap_ci(values, fn=np.mean, n=1000, alpha=0.05, seed=0) -> tuple[float,float,float]: ...
def spearman_with_ci(score, ood_drop, n_boot=1000) -> dict: ... # rho + CI + p
def cliffs_delta(a, b) -> float: ...
def holm_bonferroni(pvals) -> np.ndarray: ...
def benjamini_hochberg(pvals, q=0.05) -> np.ndarray: ...
2.8 pipeline
# stage_extract.py
def run_extract(cfg: RunConfig):
for model in cfg.models:
ext = ActivationExtractor(model, ...)
for ds_name in cfg.datasets:
base = load_dataset(ds_name, "test", cfg.max_examples_per_dist, seed=0)
dists = [base] + [filter_fidelity(s.apply(base), base) for s in shifts]
for d in dists:
if store.exists(model, ds_name, d.distribution, layer=0): continue # 幂等
ext.extract(d, store, batch_size=cfg.extract_batch[model])
del ext; free_gpu() # 关键:释放当前模型再载下一个
# stage_probe.py / stage_score.py / stage_stats.py 全程只读缓存,CPU/小 GPU 即可
3. 数据流与缓存格式
3.1 数据流
原始数据集 ──load_dataset──► DistributionSet(iid)
│
shift.apply ──┼──► bt_de / domain_x / len_* (候选)
│
MNLI fidelity 过滤 ─┘──► label-preserving DistributionSet
│
ActivationExtractor(单模型常驻)│ 一次前向抓全层 → pool → fp16 → 流式
▼
ShardStore 落盘 (阶段1结束)
│ (此后 GPU 几乎闲置)
fit_and_eval(读缓存) ──────┼──► ProbeResult{acc[dist], direction}
▼
stability.py ──► Score(双分量)
▼
stats ──► Spearman/CI/Holm-BH/Cliff's δ ──► results/*.parquet ──► figures
3.2 缓存目录与 shard 命名
分片粒度:(model, dataset, distribution) 一组目录,层维拆文件,便于按层流式与按需读取。
artifacts/cache/
└── {model}/{dataset}/{distribution}/
├── X_layer{LL}.npy # 每层一个,float16,shape (N, d_model)
├── y.npy # int8, shape (N,)
├── uids.npy # <U32 字符串 id, shape (N,)
└── meta.json # {n, n_layers, d_model, pooling, dtype, src_hash, created}
命名约定:
| 占位符 | 取值示例 | 说明 |
|---|---|---|
{model} |
pythia-70m, gpt2-medium, qwen2.5-0.5b |
HF id 安全化 |
{dataset} |
sst2, ag_news, ud_pos |
configs/datasets.yaml key |
{distribution} |
iid, bt_de, domain_books, len_long |
iid + shift 产物 |
{LL} |
00..24 |
层索引零填充两位(含 embedding 层为 0) |
3.3 dtype / shape 约定
| 数组 | dtype | shape | 备注 |
|---|---|---|---|
X_layer{LL} |
float16 | (N, d_model) | residual-stream pooled;训练时升 float32 |
y |
int8 | (N,) | 标签;多分类 ≤127 类够用 |
uids |
<U32 | (N,) | 跨分布对齐(fidelity 后 N 可不同) |
manifest.parquet |
— | 一行一 shard | 列:model,dataset,distribution,layer,n,d_model,bytes,sha1 |
约定:同一 (model,dataset) 下所有 distribution 的 d_model、n_layers 一致;X 列序 = residual_hook_points 返回顺序。
4. 显存与运行时控制策略
单模型常驻、串行换模型:外层循环按 model,处理完所有 dataset/shift 后
del model; gc.collect(); torch.cuda.empty_cache()再载下一个。任一时刻显存只住一个 LM。1.4B 在 fp16 约 2.8 GB 权重,远低于 24 GB。激活抽取一律
torch.inference_mode()+ fp16 autocast,不建图、不存梯度。一次前向用 hook 抓全层,避免逐层多次前向。流式落盘,绝不全量驻留:每个 batch 抽取→pool 成 (B,D)→cast fp16→
np.memmap追加写,显存/内存只持有一个 batch 的激活。pool 后丢弃 (B,T,D) 中间张量。batch 自适应降级:
gpu.py包装 OOM 捕获,触发即batch//=2重试,直到成功;最优 batch 记入models.yaml复用。max_len=256截断,长样本不爆显存。辅助模型(NLLB / DeBERTa-MNLI)单独阶段跑,与 LM 抽取错峰,跑完即释放;同样 fp16 + batch 流式。
探针阶段几乎不吃显存:LogReg/mass-mean 走 CPU(numpy/sklearn);MLP 用小 GPU batch(X 已是 (N,D) 向量,512 batch 占用极小)。可与抽取分进程。
mmap 读缓存:
np.load(..., mmap_mode='r'),大矩阵不一次性进内存;训练时按需切片升 float32。幂等跳过:
store.exists()命中即跳过,中断后续跑零浪费;hashing.py用 config+输入指纹做 key。
5. 4090 上各阶段运行时估算(目标 ≤30 GPU·h)
规模假设:8 数据集 × (1 IID + 3 shift 各取代表) ≈ 每数据集 ~4 distribution;每 distribution 上限 4000 样本;7 模型;max_len=256。
| 阶段 | 主要开销 | GPU·h 估算 |
|---|---|---|
| 辅助:back-translation (NLLB-600M) | 8 数据集 × |
~4.0 |
| 辅助:MNLI fidelity (DeBERTa-base) | 仅对 shift 样本,分类一次/样本 | ~1.5 |
| 阶段1:激活抽取(7 模型) | 小模型(70M/160M/410M/gpt2-s/m/qwen0.5b)合计 ~3.5;1.4B 单独 ~3.0;总样本 ~8×4×4000=128k 前向/模型 | ~8.0 |
| 阶段2:探针训练+评估 | LogReg/mass-mean 走 CPU(忽略 GPU);MLP 全配置 GPU 小 batch | ~3.0 |
| 阶段3:Score 聚合 | 纯 CPU | ~0 |
| 阶段4:统计(bootstrap/检验) | 纯 CPU(可多进程) | ~0 |
| 缓冲(重跑/调参/失败重试) | — | ~5.0 |
| 合计 | ≈ 24.5 GPU·h ✓ ≤30 |
降本旋钮:max_examples_per_dist 调到 2000 可再省 ~40% 抽取与翻译时间;shift 数从 3 减到 2;1.4B 仅在子集数据集上跑。
6. 复现入口(一条命令跑通 MVE)
configs/experiments/mve.yaml:pythia-70m × sst2 × (iid + bt_de 一个 shift) × {logreg, mass_mean, control} × seeds=[0,1],max_examples_per_dist=500。预计 < 10 分钟、< 4 GB 显存。
scripts/run_mve.sh:
#!/usr/bin/env bash
set -euo pipefail
python -m probe_stability.cli run \
--exp configs/experiments/mve.yaml \
--stages extract,probe,score,stats \
--out artifacts/
一条命令:
bash scripts/run_mve.sh
# 等价于
python -m probe_stability.cli run --exp configs/experiments/mve.yaml --stages all
产物:artifacts/cache/pythia-70m/sst2/{iid,bt_de}/X_layer*.npy、artifacts/results/{probe_results,scores,stats}.parquet、artifacts/figures/score_vs_drop.png。
全量:python -m probe_stability.cli run --exp configs/experiments/full.yaml --stages all。
CLI 子命令(便于分阶段重跑、节省 GPU·h):
probe-stability extract --exp ... # 仅写缓存(吃 GPU)
probe-stability probe --exp ... # 仅读缓存训探针(几乎不吃 GPU)
probe-stability score --exp ...
probe-stability stats --exp ...
probe-stability verify-cache # 校验分片完整性
关键工程取舍:抽取与训练彻底解耦(缓存为界)是 GPU·h 预算可控的核心——GPU 只在阶段1+辅助阶段被占用,探针的多 seed × 多探针 × 多分布组合爆炸全部落在读缓存的 CPU 侧;单模型常驻串行保证 1.4B 也安全;流式 fp16 落盘 + 幂等跳过让中断可续、零重复计算。如果需要,我可以接着把任意单个模块(如 activations.py 的 hook 抽取或 stability.py 的 Score 计算)落成可运行代码。