repro_bundle/config.py

"""Central configuration: model registry, dataset registry, shift specs, paths, hyper-params.

Importing this module is dependency-light (no torch / transformers) so it can be used by
analysis scripts on a laptop. Heavy imports live in extract_activations.py / data/*.
"""
from __future__ import annotations

import os
from dataclasses import dataclass, field
from pathlib import Path

# --------------------------------------------------------------------------------------
# Paths
# --------------------------------------------------------------------------------------
ROOT = Path(__file__).resolve().parent
CACHE_DIR = Path(os.environ.get("PROBE_CACHE", ROOT / "cache"))        # activation shards
RESULTS_DIR = Path(os.environ.get("PROBE_RESULTS", ROOT / "results"))  # metrics, tables
DATA_DIR = Path(os.environ.get("PROBE_DATA", ROOT / "data_cache"))     # shifted text cache

for _d in (CACHE_DIR, RESULTS_DIR, DATA_DIR):
    _d.mkdir(parents=True, exist_ok=True)


# --------------------------------------------------------------------------------------
# Models — the size ladder (claim C4). Keys are short ids used in shard filenames.
# --------------------------------------------------------------------------------------
@dataclass(frozen=True)
class ModelSpec:
    key: str            # short id, used on disk (no slashes)
    hf_name: str        # HF hub id
    family: str         # for grouping in mixed-effects / size ladder
    params_m: float     # parameter count in millions (for the scale axis)


MODELS: dict[str, ModelSpec] = {
    m.key: m for m in [
        ModelSpec("pythia-70m", "EleutherAI/pythia-70m", "pythia", 70),
        ModelSpec("pythia-160m", "EleutherAI/pythia-160m", "pythia", 160),
        ModelSpec("pythia-410m", "EleutherAI/pythia-410m", "pythia", 410),
        ModelSpec("pythia-1.4b", "EleutherAI/pythia-1.4b", "pythia", 1400),
        ModelSpec("pythia-6.9b", "EleutherAI/pythia-6.9b", "pythia", 6900),   # Tier 3: 7B spot-check
        ModelSpec("gpt2", "gpt2", "gpt2", 124),
        ModelSpec("gpt2-medium", "gpt2-medium", "gpt2", 355),
        ModelSpec("qwen2.5-0.5b", "Qwen/Qwen2.5-0.5B", "qwen", 500),
    ]
}

# Default ladder for the headline scale experiment (cheapest -> most expensive).
SIZE_LADDER = ["pythia-70m", "pythia-160m", "pythia-410m", "pythia-1.4b"]


# --------------------------------------------------------------------------------------
# Datasets — concept-bearing classification tasks (claim families C1/C3).
# `concept` groups datasets that probe the SAME underlying concept (for the Spearman
# generalisation analysis across concept families).
# --------------------------------------------------------------------------------------
@dataclass(frozen=True)
class DatasetSpec:
    key: str
    hf_path: str
    hf_config: str | None
    split: str
    text_field: str
    label_field: str
    concept: str            # "sentiment" | "topic" | "pos" | "truth"
    num_labels: int
    # Domain-shift partner: another DatasetSpec key whose inputs share the label space
    # but come from a different domain (used as the `domain` OOD distribution).
    domain_partner: str | None = None


#
# NOTE: datasets>=5.0 dropped legacy short names AND script-based datasets (no more
# trust_remote_code). Use fully-qualified `namespace/name` Hub ids that ship parquet.
# Verified loadable 2026-06-22: nyu-mll/glue, stanfordnlp/imdb, fancyzhx/ag_news,
# fancyzhx/dbpedia_14, fancyzhx/amazon_polarity.
DATASETS: dict[str, DatasetSpec] = {
    d.key: d for d in [
        # --- sentiment concept ---
        DatasetSpec("sst2", "nyu-mll/glue", "sst2", "train", "sentence", "label", "sentiment", 2,
                    domain_partner="imdb"),
        DatasetSpec("imdb", "stanfordnlp/imdb", None, "train", "text", "label", "sentiment", 2,
                    domain_partner="sst2"),
        DatasetSpec("amazon_polarity", "fancyzhx/amazon_polarity", None, "train", "content",
                    "label", "sentiment", 2, domain_partner="sst2"),
        # --- topic concept ---
        DatasetSpec("ag_news", "fancyzhx/ag_news", None, "train", "text", "label", "topic", 4,
                    domain_partner="dbpedia"),
        DatasetSpec("dbpedia", "fancyzhx/dbpedia_14", None, "train", "content", "label", "topic", 14),
        # --- syntactic concept (POS) ---
        # WARNING: universal_dependencies is script-based -> NOT loadable under datasets>=5.0.
        # TODO(4090): swap to a parquet POS source (e.g. a CoNLL-2003 POS parquet) before using.
        DatasetSpec("ud_pos", "universal-dependencies/universal_dependencies", "en_ewt", "train",
                    "tokens", "upos", "pos", 17),
        # --- truthfulness / factual concept (derived, label from existing fields) ---
        # TODO(4090): truthful_qa generation may be script-based; counterfact-tracing ships parquet.
        DatasetSpec("truthfulqa", "truthfulqa/truthful_qa", "generation", "validation", "question",
                    "label", "truth", 2),
        DatasetSpec("counterfact", "NeelNanda/counterfact-tracing", None, "train", "prompt",
                    "label", "truth", 2),
        # --- extra concepts for cross-concept (LOCO) power; all parquet, verified 2026-06-22 ---
        DatasetSpec("emotion", "dair-ai/emotion", None, "train", "text", "label", "emotion", 6),
        DatasetSpec("tweet_hate", "cardiffnlp/tweet_eval", "hate", "train", "text", "label",
                    "hate", 2),
        DatasetSpec("tweet_irony", "cardiffnlp/tweet_eval", "irony", "train", "text", "label",
                    "irony", 2),
        DatasetSpec("tweet_offensive", "cardiffnlp/tweet_eval", "offensive", "train", "text",
                    "label", "offensive", 2),
        DatasetSpec("subj", "SetFit/subj", None, "train", "text", "label", "subjectivity", 2),
        DatasetSpec("spam", "ucirvine/sms_spam", "plain_text", "train", "sms", "label", "spam", 2),
        # --- 3 more concepts -> 12 total (verified loadable 2026-06-22) ---
        DatasetSpec("cola", "nyu-mll/glue", "cola", "train", "sentence", "label", "grammaticality", 2),
        DatasetSpec("stance", "cardiffnlp/tweet_eval", "stance_feminist", "train", "text", "label",
                    "stance", 3),
        DatasetSpec("amazon_cf", "mteb/amazon_counterfactual", "en", "train", "text", "label",
                    "counterfactual", 2),
    ]
}


# --------------------------------------------------------------------------------------
# Distribution shifts (label-preserving). Each (dataset, distribution) pair is one
# activation shard. "iid" is the in-distribution train/test of the dataset itself.
# --------------------------------------------------------------------------------------
SHIFTS = ["iid", "paraphrase", "domain", "length"]

# Back-translation pivot for the `paraphrase` SHIFT (rotation ground truth) — keep single &
# fixed (de) so rotation measurements are consistent across runs.
BT_PIVOTS = ["de"]

# Tier 2: the AUGMENTATION predictor uses MULTIPLE pivots for genuine paraphrastic diversity
# (de/fr/ru). With PROBE_N_AUG=3 each aug uses a different pivot, so augmentation-consistency
# reflects robustness to varied surface forms (not the single-pivot artefact of the 1-aug run).
AUG_PIVOTS = ["de", "fr", "ru"]

# Length buckets (token counts) for the `length` shift: train on SHORT, eval on LONG.
LENGTH_BUCKETS = {"short": (0, 32), "long": (96, 512)}


# --------------------------------------------------------------------------------------
# Extraction hyper-params
# --------------------------------------------------------------------------------------
@dataclass
class ExtractConfig:
    pooling: str = "mean"          # "mean" (masked) | "last" (last non-pad token)
    max_length: int = 256
    batch_size: int = 32           # lower for pythia-1.4b on 24GB
    dtype: str = "float16"
    n_train: int = 2000            # examples used to FIT probes per seed (subsampled from pool)
    n_eval: int = 1000             # eval examples per (dataset, distribution) per seed (from pool)
    n_train_pool: int = 4000       # examples EXTRACTED for train (multi-seed subsamples from this)
    n_eval_pool: int = 2000        # examples EXTRACTED for eval/shifts (subsampled per seed)
    device: str = "cuda"


# --------------------------------------------------------------------------------------
# Probe / Stability-Score / stats hyper-params
# --------------------------------------------------------------------------------------
@dataclass
class ProbeConfig:
    probe_types: tuple[str, ...] = ("logreg", "mass_mean", "mlp")
    l2: float = 1.0                # inverse-C handled inside probes.py
    mlp_hidden: int = 128
    mlp_epochs: int = 50
    layer_selection: str = "iid_val"   # pick layer by IID val acc, never post-hoc on OOD


@dataclass
class StabilityConfig:
    k_bootstrap: int = 8           # K resamples for dispersion (multi-seed compensates)
    n_aug: int = 5                 # label-preserving augmentations for consistency
    # combine(dispersion, aug_consistency) -> scalar score; weights are a hyper-param we
    # report sensitivity for. Pre-registered hypothesis: combination > dispersion alone.
    w_dispersion: float = 0.5
    w_consistency: float = 0.5


@dataclass
class StatsConfig:
    seeds: tuple[int, ...] = (0, 1, 2, 3, 4)   # >=5; covers init / resample / augmentation
    n_bootstrap: int = 1000
    fdr_method: str = "holm"       # "holm" | "bh"


# Singletons used across the pipeline (override per-run in run_pipeline.py if needed).
EXTRACT = ExtractConfig()
PROBE = ProbeConfig()
STABILITY = StabilityConfig()
STATS = StatsConfig()

# Env overrides — let a small pilot run cheaply without editing code, e.g.
#   PROBE_N_TRAIN=600 PROBE_N_EVAL=400 PROBE_N_AUG=2 PROBE_BATCH=64 python run_pipeline.py ...
EXTRACT.n_train = int(os.environ.get("PROBE_N_TRAIN", EXTRACT.n_train))
EXTRACT.n_eval = int(os.environ.get("PROBE_N_EVAL", EXTRACT.n_eval))
EXTRACT.n_train_pool = int(os.environ.get("PROBE_N_TRAIN_POOL", EXTRACT.n_train_pool))
EXTRACT.n_eval_pool = int(os.environ.get("PROBE_N_EVAL_POOL", EXTRACT.n_eval_pool))
EXTRACT.batch_size = int(os.environ.get("PROBE_BATCH", EXTRACT.batch_size))
STABILITY.n_aug = int(os.environ.get("PROBE_N_AUG", STABILITY.n_aug))