repro_bundle/cache.py

"""Activation-shard cache.

One shard = activations for a single (model, dataset, distribution) triple.
Stored as a directory of `.npy` files so that the (potentially multi-GB) activation
tensor can be memory-mapped and sliced by layer without loading everything into RAM.

Layout:
    cache/<model_key>/<dataset_key>/<distribution>/
        acts.npy     float16  [N, L+1, H]   (L+1 = embeddings + L transformer layers)
        labels.npy   int64    [N]
        ids.npy      int64    [N]           (stable example ids, for cross-shard alignment)
        meta.json    {model, dataset, distribution, pooling, n, n_layers, hidden, dtype}
"""
from __future__ import annotations

import json
from pathlib import Path

import numpy as np

from config import CACHE_DIR


def shard_dir(model_key: str, dataset_key: str, distribution: str) -> Path:
    return CACHE_DIR / model_key / dataset_key / distribution


def exists(model_key: str, dataset_key: str, distribution: str) -> bool:
    d = shard_dir(model_key, dataset_key, distribution)
    return (d / "acts.npy").exists() and (d / "meta.json").exists()


def save_shard(
    model_key: str,
    dataset_key: str,
    distribution: str,
    acts: np.ndarray,        # [N, L+1, H] float16
    labels: np.ndarray,      # [N]
    ids: np.ndarray,         # [N]
    pooling: str,
) -> Path:
    d = shard_dir(model_key, dataset_key, distribution)
    d.mkdir(parents=True, exist_ok=True)
    acts = np.ascontiguousarray(acts.astype(np.float16))
    np.save(d / "acts.npy", acts)
    np.save(d / "labels.npy", labels.astype(np.int64))
    np.save(d / "ids.npy", ids.astype(np.int64))
    meta = {
        "model": model_key,
        "dataset": dataset_key,
        "distribution": distribution,
        "pooling": pooling,
        "n": int(acts.shape[0]),
        "n_layers": int(acts.shape[1]),   # includes embedding layer
        "hidden": int(acts.shape[2]),
        "dtype": "float16",
    }
    (d / "meta.json").write_text(json.dumps(meta, indent=2))
    return d


def load_meta(model_key: str, dataset_key: str, distribution: str) -> dict:
    return json.loads((shard_dir(model_key, dataset_key, distribution) / "meta.json").read_text())


def load_shard(
    model_key: str,
    dataset_key: str,
    distribution: str,
    layer: int | None = None,
    mmap: bool = True,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Return (acts, labels, ids).

    If `layer` is given, acts is [N, H] for that layer (0 = embeddings, 1..L = layers).
    Otherwise acts is the full [N, L+1, H] tensor (mmap'd by default — do not mutate).
    """
    d = shard_dir(model_key, dataset_key, distribution)
    mode = "r" if mmap else None
    acts = np.load(d / "acts.npy", mmap_mode=mode)
    labels = np.load(d / "labels.npy")
    ids = np.load(d / "ids.npy")
    if layer is not None:
        acts = np.asarray(acts[:, layer, :], dtype=np.float32)   # materialise one layer
        # sanitise fp16-overflow inf/nan (e.g. Qwen "massive activations") -> finite.
        # No-op for models without overflow (pythia/gpt2), so existing results are unchanged.
        if not np.isfinite(acts).all():
            acts = np.nan_to_num(acts, nan=0.0, posinf=65504.0, neginf=-65504.0)
    return acts, labels, ids