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"""HAC on a token dump. Image tokens only; no context; no re-encode.
K and dim come from common.py (shipped: K=32, d=64). Cluster assignment uses
sinusoidal position fusion; stored vectors are semantic means.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "shared"))
import torch
from common import (
BUDGET,
DUMP_PROTOCOL,
HEAD_DIM,
MERGE_SIZE,
WEIGHT,
atomic_torch_save,
atomic_write_json,
gib_per_million_pages,
w_tag,
)
from paths import forbid_venv_path
_G_IMAGES = None
_G_GRIDS = None
_G_BUDGET = BUDGET
_G_W = WEIGHT
_G_MERGE = MERGE_SIZE
def _cluster_one(index: int):
from hac import hac_pool
pooled = hac_pool(
_G_IMAGES[index],
_G_GRIDS[index],
budget=_G_BUDGET,
position_weight=_G_W,
merge_size=_G_MERGE,
)
return pooled.half().contiguous()
def load_image_pages(docs_path: Path):
blob = torch.load(docs_path, map_location="cpu", weights_only=False)
if blob.get("dump_protocol") not in (DUMP_PROTOCOL, None) and "packed" not in blob:
raise RuntimeError(f"unrecognized dump: {docs_path}")
packed = blob["packed"]
offsets = blob["offsets"]
starts = blob["image_start"]
ends = blob["image_end"]
grids = blob["grid_thw"]
n = int(blob["n"])
images = []
grid_list = []
for i in range(n):
a, b = int(offsets[i]), int(offsets[i + 1])
full = packed[a:b]
images.append(full[int(starts[i]) : int(ends[i])].float())
grid_list.append([int(x) for x in grids[i].tolist()])
return blob.get("ids"), images, grid_list
def pad_pooled(pooled: list[torch.Tensor], budget: int, dim: int):
n = len(pooled)
out = torch.zeros(n, budget, dim, dtype=torch.float16)
nvec = torch.zeros(n, dtype=torch.int64)
for i, vec in enumerate(pooled):
if vec.ndim != 2 or int(vec.shape[-1]) != dim:
raise RuntimeError(f"pooled[{i}] shape {tuple(vec.shape)}")
k = int(vec.shape[0])
if k > budget:
raise RuntimeError(f"pooled[{i}] has {k} > budget {budget}")
out[i, :k] = vec.half()
nvec[i] = k
return out, nvec
def cluster_one_corpus(docs_path: Path, dest: Path, w: float, budget: int, workers: int, overwrite: bool):
if dest.is_file() and not overwrite:
print(f"[cluster] skip existing {dest}", flush=True)
return json_sidecar_if_any(dest)
ids, images, grids = load_image_pages(docs_path)
n = len(images)
global _G_IMAGES, _G_GRIDS, _G_BUDGET, _G_W, _G_MERGE
_G_IMAGES = images
_G_GRIDS = grids
_G_BUDGET = int(budget)
_G_W = float(w)
_G_MERGE = MERGE_SIZE
t0 = time.time()
# Threads, not processes: sklearn releases the GIL, and fork-after-torch
# deadlocks on this node (CUDA already initialized in the parent).
if workers <= 1 or n <= 1:
pooled = [_cluster_one(i) for i in range(n)]
else:
with ThreadPoolExecutor(max_workers=workers) as pool:
pooled = list(pool.map(_cluster_one, range(n), chunksize=8))
padded, nvec = pad_pooled(pooled, budget, HEAD_DIM)
dt = time.time() - t0
stored = [int(x) for x in nvec.tolist()]
stats = {
"pages": n,
"budget": int(budget),
"position_weight": float(w),
"merge_size": MERGE_SIZE,
"export_context": False,
"avg_image_tokens": sum(int(t.shape[0]) for t in images) / n,
"avg_stored_tokens": sum(stored) / n,
"min_stored_tokens": min(stored),
"max_stored_tokens": max(stored),
"pages_below_budget": sum(s < budget for s in stored),
"gib_per_million_pages": round(gib_per_million_pages(sum(stored) / n), 4),
"compress_sec": round(dt, 2),
"workers": workers,
"corpus_id": docs_path.stem,
}
payload = {
"ids": ids,
"padded": padded,
"nvec": nvec,
"dim": HEAD_DIM,
"budget": int(budget),
"position_weight": float(w),
"export_context": False,
"dump_protocol": DUMP_PROTOCOL,
"stats": stats,
}
atomic_torch_save(dest, payload)
atomic_write_json(dest.with_suffix(".json"), stats)
print(
f"[cluster] K={budget} w={w} {docs_path.name}: {n} pages "
f"avg_stored={stats['avg_stored_tokens']:.2f} {dt:.1f}s -> {dest}",
flush=True,
)
return stats
def json_sidecar_if_any(dest: Path):
side = dest.with_suffix(".json")
if side.is_file():
return json.loads(side.read_text(encoding="utf-8"))
return None
def parse_args():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--dump-dir", required=True)
ap.add_argument("--cluster-root", required=True)
ap.add_argument("--budget", type=int, default=BUDGET)
ap.add_argument("--workers", type=int, default=128)
ap.add_argument("--overwrite", action="store_true")
return ap.parse_args()
def main():
args = parse_args()
os.environ.setdefault("OMP_NUM_THREADS", "1")
os.environ.setdefault("MKL_NUM_THREADS", "1")
os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
dump_root = forbid_venv_path(args.dump_dir, "dump-dir")
forbid_venv_path(args.cluster_root, "cluster-root")
docs_dir = dump_root / "docs"
corpora = sorted(docs_dir.glob("*.pt"))
if not corpora:
raise SystemExit(f"no dumped corpora under {docs_dir}")
weights = (WEIGHT,)
workers = max(1, int(args.workers))
print(
f"[cluster] corpora={len(corpora)} w={WEIGHT} budget={args.budget} "
f"workers={workers} no_context=1",
flush=True,
)
summary = {"weights": {}, "dump_dir": str(dump_root), "budget": args.budget, "export_context": False}
n_corpora = len(corpora)
for w in weights:
tag = w_tag(w)
dest_dir = Path(args.cluster_root) / tag / "docs"
dest_dir.mkdir(parents=True, exist_ok=True)
done_path = Path(args.cluster_root) / tag / "DONE.json"
existing_pts = list(dest_dir.glob("*.pt"))
if (
not args.overwrite
and done_path.is_file()
and len(existing_pts) == n_corpora
):
rec = json.loads(done_path.read_text(encoding="utf-8"))
if int(rec.get("n_corpora") or 0) == n_corpora:
print(f"[cluster] skip {tag} already DONE n_corpora={n_corpora}", flush=True)
summary["weights"][tag] = rec
continue
per = []
for docs_path in corpora:
dest = dest_dir / docs_path.name
per.append(
cluster_one_corpus(
docs_path, dest, w=w, budget=args.budget, workers=workers, overwrite=args.overwrite
)
)
summary["weights"][tag] = {"position_weight": w, "n_corpora": len(per), "corpora": per}
atomic_write_json(done_path, summary["weights"][tag])
atomic_write_json(Path(args.cluster_root) / "DONE.json", summary)
print(f"[cluster] done -> {Path(args.cluster_root) / 'DONE.json'}", flush=True)
if __name__ == "__main__":
main()
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