Download code/index.py from tablinum/beam-engram-tables: direct link, hf CLI and curl.
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https://huggingface.co/datasets/tablinum/beam-engram-tables/resolve/main/code/index.py
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hf download hf://datasets/tablinum/beam-engram-tables/code/index.py
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curl -L -o index.py https://huggingface.co/datasets/tablinum/beam-engram-tables/resolve/main/code/index.py
2.67 kB
| """index.py - the engram's key table: a faiss IVF (inner product, SQ8 codes) over every static passage key in emb_static/, | |
| with its inverted lists on Optane (OnDiskInvertedLists) so only the 16k centroids sit in RAM (10-19). | |
| Steps: train on a TRAIN-vector sample spread over all shards -> per-shard sub-indexes (index/shard_*.index) -> merge into | |
| index/engram.index + index/lists.ivfdata. Resumable per step. Env: EMB (emb_static/), OUT (index/), NLIST (16384), TRAIN (1000000), THREADS (4), | |
| NSHARDS (optional cap; default all shards - the old 999 default silently dropped health's last 19 shards).""" | |
| import os, glob, time | |
| import numpy as np, faiss | |
| from faiss.contrib.ondisk import merge_ondisk | |
| E = "/mnt/optane/engram"; OUT = os.environ.get("OUT", f"{E}/index"); os.makedirs(OUT, exist_ok=True) | |
| NLIST = int(os.environ.get("NLIST", 16384)); NTRAIN = int(os.environ.get("TRAIN", 1_000_000)) | |
| faiss.omp_set_num_threads(int(os.environ.get("THREADS", 4))) | |
| log = lambda *a: print(time.strftime("%H:%M:%S"), *a, flush=True) | |
| shards = sorted(f for f in glob.glob(f"{os.environ.get('EMB', E + '/emb_static')}/*.npy") if not f.endswith(".pid.npy") and ".tmp" not in f)[:int(os.environ.get("NSHARDS", 10**9))] | |
| mm = [np.load(f, mmap_mode="r") for f in shards]; D = mm[0].shape[1]; N = sum(len(m) for m in mm) | |
| log(f"{len(shards)} shards, {N} keys, dim {D}") | |
| tr = f"{OUT}/trained.index" | |
| if not os.path.exists(tr): | |
| rng = np.random.default_rng(0); per = NTRAIN // len(mm) | |
| X = np.concatenate([m[np.sort(rng.choice(len(m), min(per, len(m)), replace=False))] for m in mm]).astype(np.float32) | |
| ix = faiss.index_factory(D, f"IVF{NLIST}_HNSW32,SQ8", faiss.METRIC_INNER_PRODUCT) | |
| t0 = time.time(); ix.train(X); faiss.write_index(ix, tr); log(f"trained on {len(X)} in {time.time() - t0:.0f} s"); del X | |
| for f, m in zip(shards, mm): | |
| o = f"{OUT}/shard_{os.path.basename(f)[:-4]}.index" | |
| if os.path.exists(o): | |
| continue | |
| ix = faiss.read_index(tr); pid = np.load(f[:-4] + ".pid.npy"); t0 = time.time() | |
| for i in range(0, len(m), 500_000): | |
| ix.add_with_ids(np.ascontiguousarray(m[i:i + 500_000], dtype=np.float32), pid[i:i + 500_000]) | |
| faiss.write_index(ix, o + ".tmp"); os.replace(o + ".tmp", o); log(f"{o}: {ix.ntotal} in {time.time() - t0:.0f} s") | |
| if not os.path.exists(f"{OUT}/engram.index"): | |
| ix = faiss.read_index(tr); t0 = time.time() | |
| merge_ondisk(ix, sorted(glob.glob(f"{OUT}/shard_*.index")), f"{OUT}/lists.ivfdata") | |
| faiss.write_index(ix, f"{OUT}/engram.index"); log(f"merged {ix.ntotal} keys in {time.time() - t0:.0f} s") | |
| for f in glob.glob(f"{OUT}/shard_*.index"): | |
| os.remove(f) # their lists now live in lists.ivfdata | |