| """static_embed.py PASSAGES.parquet [...] - first-stage engram keys with sentence-transformers/static-retrieval-mrl-en-v1 |
| (Apache-2.0): mean of per-token static vectors (no special tokens), Matryoshka-truncated to DIM, L2-normalised, float16 -> |
| emb_static/<name>.npy + .pid.npy. CPU only, ~100x cheaper than a transformer encoder, so the whole of Wikipedia indexes on the |
| spare cores while Beam keeps both GPUs; the cross-encoder re-rank recovers precision (10-19). Also importable: embed(texts).""" |
| import os, sys, time |
| import numpy as np |
| from tokenizers import Tokenizer |
| from safetensors.numpy import load_file |
| S = "/mnt/optane/hf-cache/hub/models--sentence-transformers--static-retrieval-mrl-en-v1/snapshots/f60985c706f192d45d218078e49e5a8b6f15283a/0_StaticEmbedding" |
| DIM = int(os.environ.get("DIM", 256)) |
| TOK = Tokenizer.from_file(f"{S}/tokenizer.json"); TOK.no_padding(); TOK.no_truncation() |
| W = load_file(f"{S}/model.safetensors")["embedding.weight"][:, :DIM].astype(np.float32) |
|
|
|
|
| def embed(texts, maxlen=512): |
| enc = TOK.encode_batch(texts, add_special_tokens=False) |
| E = np.empty((len(texts), DIM), np.float32) |
| for i, e in enumerate(enc): |
| ids = e.ids[:maxlen] |
| E[i] = W[ids].mean(0) if ids else 0 |
| E /= np.linalg.norm(E, axis=1, keepdims=True) + 1e-12 |
| return E |
|
|
|
|
| if __name__ == "__main__": |
| import pyarrow.parquet as pq |
| out = os.environ.get("OUT", "/mnt/optane/engram/emb_static"); os.makedirs(out, exist_ok=True) |
| for src in sys.argv[1:]: |
| name = os.path.basename(src)[:-8] |
| if os.path.exists(f"{out}/{name}.npy"): |
| continue |
| t0 = time.time(); pf = pq.ParquetFile(src); n = pf.metadata.num_rows |
| E = np.lib.format.open_memmap(f"{out}/{name}.tmp.npy", "w+", np.float16, (n, DIM)); P = np.empty(n, np.int64); i = 0 |
| for b in pf.iter_batches(batch_size=8192, columns=["pid", "text"]): |
| E[i:i + b.num_rows] = embed(b.column("text").to_pylist()); P[i:i + b.num_rows] = b.column("pid").to_numpy(); i += b.num_rows |
| E.flush(); del E |
| np.save(f"{out}/{name}.pid.npy", P); os.replace(f"{out}/{name}.tmp.npy", f"{out}/{name}.npy"); txt = P |
| print(f"{time.strftime('%H:%M:%S')} {name}: {len(txt)} passages in {time.time() - t0:.0f} s", flush=True) |
|
|
