#!/usr/bin/env python3 """Explicit execution entry point for the prospective Hebbian study.""" import argparse import importlib.metadata import json import platform import re import subprocess import sys from llm_memory_editability.hebbian_learning import ( ARTIFACTS, CONFIG_PATH, DATA, calibrate, config, now, read_json, sha256, write_json, ) def prepare_sources(): import requests from transformers import AutoTokenizer from llm_memory_editability.hebbian_data import clean_candidates cfg = config() source = DATA / "source" source.mkdir(parents=True, exist_ok=True) def fetch(url, path): if path.exists(): return path.parent.mkdir(parents=True, exist_ok=True) with requests.get(url, stream=True, timeout=(30, 180)) as response: response.raise_for_status() tmp = path.with_name(path.name + ".part") with tmp.open("wb") as f: for block in response.iter_content(4 * 1024 * 1024): f.write(block) tmp.replace(path) fetch("https://rome.baulab.info/data/dsets/counterfact.json", source / "counterfact.json") model = cfg["model"] fetch( f"https://huggingface.co/api/models/{model['id']}/revision/{model['revision']}", source / "model-info.json", ) model_info = read_json(source / "model-info.json") assert model_info["sha"] == model["revision"] for item in model_info["siblings"]: name = item["rfilename"] if name != ".gitattributes": fetch( f"https://huggingface.co/{model['id']}/resolve/{model['revision']}/{name}", source / "qwen3-0.6b-base" / name, ) rev = cfg["author_revision"] for name in [ "src/hebbian/methods/hebbian/model.py", "src/hebbian/methods/hebbian/construction.py", "LICENSE", "README.md", ]: fetch( f"https://raw.githubusercontent.com/HazyResearch/hebbian-mlps/{rev}/{name}", source / "hebbian-mlps" / name, ) fetch("https://huggingface.co/api/datasets/Salesforce/wikitext", source / "wikitext-info.json") wiki_info = read_json(source / "wikitext-info.json") tokenizer = AutoTokenizer.from_pretrained(source / "qwen3-0.6b-base", local_files_only=True) import pyarrow.parquet as pq texts = {} for split in ["train", "test"]: name = f"wikitext-2-raw-v1/{split}-00000-of-00001.parquet" path = source / name fetch( f"https://huggingface.co/datasets/Salesforce/wikitext/resolve/{wiki_info['sha']}/{name}", path, ) lines = pq.read_table(path).column("text").to_pylist() documents, current = [], [] for line in lines: if re.fullmatch(r"\s*= [^=]+ =\s*", line) and current: documents.append("".join(current)) current = [] current.append(line) if current: documents.append("".join(current)) segments = [] for doc_id, doc in enumerate(documents): tokens = tokenizer(doc, add_special_tokens=False)["input_ids"] for offset in range(0, len(tokens) - 127, 128): ids = tokens[offset : offset + 128] segments.append( { "document_id": doc_id, "token_offset": offset, "document_sha256": __import__("hashlib").sha256(doc.encode()).hexdigest(), "input_ids": ids, "loss_mask": [0] + [1] * 127, "answer_start": 1, "source_split": split, } ) if len(segments) == 128: break if len(segments) == 128: break assert len(segments) == 128 texts[split] = segments write_json(DATA / "text.json", texts) files = { str(p.relative_to(source)): sha256(p) for p in source.rglob("*") if p.is_file() and not p.name.endswith(".part") } write_json( ARTIFACTS / "source-manifest.json", { "time": now(), "model_revision": model["revision"], "author_revision": rev, "wikitext_revision": wiki_info["sha"], "sha256": files, "model_license": "Apache-2.0", "author_license": (source / "hebbian-mlps/LICENSE").read_text(), "wikitext_card": wiki_info.get("cardData"), "config_sha256": sha256(CONFIG_PATH), }, ) if not (DATA / "candidates.json").exists(): clean_candidates(tokenizer) packages = {} for name in [ "torch", "transformers", "tokenizers", "datasets", "huggingface-hub", "numpy", "matplotlib", "safetensors", "pyarrow", ]: try: packages[name] = importlib.metadata.version(name) except importlib.metadata.PackageNotFoundError: packages[name] = "not installed" write_json( ARTIFACTS / "environment-lock.json", { "time": now(), "python": sys.version, "executable": sys.executable, "platform": platform.platform(), "packages": packages, "dtype": "float32", "attention": "eager", "tf32": False, "deterministic_algorithms": True, "CUBLAS_WORKSPACE_CONFIG": ":4096:8", "gpu_query": subprocess.check_output( [ "nvidia-smi", "--query-gpu=index,name,uuid,ecc.errors.uncorrected.volatile.total", "--format=csv", ], text=True, ), "excluded_gpu_indices": [0], }, ) (ARTIFACTS / "environment-freeze.txt").write_text( subprocess.check_output([sys.executable, "-m", "pip", "freeze"], text=True) ) def main(): parser = argparse.ArgumentParser() parser.add_argument( "command", choices=[ "prepare", "calibrate", "baseline", "audit-data", "lock-data", "freeze-b", "preflight", "diagnose", "form", "adapt", ], ) parser.add_argument("--device", default="cuda:3") parser.add_argument("--split", choices=["dev", "eval"], default="dev") parser.add_argument("--run-id", default="hebbian-learning-v1") parser.add_argument("--shard", type=int, default=0) parser.add_argument("--shards", type=int, default=1) args = parser.parse_args() if args.command == "prepare": prepare_sources() elif args.command == "calibrate": calibrate() elif args.command == "baseline": from llm_memory_editability.hebbian_model import run_baseline run_baseline(args.device, args.shard, args.shards) elif args.command == "audit-data": from llm_memory_editability.hebbian_data import semantic_audit semantic_audit() elif args.command == "freeze-b": from llm_memory_editability.hebbian_statistics import freeze_b freeze_b() elif args.command == "lock-data": from llm_memory_editability.hebbian_data import lock_data rows = [] for p in sorted(DATA.glob("baseline-shard-*.jsonl")): rows.extend(json.loads(line) for line in p.read_text().splitlines()) assert len({r["case_id"] for r in rows}) == len(rows) write_json(DATA / "baseline.json", sorted(rows, key=lambda r: r["case_id"])) print(lock_data()) else: from llm_memory_editability.hebbian_train import dispatch dispatch(args) if __name__ == "__main__": main()