| """Unified per-site table for the 8 focus names (+ bare_Alice control). |
| |
| Columns: name, variant, pos, update, doc_idx, block_first_mention(same-id), |
| standalone, prev_cat, next_piece, ctx_multiplicity, story_reg, dialog, |
| she_in_prefix, girl_in_prefix. Written as parquet if pyarrow available, else csv.gz. |
| """ |
| import json, os, sys |
| from collections import Counter |
| import numpy as np |
|
|
| REPO = "/mnt/nlpgpu-io1/data/maggie/explain-m0de-collapse/replicate" |
| sys.path.insert(0, REPO) |
| BASE = os.path.join(REPO, "out/alice_vs_megan") |
| K = 16; BLOCK = 1024; W = 131072; NL = 198; EOS = 100257 |
|
|
| from transformers import AutoTokenizer |
| tok = AutoTokenizer.from_pretrained( |
| os.path.join(REPO, "data/olmo3_prefix_hfstream_50b/tokenizer"), trust_remote_code=True) |
| sites = np.load(os.path.join(BASE, "sites/sites_all.npz")) |
| ctxs = np.load(os.path.join(BASE, "sites/contexts_all.npz")) |
| flags = np.load(os.path.join(BASE, "sites/block_register_flags.npz")) |
| eos = np.load(os.path.join(BASE, "sites/eos_positions.npy")) |
| h0 = json.load(open(os.path.join(BASE, "sites/h0_counts.json"))) |
|
|
| dec_cache = {} |
| def dec(i): |
| i = int(i) |
| if i not in dec_cache: |
| dec_cache[i] = tok.decode([i]) |
| return dec_cache[i] |
|
|
| HONOR = {"mrs","mr","ms","miss","aunt","auntie","aunty","lady","princess","queen", |
| "sister","grandma","grandmother","mother","mom","mama","cousin","dr", |
| "madam","madame","saint","st","mrs.","ms.","dr.","st.","dame"} |
| PREPS = {"about","with","to","from","for","of","as","at","on","in","like"} |
| QUOTES = set('"“”‘’\'`«»') |
|
|
| def prev_cat(pid): |
| s = dec(pid) |
| if pid == EOS: return "doc_start" |
| if pid == NL or s.endswith("\n"): return "newline" |
| t = s.strip(); tl = t.lower() |
| if not t: return "space" |
| if tl in ("named","called"): return "named_called" |
| if tl == "dear": return "dear" |
| if tl == "and": return "and" |
| if tl in HONOR: return "honorific_kin" |
| if t[-1] in ".!?…": return "sentence_end" |
| if t[-1] in QUOTES: return "quote_adj" |
| if t[-1] == ":": return "colon" |
| if t[-1] == ",": return "comma" |
| if tl == "by": return "by" |
| if tl in PREPS: return "prep" |
| if t[0].isdigit(): return "digit" |
| if s[0] == " " and t[0].isupper(): return "cap_word" |
| if s[0] == " " and t[0].islower(): return "lower_word" |
| if t[0].islower(): return "glue_lower" |
| return "other" |
|
|
| def is_standalone(nid): |
| s = dec(nid); t = s.strip() |
| if not s: return True |
| if s[0].isalnum(): return False |
| cap = bool(t) and s[0] == " " and t[0].isupper() and t.replace("-", "").replace(".", "").isalpha() |
| return not cap |
|
|
| recs = [] |
| FOCUS = ["Alice", "Mary", "Sarah", "Maria", "Julia", "Megan", "Karen", "Ann"] |
| for nm in FOCUS + ["bare_Alice"]: |
| lab = nm if nm.startswith("bare") else f"sp_{nm}" |
| p = sites[f"pos_{lab}"]; C = ctxs[f"ctx_{lab}"] |
| if not len(p): continue |
| seqlen = len(h0["patterns"][lab]["ids"]) |
| cs = ((p - 1) // BLOCK) * BLOCK |
| prev_same = np.concatenate(([np.int64(-1)], p[:-1])) |
| fm = prev_same < cs |
| rows = [tuple(r) for r in C.tolist()] |
| mult = Counter(rows) |
| doc_idx = np.searchsorted(eos, p) |
| has_flags = (not nm.startswith("bare")) and (f"{nm}_story_reg" in flags.files) |
| for i in range(len(p)): |
| recs.append({ |
| "name": nm.replace("bare_", ""), "variant": "bare" if nm.startswith("bare") else "sp", |
| "pos": int(p[i]), "update": int(p[i] // W), "doc_idx": int(doc_idx[i]), |
| "first_mention_block": bool(fm[i]), |
| "standalone": is_standalone(C[i, K + seqlen]), |
| "prev_cat": prev_cat(int(C[i, K - 1])), |
| "next_piece": dec(C[i, K + seqlen])[:20], |
| "ctx_multiplicity": int(mult[rows[i]]), |
| "story_reg": bool(flags[f"{nm}_story_reg"][i]) if has_flags else None, |
| "dialog_prefix": bool(flags[f"{nm}_dialog"][i]) if has_flags else None, |
| "she_in_prefix": bool(flags[f"{nm}_she"][i]) if has_flags else None, |
| "girl_in_prefix": bool(flags[f"{nm}_girl"][i]) if has_flags else None, |
| }) |
|
|
| out_base = os.path.join(BASE, "per_site_table") |
| try: |
| import pandas as pd |
| df = pd.DataFrame(recs) |
| try: |
| df.to_parquet(out_base + ".parquet", index=False) |
| print("wrote", out_base + ".parquet", len(df), "rows") |
| except Exception as e: |
| df.to_csv(out_base + ".csv.gz", index=False, compression="gzip") |
| print("parquet failed (", e, ") -> wrote csv.gz", len(df)) |
| except ImportError: |
| import csv, gzip |
| with gzip.open(out_base + ".csv.gz", "wt", newline="") as f: |
| w = csv.DictWriter(f, fieldnames=list(recs[0])) |
| w.writeheader(); w.writerows(recs) |
| print("wrote", out_base + ".csv.gz", len(recs), "rows") |
|
|
