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adf912b 8086c56 454b3e6 adf912b 454b3e6 adf912b 454b3e6 adf912b 8086c56 454b3e6 adf912b 8086c56 454b3e6 adf912b 8086c56 adf912b 8086c56 adf912b 454b3e6 adf912b 454b3e6 adf912b 8086c56 adf912b 454b3e6 adf912b 8086c56 adf912b 8086c56 adf912b 454b3e6 adf912b 8086c56 adf912b 454b3e6 adf912b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | """cases.jsonl + pages.jsonl -> tokenized training items (train split) and eval cases (held-out pages).
python finetune/build_items.py out/pages.jsonl out/cases.jsonl out/
"""
import json, random, os, sys
from array import array
import torch
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from common_ft import build_request, gold_for, goal_done_question
from transformers import AutoTokenizer
from laya.common import QTYPES, build_sequence, render_options
MAX_LEN, HEAD_MAX_LEN = int(os.environ.get('LAYA_MAXLEN', '1024')), int(os.environ.get('LAYA_HEAD', '512'))
EVAL_EVERY = 5 # pages with index % 5 == 0 are held out
MAX_TARGETS = int(os.environ.get('MAX_TARGETS', '40'))
FINAL_P = float(os.environ.get('FINAL_P', '0.2')) # share of target items built like the server's final round
# NOUL=1: also emit the yes/no question "is the whole goal visibly done on this page?" (common_ft.GOAL_DONE).
# yes = states whose gold is DONE; no = every mid-trajectory state (incl. webgym's results-shown-but-filter-missing
# states). NOUL_NEG_P keeps the negatives at roughly 60:40 (no-heavy, to counter the DONE-too-early bias).
NOUL = os.environ.get('NOUL') == '1'
NOUL_NEG_P = float(os.environ.get('NOUL_NEG_P', '0.3'))
def main():
pages_f, cases_f, out = sys.argv[1:4]
pages = [json.loads(l) for l in open(pages_f)]
cases = [json.loads(l) for l in open(cases_f)]
for extra in sys.argv[4:]:
cases += [json.loads(l) for l in open(extra)]
snap = os.environ.get("LAYA_BASE")
tok = AutoTokenizer.from_pretrained(os.path.join(snap, "tokenizer"))
items, n_skip, ev = [], 0, []
import hashlib
STOPS = [" Stop when it is open.", " Stop once that page is visible.", " Then stop.", " Finish when it has loaded.", ""]
# evaluation hygiene: never train on a suite task's start page (suite A and B start URLs), whatever the source
import re as _re
def _norm(u): return (u or "").split("#")[0].rstrip("/").replace("://www.", "://")
suite_urls = set()
for f in ("apps/browser_suite.py", "apps/browser_suite_b.py"):
if os.path.exists(f):
suite_urls |= {_norm(u) for u in _re.findall(r'\("[a-z0-9-]+", "(https?://[^"]+)"', open(f).read())}
n_suite = 0
if os.environ.get("LAYA_FMT") == "v6":
# v6 history carries each action's result = the page the NEXT step starts on (same trajectory, one more step)
traj = lambda c: (c.get("source"), c.get("task_id") or c.get("goal"), c.get("website"))
at = {}
for c in cases:
pg = c.get("page_obj") or (pages[c["page"]] if c.get("page", -1) >= 0 else {})
at.setdefault((traj(c), len(c.get("history") or [])), (pg.get("url") or c.get("url"), pg.get("title") or c.get("title")))
filled = 0
for c in cases:
for i, h in enumerate(c.get("history") or []):
if not h.get("url") and (traj(c), i + 1) in at:
h["url"], h["title"] = at[(traj(c), i + 1)]; filled += 1
print("v6: history steps given their result page:", filled, flush=True)
keep = float(os.environ.get("CASE_KEEP", "1")) # CASE_KEEP=p: build from a random p of the cases (smaller, faster)
if keep < 1:
cases = [c for c in cases if int(hashlib.md5((c["goal"] + str(len(c.get("history") or []))).encode()).hexdigest()[:8], 16) / 0xffffffff < keep]
print("CASE_KEEP", keep, "->", len(cases), "cases", flush=True)
for c in cases:
if _norm(c.get("url") or (c.get("page_obj") or {}).get("url")) in suite_urls or (c.get("page", -1) >= 0 and _norm(pages[c["page"]]["url"]) in suite_urls):
n_suite += 1; continue
if c.get("source") == "dagger":
continue
if c["kind"] == "done" and "page_obj" not in c:
continue # template DONE cases leak phrasing; real ones come from make_done_cases.py
page = c.get("page_obj") or pages[c["page"]]
h = int(hashlib.md5(c["goal"].encode()).hexdigest(), 16)
is_sub = str(c.get("skill", "")).endswith("@sub") or c.get("skill") == "subgoal_done"
goal = c["goal"] + ("" if is_sub or c.get("source") == "webgym" else STOPS[h % len(STOPS)])
c = {**c, "goal": goal}
state, questions, targets, controls = build_request(page, goal, c.get("history", []))
if c.get("noul_only"):
# a state where a failed run wrongly stopped: only the completion question, answered "not done"
if NOUL and not ((hashlib.md5(c["website"].encode()).digest()[0] % EVAL_EVERY == 0)):
nq = goal_done_question(goal)
qq = {"t": "noul", "ins": json.dumps(nq["instructions"]), "crit": None}
seq, markers = build_sequence(tok, state, qq, MAX_LEN, HEAD_MAX_LEN)
if len(markers) == 2:
items.extend([{"ids": array("i", seq), "markers": markers, "qtype": QTYPES["noul"], "target": [1.0, 0.0], "label": 0,
"qid": "goal_done", "gold_op": "NOT_DONE", "src": c.get("source")}] * 2)
elif NOUL:
ev.append({**c, "gold_index": None})
continue
gop, gidx = gold_for(c, targets, controls)
if gop is None:
n_skip += 1; continue
held = (hashlib.md5(c["website"].encode()).digest()[0] % EVAL_EVERY == 0) if c.get("source") in ("mind2web", "nnetnav", "webchain", "gobrowse") else (c["page"] % EVAL_EVERY == 0)
if held:
ev.append({**c, "gold_index": gidx}); continue
golds = {"operation": gop}
if gidx is not None:
golds[gop.lower() + "_target"] = gidx
for qid, gold in golds.items():
q = questions[qid]; keys = list(q["criteria"])
rs = random.Random(h + len(keys) + len(qid))
if qid != "operation" and len(keys) > 8 and rs.random() < FINAL_P:
width = rs.randint(2, 6) # the server's final round: only the chunk winners (2-6 options)
else:
width = MAX_TARGETS
if qid != "operation" and len(keys) > width:
# inference splits wide choices into chunks of <= MAX_TARGETS (systemone_server MAXOPT); train on the same
# widths, so option labels are never truncated to a few tokens: gold + a random sample, page order kept
keep = set(rs.sample([k for k in keys if k != gold], width - 1)) | {gold}
keys = [k for k in keys if k in keep]
q = {**q, "criteria": {k: q["criteria"][k] for k in keys}}
target = [1.0 if k == gold else 0.0 for k in keys]
qq = {"t": "choice", "ins": json.dumps(q["instructions"]), "crit": q["criteria"]}
seq, markers = build_sequence(tok, state, qq, MAX_LEN, HEAD_MAX_LEN)
if len(markers) != len(render_options(qq)):
n_skip += 1; continue
item = {"ids": array("i", seq), "markers": markers, "qtype": QTYPES["choice"], "target": target, "label": keys.index(gold), "qid": qid, "gold_op": gop, "src": c.get("source")}
# class balance: CLICK dominates the operation question, so repeat the rare operations
reps = {"DONE": 4, "TYPE_TEXT": 3, "SELECT": 3, "PRESS_ENTER": 4, "SCROLL_DOWN": 2}.get(gop, 1) if qid == "operation" else 1
if c.get("source") == "dagger":
reps *= 5
if c.get("source") == "webgym" or str(c.get("skill", "")).endswith("@sub"):
reps = 1 # webgym already pairs every DONE with its "keep going" twin; no op-level oversampling
if c.get("source") == "rollout":
reps *= 3 # scripted scroll / search-submit / select trajectories: the skills the model lacked # on-policy teacher corrections from real tasks: few but exactly where the policy fails
items.extend([item] * reps)
if NOUL:
yes = gop == "DONE"
rn = random.Random(h + 7)
if yes or rn.random() < NOUL_NEG_P:
nq = goal_done_question(goal)
qq = {"t": "noul", "ins": json.dumps(nq["instructions"]), "crit": None}
seq, markers = build_sequence(tok, state, qq, MAX_LEN, HEAD_MAX_LEN)
if len(markers) == 2:
items.append({"ids": array("i", seq), "markers": markers, "qtype": QTYPES["noul"], "target": [0.0, 1.0] if yes else [1.0, 0.0],
"label": int(yes), "qid": "goal_done", "gold_op": gop, "src": c.get("source")})
torch.save(items, os.path.join(out, "train_items.pt"))
with open(os.path.join(out, "eval_cases.jsonl"), "w") as f:
for c in ev: f.write(json.dumps(c, ensure_ascii=False) + "\n")
print(f"dropped {n_suite} cases on suite start pages; dagger cases are never used")
lens = [len(i["ids"]) for i in items]
import collections
print("goal_done (noul) items:", collections.Counter(i["label"] for i in items if i["qid"] == "goal_done"))
print("operation label counts:", dict(collections.Counter(i["gold_op"] for i in items if i["qid"] == "operation")))
print(f"train items {len(items)} (op {sum(i['qid']=='operation' for i in items)}, target {sum(i['qid']!='operation' for i in items)}), "
f"eval cases {len(ev)}, skipped {n_skip}, seq len mean {sum(lens)/len(lens):.0f} max {max(lens)}")
if __name__ == "__main__":
main()
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