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run decode tracks A, C (both key modes) and B for one model; development only
score Top-k rates (A, C), keystroke savings with the tuned threshold (B), coverage (D)
Protocol: docs/zh-CN/benchmarks/predict-zh-plan.md. Decoder: vimeml.benchmarks.predict_zh.
The blind split is never read here.
"""
import argparse
import json
import math
import subprocess
import sys
import time
import warnings
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "src"))
import torch # noqa: E402
from vimeml.benchmarks import predict_zh as P # noqa: E402
from vimeml.training.data import file_sha, write_json # noqa: E402
VERSION = "ime-predict-zh-v1-r1"
FROZEN = ROOT / "outputs/benchmarks/ime-predict-zh-v1/frozen-r1"
FROZEN_SHA = {
"items-a.jsonl": "493fc4702b9dc37b1f645fcfdcc3efa515f840adcef7e1423c3d233b21eea87f",
"messages-b.jsonl": "2640d6b573fb2e45996a7b6556a2aeafe9af3e42e96603588947ba75fb7a1c71",
"items-c.jsonl": "dc88bc5f0a7e1ed2e3c0ee2e9ae7841ca2712e13a34151e293e2b205df21fb2e",
"manifest.json": "002dc9a39113e1831b5d2352187a079e15705770310dc81de6e06a87e68f6415",
}
RIME = ROOT / "datasets/ime-standard-zh-v1/engine/rime-ice"
QUANTILES = [q / 100 for q in range(5, 100, 5)]
MODES = (("first_syllable", "syllable", "keys_first_syllable"), ("initials", "initials", "keys_initials"))
def log(*parts):
print(time.strftime("%H:%M:%S"), *parts, file=sys.stderr, flush=True)
def development(name, limit):
rows = [r for r in P.read_jsonl(FROZEN / name) if r["split"] == "development"]
if limit: # smoke runs: the first `limit` items of every source
seen = {}
rows = [r for r in rows if seen.setdefault(r["source"], []).append(1) or len(seen[r["source"]]) <= limit]
return rows
def write_jsonl(path, rows):
with open(path, "w", encoding="utf-8") as stream:
for row in rows:
stream.write(json.dumps(row, ensure_ascii=False) + "\n")
def quantile(values, q):
values = sorted(values)
position = q * (len(values) - 1)
low = math.floor(position)
high = min(low + 1, len(values) - 1)
return values[low] + (values[high] - values[low]) * (position - low)
def load_model(args):
if args.model != "vime":
raise SystemExit(f"Unknown model adapter {args.model}")
from vimeml.benchmarks.zh13_lm_scores import load
from vimeml.training.joint_zh13 import SPECIAL
model, processor, meta = load(args.checkpoint, args.tokenizer, args.device)
return P.VimeLM(model, processor, args.device, SPECIAL), meta
def run(args):
for name, sha in FROZEN_SHA.items():
if file_sha(FROZEN / name) != sha:
raise SystemExit(f"{name} differs from the frozen {VERSION}")
out = Path(args.out)
out.mkdir(parents=True, exist_ok=True)
lm, model_meta = load_model(args)
dicts = RIME / "cn_dicts"
readings = P.load_readings(dicts)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import jieba
import pypinyin
jieba.setLogLevel(60)
rime_commit = subprocess.run(["git", "-C", str(RIME), "rev-parse", "HEAD"], capture_output=True,
text=True).stdout.strip()
meta = {
"version": VERSION, "split": "development", "limit_per_source": args.limit,
"model": {"adapter": args.model, **model_meta, "device": args.device, "dtype": "float32"},
"decoder": {"beam": P.BEAM, "max_chars": P.MAX_CHARS, "min_chars": P.MIN_CHARS, "top": P.TOP,
"show": P.SHOW, "reserve": P.RESERVE, "context_length": lm.context_length},
"frozen_sha256": FROZEN_SHA,
"readings": {"rime_ice_commit": rime_commit,
**{n: file_sha(dicts / n) for n in ("8105.dict.yaml", "41448.dict.yaml")}},
"jieba": jieba.__version__, "pypinyin": pypinyin.__version__, "torch": torch.__version__,
}
started = time.time()
rows_a, items = [], development("items-a.jsonl", args.limit)
for k, item in enumerate(items, 1):
candidates = P.predict(lm, item["previous_turns"], item["prefix"])
rank, chars = P.rank_of_hit(candidates, item["continuation"])
rows_a.append({"id": item["id"], "source": item["source"], "dialogue": item["dialogue"],
"candidates": candidates, "rank": rank, "hit_chars": chars,
"confidence": candidates[0][1] if candidates else None})
if k % 500 == 0:
log("A", k, len(items))
write_jsonl(out / "track-a.jsonl", rows_a)
meta["seconds_a"] = round(time.time() - started, 1)
rows_c, items = [], development("items-c.jsonl", args.limit)
for k, item in enumerate(items, 1):
for mode, kind, field in MODES:
constraint = P.Constraint(kind, item[field])
candidates = P.predict(lm, item["previous_turns"], item["prefix"], constraint, readings)
rows_c.append({"id": item["id"], "source": item["source"], "dialogue": item["dialogue"],
"mode": mode, "keys": item[field], "candidates": candidates,
"rank": P.rank_exact(candidates, item["target"])})
if k % 500 == 0:
log("C", k, len(items))
write_jsonl(out / "track-c.jsonl", rows_c)
meta["seconds_c"] = round(time.time() - started - meta["seconds_a"], 1)
scores = [r["confidence"] for r in rows_a if r["confidence"] is not None]
grid = [P.NEG] + [quantile(scores, q) for q in QUANTILES]
meta["threshold_grid"] = [P.finite(t) for t in grid]
def pinyin(run):
return pypinyin.lazy_pinyin(run, style=pypinyin.Style.NORMAL)
def cut(message):
return jieba.lcut(message, HMM=False)
rows_b, items, decodes = [], development("messages-b.jsonl", args.limit), 0
for k, item in enumerate(items, 1):
message, memo = item["message"], {}
def predictions(position):
nonlocal decodes
if position not in memo:
memo[position] = P.predict(lm, item["previous_turns"], message[:position])
decodes += 1
return memo[position]
costs, ends = P.keystrokes(message, pinyin), P.boundaries(message, cut)
results = [P.simulate(message, costs, ends, predictions, t) for t in grid]
rows_b.append({"id": item["id"], "source": item["source"], "dialogue": item["dialogue"],
"results": results})
if k % 500 == 0:
log("B", k, len(items), "decodes", decodes)
write_jsonl(out / "track-b.jsonl", rows_b)
meta["decodes_b"] = decodes
meta["seconds_total"] = round(time.time() - started, 1)
write_json(out / "meta.json", meta)
log("done", meta["seconds_total"], "s")
def savings(rows, index):
keys = sum(r["results"][index]["keys"] for r in rows)
base = sum(r["results"][index]["baseline_keys"] for r in rows)
shows = sum(r["results"][index]["shows"] for r in rows)
clicks = sum(r["results"][index]["clicks"] for r in rows)
return {"savings": 1 - keys / base, "keys": keys, "baseline_keys": base, "shows": shows,
"clicks": clicks, "click_rate": clicks / shows if shows else None}
def score(args):
run_dir = Path(args.run)
meta = json.loads((run_dir / "meta.json").read_text(encoding="utf-8"))
rows_a = P.read_jsonl(run_dir / "track-a.jsonl")
hits3 = [r["hit_chars"] for r in rows_a if r["rank"] and r["rank"] <= 3]
result = {"version": meta["version"], "split": meta["split"], "model": meta["model"],
"track_a": {**P.rates(rows_a, "rank"),
"mean_hit_chars_top3": sum(hits3) / len(hits3) if hits3 else None}}
rows_c = P.read_jsonl(run_dir / "track-c.jsonl")
result["track_c"] = {mode: P.rates([r for r in rows_c if r["mode"] == mode], "rank") for mode, _, _ in MODES}
rows_b = P.read_jsonl(run_dir / "track-b.jsonl")
grid = meta["threshold_grid"]
table = [{"threshold": t, **savings(rows_b, i)} for i, t in enumerate(grid)]
# best savings; ties go to the higher threshold (fewer shows)
best = max(range(len(grid)), key=lambda i: (table[i]["savings"], i))
sources = sorted({r["source"] for r in rows_b})
result["track_b"] = {"chosen": table[best],
"by_source": {s: savings([r for r in rows_b if r["source"] == s], best)["savings"]
for s in sources},
"grid": table}
result["track_d"] = P.coverage_curve([{"id": r["id"], "confidence": r["confidence"], "rank": r["rank"]}
for r in rows_a])
write_json(run_dir / "score.json", result)
a, b = result["track_a"], result["track_b"]["chosen"]
print(f"A top1 {a['top1']['micro']:.4f} top3 {a['top3']['micro']:.4f} (macro {a['top3']['macro']:.4f})"
f" top5 {a['top5']['micro']:.4f} hit chars {a['mean_hit_chars_top3']:.2f}")
for mode in result["track_c"]:
c = result["track_c"][mode]
print(f"C {mode:14s} top1 {c['top1']['micro']:.4f} top3 {c['top3']['micro']:.4f}"
f" top5 {c['top5']['micro']:.4f}")
print(f"B savings {b['savings']:.4f} threshold {b['threshold']} shows {b['shows']}"
f" clicks {b['clicks']} click rate {b['click_rate']}")
print("D ", {k: round(v["top1"], 4) for k, v in result["track_d"].items()})
def main():
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
sub = parser.add_subparsers(dest="command", required=True)
r = sub.add_parser("run")
r.add_argument("--model", default="vime")
r.add_argument("--checkpoint", default=str(ROOT / "artifacts/models/tiny-zh13-12k-fresh-s1-d384-l9/final.pt"))
r.add_argument("--tokenizer", default=str(ROOT / "artifacts/tokenizers/zh-v13/zh-unigram-12288/tokenizer.model"))
r.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
r.add_argument("--limit", type=int, default=0, help="items per source, for smoke runs")
r.add_argument("--out", required=True)
s = sub.add_parser("score")
s.add_argument("--run", required=True)
args = parser.parse_args()
run(args) if args.command == "run" else score(args)
if __name__ == "__main__":
main()
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