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a61b883 0df0bf9 a61b883 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | # -*- coding: utf-8 -*-
r"""Local schema v2 synthetic-augmentation training runner.
This wrapper keeps the training structure reproducible:
1. Build a combined Rust encoded cache from the hard-focus JSONL plus synthetic
augmentation JSONL.
2. Train with ``anifilebert.train --encoded-cache-dir`` so Python training never
has to re-split raw mixed JSONL in a non-comparable way.
Typical usage from the repo root on the local Windows GPU machine:
.\.venv\Scripts\python.exe -m tools.train_schema_v2_synthetic
"""
from __future__ import annotations
import argparse
import datetime as dt
import json
from pathlib import Path
import shlex
import shutil
import subprocess
import sys
from typing import Any, Sequence
try:
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
except AttributeError:
pass
def utc_now() -> str:
return dt.datetime.now(dt.timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
def command_text(args: Sequence[Any]) -> str:
return " ".join(shlex.quote(str(arg)) for arg in args)
def run(args: Sequence[Any], *, dry_run: bool, command_log: list[dict[str, Any]]) -> None:
entry: dict[str, Any] = {
"cmd": command_text(args),
"started_at": utc_now(),
"dry_run": dry_run,
}
command_log.append(entry)
print(f"\n$ {entry['cmd']}")
if dry_run:
entry["returncode"] = 0
entry["finished_at"] = utc_now()
return
proc = subprocess.Popen(
list(map(str, args)),
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
encoding="utf-8",
errors="replace",
bufsize=1,
)
assert proc.stdout is not None
for line in proc.stdout:
print(line, end="")
proc.wait()
entry["returncode"] = proc.returncode
entry["finished_at"] = utc_now()
if proc.returncode != 0:
raise RuntimeError(f"Command failed with exit code {proc.returncode}: {entry['cmd']}")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Train schema v2 hard-focus + synthetic augmentation with Rust cache")
parser.add_argument("--primary-data-file", default="data/schema_v2_hard_focus_char_seed63.jsonl")
parser.add_argument("--synthetic-data-file", default="data/schema_v2_synthetic_aug.jsonl")
parser.add_argument("--synthetic-repeat", type=int, default=3)
parser.add_argument("--vocab-file", default="datasets/AnimeName/vocab.char.json")
parser.add_argument("--label-schema-file", default="label_schema.json")
parser.add_argument("--encoded-cache-dir", default="data/encoded_cache/schema_v2_hard_focus_seed63_synth_pathleaf_repeat3")
parser.add_argument("--save-dir", default="checkpoints/schema-v2-best-hardfocus-synth-pathleaf-cache")
parser.add_argument("--init-model-dir", default="checkpoints/ablation-schema-v2-hardfocus-cache-repaired-from-baseline-seed62-10epoch-rerun/final")
parser.add_argument("--case-eval-output", default="reports/schema_v2_best_hardfocus_synth_pathleaf_cache_case_metrics.json")
parser.add_argument("--experiment-name", default="schema-v2-best-hardfocus-synth-pathleaf-cache")
parser.add_argument("--max-length", type=int, default=128)
parser.add_argument("--train-split", type=float, default=0.995)
parser.add_argument("--seed", type=int, default=63)
parser.add_argument("--shard-size", type=int, default=25000)
parser.add_argument("--threads", type=int, default=16)
parser.add_argument("--epochs", type=float, default=2)
parser.add_argument("--batch-size", type=int, default=512)
parser.add_argument("--learning-rate", type=float, default=0.00004)
parser.add_argument("--warmup-steps", type=int, default=120)
parser.add_argument("--checkpoint-steps", type=int, default=1000)
parser.add_argument("--save-total-limit", type=int, default=3)
parser.add_argument("--parse-eval-limit", type=int, default=2048)
parser.add_argument("--case-eval-file", default="data/parser_regression_cases.json")
parser.add_argument("--force-cache", action="store_true", help="Delete and rebuild the encoded cache even if manifest exists")
parser.add_argument("--skip-cache", action="store_true", help="Reuse the existing encoded cache")
parser.add_argument("--dry-run", action="store_true")
return parser.parse_args()
def main() -> None:
args = parse_args()
command_log: list[dict[str, Any]] = []
cache_dir = Path(args.encoded_cache_dir)
manifest_path = cache_dir / "manifest.json"
if args.force_cache and cache_dir.exists():
print(f"Removing existing cache: {cache_dir}")
if not args.dry_run:
shutil.rmtree(cache_dir)
if not args.skip_cache and not manifest_path.exists():
cache_cmd = [
"cargo", "run", "--release",
"--manifest-path", "tools/encoded_dataset_cache/Cargo.toml",
"--",
"--input", args.primary_data_file,
"--input", args.synthetic_data_file,
"--input-repeat", "1",
"--input-repeat", str(max(1, args.synthetic_repeat)),
"--vocab-file", args.vocab_file,
"--label-schema-file", args.label_schema_file,
"--output-dir", args.encoded_cache_dir,
"--max-length", str(args.max_length),
"--train-split", str(args.train_split),
"--seed", str(args.seed),
"--shard-size", str(args.shard_size),
"--threads", str(args.threads),
]
run(cache_cmd, dry_run=args.dry_run, command_log=command_log)
else:
print(f"Using existing encoded cache: {cache_dir}")
train_cmd = [
sys.executable, "-m", "anifilebert.train",
"--tokenizer", "char",
"--data-file", args.primary_data_file,
"--vocab-file", args.vocab_file,
"--encoded-cache-dir", args.encoded_cache_dir,
"--save-dir", args.save_dir,
"--init-model-dir", args.init_model_dir,
"--epochs", str(args.epochs),
"--batch-size", str(args.batch_size),
"--learning-rate", str(args.learning_rate),
"--warmup-steps", str(args.warmup_steps),
"--max-seq-length", str(args.max_length),
"--train-split", str(args.train_split),
"--num-workers", "0",
"--checkpoint-steps", str(args.checkpoint_steps),
"--save-total-limit", str(args.save_total_limit),
"--no-periodic-eval",
"--bf16",
"--auto-find-batch-size",
"--parse-eval-limit", str(args.parse_eval_limit),
"--case-eval-file", args.case_eval_file,
"--case-eval-output", args.case_eval_output,
"--seed", str(args.seed),
"--experiment-name", args.experiment_name,
]
run(train_cmd, dry_run=args.dry_run, command_log=command_log)
run_manifest = {
"name": args.experiment_name,
"started_at": command_log[0]["started_at"] if command_log else utc_now(),
"finished_at": utc_now(),
"primary_data_file": args.primary_data_file,
"synthetic_data_file": args.synthetic_data_file,
"synthetic_repeat": args.synthetic_repeat,
"encoded_cache_dir": args.encoded_cache_dir,
"save_dir": args.save_dir,
"init_model_dir": args.init_model_dir,
"commands": command_log,
}
manifest_output = Path(args.save_dir) / "schema_v2_synthetic_train_manifest.json"
print(f"Writing run manifest: {manifest_output}")
if not args.dry_run:
manifest_output.parent.mkdir(parents=True, exist_ok=True)
manifest_output.write_text(json.dumps(run_manifest, ensure_ascii=False, indent=2), encoding="utf-8")
if __name__ == "__main__":
main()
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