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c2ca866 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | #!/usr/bin/env python3
"""
convert_data.py: turn local ChatML datasets into tokenized train.pt/val.pt
for train_chat.py.
Accepts .jsonl, .json, and .parquet files (auto-detected by extension), and
these record shapes (one conversation per record):
{"messages": [{"role": "user", "content": ...}, ...]} # HF ChatML style
[{"role": ..., "content": ...}, ...] # bare message list
{"conversation": [...]} or {"chat": [...]} # aliases
{"instruction": ..., "input": ..., "output": ...} # alpaca style (converted)
{"data": [...]} / {"conversations": [...]} # JSON containers of the above
Parquet rows may store the messages column as a list of dicts or as a JSON
string (both work).
Usage:
python3 convert_data.py --model-path . --input my_data.jsonl \
--output data/my_data
python3 convert_data.py --model-path . --input a.jsonl b.jsonl \
--output data/mixed
python3 convert_data.py --model-path . --input ./folder \
--output data/folder
Then train:
python3 train_chat.py --model-path . --data-dir data/my_data \
--output-dir my_checkpoints --lr 7e-5 --epochs 3
"""
import argparse
import json
import sys
from pathlib import Path
import numpy as np
from transformers import AutoTokenizer
sys.path.insert(0, str(Path(__file__).resolve().parent))
from prepare_data import ( # noqa: E402
add_chat_tokens,
build_conv_segments,
save_dataset,
tokenize_and_split,
)
SUPPORTED_EXTS = {".jsonl", ".json", ".parquet"}
CONTAINER_KEYS = ("conversations", "data", "rows", "examples")
MESSAGE_KEYS = ("messages", "conversation", "chat")
# ---------------------------------------------------------------------------
# File reading
# ---------------------------------------------------------------------------
def collect_files(paths):
"""Expand --input args (files and/or dirs) into a sorted file list."""
files = []
for p in paths:
p = Path(p)
if p.is_dir():
files.extend(f for f in sorted(p.iterdir())
if f.suffix.lower() in SUPPORTED_EXTS)
elif p.suffix.lower() in SUPPORTED_EXTS:
files.append(p)
else:
print(f"[!] Skipping unsupported file: {p} (want .jsonl/.json/.parquet)")
return files
def iter_records(path):
"""Yield one raw record (dict or list) per conversation from a file."""
ext = path.suffix.lower()
if ext == ".jsonl":
with open(path) as f:
for line in f:
line = line.strip()
if not line:
continue
yield json.loads(line)
elif ext == ".json":
with open(path) as f:
obj = json.load(f)
if isinstance(obj, list):
yield from obj
elif isinstance(obj, dict):
for key in CONTAINER_KEYS:
if isinstance(obj.get(key), list):
yield from obj[key]
return
yield obj # single-conversation file
elif ext == ".parquet":
import pandas as pd # lazy: only needed for parquet
df = pd.read_parquet(path)
for _, row in df.iterrows():
yield dict(row)
else:
raise ValueError(f"Unsupported extension: {path}")
def normalize_record(rec):
"""Turn one record into a list of {role, content} messages, or None."""
if isinstance(rec, list):
msgs = [m for m in rec
if isinstance(m, dict) and m.get("content")]
return msgs or None
if not isinstance(rec, dict):
return None
# ChatML-style keys (value may be a list of dicts, a numpy array of dicts
# from parquet round-trips, or a JSON string)
for key in MESSAGE_KEYS:
v = rec.get(key)
if isinstance(v, str):
try:
v = json.loads(v)
except json.JSONDecodeError:
continue
if isinstance(v, (list, np.ndarray)):
msgs = [m for m in v
if isinstance(m, dict) and m.get("content")]
if msgs:
return msgs
# Alpaca-style record: instruction / input / output
if rec.get("instruction") and rec.get("output"):
user = rec["instruction"]
if rec.get("input"):
user += f"\n\n{rec['input']}"
return [{"role": "user", "content": user},
{"role": "assistant", "content": rec["output"]}]
return None
def load_convs_from_files(files):
"""Build (roles, segs) conversations from all files."""
convs = []
for path in files:
n_before = len(convs)
for rec in iter_records(path):
msgs = normalize_record(rec)
if msgs is None:
continue
segs, roles = build_conv_segments(msgs)
if not segs:
continue
convs.append((roles, segs))
print(f"[*] {path.name}: {len(convs) - n_before} conversations")
return convs
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(
description="Convert local ChatML data (jsonl/json/parquet) to tokenized .pt")
parser.add_argument("--model-path", default=".",
help="Dir with tokenizer.json (the release dir works)")
parser.add_argument("--input", nargs="+", required=True,
help="File(s) or dir(s): .jsonl, .json, .parquet")
parser.add_argument("--output", default="data/converted",
help="Output dir (train.pt, val.pt, tokenizer/, stats.json)")
parser.add_argument("--val-size", type=int, default=500, help="Held-out examples")
parser.add_argument("--max-len", type=int, default=1024, help="Max tokens per example")
parser.add_argument("--max-resp-tokens", type=int, default=256,
help="Cap on target response tokens (keeps <|im_end|>)")
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
out = Path(args.output)
out.mkdir(parents=True, exist_ok=True)
print(f"[*] Loading tokenizer from {args.model_path}")
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
add_chat_tokens(tokenizer)
tokenizer.save_pretrained(out / "tokenizer")
files = collect_files(args.input)
if not files:
print("[!] No .jsonl/.json/.parquet files found in the inputs.")
sys.exit(1)
print(f"[*] Files: {', '.join(f.name for f in files)}")
convs = load_convs_from_files(files)
if not convs:
print("[!] No conversations parsed (check the record shapes in the docstring).")
sys.exit(1)
print(f"[*] Total: {len(convs)} conversations")
# Auto-scale the val split: never take everything for small datasets
val_size = min(args.val_size, max(1, len(convs) // 10))
if val_size != args.val_size:
print(f"[*] Small dataset: using val_size={val_size}")
train_examples, val_examples, skipped, dupes = tokenize_and_split(
convs, set(), tokenizer, val_size, args.max_len,
args.max_resp_tokens, args.seed)
save_dataset(out, train_examples, val_examples, tokenizer, skipped, dupes)
if __name__ == "__main__":
main()
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