train.py

import argparse, os
from pathlib import Path
from datasets import load_dataset
from transformers import (
    AutoTokenizer, AutoModelForCausalLM,
    DataCollatorForLanguageModeling, Trainer, TrainingArguments
)
import zipfile

ROOT = Path(_file_).resolve().parent

def parse_args():
    ap = argparse.ArgumentParser()
    ap.add_argument("--dataset", required=True, help="Path to .jsonl (or a folder you adapt later)")
    ap.add_argument("--output",  default=str(ROOT / "trained_model"))
    ap.add_argument("--zip_path", default=str(ROOT / "trained_model.zip"))
    ap.add_argument("--model_name", default="Salesforce/codegen-350M-multi")
    ap.add_argument("--epochs", type=float, default=1.0)
    ap.add_argument("--batch_size", type=int, default=2)
    ap.add_argument("--block_size", type=int, default=256)
    ap.add_argument("--learning_rate", type=float, default=5e-5)
    return ap.parse_args()

def main():
    a = parse_args()
    out_dir  = Path(a.output).resolve()
    zip_path = Path(a.zip_path).resolve()

    print(f"📦 Loading dataset from: {a.dataset}", flush=True)
    ds = load_dataset("json", data_files=a.dataset, split="train")
    cols = ds.column_names
    print("🧾 Columns:", cols, flush=True)

    tok = AutoTokenizer.from_pretrained(a.model_name, use_fast=True)
    if tok.pad_token is None and tok.eos_token is not None:
        tok.pad_token = tok.eos_token
    model = AutoModelForCausalLM.from_pretrained(a.model_name)

    def tokenize(batch):
        if "text" in batch:
            texts = batch["text"]
        elif "prompt" in batch and "completion" in batch:
            texts = [str(p).rstrip() + "\n" + str(c) for p, c in zip(batch["prompt"], batch["completion"])]
        else:
            raise ValueError("Dataset must have 'text' or 'prompt' + 'completion'.")
        return tok(texts, padding="max_length", truncation=True, max_length=a.block_size)

    print("🔁 Tokenizing…", flush=True)
    tokenized = ds.map(tokenize, batched=True, remove_columns=cols)
    collator = DataCollatorForLanguageModeling(tokenizer=tok, mlm=False)

    args = TrainingArguments(
        output_dir=str(out_dir),
        overwrite_output_dir=True,
        per_device_train_batch_size=a.batch_size,
        num_train_epochs=a.epochs,
        learning_rate=a.learning_rate,
        logging_steps=5,
        save_strategy="no",
        report_to=[],
        fp16=False,
    )

    print("⚙ Trainer…", flush=True)
    trainer = Trainer(model=model, args=args, train_dataset=tokenized,
                      tokenizer=tok, data_collator=collator)

    print("🚀 Training…", flush=True)
    trainer.train()

    print(f"💾 Saving to {out_dir}", flush=True)
    os.makedirs(out_dir, exist_ok=True)
    trainer.save_model(out_dir)
    tok.save_pretrained(out_dir)

    # Zip the folder
    if zip_path.exists():
        zip_path.unlink()
    print(f"📦 Zipping → {zip_path.name}", flush=True)
    with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z:
        for p in out_dir.rglob("*"):
            z.write(p, arcname=p.relative_to(out_dir))

    print("✅ Done.", flush=True)

if __name__ == "__main__":
    main()