t2a-finetune/runpod/train_runpod.py

#!/usr/bin/env python3
"""
Fine-tuning QLoRA de gemma3:12b sur RunPod (A100 40/80GB).

Différences vs local (RTX 5070 12GB) :
  - max_seq_length=2048 (vs 512) — prompts pipeline non tronqués
  - batch_size=4 (vs 1) — convergence plus stable
  - 3 epochs (vs 1) — meilleure mémorisation
  - Dataset complet sans sous-échantillonnage

Usage sur RunPod :
  1. Créer un pod avec template PyTorch 2.4+ / CUDA 12.x
  2. rsync -avz runpod/ runpod_host:/workspace/t2a-finetune/
  3. bash /workspace/t2a-finetune/setup.sh
  4. python /workspace/t2a-finetune/train_runpod.py [--epochs 3] [--export-gguf]
"""

import argparse
import json
import os
from pathlib import Path

BASE = Path(__file__).resolve().parent
DATASETS = BASE / "data"
OUTPUT = BASE / "models"
OUTPUT.mkdir(parents=True, exist_ok=True)


def check_prerequisites():
    import torch

    if not torch.cuda.is_available():
        raise RuntimeError("CUDA non disponible.")

    gpu_name = torch.cuda.get_device_name(0)
    vram_total = torch.cuda.get_device_properties(0).total_memory / 1024**3

    print(f"GPU: {gpu_name}")
    print(f"VRAM: {vram_total:.1f} Go")

    train_path = DATASETS / "pmsi_train.jsonl"
    eval_path = DATASETS / "pmsi_eval.jsonl"
    if not train_path.exists() or not eval_path.exists():
        raise FileNotFoundError("Dataset non trouvé dans data/")

    with open(train_path) as f:
        n_train = sum(1 for _ in f)
    with open(eval_path) as f:
        n_eval = sum(1 for _ in f)

    print(f"Dataset: {n_train} train + {n_eval} eval")

    # Adapter le batch size à la VRAM
    if vram_total >= 70:
        suggested_batch = 8
    elif vram_total >= 35:
        suggested_batch = 4
    else:
        suggested_batch = 2

    print(f"Batch size suggéré: {suggested_batch}")
    return train_path, eval_path, suggested_batch


def load_model(model_name, max_seq_length, load_in_4bit=True):
    from unsloth import FastLanguageModel

    print(f"\nChargement de {model_name} (4-bit={load_in_4bit})...")

    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=model_name,
        max_seq_length=max_seq_length,
        dtype=None,
        load_in_4bit=load_in_4bit,
    )

    print(f"  Modèle chargé : {model.config._name_or_path}")
    print(f"  Paramètres : {model.num_parameters() / 1e9:.1f}B")
    return model, tokenizer


def attach_lora(model, r=32, alpha=64, dropout=0.0):
    from unsloth import FastLanguageModel

    print(f"\nLoRA (r={r}, alpha={alpha})...")

    model = FastLanguageModel.get_peft_model(
        model,
        r=r,
        target_modules=[
            "q_proj", "k_proj", "v_proj", "o_proj",
            "gate_proj", "up_proj", "down_proj",
        ],
        lora_alpha=alpha,
        lora_dropout=dropout,
        bias="none",
        use_gradient_checkpointing="unsloth",
        random_state=42,
    )

    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
    total = sum(p.numel() for p in model.parameters())
    print(f"  Entraînables : {trainable / 1e6:.1f}M / {total / 1e9:.1f}B ({100 * trainable / total:.2f}%)")
    return model


def load_dataset(train_path, eval_path):
    from datasets import Dataset

    def load_jsonl(path):
        examples = []
        with open(path) as f:
            for line in f:
                examples.append(json.loads(line.strip()))
        return examples

    train_ds = Dataset.from_list(load_jsonl(train_path))
    eval_ds = Dataset.from_list(load_jsonl(eval_path))

    print(f"\nDataset : {len(train_ds)} train + {len(eval_ds)} eval")
    return train_ds, eval_ds


def format_chat(example, tokenizer):
    text = tokenizer.apply_chat_template(
        example["messages"],
        tokenize=False,
        add_generation_prompt=False,
    )
    return {"text": text}


def train(model, tokenizer, train_ds, eval_ds, args):
    from trl import SFTTrainer, SFTConfig

    print(f"\nConfig entraînement :")
    print(f"  Epochs : {args.epochs}")
    print(f"  LR : {args.lr}")
    print(f"  Batch : {args.batch} x grad_accum={args.grad_accum} = {args.batch * args.grad_accum}")
    print(f"  Max seq length : {args.max_seq_length}")

    train_ds = train_ds.map(lambda x: format_chat(x, tokenizer), num_proc=4)
    eval_ds = eval_ds.map(lambda x: format_chat(x, tokenizer), num_proc=4)

    output_dir = OUTPUT / "pmsi-lora-checkpoints"

    # Wandb optionnel
    report = "none"
    callbacks = []
    try:
        import wandb
        wandb.init(project="pmsi-coder", name=f"runpod-{args.epochs}ep-seq{args.max_seq_length}")
        report = "wandb"
        print("  Tracking : wandb")
    except ImportError:
        print("  Tracking : none (pip install wandb pour activer)")

    training_args = SFTConfig(
        output_dir=str(output_dir),
        num_train_epochs=args.epochs,
        per_device_train_batch_size=args.batch,
        per_device_eval_batch_size=args.batch,
        gradient_accumulation_steps=args.grad_accum,
        learning_rate=args.lr,
        weight_decay=0.01,
        warmup_ratio=0.05,
        lr_scheduler_type="cosine",
        logging_steps=10,
        eval_strategy="steps",
        eval_steps=500,
        save_strategy="steps",
        save_steps=500,
        save_total_limit=3,
        fp16=False,
        bf16=True,
        max_seq_length=args.max_seq_length,
        dataset_text_field="text",
        seed=42,
        report_to=report,
    )

    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_ds,
        eval_dataset=eval_ds,
        args=training_args,
        callbacks=callbacks,
    )

    total_steps = len(train_ds) * args.epochs // (args.batch * args.grad_accum)
    print(f"\n  Steps estimés : ~{total_steps}")
    print(f"  Démarrage...")

    if args.resume:
        trainer.train(resume_from_checkpoint=True)
    else:
        trainer.train()

    final_dir = OUTPUT / "pmsi-lora-final"
    model.save_pretrained(str(final_dir))
    tokenizer.save_pretrained(str(final_dir))
    print(f"\nLoRA sauvegardé : {final_dir}")

    return trainer, final_dir


def export_gguf(model, tokenizer, final_dir, quantization="q4_k_m"):
    print(f"\nExport GGUF ({quantization})...")

    gguf_dir = OUTPUT / "pmsi-gguf"
    gguf_dir.mkdir(parents=True, exist_ok=True)

    model.save_pretrained_gguf(
        str(gguf_dir),
        tokenizer,
        quantization_method=quantization,
    )

    gguf_files = list(gguf_dir.glob("*.gguf"))
    if gguf_files:
        gguf_path = gguf_files[0]
        size_gb = gguf_path.stat().st_size / 1024**3
        print(f"  GGUF : {gguf_path.name} ({size_gb:.1f} Go)")

        modelfile_path = gguf_dir / "Modelfile"
        with open(modelfile_path, "w") as f:
            f.write(f"FROM {gguf_path.name}\n\n")
            f.write("PARAMETER temperature 0.3\n")
            f.write("PARAMETER top_p 0.9\n")
            f.write("PARAMETER num_ctx 8192\n")

        print(f"  Modelfile créé")
        print(f"\n  Pour récupérer : scp runpod:{gguf_path} .")
        print(f"  Puis : ollama create pmsi-coder -f Modelfile")


def main():
    parser = argparse.ArgumentParser(description="Fine-tuning QLoRA RunPod")

    parser.add_argument("--model", default="unsloth/gemma-3-12b-it-bnb-4bit")
    parser.add_argument("--max-seq-length", type=int, default=2048)

    parser.add_argument("--lora-r", type=int, default=32)
    parser.add_argument("--lora-alpha", type=int, default=64)
    parser.add_argument("--lora-dropout", type=float, default=0.0)

    parser.add_argument("--epochs", type=int, default=3)
    parser.add_argument("--lr", type=float, default=2e-4)
    parser.add_argument("--batch", type=int, default=0, help="0=auto-detect")
    parser.add_argument("--grad-accum", type=int, default=4)

    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--export-gguf", action="store_true")
    parser.add_argument("--gguf-quant", default="q4_k_m")

    args = parser.parse_args()

    train_path, eval_path, suggested_batch = check_prerequisites()

    if args.batch == 0:
        args.batch = suggested_batch
        print(f"Batch auto-détecté : {args.batch}")

    model, tokenizer = load_model(args.model, args.max_seq_length)
    model = attach_lora(model, r=args.lora_r, alpha=args.lora_alpha, dropout=args.lora_dropout)
    train_ds, eval_ds = load_dataset(train_path, eval_path)
    trainer, final_dir = train(model, tokenizer, train_ds, eval_ds, args)

    if args.export_gguf:
        export_gguf(model, tokenizer, final_dir, args.gguf_quant)

    print("\n" + "=" * 50)
    print("Fine-tuning terminé !")
    print(f"  LoRA : {final_dir}")
    if args.export_gguf:
        print(f"  GGUF : {OUTPUT / 'pmsi-gguf'}")
    print("=" * 50)


if __name__ == "__main__":
    main()