test/merge_lora.py

"""
Step 1: Merge DFlash-LoRA adapter into base model.
Usage:
    conda activate sglang
    python3 merge_lora.py
    python3 merge_lora.py --ckpt epoch_2_step_15000   # 测其他 checkpoint
"""
import argparse
import os

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL  = "/workspace/models/Qwen3-8B"
OUTPUT_ROOT = "/workspace/hanrui/syxin_old/Specforge/outputs/qwen3-8b-sft-32gpu-v2"
MERGE_ROOT  = "/workspace/hanrui/syxin_old/Specforge/outputs/qwen3-8b-sft-32gpu-v2-merged"

def parse_args():
    p = argparse.ArgumentParser()
    p.add_argument("--ckpt", default="epoch_0_step_3000",
                   help="Checkpoint folder name under OUTPUT_ROOT")
    p.add_argument("--merged-path", default=MERGE_ROOT,
                   help="Where to save the merged model")
    return p.parse_args()


def main():
    args = parse_args()
    adapter_path = os.path.join(OUTPUT_ROOT, args.ckpt)
    merged_path  = args.merged_path

    if os.path.exists(merged_path):
        print(f"[skip] Merged model already exists: {merged_path}")
        return

    assert os.path.isdir(adapter_path), f"Adapter not found: {adapter_path}"

    print(f"Base model  : {BASE_MODEL}")
    print(f"Adapter     : {adapter_path}")
    print(f"Output      : {merged_path}")
    print()

    print("[1/4] Loading base model to CPU ...")
    model = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL,
        torch_dtype=torch.bfloat16,
        device_map="cpu",
    )

    print("[2/4] Loading LoRA adapter ...")
    model = PeftModel.from_pretrained(model, adapter_path)

    print("[3/4] Merging weights ...")
    model = model.merge_and_unload()

    print("[4/4] Saving merged model ...")
    os.makedirs(merged_path, exist_ok=True)
    model.save_pretrained(merged_path, safe_serialization=True)
    AutoTokenizer.from_pretrained(BASE_MODEL).save_pretrained(merged_path)

    print(f"\nDone. Merged model saved to: {merged_path}")


if __name__ == "__main__":
    main()