convert_model.py

import os
import sys

__package__ = "scripts"
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import warnings
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM
from model.model_minimind import MiniMindConfig, MiniMindForCausalLM

warnings.filterwarnings('ignore', category=UserWarning)


# MoE模型需使用此函数转换
def convert_torch2transformers_minimind(torch_path, transformers_path, dtype=torch.bfloat16):
    MiniMindConfig.register_for_auto_class()
    MiniMindForCausalLM.register_for_auto_class("AutoModelForCausalLM")
    lm_model = MiniMindForCausalLM(lm_config)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    state_dict = torch.load(torch_path, map_location=device)
    lm_model.load_state_dict(state_dict, strict=False)
    lm_model = lm_model.to(dtype)  # 转换模型权重精度
    model_params = sum(p.numel() for p in lm_model.parameters() if p.requires_grad)
    print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)')
    lm_model.save_pretrained(transformers_path, safe_serialization=False)
    tokenizer = AutoTokenizer.from_pretrained('./model/')
    tokenizer.save_pretrained(transformers_path)
    print(f"模型已保存为 Transformers-MiniMind 格式: {transformers_path}")


# LlamaForCausalLM结构兼容第三方生态
def convert_torch2transformers_llama(torch_path, transformers_path, dtype=torch.bfloat16):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    state_dict = torch.load(torch_path, map_location=device)
    llama_config = LlamaConfig(
        vocab_size=lm_config.vocab_size,
        hidden_size=lm_config.hidden_size,
        intermediate_size=64 * ((int(lm_config.hidden_size * 8 / 3) + 64 - 1) // 64),
        num_hidden_layers=lm_config.num_hidden_layers,
        num_attention_heads=lm_config.num_attention_heads,
        num_key_value_heads=lm_config.num_key_value_heads,
        max_position_embeddings=lm_config.max_seq_len,
        rms_norm_eps=lm_config.rms_norm_eps,
        rope_theta=lm_config.rope_theta,
    )
    llama_model = LlamaForCausalLM(llama_config)
    llama_model.load_state_dict(state_dict, strict=False)
    llama_model = llama_model.to(dtype)  # 转换模型权重精度
    llama_model.save_pretrained(transformers_path)
    model_params = sum(p.numel() for p in llama_model.parameters() if p.requires_grad)
    print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)')
    tokenizer = AutoTokenizer.from_pretrained('./model/')
    tokenizer.save_pretrained(transformers_path)
    print(f"模型已保存为 Transformers-Llama 格式: {transformers_path}")


def convert_transformers2torch(transformers_path, torch_path):
    model = AutoModelForCausalLM.from_pretrained(transformers_path, trust_remote_code=True)
    torch.save(model.state_dict(), torch_path)
    print(f"模型已保存为 PyTorch 格式: {torch_path}")



if __name__ == '__main__':
    lm_config = MiniMindConfig(hidden_size=512, num_hidden_layers=8, max_seq_len=8192, use_moe=False)
    # import os
    # print(os.getcwd())
    torch_path = f"./out/full_sft_{lm_config.hidden_size}{'_moe' if lm_config.use_moe else ''}.pth"
    transformers_path = './MiniMind2'

    convert_torch2transformers_minimind(torch_path, transformers_path)

    # # # convert transformers to torch model
    # # convert_transformers2torch(transformers_path, torch_path)