utils.py

import os
from typing import Dict, Tuple, Union, Optional

from torch.nn import Module
from transformers import AutoModel


def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
 # transformer.word_embeddings 占用1层
 # transformer.final_layernorm 和 lm_head 占用1层
 # transformer.layers 占用 28 层
 # 总共30层分配到num_gpus张卡上
 num_trans_layers = 28
 per_gpu_layers = 30 / num_gpus

 # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
 # windows下 model.device 会被设置成 transformer.word_embeddings.device
 # linux下 model.device 会被设置成 lm_head.device
 # 在调用chat或者stream_chat时,input_ids会被放到model.device上
 # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
 # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
 device_map = {'transformer.word_embeddings': 0,
 'transformer.final_layernorm': 0, 'lm_head': 0}

 used = 2
 gpu_target = 0
 for i in range(num_trans_layers):
 if used >= per_gpu_layers:
 gpu_target += 1
 used = 0
 assert gpu_target < num_gpus
 device_map[f'transformer.layers.{i}'] = gpu_target
 used += 1

 return device_map


def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
 device_map: Optional[Dict[str, int]] = None, **kwargs) -> Module:
 if num_gpus < 2 and device_map is None:
 model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half().cuda()
 else:
 from accelerate import dispatch_model

 model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half()

 if device_map is None:
 device_map = auto_configure_device_map(num_gpus)

 model = dispatch_model(model, device_map=device_map)

 return model