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 # 本文件来源于https://github.com/THUDM/ChatGLM-6B/blob/main/utils.py # 仅此处做少许修改以支持ChatGLM2 device_map = { 'transformer.embedding.word_embeddings': 0, 'transformer.encoder.final_layernorm': 0, 'transformer.output_layer': 0, 'transformer.rotary_pos_emb': 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.encoder.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