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- 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
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