mirror of https://github.com/THUDM/ChatGLM2-6B
Zhengxiao Du
1 year ago
committed by
GitHub
7 changed files with 94 additions and 9 deletions
@ -0,0 +1,59 @@
|
||||
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 |
Loading…
Reference in new issue