Unverified 提交 86c4147b 作者: imClumsyPanda 提交者: GitHub

Merge pull request #128 from Viscount/dev

1. 参考ChatGLM-6B代码实现模型多卡部署
......@@ -5,6 +5,8 @@ from transformers import AutoTokenizer, AutoModel
import torch
from configs.model_config import LLM_DEVICE
from typing import Dict, Tuple, Union, Optional
DEVICE = LLM_DEVICE
DEVICE_ID = "0" if torch.cuda.is_available() else None
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
......@@ -17,6 +19,36 @@ def torch_gc():
torch.cuda.ipc_collect()
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
class ChatGLM(LLM):
max_token: int = 10000
temperature: float = 0.01
......@@ -51,19 +83,34 @@ class ChatGLM(LLM):
def load_model(self,
model_name_or_path: str = "THUDM/chatglm-6b",
llm_device=LLM_DEVICE):
llm_device=LLM_DEVICE,
device_map: Optional[Dict[str, int]] = None,
**kwargs):
self.tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path,
trust_remote_code=True
)
if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
self.model = (
AutoModel.from_pretrained(
model_name_or_path,
trust_remote_code=True)
.half()
.cuda()
)
# 根据当前设备GPU数量决定是否进行多卡部署
num_gpus = torch.cuda.device_count()
if num_gpus < 2 and device_map is None:
self.model = (
AutoModel.from_pretrained(
model_name_or_path,
trust_remote_code=True,
**kwargs)
.half()
.cuda()
)
else:
from accelerate import dispatch_model
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True, **kwargs).half()
# 可传入device_map自定义每张卡的部署情况
if device_map is None:
device_map = auto_configure_device_map(num_gpus)
self.model = dispatch_model(model, device_map=device_map)
else:
self.model = (
AutoModel.from_pretrained(
......
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