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修复本地模型在Windows下的加载BUG

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qingxu fu
2023-11-11 21:11:55 +08:00
parent e4409b94d1
commit f75e39dc27
8 changed files with 25 additions and 25 deletions
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3
request_llms/bridge_chatgpt.py
View File

@@ -7,8 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
具备多线程调用能力的函数
2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
2. predict_no_ui_long_connection：支持多线程
"""
import json

3
request_llms/bridge_chatgpt_website.py
View File

@@ -7,8 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
具备多线程调用能力的函数
2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
2. predict_no_ui_long_connection：支持多线程
"""
import json

2
request_llms/bridge_claude.py
View File

@@ -7,7 +7,7 @@
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
具备多线程调用能力的函数
2. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
2. predict_no_ui_long_connection：支持多线程
"""
import os

17
request_llms/bridge_internlm.py
View File

@@ -5,7 +5,7 @@ from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
@@ -52,14 +52,15 @@ class GetInternlmHandle(LocalLLMHandle):
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = get_conf('LOCAL_MODEL_DEVICE')
if self._model is None:
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
if device=='cpu':
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
else:
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
with ProxyNetworkActivate('Download_LLM'):
if self._model is None:
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
if device=='cpu':
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
else:
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
model = model.eval()
model = model.eval()
return model, tokenizer
def llm_stream_generator(self, **kwargs):

15
request_llms/bridge_qwen.py
View File

@@ -6,7 +6,7 @@ from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
@@ -29,12 +29,13 @@ class GetONNXGLMHandle(LocalLLMHandle):
import platform
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = 'qwen/Qwen-7B-Chat'
self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
self._model = model
with ProxyNetworkActivate('Download_LLM'):
model_id = 'qwen/Qwen-7B-Chat'
self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
self._model = model
return self._model, self._tokenizer

2
request_llms/local_llm_class.py
View File

@@ -201,7 +201,7 @@ class LocalLLMHandle(Process):
if res.startswith(self.std_tag):
new_output = res[len(self.std_tag):]
std_out = std_out[:std_out_clip_len]
# print(new_output, end='')
print(new_output, end='')
std_out = new_output + std_out
yield self.std_tag + '\n```\n' + std_out + '\n```\n'
elif res == '[Finish]':