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修复本地模型在Windows下的加载BUG

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This commit is contained in:
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
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@ -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 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 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 2. predict_no_ui_long_connection：支持多线程
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
""" """
import json import json

3
request_llms/bridge_chatgpt_website.py
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@ -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 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 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 2. predict_no_ui_long_connection：支持多线程
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
""" """
import json import json

2
request_llms/bridge_claude.py
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@ -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 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 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 import os

3
request_llms/bridge_internlm.py
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@ -5,7 +5,7 @@ from transformers import AutoModel, AutoTokenizer
import time import time
import threading import threading
import importlib import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
@ -52,6 +52,7 @@ class GetInternlmHandle(LocalLLMHandle):
import torch import torch
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
device = get_conf('LOCAL_MODEL_DEVICE') device = get_conf('LOCAL_MODEL_DEVICE')
with ProxyNetworkActivate('Download_LLM'):
if self._model is None: if self._model is None:
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
if device=='cpu': if device=='cpu':

3
request_llms/bridge_qwen.py
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@ -6,7 +6,7 @@ from transformers import AutoModel, AutoTokenizer
import time import time
import threading import threading
import importlib import importlib
from toolbox import update_ui, get_conf from toolbox import update_ui, get_conf, ProxyNetworkActivate
from multiprocessing import Process, Pipe from multiprocessing import Process, Pipe
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
@ -29,6 +29,7 @@ class GetONNXGLMHandle(LocalLLMHandle):
import platform import platform
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
with ProxyNetworkActivate('Download_LLM'):
model_id = 'qwen/Qwen-7B-Chat' model_id = 'qwen/Qwen-7B-Chat'
self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True) self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
# use fp16 # use fp16

2
request_llms/local_llm_class.py
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@ -201,7 +201,7 @@ class LocalLLMHandle(Process):
if res.startswith(self.std_tag): if res.startswith(self.std_tag):
new_output = res[len(self.std_tag):] new_output = res[len(self.std_tag):]
std_out = std_out[:std_out_clip_len] std_out = std_out[:std_out_clip_len]
# print(new_output, end='') print(new_output, end='')
std_out = new_output + std_out std_out = new_output + std_out
yield self.std_tag + '\n```\n' + std_out + '\n```\n' yield self.std_tag + '\n```\n' + std_out + '\n```\n'
elif res == '[Finish]': elif res == '[Finish]':

4
tests/test_llms.py
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@ -15,11 +15,11 @@ if __name__ == "__main__":
# from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection # from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
# from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection # from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection
# from request_llms.bridge_claude import predict_no_ui_long_connection # from request_llms.bridge_claude import predict_no_ui_long_connection
# from request_llms.bridge_internlm import predict_no_ui_long_connection from request_llms.bridge_internlm import predict_no_ui_long_connection
# from request_llms.bridge_qwen import predict_no_ui_long_connection # from request_llms.bridge_qwen import predict_no_ui_long_connection
# from request_llms.bridge_spark import predict_no_ui_long_connection # from request_llms.bridge_spark import predict_no_ui_long_connection
# from request_llms.bridge_zhipu import predict_no_ui_long_connection # from request_llms.bridge_zhipu import predict_no_ui_long_connection
from request_llms.bridge_chatglm3 import predict_no_ui_long_connection # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
llm_kwargs = { llm_kwargs = {
'max_length': 4096, 'max_length': 4096,

4
version
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@ -1,5 +1,5 @@
{ {
"version": 3.57, "version": 3.58,
"show_feature": true, "show_feature": true,
"new_feature": "支持文心一言v4和星火v3 <-> 支持GLM3和智谱的API <-> 解决本地模型并发BUG <-> 支持动态追加基础功能按钮 <-> 新汇报PDF汇总页面 <-> 重新编译Gradio优化使用体验" "new_feature": "修复本地模型在Windows下的加载BUG <-> 支持文心一言v4和星火v3 <-> 支持GLM3和智谱的API <-> 解决本地模型并发BUG <-> 支持动态追加基础功能按钮 <-> 新汇报PDF汇总页面 <-> 重新编译Gradio优化使用体验"
} }