497 lines
18 KiB
Python
497 lines
18 KiB
Python
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"""
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该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节
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不具备多线程能力的函数：正常对话时使用，具备完备的交互功能，不可多线程
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1. predict(...)
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具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
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2. predict_no_ui_long_connection(...)
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"""
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import tiktoken
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from functools import lru_cache
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from concurrent.futures import ThreadPoolExecutor
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from toolbox import get_conf, trimmed_format_exc
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from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
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from .bridge_chatgpt import predict as chatgpt_ui
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from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
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from .bridge_chatglm import predict as chatglm_ui
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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class LazyloadTiktoken(object):
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def __init__(self, model):
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self.model = model
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@staticmethod
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@lru_cache(maxsize=128)
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def get_encoder(model):
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print('正在加载tokenizer，如果是第一次运行，可能需要一点时间下载参数')
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tmp = tiktoken.encoding_for_model(model)
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print('加载tokenizer完毕')
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return tmp
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def encode(self, *args, **kwargs):
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encoder = self.get_encoder(self.model)
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return encoder.encode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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encoder = self.get_encoder(self.model)
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return encoder.decode(*args, **kwargs)
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# Endpoint 重定向
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API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
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openai_endpoint = "https://api.openai.com/v1/chat/completions"
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api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
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newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
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azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
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# 兼容旧版的配置
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try:
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API_URL, = get_conf("API_URL")
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if API_URL != "https://api.openai.com/v1/chat/completions":
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openai_endpoint = API_URL
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print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
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except:
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pass
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# 新版配置
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if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
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if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
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if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
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# 获取tokenizer
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tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
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tokenizer_gpt4 = LazyloadTiktoken("gpt-4")
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get_token_num_gpt35 = lambda txt: len(tokenizer_gpt35.encode(txt, disallowed_special=()))
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get_token_num_gpt4 = lambda txt: len(tokenizer_gpt4.encode(txt, disallowed_special=()))
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# 开始初始化模型
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AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
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AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
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# -=-=-=-=-=-=- 以下这部分是最早加入的最稳定的模型 -=-=-=-=-=-=-
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model_info = {
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# openai
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"gpt-3.5-turbo": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": openai_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gpt-3.5-turbo-16k": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": openai_endpoint,
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"max_token": 1024*16,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gpt-3.5-turbo-0613": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": openai_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gpt-3.5-turbo-16k-0613": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": openai_endpoint,
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"max_token": 1024 * 16,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gpt-4": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": openai_endpoint,
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"max_token": 8192,
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"tokenizer": tokenizer_gpt4,
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"token_cnt": get_token_num_gpt4,
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},
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# azure openai
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"azure-gpt-3.5":{
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": azure_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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# api_2d
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"api2d-gpt-3.5-turbo": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": api2d_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"api2d-gpt-4": {
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"fn_with_ui": chatgpt_ui,
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"fn_without_ui": chatgpt_noui,
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"endpoint": api2d_endpoint,
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"max_token": 8192,
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"tokenizer": tokenizer_gpt4,
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"token_cnt": get_token_num_gpt4,
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},
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# 将 chatglm 直接对齐到 chatglm2
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"chatglm": {
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"fn_with_ui": chatglm_ui,
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"fn_without_ui": chatglm_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"chatglm2": {
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"fn_with_ui": chatglm_ui,
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"fn_without_ui": chatglm_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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}
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# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
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if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
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from .bridge_claude import predict_no_ui_long_connection as claude_noui
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from .bridge_claude import predict as claude_ui
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model_info.update({
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"claude-1-100k": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": None,
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"max_token": 8196,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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model_info.update({
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"claude-2": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": None,
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"max_token": 8196,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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if "jittorllms_rwkv" in AVAIL_LLM_MODELS:
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from .bridge_jittorllms_rwkv import predict_no_ui_long_connection as rwkv_noui
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from .bridge_jittorllms_rwkv import predict as rwkv_ui
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model_info.update({
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"jittorllms_rwkv": {
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"fn_with_ui": rwkv_ui,
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"fn_without_ui": rwkv_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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if "jittorllms_llama" in AVAIL_LLM_MODELS:
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from .bridge_jittorllms_llama import predict_no_ui_long_connection as llama_noui
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from .bridge_jittorllms_llama import predict as llama_ui
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model_info.update({
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"jittorllms_llama": {
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"fn_with_ui": llama_ui,
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"fn_without_ui": llama_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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if "jittorllms_pangualpha" in AVAIL_LLM_MODELS:
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from .bridge_jittorllms_pangualpha import predict_no_ui_long_connection as pangualpha_noui
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from .bridge_jittorllms_pangualpha import predict as pangualpha_ui
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model_info.update({
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"jittorllms_pangualpha": {
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"fn_with_ui": pangualpha_ui,
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"fn_without_ui": pangualpha_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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if "moss" in AVAIL_LLM_MODELS:
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from .bridge_moss import predict_no_ui_long_connection as moss_noui
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from .bridge_moss import predict as moss_ui
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model_info.update({
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"moss": {
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"fn_with_ui": moss_ui,
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"fn_without_ui": moss_noui,
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"endpoint": None,
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"max_token": 1024,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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if "stack-claude" in AVAIL_LLM_MODELS:
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from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui
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from .bridge_stackclaude import predict as claude_ui
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model_info.update({
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"stack-claude": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": None,
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"max_token": 8192,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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if "newbing-free" in AVAIL_LLM_MODELS:
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try:
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from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
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from .bridge_newbingfree import predict as newbingfree_ui
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model_info.update({
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"newbing-free": {
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"fn_with_ui": newbingfree_ui,
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"fn_without_ui": newbingfree_noui,
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"endpoint": newbing_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "newbing" in AVAIL_LLM_MODELS: # same with newbing-free
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try:
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from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
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from .bridge_newbingfree import predict as newbingfree_ui
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model_info.update({
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"newbing": {
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"fn_with_ui": newbingfree_ui,
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"fn_without_ui": newbingfree_noui,
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"endpoint": newbing_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "chatglmft" in AVAIL_LLM_MODELS: # same with newbing-free
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try:
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from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
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from .bridge_chatglmft import predict as chatglmft_ui
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model_info.update({
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"chatglmft": {
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"fn_with_ui": chatglmft_ui,
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"fn_without_ui": chatglmft_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "internlm" in AVAIL_LLM_MODELS:
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try:
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from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
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from .bridge_internlm import predict as internlm_ui
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model_info.update({
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"internlm": {
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"fn_with_ui": internlm_ui,
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"fn_without_ui": internlm_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "chatglm_onnx" in AVAIL_LLM_MODELS:
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try:
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from .bridge_chatglmonnx import predict_no_ui_long_connection as chatglm_onnx_noui
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from .bridge_chatglmonnx import predict as chatglm_onnx_ui
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model_info.update({
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"chatglm_onnx": {
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"fn_with_ui": chatglm_onnx_ui,
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"fn_without_ui": chatglm_onnx_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "qwen" in AVAIL_LLM_MODELS:
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try:
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from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
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from .bridge_qwen import predict as qwen_ui
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model_info.update({
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"qwen": {
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"fn_with_ui": qwen_ui,
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"fn_without_ui": qwen_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "chatgpt_website" in AVAIL_LLM_MODELS: # 接入一些逆向工程https://github.com/acheong08/ChatGPT-to-API/
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try:
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from .bridge_chatgpt_website import predict_no_ui_long_connection as chatgpt_website_noui
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from .bridge_chatgpt_website import predict as chatgpt_website_ui
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model_info.update({
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"chatgpt_website": {
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"fn_with_ui": chatgpt_website_ui,
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"fn_without_ui": chatgpt_website_noui,
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"endpoint": openai_endpoint,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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if "spark" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
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try:
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from .bridge_spark import predict_no_ui_long_connection as spark_noui
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from .bridge_spark import predict as spark_ui
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model_info.update({
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"spark": {
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"fn_with_ui": spark_ui,
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"fn_without_ui": spark_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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print(trimmed_format_exc())
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def LLM_CATCH_EXCEPTION(f):
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"""
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装饰器函数，将错误显示出来
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"""
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def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
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try:
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return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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except Exception as e:
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tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
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observe_window[0] = tb_str
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return tb_str
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return decorated
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def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):
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"""
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发送至LLM，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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LLM的内部调优参数
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history：
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是之前的对话列表
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observe_window = None：
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用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
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"""
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import threading, time, copy
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model = llm_kwargs['llm_model']
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n_model = 1
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if '&' not in model:
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assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
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# 如果只询问1个大语言模型：
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method = model_info[model]["fn_without_ui"]
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return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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else:
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# 如果同时询问多个大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
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executor = ThreadPoolExecutor(max_workers=4)
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models = model.split('&')
|
||
n_model = len(models)
|
||
|
||
window_len = len(observe_window)
|
||
assert window_len==3
|
||
window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
|
||
|
||
futures = []
|
||
for i in range(n_model):
|
||
model = models[i]
|
||
method = model_info[model]["fn_without_ui"]
|
||
llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
|
||
llm_kwargs_feedin['llm_model'] = model
|
||
future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
|
||
futures.append(future)
|
||
|
||
def mutex_manager(window_mutex, observe_window):
|
||
while True:
|
||
time.sleep(0.25)
|
||
if not window_mutex[-1]: break
|
||
# 看门狗（watchdog）
|
||
for i in range(n_model):
|
||
window_mutex[i][1] = observe_window[1]
|
||
# 观察窗（window）
|
||
chat_string = []
|
||
for i in range(n_model):
|
||
chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
|
||
res = '<br/><br/>\n\n---\n\n'.join(chat_string)
|
||
# # # # # # # # # # #
|
||
observe_window[0] = res
|
||
|
||
t_model = threading.Thread(target=mutex_manager, args=(window_mutex, observe_window), daemon=True)
|
||
t_model.start()
|
||
|
||
return_string_collect = []
|
||
while True:
|
||
worker_done = [h.done() for h in futures]
|
||
if all(worker_done):
|
||
executor.shutdown()
|
||
break
|
||
time.sleep(1)
|
||
|
||
for i, future in enumerate(futures): # wait and get
|
||
return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
|
||
|
||
window_mutex[-1] = False # stop mutex thread
|
||
res = '<br/><br/>\n\n---\n\n'.join(return_string_collect)
|
||
return res
|
||
|
||
|
||
def predict(inputs, llm_kwargs, *args, **kwargs):
|
||
"""
|
||
发送至LLM，流式获取输出。
|
||
用于基础的对话功能。
|
||
inputs 是本次问询的输入
|
||
top_p, temperature是LLM的内部调优参数
|
||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||
"""
|
||
|
||
method = model_info[llm_kwargs['llm_model']]["fn_with_ui"] # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
|
||
yield from method(inputs, llm_kwargs, *args, **kwargs)
|
||
|