from __future__ import annotations from typing import TYPE_CHECKING, List import logging import json import commentjson as cjson import os import sys import requests import urllib3 import platform from dataclasses import dataclass, field from transformers import HfArgumentParser from tqdm import tqdm import colorama from duckduckgo_search import ddg import asyncio import aiohttp from enum import Enum from .presets import * from .llama_func import * from .utils import * from . import shared from .config import retrieve_proxy from modules import config from .base_model import BaseLLMModel, ModelType class OpenAIClient(BaseLLMModel): def __init__( self, model_name, api_key, system_prompt=INITIAL_SYSTEM_PROMPT, temperature=1.0, top_p=1.0, ) -> None: super().__init__( model_name=model_name, temperature=temperature, top_p=top_p, system_prompt=system_prompt, ) self.api_key = api_key self.headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}", } def get_answer_stream_iter(self): response = self._get_response(stream=True) if response is not None: iter = self._decode_chat_response(response) partial_text = "" for i in iter: partial_text += i yield partial_text else: yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG def get_answer_at_once(self): response = self._get_response() response = json.loads(response.text) content = response["choices"][0]["message"]["content"] total_token_count = response["usage"]["total_tokens"] return content, total_token_count def count_token(self, user_input): input_token_count = count_token(construct_user(user_input)) if self.system_prompt is not None and len(self.all_token_counts) == 0: system_prompt_token_count = count_token( construct_system(self.system_prompt) ) return input_token_count + system_prompt_token_count return input_token_count def billing_info(self): try: curr_time = datetime.datetime.now() last_day_of_month = get_last_day_of_month(curr_time).strftime("%Y-%m-%d") first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d") usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}" try: usage_data = self._get_billing_data(usage_url) except Exception as e: logging.error(f"获取API使用情况失败:" + str(e)) return f"**获取API使用情况失败**" rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100) return f"**本月使用金额** \u3000 ${rounded_usage}" except requests.exceptions.ConnectTimeout: status_text = ( STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG ) return status_text except requests.exceptions.ReadTimeout: status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG return status_text except Exception as e: logging.error(f"获取API使用情况失败:" + str(e)) return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG def set_token_upper_limit(self, new_upper_limit): pass @shared.state.switching_api_key # 在不开启多账号模式的时候，这个装饰器不会起作用 def _get_response(self, stream=False): openai_api_key = self.api_key system_prompt = self.system_prompt history = self.history logging.debug(colorama.Fore.YELLOW + f"{history}" + colorama.Fore.RESET) headers = { "Content-Type": "application/json", "Authorization": f"Bearer {openai_api_key}", } if system_prompt is not None: history = [construct_system(system_prompt), *history] payload = { "model": self.model_name, "messages": history, "temperature": self.temperature, "top_p": self.top_p, "n": self.n_choices, "stream": stream, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, } if self.max_generation_token is not None: payload["max_tokens"] = self.max_generation_token if self.stop_sequence is not None: payload["stop"] = self.stop_sequence if self.logit_bias is not None: payload["logit_bias"] = self.logit_bias if self.user_identifier is not None: payload["user"] = self.user_identifier if stream: timeout = TIMEOUT_STREAMING else: timeout = TIMEOUT_ALL # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求 if shared.state.completion_url != COMPLETION_URL: logging.info(f"使用自定义API URL: {shared.state.completion_url}") with retrieve_proxy(): try: response = requests.post( shared.state.completion_url, headers=headers, json=payload, stream=stream, timeout=timeout, ) except: return None return response def _get_billing_data(self, usage_url): with retrieve_proxy(): response = requests.get( usage_url, headers=self.headers, timeout=TIMEOUT_ALL, ) if response.status_code == 200: data = response.json() return data else: raise Exception( f"API request failed with status code {response.status_code}: {response.text}" ) def _decode_chat_response(self, response): for chunk in response.iter_lines(): if chunk: chunk = chunk.decode() chunk_length = len(chunk) try: chunk = json.loads(chunk[6:]) except json.JSONDecodeError: print(f"JSON解析错误,收到的内容: {chunk}") continue if chunk_length > 6 and "delta" in chunk["choices"][0]: if chunk["choices"][0]["finish_reason"] == "stop": break try: yield chunk["choices"][0]["delta"]["content"] except Exception as e: # logging.error(f"Error: {e}") continue class ChatGLM_Client(BaseLLMModel): def __init__(self, model_name) -> None: super().__init__(model_name=model_name) from transformers import AutoTokenizer, AutoModel import torch system_name = platform.system() model_path=None if os.path.exists("models"): model_dirs = os.listdir("models") if model_name in model_dirs: model_path = f"models/{model_name}" if model_path is not None: model_source = model_path else: model_source = f"THUDM/{model_name}" self.tokenizer = AutoTokenizer.from_pretrained( model_source, trust_remote_code=True ) quantified = False if "int4" in model_name: quantified = True if quantified: model = AutoModel.from_pretrained( model_source, trust_remote_code=True ).float() else: model = AutoModel.from_pretrained( model_source, trust_remote_code=True ).half() if torch.cuda.is_available(): # run on CUDA logging.info("CUDA is available, using CUDA") model = model.cuda() # mps加速还存在一些问题，暂时不使用 elif system_name == "Darwin" and model_path is not None and not quantified: logging.info("Running on macOS, using MPS") # running on macOS and model already downloaded model = model.to("mps") else: logging.info("GPU is not available, using CPU") model = model.eval() self.model = model def _get_glm_style_input(self): history = [x["content"] for x in self.history] query = history.pop() logging.debug(colorama.Fore.YELLOW + f"{history}" + colorama.Fore.RESET) assert ( len(history) % 2 == 0 ), f"History should be even length. current history is: {history}" history = [[history[i], history[i + 1]] for i in range(0, len(history), 2)] return history, query def get_answer_at_once(self): history, query = self._get_glm_style_input() response, _ = self.model.chat(self.tokenizer, query, history=history) return response, len(response) def get_answer_stream_iter(self): history, query = self._get_glm_style_input() for response, history in self.model.stream_chat( self.tokenizer, query, history, max_length=self.token_upper_limit, top_p=self.top_p, temperature=self.temperature, ): yield response @dataclass class ChatbotArguments: pass class LLaMA_Client(BaseLLMModel): def __init__( self, model_name, lora_path=None, ) -> None: super().__init__(model_name=model_name) from lmflow.datasets.dataset import Dataset from lmflow.pipeline.auto_pipeline import AutoPipeline from lmflow.models.auto_model import AutoModel from lmflow.args import ModelArguments, DatasetArguments, AutoArguments, InferencerArguments model_path = None if os.path.exists("models"): model_dirs = os.listdir("models") if model_name in model_dirs: model_path = f"models/{model_name}" if model_path is not None: model_source = model_path else: raise Exception(f"models目录下没有这个模型: {model_name}") self.max_generation_token = 1000 pipeline_name = "inferencer" PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name) """ if [ $# -ge 2 ]; then lora_args="--lora_model_path $2" fi CUDA_VISIBLE_DEVICES=2 \ deepspeed examples/chatbot.py \ --deepspeed configs/ds_config_chatbot.json \ --model_name_or_path ${model} \ ${lora_args} model_args: ModelArguments(model_name_or_path='/home/guest/llm_models/llama/7B', lora_model_path='/home/guest/llm_models/lora/baize-lora-7B', model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None, use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True) pipeline_args: InferencerArguments(local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16') """ # parser = HfArgumentParser( # ( # ModelArguments, # PipelineArguments, # ChatbotArguments, # ) # ) model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None, use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True) pipeline_args = InferencerArguments(local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16') # model_args, pipeline_args, chatbot_args = parser.parse_args_into_dataclasses() with open(pipeline_args.deepspeed, "r") as f: ds_config = json.load(f) self.model = AutoModel.get_model( model_args, tune_strategy="none", ds_config=ds_config, ) # We don't need input data, we will read interactively from stdin data_args = DatasetArguments(dataset_path=None) self.dataset = Dataset(data_args) self.inferencer = AutoPipeline.get_pipeline( pipeline_name=pipeline_name, model_args=model_args, data_args=data_args, pipeline_args=pipeline_args, ) # Chats model_name = model_args.model_name_or_path if model_args.lora_model_path is not None: model_name += f" + {model_args.lora_model_path}" # context = ( # "You are a helpful assistant who follows the given instructions" # " unconditionally." # ) self.end_string = "\n\n" def _get_llama_style_input(self): history = [x["content"] for x in self.history] context = "\n".join(history) return context def get_answer_at_once(self): context = self._get_llama_style_input() input_dataset = self.dataset.from_dict( {"type": "text_only", "instances": [{"text": context}]} ) output_dataset = self.inferencer.inference( model=self.model, dataset=input_dataset, max_new_tokens=self.max_generation_token, temperature=self.temperature, ) response = output_dataset.to_dict()["instances"][0]["text"] try: index = response.index(self.end_string) except ValueError: response += self.end_string index = response.index(self.end_string) response = response[: index + 1] return response, len(response) def get_answer_stream_iter(self): response, _ = self.get_answer_at_once() yield response class ModelManager: def __init__(self, **kwargs) -> None: self.get_model(**kwargs) def get_model( self, model_name, lora_model_path=None, access_key=None, temperature=None, top_p=None, system_prompt=None, ) -> BaseLLMModel: print(lora_model_path) msg = f"模型设置为了： {model_name}" logging.info(msg) model_type = ModelType.get_type(model_name) lora_selector_visibility = False lora_choices = [] dont_change_lora_selector = False if model_type != ModelType.OpenAI: config.local_embedding = True model = None try: if model_type == ModelType.OpenAI: model = OpenAIClient( model_name=model_name, api_key=access_key, system_prompt=system_prompt, temperature=temperature, top_p=top_p, ) elif model_type == ModelType.ChatGLM: model = ChatGLM_Client(model_name) elif model_type == ModelType.LLaMA and lora_model_path == "": msg = "现在请选择LoRA模型" logging.info(msg) lora_selector_visibility = True if os.path.isdir("lora"): lora_choices = get_file_names("lora", plain=True, filetypes=[""]) lora_choices = ["No LoRA"] + lora_choices elif model_type == ModelType.LLaMA and lora_model_path != "": dont_change_lora_selector = True if lora_model_path == "No LoRA": lora_model_path = None msg += " + No LoRA" else: msg += f" + {lora_model_path}" model = LLaMA_Client(model_name, lora_model_path) pass elif model_type == ModelType.Unknown: raise ValueError(f"未知模型: {model_name}") except Exception as e: logging.error(e) msg = f"{STANDARD_ERROR_MSG}: {e}" if model is not None: self.model = model if dont_change_lora_selector: return msg else: return msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility) def predict(self, *args): iter = self.model.predict(*args) for i in iter: yield i def billing_info(self): return self.model.billing_info() def set_key(self, *args): return self.model.set_key(*args) def load_chat_history(self, *args): return self.model.load_chat_history(*args) def interrupt(self, *args): return self.model.interrupt(*args) def reset(self, *args): return self.model.reset(*args) def retry(self, *args): iter = self.model.retry(*args) for i in iter: yield i def delete_first_conversation(self, *args): return self.model.delete_first_conversation(*args) def delete_last_conversation(self, *args): return self.model.delete_last_conversation(*args) def set_system_prompt(self, *args): return self.model.set_system_prompt(*args) def save_chat_history(self, *args): return self.model.save_chat_history(*args) def export_markdown(self, *args): return self.model.export_markdown(*args) def load_chat_history(self, *args): return self.model.load_chat_history(*args) def set_token_upper_limit(self, *args): return self.model.set_token_upper_limit(*args) def set_temperature(self, *args): self.model.set_temperature(*args) def set_top_p(self, *args): self.model.set_top_p(*args) def set_n_choices(self, *args): self.model.set_n_choices(*args) def set_stop_sequence(self, *args): self.model.set_stop_sequence(*args) def set_max_tokens(self, *args): self.model.set_max_tokens(*args) def set_presence_penalty(self, *args): self.model.set_presence_penalty(*args) def set_frequency_penalty(self, *args): self.model.set_frequency_penalty(*args) def set_logit_bias(self, *args): self.model.set_logit_bias(*args) def set_user_identifier(self, *args): self.model.set_user_identifier(*args) if __name__ == "__main__": with open("config.json", "r") as f: openai_api_key = cjson.load(f)["openai_api_key"] # set logging level to debug logging.basicConfig(level=logging.DEBUG) # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key) client = ModelManager(model_name="chatglm-6b-int4") chatbot = [] stream = False # 测试账单功能 logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET) logging.info(client.billing_info()) # 测试问答 logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET) question = "巴黎是中国的首都吗？" for i in client.predict(inputs=question, chatbot=chatbot, stream=stream): logging.info(i) logging.info(f"测试问答后history : {client.history}") # 测试记忆力 logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET) question = "我刚刚问了你什么问题？" for i in client.predict(inputs=question, chatbot=chatbot, stream=stream): logging.info(i) logging.info(f"测试记忆力后history : {client.history}") # 测试重试功能 logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET) for i in client.retry(chatbot=chatbot, stream=stream): logging.info(i) logging.info(f"重试后history : {client.history}") # # 测试总结功能 # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET) # chatbot, msg = client.reduce_token_size(chatbot=chatbot) # print(chatbot, msg) # print(f"总结后history: {client.history}")