modules/models.py

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 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.need_api_key = True
        self._refresh_header()

    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

    def set_key(self, new_access_key):
        self.api_key = new_access_key.strip()
        self._refresh_header()
        msg = f"API密钥更改为了{hide_middle_chars(self.api_key)}"
        logging.info(msg)
        return msg

    @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 _refresh_header(self):
        self.headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.api_key}",
        }

    def _get_billing_data(self, billing_url):
        with retrieve_proxy():
            response = requests.get(
                billing_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):
        error_msg = ""
        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}")
                    error_msg+=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
        if error_msg:
            raise Exception(error_msg)


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


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, 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}")
        if lora_path is not None:
            lora_path = f"lora/{lora_path}"
        self.max_generation_token = 1000
        pipeline_name = "inferencer"
        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')

        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
        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):
        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]
        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:
        msg = f"模型设置为了： {model_name}"
        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}")
            logging.info(msg)
        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}")