import json import os import time from concurrent.futures import ThreadPoolExecutor from threading import Lock from typing import Dict, List, Optional, Union import requests from opencompass.registry import MODELS from opencompass.utils.prompt import PromptList from .base_api import BaseAPIModel PromptType = Union[PromptList, str] OPENAI_API_BASE = 'https://api.openai.com/v1/chat/completions' @MODELS.register_module() class OpenAI(BaseAPIModel): """Model wrapper around OpenAI's models. Args: path (str): The name of OpenAI's model. max_seq_len (int): The maximum allowed sequence length of a model. Note that the length of prompt + generated tokens shall not exceed this value. Defaults to 2048. query_per_second (int): The maximum queries allowed per second between two consecutive calls of the API. Defaults to 1. retry (int): Number of retires if the API call fails. Defaults to 2. key (str or List[str]): OpenAI key(s). In particular, when it is set to "ENV", the key will be fetched from the environment variable $OPENAI_API_KEY, as how openai defaults to be. If it's a list, the keys will be used in round-robin manner. Defaults to 'ENV'. org (str or List[str], optional): OpenAI organization(s). If not specified, OpenAI uses the default organization bound to each API key. If specified, the orgs will be posted with each request in round-robin manner. Defaults to None. meta_template (Dict, optional): The model's meta prompt template if needed, in case the requirement of injecting or wrapping of any meta instructions. openai_api_base (str): The base url of OpenAI's API. Defaults to 'https://api.openai.com/v1/chat/completions'. temperature (float, optional): What sampling temperature to use. If not None, will override the temperature in the `generate()` call. Defaults to None. """ is_api: bool = True def __init__(self, path: str = 'gpt-3.5-turbo', max_seq_len: int = 4096, query_per_second: int = 1, retry: int = 2, key: Union[str, List[str]] = 'ENV', org: Optional[Union[str, List[str]]] = None, meta_template: Optional[Dict] = None, openai_api_base: str = OPENAI_API_BASE, temperature: Optional[float] = None): super().__init__(path=path, max_seq_len=max_seq_len, meta_template=meta_template, query_per_second=query_per_second, retry=retry) import tiktoken self.tiktoken = tiktoken self.temperature = temperature if isinstance(key, str): self.keys = [os.getenv('OPENAI_API_KEY') if key == 'ENV' else key] else: self.keys = key self.key_ctr = 0 if isinstance(org, str): self.orgs = [org] else: self.orgs = org self.org_ctr = 0 self.url = openai_api_base def generate( self, inputs: List[str or PromptList], max_out_len: int = 512, temperature: float = 0.7, ) -> List[str]: """Generate results given a list of inputs. Args: inputs (List[str or PromptList]): A list of strings or PromptDicts. The PromptDict should be organized in OpenCompass' API format. max_out_len (int): The maximum length of the output. temperature (float): What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. Defaults to 0.7. Returns: List[str]: A list of generated strings. """ if self.temperature is not None: temperature = self.temperature with ThreadPoolExecutor() as executor: results = list( executor.map(self._generate, inputs, [max_out_len] * len(inputs), [temperature] * len(inputs))) return results def _generate(self, input: str or PromptList, max_out_len: int, temperature: float) -> str: """Generate results given a list of inputs. Args: inputs (str or PromptList): A string or PromptDict. The PromptDict should be organized in OpenCompass' API format. max_out_len (int): The maximum length of the output. temperature (float): What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. Returns: str: The generated string. """ assert isinstance(input, (str, PromptList)) if isinstance(input, str): messages = [{'role': 'user', 'content': input}] else: messages = [] for item in input: msg = {'content': item['prompt']} if item['role'] == 'HUMAN': msg['role'] = 'user' elif item['role'] == 'BOT': msg['role'] = 'assistant' elif item['role'] == 'SYSTEM': msg['role'] = 'system' messages.append(msg) # max num token for gpt-3.5-turbo is 4097 max_out_len = min( max_out_len, self.max_seq_len - 50 - self.get_token_len(str(input))) if max_out_len <= 0: return '' max_num_retries = 0 while max_num_retries < self.retry: self.wait() if hasattr(self, 'keys'): with Lock(): self.key_ctr += 1 if self.key_ctr == len(self.keys): self.key_ctr = 0 header = { 'Authorization': f'Bearer {self.keys[self.key_ctr]}', 'content-type': 'application/json', } if self.orgs: with Lock(): self.org_ctr += 1 if self.org_ctr == len(self.orgs): self.org_ctr = 0 header['OpenAI-Organization'] = self.orgs[self.org_ctr] try: data = dict( model=self.path, messages=messages, max_tokens=max_out_len, n=1, stop=None, temperature=temperature, ) raw_response = requests.post(self.url, headers=header, data=json.dumps(data)) except requests.ConnectionError: self.logger.error('Got connection error, retrying...') continue try: response = raw_response.json() except requests.JSONDecodeError: self.logger.error('JsonDecode error, got', str(raw_response.content)) continue try: return response['choices'][0]['message']['content'].strip() except KeyError: if 'error' in response: if response['error']['code'] == 'rate_limit_exceeded': time.sleep(1) continue self.logger.error('Find error message in response: ', str(response['error'])) max_num_retries += 1 raise RuntimeError('Calling OpenAI failed after retrying for ' f'{max_num_retries} times. Check the logs for ' 'details.') def get_token_len(self, prompt: str) -> int: """Get lengths of the tokenized string. Only English and Chinese characters are counted for now. Users are encouraged to override this method if more accurate length is needed. Args: prompt (str): Input string. Returns: int: Length of the input tokens """ enc = self.tiktoken.encoding_for_model(self.path) return len(enc.encode(prompt))