import json import os import re import time from concurrent.futures import ThreadPoolExecutor from threading import Lock from typing import Dict, List, Optional, Union import jieba 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'. mode (str, optional): The method of input truncation when input length exceeds max_seq_len. 'front','mid' and 'rear' represents the part of input to truncate. Defaults to 'none'. 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, rpm_verbose: bool = False, 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, mode: str = 'none', temperature: Optional[float] = None): super().__init__(path=path, max_seq_len=max_seq_len, meta_template=meta_template, query_per_second=query_per_second, rpm_verbose=rpm_verbose, retry=retry) import tiktoken self.tiktoken = tiktoken self.temperature = temperature assert mode in ['none', 'front', 'mid', 'rear'] self.mode = mode if isinstance(key, str): self.keys = [os.getenv('OPENAI_API_KEY') if key == 'ENV' else key] else: self.keys = key # record invalid keys and skip them when requesting API # - keys have insufficient_quota self.invalid_keys = set() self.key_ctr = 0 if isinstance(org, str): self.orgs = [org] else: self.orgs = org self.org_ctr = 0 self.url = openai_api_base self.path = path 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)) # max num token for gpt-3.5-turbo is 4097 context_window = 4096 if '32k' in self.path: context_window = 32768 elif '16k' in self.path: context_window = 16384 elif 'gpt-4' in self.path: context_window = 8192 # will leave 100 tokens as prompt buffer, triggered if input is str if isinstance(input, str) and self.mode != 'none': context_window = self.max_seq_len input = self.bin_trim(input, context_window - 100 - max_out_len) 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) # Hold out 100 tokens due to potential errors in tiktoken calculation max_out_len = min( max_out_len, context_window - self.get_token_len(str(input)) - 100) if max_out_len <= 0: return '' max_num_retries = 0 while max_num_retries < self.retry: self.wait() with Lock(): if len(self.invalid_keys) == len(self.keys): raise RuntimeError('All keys have insufficient quota.') # find the next valid key while True: self.key_ctr += 1 if self.key_ctr == len(self.keys): self.key_ctr = 0 if self.keys[self.key_ctr] not in self.invalid_keys: break key = self.keys[self.key_ctr] header = { 'Authorization': f'Bearer {key}', '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 elif response['error']['code'] == 'insufficient_quota': self.invalid_keys.add(key) self.logger.warn(f'insufficient_quota key: {key}') 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)) def bin_trim(self, prompt: str, num_token: int) -> str: """Get a suffix of prompt which is no longer than num_token tokens. Args: prompt (str): Input string. num_token (int): The upper bound of token numbers. Returns: str: The trimmed prompt. """ token_len = self.get_token_len(prompt) if token_len <= num_token: return prompt pattern = re.compile(r'[\u4e00-\u9fa5]') if pattern.search(prompt): words = list(jieba.cut(prompt, cut_all=False)) sep = '' else: words = prompt.split(' ') sep = ' ' l, r = 1, len(words) while l + 2 < r: mid = (l + r) // 2 if self.mode == 'front': cur_prompt = sep.join(words[-mid:]) elif self.mode == 'mid': cur_prompt = sep.join(words[:mid]) + sep.join(words[-mid:]) elif self.mode == 'rear': cur_prompt = sep.join(words[:mid]) if self.get_token_len(cur_prompt) <= num_token: l = mid # noqa: E741 else: r = mid if self.mode == 'front': prompt = sep.join(words[-l:]) elif self.mode == 'mid': prompt = sep.join(words[:l]) + sep.join(words[-l:]) elif self.mode == 'rear': prompt = sep.join(words[:l]) return prompt class OpenAIAllesAPIN(OpenAI): """Model wrapper around OpenAI-AllesAPIN. Args: path (str): The name of OpenAI's model. url (str): URL to AllesAPIN. key (str): AllesAPIN key. query_per_second (int): The maximum queries allowed per second between two consecutive calls of the API. Defaults to 1. max_seq_len (int): Unused here. meta_template (Dict, optional): The model's meta prompt template if needed, in case the requirement of injecting or wrapping of any meta instructions. retry (int): Number of retires if the API call fails. Defaults to 2. """ is_api: bool = True def __init__(self, path: str, url: str, key: str, temperature: float = 1.0, query_per_second: int = 1, rpm_verbose: bool = False, max_seq_len: int = 2048, meta_template: Optional[Dict] = None, retry: int = 2): super().__init__(path=path, max_seq_len=max_seq_len, query_per_second=query_per_second, rpm_verbose=rpm_verbose, meta_template=meta_template, retry=retry) self.url = url self.temperature = temperature self.headers = { 'alles-apin-token': key, 'content-type': 'application/json', } def _generate(self, input: str or PromptList, max_out_len: int, temperature: float) -> str: """Generate results given an input. 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) # model can be response with user and system # when it comes with agent involved. assert msg['role'] in ['user', 'system'] data = { 'model': self.path, 'messages': messages, 'temperature': temperature } for _ in range(self.retry): self.wait() raw_response = requests.post(self.url, headers=self.headers, data=json.dumps(data)) try: response = raw_response.json() except requests.JSONDecodeError: self.logger.error('JsonDecode error, got', str(raw_response.content)) time.sleep(1) continue if raw_response.status_code == 200 and response[ 'msgCode'] == '10000': data = response['data'] choices = data['choices'] if choices is None: self.logger.error(data) else: return choices[0]['message']['content'].strip() try: match = re.match(r'Error code: \d+ - (.*)', response['data']) err = eval(match.group(1))['error'] if err['code'] == 'content_filter' and err['status'] == 400: return err['message'] except Exception: pass self.logger.error(response['msg']) self.logger.error(response) time.sleep(1) raise RuntimeError('API call failed.') 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))