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* update gemini api and add gemini models * add openai models * update CHARM evaluation * add CHARM memorization tasks * add CharmMemSummarizer (output eval details for memorization-independent reasoning analysis * update CHARM readme --------- Co-authored-by: wujiang <wujiang@pjlab.org.cn>
204 lines
6.7 KiB
Python
204 lines
6.7 KiB
Python
# flake8: noqa: E501
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import json
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor
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from typing import Dict, List, Optional, Union
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import requests
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from opencompass.utils.prompt import PromptList
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from .base_api import BaseAPIModel
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PromptType = Union[PromptList, str, float]
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class Gemini(BaseAPIModel):
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"""Model wrapper around Gemini models.
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Documentation:
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Args:
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path (str): The name of Gemini model.
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e.g. `gemini-pro`
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key (str): Authorization key.
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query_per_second (int): The maximum queries allowed per second
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between two consecutive calls of the API. Defaults to 1.
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max_seq_len (int): Unused here.
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meta_template (Dict, optional): The model's meta prompt
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template if needed, in case the requirement of injecting or
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wrapping of any meta instructions.
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retry (int): Number of retires if the API call fails. Defaults to 2.
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"""
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def __init__(
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self,
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key: str,
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path: str,
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query_per_second: int = 2,
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max_seq_len: int = 2048,
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meta_template: Optional[Dict] = None,
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retry: int = 2,
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temperature: float = 1.0,
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top_p: float = 0.8,
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top_k: float = 10.0,
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):
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super().__init__(path=path,
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max_seq_len=max_seq_len,
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query_per_second=query_per_second,
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meta_template=meta_template,
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retry=retry)
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assert isinstance(key, str)
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if key == 'ENV':
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if 'GEMINI_API_KEY' not in os.environ:
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raise ValueError('GEMINI API key is not set.')
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key = os.getenv('GEMINI_API_KEY')
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assert path in [
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'gemini-1.0-pro', 'gemini-pro', 'gemini-1.5-flash',
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'gemini-1.5-pro'
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] # https://ai.google.dev/gemini-api/docs/models/gemini#model-variations
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self.url = f'https://generativelanguage.googleapis.com/v1beta/models/{path}:generateContent?key={key}'
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self.temperature = temperature
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self.top_p = top_p
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self.top_k = top_k
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self.headers = {
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'content-type': 'application/json',
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}
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def generate(
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self,
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inputs: List[PromptType],
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max_out_len: int = 512,
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) -> List[str]:
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"""Generate results given a list of inputs.
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Args:
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inputs (List[PromptType]): A list of strings or PromptDicts.
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The PromptDict should be organized in OpenCompass'
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API format.
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max_out_len (int): The maximum length of the output.
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Returns:
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List[str]: A list of generated strings.
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"""
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with ThreadPoolExecutor() as executor:
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results = list(
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executor.map(self._generate, inputs,
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[max_out_len] * len(inputs)))
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self.flush()
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return results
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def _generate(
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self,
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input: PromptType,
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max_out_len: int = 512,
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) -> str:
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"""Generate results given an input.
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Args:
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inputs (PromptType): A string or PromptDict.
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The PromptDict should be organized in OpenCompass'
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API format.
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max_out_len (int): The maximum length of the output.
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Returns:
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str: The generated string.
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"""
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assert isinstance(input, (str, PromptList))
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if isinstance(input, str):
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messages = [{'role': 'user', 'parts': [{'text': input}]}]
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else:
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messages = []
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system_prompt = None
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for item in input:
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if item['role'] == 'SYSTEM':
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system_prompt = item['prompt']
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for item in input:
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if system_prompt is not None:
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msg = {
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'parts': [{
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'text': system_prompt + '\n' + item['prompt']
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}]
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}
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else:
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msg = {'parts': [{'text': item['prompt']}]}
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if item['role'] == 'HUMAN':
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msg['role'] = 'user'
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messages.append(msg)
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elif item['role'] == 'BOT':
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msg['role'] = 'model'
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messages.append(msg)
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elif item['role'] == 'SYSTEM':
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pass
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# model can be response with user and system
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# when it comes with agent involved.
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assert msg['role'] in ['user', 'system']
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data = {
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'model':
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self.path,
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'contents':
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messages,
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'safetySettings': [
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{
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'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',
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'threshold': 'BLOCK_NONE'
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},
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{
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'category': 'HARM_CATEGORY_HATE_SPEECH',
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'threshold': 'BLOCK_NONE'
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},
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{
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'category': 'HARM_CATEGORY_HARASSMENT',
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'threshold': 'BLOCK_NONE'
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},
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{
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'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',
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'threshold': 'BLOCK_NONE'
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},
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],
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'generationConfig': {
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'candidate_count': 1,
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'temperature': self.temperature,
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'maxOutputTokens': 2048,
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'topP': self.top_p,
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'topK': self.top_k
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}
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}
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for _ in range(self.retry):
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self.wait()
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raw_response = requests.post(self.url,
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headers=self.headers,
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data=json.dumps(data))
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try:
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response = raw_response.json()
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except requests.JSONDecodeError:
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self.logger.error('JsonDecode error, got',
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str(raw_response.content))
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time.sleep(1)
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continue
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if raw_response.status_code == 200:
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if 'candidates' not in response:
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self.logger.error(response)
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else:
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if 'content' not in response['candidates'][0]:
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return "Due to Google's restrictive policies, I am unable to respond to this question."
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else:
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return response['candidates'][0]['content']['parts'][
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0]['text'].strip()
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try:
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msg = response['error']['message']
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self.logger.error(msg)
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except KeyError:
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pass
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self.logger.error(response)
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time.sleep(1)
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raise RuntimeError('API call failed.')
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