""" LLM wrapper for LLMs running on ColossalCloud Platform Usage: os.environ['URL'] = "" os.environ['HOST'] = "" gen_config = { 'max_new_tokens': 100, # 'top_k': 2, 'top_p': 0.9, 'temperature': 0.5, 'repetition_penalty': 2, } llm = ColossalCloudLLM(n=1) llm.set_auth_config() resp = llm(prompt='What do you call a three-ton kangaroo?', **gen_config) print(resp) # super-heavyweight awesome-natured yawning Australian creature! """ import json from typing import Any, List, Mapping, Optional import requests from langchain.llms.base import LLM from langchain.utils import get_from_dict_or_env class ColossalCloudLLM(LLM): """ A custom LLM class that integrates LLMs running on the ColossalCloud Platform """ n: int gen_config: dict = None auth_config: dict = None valid_gen_para: list = ['max_new_tokens', 'top_k', 'top_p', 'temperature', 'repetition_penalty'] def __init__(self, gen_config=None, **kwargs): """ Args: gen_config: config for generation, max_new_tokens: 50 by default top_k: (1, vocab_size) top_p: (0, 1) if not None temperature: (0, inf) if not None repetition_penalty: (1, inf) if not None """ super(ColossalCloudLLM, self).__init__(**kwargs) if gen_config is None: self.gen_config = {"max_new_tokens": 50} else: assert "max_new_tokens" in gen_config, "max_new_tokens is a compulsory key in the gen config" self.gen_config = gen_config @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {"n": self.n} @property def _llm_type(self) -> str: return 'ColossalCloudLLM' def set_auth_config(self, **kwargs): url = get_from_dict_or_env(kwargs, "url", "URL") host = get_from_dict_or_env(kwargs, "host", "HOST") auth_config = {} auth_config['endpoint'] = url auth_config['Host'] = host self.auth_config = auth_config def _call(self, prompt: str, stop=None, **kwargs: Any) -> str: """ Args: prompt: The prompt to pass into the model. stop: A list of strings to stop generation when encountered Returns: The string generated by the model """ # Update the generation arguments for key, value in kwargs.items(): if key not in self.valid_gen_para: raise KeyError(f"Invalid generation parameter: '{key}'. Valid keys are: {', '.join(self.valid_gen_para)}") if key in self.gen_config: self.gen_config[key] = value resp_text = self.text_completion(prompt, self.gen_config, self.auth_config) # TODO: This may cause excessive tokens count if stop is not None: for stopping_words in stop: if stopping_words in resp_text: resp_text = resp_text.split(stopping_words)[0] return resp_text def text_completion(self, prompt, gen_config, auth_config): # Complusory Parameters endpoint = auth_config.pop('endpoint') max_new_tokens = gen_config.pop('max_new_tokens') # Optional Parameters optional_params = ['top_k', 'top_p', 'temperature', 'repetition_penalty'] # Self.optional gen_config = {key: gen_config[key] for key in optional_params if key in gen_config} # Define the data payload data = { "max_new_tokens": max_new_tokens, "history": [ {"instruction": prompt, "response": ""} ], **gen_config } headers = { "Content-Type": "application/json", **auth_config # 'Host', } # Make the POST request response = requests.post(endpoint, headers=headers, data=json.dumps(data)) response.raise_for_status() # raise error if return code is not 200(success) # Check the response return response.text