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187 lines
5.0 KiB
Python
187 lines
5.0 KiB
Python
"""Client class."""
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import logging
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import requests
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from manifest.request import Request
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logger = logging.getLogger(__name__)
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class Client(ABC):
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"""Client class."""
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# Must be overridden by child class
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PARAMS: Dict[str, Tuple[str, Any]] = {}
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REQUEST_CLS = Request
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def __init__(
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self, connection_str: Optional[str] = None, client_args: Dict[str, Any] = {}
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):
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"""
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Initialize client.
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kwargs are passed to client as default parameters.
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For clients like OpenAI that do not require a connection,
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the connection_str can be None.
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Args:
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connection_str: connection string for client.
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client_args: client arguments.
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"""
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self.connect(connection_str, client_args)
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@abstractmethod
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def connect(
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self, connection_str: Optional[str], client_args: Dict[str, Any]
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) -> None:
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"""
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Connect to client.
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Args:
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connection_str: connection string.
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"""
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raise NotImplementedError()
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@abstractmethod
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def close(self) -> None:
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"""Close the client."""
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raise NotImplementedError()
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@abstractmethod
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def get_generation_url(self) -> str:
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"""Get generation URL."""
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raise NotImplementedError()
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@abstractmethod
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def get_generation_header(self) -> Dict[str, str]:
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"""
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Get generation header.
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Returns:
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header.
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"""
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raise NotImplementedError()
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@abstractmethod
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def supports_batch_inference(self) -> bool:
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"""Return whether the client supports batch inference."""
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raise NotImplementedError()
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@abstractmethod
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def get_model_params(self) -> Dict:
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"""
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Get model params.
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By getting model params from the server, we can add to request
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and make sure cache keys are unique to model.
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Returns:
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model params.
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"""
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raise NotImplementedError()
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def get_model_inputs(self) -> List:
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"""
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Get allowable model inputs.
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Returns:
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model inputs.
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"""
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return list(self.PARAMS.keys())
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def get_request_params(
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self, prompt: Union[str, List[str]], request_args: Dict[str, Any]
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) -> Request:
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"""
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Parse model kwargs to request.
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Args:
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prompt: prompt.
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request_args: request arguments.
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Returns:
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request.
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"""
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params = {"prompt": prompt}
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for key in self.PARAMS:
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params[key] = request_args.pop(key, getattr(self, key))
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return self.REQUEST_CLS(**params)
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def format_response(self, response: Dict) -> Dict[str, Any]:
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"""
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Format response to dict.
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Args:
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response: response
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Return:
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response as dict
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"""
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if "choices" not in response:
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raise ValueError(f"Invalid response: {response}")
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return response
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def get_request(self, request: Request) -> Tuple[Callable[[], Dict], Dict]:
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"""
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Get request string function.
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Args:
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request: request.
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Returns:
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request function that takes no input.
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request parameters as dict.
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"""
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if isinstance(request.prompt, list) and not self.supports_batch_inference():
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raise ValueError(
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f"{self.__class__.__name__} does not support batch inference."
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)
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request_params = request.to_dict(self.PARAMS)
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retry_timeout = request_params.pop("client_timeout")
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def _run_completion() -> Dict:
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post_str = self.get_generation_url()
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try:
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res = requests.post(
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post_str,
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headers=self.get_generation_header(),
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json=request_params,
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timeout=retry_timeout,
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)
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res.raise_for_status()
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except requests.Timeout as e:
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logger.error(
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f"{self.__class__.__name__} request timed out."
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" Increase client_timeout."
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)
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raise e
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except requests.exceptions.HTTPError:
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logger.error(res.json())
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raise requests.exceptions.HTTPError(res.json())
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return self.format_response(res.json())
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return _run_completion, request_params
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def get_score_prompt_request(
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self,
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request: Request,
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) -> Tuple[Callable[[], Dict], Dict]:
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"""
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Get the logit score of the prompt via a forward pass of the model.
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Args:
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request: request.
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Returns:
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request function that takes no input.
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request parameters as dict.
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"""
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raise NotImplementedError(
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f"{self.__class__.__name__} does not support prompt scoring request."
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)
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