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129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
"""Hugging Face client."""
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import logging
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from typing import Any, Dict, Optional
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import requests
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from manifest.clients.client import Client
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from manifest.request import DEFAULT_REQUEST_KEYS, LMRequest, LMScoreRequest
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from manifest.response import LMModelChoice, ModelChoices, Response
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logger = logging.getLogger(__name__)
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class HuggingFaceClient(Client):
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"""HuggingFace client."""
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# User param -> (client param, default value)
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PARAMS = {
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"temperature": ("temperature", 1.0),
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"max_tokens": ("max_tokens", 10),
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"n": ("n", 1),
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"top_p": ("top_p", 1.0),
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"top_k": ("top_k", 50),
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"repetition_penalty": ("repetition_penalty", 1.0),
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"do_sample": ("do_sample", True),
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}
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REQUEST_CLS = LMRequest
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NAME = "huggingface"
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def connect(
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self,
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connection_str: Optional[str] = None,
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client_args: Dict[str, Any] = {},
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) -> None:
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"""
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Connect to the HuggingFace url.
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Arsg:
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connection_str: connection string.
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client_args: client arguments.
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"""
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if not connection_str:
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raise ValueError("Must provide connection string")
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self.host = connection_str.rstrip("/")
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for key in self.PARAMS:
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setattr(self, key, client_args.pop(key, self.PARAMS[key][1]))
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def close(self) -> None:
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"""Close the client."""
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pass
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def get_generation_url(self) -> str:
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"""Get generation URL."""
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return self.host + "/completions"
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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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return {}
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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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return True
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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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res = requests.post(self.host + "/params").json()
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res["client_name"] = self.NAME
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return res
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def get_score_prompt_request(
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self,
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request: LMScoreRequest,
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) -> Response:
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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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request_params = self.get_request_params(request)
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retry_timeout = request_params.pop("client_timeout")
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for key in DEFAULT_REQUEST_KEYS:
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request_params.pop(key, None)
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# Do not add params like we do with request as the model isn't sampling
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request_params = {"prompt": request.prompt}
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post_str = self.host + "/score_sequence"
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try:
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res = requests.post(
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post_str,
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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("HF request timed out. Increase client_timeout.")
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raise e
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except requests.exceptions.HTTPError as e:
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logger.error(res.text)
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raise e
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response_dict = res.json()
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return Response(
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response=ModelChoices(
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choices=[LMModelChoice(**choice) for choice in response_dict["choices"]]
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),
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cached=False,
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request=request,
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usages=None,
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response_type="text",
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request_type=LMScoreRequest,
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)
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