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168 lines
4.4 KiB
Python
168 lines
4.4 KiB
Python
"""Dummy client."""
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import logging
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from typing import Any, Dict, Optional
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from manifest.clients.client import Client
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from manifest.request import LMRequest, LMScoreRequest, Request
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from manifest.response import LMModelChoice, ModelChoices, Response, Usage, Usages
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logger = logging.getLogger(__name__)
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class DummyClient(Client):
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"""Dummy client."""
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# User param -> (client param, default value)
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PARAMS = {
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"n": ("num_results", 1),
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}
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REQUEST_CLS = LMRequest
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NAME = "dummy"
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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 dummpy server.
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This is a dummy client that returns identity responses. Used for testing.
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Args:
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connection_str: connection string.
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client_args: client arguments.
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"""
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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 "dummy"
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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_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 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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return {"engine": "dummy"}
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def run_request(self, request: Request) -> Response:
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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):
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num_results = len(request.prompt)
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else:
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num_results = 1
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request_params = request.to_dict(self.PARAMS)
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return Response(
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response=ModelChoices(
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choices=[LMModelChoice(text="hello")] # type: ignore
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* int(request_params["num_results"])
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* num_results
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),
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cached=False,
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request=request,
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usages=Usages(
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usages=[
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Usage(
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**{
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"prompt_tokens": 1,
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"completion_tokens": 1,
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"total_tokens": 2,
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}
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)
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]
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* int(request_params["num_results"])
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* num_results
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),
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response_type="text",
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request_type=self.REQUEST_CLS,
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)
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async def arun_batch_request(self, request: Request) -> Response:
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"""
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Get async request string function.
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Args:
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request: request.
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Returns:
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response.
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"""
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return self.run_request(request)
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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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if isinstance(request.prompt, list):
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num_results = len(request.prompt)
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else:
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num_results = 1
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response_dict = {
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"choices": [
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{
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"text": request.prompt
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if isinstance(request.prompt, str)
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else request.prompt[i],
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"token_logprobs": [0.3],
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}
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for i in range(num_results)
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]
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}
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return Response(
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response=ModelChoices(
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choices=[
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LMModelChoice(**choice) # type: ignore
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for choice in response_dict["choices"]
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]
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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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