52 lines
2.5 KiB
Python
52 lines
2.5 KiB
Python
from typing import Tuple
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from flask import jsonify
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from vllm import SamplingParams
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from llm_server.database.log_to_db import log_to_db
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from llm_server.llm.llm_backend import LLMBackend
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class VLLMBackend(LLMBackend):
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_default_params = vars(SamplingParams())
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def handle_response(self, success, request, response_json_body, response_status_code, client_ip, token, prompt: str, elapsed_time, parameters, headers):
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if len(response_json_body.get('text', [])):
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# Does vllm return the prompt and the response together???
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backend_response = response_json_body['text'][0].split(prompt)[1].strip(' ').strip('\n')
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else:
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# Failsafe
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backend_response = ''
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log_to_db(ip=client_ip, token=token, prompt=prompt, response=backend_response, gen_time=elapsed_time, parameters=parameters, headers=headers, backend_response_code=response_status_code, request_url=request.url,
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response_tokens=response_json_body.get('details', {}).get('generated_tokens'), backend_url=self.backend_url)
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return jsonify({'results': [{'text': backend_response}]}), 200
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def get_parameters(self, parameters) -> Tuple[dict | None, str | None]:
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try:
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# top_k == -1 means disabled
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top_k = parameters.get('top_k', self._default_params['top_k'])
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if top_k <= 0:
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top_k = -1
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sampling_params = SamplingParams(
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temperature=parameters.get('temperature', self._default_params['temperature']),
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top_p=parameters.get('top_p', self._default_params['top_p']),
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top_k=top_k,
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use_beam_search=True if parameters.get('num_beams', 0) > 1 else False,
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stop=list(set(parameters.get('stopping_strings') or parameters.get('stop', self._default_params['stop']))),
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ignore_eos=parameters.get('ban_eos_token', False),
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max_tokens=parameters.get('max_new_tokens') or parameters.get('max_tokens', self._default_params['max_tokens']),
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presence_penalty=parameters.get('presence_penalty', self._default_params['presence_penalty']),
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frequency_penalty=parameters.get('frequency_penalty', self._default_params['frequency_penalty'])
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)
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except ValueError as e:
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return None, str(e).strip('.')
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# We use max_new_tokens throughout the server.
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result = vars(sampling_params)
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result['max_new_tokens'] = result.pop('max_tokens')
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return result, None
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