71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
from typing import List, Optional
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from tqdm import tqdm
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from cacheflow.outputs import RequestOutput
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from cacheflow.sampling_params import SamplingParams
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from cacheflow.server.arg_utils import ServerArgs
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from cacheflow.server.llm_server import LLMServer
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from cacheflow.utils import Counter
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class LLM:
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def __init__(
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self,
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model: str,
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tensor_parallel_size: int = 1,
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dtype: str = "default",
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seed: int = 0,
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**kwargs,
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) -> None:
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if "disable_log_stats" not in kwargs:
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kwargs["disable_log_stats"] = True
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server_args = ServerArgs(
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model=model,
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tensor_parallel_size=tensor_parallel_size,
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dtype=dtype,
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seed=seed,
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**kwargs,
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)
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self.llm_server = LLMServer.from_server_args(server_args)
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self.request_counter = Counter()
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def generate(
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self,
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prompts: List[str],
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sampling_params: Optional[SamplingParams] = None,
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prompt_token_ids: Optional[List[List[int]]] = None,
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use_tqdm: bool = True,
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) -> List[RequestOutput]:
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if sampling_params is None:
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# Use default sampling params.
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sampling_params = SamplingParams()
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# Initialize tqdm.
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if use_tqdm:
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pbar = tqdm(total=len(prompts), desc="Processed prompts")
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# Add requests to the server.
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for i in range(len(prompts)):
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prompt = prompts[i]
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if prompt_token_ids is None:
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token_ids = None
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else:
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token_ids = prompt_token_ids[i]
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request_id = str(next(self.request_counter))
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self.llm_server.add_request(request_id, prompt, sampling_params,
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token_ids)
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# Run the server.
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outputs: List[RequestOutput] = []
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while self.llm_server.has_unfinished_requests():
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step_outputs = self.llm_server.step()
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for output in step_outputs:
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if output.done:
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outputs.append(output)
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if use_tqdm:
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pbar.update(1)
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if use_tqdm:
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pbar.close()
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return outputs
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