110 lines
3.1 KiB
Python
110 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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from vllm.inputs import TokenInputs, token_inputs
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from vllm.logprobs import Logprob
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from vllm.lora.request import LoRARequest
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from vllm.multimodal.inputs import MultiModalInputs, mm_inputs
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@dataclass
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class BeamSearchSequence:
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"""A sequence for beam search.
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It keeps track of the tokens and the log probability of the sequence.
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The text field is optional and will only be filled when the sequence is
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about to be returned to the user.
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"""
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orig_prompt: TokenInputs | MultiModalInputs
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# The tokens include the prompt.
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tokens: list[int]
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logprobs: list[dict[int, Logprob]]
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lora_request: LoRARequest | None = None
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cum_logprob: float = 0.0
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text: str | None = None
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finish_reason: str | None = None
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stop_reason: int | str | None = None
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def get_prompt(self):
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prompt = self.orig_prompt
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prompt_text = prompt.get("prompt")
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cache_salt = prompt.get("cache_salt")
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if prompt["type"] == "token":
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return token_inputs(
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self.tokens,
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prompt=prompt_text,
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cache_salt=cache_salt,
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)
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return mm_inputs(
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prompt_token_ids=self.tokens,
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mm_kwargs=prompt["mm_kwargs"],
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mm_hashes=prompt["mm_hashes"],
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mm_placeholders=prompt["mm_placeholders"],
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prompt=prompt_text,
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cache_salt=cache_salt,
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)
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@dataclass
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class BeamSearchOutput:
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"""The output of beam search.
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It contains the list of the best beam search sequences.
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The length of the list is equal to the beam width.
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"""
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sequences: list[BeamSearchSequence]
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class BeamSearchInstance:
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def __init__(
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self,
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prompt: TokenInputs | MultiModalInputs,
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lora_request: LoRARequest | None = None,
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logprobs: list[dict[int, Logprob]] | None = None,
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**kwargs,
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):
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self.beams: list[BeamSearchSequence] = [
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BeamSearchSequence(
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orig_prompt=prompt,
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tokens=prompt["prompt_token_ids"],
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logprobs=[] if logprobs is None else list(logprobs),
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lora_request=lora_request,
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**kwargs,
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)
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]
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self.completed: list[BeamSearchSequence] = []
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def get_beam_search_score(
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tokens: list[int],
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cumulative_logprob: float,
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eos_token_id: int,
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length_penalty: float = 1.0,
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) -> float:
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"""Calculate the beam search score with length penalty.
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Adapted from
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https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938
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"""
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seq_len = len(tokens)
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if tokens[-1] == eos_token_id:
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seq_len -= 1
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return cumulative_logprob / (seq_len**length_penalty)
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def create_sort_beams_key_function(eos_token_id: int, length_penalty: float):
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def sort_beams_key(x: BeamSearchSequence) -> float:
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return get_beam_search_score(
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x.tokens, x.cum_logprob, eos_token_id, length_penalty
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)
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return sort_beams_key
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