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- from abc import ABC, abstractmethod
- from dataclasses import dataclass
- import torch
- from aphrodite.common.sequence import ExecuteModelRequest
- @dataclass
- class SpeculativeProposals:
- """Datastructure used to represent proposal tokens from some proposer. It
- also tracks how many speculative tokens each sequence has.
- """
- # Speculative proposal tokens.
- proposal_token_ids: torch.Tensor
- # Probabilities of the proposal tokens according to the proposer.
- proposal_probs: torch.Tensor
- # The valid length of each proposal; can be zero.
- proposal_lens: torch.Tensor
- def __repr__(self):
- return (f"SpeculativeProposals("
- f"proposal_token_ids={self.proposal_token_ids}, "
- f"proposal_probs={self.proposal_probs.shape}, "
- f"proposal_lens={self.proposal_lens})")
- @dataclass
- class SpeculativeScores:
- """Datastructure used to represent the scores of speculative tokens
- according to the scoring model.
- """
- # Probabilities of the speculative tokens according to the scoring model.
- probs: torch.Tensor
- # Log-probabilities of the speculative tokens according to the scoring
- # model. These values can be used to generate Logprob objects that are
- # returned to the user.
- logprobs: torch.Tensor
- # Token ids sampled from the scoring model. Used for speculative bonus
- # tokens and also non-speculative normal decoding.
- token_ids: torch.Tensor
- def __repr__(self):
- return (f"SpeculativeScores("
- f"probs={self.probs.shape}, "
- f"token_ids={self.token_ids.shape})")
- class SpeculativeProposer(ABC):
- @abstractmethod
- def get_proposals(
- self,
- execute_model_req: ExecuteModelRequest,
- ) -> SpeculativeProposals:
- raise NotImplementedError
- class SpeculativeScorer(ABC):
- @abstractmethod
- def score_proposals(
- self,
- execute_model_req: ExecuteModelRequest,
- proposals: SpeculativeProposals,
- ) -> SpeculativeScores:
- raise NotImplementedError
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