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46 lines
2.1 KiB
Python
46 lines
2.1 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 typing import List, Optional
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from vllm.sequence import SequenceGroupMetadata
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from vllm.worker.model_runner_base import (ModelRunnerBase,
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ModelRunnerInputBase,
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ModelRunnerWrapperBase)
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class TargetModelRunner(ModelRunnerWrapperBase):
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"""Specialized model runner for speculative decoding target model.
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In speculative decoding, the log probabilities selected finally may not
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be the same ones as selected by the target model sampling. This means
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that the time spent in the log probability calculation of the target model
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is time wasted, since we calculate log probabilities after deciding which
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tokens are accepted. For this reason disabling log probabilities in the
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target model will make decode faster. The model runner sets the
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SamplingMetadata parameters according to whether log probabilities are
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requested or not.
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"""
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def __init__(self, model_runner: ModelRunnerBase):
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# An internal boolean member variable to indicate if token log
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# probabilities are needed or not.
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super().__init__(model_runner)
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self.disable_logprobs = True
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def prepare_model_input(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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virtual_engine: int = 0,
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finished_requests_ids: Optional[List[str]] = None,
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) -> ModelRunnerInputBase:
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model_input: ModelRunnerInputBase =\
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self.model_runner.prepare_model_input(
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seq_group_metadata_list, virtual_engine, finished_requests_ids)
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# If token log probabilities is disabled then skip generating sampler
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# CPU output. We directly serialize the GPU sampled_token_id tensors
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# as needed. If log probabilities is enabled then synchronize all the
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# sampling related tensors which includes the logprobs tensors.
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model_input.sampling_metadata.skip_sampler_cpu_output = (
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self.disable_logprobs)
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return model_input
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