81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
import typing
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import unittest
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import torch
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from sglang.srt.sampling.penaltylib.penalizers.frequency_penalty import (
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BatchedFrequencyPenalizer,
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)
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from sglang.test.srt.sampling.penaltylib.utils import (
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BaseBatchedPenalizerTest,
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MockSamplingParams,
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Step,
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StepType,
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Subject,
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)
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FREQUENCY_PENALTY = 0.12
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class TestBatchedFrequencyPenalizer(BaseBatchedPenalizerTest):
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Penalizer = BatchedFrequencyPenalizer
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def _create_subject(self, frequency_penalty: float) -> Subject:
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return Subject(
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sampling_params=MockSamplingParams(
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frequency_penalty=frequency_penalty,
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),
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steps=[
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Step(
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type=StepType.INPUT,
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token_ids=[0, 1, 2],
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expected_tensors={
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"frequency_penalties": self.tensor(
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[[frequency_penalty] * self.vocab_size], dtype=torch.float32
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),
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"cumulated_frequency_penalties": self.tensor(
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[[0.0] * self.vocab_size], dtype=torch.float32
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),
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},
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expected_logits=self.tensor(
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[[1] * self.vocab_size], dtype=torch.float32
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),
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),
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Step(
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type=StepType.OUTPUT,
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token_ids=[1, 2, 2],
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expected_tensors={
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"frequency_penalties": self.tensor(
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[[frequency_penalty] * self.vocab_size], dtype=torch.float32
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),
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"cumulated_frequency_penalties": self.tensor(
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[
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[
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frequency_penalty * i if i in {1, 2} else 0.0
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for i in range(self.vocab_size)
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],
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],
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dtype=torch.float32,
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),
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},
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expected_logits=self.tensor(
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[
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[
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1.0 - frequency_penalty * i if i in {1, 2} else 1.0
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for i in range(self.vocab_size)
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],
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],
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dtype=torch.float32,
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),
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),
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],
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)
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def create_test_subjects(self) -> typing.List[Subject]:
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self.enabled = self._create_subject(frequency_penalty=FREQUENCY_PENALTY)
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self.disabled = self._create_subject(frequency_penalty=0.0)
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if __name__ == "__main__":
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unittest.main()
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