94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import unittest
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from typing import List
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import torch
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from sglang.srt.sampling.penaltylib.penalizers.presence_penalty import (
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BatchedPresencePenalizer,
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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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class BaseBatchedPresencePenalizerTest(BaseBatchedPenalizerTest):
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Penalizer = BatchedPresencePenalizer
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presence_penalty: float
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def setUp(self):
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if self.__class__ == BaseBatchedPresencePenalizerTest:
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self.skipTest("Base class for presence_penalty tests")
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super().setUp()
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def _create_subject(self, presence_penalty: float) -> Subject:
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return Subject(
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sampling_params=MockSamplingParams(
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presence_penalty=presence_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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"presence_penalties": self.tensor(
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[[presence_penalty] * self.vocab_size], dtype=torch.float32
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),
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"cumulated_presence_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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"presence_penalties": self.tensor(
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[[presence_penalty] * self.vocab_size], dtype=torch.float32
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),
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"cumulated_presence_penalties": self.tensor(
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[
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[
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presence_penalty 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 - presence_penalty 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) -> List[Subject]:
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self.enabled = self._create_subject(presence_penalty=self.presence_penalty)
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self.disabled = self._create_subject(presence_penalty=0.0)
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class TestBatchedPresencePenalizerPositiveValue(BaseBatchedPresencePenalizerTest):
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presence_penalty = 0.12
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class TestBatchedPresencePenalizerNegativeValue(BaseBatchedPresencePenalizerTest):
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presence_penalty = -0.12
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if __name__ == "__main__":
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unittest.main()
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