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[Refactor] Import global var form vllm instead of overwirte it (#5469)

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### What this PR does / why we need it?
Import global var form vllm instead of overwirte it, so that we could
use the correct global variant value

- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2026-01-07 18:41:45 +08:00
committed by GitHub
parent 380f089fbf
commit 3f4f2b4ae6
10 changed files with 7 additions and 157 deletions
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40
tests/ut/sample/logits_processor/test_builtin.py
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import torch
from pytest_mock import MockerFixture
from vllm.config import SchedulerConfig, VllmConfig
from tests.ut.base import PytestBase
from vllm_ascend.sample.logits_processor import AscendMinPLogitsProcessor
class TestMinPLogitsProcessorInitFunc(PytestBase):
def test_init_func_with_decode_max_num_seqs(self, mocker: MockerFixture):
device_cpu = torch.device("cpu")
device_npu = torch.device("npu")
is_pin_memory = False
mock_vllm_config = mocker.MagicMock(spec=VllmConfig)
mock_scheduler_config = mocker.MagicMock(spec=SchedulerConfig)
mock_scheduler_config.decode_max_num_seqs = 0
mock_scheduler_config.max_num_seqs = 128
mock_vllm_config.scheduler_config = mock_scheduler_config
# torch.zeros/torch.empty returns error on online ut machine, so mock it
mock_tensor = torch.zeros((256, ),
dtype=torch.float32,
pin_memory=False)
mocker.patch("torch.zeros", return_value=mock_tensor)
mock_empty_tensor = torch.empty((256, ), dtype=torch.float32)
mocker.patch("torch.empty", return_value=mock_empty_tensor)
processor_cpu = AscendMinPLogitsProcessor(mock_vllm_config, device_cpu,
is_pin_memory)
assert processor_cpu.min_p is not None
assert processor_cpu.use_double_tensor is False
assert processor_cpu.min_p_cpu.shape[0] == 256
processor_cpu = AscendMinPLogitsProcessor(mock_vllm_config, device_npu,
is_pin_memory)
assert processor_cpu.min_p is not None
assert processor_cpu.use_double_tensor is True
assert processor_cpu.min_p_cpu.shape[0] == 256