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xc-llm-ascend/tests/ut/torchair/test_torchair_model_runner.py

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from unittest.mock import MagicMock, Mock
import pytest
import torch
from pytest_mock import MockerFixture
from vllm.config import VllmConfig
from tests.ut.base import PytestBase
from vllm_ascend.torchair.torchair_model_runner import NPUTorchairModelRunner
class TestNPUTorchairModelRunner(PytestBase):
@pytest.fixture
def setup_npu_torchair_model_runner(self, mocker: MockerFixture):
mocker.patch.object(NPUTorchairModelRunner, "__init__",
lambda self, *args, **kwargs: None)
runner = NPUTorchairModelRunner(Mock(), Mock())
runner.device = torch.device("cpu")
runner.vllm_config = MagicMock(spec=VllmConfig)
runner.speculative_config = MagicMock(
method="deepseek_mtp",
num_speculative_tokens=4,
disable_padded_drafter_batch=False)
runner.ascend_config = MagicMock(enable_shared_expert_dp=False,
torchair_graph_config=MagicMock(
use_cached_graph=True,
graph_batch_sizes=[1, 2, 4]))
runner.decode_token_per_req = 2
runner.is_kv_consumer = True
runner.max_num_reqs = 100
runner.model_config = MagicMock(hf_config=MagicMock(index_topk=2))
runner.attn_backend = MagicMock(get_builder_cls=lambda: Mock())
return runner
def test_init(self, mocker: MockerFixture,
setup_npu_torchair_model_runner):
runner = setup_npu_torchair_model_runner
assert isinstance(runner, NPUTorchairModelRunner)