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[CI]: Remove unnecessary imports from test_lmache_integration (#30157)

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Signed-off-by: Samuel Shen <slshen@uchicago.edu>
Co-authored-by: Samuel Shen <slshen@uchicago.edu>
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Samuel Shen 2025-12-05 20:53:34 -08:00 committed by GitHub
parent dc839ad03d
commit 7e31c3a3f6
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1 changed files with 2 additions and 60 deletions
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62
tests/v1/kv_connector/unit/test_lmcache_integration.py
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@ -64,22 +64,6 @@ def test_multimodal_interface():
assumes(PlaceholderRange, "offset")
assumes(PlaceholderRange, "length")
# test a minimal case
import torch
from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
apply_mm_hashes_to_token_ids,
)
token_ids = torch.arange(10, dtype=torch.long)
mm_hashes = ["0000", "1111"] # hex repr of 0 and 4369
mm_positions = [
PlaceholderRange(offset=0, length=4),
PlaceholderRange(offset=5, length=4),
]
apply_mm_hashes_to_token_ids(token_ids, mm_hashes, mm_positions)
assert token_ids.tolist() == [0, 0, 0, 0, 4, 4369, 4369, 4369, 4369, 9]
@pytest.mark.skipif(
current_platform.is_rocm(), reason="Requires libcudart.so, not available on ROCm"
@ -122,16 +106,6 @@ def test_config_interface():
assumes(CacheConfig, "block_size")
assumes(CacheConfig, "gpu_memory_utilization")
# mla metadata minimal cases
from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
mla_enabled,
)
model_config = ModelConfig(model="deepseek-ai/DeepSeek-R1")
assert mla_enabled(model_config)
model_config = ModelConfig(model="Qwen/Qwen3-0.6B")
assert not mla_enabled(model_config)
# kv metadata minimal case
from vllm.utils.torch_utils import get_kv_cache_torch_dtype
@ -139,7 +113,7 @@ def test_config_interface():
parallel_config = ParallelConfig()
cache_config = CacheConfig(cache_dtype="bfloat16")
kv_dtype = get_kv_cache_torch_dtype(cache_config.cache_dtype, model_config.dtype)
use_mla = mla_enabled(model_config)
use_mla = False
chunk_size = 256
num_layer = model_config.get_num_layers(parallel_config)
num_kv_head = model_config.get_num_kv_heads(parallel_config)
@ -184,43 +158,11 @@ def test_request_interface():
assumes(req, "num_tokens")
assumes(req, "kv_transfer_params", is_instance_of=(dict, NoneType))
from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalKwargsItem
from vllm.multimodal.inputs import MultiModalFeatureSpec
assumes(MultiModalFeatureSpec, "identifier")
assumes(MultiModalFeatureSpec, "mm_position")
# minimal case:
from vllm.multimodal.inputs import PlaceholderRange
request = Request(
request_id="test_request",
prompt_token_ids=[1, 2, 3],
sampling_params=SamplingParams(max_tokens=10),
pooling_params=None,
eos_token_id=100,
lora_request=None,
mm_features=[
MultiModalFeatureSpec(
modality="image",
identifier="0000",
data=MultiModalKwargsItem.dummy("dummy_m"),
mm_position=PlaceholderRange(offset=0, length=10),
)
],
)
from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
extract_mm_features,
)
mm_hashes, mm_positions = extract_mm_features(request)
assert isinstance(mm_hashes, list)
assert len(mm_hashes) == 1
assert isinstance(mm_positions, list)
assert len(mm_positions) == 1
assert mm_positions[0].offset == 0
assert mm_positions[0].length == 10
def test_new_request_interface():
# protect against interface changes