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[UT][intel GPU] use current_platform instead of device hardcode in v1 tests (#20169)

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Signed-off-by: Ma, Liangliang <liangliang.ma@intel.com>
This commit is contained in:
Liangliang Ma
2025-07-02 09:06:04 +08:00
committed by GitHub
parent 3be8d312a2
commit a0389e0554
10 changed files with 44 additions and 26 deletions
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23
tests/v1/spec_decode/test_eagle.py
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@@ -10,6 +10,7 @@ from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, ModelConfig,
ParallelConfig, SchedulerConfig, SpeculativeConfig,
VllmConfig)
from vllm.model_executor.models.llama import LlamaForCausalLM
from vllm.platforms import current_platform
from vllm.v1.spec_decode.eagle import EagleProposer
model_dir = "meta-llama/Llama-3.1-8B-Instruct"
@@ -38,15 +39,17 @@ def _create_proposer(method: str, k: int) -> EagleProposer:
num_speculative_tokens=k,
)
vllm_config = VllmConfig(model_config=model_config,
cache_config=CacheConfig(),
speculative_config=speculative_config,
device_config=DeviceConfig(device="cuda"),
parallel_config=ParallelConfig(),
load_config=LoadConfig(),
scheduler_config=SchedulerConfig())
vllm_config = VllmConfig(
model_config=model_config,
cache_config=CacheConfig(),
speculative_config=speculative_config,
device_config=DeviceConfig(device=current_platform.device_type),
parallel_config=ParallelConfig(),
load_config=LoadConfig(),
scheduler_config=SchedulerConfig())
return EagleProposer(vllm_config=vllm_config, device='cuda')
return EagleProposer(vllm_config=vllm_config,
device=current_platform.device_type)
def test_prepare_inputs():
@@ -59,7 +62,7 @@ def test_prepare_inputs():
a, a + 1, ..., a + b - n2 - 1,
a + b, a + b + 1, ..., a + b + c - n3 - 1]
"""
device = torch.device('cuda')
device = torch.device(current_platform.device_type)
# a = 4, b = 7, c = 5
# n1 = 1, n2 = 3, n3 = 2
@@ -198,7 +201,7 @@ def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method,
@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 8])
def test_propose(num_speculative_tokens):
# Use GPU device
device = torch.device('cuda')
device = torch.device(current_platform.device_type)
# Setup test parameters
batch_size = 2