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[Lint]Style: Convert test/ to ruff format(Batch #5) (#6747)

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### What this PR does / why we need it?
| File Path |
| :--- |
| `tests/e2e/singlecard/compile/backend.py` |
| `tests/e2e/singlecard/compile/test_graphex_norm_quant_fusion.py` |
| `tests/e2e/singlecard/compile/test_graphex_qknorm_rope_fusion.py` |
| `tests/e2e/singlecard/compile/test_norm_quant_fusion.py` |
| `tests/e2e/singlecard/model_runner_v2/test_basic.py` |
| `tests/e2e/singlecard/test_aclgraph_accuracy.py` |
| `tests/e2e/singlecard/test_aclgraph_batch_invariant.py` |
| `tests/e2e/singlecard/test_aclgraph_mem.py` |
| `tests/e2e/singlecard/test_async_scheduling.py` |
| `tests/e2e/singlecard/test_auto_fit_max_mode_len.py` |
| `tests/e2e/singlecard/test_batch_invariant.py` |
| `tests/e2e/singlecard/test_camem.py` |
| `tests/e2e/singlecard/test_completion_with_prompt_embeds.py` |
| `tests/e2e/singlecard/test_cpu_offloading.py` |
| `tests/e2e/singlecard/test_guided_decoding.py` |
| `tests/e2e/singlecard/test_ilama_lora.py` |
| `tests/e2e/singlecard/test_llama32_lora.py` |
| `tests/e2e/singlecard/test_models.py` |
| `tests/e2e/singlecard/test_multistream_overlap_shared_expert.py` |
| `tests/e2e/singlecard/test_quantization.py` |
| `tests/e2e/singlecard/test_qwen3_multi_loras.py` |
| `tests/e2e/singlecard/test_sampler.py` |
| `tests/e2e/singlecard/test_vlm.py` |
| `tests/e2e/singlecard/test_xlite.py` |
| `tests/e2e/singlecard/utils.py` |

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-02-24 15:50:00 +08:00
committed by GitHub
parent 747484cb64
commit 62ea664aa7
26 changed files with 859 additions and 1052 deletions
Download Patch File Download Diff File Expand all files Collapse all files

54
tests/e2e/singlecard/test_vlm.py
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@@ -20,6 +20,7 @@
Run `pytest tests/test_offline_inference.py`.
"""
import os
from unittest.mock import patch
@@ -44,11 +45,13 @@ def test_multimodal_vl(vl_config):
images = [image] * len(img_questions)
prompts = vl_config["prompt_fn"](img_questions)
with VllmRunner(vl_config["model"],
mm_processor_kwargs=vl_config["mm_processor_kwargs"],
max_model_len=8192,
cudagraph_capture_sizes=[1, 2, 4, 8],
limit_mm_per_prompt={"image": 1}) as vllm_model:
with VllmRunner(
vl_config["model"],
mm_processor_kwargs=vl_config["mm_processor_kwargs"],
max_model_len=8192,
cudagraph_capture_sizes=[1, 2, 4, 8],
limit_mm_per_prompt={"image": 1},
) as vllm_model:
outputs = vllm_model.generate_greedy(
prompts=prompts,
images=images,
@@ -63,35 +66,30 @@ def test_multimodal_vl(vl_config):
@patch.dict(os.environ, {"VLLM_WORKER_MULTIPROC_METHOD": "spawn"})
def test_multimodal_audio():
audio_prompt = "".join([
f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
for idx in range(2)
])
audio_prompt = "".join([f"Audio {idx + 1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(2)])
question = "What sport and what nursery rhyme are referenced?"
prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
"<|im_start|>user\n"
f"{audio_prompt}{question}<|im_end|>\n"
"<|im_start|>assistant\n")
prompt = (
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
"<|im_start|>user\n"
f"{audio_prompt}{question}<|im_end|>\n"
"<|im_start|>assistant\n"
)
mm_data = {
"audio": [
asset.audio_and_sample_rate for asset in
[AudioAsset("mary_had_lamb"),
AudioAsset("winning_call")]
]
"audio": [asset.audio_and_sample_rate for asset in [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]]
}
inputs = {"prompt": prompt, "multi_modal_data": mm_data}
sampling_params = SamplingParams(temperature=0.2,
max_tokens=10,
stop_token_ids=None)
sampling_params = SamplingParams(temperature=0.2, max_tokens=10, stop_token_ids=None)
with VllmRunner("Qwen/Qwen2-Audio-7B-Instruct",
max_model_len=4096,
max_num_seqs=5,
dtype="bfloat16",
limit_mm_per_prompt={"audio": 2},
cudagraph_capture_sizes=[1, 2, 4, 8],
gpu_memory_utilization=0.9) as runner:
with VllmRunner(
"Qwen/Qwen2-Audio-7B-Instruct",
max_model_len=4096,
max_num_seqs=5,
dtype="bfloat16",
limit_mm_per_prompt={"audio": 2},
cudagraph_capture_sizes=[1, 2, 4, 8],
gpu_memory_utilization=0.9,
) as runner:
outputs = runner.generate(inputs, sampling_params=sampling_params)
assert outputs is not None, "Generated outputs should not be None."