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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
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88
tests/e2e/singlecard/test_guided_decoding.py
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@@ -17,7 +17,7 @@
# limitations under the License.
#
import json
from typing import Any, Dict
from typing import Any
import jsonschema
import pytest
@@ -34,8 +34,10 @@ GuidedDecodingBackend = ["xgrammar", "guidance", "outlines"]
@pytest.fixture(scope="module")
def sample_regex():
return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
return (
r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)"
)
@pytest.fixture(scope="module")
@@ -43,66 +45,41 @@ def sample_json_schema():
return {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"skills": {
"type": "array",
"items": {
"type": "string",
"maxLength": 10
},
"minItems": 3
},
"name": {"type": "string"},
"age": {"type": "integer"},
"skills": {"type": "array", "items": {"type": "string", "maxLength": 10}, "minItems": 3},
"work_history": {
"type": "array",
"items": {
"type": "object",
"properties": {
"company": {
"type": "string"
},
"duration": {
"type": "number"
},
"position": {
"type": "string"
}
"company": {"type": "string"},
"duration": {"type": "number"},
"position": {"type": "string"},
},
"required": ["company", "position"]
}
}
"required": ["company", "position"],
},
},
},
"required": ["name", "age", "skills", "work_history"]
"required": ["name", "age", "skills", "work_history"],
}
@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
def test_guided_json_completion(guided_decoding_backend: str,
sample_json_schema):
runner_kwargs: Dict[str, Any] = {}
def test_guided_json_completion(guided_decoding_backend: str, sample_json_schema):
runner_kwargs: dict[str, Any] = {}
sampling_params = SamplingParams(
temperature=1.0,
max_tokens=500,
structured_outputs=StructuredOutputsParams(json=sample_json_schema))
temperature=1.0, max_tokens=500, structured_outputs=StructuredOutputsParams(json=sample_json_schema)
)
runner_kwargs = {
"cudagraph_capture_sizes": [1, 2, 4, 8],
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
"structured_outputs_config": {"backend": guided_decoding_backend},
}
with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
prompts = [
f"Give an example JSON for an employee profile "
f"that fits this schema: {sample_json_schema}"
] * 2
prompts = [f"Give an example JSON for an employee profile that fits this schema: {sample_json_schema}"] * 2
inputs = vllm_model.get_inputs(prompts)
outputs = vllm_model.model.generate(inputs,
sampling_params=sampling_params)
outputs = vllm_model.model.generate(inputs, sampling_params=sampling_params)
assert outputs is not None
@@ -115,34 +92,27 @@ def test_guided_json_completion(guided_decoding_backend: str,
assert generated_text is not None
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
output_json = json.loads(generated_text)
jsonschema.validate(instance=output_json,
schema=sample_json_schema)
jsonschema.validate(instance=output_json, schema=sample_json_schema)
@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
def test_guided_regex(guided_decoding_backend: str, sample_regex):
if guided_decoding_backend == "outlines":
pytest.skip("Outlines doesn't support regex-based guided decoding.")
runner_kwargs: Dict[str, Any] = {}
runner_kwargs: dict[str, Any] = {}
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
structured_outputs=StructuredOutputsParams(regex=sample_regex))
temperature=0.8, top_p=0.95, structured_outputs=StructuredOutputsParams(regex=sample_regex)
)
runner_kwargs = {
"cudagraph_capture_sizes": [1, 2, 4, 8],
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
"structured_outputs_config": {"backend": guided_decoding_backend},
}
with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
prompts = [
f"Give an example IPv4 address with this regex: {sample_regex}"
] * 2
prompts = [f"Give an example IPv4 address with this regex: {sample_regex}"] * 2
inputs = vllm_model.get_inputs(prompts)
outputs = vllm_model.model.generate(inputs,
sampling_params=sampling_params)
outputs = vllm_model.model.generate(inputs, sampling_params=sampling_params)
assert outputs is not None
for output in outputs:
assert output is not None