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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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168
tests/e2e/singlecard/test_aclgraph_accuracy.py
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@@ -15,20 +15,22 @@
# limitations under the License.
#
import pytest
# ruff: noqa: E501
import os
from tests.e2e.singlecard.utils import (PROMPTS_LONG, PROMPTS_SHORT,
LLMTestCase, gen_and_valid)
import pytest
from tests.e2e.singlecard.utils import PROMPTS_LONG, PROMPTS_SHORT, LLMTestCase, gen_and_valid
CASE_QWEN_ACLGRAPH = LLMTestCase(
model="Qwen/Qwen3-0.6B",
prompts=PROMPTS_SHORT,
golden_answers=[
" Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the",
' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
' Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of',
' not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and'
" the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president",
" Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of",
" not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and",
],
)
@@ -37,10 +39,10 @@ CASE_DS_ACLGRAPH = LLMTestCase(
quantization="ascend",
prompts=PROMPTS_SHORT,
golden_answers=[
'\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2',
' a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the',
' Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art',
' here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of'
"\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2",
" a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the",
" Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art",
" here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of",
],
)
@@ -49,9 +51,9 @@ CASE_QWEN_FULL = LLMTestCase(
prompts=PROMPTS_SHORT,
golden_answers=[
" Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the",
' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
' Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of',
' not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and'
" the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president",
" Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of",
" not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and",
],
)
@@ -60,10 +62,10 @@ CASE_DS_FULL = LLMTestCase(
quantization="ascend",
prompts=PROMPTS_SHORT,
golden_answers=[
'\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2',
' a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the',
' Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art',
' here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of'
"\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2",
" a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the",
" Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art",
" here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of",
],
)
@@ -71,10 +73,11 @@ CASE_QWEN_FULL_DECODE_ONLY = LLMTestCase(
model="Qwen/Qwen3-0.6B",
prompts=PROMPTS_LONG,
golden_answers=[
' \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the',
" \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the",
" \n\nTo solve this problem, we can use the fact that the expected value of the area of a triangle with vertices on a square can be calculated by integrating over",
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can'
])
" \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can",
],
)
CASE_DS_FULL_DECODE_ONLY = LLMTestCase(
model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
@@ -83,26 +86,31 @@ CASE_DS_FULL_DECODE_ONLY = LLMTestCase(
golden_answers=[
"\n\nSelect an assignment template",
"\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use",
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations"
])
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations",
],
)
CASE_QWEN_EX = LLMTestCase(
model="Qwen/Qwen3-0.6B",
prompts=PROMPTS_LONG,
golden_answers=[
' \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the',
" \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the",
" \n\nTo solve this problem, we can use the fact that the expected value of the area of a triangle with vertices on a square can be calculated by integrating over",
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can'
])
" \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can",
],
)
CASE_DS_EX = LLMTestCase(
model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
quantization="ascend",
prompts=PROMPTS_LONG,
golden_answers=[
"\n\nSelect an assignment template",
"\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use",
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations",
],
)
CASE_DS_EX = LLMTestCase(model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
quantization="ascend",
prompts=PROMPTS_LONG,
golden_answers=[
"\n\nSelect an assignment template",
"\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use",
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations"
])
@pytest.mark.parametrize("cur_case", [CASE_QWEN_ACLGRAPH, CASE_DS_ACLGRAPH])
def test_piecewise_res_consistency(cur_case: LLMTestCase):
@@ -112,51 +120,48 @@ def test_piecewise_res_consistency(cur_case: LLMTestCase):
"cudagraph_capture_sizes": [1, 2, 4, 8],
"quantization": cur_case.quantization,
}
gen_and_valid(runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers)
gen_and_valid(
runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers,
)
@pytest.mark.parametrize(
"cur_case", [CASE_QWEN_FULL, CASE_DS_FULL])
@pytest.mark.parametrize("cur_case", [CASE_QWEN_FULL, CASE_DS_FULL])
def test_full_res_consistency(cur_case: LLMTestCase, monkeypatch):
monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
runner_kwargs = {
"model_name": cur_case.model,
"max_model_len": 1024,
"compilation_config": {
"cudagraph_capture_sizes": [4, 8, 32, 64],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
"compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"},
"quantization": cur_case.quantization,
}
gen_and_valid(runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers)
gen_and_valid(
runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers,
)
@pytest.mark.parametrize(
"cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY])
@pytest.mark.parametrize("cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY])
def test_full_decode_only_res_consistency(cur_case: LLMTestCase, monkeypatch):
monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
runner_kwargs = {
"model_name": cur_case.model,
"max_model_len": 1024,
"compilation_config": {
"cudagraph_capture_sizes": [4, 8, 32, 64],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
"compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"},
"quantization": cur_case.quantization,
"additional_config": {
"npugraph_ex_config": {
"enable": False
}
},
"additional_config": {"npugraph_ex_config": {"enable": False}},
}
gen_and_valid(runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers)
gen_and_valid(
runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers,
)
@pytest.mark.parametrize("cur_case", [CASE_QWEN_EX, CASE_DS_EX])
def test_npugraph_ex_res_consistency(cur_case: LLMTestCase, monkeypatch):
@@ -165,20 +170,16 @@ def test_npugraph_ex_res_consistency(cur_case: LLMTestCase, monkeypatch):
"model_name": cur_case.model,
"quantization": cur_case.quantization,
"max_model_len": 1024,
"compilation_config": {
"cudagraph_capture_sizes": [4, 8, 32, 64],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
"additional_config": {
"npugraph_ex_config": {
"enable": True
}
},
"compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"},
"additional_config": {"npugraph_ex_config": {"enable": True}},
}
gen_and_valid(runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers)
gen_and_valid(
runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers,
)
# The accuracy has already been verified in the previous test case.
# This test case is used to check whether the functionality works properly
@@ -190,10 +191,7 @@ def test_npugraph_ex_with_static_kernel(cur_case: LLMTestCase, monkeypatch):
"model_name": cur_case.model,
"quantization": cur_case.quantization,
"max_model_len": 1024,
"compilation_config": {
"cudagraph_capture_sizes": [4, 8],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
"compilation_config": {"cudagraph_capture_sizes": [4, 8], "cudagraph_mode": "FULL_DECODE_ONLY"},
"additional_config": {
"npugraph_ex_config": {
"enable": True,
@@ -201,12 +199,14 @@ def test_npugraph_ex_with_static_kernel(cur_case: LLMTestCase, monkeypatch):
}
},
}
gen_and_valid(runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers)
gen_and_valid(
runner_kwargs=runner_kwargs,
prompts=cur_case.prompts,
sampling_params=cur_case.sampling_params,
golden_answers=cur_case.golden_answers,
)
# Check whether the static kernel is properly uninstall
ascend_home_path = os.environ["ASCEND_HOME_PATH"]
static_kernel_install_path = os.path.join(ascend_home_path, 'opp/static_kernel/ai_core')
static_kernel_install_path = os.path.join(ascend_home_path, "opp/static_kernel/ai_core")
assert not os.path.exists(static_kernel_install_path)