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[1/N][CI] Move linting system to pre-commits hooks (#1256)

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### What this PR does / why we need it?

Follow vllm-project/vllm lint way:
https://github.com/vllm-project/vllm/blob/main/.pre-commit-config.yaml

Enable pre-commit to avoid some low level error  AMAP.

This pr is one step of #1241, The purpose is make linting system more
clear and convenient, on this step, Mainly did the following things:
yapf, actionlint, ruff, typos, isort, mypy, png-lint, signoff-commit,
enforce-import-regex-instead-of-re.

TODO: 
- clang-format(check for csrc with google style)
need clean code, disable for now 
- pymarkdown
need clean code, disable for now 
- shellcheck
need clean code, disable for now 

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

Only developer UX change:

https://vllm-ascend--1256.org.readthedocs.build/en/1256/developer_guide/contributing.html#run-lint-locally

```
pip install -r requirements-lint.txt && pre-commit install
bash format.sh
```

### How was this patch tested?

CI passed with new added/existing test.

Co-authored-by: Yikun [yikunkero@gmail.com](mailto:yikunkero@gmail.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
- vLLM version: v0.9.1
- vLLM main:
5358cce5ff

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2025-07-10 14:17:15 +08:00
committed by GitHub
parent 643e6f5486
commit c7446438a9
28 changed files with 753 additions and 667 deletions
Download Patch File Download Diff File Expand all files Collapse all files

130
benchmarks/scripts/run_accuracy.py
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@@ -44,82 +44,72 @@ BATCH_SIZE = {"ceval-valid": 1, "mmlu": 1, "gsm8k": "auto", "mmmu_val": 1}
MODEL_TYPE = {
"Qwen/Qwen3-8B-Base": "vllm",
"Qwen/Qwen3-30B-A3B": "vllm",
"Qwen/Qwen2.5-VL-7B-Instruct": "vllm-vlm"
"Qwen/Qwen2.5-VL-7B-Instruct": "vllm-vlm",
}
# Command templates for running evaluations
MODEL_RUN_INFO = {
"Qwen/Qwen3-30B-A3B":
("export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True'\n"
"lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
),
"Qwen/Qwen3-8B-Base":
("export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6'\n"
"lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
),
"Qwen/Qwen2.5-VL-7B-Instruct":
("export MODEL_ARGS='pretrained={model},max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2'\n"
"lm_eval --model vllm-vlm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --batch_size 1"),
"Qwen/Qwen3-30B-A3B": (
"export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True'\n"
"lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
),
"Qwen/Qwen3-8B-Base": (
"export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6'\n"
"lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
),
"Qwen/Qwen2.5-VL-7B-Instruct": (
"export MODEL_ARGS='pretrained={model},max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2'\n"
"lm_eval --model vllm-vlm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
"--apply_chat_template --fewshot_as_multiturn --batch_size 1"
),
}
# Evaluation metric filters per task
FILTER = {
"gsm8k": "exact_match,flexible-extract",
"ceval-valid": "acc,none",
"mmmu_val": "acc,none"
"mmmu_val": "acc,none",
}
# Expected accuracy values for models
EXPECTED_VALUE = {
"Qwen/Qwen3-30B-A3B": {
"ceval-valid": 0.83,
"gsm8k": 0.85
},
"Qwen/Qwen3-8B-Base": {
"ceval-valid": 0.82,
"gsm8k": 0.83
},
"Qwen/Qwen2.5-VL-7B-Instruct": {
"mmmu_val": 0.51
}
"Qwen/Qwen3-30B-A3B": {"ceval-valid": 0.83, "gsm8k": 0.85},
"Qwen/Qwen3-8B-Base": {"ceval-valid": 0.82, "gsm8k": 0.83},
"Qwen/Qwen2.5-VL-7B-Instruct": {"mmmu_val": 0.51},
}
PARALLEL_MODE = {
"Qwen/Qwen3-8B-Base": "TP",
"Qwen/Qwen2.5-VL-7B-Instruct": "TP",
"Qwen/Qwen3-30B-A3B": "EP"
"Qwen/Qwen3-30B-A3B": "EP",
}
# Execution backend configuration
EXECUTION_MODE = {
"Qwen/Qwen3-8B-Base": "ACLGraph",
"Qwen/Qwen2.5-VL-7B-Instruct": "ACLGraph",
"Qwen/Qwen3-30B-A3B": "ACLGraph"
"Qwen/Qwen3-30B-A3B": "ACLGraph",
}
# Model arguments for evaluation
MODEL_ARGS = {
"Qwen/Qwen3-8B-Base":
"pretrained=Qwen/Qwen3-8B-Base,max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6",
"Qwen/Qwen2.5-VL-7B-Instruct":
"pretrained=Qwen/Qwen2.5-VL-7B-Instruct,max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2",
"Qwen/Qwen3-30B-A3B":
"pretrained=Qwen/Qwen3-30B-A3B,max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True"
"Qwen/Qwen3-8B-Base": "pretrained=Qwen/Qwen3-8B-Base,max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6",
"Qwen/Qwen2.5-VL-7B-Instruct": "pretrained=Qwen/Qwen2.5-VL-7B-Instruct,max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2",
"Qwen/Qwen3-30B-A3B": "pretrained=Qwen/Qwen3-30B-A3B,max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True",
}
# Whether to apply chat template formatting
APPLY_CHAT_TEMPLATE = {
"Qwen/Qwen3-8B-Base": True,
"Qwen/Qwen2.5-VL-7B-Instruct": True,
"Qwen/Qwen3-30B-A3B": False
"Qwen/Qwen3-30B-A3B": False,
}
# Few-shot examples handling as multi-turn dialogues.
FEWSHOT_AS_MULTITURN = {
"Qwen/Qwen3-8B-Base": True,
"Qwen/Qwen2.5-VL-7B-Instruct": True,
"Qwen/Qwen3-30B-A3B": False
"Qwen/Qwen3-30B-A3B": False,
}
# Relative tolerance for accuracy checks
@@ -136,7 +126,7 @@ def run_accuracy_test(queue, model, dataset):
"tasks": dataset,
"apply_chat_template": APPLY_CHAT_TEMPLATE[model],
"fewshot_as_multiturn": FEWSHOT_AS_MULTITURN[model],
"batch_size": BATCH_SIZE[dataset]
"batch_size": BATCH_SIZE[dataset],
}
if MODEL_TYPE[model] == "vllm":
@@ -151,7 +141,7 @@ def run_accuracy_test(queue, model, dataset):
queue.put(e)
sys.exit(1)
finally:
if 'results' in locals():
if "results" in locals():
del results
gc.collect()
torch.npu.empty_cache()
@@ -161,16 +151,15 @@ def run_accuracy_test(queue, model, dataset):
def generate_md(model_name, tasks_list, args, datasets):
"""Generate Markdown report with evaluation results"""
# Format the run command
run_cmd = MODEL_RUN_INFO[model_name].format(model=model_name,
datasets=datasets)
run_cmd = MODEL_RUN_INFO[model_name].format(model=model_name, datasets=datasets)
model = model_name.split("/")[1]
# Version information section
version_info = (
f"**vLLM Version**: vLLM: {args.vllm_version} "
f"([{args.vllm_commit}]({VLLM_URL+args.vllm_commit})), "
f"([{args.vllm_commit}]({VLLM_URL + args.vllm_commit})), "
f"vLLM Ascend: {args.vllm_ascend_version} "
f"([{args.vllm_ascend_commit}]({VLLM_ASCEND_URL+args.vllm_ascend_commit})) "
f"([{args.vllm_ascend_commit}]({VLLM_ASCEND_URL + args.vllm_ascend_commit})) "
)
# Report header with system info
@@ -218,21 +207,39 @@ def generate_md(model_name, tasks_list, args, datasets):
else:
n_shot = "0"
flag = ACCURACY_FLAG.get(task_name, "")
row = (f"| {task_name:<37} "
f"| {flt:<6} "
f"| {n_shot:6} "
f"| {metric:<6} "
f"| {flag}{value:>5.4f} "
f"| ± {stderr:>5.4f} |")
row = (
f"| {task_name:<37} "
f"| {flt:<6} "
f"| {n_shot:6} "
f"| {metric:<6} "
f"| {flag}{value:>5.4f} "
f"| ± {stderr:>5.4f} |"
)
if not task_name.startswith("-"):
rows.append(row)
rows_sub.append("<details>" + "\n" + "<summary>" + task_name +
" details" + "</summary>" + "\n" * 2 + header)
rows_sub.append(
"<details>"
+ "\n"
+ "<summary>"
+ task_name
+ " details"
+ "</summary>"
+ "\n" * 2
+ header
)
rows_sub.append(row)
rows_sub.append("</details>")
# Combine all Markdown sections
md = preamble + "\n" + header + "\n" + "\n".join(rows) + "\n" + "\n".join(
rows_sub) + "\n"
md = (
preamble
+ "\n"
+ header
+ "\n"
+ "\n".join(rows)
+ "\n"
+ "\n".join(rows_sub)
+ "\n"
)
print(md)
return md
@@ -262,8 +269,9 @@ def main(args):
# Evaluate model on each dataset
for dataset in datasets:
accuracy_expected = EXPECTED_VALUE[args.model][dataset]
p = multiprocessing.Process(target=run_accuracy_test,
args=(result_queue, args.model, dataset))
p = multiprocessing.Process(
target=run_accuracy_test, args=(result_queue, args.model, dataset)
)
p.start()
p.join()
if p.is_alive():
@@ -274,8 +282,11 @@ def main(args):
time.sleep(10)
result = result_queue.get()
print(result)
if accuracy_expected - RTOL < result[dataset][
FILTER[dataset]] < accuracy_expected + RTOL:
if (
accuracy_expected - RTOL
< result[dataset][FILTER[dataset]]
< accuracy_expected + RTOL
):
ACCURACY_FLAG[dataset] = "✅"
else:
ACCURACY_FLAG[dataset] = "❌"
@@ -285,10 +296,11 @@ def main(args):
if __name__ == "__main__":
multiprocessing.set_start_method('spawn', force=True)
multiprocessing.set_start_method("spawn", force=True)
# Initialize argument parser
parser = argparse.ArgumentParser(
description="Run model accuracy evaluation and generate report")
description="Run model accuracy evaluation and generate report"
)
parser.add_argument("--output", type=str, required=True)
parser.add_argument("--model", type=str, required=True)
parser.add_argument("--vllm_ascend_version", type=str, required=False)