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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

82
benchmarks/ops/ben_vocabparallelembedding.py
View File

@@ -12,12 +12,12 @@ import vllm_ascend.platform # noqa: F401
def benchmark_npu(fn, num_iterations=100, num_warmup_iterations=50):
"""
Benchmark function for NPU operations
Args:
fn: Function to benchmark
num_iterations: Number of timing iterations
num_warmup_iterations: Number of warmup iterations
Returns:
float: Minimum elapsed time in seconds
"""
@@ -41,19 +41,26 @@ def benchmark_npu(fn, num_iterations=100, num_warmup_iterations=50):
def get_masked_input_and_mask_ref(
input_: torch.Tensor, org_vocab_start_index: int,
org_vocab_end_index: int, num_org_vocab_padding: int,
added_vocab_start_index: int,
added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
input_: torch.Tensor,
org_vocab_start_index: int,
org_vocab_end_index: int,
num_org_vocab_padding: int,
added_vocab_start_index: int,
added_vocab_end_index: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Reference implementation for verification"""
org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ <
org_vocab_end_index)
org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ < org_vocab_end_index)
added_vocab_mask = (input_ >= added_vocab_start_index) & (
input_ < added_vocab_end_index)
added_offset = added_vocab_start_index - (
org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding
valid_offset = (org_vocab_start_index *
org_vocab_mask) + (added_offset * added_vocab_mask)
input_ < added_vocab_end_index
)
added_offset = (
added_vocab_start_index
- (org_vocab_end_index - org_vocab_start_index)
- num_org_vocab_padding
)
valid_offset = (org_vocab_start_index * org_vocab_mask) + (
added_offset * added_vocab_mask
)
vocab_mask = org_vocab_mask | added_vocab_mask
masked_input = vocab_mask * (input_ - valid_offset)
return masked_input, ~vocab_mask
@@ -94,21 +101,25 @@ def test_get_masked_input_and_mask(
# Define reference function
def ref_fn():
return get_masked_input_and_mask_ref(input_tensor,
test_case["org_start"],
test_case["org_end"],
test_case["padding"],
test_case["added_start"],
test_case["added_end"])
return get_masked_input_and_mask_ref(
input_tensor,
test_case["org_start"],
test_case["org_end"],
test_case["padding"],
test_case["added_start"],
test_case["added_end"],
)
# Define custom function
def custom_fn():
return torch.ops._C.get_masked_input_and_mask(input_tensor,
test_case["org_start"],
test_case["org_end"],
test_case["padding"],
test_case["added_start"],
test_case["added_end"])
return torch.ops._C.get_masked_input_and_mask(
input_tensor,
test_case["org_start"],
test_case["org_end"],
test_case["padding"],
test_case["added_start"],
test_case["added_end"],
)
# Get results for correctness testing
ref_masked_input, ref_mask = ref_fn()
@@ -120,9 +131,9 @@ def test_get_masked_input_and_mask(
# Print performance results
print("\nPerformance Results:")
print(f"Reference implementation: {ref_time*1000:.3f} ms")
print(f"Custom implementation: {custom_time*1000:.3f} ms")
print(f"Speedup: {ref_time/custom_time:.2f}x")
print(f"Reference implementation: {ref_time * 1000:.3f} ms")
print(f"Custom implementation: {custom_time * 1000:.3f} ms")
print(f"Speedup: {ref_time / custom_time:.2f}x")
# Compare results for correctness
ref_masked_input = ref_masked_input.to(dtype)
@@ -136,9 +147,12 @@ def test_get_masked_input_and_mask(
ref_masked_input,
rtol=1e-5,
atol=1e-5,
msg=f"Masked input mismatch for case: {test_case}")
torch.testing.assert_close(custom_mask,
ref_mask,
rtol=1e-5,
atol=1e-5,
msg=f"Mask mismatch for case: {test_case}")
msg=f"Masked input mismatch for case: {test_case}",
)
torch.testing.assert_close(
custom_mask,
ref_mask,
rtol=1e-5,
atol=1e-5,
msg=f"Mask mismatch for case: {test_case}",
)