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Add pre-commit hooks #286

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Merged
Anerudhan merged 4 commits into from
Jun 5, 2026
Merged

Add pre-commit hooks #286

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305599a
Fix clang format issues
Anerudhan Jun 5, 2026
9f063d3
Fix clang-format
Anerudhan Jun 5, 2026
a7651a0
Add pre-commit hooks and fix pre-commit
Anerudhan Jun 5, 2026
2f24b10
Fix the black issues
Anerudhan Jun 5, 2026
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29 changes: 29 additions & 0 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,29 @@
repos:
- repo: https://github.com/pre-commit/mirrors-clang-format
rev: v21.1.6
hooks:
- id: clang-format
types_or: [c++, c, cuda]
- repo: https://github.com/psf/black-pre-commit-mirror
rev: 26.1.0
hooks:
- id: black
name: black
description: "Black: The uncompromising Python code formatter"
entry: black
language: python
minimum_pre_commit_version: 2.9.2
require_serial: true
types_or: [python, pyi]
args: ["--line-length", "160"]
Comment on lines +7 to +18

@coderabbitai coderabbitai Bot Jun 5, 2026

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⚠️ Potential issue | 🔴 Critical | ⚡ Quick win

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Description: Verify black version 26.1.0 exists and check for security issues

# Check PyPI for black versions
curl -s https://pypi.org/pypi/black/json | jq -r '.releases | keys[]' | grep '^26\.1\.0$'

# Get latest version for comparison
echo "=== Latest black version ==="
curl -s https://pypi.org/pypi/black/json | jq -r '.info.version'

# Check for security advisories
gh api graphql -f query='
{
  securityVulnerabilities(first: 5, ecosystem: PIP, package: "black") {
    nodes {
      advisory {
        summary
        severity
        publishedAt
      }
      vulnerableVersionRange
      firstPatchedVersion {
        identifier
      }
    }
  }
}'

Repository: NVIDIA/cudnn-frontend

Length of output: 635

Update the pinned Black pre-commit hook to address the HIGH security advisory.

rev: 26.1.0 exists but falls under a HIGH-severity vulnerability affecting versions < 26.3.1 (“Arbitrary file writes from unsanitized user input in cache file name”); bump rev in .pre-commit-config.yaml to 26.3.1 or later (latest available: 26.5.1).

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In @.pre-commit-config.yaml around lines 7 - 18, The pinned Black pre-commit
hook rev is currently set to "rev: 26.1.0" (under the hook with id: black/name:
black); update that rev to a fixed secure version (e.g., "26.3.1" or preferably
the latest "26.5.1") in .pre-commit-config.yaml so the Black hook no longer uses
a version affected by the HIGH-severity vulnerability.

- id: black-jupyter
name: black-jupyter
description:
"Black: The uncompromising Python code formatter (with Jupyter Notebook support)"
entry: black
language: python
minimum_pre_commit_version: 2.9.2
require_serial: true
types_or: [python, pyi, jupyter]
additional_dependencies: [".[jupyter]"]
args: ["--line-length", "160"]
7 changes: 3 additions & 4 deletions include/cudnn_frontend/node/reduction.h
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Expand Up @@ -153,10 +153,9 @@ class ReductionNode : public NodeCRTP<ReductionNode> {
// 5. Set the optional group offset tensor descriptor
if (Group_offset != attributes.inputs.end() && Group_offset->second != nullptr) {
#if (CUDNN_VERSION >= 92400)
RETURN_CUDNN_FRONTEND_ERROR_IF(
detail::get_backend_version() < 92400,
error_code_t::GRAPH_NOT_SUPPORTED,
"Reduction group_offset is not supported in cudnn version < 9.24.0");
RETURN_CUDNN_FRONTEND_ERROR_IF(detail::get_backend_version() < 92400,
error_code_t::GRAPH_NOT_SUPPORTED,
"Reduction group_offset is not supported in cudnn version < 9.24.0");
auto group_offset_backend_desc = tensors.at(Group_offset->second->get_uid())->get_raw_desc();

_CUDNN_CHECK_CUDNN_ERROR(detail::set_attribute(reduction_operation.get_raw_desc(),
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4 changes: 1 addition & 3 deletions test/python/test_matmul_bias_relu.py
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Expand Up @@ -206,9 +206,7 @@ def test_matmul_bias(param_extract, cudnn_handle):
X_gpu = torch.randn(b, s, e, requires_grad=False, device="cuda", dtype=input_type)
W_gpu = torch.randn(1, e, e * 4, requires_grad=False, device="cuda", dtype=input_type)
B_gpu = torch.randn(1, 1, e * 4, requires_grad=False, device="cuda", dtype=input_type)
Y_expected = torch.nn.functional.linear(
X_gpu.float(), W_gpu.squeeze().float().T, bias=B_gpu.squeeze().float()
).to(input_type)
Y_expected = torch.nn.functional.linear(X_gpu.float(), W_gpu.squeeze().float().T, bias=B_gpu.squeeze().float()).to(input_type)

stream = torch.cuda.current_stream().cuda_stream
cudnn.set_stream(handle=cudnn_handle, stream=stream)
Expand Down