triton-operator-code-review

To perform static code review on Triton kernels for Ascend NPU, analyze host and device code to identify misuses and static defects. This enforces masking for tl.load/tl.store, checks constexpr BLOCK sizes, and validates supported dtypes for dot inputs.

Common Triton API misuses affecting Ascend NPU execution include missing masks for tl.load/tl.store operations, unaligned BLOCK sizes, unsupported dot input dtypes, and implicit out_dtype handling during reductions, which static analysis can detect.

Yes, you can integrate Triton static analysis into your development CI pipeline. It performs one-time compliance reviews of Triton auxiliary kernels, generating code review reports that ensure security, correctness, and performance before deployment.

Ascend NPU requires constexpr BLOCK sizes to be aligned to 16 multiples. Static analysis enforces this constraint during Triton kernel code review to ensure correct memory access patterns and optimal hardware utilization.

Triton reduction operations fail precision checks on Ascend NPU when missing explicit out_dtype handling. Static code review identifies these precision issues by requiring explicit output data type declarations during reduction operations.

The best way to check Triton code compliance for memory safety and hardcoded values is running a comprehensive static analysis scan. It detects hardcoded values, memory risks, and precision problems across both Host and Device kernels.