triton-operator-precision-eval

You can verify Triton operator precision by benchmarking outputs against PyTorch reference implementations across diverse shapes, dtypes, and epsilon settings to produce a standardized accuracy report.

Triton kernel accuracy verification uses MERE, MARE, and maximum relative error metrics to calculate error against PyTorch references and generate a reproducible precision report.

Automating precision evaluation for a fused Triton kernel involves running an end-to-end workflow that compares kernel outputs with PyTorch references and automatically generates a report with error metrics.

Yes, you need pytest and numpy, along with triton, torch, and torch_npu, to execute the benchmarking scripts that compare operator outputs and validate numerical consistency.

The best way to ensure numerical consistency before performance optimization is to run automated precision verification that validates your Triton operator against a PyTorch reference implementation across multiple configurations.

Mismatches across different shapes occur because precision varies by dtype and shape configurations; running a comprehensive benchmark evaluation identifies these specific numerical inconsistencies.