pypto-optimization

Tuning tile shapes and loop counts improves kernel performance by optimizing memory bandwidth and compute efficiency. PyPTO applies rule-based allocation to automatically select and evaluate configurations for common operators like softmax, reduction, and norm.

Optimizing a 3D tensor reduction requires comparing different tiling strategies across multiple axes. PyPTO generates and evaluates tile and loop configurations based on your operator's shape and backend to identify the fastest configuration.

Reduction axis strategies need adjustment when optimizing operators with tiling and contiguous memory access patterns. PyPTO allocates optimization rules to maximize kernel performance on selected backends while ensuring consistent results.

PyPTO supports kernel tuning for operators including softmax, reduction, norm, and loss. It applies to operators with tiling, loop partitioning, and contiguous memory access patterns across supported backends.

Kernel optimization requires careful evaluation to avoid undefined behavior and ensure consistent results. PyPTO reduces manual trial-and-error by automatically selecting and comparing tile shapes, loop counts, and reduction axis strategies.

PyPTO automatically selects and compares tile shapes and loop counts across common operators. Running it with your operator's shape and backend generates and evaluates configurations to identify the best performance.