pypto-case-norm-batchnorm

You can implement per-channel normalization on a 3D tensor using the demonstrated Pypto kernel. It processes tensors shaped (B, C, spatial) by computing per-channel statistics and applying normalization with configurable batch, channels, spatial, and epsilon parameters.

Broadcasting for 3D BatchNorm uses expand_clone to align computed statistics back to the original input shape. This ensures proper elementwise operations during normalization across batch and spatial dimensions.

The best way to perform multi-axis reduction is using a modular Pypto kernel that executes sequential reductions across specified axes. It first reduces across a spatial axis, then reduces across the channel or batch axis as needed.

Yes, you can configure batch, channels, and spatial dimensions for 3D BatchNorm in Pypto. You create and execute the normalization kernel using create_batchnorm_kernel with your desired dimensional parameters.

No, this 3D normalization approach does not require external dependencies. It operates entirely using the Pypto kernel framework to perform multi-axis reductions and broadcasting via expand_clone.

You need expand_clone to broadcast the computed per-channel statistics back to the original 3D tensor shape. This alignment is necessary to perform elementwise normalization operations correctly across batch and spatial dimensions.