[ExecuTorch][WebGPU] Add batch_norm op by JCNTH · Pull Request #20993 · pytorch/executorch

JCNTH · 2026-07-16T21:49:23Z

Stack from ghstack (oldest at bottom):

Add aten._native_batch_norm_legit_no_training.default to the WebGPU backend — inference batch norm on MODNet's decoder and CNN backbones.

Problem: The WebGPU delegate had no batch-norm handler, so MODNet (background removal) and other CNN models could not fully delegate to the GPU.

Solution: A 4D NCHW fp32 kernel applying the per-channel inference affine y = (x - running_mean) / sqrt(running_var + eps) * weight + bias. weight/bias are optional (affine=False → unit scale / zero shift), bound via utils::make_optional_binding with a dummy buffer when absent.

Implementation:

runtime/ops/batch_norm/{BatchNorm.cpp,batch_norm.wgsl,batch_norm_wgsl.h} registering aten._native_batch_norm_legit_no_training.default; uses utils::make_compute_pipeline + utils::make_optional_binding.
Multi-output op: reads the out entry of the output ValueList (save_mean/save_invstd unused in inference).
Mirrors the Vulkan _native_batch_norm_legit_no_training delegate.
CMake WEBGPU_SRCS entry.

Constraints: fp32-only, 4D in/out — throws on rank/shape/dtype mismatch (fail-loud); no change to existing ops.

Differential Revision: D112417288

[ghstack-poisoned]

pytorch-bot · 2026-07-16T21:49:26Z

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[ExecuTorch][WebGPU] Add batch_norm op#20993

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