Hi PyTorch compiler folks,
I put together a small benchmark repo on custom-kernel attribution for decode/generation workloads:
The narrow claim is not that custom kernels are useless. The claim is that in these public-style inference optimization repos, the large end-to-end gains often come from static cache / graph capture / batching, while the marginal handwritten Triton/CUDA kernel contribution is much smaller. In the two reproduced case studies, a correctly configured `torch.compile` baseline matches or beats the custom-kernel path.
I would appreciate a review from the PyTorch compiler side on:
1. Whether the `torch.compile` baselines are configured fairly.
2. Whether the Qwen3.5 and Qwen3-TTS compile boundaries are reasonable.
3. Whether the benchmark framing around `StaticCache`, CUDA graphs, and decode workloads is technically accurate.
4. Any missing PyTorch compiler features/docs that would make this kind of baseline easier for users to discover.
I am not looking for endorsement of the conclusion; I would most value critical feedback on the benchmark methodology.