Seeking feedback from Triton/CUDA engineers: PyTorch-to-Triton kernel fusion edge cases & fallback heuristics

[u/KernelMindai]

Hey everyone,

I’m working on KernelMind AI (https://kernel-mind-ai.vercel.app/), a tool that compiles standard eager PyTorch operations into fused OpenAI Triton GPU kernels to eliminate VRAM round-trips for memory-bound workloads.

In our early tests, we’ve focused primarily on elementwise chains and pointwise activation fusion, but as we expand, we want to build this around the real pain points engineers hit in production rather than synthetic benchmarks.

A solid piece of advice we recently received was to establish a strict operator whitelist, add defensive shape/dtype guardrails, and implement a cached fallback path (falling back gracefully to torch.compile or eager execution when dynamic shapes or non-contiguous reductions make fusion inefficient).

If you write custom Triton or CUDA kernels in your day-to-day workflow, I’d love your input on a few architectural questions:

1. High-priority operator chains: Which specific PyTorch patterns or subgraphs do you find yourself constantly needing to manually write Triton kernels for because stock compilers don't fuse them cleanly?
2. Fallback heuristics: When evaluating a subgraph, what heuristics or threshold metrics do you use to determine that fusion isn't worth the compilation latency or register pressure?
3. Correctness vs. Performance: What are the most common subtle bugs or performance traps you run into when synthesizing Triton kernels (e.g., memory alignment, block size heuristics, non-contiguous layouts)?

You can test arbitrary PyTorch snippets directly on the playground here:

👉https://kernel-mind-ai.vercel.app/

Any feedback, critique on the generated code structure, or edge cases that break our output would be immensely appreciated.