thanks so much @amd-asalykov, validating on a bare metal MI350X right now. Also thanks @laasya-konidala for setting things up and checking the codebase + verifying!

@simonguozirui you might have noticed that in the current implementation we rely on os.environ["CXX"] = "hipcc" in src/kernelbench/prompts/model_new_ex_add_hip.py to make pytorch's load_inline work with hip kernels. We didn't explicitly tell LLMs to include this LOC in the generated kernels. Instead we expect that LLMs will include it automatically based on the model_new_ex_add_hip.py example. As an alternative, we could introduce a backend-specific prompt and explicitly ask LLMs to include os.environ["CXX"] = "hipcc"

Gotcha os.environ["CXX"] = "hipcc" triggers the HIP compiler and prevents it from running hipify (we had this issue last year when @willhu-jpg and I tried to implement AMD support; it would just hipify CUDA code #37). We can keep that as part of the in-context example which becomes a part of the prompt automatically, the same way we treat other backends like TK that need special include library (no need for backend specific warnings and instructions)

Added a few guardrails to ensure a separate AMD and NVIDIA code path

Two more small things to close things off

• reward hack checking -> hip kernel must use hip-related keywords (regex-based matching)
• L2 cache thrashing, for clearing cache rn we allocate a big tensor to thrash it, so for AMD need to see if the tensor size still works well there