AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization

Abstract:We introduce AMDKernelVault, an open HIP and Triton kernel corpus and training framework for recent AMD CDNA GPUs. Existing LLM-based kernel agents are largely CUDA/NVIDIA-centric and often depend on repeated frontier-LLM calls for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that transform PyTorch references into HIP or Triton kernels, compile and validate candidates under ROCm, and latency-profile them on AMD hardware. The corpus contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. We further train Qwen3-8B with supervised fine-tuning and execution-aware reinforcement learning as a demonstration of the corpus's utility. Under fixed evaluation budgets, it achieves the highest correctness among the compared models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), but does not uniformly lead compilation or speed metrics. The corpus and documentation are available at this https URL, and the associated training and kernel-generation code is available at this https URL.

N pages, 3 figures, including appendix. First four authors contributed equally. Code: this https URL Data: this https URL

Computation and Language (cs.CL)

arXiv:2609.12471 [cs.CL]

(or arXiv:2609.12471v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.12471 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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