We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus’s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets.

Machine learning practitioners routinely rely on public code repositories accompanying research papers, libraries, and open-source projects to evaluate, baseline, or build upon prior work. Yet getting such repositories to run and reproduce results often involves non-trivial effort, and is especially challenging when the target software and hardware stack differs from the one the original code was developed on. A prominent instance is running repositories originally built for CUDA-enabled hardware on AMD GPUs supporting the ROCm software stack. Here, successful installation alone is not a sufficient signal: a range of subtler issues may cause the code to either fail outright or silently fall back to inefficient execution. We present Repo2ROCm, an agentic system that migrates CUDA-centric ML repositories to ROCm in an automated manner. Repo2ROCm reconstructs a ROCm-compatible environment, adapts hardware-specific assumptions, executes a real workload, and confirms GPU-backed execution emitting auditable artifacts at every step (environment specs, source patches, execution logs, and verification evidence). We evaluate on ResearchBench, 60 repositories belonging to recent papers from top AI conferences. While only 18/60 run out of the box, Repo2ROCm reaches verified functional reproduction for 59/60 (3.3× improvement). Against popular general-purpose coding agents on a curated ten-repository diagnostic set (with all agents using the same Claude Sonnet 4 backbone), Repo2ROCm achieves the highest Portability Fidelity Score (PFS), a measure of how faithfully a run preserves the accelerator-critical execution path, real workload, and verified environment, scoring 77.3 vs. 61.4 (Claude Code), 58.7 (Mini-SWE-Agent), and 40.3 (Cursor Agent).