AMDKernelVault：面向AMD GPU内核优化的大规模数据集与智能体训练
AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
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中文总结 AI 辅助
本文提出AMDKernelVault，一个包含HIP和Triton内核的大规模语料库及智能体训练框架，通过HIPKernelGen和TritonKernelGen流水线生成并验证内核，训练Qwen3-8B在多个基准上取得最高正确率，弥补了AMD GPU内核优化的空白。
中文摘要 AI 辅助
我们推出了AMDKernelVault，这是一个面向近期AMD CDNA GPU的开源HIP和Triton内核语料库及训练框架。现有的基于LLM的内核智能体主要围绕CUDA/NVIDIA，且常常依赖反复调用前沿LLM进行生成、反思和优化。为弥补这一空白，我们开发了HIPKernelGen和TritonKernelGen，这两个智能体驱动的流水线将PyTorch参考实现转换为HIP或Triton内核，在ROCm下编译并验证候选内核，并在AMD硬件上进行延迟性能分析。该语料库包含62,153个经执行验证的HIP内核样本、2,377条基于生产环境的ROCm库问答条目，以及39,893个Triton内核。我们进一步使用监督微调和执行感知的强化学习训练Qwen3-8B，以展示该语料库的实用性。在固定的评估预算下，该模型在PyTorch到HIP转换（34.0% Pass@1）、TritonBench-G（33.2% Corr@3）和ROCmBench（41.94% Corr@3）上取得了对比模型中最高的正确率，但在编译或速度指标上并未全面领先。语料库和文档可在该https URL获取，相关的训练和内核生成代码可在该https URL获取。
英文摘要
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 https://huggingface.co/datasets/amd/AIG-Datasets, and the associated training and kernel-generation code is available at https://github.com/AMD-AGI/hip_kernel_llm_lab.
发表机构
- Advanced Micro Devices, Inc.（超威半导体公司）
机构由 AI 辅助整理，请以论文原文为准。
