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Tencent/hpc-ops ★ 1184 145 APPLICATION

This repository is a production-grade, high-performance GPU operator library for LLM inference, developed by Tencent's Hunyuan AI Infra team. It provides a comprehensive set of fused CUDA kernels for attention (prefill/decode with FP8 quantization, block-sparse, paged KV-cache), Mixture-of-Experts (fused MoE with FP8 per-tensor/blockwise scaling), GEMM (BF16×FP32, gated MLA), normalization (fused allreduce+RMSNorm), sampling (fused top-k/top-p/temperature), RoPE with KV cache, and activation quantization. The library is exposed as a Python package (`hpc`) that auto-detects the GPU architecture (SM90/SM100/SM103) and loads the corresponding compiled extension. It integrates with PyTorch via custom ops registered through `torch.library`, and includes a vendored copy of NVIDIA CUTLASS for underlying GEMM primitives. The codebase demonstrates advanced CUDA techniques (TMA, PDL, cp.async, NVLink multicast) and is benchmarked against vLLM, SGLang, FlashInfer, and cuBLAS. While heavily optimized for specific GPU generations, the architecture-aware dispatch and clean Python API make it a strong reference for high-performance LLM inference kernels.

RATING: 8.3/10 MAINT: active DRIFT: low
#llm-inference #cuda-kernels #fused-operators #attention #moe #fp8 #pytorch-custom-op #high-performance
▸ 5 reusable components found
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