FlashInfer
FlashInfer is a high-performance GPU kernel library designed for large-scale language modeling services.
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- Efficient Sparse/Dense Attention Kernel: Supports single and batch sparse and dense KV storage attention computation, enabling high performance on CUDA and Tensor cores.
- Load balancing scheduling: By decoupling the planning and execution stages of attention computation, optimizing the computation scheduling of variable length inputs, and reducing load imbalance issues.
- Memory efficiency optimization: Provides a cascaded attention mechanism, supports hierarchical KV caching, and achieves efficient memory utilization.
- Custom attention mechanism: supports user-defined attention variants through JIT compilation.
- Compatible with CUDAGraph and torch.compile: The FlashInfer kernel can be captured by CUDAGraph and torch.compile to achieve low latency inference.
- Efficient LLM specific operations: Provides high-performance Top-P, Top-K/Min-P sampling fusion kernels without the need for sorting operations.
- Supports multiple APIs: Supports PyTorch, TVM, and C++(header file) APIs for easy integration into different projects.
Product Details
FlashInfer is a high-performance GPU kernel library designed specifically for Large Language Modeling (LLM) services. It significantly improves the performance of LLM in inference and deployment by providing efficient sparse/dense attention mechanisms, load balancing scheduling, memory efficiency optimization, and other functions. FlashInfer supports PyTorch, TVM, and C++APIs, making it easy to integrate into existing projects. Its main advantages include efficient kernel implementation, flexible customization capabilities, and extensive compatibility. The development background of FlashInfer is to meet the growing demand for LLM applications and provide more efficient and reliable inference support.
