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Flashinfer
Overview :
FlashInfer is a high-performance GPU kernel library specifically tailored for large language model (LLM) services. It significantly improves LLM performance during inference and deployment by providing efficient sparse/dense attention mechanisms, load-balancing scheduling, and memory efficiency optimizations. FlashInfer supports PyTorch, TVM, and C++ APIs, making it easy to integrate into existing projects. Its main advantages include efficient kernel implementations, flexible customization options, and broad compatibility. FlashInfer was developed to meet the increasing demand for LLM applications and to provide more efficient and reliable inference support.
Target Users :
FlashInfer is designed for developers and researchers who require high-performance LLM inference and deployment, particularly in applications that involve large-scale language model inference on GPUs.
Use Cases
Accelerate the inference process of large language models using FlashInfer in natural language processing tasks, improving model response time.
Optimize the attention mechanism of models in machine translation applications with FlashInfer, enhancing translation quality and efficiency.
Utilize FlashInfer's efficient kernels to implement rapid text generation and retrieval functionalities in intelligent Q&A systems.
Features
Efficient sparse/dense attention kernels: Support for both single and batch sparse and dense key-value (KV) storage attention computations, achieving high performance on CUDA cores and Tensor cores.
Load-balancing scheduling: Optimize computation scheduling for variable-length input by decoupling the scheduling and execution phases of attention calculations, reducing load imbalance issues.
Memory efficiency optimization: Provide cascading attention mechanisms with hierarchical KV caching for efficient memory utilization.
Custom attention mechanisms: Support user-defined attention variants through JIT compilation.
Compatibility with CUDAGraph and torch.compile: FlashInfer kernels can be captured by CUDAGraphs and torch.compile for low-latency inference.
High-performance LLM-specific operations: Provide efficient Top-P and Top-K/Min-P sampling fusion kernels without the need for sorting operations.
Support for multiple APIs: Compatible with PyTorch, TVM, and C++ (header files) API, facilitating integration into diverse projects.
How to Use
1. Install FlashInfer: Choose the appropriate pre-compiled wheel for your system and CUDA version, or build from source.
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2. Import the FlashInfer library: Include the FlashInfer module in your Python script.
3. Prepare input data: Generate or load the input data needed for attention calculations.
4. Invoke FlashInfer's API: Utilize the APIs provided by FlashInfer for attention calculations or other operations.
5. Retrieve results: Process and analyze the computed results for specific applications.
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