xformers is a collection of specialized toolsets for fused GPU operators, sparse attention mechanisms, modular transformer components, and performance benchmarking. It provides a library of optimized and interoperable building blocks used to construct and experiment with transformer architectures.
The project features a fused CUDA operator library that combines common layers into single GPU operations to increase throughput. It includes a sparse attention framework and memory-efficient attention kernels that utilize tiling strategies and structured sparsity patterns to reduce computational overhead and memory usage.
The toolkit covers a broad surface of performance optimization, including kernel fusion and an operator benchmarking framework for measuring the execution latency and memory footprint of individual model components. It also supports composable block assembly and custom component extensions to facilitate architectural experimentation.