Optimizing Token Generation in PyTorch Decoder Models

Optimizing token generation in PyTorch decoder models involves several key techniques to reduce latency and improve memory usage. The process begins by implementing KV caching to reduce computational complexity from quadratic to linear time, followed by using expandable memory segments and static caching to address memory fragmentation. The primary technique demonstrated is using CUDA stream interleaving to hide the latency of host-device synchronization by pipelining the model's execution steps. These methods are demonstrated on a GPT-2 model, with code examples and benchmarks showing significant gains in runtime and memory efficiency.