Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Performance profiling in PyTorch is explored by analyzing the transition from a basic matmul-add operation to a standard `nn.Linear` layer. The analysis uses profiler traces to examine the CPU dispatch chain and the resulting GPU kernel execution for this fundamental building block. Stacking these layers creates a Multilayer Perceptron (MLP), and the post investigates how tools like `torch.compile` optimize its performance. This optimization involves fusing multiple operations into a single, efficient Triton kernel, which significantly reduces scheduling overhead and improves GPU utilization.