Hugging Face’s second PyTorch profiling post examines how nn.Linear maps to matrix multiplication plus bias addition, then extends the analysis to a three-layer MLP with activation. It walks through profiler traces, torch.compile behavior, kernel layouts, and fused Triton or hand-tuned kernels to show how MLP performance can be optimized on an NVIDIA A100 GPU.

The second part of a PyTorch profiling series explores how a basic `nn.Linear` layer operates under the hood, breaking down its matrix multiplication and addition steps. It then builds a fused Multilayer Perceptron (MLP) block by stacking three linear layers with activations, analyzing performance improvements through kernel fusion and `torch.compile`. The post includes hands-on scripts and traces to demonstrate optimizations on NVIDIA A100 GPUs.