Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
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.
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.