In the first part of this series "Profiling in PyTorch", we used torch.add(torch.matmul(x, w), b) to learn how to read PyTorch profiler traces. We also discussed several other topics that came our way - the CPU dispatch chain, launch overhead, the difference between an overhead-bound and a compute-bound regime, and some internals of torch.compile.

In the second iteration (this blog post), we climb one rung up the ladder. We replace the hand-written matmul-add pair with an nn.Linear (with bias=True). This is the building block every deep learning model uses. We then stack three of them (specific to our example), with an activation in between, to form a Multilayer Perceptron (MLP) block.

The scripts for this blog post live here: 02_linear.py, 03_simple_mlp.py, and 03_kernels_mlp.py. Like before, it helps to open them in a separate tab and walk through the code as you read. We use an NVIDIA A100-SXM4-80GB GPU to run the scripts. It is really easy to set up a GPU on the Hugging Face infrastructure and experiment with the scripts using Dev Mode with Spaces. One could also run the scripts with the Hugging Face Jobs pipeline.

nn.Linear is a module wrapper around the same matrix multiplication and addition we already profiled in Part 1. The only difference is that it owns its weight and bias as parameters and exposes a forward method that PyTorch users have grown familiar with.

Where x is the input, w is the weight and b is the bias. Let's run 02_linear.py and check the profile.

trace-util is a utility that will sync your traces to a Hugging Face bucket and then provide the Preffeto URLs on your terminal.