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

In the second part of the "Profiling in PyTorch" series, the authors explore the transition from using basic matrix multiplication and addition to implementing a more sophisticated Multilayer Perceptron (MLP) using the nn.Linear module with the PyTorch profiler. The discussion highlights how nn.Linear wraps matrix multiplication with built-in bias handling, optimizing the process by using epilogues to merge operations and reduce memory traffic. The series progresses by profiling a GeGLU MLP, where the authors observe the benefits of operation fusion using torch.compile, which reduces the number of GPU kernel launches by combining pointwise operations. The exploration also includes a comparison with hand-tuned Triton kernels from the Hugging Face Hub, demonstrating the trade-offs between generic and specialized kernels in terms of performance and flexibility. Through these analyses, the authors emphasize the importance of forming hypotheses before examining profiler traces to effectively diagnose and optimize PyTorch models.

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