Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.
huggingface ·
This article introduces a systematic approach to profiling inference performance on AWS Trainium using TorchNeuron, an open-source PyTorch backend. It guides readers through setting up the NeuronConfig and NeuronProfiler to capture device and runtime traces for analysis. The author demonstrates these tools by profiling three variations of the RMSNorm operation: naive PyTorch operations, the built-in torch.rms_norm, and a compiled version using torch.compile. Results show that both the fused kernel and the compiled graph significantly reduce Neuron device time compared to the naive implementation by eliminating intermediate tensor materializations. The guide explains how to interpret the key_averages() table to identify performance bottlenecks and uses Neuron Explorer to visualize the execution lifecycle from framework dispatch to hardware execution. This workflow allows practitioners to pinpoint hotspots and optimize models by leveraging fusion or compilation techniques. The post concludes by previewing Part 2, which will focus on device-level profiling and roofline analysis.
huggingface