Hugging Face explains profiling PyTorch RMSNorm on AWS Trainium with TorchNeuron
Overview
Hugging Face's blog post shows how to profile AWS Trainium workloads with TorchNeuron, the open source native PyTorch backend, using torch.profiler configured through NeuronConfig and NeuronProfiler.
The walkthrough is Part 1 of a series on analyzing inference performance and finding where time is spent. It compares three RMSNorm implementations: naive PyTorch operations, the built-in torch.rms_norm() lowered to a fused Neuron kernel, and naive operations under torch.compile. The scripts use matched inputs and profiler settings, and the output is inspected in Neuron Explorer.
Written by AI from the articles below · updated Oct 21, 1:16 PM ET
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- Hugging Face BlogOfficialProfiling TorchNeuron on AWS Trainium: system-level profiling walkthrough
AIHugging Face's blog post walks through profiling AWS Trainium workloads with TorchNeuron, the open source native PyTorch backend, using torch.profiler configured through NeuronConfig and NeuronProfiler. The example profiles three RMSNorm implementations: naive PyTorch operations, the built-in torch.rms_norm() lowered to a fused Neuron kernel, and naive operations under torch.compile. The scripts use matched inputs and profiler settings so results can be compared, and the output is inspected in Neuron Explorer.
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