Training Compiled PyTorch 2.0 with PyTorch Lightning

PyTorch 2.0 introduces `torch.compile`, a compiler-based feature intended to accelerate model training and inference while preserving PyTorch’s Pythonic workflow and backward compatibility. Reported tests across 163 open-source models found average training improvements of 51% with AMP on NVIDIA A100 GPUs, while benchmarks using TorchVision ResNet models showed gains of roughly 69% for ResNet-50 and 74% for ResNet-152 over eager execution. PyTorch Lightning 2.0 supports this capability directly, allowing developers to compile a LightningModule and its training, validation, and prediction steps with a single call before using the Trainer. The tutorial demonstrates loading CIFAR-10 data, defining a ResNet-18-based LightningModule with training and validation logic, selecting an optimizer, and training the compiled model, with compilation modes including default, reduce-overhead, and max-autotune; the authors report that reduce-overhead performed best in their benchmarks. Lightning’s Trainer also automates device selection and training operations, while Fabric is suggested for users requiring greater control over complex workloads such as reinforcement learning or large language model training.

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