In this tutorial, we'll dive deep into gradient checkpointing in PyTorch, a memory optimization technique for neural network training. We'll cover its purpose, mechanics, and provide detailed insights into PyTorch's internal implementation, including advanced features like early stopping and selective checkpointing. Additionally, we'll address the user's specific requests for background on Torch operations, Torch dispatch, and detailed implementation analysis. Practical examples, flowcharts, and comprehensive explanations are included to ensure clarity.
Gradient checkpointing reduces memory consumption during the training of deep neural networks. Normally, during the forward pass, all intermediate activations are stored in memory for gradient computation in the backward pass. This can lead to high memory usage, especially for large models. Gradient checkpointing addresses t
