Manual backward pass: Ditching Autograd for extreme memory optimization
YouJiacheng · x · 2026-08-18
The author shares a practice of abandoning PyTorch Autograd and nn.Module abstractions to manually write the forwardbackward function. By explicitly managing gradients and intermediate activations, and applying Claude's optimization tips—such as skipping high-memory layers and recomputing ReLU during the backward pass—the activation memory footprint for the Nanochat architecture was significantly reduced.
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