Gradient Clipping and Mixed Precision

The optimizer and schedule from the previous lesson assume gradients are sane. They usually are not. A single bad batch can spike the gradient norm by three orders of magnitude. Mixed-precision training amplifies this by introducing FP16 overflow on the loss side. This lesson builds the two safety belts that production training cannot ship without: gradient clipping to a configured global L2 norm, and a mixed-precision loop with autocast and GradScaler that detects NaN and Inf, skips the step cleanly, and logs the scaling factor for forensics.