Loss Scaling Download ^new^ Review

However, FP16 has a serious limitation: its dynamic range is roughly ( 5.96 \times 10^-8 ) to ( 65504 ). (common in deep networks) can become zero when rounded to FP16. This is called underflow .

for data, target in dataloader: optimizer.zero_grad() loss scaling download

✅ — it’s a feature, not a library. However, FP16 has a serious limitation: its dynamic

with autocast(): # FP16 forward pass output = model(data) loss = criterion(output, target) loss scaling download