Loss Scaling Download [exclusive] Jun 2026

Loss scaling is widely used in deep learning frameworks, such as TensorFlow and PyTorch. Here's an example of how to implement loss scaling in PyTorch:

# Enable loss scaling with a scaling factor of 128 scaling_factor = 128

with torch.cuda.amp.autocast(): output = model(input) loss = criterion(output, target) loss scaling download

Loss scaling is a simple yet effective technique for preventing exploding gradients during the training process. By scaling the loss function, gradients become smaller, and the model's parameters are updated by smaller amounts, resulting in stable and efficient training. Loss scaling is widely used in deep learning frameworks and can be easily implemented in various machine learning models.

# Define a simple model model = nn.Linear(5, 3) Loss scaling is widely used in deep learning

scaled_loss = scaling_factor * loss

✅ — it’s a feature, not a library. Calls backward() on scaled loss to create scaled gradients

from torch.cuda.amp import autocast, GradScaler

Loss scaling is a simple, elegant fix:

# Scales loss. Calls backward() on scaled loss to create scaled gradients. scaler.scale(loss).backward()