mixed precision

[2026-03-21 14:35] xenonfun: --- ## NEEDS TESTING (builds, untested) - [ ] LoheSphericalComplexAttention (lohe_complex.rs) - [ ] LoheSphericalComplexSplitAttention (lohe_complex_split.rs) - [ ] QuaternionEncoder (quaternion_enc.rs) - [ ] …

# Calculate average loss for the epoch avg_loss = running_loss / len(data_loader) print(f'Epoch [{epoch + 1}/100], Loss: {avg_loss:.4f}, LR: {optimizer.param_groups[0]["lr"]}') # Step the scheduler s…

loss = criterion(outputs, batch_targets) # Normalize the loss because it is accumulated loss = loss / accumulation_steps # Backward pass loss.backward() # Update wei…

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from torch.cuda.amp import GradScaler, autocast # Initialize PyTorch model model = nn.Sequential( nn.Linear(128, 128)…

loss = loss / accumulation_steps # Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …

To profile your code and identify bottlenecks, you can use `torch.autograd.profiler`. Here's a quick example of how to profile your training loop: ```python from torch.autograd import profiler # Training loop with profiling for epoch in r…