PyTorch Loss Implementation

Key Concepts

1. Tensor Operations

import torch

# Sigmoid function
sigmoid_output = torch.sigmoid(input_tensor)

# Log function
log_output = torch.log(input_tensor)

# Mean reduction
mean_loss = loss.mean()

# Sum reduction
sum_loss = loss.sum()

2. Batch Processing

# Batch dimension handling
batch_size = tensor.shape[0]
x = tensor[:batch_size//2]  # First half
y = tensor[batch_size//2:]  # Second half

# Ensure same device and dtype
tensor = tensor.to(device=model.device, dtype=torch.float32)

3. Numerical Stability

Log-Sigmoid Stability

# Avoid: log(sigmoid(x)) can cause numerical issues
# Instead use:
stable_loss = torch.nn.functional.logsigmoid(x)
# Or manually:
loss = -torch.log(torch.sigmoid(x) + 1e-10)

Handling Small Values

# Add epsilon to avoid log(0)
safe_log = torch.log(value + 1e-8)

# Clamp to valid range
clamped = torch.clamp(value, min=1e-10, max=1.0)

4. Device Handling

# Ensure all tensors on same device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tensor = tensor.to(device)

# Or get from model
device = next(model.parameters()).device
tensor = tensor.to(device)

Loss Function Pattern

def compute_loss(logits, labels, temperature=1.0, margin=0.5):
    # 1. Normalize/compute rewards
    rewards = logits / (sequence_length + 1e-8)

    # 2. Compute differences
    diff = temperature * rewards[:n//2] - temperature * rewards[n//2:] - margin

    # 3. Apply objective
    loss_per_pair = -torch.log(torch.sigmoid(diff) + 1e-10)

    # 4. Reduce
    loss = loss_per_pair.mean()

    return loss

Debugging Tips

1. Check tensor shapes at each step
2. Use .detach() for inspecting values without affecting gradients
3. Verify numerical stability with small inputs
4. Test gradients with loss.backward()
5. Print intermediate values for debugging

Performance Tips

• Use in-place operations where safe: tensor.log_()
• Avoid unnecessary cloning/copying
• Batch operations are faster than loops
• Use PyTorch functions over custom loops