CUDA device-side assert triggered in PyTorch

The "CUDA error: device-side assert triggered" in PyTorch is a common but frustrating error that occurs when working with GPU acceleration. This error often provides minimal information, making debugging challenging. This article explores the root causes and provides systematic approaches to resolve this issue.

Problem Overview

When executing PyTorch code on CUDA-enabled devices like Google Colab's GPU, you might encounter:

RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1

Even setting CUDA_LAUNCH_BLOCKING=1 may not always provide additional details, leaving developers searching for solutions.

Common Causes

Based on community experiences, the most frequent causes include:

1. Label/index mismatches between model output and target tensors
2. Vocabulary/embedding dimension mismatches
3. Invalid tensor values (e.g., out-of-bounds indices)
4. GPU memory issues or stuck processes
5. Missing activation functions before loss computation
6. Tokenizer/model dimension mismatches in transformer models

Debugging Strategies

1. Switch to CPU for Better Error Messages

The most effective approach is to temporarily switch to CPU execution:

# Force CPU debugging
device = torch.device('cpu')

# Re-run your code to get detailed error messages
t = torch.tensor([1, 2], device=device)

CPU execution typically provides more informative error messages that pinpoint the exact issue, such as index out-of-bounds errors or dimension mismatches.

2. Check for Label/Index Issues

Many reported cases involve label/index problems:

# Example: Converting string labels to numeric indices
label_mapping = {'class_a': 0, 'class_b': 1, 'class_c': 2}
labels = [label_mapping[label] for label in raw_labels]

# Ensure labels start from 0 and are consecutive
assert min(labels) == 0, "Labels should start from 0"
assert max(labels) == len(set(labels)) - 1, "Labels should be consecutive"

3. Verify Model Architecture Compatibility

Ensure your model's output layer matches your classification task:

# Incorrect: Output layer doesn't match number of classes
model.fc = nn.Linear(hidden_size, 2)  # Only 2 output nodes

# Correct: Match output dimension to number of classes
num_classes = 4  # For 4-class classification
model.fc = nn.Linear(hidden_size, num_classes)

4. Check Tokenizer and Model Alignment

For transformer models, ensure tokenizer and model dimensions match:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

# Add special tokens if needed
tokenizer.add_special_tokens({'pad_token': '<pad>'})

# Resize model embeddings to match tokenizer vocabulary
model.resize_token_embeddings(len(tokenizer))

5. Validate Input Data and Transformations

Incorrect data transformations can cause subtle issues:

# Review your data preprocessing pipeline
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                         std=[0.229, 0.224, 0.225])
])

# Ensure masks and images receive appropriate transformations
# (e.g., don't apply color transformations to mask images)

Advanced Debugging Techniques

Environment Variable Debugging

Enable more detailed CUDA error reporting:

import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

# Your code here - may provide better stack traces

Memory Management

Clear GPU memory and processes:

# Clean up GPU memory
torch.cuda.empty_cache()

# For Colab, sometimes a complete runtime restart is needed
# Runtime → Restart runtime or Factory reset runtime

WARNING

Once a CUDA assert error occurs, GPU operations may remain unstable until you restart your runtime/kernel.

Specific Scenarios and Solutions

Hugging Face Transformers

For issues with Hugging Face's Trainer:

# Check for tokenizer-model dimension mismatches
print(f"Tokenizer vocab size: {len(tokenizer)}")
print(f"Model embedding size: {model.config.vocab_size}")

# Resize if necessary
if len(tokenizer) != model.config.vocab_size:
    model.resize_token_embeddings(len(tokenizer))

Multi-GPU Environments

Verify GPU device configuration:

# Check available GPUs
print(f"Available GPUs: {torch.cuda.device_count()}")

# Explicitly set device if needed
device = torch.device(f'cuda:0' if torch.cuda.is_available() else 'cpu')

Loss Function Issues

Ensure proper activation functions before loss computation:

# For binary classification with BCE loss
output = model(input_data)
# Apply sigmoid activation before BCE loss
loss = F.binary_cross_entropy_with_logits(output, targets)

# Alternatively, apply sigmoid first then use BCE loss
# output = torch.sigmoid(model(input_data))
# loss = F.binary_cross_entropy(output, targets)

Prevention Best Practices

1. Validate data dimensions before training
2. Use consistent label encoding (0-indexed, consecutive integers)
3. Regularly check model-config compatibility
4. Implement data sanity checks:

def check_data_consistency(dataloader, model, num_classes):
    batch = next(iter(dataloader))
    inputs, targets = batch
    
    # Check target range
    assert targets.min() >= 0, "Targets contain negative values"
    assert targets.max() < num_classes, f"Targets exceed number of classes ({num_classes})"
    
    # Check model output dimension
    with torch.no_grad():
        output = model(inputs)
        assert output.shape[1] == num_classes, "Model output dimension doesn't match num_classes"

When to Seek Alternatives

If persistent issues occur specifically on Google Colab:

• Try alternative GPU providers (Kaggle, SageMaker, or local GPU)
• Verify Colab GPU availability and quotas
• Consider using Colab Pro for more stable GPU access

Conclusion

The "device-side assert triggered" error typically stems from data-model mismatches rather than GPU hardware issues. The most effective approach is:

1. Switch to CPU for detailed error messages
2. Validate label/index ranges and dimensions
3. Ensure model architecture matches your data characteristics
4. Restart runtime if GPU state becomes unstable
5. Implement preventive checks in your data processing pipeline

By systematically addressing these areas, you can resolve this error and build more robust deep learning applications.