How to stop memory leak while using adahessian?

[Shubh-Goyal-07]

No description provided.

[VadisettyRahul]

1. Use torch.no_grad() Where Applicable: Ensure that gradient calculations are disabled when not needed, such as during validation or inference, to save memory.

with torch.no_grad():
    # Validation or inference code

2. Delete Unused Variables: Remove any intermediate tensors or variables that are no longer needed. This can be done with Python’s del statement followed by clearing the GPU cache if using CUDA.

del variable_name
torch.cuda.empty_cache()  # Clears GPU memory

3. Enable Gradient Checkpointing: This reduces memory consumption by recomputing parts of the graph during the backward pass rather than storing all intermediate activations. Useful when dealing with large models.
   from torch.utils.checkpoint import checkpoint

# Example of gradient checkpointing
output = checkpoint(model, input_data)

4. Optimize Batch Size: Large batch sizes consume more memory. Reducing the batch size helps prevent memory overflows.

5. Detach Unnecessary Tensors: Use detach() to prevent PyTorch from retaining computation graphs for tensors that no longer require gradient tracking.
   tensor = tensor.detach()

6. Use torch.cuda.empty_cache() Regularly: In GPU operations, periodically clearing the cache can help release memory back to the GPU and prevent leaks.
   torch.cuda.empty_cache()

7. Monitor Memory Usage: Use PyTorch's memory profiling tools to track and profile memory usage during the training process.

import torch
print(torch.cuda.memory_summary())

8. Check for Redundant Hessian Calculations: Since AdaHessian performs second-order derivative calculations, ensure they are optimized and not needlessly repeated, as these calculations are memory-intensive.