LLM specific
For an interview focusing on creative problem-solving and being clever with PyTorch, you can expect questions that test your ability to:
1. Optimize Model Training & Memory Usage
• How would you efficiently train a large model on limited GPU memory?
• How do mixed precision training and gradient checkpointing work?
• Implement a custom torch.autograd.Function to save memory.
2. Custom Implementations & PyTorch Internals
• Implement a custom activation function with PyTorch.
• How does torch.nn.Module work internally?
• Explain how PyTorch’s autograd computes gradients.
3. Efficient Tensor Operations
• Optimize a given PyTorch operation to minimize GPU memory and maximize speed.
• Implement a function that computes a rolling window mean using efficient tensor operations.
• Why should we prefer torch.einsum over explicit loops?
4. Parallelism & Multi-GPU Training
• Implement a simple data parallel training loop.
• What is the difference between torch.nn.DataParallel and torch.nn.parallel.DistributedDataParallel?
• How do you handle synchronization issues in multi-GPU training?
5. Custom Loss Functions & Gradients
• Implement a custom loss function that requires second-order gradients.
• How do you stop gradients from flowing through part of the computation graph?
6. Debugging & Profiling Performance Issues
• How would you debug a PyTorch model that is training extremely slowly?
• Use torch.profiler to identify bottlenecks in a model’s training loop.
7. Reinforcement Learning / Optimization-Specific Questions
• Implement a basic reinforcement learning policy network in PyTorch.
• How would you use PyTorch for differentiable optimization tasks?