Stable Panther

PyTorch is great for research, but debugging PyTorch models in production is a different beast. Here’s what I’ve learned from debugging models that actually matter.

The Debugging Reality

Gradient issues are everywhere. Vanishing gradients, exploding gradients, gradient clipping that’s too aggressive or too lenient. Most model problems start with gradients that aren’t behaving properly.

Memory leaks in training loops. PyTorch’s automatic differentiation is powerful, but it can eat memory if you’re not careful. That .detach() call you forgot? It’s probably causing a memory leak.

Device mismatches are the bane of existence. CPU tensors mixed with GPU tensors, different devices in the same model, tensors that mysteriously end up on the wrong device. It’s the most common error in PyTorch code.

What Actually Works

Use torch.autograd.set_detect_anomaly(True) liberally. It’s slow, but it catches gradient issues that would otherwise be silent. Turn it on when debugging, turn it off for production.

Check tensor shapes constantly. print(tensor.shape) is your best friend. Most PyTorch errors come from shape mismatches that could have been caught early.

Use torch.jit.trace() for debugging. It forces you to think about your model as a computation graph, which makes debugging much easier. Plus, it catches issues that regular PyTorch might miss.

Profile your memory usage. torch.cuda.memory_summary() is invaluable. If your memory usage is growing over time, you’ve got a leak somewhere.

Common Gotchas

Don’t use torch.no_grad() everywhere. It’s tempting to wrap everything in torch.no_grad(), but it can hide gradient issues. Use it only when you actually don’t need gradients.

Be careful with torch.cat() and torch.stack(). They’re not the same thing, and using the wrong one will give you shape errors that are hard to debug.

Watch out for in-place operations. tensor += 1 might not do what you think it does. Use tensor = tensor + 1 instead, or be very careful about when you use in-place operations.

Production Debugging

Log everything. Tensor shapes, device locations, memory usage, gradient norms - log it all. You’ll thank yourself when something breaks in production.

Use torch.jit.script() for complex models. It catches a lot of issues that regular PyTorch doesn’t, and it makes your models faster too.

Test on different devices. Your model might work on your GPU but fail on someone else’s. Test on CPU, different GPUs, and different PyTorch versions.

The Bottom Line

PyTorch debugging is about understanding the computation graph, not just the code. Most issues come from not thinking about how tensors flow through your model.

The key is to catch issues early, log everything, and test thoroughly. PyTorch is powerful, but with great power comes great responsibility to debug properly.

Struggling with PyTorch debugging? Contact us for help with model optimization and debugging strategies.