Appendix E: Common Errors & Debugging Tips in PyTorch
Abstract: Common errors in PyTorch often stem from issues in tensor manipulation, gradient computation, and device management. Debugging these errors typically involves systematic checks and utilizing PyTorch's built-in tools. Common Errors: Shape Mismatches: Occur during operations like matrix multiplication, concatenation, or view/reshape operations when tensor dimensions do not align. Debugging Tip: Use tensor.shape or tensor.size() to inspect dimensions at various points in your code. RuntimeError: Trying to backward through the graph a second time : This happens when attempting to compute gradients for a tensor that has already been freed or has its graph detached. Debugging Tip: Ensure loss.backward() is called only once per computation graph. If you need to retain the graph for multiple backward calls, use loss.backward(retain_graph=True) , but be mindful of memory usage. Alternatively, recom...