Just wanted to make a thread with some information I wish I found before spending 4 hours trying to debug a memory leak. Most of the memory leak threads I found were unhelpful so I wanted to throw together a few tips here.
causes of leaks:
i) most threads talk about leaks caused by creating an array that holds tensors, if you continually add tensors to this array, you will at some point fill up the memory.
ii) something i didn’t see mentioned is Autograd leaks, i.e. if you do a computation with a tensor and store it somewhere that never gets back-propped, you will never clear the computational graph and so the computational graph just keeps growing and growing. In my case I was measuring solution sparsity with a penalty function that was never used for backprop, I was then calculating the exponential running average of this which is why even after penalty would get garbage collected, the computational graph for the average remained. This issue can be avoided by using .detach() for any tensor computation that isn’t strictly for training the network.
torch.cuda.empty_cache() (in most cases) is nothing more than a bandaid, its not going to fix the underlying issue though it may delay the error for a while by clearing other stuff while ignoring the actual problem
the most useful way I found to debug is to use torch.cuda.memory_allocated() and torch.cuda.max_memory_allocated() to print a percent of used memory at the top of the training loop. Then look at your training loop, add a continue statement right below the first line and run the training loop. If your memory usage holds steady, move the continue to the next line and so on until you find the leak.
Thanks a lot. having a clearer title would help alot imho. sth like, “how to find and fix a possible memory leak” or “what I found helpful in fixing a memory leak” or things like this .
Anyway enjoyed this and thank you for this.
Another one, a mix between 1.i) and 1.ii): if you append tensors with computed gradients to python lists for tracking purposes, the gradients also get inserted in the list and it grows a bit more than expected!
Also, leaks can find their way in computer memory (RAM, not GPU mem), so it can be useful to log RAM usage as well during training.
I’m having trouble finding my memory leak, and I’m trying your 3rd tip which is using the continue after each line and check. I have a small question about it: if we continue right after a forward call, should the memory consumption stay constant? Here is my code:
Yeah, the goal is to just isolate each line individually until you find the part with the memory leak. If you put the continue above that line without issue, but below it there’s a leak then that’s your problem. If I were to guess this looks like an autograd memory leak i.e. pytorch is storing each calculation step so it can calculate the gradient of the loss but if you never actually do the gradient step, it just continually stores a record of all calculations.
Try using a “with no gradient:” statement above your forward call to check if that’s the issue.
Thanks for the prompt reply, but when I run with the wrapper torch.no_grad(), this error occurs:
File "main_pred.py", line 145, in <module>
train_res = train_model(train_loader, optim, epoch, args.epochs, writer, model, args, weight_balancing, device)
File "/home/chris/CSD_graph_detection/modules/utils.py", line 321, in train_model
return eval_model(loader, optim, epoch, epochs, writer, model, args, weight_balancing, device, True)
File "/home/chris/CSD_graph_detection/modules/utils.py", line 228, in eval_model
File "/home/chris/anaconda3/envs/CSD/lib/python3.7/site-packages/torch/tensor.py", line 198, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/chris/anaconda3/envs/CSD/lib/python3.7/site-packages/torch/autograd/__init__.py", line 100, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
hi，I have same problem as ii) .When I am using save_tensor, I have some layer forward, but this layer do not backward. The memory would be leaked. I could not use .detach() . Do you have any function to slove the problem.
Thank you for this thread. I was having issues with my training step because the model would occupy my entire RAM and would just freeze mid-training. After reading your thread and looking carefully at my code, I noticed my custom loss function wasn’t using detach on the tensors I was creating, and thus it was freezing everything!