Increasingly occupied GPU memory

[LeePleased]

Hi~, I meet some problem while running your code on GPU. During training, the program will unexpectly consume GPU memory continuously, like from 2000 Mb -> 3000 Mb -> ... -> and finally explode. I use Python 3.6, Pytorch 0.4 and GPU with 12 GB memory.

[LeePleased]

I guess it may relate to RNN compact weight, but I don't know how to fix it.

[S-Abdelnabi]

Hi,

Have you found a fix to this? I am having a similar issue. It was working on pytorch 0.4.1, the compact weight warning was displayed once at the beginning only, and it continues normally until the end of the training. However, I updated to pytorch 1.2 and I am facing the same issue as yours. The warning is displayed at each call of the forward, and it stops training with OOM after around 100 epochs. I tried to call flatten_parameters() at the forward function of WeightDrop class. But I still get the warning.

Thanks a lot.

[rewicks]

Also having this issue. I don't think it's related to the flatten_parameters() warnings. It seems to be correlated with the optimizer--specifically, the memory usage only starts to increase after it is switched to ASGD.

[AndreaLK3]

@rewicks good call, the memory usage increases only with the ASGD optimizer. I think I have found the problem with it, but I am not sure how to solve it.

I printed the tensors living in memory using the GPU memory profiling code mentioned at https://discuss.pytorch.org/t/how-to-debug-causes-of-gpu-memory-leaks/6741/3 , and used the PyCharm debugger to see the variables during training.

The ASGD optimizer is an object that contains:

• defaults: its default settings
• param_groups = list containing 1 dictionary, with the hyperparameters ‘lr’, ‘alpha’, ‘lambd’, ‘t0’, ‘weight_decay’ and ’params’=a list of 14 Parameters w/Tensors
• state = defaultdict:20 {Parameter containing Tensor, Parameter containing Tensor, etc etc.}

As the epochs go on and on, optimizer.state will contain 20,23,26,29,... (un-named) Tensors. My hypothesis:

• either the ASGD averages over all the previous epochs, and thus eventually breaks memory
• or, more likely, the Tensors with the past gradients are never de-allocated from memory, we always allocate into new ones

Should we change the t0 parameter, increasing it by 1 each epoch? Or should we delete manually tensors from optimizer.state? I would like to hear your opinions, and possibly from the authors as well - although maybe they didn't have this problem because they did not have resource constraints (I break memory on a GPU that has 10GB of memory)

[AndreaLK3]

I have found a solution. If it works for others as well, this issue can be closed.

I have modified the ASGD optimizer using @mourga's port of AWD-LSTM for PyTorch 1.2.0, from: https://github.com/mourga/awd-lstm-lm

In particular, in main.py, you have to replace:

• lines 243-245 with :

for prm in model.parameters():
       if prm in optimizer.state.keys():
                    tmp[prm] = prm.data.detach()
                    prm.data = optimizer.state[prm]['ax'].detach()

• lines 259-260 with:

for prm in model.parameters():
      if prm in tmp.keys():
             prm.data = tmp[prm].detach()
             prm.requires_grad = True
del tmp

[zhilizju]

I have found a solution. If it works for others as well, this issue can be closed.

I have modified the ASGD optimizer using @mourga's port of AWD-LSTM for PyTorch 1.2.0, from: https://github.com/mourga/awd-lstm-lm

In particular, in main.py, you have to replace:

• lines 243-245 with :

for prm in model.parameters():
       if prm in optimizer.state.keys():
                    tmp[prm] = prm.data.detach()
                    prm.data = optimizer.state[prm]['ax'].detach()

• lines 259-260 with:

for prm in model.parameters():
      if prm in tmp.keys():
             prm.data = tmp[prm].detach()
             prm.requires_grad = True
del tmp

hi, @AndreaLK3. It works for me as well. However, I don‘t achieve the perplexities that this instruction declares.

The instruction below trains a PTB model that without finetuning achieves perplexities of approximately 61.2 / 58.8 (validation / testing) .python main.py --batch_size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --epoch 500 --save PTB.pt

I just achieve perplexities of 64.74/62.23(validation/testing) with the same command. My torch version is 1.5.0 and cuda version is 10.1. I'd like to know your experiment result and your advice.