Memory leak when performing inner loop on a copy

When running the following script, memory usage seems to continually increase (not all iterations, but many of them in the beginning). Explicitly decrementing the reference counts for the functional model/differentiable optimizer seems to help, but not completely fix the issue. Script for reproducing attached. Monitoring GPU memory usage with watch -n 0.1 nvidia-smi. Running PyTorch version 1.3.0, torchvision 0.4.1, higher version 0.1.3@59537fa. Let me know if any other information would be helpful.

Code to reproduce the issue:

import torch, torchvision
import higher
import time
from copy import deepcopy


print(torch.__version__, torchvision.__version__)

inner_loop_copy = True # Setting this to False gives no memory usage increase

device = torch.device('cuda:0')
model = torchvision.models.resnet18().to(device)
opt = torch.optim.SGD(model.parameters(), lr=1e-5)

for idx in range(100):
    print(idx)
    if inner_loop_copy:
        model_ = deepcopy(model)
        opt_ = torch.optim.SGD(model_.parameters(), lr=1e-5)
    else:
        model_ = model
        opt_ = opt

    with higher.innerloop_ctx(model_, opt_) as (fm, do):
        pass

    # del fm, do # Uncommenting this helps, but doesn't completely eliminate the memory usage increase

[eric-mitchell]

Got it. Not sure I will be able to devote much time to it either. For the moment, though, it seems to be at least partially alleviated by explicitly deleting the functional model/differentiable optimizer.

[egrefen]

Fixed in #15 (we believe). Please let us know if you have ongoing issues.