Reproducing results on proteins about how_attentive_are_gats HOT 4 CLOSED

That has to be it, library differences. I just cloned the repo and ran bash run_proteins.sh as it is. Funny enough, with n_workers=0 I am using only 3GB of memory.

Anyways, it seems that now I can run your experiments, with the only difference that I am not using BatchSampler, but I guess it is enough to set shuffle=true in NodeDataLoader to obtain the same effect.

I will close the issue and open a new one if I find myself into more problems.

Thanks for the help!

from how_attentive_are_gats.

Hi @adrianjav!

The error you describe happens when you feed the BatchNorm layer with batchsize=1.
In this case, that means that there is only one subgarph:

Since you removed the batch sampler, you probably damaged this logic.

I guess that the solution will be to replace the following in proteins_exp.py

- from dgl.dataloading.pytorch import NodeDataLoader
+ from dgl.dataloading import DataLoader

lines 159-157:

+   train_dataloader = DataLoader(
-   train_dataloader = DataLoaderWrapper(
-       NodeDataLoader(
            graph.cpu(),
            train_idx.cpu(),
            train_sampler,
-          batch_sampler=BatchSampler(len(train_idx), batch_size=train_batch_size),
+         batch_size=train_batch_size,
            num_workers=4,
-       )
    )

lines 171-179:

+   eval_dataloader = DataLoader(
-   eval_dataloader = DataLoaderWrapper(
-       NodeDataLoader(
            graph.cpu(),
            torch.cat([train_idx.cpu(), val_idx.cpu(), test_idx.cpu()]),
            eval_sampler,
-           batch_sampler=BatchSampler(graph.number_of_nodes(), batch_size=32768),
+           batch_size=32768,
            num_workers=4,
-       )
    )

Please update me on how it goes!

from how_attentive_are_gats.

Thanks for the prompt reply.

Quite an oversight on my side. I added the batch size, but I was still running on problems regarding the memory usage. I fixed it doing the following (running at the moment, but it seems ok).

+   train_dataloader = NodeDataLoader(
-   train_dataloader = DataLoaderWrapper(
-       NodeDataLoader(
            graph.cpu(),
            train_idx.cpu(),
            train_sampler,
-          batch_sampler=BatchSampler(len(train_idx), batch_size=train_batch_size),
+         batch_size=train_batch_size,
-           num_workers=4,
+           num_workers=0,
-       )
    )

Out of curiosity: What were the specs of the machine you used for the experiments? I am using a V100 with 16GB of RAM, and with 2 workers, memory usage skyrocketed to 14GB in 10seconds.

from how_attentive_are_gats.

We also used V100 with 16GB of RAM.

I don't know how the changes in the new version of DGL affect memory usage (if any at all).

Please have a look at the arguments in run_proteins.sh and the default arguments in proteins_exp.py which were used for our experiments:

Maybe you use other hyperparameters that affect memory usage.

from how_attentive_are_gats.