I have access to Tesla K20c, I am running ResNet50 on CIFAR10 dataset... Then I get the error as:
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generated/../generic/THCTensorMathPointwise.cu line=265 error=59 : device-side assert triggered
Traceback (most recent call last): File "main.py", line 109, in <module> train(loader_train, model, criterion, optimizer) File "main.py", line 54, in train optimizer.step() File "/usr/local/anaconda35/lib/python3.6/site-packages/torch/optim/sgd.py", line 93, in step d_p.add_(weight_decay, p.data)
RuntimeError: cuda runtime error (59) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generated/../generic/THCTensorMathPointwise.cu:265How to resolve this error?
69 Answers
I have encountered this problem several times. And I find it to be an index issue.
For example, if your ground truth label starts at 1: target = [1,2,3,4,5], then you should subtract 1 for every label, change it to: [0,1,2,3,4].
This solves my problem every time.
5In general, when encountering cuda runtine errors, it is advisable to run your program again using the CUDA_LAUNCH_BLOCKING=1 flag to obtain an accurate stack trace.
In your specific case, the targets of your data were too high (or low) for the specified number of classes.
1I encountered this error when running BertModel.from_pretrained('bert-base-uncased'). I found the solution by moving to the CPU when the error message changed to 'IndexError: index out of range in self'. Which led me to this post. The solution was to truncate sentences to length 512.
One way to raise the "CUDA error: device-side assert triggered" RuntimeError, is by indexing into a GPU torch.Tensor using a list having out of dimension indices.
So, this snippet would raise an IndexError with the message "IndexError: index 3 is out of bounds for dimension 0 with size 3", not the CUDA error
data = torch.randn((3,10), device=torch.device("cuda"))
data[3,:]whereas, this one would raise the CUDA "device-side assert triggered" RuntimeError
data = torch.randn((3,10), device=torch.device("cuda"))
indices = [1,3]
data[indices,:]which could mean that in case of class labels, such as in the answer by @Rainy, it's the final class label (i.e. when label == num_classes) that is causing the error, when the labels start from 1 rather than 0.
Also, when device is "cpu" the error thrown is IndexError such as the one thrown by the first snippet.
This error can be made more elaborative if you switch to CPU first. Once you switch to CPU, it will show the exact error, which is most probably related to the indexing problem, which is IndexError: Target 2 is out of bounds in my case and could be related in yours case. The issue is "How many classes are you currently using and what is the shape of your output?", you can find the classes like this
max(train_labels)
min(train_labels)which in my case gave me 2 and 0, the problem is caused by missing 1 index, so a quick hack is to quickly replace all 2s with 1s , which can be done through this code:
train_=train.copy()
train_['label'] =train_['label'].replace(2,1)then you run the same code and see the results, it should work
class NDataset(torch.utils.data.Dataset): def __init__(self, encodings, labels): self.encodings = encodings self.labels = labels def __getitem__(self, idx): item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} item['labels'] = torch.tensor(self.labels[idx]) return item def __len__(self): return len(self.labels)
train_dataset = NDataset(train_encodings, train_labels)
val_dataset = NDataset(val_encodings, val_labels)
test_dataset = NDataset(test_encodings, test_labels)I found I got this error when I had a label with an invalid value.
1Happened to me multiple time when the target or label of the bce or ce loss would be <= 0.
This can also be caused by nan values in your model input data. One easy way to "treat" this problem is to convert any that pop up into zeros on the fly:
batch_data = batch_data[batch_data != batch_data] = 0Another situation where this can happen: you are training a dataset with more classes than you last layer expects. It's another unexpected index situation
