CODEWITHSUNDEEP
gpupytorchgoogle-colaboratory
u2gilles
u2gilles

Reputation: 7393

Pytorch on google-colaboratory GPU - Illegal memory access

I am using pytorch(0.4.0) on google-colaboratory ( NVIDIA-SMI 396.44 Driver Version: 396.44)

When running my code outside any function, I am able to send pytorch tensors and model to the GPU :

...
model.cuda()
data_tensor = data_tensor.cuda()
...

And my CNN model is trained successfully with 98% accurancy.

But when I put the same code in a function,

def main(...):
    ....
    model.cuda()
    data_tensor= data_tensor.cuda()
    ...

if __name__ == "__main__":
    main('...)

I have the following error:

cuda runtime error (77) : an illegal memory access was encountered at /pytorch/aten/src/THC/generic/THCTensorCopy.c:20

UPDATE(18/11/21):

It turned out that being part or not of a function is irrelevant. Usually, I have first a CUDNN_STATUS_EXECUTION_FAILED error then the second time a cuda runtime error (77) as shown below. But it sometimes works a few times before failing.

CUDNN_STATUS_EXECUTION_FAILED (first try) :

RuntimeError                              Traceback (most recent call last)
<ipython-input-27-53476e08e017> in <module>()
      1 main('mnist', 'to', 'ndd', Xd=16, epo=5, bs=100, tXn=-1, vXn=300,
----> 2      lr=0.05, suf="s1", n_class=10, cuda=True)

<ipython-input-23-918584456207> in main(ds, framework, format, Xd, epo, bs, tXn, vXn, lr, suf, n_class, cuda)
     12     opt = torch.optim.SGD(net.parameters(), lr)
     13 
---> 14     train(net, opt, Xd, epo, bs, cuda, tXn, tX, tT, vX, vT,lr)
     15 

<ipython-input-26-6b574a9e8af6> in train(model, optimizer, Xd, epo, bs, cuda, Xn, tX, tT, vX, vT, lr)
     26             #t = t.cuda()
     27             optimizer.zero_grad()
---> 28             z = model(x)
     29             bat_loss = criterion(z, t)
     30             bat_loss.backward()

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    489             result = self._slow_forward(*input, **kwargs)
    490         else:
--> 491             result = self.forward(*input, **kwargs)
    492         for hook in self._forward_hooks.values():
    493             hook_result = hook(self, input, result)

<ipython-input-22-b4bc2e0b39b8> in forward(self, X)
     10         H0 = torch.zeros(self.n_H, X.size(0), self.Wh)
     11         C0 = torch.zeros(self.n_H, X.size(0), self.Wh)
---> 12         O, (Hn, Cn), = self.lstm1(X, (H0, C0))
     13         O = self.linear1(O[:, -1, :])
     14         return O

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    489             result = self._slow_forward(*input, **kwargs)
    490         else:
--> 491             result = self.forward(*input, **kwargs)
    492         for hook in self._forward_hooks.values():
    493             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
    190             flat_weight=flat_weight
    191         )
--> 192         output, hidden = func(input, self.all_weights, hx, batch_sizes)
    193         if is_packed:
    194             output = PackedSequence(output, batch_sizes)

/usr/local/lib/python3.6/dist-packages/torch/nn/_functions/rnn.py in forward(input, *fargs, **fkwargs)
    321             func = decorator(func)
    322 
--> 323         return func(input, *fargs, **fkwargs)
    324 
    325     return forward

/usr/local/lib/python3.6/dist-packages/torch/nn/_functions/rnn.py in forward(input, weight, hx, batch_sizes)
    285             batch_first, dropout, train, bool(bidirectional),
    286             list(batch_sizes.data) if variable_length else (),
--> 287             dropout_ts)
    288 
    289         if cx is not None:

RuntimeError: CUDNN_STATUS_EXECUTION_FAILED

cuda runtime error (77) (other tries):

RuntimeError                              Traceback (most recent call last)
<ipython-input-28-53476e08e017> in <module>()
      1 main('mnist', 'to', 'ndd', Xd=16, epo=5, bs=100, tXn=-1, vXn=300,
----> 2      lr=0.05, suf="s1", n_class=10, cuda=True)

<ipython-input-23-918584456207> in main(ds, framework, format, Xd, epo, bs, tXn, vXn, lr, suf, n_class, cuda)
     12     opt = torch.optim.SGD(net.parameters(), lr)
     13 
---> 14     train(net, opt, Xd, epo, bs, cuda, tXn, tX, tT, vX, vT,lr)
     15 

<ipython-input-26-6b574a9e8af6> in train(model, optimizer, Xd, epo, bs, cuda, Xn, tX, tT, vX, vT, lr)
      4     if cuda and torch.cuda.is_available():
      5         print("tX type (before):", tX.type())
----> 6         model.cuda()
      7         tX = tX.cuda()
      8         tT = tT.cuda()

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in cuda(self, device)
    247             Module: self
    248         """
--> 249         return self._apply(lambda t: t.cuda(device))
    250 
    251     def cpu(self):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _apply(self, fn)
    174     def _apply(self, fn):
    175         for module in self.children():
--> 176             module._apply(fn)
    177 
    178         for param in self._parameters.values():

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in _apply(self, fn)
    109 
    110     def _apply(self, fn):
--> 111         ret = super(RNNBase, self)._apply(fn)
    112         self.flatten_parameters()
    113         return ret

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _apply(self, fn)
    180                 # Tensors stored in modules are graph leaves, and we don't
    181                 # want to create copy nodes, so we have to unpack the data.
--> 182                 param.data = fn(param.data)
    183                 if param._grad is not None:
    184                     param._grad.data = fn(param._grad.data)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in <lambda>(t)
    247             Module: self
    248         """
--> 249         return self._apply(lambda t: t.cuda(device))
    250 
    251     def cpu(self):

RuntimeError: cuda runtime error (77) : an illegal memory access was encountered at /pytorch/aten/src/THC/generic/THCTensorCopy.c:20

Upvotes: 2

Views: 4520

Answers (1)

u2gilles
u2gilles

Reputation: 7393

It now works with Pytorch 1.0 using:

!pip3 install https://download.pytorch.org/whl/cu80/torch-1.0.0-cp36-cp36m-linux_x86_64.whl

Upvotes: 2

Related Questions