Printing attack adversaries in a verbose mode, for better debugging purpose

[stefan-matcovici]

Full stacktrace:

/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [64,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [65,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [66,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [67,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [68,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
............................
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [30,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
/pytorch/aten/src/THC/THCTensorScatterGather.cu:188: void THCudaTensor_scatterFillKernel(TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, Real, int, IndexType) [with IndexType = unsigned int, Real = long, Dims = 2]: block: [0,0,0], thread: [31,0,0] Assertion `indexValue >= 0 && indexValue < tensor.sizes[dim]` failed.
Traceback (most recent call last):
  File "adversarial_test_model.py", line 56, in <module>
    advdata = adversary.perturb(clndata, target)
  File "/nfshomes/smatcovi/works/pilot-project/ppenv/lib/python3.7/site-packages/advertorch/attacks/carlini_wagner.py", line 209, in perturb
    final_l2distsqs = torch.FloatTensor(final_l2distsqs).to(x.device)
RuntimeError: CUDA error: device-side assert triggered

I am trying to use the CarliniWagnerL2Attack. I am using the code tutorial_train_mnist.py but for checking robust accuracy. When I run locally (GTX1050) everything works. When I try to run on a nvidia K20 or M40 I get this error.

[stefan-matcovici]

After running with CUDA_LAUNCH_BLOCKING=1, I might get a more specific stacktrace:

Traceback (most recent call last):
  File "adversarial_test_model.py", line 56, in <module>
    advdata = adversary.perturb(clndata, target)
  File "/nfshomes/smatcovi/works/pilot-project/ppenv/lib/python3.7/site-packages/advertorch/attacks/carlini_wagner.py", line 207, in perturb
    y_onehot = to_one_hot(y, self.num_classes).float()
  File "/nfshomes/smatcovi/works/pilot-project/ppenv/lib/python3.7/site-packages/advertorch/utils.py", line 83, in to_one_hot
    y_one_hot = y.new_zeros((y.size()[0], num_classes)).scatter_(1, y, 1)

[gwding]

I did some search, and found something like the following
fastai/fastai#440
https://forums.fast.ai/t/pytorch-not-working-with-an-old-nvidia-card/14632/17
https://discuss.pytorch.org/t/pytorch-no-longer-supports-this-gpu-because-it-is-too-old/13803
https://en.wikipedia.org/wiki/Nvidia_Tesla

It seems that pre-complied pytorch does not work old nvidia gpus, and compile from scratch might make it work. But I don't have an old gpu in hand so cannot do this test myself.

[stefan-matcovici]

I might have sent you on the wrong track. Sorry about that.
I was calling the attack with True as an argument for the num_of_classes. Maybe this is an issue after all because this wasn't caught until the GPU computations.
Also, as a future feature, I think it would be a great idea to add some logging about the attack like how many iterations, the current loss, etc.

That being said, you may close the issue and sorry one more time

[gwding]

@stefan-matcovici Thanks for the suggestion. I changed the title of this issue, and labeled it as a todo item.