RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle)` with GPU only

user_10621921 0
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I'm working on the CNN with one-dimensional signal. It works totally fine with CPU device. However, when I training model in GPU, CUDA error occurred. I set os.environ['CUDA_LAUNCH_BLOCKING'] = "1" command after I got RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling cublasCreate(handle). With doing this, a cublasSgemm error occurred instead of cublasCreate error. Though the nvidia document doubt the hardware problem, I can training other CNN with images without any error. Below is my code for the data loading and set data in training model.

    idx = np.arange(len(dataset))  # dataset & label shuffle in once
    np.random.shuffle(idx)

    dataset = dataset[idx]
    sdnn = np.array(sdnn)[idx.astype(int)]        

    train_data, val_data = dataset[:int(0.8 * len(dataset))], dataset[int(0.8 * len(dataset)):]
    train_label, val_label = sdnn[:int(0.8 * len(sdnn))], sdnn[int(0.8 * len(sdnn)):]
    train_set = DataLoader(dataset=train_data, batch_size=opt.batch_size, num_workers=opt.workers)

    for i, data in enumerate(train_set, 0):  # data.shape = [batch_size, 3000(len(signal)), 1(channel)] tensor

        x = data.transpose(1, 2)
        label = torch.Tensor(train_label[i * opt.batch_size:i * opt.batch_size + opt.batch_size])
        x = x.to(device, non_blocking=True)
        label = label.to(device, non_blocking=True) # [batch size]
        label = label.view([len(label), 1])
        optim.zero_grad()

        # Feature of signal extract
        y_predict = model(x) # [batch size, fc3 output] # Error occurred HERE
        loss = mse(y_predict, label)

Below is the error message from this code.

File C:/Users/Me/Desktop/Me/Study/Project/Analysis/Regression/main.py", line 217, in Processing
    y_predict = model(x) # [batch size, fc3 output]
  File "C:\Anaconda\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\ME\Desktop\ME\Study\Project\Analysis\Regression\cnn.py", line 104, in forward
    x = self.fc1(x)
  File "C:\Anaconda\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Anaconda\envs\torch\lib\site-packages\torch\nn\modules\linear.py", line 91, in forward
    return F.linear(input, self.weight, self.bias)
  File "C:\Anaconda\envs\torch\lib\site-packages\torch\nn\functional.py", line 1674, in linear
    ret = torch.addmm(bias, input, weight.t())
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`

I've tried to solve this error for weeks but can't find the solution. If you can see anything wrong here, please let me know.

conv-neural-network pytorch gpu python
3 Answers

Please know that, it can also be caused if you have a mismatch between the dimension of your input tensor and the dimensions of your nn.Linear module. (ex. input.shape = (a, b) and nn.Linear(c, c, bias=False) with c not matching).

user_64122120

With searched with the partial keywords, I finally got the similar situation. Because of the stability, I used the CUDA 10.2 version. The reference asked to upgrade CUDA toolkit to higher - 11.2 in my case - and problem solved! I've deal with other training processes but this one only caused error. As the CUDA error occurred with various reasons, changes the version could be counted for solution.

user_106219210

Rightly said by Loich, and I think shape mismatch is a prime reason why this error is thrown.

I too got this error while training a image recognition model, where the shapes of - output of final Conv2d and input of first Linear layers was not same.

If none of that works, then the best thing to do is to run a smaller version of the process on CPU and recreate the error. When running it on CPU instead of CUDA, you will get a more useful traceback that can solve your error.

One remedy explained in this answer (quoted above) is, with disabled gpu try to recreate similar situation by executing the code (without changing any line) on cpu, it should give better and understandable error.

P.S.: Although, the original question states that their code is executing fine on cpu, I've posted this answer for someone with similar error and not as a result of Cuda version mismatch.

user_126487890
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