Hi, I get the same error with a slightly different situation. My model was working fine with torch 0.4 and python 2.7. Now, after updating to python 3.5 and torch 1.0.1 it gives that same error when adding the rnn layer:
class BidirectionalLSTM(nn.Module):
    # Module to extract BLSTM features from convolutional feature map

    def __init__(self, nIn, nHidden, nOut):
        super(BidirectionalLSTM, self).__init__()

        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
        self.embedding = nn.Linear(nHidden * 2, nOut)

    def forward(self, input):
        recurrent, _ = self.rnn(input)
        T, b, h = recurrent.size()
        t_rec = recurrent.view(T * b, h)

        output = self.embedding(t_rec)  # [T * b, nOut]
        output = output.view(T, b, -1)

        return output


class RecognitionModel(nn.Module):


    def __init__(self,feature_size = 256,pool_h=32,alphabet_len=38):
        super(RecognitionModel,self).__init__()
        self.alphabet_len = alphabet_len
        nh= 256
        self.conv1 = nn.Conv2d(feature_size, nh, kernel_size=3, padding=1)
        self.act1 = nn.ReLU()

        self.conv2 = nn.Conv2d(nh, nh, kernel_size=3, padding=1)
        self.act2 = nn.ReLU()

        self.rnn = nn.Sequential(
        BidirectionalLSTM(pool_h*nh, nh,nh),
        BidirectionalLSTM(nh,nh, pool_h*nh))


        self.output = nn.Linear(in_features=nh*pool_h,out_features=self.alphabet_len)

    def forward(self,x):
        x= self.conv1(x)
        x = self.act1(x)

        x = self.conv2(x)
        x = self.act2(x)

        x=x.view(x.shape[0],x.shape[1]*x.shape[2],x.shape[3]).transpose(1,2)

        x = self.rnn(x)
        output = self.output(x)
        return output


If I remove the declaration of self.rnn and it’s call in forward the model works fine, but when adding it it gives the error
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED
The code used to work with CUDA 8.0 and GeForce GTX 1080 Ti GPU, now I use CUDA 10.0 with a TITAN RTX.
Any input is appreciated.