I read the tutorial about lstm. http://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html 102
I tried to use the cuda but failed. I do not know how to figure the bug.
Here is the code that I change.
model = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix))
use_gpu=torch.cuda.is_available()
if(use_gpu):
    model=model.cuda()

loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# See what the scores are before training
# Note that element i,j of the output is the score for tag j for word i.
#print(tag_scores)

for epoch in range(10):  # again, normally you would NOT do 300 epochs, it is toy data
    #print(epoch,end=',')

    for sentence, tags in training_data:
        # Step 1. Remember that Pytorch accumulates gradients.
        # We need to clear them out before each instance
        model.zero_grad()

        # Also, we need to clear out the hidden state of the LSTM,
        # detaching it from its history on the last instance.
        model.hidden = model.init_hidden()

        # Step 2. Get our inputs ready for the network, that is, turn them into
        # Variables of word indices.
        sentence_in = prepare_sequence(sentence, word_to_ix)
        if(use_gpu):
            sentence_in=sentence_in.cuda()
        

        targets = prepare_sequence(tags, tag_to_ix)
        if(use_gpu):
            targets=targets.cuda()
        
        # Step 3. Run our forward pass.
        tag_scores = model(sentence_in)

        # Step 4. Compute the loss, gradients, and update the parameters by
        #  calling optimizer.step()
        loss = loss_function(tag_scores, targets)
        loss.backward()
        optimizer.step()

Traceback (most recent call last):
  File "ner.py", line 169, in <module>
    tag_scores = model(sentence_in)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "ner.py", line 121, in forward
    lstm_out, self.hidden = self.lstm(embeds.view(len(sentence), 1, -1), self.hidden)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/rnn.py", line 162, in forward
    output, hidden = func(input, self.all_weights, hx)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/_functions/rnn.py", line 351, in forward
    return func(input, *fargs, **fkwargs)
  File "/usr/local/lib/python3.5/dist-packages/torch/autograd/function.py", line 284, in _do_forward
    flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
  File "/usr/local/lib/python3.5/dist-packages/torch/autograd/function.py", line 306, in forward
    result = self.forward_extended(*nested_tensors)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/_functions/rnn.py", line 293, in forward_extended
    cudnn.rnn.forward(self, input, hx, weight, output, hy)
  File "/usr/local/lib/python3.5/dist-packages/torch/backends/cudnn/rnn.py", line 242, in forward
    fn.hx_desc = cudnn.descriptor(hx)
  File "/usr/local/lib/python3.5/dist-packages/torch/backends/cudnn/__init__.py", line 310, in descriptor
    descriptor.set(tensor)
  File "/usr/local/lib/python3.5/dist-packages/torch/backends/cudnn/__init__.py", line 116, in set
    self, _typemap[tensor.type()], tensor.dim(),
KeyError: 'torch.FloatTensor'