traceback

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-70-1639bf8d1c6f> in <module>()
----> 1 model.forward(x)

<ipython-input-66-0c01578a2402> in forward(self, x)
     11         c0 = Variable(torch.zeros([1, batch_size, self.hidden_dim]), requires_grad=False)
     12         print(h0, c0)
---> 13         fx, _ = self.lstm.forward(x, (h0, c0))
     14         return self.linear.forward(fx[-1])
     15 

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
     89             dropout_state=self.dropout_state
     90         )
---> 91         output, hidden = func(input, self.all_weights, hx)
     92         if is_packed:
     93             output = PackedSequence(output, batch_sizes)

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, *fargs, **fkwargs)
    341         else:
    342             func = AutogradRNN(*args, **kwargs)
--> 343         return func(input, *fargs, **fkwargs)
    344 
    345     return forward

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, weight, hidden)
    241             input = input.transpose(0, 1)
    242 
--> 243         nexth, output = func(input, hidden, weight)
    244 
    245         if batch_first and batch_sizes is None:

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, hidden, weight)
     81                 l = i * num_directions + j
     82 
---> 83                 hy, output = inner(input, hidden[l], weight[l])
     84                 next_hidden.append(hy)
     85                 all_output.append(output)

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, hidden, weight)
    110         steps = range(input.size(0) - 1, -1, -1) if reverse else range(input.size(0))
    111         for i in steps:
--> 112             hidden = inner(input[i], hidden, *weight)
    113             # hack to handle LSTM
    114             output.append(isinstance(hidden, tuple) and hidden[0] or hidden)

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in LSTMCell(input, hidden, w_ih, w_hh, b_ih, b_hh)
     28 
     29     hx, cx = hidden
---> 30     gates = F.linear(input, w_ih, b_ih) + F.linear(hx, w_hh, b_hh)
     31 
     32     ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias)
    447 def linear(input, weight, bias=None):
    448     state = _functions.linear.Linear()
--> 449     return state(input, weight) if bias is None else state(input, weight, bias)
    450 
    451 

/home/monorhesus/.conda/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/linear.py in forward(self, input, weight, bias)
      8         self.save_for_backward(input, weight, bias)
      9         output = input.new(input.size(0), weight.size(0))
---> 10         output.addmm_(0, 1, input, weight.t())
     11         if bias is not None:
     12             # cuBLAS doesn't support 0 strides in sger, so we can't use expand

RuntimeError: size mismatch, m1: [20 x 1], m2: [5 x 512] at /py/conda-bld/pytorch_1493681908901/work/torch/lib/TH/generic/THTensorMath.c:1237