Pytorch: Error in DataParallel for RNN model - pytorch

Question

I'm trying to use torch.nn.DataParallel for a RNN model. My model looks like this:
class EncoderRNN(nn.Module):
def __init__(self, vocal_size, hidden_size):
super(EncoderRNN, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(vocal_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
def forward(self, input_batch, input_batch_length, hidden):
embedded = self.embedding(input_batch)
packed_input = nn.utils.rnn.pack_padded_sequence(embedded, input_batch_length.cpu().numpy(), batch_first=True)
output, hidden = self.gru(packed_input, hidden)
return output, hidden
class DecoderRNN(nn.Module):
def __init__(self, hidden_size, vocab_size):
super(DecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
self.out = nn.Linear(hidden_size, vocab_size)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, target_batch, target_batch_length, hidden, train=False):
embedded = self.embedding(target_batch)
output = F.relu(embedded)
if train:
# minus 1 to eliminate <EOS>
packed_target = nn.utils.rnn.pack_padded_sequence(output, (target_batch_length - 1).cpu().numpy(),
batch_first=True)
output, hidden = self.gru(packed_target, hidden)
output = self.softmax(self.out(output[0]))
return output, hidden
And I implemented DataParallel like this when declaring the model:
encoder = nn.DataParallel(encoder)
decoder = nn.DataParallel(decoder)
The code runs on a server with 4 GPUs, and I received following error message:
/home/cjunjie/NLP/DocSummarization/model.py:18: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
output, hidden = self.gru(packed_input, hidden)
Traceback (most recent call last):
File "train.py", line 144, in <module>
train_iteration(encoder, decoder, fileDataSet)
File "train.py", line 110, in train_iteration
target_indices, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
File "train.py", line 41, in train
encoder_output, encoder_hidden = encoder(input_batch, input_batch_length, encoder_hidden)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 357, in __call__
result = self.forward(*input, **kwargs)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 74, in forward
return self.gather(outputs, self.output_device)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 86, in gather
return gather(outputs, output_device, dim=self.dim)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 65, in gather
return gather_map(outputs)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
return type(out)(map(gather_map, zip(*outputs)))
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
return type(out)(map(gather_map, zip(*outputs)))
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/utils/rnn.py", line 39, in __new__
return super(PackedSequence, cls).__new__(cls, *args[0])
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 57, in gather_map
return Gather.apply(target_device, dim, *outputs)
File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/_functions.py", line 58, in forward
assert all(map(lambda i: i.is_cuda, inputs))
AssertionError
I searched for the same problem, but none of them have a solution. Can anyone help?

Answer 1

In order to run the code on GPUs you need to copy both variables and model weights to cuda. I suspect you did not copy model weights to cuda. To do that you need to do
encoder.cuda()
decoder.cuda()