Graph not resetting between backward passes?
I guess I’m missing something obvious here, but why running the model again doesn’t refill the buffers? I was going to implement training in a similar loop. Code to reproduce: <SCODE>import torch as th
import torch.nn as nn
from torch.autograd import Variable

# borrowed from `word_language_model`
class RNNModel(nn.Module):
    """Container module with an encoder, a recurrent module, and a decoder."""

    def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers):
        super(RNNModel, self).__init__()
        self.encoder = nn.Embedding(ntoken, ninp)
        self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, bias=False)
        self.decoder = nn.Linear(nhid, ntoken)

        self.init_weights()

        self.rnn_type = rnn_type
        self.nhid = nhid
        self.nlayers = nlayers

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.fill_(0)
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, input, hidden):
        emb = self.encoder(input)
        output, hidden = self.rnn(emb, hidden)
        decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2)))
        return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden

    def init_hidden(self, bsz):
        weight = next(self.parameters()).data
        if self.rnn_type == 'LSTM':
            return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
                    Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()))
        else:
            return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())

vocab_size = 256
batch_size = 64

model = RNNModel('GRU', vocab_size, 100, 100, 3)
model.train()

optimizer = th.optim.Adam(model.parameters(), lr=1e-2)
criterion = nn.CrossEntropyLoss()

x = Variable(th.LongTensor(50, 64))
x[:] = 1

state = model.init_hidden(batch_size)

print('first pass')
optimizer.zero_grad()
logits, state = model(x, state)
loss = criterion(logits.view(-1, vocab_size), x.view(-1))
loss.backward()
optimizer.step()

print('second pass')
optimizer.zero_grad()
logits, state = model(x, state)
loss = criterion(logits.view(-1, vocab_size), x.view(-1))
loss.backward()   # <-- error occurs here
optimizer.step()<ECODE>
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