Trying to bring Ignite into my training pipeline, but I’ve come across some issues with it, any help would be appreciated.
Not sure if I’m doing something wrong, but I tried to use Ignite with a basic LSTM. It kept throwing “RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed”, caused by:
def forward(self, sentence):
        num_timesteps = sentence.size()[1]
        lstm_out, self.hidden = self.lstm(
            sentence.view(num_timesteps, 1, -1), self.hidden)
        score_space = self.hidden2output(lstm_out.view(num_timesteps, -1))
        scores = torch.sigmoid(score_space).view(score_space.size()[1], score_space.size()[0])

        return scores   

This was based on the tutorial code for Pytorch, and works with the training loop made from vanilla Pytorch.
I fixed the error by changing it to:
def forward(self, sentence):
        num_timesteps = sentence.size()[1]
        lstm_out, hidden_temp = self.lstm(
            sentence.view(num_timesteps, 1, -1), (self.hidden, self.cell))
        self.hidden = hidden_temp[0]
        self.cell = hidden_temp[1]
        self.hidden = self.hidden.detach()
        self.cell = self.cell.detach()
        score_space = self.hidden2output(lstm_out.view(num_timesteps, -1))
        scores = torch.sigmoid(score_space).view(score_space.size()[1], score_space.size()[0])

        return scores

Is this intended behaviour, and does my fix negatively impact something I haven’t taken into account?
And also, I find that it takes over 3x as many epochs to converge on a single sample using Ignite vs. my own training loop, could someone shed light on that behaviour?
Thanks!