Hi,
In my LSTM model, I found that the tensor variables must be re-allocate each iteration(each backward() calling), or the error
RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time.```
will be raised.

Sample code:
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
BATCH_SIZE = 1

class LSTM(nn.Module):
    """
    LSTM extractor for Peptide
    """

    def __init__(self, input_channels, lstm_hidden_size=100, lstm_num_layers=2):
        super(LSTM, self).__init__()
        self.lstm = nn.LSTM(input_channels,
                            lstm_hidden_size,
                            lstm_num_layers,
                            bias=False,
                            bidirectional=True)
        self.lstm_num_layers = lstm_num_layers
        self.lstm_hidden_size = lstm_hidden_size
        self.lstm_states = self.int_hidden_states()
    
    def int_hidden_states(self):
        return (
            torch.zeros((self.lstm_num_layers*2, BATCH_SIZE, self.lstm_hidden_size)).to(device),
            torch.zeros((self.lstm_num_layers*2, BATCH_SIZE, self.lstm_hidden_size)).to(device),
        )

    def forward(self, peptide):
        lstm_out, self.lstm_states = self.lstm(peptide, self.lstm_states)
        return lstm_out[-1]


def main():
    model = LSTM(10)
    for _ in range(5):
        a = model.forward(torch.rand(15, BATCH_SIZE, 10, dtype=torch.float).to(device))

        print(a.shape)

        ## This is required
        model.lstm_states = model.int_hidden_states()

        a.backward(torch.rand(a.shape))
        print('done')

if __name__ == '__main__':
    main()

What’s the logical behind this? Why parameter tensors of a module/layer do not require these actions?
Ii is good to use retain_graph=True here?