Error in LSTM-based RNN training: "IndexError: index out of range" during DataLoader iteration
Author: capdescx
Pull request: no

I'm currently working on training an LSTM-based RNN using PyTorch and encountering an error during the training loop I've tried various solutions to resolve this issue but I'm still facing the same error message. Here's the relevant code snippet

```import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torch.nn.utils.rnn import pad_sequence
import pandas as pd
import json

class RNNModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(RNNModel, self).__init__()
        # Define your layers here
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x):
        embedded = self.embedding(x)
        output, (h_n, c_n) = self.rnn(embedded)
        return self.fc(output)


with open('cleaned_vocab.json', 'r', encoding='utf-8') as vocab_file:
    vocab = json.load(vocab_file)

class CustomDataset(Dataset):
    def __init__(self, csv_path, max_seq_length):
        self.data = pd.read_csv(csv_path)
        self.max_seq_length = max_seq_length
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        text = self.data.loc[idx, 'text']
        tokens = [int(token) for token in text.split()]
        
        if len(tokens) > self.max_seq_length:
            tokens = tokens[:self.max_seq_length]
        
        padded_sequence = tokens + [0] * (self.max_seq_length - len(tokens))
        input_sequence = torch.tensor(padded_sequence[:-1])  # Input sequence without last token
        target_sequence = torch.tensor(padded_sequence[1:])   # Target sequence without first token
        return input_sequence, target_sequence


class CustomCollate:
    def __init__(self, pad_idx):
        self.pad_idx = pad_idx
    
    def __call__(self, batch):
        input_seqs, target_seqs = zip(*batch)
        padded_input_seqs = pad_sequence(input_seqs, batch_first=True, padding_value=self.pad_idx)
        padded_target_seqs = pad_sequence(target_seqs, batch_first=True, padding_value=self.pad_idx)
        return padded_input_seqs, padded_target_seqs

max_sequence_length = 30  # Define your desired maximum sequence length
dataset = CustomDataset('processed_data.csv', max_sequence_length)


dataloader = DataLoader(dataset, batch_size=32, shuffle=True, collate_fn=CustomCollate(0))

vocab_size = len(vocab)
embedding_dim = 128
hidden_dim = 256
rnn_model = RNNModel(vocab_size, embedding_dim, hidden_dim)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(rnn_model.parameters(), lr=0.001)


num_epochs = 10
for epoch in range(num_epochs):
    for input_batch, target_batch in dataloader:
        optimizer.zero_grad()


        output = rnn_model(input_batch)

        # Calculate loss and backpropagate
        loss = criterion(output.transpose(1, 2), target_batch)
        loss.backward()
        optimizer.step()


torch.save(rnn_model.state_dict(), 'rnn_model.pth')

print("Training completed.")
```
I've verified my csv file adjusted the 'max_seq_length' parameter to ensure it is appropriate for my data Double-checked the data pre-processing steps, including padding and formatting

Any suggestions on how to further debug and resolve this issue would be greatly appreciated Thank you in advance!

Comments:
I'm not too sure why you are opening an issue here as this has nothing to do with the Transformers library. You should try the PyTorch forums.

@sgugger sorry and thank you

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