How the function nn.LSTM behaves within the batches/ seq_len?

Question

I’m currently learning to use nn.LSTM with pytorch and had to ask how the function is working.

Basically I’m trying to feed my dataset matrix (M x N). Since the dataset is a matrix, I wanted to feed the dataset recursively(as timesteps) into the LSTM network with Dataloader(utils.data.Dataset).

The point where i got confused was the size of input(seq_len, batch, input_size)

Let’s say I’m getting my data_loader with batch_size=10. In order to generate the train_loader with the right form, I had to make the previous size of (M x N) into the size including the sequence_length which could simply be transformed to (M/seq_len, seq_len, N).

Then the input size of my nn.LSTM would be like: (M/seq_len/batch_size, seq_len, N)

So, my main question comes:

1. If i feed this data size into the LSTM model nn.LSTM(N, hidden_size), is the LSTM model already doing the recursive feed-forward within the whole batch?

2. I'm also confused with the seq_len, while seq_len>1, the output will get the dimension of seq_len. Would that mean the output contains the recursive operations of sequences?

I’m not sure i made the questions clear, but my understanding is getting quite messed up..lol Hope somebody could help me organizing the right understanding.

Answer 1

1. Yes, provided each sample's sequence length is the same (which seems to be the case here). If not, you have to pad with torch.nn.utils.rnn.pad_sequence for example.

2. Yes, LSTM is expanded to each timestep and there is output for each timestep already. Hence you don't have to apply it for each element separately.