Reshape time series data for RNNs

This is a crosspost from

I wish to train a RNN model such that I can predict for T steps ahead in a time series model. Most of the examples that I have seen so far are centred around text.

The toy example that I have is to predict 3 sine waves as shown below:

x = torch.arange(0,30,0.05)
y = [torch.sin(x), torch.sin(x-np.pi), torch.sin(x-np.pi/2)]
y = torch.stack(y)
y = y.t()

y is of shape 600,3. However in order for the LSTM to accept it the input needs to be of shape (seq_len, batch, input_size). I was wondering if there is a function in pytorch that converts them to required format. Suppose that in my case I want seq_len=50 and batch_size=32.

This snippet of code from machinelearningmastery was the only snippet of code I found.

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)

Does pad_packed_sequence or anything similar in pytorch natively do this.

If anyone is interested, this is my LSTM model:

class LSTM(nn.Module):
    def __init__(self, n_features, h, num_layers=2):
        self.lstm = nn.LSTM(n_features, h, num_layers)
        self.linear = nn.Linear(h, n_features)
    def forward(self, input, h=None):
        lstm_out, self.hidden = self.lstm(input, h)
        return self.linear(lstm_out)

[optional Q] For whatever solution that I end up with, is there a way to ensure that I can do stateful training?