Dear Sir/Mdm at PyTorch,
I have a dimensionality problem which might be due to bug in LSTM. I have a text input of
Sample input size: torch.Size([256, 20]) in my training and test DataLoader. (My texts sequence length is only 20 and very short, my batch size is 256).
Here is the error:
Error: Expected hidden dimension of (2, 229, 256) but got (2, 256, 256)
I find it strange that the hidden dimension changes in LSTM. I have n_layer=2, batch_size=256, and hidden_dim=256. The error is found when I do the validation calculation (I have marked the erroneous line it in the training code I post below) but has no problem at all during backprops in the training. Please refer to the training code at the last code snippet.
The following model is my model:
import torch.nn as nn
import torch.nn.functional
class SentimentRNN(nn.Module):
"""
The RNN model that will be used to perform Sentiment analysis.
"""
def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
"""
Initialize the model by setting up the layers.
"""
super(SentimentRNN, self).__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
# embedding and LSTM layers
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers,
dropout=drop_prob, batch_first=True)
# dropout layer
self.dropout = nn.Dropout(0.3)
# linear and sigmoid layers
self.fc = nn.Linear(hidden_dim, output_size)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x, hidden):
"""
Perform a forward pass of our model on some input and hidden state.
"""
batch_size = x.size(0)
# embeddings and lstm_out
x = x.long()
embeds = self.embedding(x)
lstm_out, hidden = self.lstm(embeds, hidden)
# stack up lstm outputs
lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)
# dropout and fully-connected layer
out = self.dropout(lstm_out)
out = self.fc(out)
# sigmoid function
softmax_out = self.softmax(out)
# reshape to be batch_size first
softmax_out = softmax_out.view(batch_size, -1, output_size)
softmax_out = softmax_out[:, -1] # get last batch of labels
# return last sigmoid output and hidden state
return softmax_out, hidden
def init_hidden(self, batch_size):
''' Initializes hidden state '''
# Create two new tensors with sizes n_layers x batch_size x hidden_dim,
# initialized to zero, for hidden state and cell state of LSTM
weight = next(self.parameters()).data
if (train_on_gpu):
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())
else:
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())
return hidden
And a training code:
# Instantiate the model w/ hyperparams
vocab_size = len(embedding_int)+1 # +1 for the 0 padding + our word tokens
output_size = 17
embedding_dim = 40
hidden_dim = 256
n_layers = 2
net = SentimentRNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)
# loss and optimization functions
lr=0.001
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
def train():
# training params
epochs = 10
counter = 0
print_every = 10000
clip=5 # gradient clipping
# move model to GPU, if available
if(train_on_gpu):
net.cuda()
for e in range(epochs):
net.train()
h = net.init_hidden(batch_size)
# batch loop
for inputs, labels in train_loader:
counter += 1
if(train_on_gpu):
inputs, labels = inputs.cuda(), labels.cuda()
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
h = tuple([each.data for each in h])
# zero accumulated gradients
net.zero_grad()
# get the output from the model
output, h = net(inputs, h)
# calculate the loss and perform backprop
loss = criterion(output.squeeze(), labels)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
nn.utils.clip_grad_norm_(net.parameters(), clip)
optimizer.step()
if count%print_every == 0:
# Get validation loss
val_h = net.init_hidden(batch_size)
val_losses = []
net.eval()
for inputs, labels in valid_loader:
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
val_h = tuple([each.data for each in val_h])
if(train_on_gpu):
inputs, labels = inputs.cuda(), labels.cuda()
output, val_h = net(inputs, val_h) ######### ERROR HERE #############
val_loss = criterion(output.squeeze(), labels)
val_losses.append(val_loss.item())
net.train()
print("Epoch: {}/{}...".format(e+1, epochs),
"Step: {}...".format(counter),
"Loss: {:.6f}...".format(loss.item()),
"Val Loss: {:.6f}".format(np.mean(val_losses)))