 # AWD-LSTM chunks

#1

I’m seeking an intuitive explanation of one of the features of AWD-LSTM.

Notebook 12a includes a Python version of AWD-LSTM, in which Jeremy splits the input into 4 pieces and runs each one through a gate in the `forward` method of the LSTMCell Class. That seems to match the equations given. However, I noticed that in Colah’s Understanding LSTM Networks equations, the entire input seems to be used for each of the gates.

Splitting the input into 4 pieces doesn’t make sense to me intuitively, since I don’t see how you would decide which piece goes through which gate. Is there a trick I’m missing, like randomizing the split or perhaps pre-copying the matrix so that when you split it, you are back to a single copy for each gate? I don’t quite see that in the code.

Thanks,
John

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(Abhinav Verma) #2
``````class LSTMCell(nn.Module):
def __init__(self, ni, nh):
super().__init__()
self.ih = nn.Linear(ni,4*nh)
self.hh = nn.Linear(nh,4*nh)

def forward(self, input, state):
h,c = state
#One big multiplication for all the gates is better than 4 smaller ones
gates = (self.ih(input) + self.hh(h)).chunk(4, 1)
ingate,forgetgate,outgate = map(torch.sigmoid, gates[:3])
cellgate = gates.tanh()

c = (forgetgate*c) + (ingate*cellgate)
h = outgate * c.tanh()
return h, (h,c)
``````

Are you referring to this code? I think he’s initializing his layers that way in init self.ih and self.hh are initialized as 4 layered tensors. Will go through the video again once I’m there to see if this explanation is valid though. That’s my understanding for now

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#3

Thanks, yes that is the code that confuses me. `chunk(4,1)` really does split it into 4 pieces.

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(Abhinav Verma) #4

So he is chunking the product but he’s actually doing a big multiplication . self.ih and self.hh are basically are a bigger tensor than initialized . Generally you would copy the self.ih 4 times and do the multiplication however if you initialize it as 4 layers Jeremy says its better. For e.g say  is a tensor . Normally you would copy  4 different times for 4 different multiplications. But initialize it like this [2,2,2,2] and do one big multiplication with 4 different numbers like this [1,2,3,4]. This Jeremy reckons is better as its less memory intensive. It’s a really simple example that I’ve chosen. Hope it’s clear enough . 0 Likes