AWD LSTM from Smerity et al.

Basic NLP modules

On top of the pytorch or the fastai layers, the language models use some custom layers specific to NLP.



 dropout_mask (x:torch.Tensor, sz:list, p:float)

Return a dropout mask of the same type as x, size sz, with probability p to cancel an element.

Type Details
x Tensor Source tensor, output will be of the same type as x
sz list Size of the dropout mask as ints
p float Dropout probability
Returns Tensor Multiplicative dropout mask
t = dropout_mask(torch.randn(3,4), [4,3], 0.25)
test_eq(t.shape, [4,3])
assert ((t == 4/3) + (t==0)).all()



 RNNDropout (p:float=0.5)

Dropout with probability p that is consistent on the seq_len dimension.

dp = RNNDropout(0.3)
tst_inp = torch.randn(4,3,7)
tst_out = dp(tst_inp)
for i in range(4):
    for j in range(7):
        if tst_out[i,0,j] == 0: assert (tst_out[i,:,j] == 0).all()
        else: test_close(tst_out[i,:,j], tst_inp[i,:,j]/(1-0.3))

It also supports doing dropout over a sequence of images where time dimesion is the 1st axis, 10 images of 3 channels and 32 by 32.

_ = dp(torch.rand(4,10,3,32,32))



 WeightDropout (module:nn.Module, weight_p:float,

A module that wraps another layer in which some weights will be replaced by 0 during training.

Type Default Details
module nn.Module Wrapped module
weight_p float Weight dropout probability
layer_names str | MutableSequence weight_hh_l0 Name(s) of the parameters to apply dropout to
module = nn.LSTM(5,7)
dp_module = WeightDropout(module, 0.4)
wgts = dp_module.module.weight_hh_l0
tst_inp = torch.randn(10,20,5)
h = torch.zeros(1,20,7), torch.zeros(1,20,7)
x,h = dp_module(tst_inp,h)
loss = x.sum()
new_wgts = getattr(dp_module.module, 'weight_hh_l0')
test_eq(wgts, getattr(dp_module, 'weight_hh_l0_raw'))
assert 0.2 <= (new_wgts==0).sum().float()/new_wgts.numel() <= 0.6
assert dp_module.weight_hh_l0_raw.requires_grad
assert dp_module.weight_hh_l0_raw.grad is not None
assert ((dp_module.weight_hh_l0_raw.grad == 0.) & (new_wgts == 0.)).any()



 EmbeddingDropout (emb:nn.Embedding, embed_p:float)

Apply dropout with probability embed_p to an embedding layer emb.

Type Details
emb nn.Embedding Wrapped embedding layer
embed_p float Embdedding layer dropout probability
enc = nn.Embedding(10, 7, padding_idx=1)
enc_dp = EmbeddingDropout(enc, 0.5)
tst_inp = torch.randint(0,10,(8,))
tst_out = enc_dp(tst_inp)
for i in range(8):
    assert (tst_out[i]==0).all() or torch.allclose(tst_out[i], 2*enc.weight[tst_inp[i]])



 AWD_LSTM (vocab_sz:int, emb_sz:int, n_hid:int, n_layers:int,
           pad_token:int=1, hidden_p:float=0.2, input_p:float=0.6,
           embed_p:float=0.1, weight_p:float=0.5, bidir:bool=False)

AWD-LSTM inspired by

Type Default Details
vocab_sz int Size of the vocabulary
emb_sz int Size of embedding vector
n_hid int Number of features in hidden state
n_layers int Number of LSTM layers
pad_token int 1 Padding token id
hidden_p float 0.2 Dropout probability for hidden state between layers
input_p float 0.6 Dropout probability for LSTM stack input
embed_p float 0.1 Embedding layer dropout probabillity
weight_p float 0.5 Hidden-to-hidden wight dropout probability for LSTM layers
bidir bool False If set to True uses bidirectional LSTM layers

This is the core of an AWD-LSTM model, with embeddings from vocab_sz and emb_sz, n_layers LSTMs potentially bidir stacked, the first one going from emb_sz to n_hid, the last one from n_hid to emb_sz and all the inner ones from n_hid to n_hid. pad_token is passed to the PyTorch embedding layer. The dropouts are applied as such:

  • the embeddings are wrapped in EmbeddingDropout of probability embed_p;
  • the result of this embedding layer goes through an RNNDropout of probability input_p;
  • each LSTM has WeightDropout applied with probability weight_p;
  • between two of the inner LSTM, an RNNDropout is applied with probability hidden_p.

THe module returns two lists: the raw outputs (without being applied the dropout of hidden_p) of each inner LSTM and the list of outputs with dropout. Since there is no dropout applied on the last output, those two lists have the same last element, which is the output that should be fed to a decoder (in the case of a language model).

tst = AWD_LSTM(100, 20, 10, 2, hidden_p=0.2, embed_p=0.02, input_p=0.1, weight_p=0.2)
x = torch.randint(0, 100, (10,5))
r = tst(x)
test_eq(, 10)
test_eq(len(tst.hidden), 2)
test_eq([h_.shape for h_ in tst.hidden[0]], [[1,10,10], [1,10,10]])
test_eq([h_.shape for h_ in tst.hidden[1]], [[1,10,20], [1,10,20]])

test_eq(r.shape, [10,5,20])
test_eq(r[:,-1], tst.hidden[-1][0][0]) #hidden state is the last timestep in raw outputs




 awd_lstm_lm_split (model)

Split a RNN model in groups for differential learning rates.



 awd_lstm_clas_split (model)

Split a RNN model in groups for differential learning rates.