#

Transformer XL

This is an implementation of Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context in PyTorch.

Transformer has a limited attention span, equal to the length of the sequence trained in parallel. All these positions have a fixed positional encoding. Transformer XL increases this attention span by letting each of the positions pay attention to precalculated past embeddings. For instance if the context length is $l$ , it will keep the embeddings of all layers for previous batch of length $l$ and feed them to current step. If we use fixed-positional encodings these pre-calculated embeddings will have the same positions as the current context. They introduce relative positional encoding, where the positional encodings are introduced at the attention calculation.

Annotated implementation of relative multi-headed attention is in relative_mha.py .

Here's the training code and a notebook for training a transformer XL model on Tiny Shakespeare dataset.

36from typing import List, Optional
37
38import torch
39import torch.nn as nn
40
41from labml_helpers.module import Module
42from labml_nn.utils import clone_module_list
43from .relative_mha import RelativeMultiHeadAttention
44from ..feed_forward import FeedForward

#

Transformer XL Layer

The transformer XL model comprises of a number of these layers.

47class TransformerXLLayer(Module):

#

d_model is the token embedding size
self_attn is the self attention module
feed_forward is the feed forward module
dropout_prob is the probability of dropping out after self attention and FFN

53    def __init__(self, *,
54                 d_model: int,
55                 self_attn: RelativeMultiHeadAttention,
56                 feed_forward: FeedForward,
57                 dropout_prob: float):

#

64        super().__init__()
65        self.size = d_model
66        self.self_attn = self_attn
67        self.feed_forward = feed_forward
68        self.dropout = nn.Dropout(dropout_prob)
69        self.norm_self_attn = nn.LayerNorm([d_model])
70        self.norm_ff = nn.LayerNorm([d_model])

#

x is a tensor of the token level feature vectors of shape [seq_len, batch_size, d_model]
mem is a tensor of the past token level feature vectors of shape [mem_len, batch_size, d_model]
mask is a matrix of shape [seq_len, mem_len + seq_len, batch_size] or [seq_len, mem_len + seq_len, 1] . mask[i, j] is true if token at i can see token at j .

72    def forward(self, *,
73                x: torch.Tensor,
74                mem: Optional[torch.Tensor],
75                mask: torch.Tensor):

#

Normalize the vectors before doing self attention

83        z = self.norm_self_attn(x)

#

If there is memory

85        if mem is not None:

#

Normalize it

87            mem = self.norm_self_attn(mem)

#

Concatenate with z

89            m_z = torch.cat((mem, z), dim=0)

#

Ignore if there is no memory

91        else:
92            m_z = z

#

Attention

94        self_attn = self.self_attn(query=z, key=m_z, value=m_z, mask=mask)

#

Add the attention results

96        x = x + self.dropout(self_attn)

#

Normalize for feed-forward

99        z = self.norm_ff(x)

#

Pass through the feed-forward network

101        ff = self.feed_forward(z)

#

Add the feed-forward results back

103        x = x + self.dropout(ff)

#

106        return x

#

Transformer XL Model

This consists of multiple transformer XL layers

109class TransformerXL(Module):

#

116    def __init__(self, layer: TransformerXLLayer, n_layers: int):
117        super().__init__()

#

Make copies of the transformer layer

119        self.layers = clone_module_list(layer, n_layers)

#

Final normalization layer

121        self.norm = nn.LayerNorm([layer.size])

#

x is a tensor of the token embeddings vectors of shape [seq_len, batch_size, d_model]
mem is a list of tensors of the past token level feature vectors of shape [mem_len, batch_size, d_model] for each layer
mask is the masking matrix

123    def forward(self, x: torch.Tensor, mem: List[torch.Tensor], mask: torch.Tensor):

#

List to store token level feature vectors, which will become the memories for the next sequential batch.

132        new_mem = []

#

Run through each transformer layer

134        for i, layer in enumerate(self.layers):

#

Add to the list of feature vectors

136            new_mem.append(x.detach())

#

Memory

138            m = mem[i] if mem else None

#

Run through the transformer XL layer

140            x = layer(x=x, mem=m, mask=mask)

#

Finally, normalize the vectors

142        return self.norm(x), new_mem