13import math
14
15import torch
16import torch.nn as nn
17
18from labml_nn.utils import clone_module_list
19from .feed_forward import FeedForward
20from .mha import MultiHeadAttention
21from .positional_encoding import get_positional_encoding
24class EmbeddingsWithPositionalEncoding(nn.Module):
31 def __init__(self, d_model: int, n_vocab: int, max_len: int = 5000):
32 super().__init__()
33 self.linear = nn.Embedding(n_vocab, d_model)
34 self.d_model = d_model
35 self.register_buffer('positional_encodings', get_positional_encoding(d_model, max_len))
37 def forward(self, x: torch.Tensor):
38 pe = self.positional_encodings[:x.shape[0]].requires_grad_(False)
39 return self.linear(x) * math.sqrt(self.d_model) + pe
42class EmbeddingsWithLearnedPositionalEncoding(nn.Module):
49 def __init__(self, d_model: int, n_vocab: int, max_len: int = 5000):
50 super().__init__()
51 self.linear = nn.Embedding(n_vocab, d_model)
52 self.d_model = d_model
53 self.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)
55 def forward(self, x: torch.Tensor):
56 pe = self.positional_encodings[:x.shape[0]]
57 return self.linear(x) * math.sqrt(self.d_model) + pe
This can act as an encoder layer or a decoder layer.
🗒 Some implementations, including the paper seem to have differences in where the layer-normalization is done. Here we do a layer normalization before attention and feed-forward networks, and add the original residual vectors. Alternative is to do a layer normalization after adding the residuals. But we found this to be less stable when training. We found a detailed discussion about this in the paper On Layer Normalization in the Transformer Architecture.
60class TransformerLayer(nn.Module):
d_model
is the token embedding size self_attn
is the self attention module src_attn
is the source attention module (when this is used in a decoder) feed_forward
is the feed forward module dropout_prob
is the probability of dropping out after self attention and FFN78 def __init__(self, *,
79 d_model: int,
80 self_attn: MultiHeadAttention,
81 src_attn: MultiHeadAttention = None,
82 feed_forward: FeedForward,
83 dropout_prob: float):
91 super().__init__()
92 self.size = d_model
93 self.self_attn = self_attn
94 self.src_attn = src_attn
95 self.feed_forward = feed_forward
96 self.dropout = nn.Dropout(dropout_prob)
97 self.norm_self_attn = nn.LayerNorm([d_model])
98 if self.src_attn is not None:
99 self.norm_src_attn = nn.LayerNorm([d_model])
100 self.norm_ff = nn.LayerNorm([d_model])
Whether to save input to the feed forward layer
102 self.is_save_ff_input = False
104 def forward(self, *,
105 x: torch.Tensor,
106 mask: torch.Tensor,
107 src: torch.Tensor = None,
108 src_mask: torch.Tensor = None):
Normalize the vectors before doing self attention
110 z = self.norm_self_attn(x)
Run through self attention, i.e. keys and values are from self
112 self_attn = self.self_attn(query=z, key=z, value=z, mask=mask)
Add the self attention results
114 x = x + self.dropout(self_attn)
If a source is provided, get results from attention to source. This is when you have a decoder layer that pays attention to encoder outputs
119 if src is not None:
Normalize vectors
121 z = self.norm_src_attn(x)
Attention to source. i.e. keys and values are from source
123 attn_src = self.src_attn(query=z, key=src, value=src, mask=src_mask)
Add the source attention results
125 x = x + self.dropout(attn_src)
Normalize for feed-forward
128 z = self.norm_ff(x)
Save the input to the feed forward layer if specified
130 if self.is_save_ff_input:
131 self.ff_input = z.clone()
Pass through the feed-forward network
133 ff = self.feed_forward(z)
Add the feed-forward results back
135 x = x + self.dropout(ff)
136
137 return x
140class Encoder(nn.Module):
147 def __init__(self, layer: TransformerLayer, n_layers: int):
148 super().__init__()
Make copies of the transformer layer
150 self.layers = clone_module_list(layer, n_layers)
Final normalization layer
152 self.norm = nn.LayerNorm([layer.size])
154 def forward(self, x: torch.Tensor, mask: torch.Tensor):
Run through each transformer layer
156 for layer in self.layers:
157 x = layer(x=x, mask=mask)
Finally, normalize the vectors
159 return self.norm(x)
162class Decoder(nn.Module):
169 def __init__(self, layer: TransformerLayer, n_layers: int):
170 super().__init__()
Make copies of the transformer layer
172 self.layers = clone_module_list(layer, n_layers)
Final normalization layer
174 self.norm = nn.LayerNorm([layer.size])
176 def forward(self, x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor):
Run through each transformer layer
178 for layer in self.layers:
179 x = layer(x=x, mask=tgt_mask, src=memory, src_mask=src_mask)
Finally, normalize the vectors
181 return self.norm(x)
This predicts the tokens and gives the lof softmax of those. You don't need this if you are using nn.CrossEntropyLoss
.
184class Generator(nn.Module):
194 def __init__(self, n_vocab: int, d_model: int):
195 super().__init__()
196 self.projection = nn.Linear(d_model, n_vocab)
198 def forward(self, x):
199 return self.projection(x)
202class EncoderDecoder(nn.Module):
209 def __init__(self, encoder: Encoder, decoder: Decoder, src_embed: nn.Module, tgt_embed: nn.Module, generator: nn.Module):
210 super().__init__()
211 self.encoder = encoder
212 self.decoder = decoder
213 self.src_embed = src_embed
214 self.tgt_embed = tgt_embed
215 self.generator = generator
This was important from their code. Initialize parameters with Glorot / fan_avg.
219 for p in self.parameters():
220 if p.dim() > 1:
221 nn.init.xavier_uniform_(p)
223 def forward(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor):
Run the source through encoder
225 enc = self.encode(src, src_mask)
Run encodings and targets through decoder
227 return self.decode(enc, src_mask, tgt, tgt_mask)
229 def encode(self, src: torch.Tensor, src_mask: torch.Tensor):
230 return self.encoder(self.src_embed(src), src_mask)
232 def decode(self, memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor):
233 return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)