File size: 5,303 Bytes
158b61b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
"""
Implementation of "Attention is All You Need"
"""
import torch.nn as nn
from onmt.encoders.encoder import EncoderBase
from onmt.modules import MultiHeadedAttention
from onmt.modules.position_ffn import PositionwiseFeedForward
from onmt.modules.position_ffn import ActivationFunction
from onmt.utils.misc import sequence_mask
class TransformerEncoderLayer(nn.Module):
"""
A single layer of the transformer encoder.
Args:
d_model (int): the dimension of keys/values/queries in
MultiHeadedAttention, also the input size of
the first-layer of the PositionwiseFeedForward.
heads (int): the number of head for MultiHeadedAttention.
d_ff (int): the second-layer of the PositionwiseFeedForward.
dropout (float): dropout probability(0-1.0).
pos_ffn_activation_fn (ActivationFunction):
activation function choice for PositionwiseFeedForward layer
"""
def __init__(self, d_model, heads, d_ff, dropout, attention_dropout,
max_relative_positions=0,
pos_ffn_activation_fn=ActivationFunction.relu):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = MultiHeadedAttention(
heads, d_model, dropout=attention_dropout,
max_relative_positions=max_relative_positions)
self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout,
pos_ffn_activation_fn)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
self.dropout = nn.Dropout(dropout)
def forward(self, inputs, mask):
"""
Args:
inputs (FloatTensor): ``(batch_size, src_len, model_dim)``
mask (LongTensor): ``(batch_size, 1, src_len)``
Returns:
(FloatTensor):
* outputs ``(batch_size, src_len, model_dim)``
"""
input_norm = self.layer_norm(inputs)
context, _ = self.self_attn(input_norm, input_norm, input_norm,
mask=mask, attn_type="self")
out = self.dropout(context) + inputs
return self.feed_forward(out)
def update_dropout(self, dropout, attention_dropout):
self.self_attn.update_dropout(attention_dropout)
self.feed_forward.update_dropout(dropout)
self.dropout.p = dropout
class TransformerEncoder(EncoderBase):
"""The Transformer encoder from "Attention is All You Need"
:cite:`DBLP:journals/corr/VaswaniSPUJGKP17`
.. mermaid::
graph BT
A[input]
B[multi-head self-attn]
C[feed forward]
O[output]
A --> B
B --> C
C --> O
Args:
num_layers (int): number of encoder layers
d_model (int): size of the model
heads (int): number of heads
d_ff (int): size of the inner FF layer
dropout (float): dropout parameters
embeddings (onmt.modules.Embeddings):
embeddings to use, should have positional encodings
pos_ffn_activation_fn (ActivationFunction):
activation function choice for PositionwiseFeedForward layer
Returns:
(torch.FloatTensor, torch.FloatTensor):
* embeddings ``(src_len, batch_size, model_dim)``
* memory_bank ``(src_len, batch_size, model_dim)``
"""
def __init__(self, num_layers, d_model, heads, d_ff, dropout,
attention_dropout, embeddings, max_relative_positions,
pos_ffn_activation_fn=ActivationFunction.relu):
super(TransformerEncoder, self).__init__()
self.embeddings = embeddings
self.transformer = nn.ModuleList(
[TransformerEncoderLayer(
d_model, heads, d_ff, dropout, attention_dropout,
max_relative_positions=max_relative_positions,
pos_ffn_activation_fn=pos_ffn_activation_fn)
for i in range(num_layers)])
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
@classmethod
def from_opt(cls, opt, embeddings):
"""Alternate constructor."""
return cls(
opt.enc_layers,
opt.enc_rnn_size,
opt.heads,
opt.transformer_ff,
opt.dropout[0] if type(opt.dropout) is list else opt.dropout,
opt.attention_dropout[0] if type(opt.attention_dropout)
is list else opt.attention_dropout,
embeddings,
opt.max_relative_positions,
pos_ffn_activation_fn=opt.pos_ffn_activation_fn,
)
def forward(self, src, lengths=None):
"""See :func:`EncoderBase.forward()`"""
self._check_args(src, lengths)
emb = self.embeddings(src)
out = emb.transpose(0, 1).contiguous()
mask = ~sequence_mask(lengths).unsqueeze(1)
# Run the forward pass of every layer of the tranformer.
for layer in self.transformer:
out = layer(out, mask)
out = self.layer_norm(out)
return emb, out.transpose(0, 1).contiguous(), lengths
def update_dropout(self, dropout, attention_dropout):
self.embeddings.update_dropout(dropout)
for layer in self.transformer:
layer.update_dropout(dropout, attention_dropout)
|