File size: 9,780 Bytes
cd89176 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
#LlTRA = Language to Language Transformer model.
import math
import torch
import torch.nn as nn
class InputEmbeddingsLayer(nn.Module):
def __init__(self, d_model: int, vocab_size: int) -> None:
super().__init__()
self.d_model = d_model
self.vocab_size = vocab_size
self.embedding = nn.Embedding(vocab_size, d_model)
def forward(self, x):
return self.embedding(x) * math.sqrt(self.d_model)
class PositionalEncodingLayer(nn.Module):
def __init__(self, d_model: int, sequence_length: int, dropout: float) -> None:
super().__init__()
self.d_model = d_model
self.sequence_length = sequence_length
self.dropout = nn.Dropout(dropout)
PE = torch.zeros(sequence_length, d_model)
Position = torch.arange(0, sequence_length, dtype=torch.float).unsqueeze(1)
deviation_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
PE[:, 0::2] = torch.sin(Position * deviation_term)
PE[:, 1::2] = torch.cos(Position * deviation_term)
PE = PE.unsqueeze(0)
self.register_buffer('PE', PE)
def forward(self, x):
x = x + (self.PE[:, :x.shape[1], :]).requires_grad_(False)
return self.dropout(x)
class NormalizationLayer(nn.Module):
def __init__(self, Epslone: float = 10**-6) -> None:
super().__init__()
self.Epslone = Epslone
self.Alpha = nn.Parameter(torch.ones(1))
self.Bias = nn.Parameter(torch.ones(1))
def forward(self, x):
mean = x.mean(dim = -1, keepdim = True)
std = x.std(dim = -1, keepdim = True)
return self.Alpha * (x - mean) / (std + self.Epslone) + self.Bias
class FeedForwardBlock(nn.Module):
def __init__(self, d_model: int, d_ff: int, dropout: float) -> None:
super().__init__()
self.Linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.Linear_2 = nn.Linear(d_ff, d_model)
def forward(self, x):
return self.Linear_2(self.dropout(torch.relu(self.Linear_1(x))))
class MultiHeadAttentionBlock(nn.Module):
def __init__(self, d_model: int, heads: int, dropout: float) -> None:
super().__init__()
self.d_model = d_model
self.heads = heads
assert d_model % heads == 0 , "d_model is not divisible by heads"
self.d_k = d_model // heads
self.W_Q = nn.Linear(d_model, d_model)
self.W_K = nn.Linear(d_model, d_model)
self.W_V = nn.Linear(d_model, d_model)
self.W_O = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
@staticmethod
def Attention(query, key, value, mask, dropout: nn.Dropout):
d_k = query.shape[-1]
self_attention_scores = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
self_attention_scores.masked_fill_(mask == 0, -1e9)
self_attention_scores = self_attention_scores.softmax(dim=-1)
if dropout is not None:
self_attention_scores = dropout(self_attention_scores)
return (self_attention_scores @ value), self_attention_scores
def forward(self, query, key, value, mask):
Query = self.W_Q(query)
Key = self.W_K(key)
Value = self.W_V(value)
Query = Query.view(Query.shape[0], Query.shape[1], self.heads, self.d_k).transpose(1,2)
Key = Key.view(Key.shape[0], Key.shape[1], self.heads, self.d_k).transpose(1,2)
Value = Value.view(Value.shape[0], Value.shape[1], self.heads, self.d_k).transpose(1,2)
x, self.self_attention_scores = MultiHeadAttentionBlock.Attention(Query, Key, Value, mask, self.dropout)
x = x.transpose(1,2).contiguous().view(x.shape[0], -1, self.heads * self.d_k)
return self.W_O(x)
class ResidualConnection(nn.Module):
def __init__(self, dropout: float) -> None:
super().__init__()
self.dropout = nn.Dropout(dropout)
self.normalization = NormalizationLayer()
def forward(self, x, subLayer):
return x + self.dropout(subLayer(self.normalization(x)))
class EncoderBlock(nn.Module):
def __init__(self, encoder_self_attention_block: MultiHeadAttentionBlock, encoder_feed_forward_block: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.encoder_self_attention_block = encoder_self_attention_block
self.encoder_feed_forward_block = encoder_feed_forward_block
self.residual_connection = nn.ModuleList([ResidualConnection(dropout) for _ in range(2)])
def forward(self, x, source_mask):
x = self.residual_connection[0](x, lambda x: self.encoder_self_attention_block(x, x, x, source_mask))
x = self.residual_connection[1](x, self.encoder_feed_forward_block)
return x
class Encoder(nn.Module):
def __init__(self, Layers: nn.ModuleList) -> None:
super().__init__()
self.Layers = Layers
self.normalization = NormalizationLayer()
def forward(self, x, source_mask):
for layer in self.Layers:
x = layer(x, source_mask)
return self.normalization(x)
class DecoderBlock(nn.Module):
def __init__(self, decoder_self_attention_block: MultiHeadAttentionBlock, decoder_cross_attention_block: MultiHeadAttentionBlock, decoder_feed_forward_block: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.decoder_self_attention_block = decoder_self_attention_block
self.decoder_cross_attention_block = decoder_cross_attention_block
self.decoder_feed_forward_block = decoder_feed_forward_block
self.residual_connection = nn.ModuleList([ResidualConnection(dropout) for _ in range(3)])
def forward(self, x, Encoder_output, maks, target_mask):
x = self.residual_connection[0](x, lambda x: self.decoder_self_attention_block(x, x, x, target_mask))
x = self.residual_connection[1](x, lambda x: self.decoder_cross_attention_block(x, Encoder_output, Encoder_output, target_mask))
x = self.residual_connection[2](x, self.decoder_feed_forward_block)
return x
class Decoder(nn.Module):
def __init__(self, Layers: nn.ModuleList) -> None:
super().__init__()
self.Layers = Layers
self.normalization = NormalizationLayer()
def forward(self, x, Encoder_output, mask, target_mask):
for layer in self.Layers:
x = layer(x, Encoder_output, mask, target_mask)
return self.normalization(x)
class LinearLayer(nn.Module):
def __init__(self, d_model: int, vocab_size: int) -> None:
super().__init__()
self.Linear = nn.Linear(d_model, vocab_size)
def forward(self, x):
return self.Linear(x)
class TransformerBlock(nn.Module):
def __init__(self, encoder: Encoder, decoder: Decoder, source_embedding: InputEmbeddingsLayer, target_embedding: InputEmbeddingsLayer, source_position: PositionalEncodingLayer, target_position: PositionalEncodingLayer, Linear: LinearLayer) -> None:
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.source_embedding = source_embedding
self.target_embedding = target_embedding
self.source_position = source_position
self.target_position = target_position
self.Linear = Linear
def encode(self, source_language, source_mask):
source_language = self.source_embedding(source_language)
source_language = self.source_position(source_language)
return self.encoder(source_language, source_mask)
def decode(self, Encoder_output, mask, target_language, target_mask):
target_language = self.target_embedding(target_language)
target_language = self.target_position(target_language)
return self.decoder(target_language, Encoder_output, mask, target_mask)
def linear(self, x):
return self.Linear(x)
def TransformerModel(source_vocab_size: int, target_vocab_size: int, source_sequence_length: int, target_sequence_length: int, d_model: int = 512, Layers: int = 6, heads: int = 8, dropout: float = 0.1, d_ff: int = 2048)->TransformerBlock:
source_embedding = InputEmbeddingsLayer(d_model, source_vocab_size)
source_position = PositionalEncodingLayer(d_model, source_sequence_length, dropout)
target_embedding = InputEmbeddingsLayer(d_model, target_vocab_size)
target_position = PositionalEncodingLayer(d_model, target_sequence_length, dropout)
EncoderBlocks = []
for _ in range(Layers):
encoder_self_attention_block = MultiHeadAttentionBlock(d_model, heads, dropout)
encoder_feed_forward_block = FeedForwardBlock(d_model, d_ff, dropout)
encoder_block = EncoderBlock(encoder_self_attention_block, encoder_feed_forward_block, dropout)
EncoderBlocks.append(encoder_block)
DecoderBlocks = []
for _ in range(Layers):
decoder_self_attention_block = MultiHeadAttentionBlock(d_model, heads, dropout)
decoder_cross_attention_block = MultiHeadAttentionBlock(d_model, heads, dropout)
decoder_feed_forward_block = FeedForwardBlock(d_model, d_ff, dropout)
decoder_block = DecoderBlock(decoder_self_attention_block, decoder_cross_attention_block, decoder_feed_forward_block, dropout)
DecoderBlocks.append(decoder_block)
encoder = Encoder(nn.ModuleList(EncoderBlocks))
decoder = Decoder(nn.ModuleList(DecoderBlocks))
linear = LinearLayer(d_model, target_vocab_size)
Transformer = TransformerBlock(encoder, decoder, source_embedding, target_embedding, source_position, target_position, linear)
for T in Transformer.parameters():
if T.dim() > 1:
nn.init.xavier_uniform(T)
return Transformer
|