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from dataclasses import dataclass
import math
import torch
import torch.nn as nn
@dataclass
class Args:
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
class InputEmbeddingLayer(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, Epsilon: float = 10**-4) -> None:
super().__init__()
self.Epsilon = Epsilon
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.Epsilon) + 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 divisable 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):
d_k = Query.shape[-1]
self_attention_scores = (Query @ Key.traspose(-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
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().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_layer = NormalizationLayer()
def forward(self, x, subLayer):
return self.dropout(subLayer(self.normalization_layer))
class EncoderBlock(nn.Module):
def __init__(self, self_attetion_block: MultiHeadAttentionBlock, feed_forward_block: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.self_attention_block = self_attetion_block
self.feed_forward_block = 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.self_attention_block(x, x, x, source_mask))
x = self.residual_connection[1](x, self.feed_forward_block)
return x
class Encoder(nn.Module):
def __init__(self, Layers: nn.ModuleList) -> None:
super().__init__()
self.Layers = Layers
self.normalization_layer = NormalizationLayer()
def forward(self, x, source_mask):
for layer in self.Layers:
x = layer(x, source_mask)
return self.normalization_layer(x)
class DecoderBlock(nn.Module):
def __init__(self, masked_self_attention_block: MultiHeadAttentionBlock, self_attention_block: MultiHeadAttentionBlock, feedforwardblock: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.masked_self_attention_block = masked_self_attention_block
self.self_attention_block = self_attention_block
self.feedforwardblock = feedforwardblock
self.residual_connection = nn.ModuleList([ResidualConnection(dropout) for _ in range(3)])
def forward(self, x, Encoder_output, source_mask, target_mask):
x = self.residual_connection[0](x, lambda x: self.masked_self_attention_block(x, x, x, source_mask))
x = self.residual_connection[1](x, lambda x: self.self_attention_block(x, Encoder_output, Encoder_output, target_mask))
x = self.residual_connection[1](x, self.feedforwardblock)
return x
class Decoder(nn.Module):
def __init__(self, Layers: nn.ModuleList) -> None:
super().__init__()
self.Layers = Layers
self.normalization_layer = NormalizationLayer()
def forward(self, x, Encoder_output, source_mask, target_mask):
for layer in self.Layers:
x = layer(x, Encoder_output, source_mask, target_mask)
return self.normalization_layer(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: InputEmbeddingLayer,
target_embedding: InputEmbeddingLayer,
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, source_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, source_mask, target_mask)
def linear(self, x):
return self.Linear(x)
def Transformer_model(Args: Args)->TransformerBlock:
source_embedding = InputEmbeddingLayer(Args.d_model, Args.source_vocab_size)
source_position = PositionalEncodingLayer(Args.d_model, Args.source_sequence_length, Args.dropout)
target_embedding = InputEmbeddingLayer(Args.d_model, Args.target_vocab_size)
target_position = PositionalEncodingLayer(Args.d_model, Args.target_sequence_length, Args.dropout)
Encoder_Blocks = []
for _ in range(Args.Layers):
encoder_self_attention_block = MultiHeadAttentionBlock(Args.d_model, Args.heads, Args.dropout)
encoder_feed_forward_block = FeedForwardBlock(Args.d_model, Args.d_ff, Args.dropout)
encoder_block = EncoderBlock(encoder_self_attention_block, encoder_feed_forward_block, Args.dropout)
Encoder_Blocks.append(encoder_block)
Decoder_Blocks = []
for _ in range(Args.Layers):
decoder_self_attention_block = MultiHeadAttentionBlock(Args.d_model, Args.heads, Args.dropout)
decoder_cross_attention_block = MultiHeadAttentionBlock(Args.d_model, Args.heads, Args.dropout)
decoder_feed_forward_block = FeedForwardBlock(Args.d_model, Args.d_ff, Args.dropout)
decoder_block = DecoderBlock(decoder_self_attention_block, decoder_cross_attention_block, decoder_feed_forward_block, Args.dropout)
Decoder_Blocks.append(decoder_block)
encoder = Encoder(nn.ModuleList(Encoder_Blocks))
decoder = Decoder(nn.ModuleList(Decoder_Blocks))
linear = LinearLayer(Args.d_model, Args.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 |