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import torch
import torch.nn as nn
import math
class LayerNormalization(nn.Module):
def __init__(self, features: int, eps: float = 1e-6) -> None:
super().__init__()
self.eps = eps
self.alpha = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
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.eps) + self.bias
class FeedForwardBlock(nn.Module):
def __init__(self, d_model: int, d_ff: int, dropout: float) -> None:
super().__init__()
self.fc1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.fc2 = nn.Linear(d_ff, d_model)
def forward(self, x):
return self.fc2(self.dropout(torch.relu(self.fc1(x))))
class InputEmbeddings(nn.Module):
def __init__(self, d_model: int, vocab_size: int) -> None:
super().__init__()
self.d_model = d_model
self.embedding = nn.Embedding(vocab_size, d_model)
def forward(self, x):
return self.embedding(x) * math.sqrt(self.d_model)
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, seq_len: int, dropout: float) -> None:
super().__init__()
self.dropout = nn.Dropout(dropout)
pe = torch.zeros(seq_len, d_model)
position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_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 ResidualConnection(nn.Module):
def __init__(self, features: int, dropout: float) -> None:
super().__init__()
self.dropout = nn.Dropout(dropout)
self.norm = LayerNormalization(features)
def forward(self, x, sublayer):
return x + self.dropout(sublayer(self.norm(x)))
class MultiHeadAttentionBlock(nn.Module):
def __init__(self, d_model: int, num_heads: int, dropout: float) -> None:
super().__init__()
self.num_heads = num_heads
self.d_k = d_model // num_heads
self.w_q = nn.Linear(d_model, d_model, bias=False)
self.w_k = nn.Linear(d_model, d_model, bias=False)
self.w_v = nn.Linear(d_model, d_model, bias=False)
self.w_o = nn.Linear(d_model, d_model, bias=False)
self.dropout = nn.Dropout(dropout)
@staticmethod
def attention(query, key, value, mask, dropout: nn.Dropout):
d_k = query.shape[-1]
scores = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores.masked_fill_(mask == 0, -1e9)
scores = scores.softmax(dim=-1)
if dropout is not None:
scores = dropout(scores)
return scores @ value, scores
def forward(self, q, k, v, mask):
query = self.w_q(q)
key = self.w_k(k)
value = self.w_v(v)
query = query.view(query.shape[0], query.shape[1], self.num_heads, self.d_k).transpose(1, 2)
key = key.view(key.shape[0], key.shape[1], self.num_heads, self.d_k).transpose(1, 2)
value = value.view(value.shape[0], value.shape[1], self.num_heads, self.d_k).transpose(1, 2)
x, self.attention_scores = MultiHeadAttentionBlock.attention(query, key, value, mask, self.dropout)
x = x.transpose(1, 2).contiguous().view(x.shape[0], -1, self.num_heads * self.d_k)
return self.w_o(x)
class EncoderBlock(nn.Module):
def __init__(self, features: int, self_attention: MultiHeadAttentionBlock, feed_forward: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.self_attention = self_attention
self.feed_forward = feed_forward
self.residuals = nn.ModuleList([ResidualConnection(features, dropout) for _ in range(2)])
def forward(self, x, src_mask):
x = self.residuals[0](x, lambda x: self.self_attention(x, x, x, src_mask))
x = self.residuals[1](x, self.feed_forward)
return x
class Encoder(nn.Module):
def __init__(self, features: int, layers: nn.ModuleList) -> None:
super().__init__()
self.layers = layers
self.norm = LayerNormalization(features)
def forward(self, x, mask):
for layer in self.layers:
x = layer(x, mask)
return self.norm(x)
class DecoderBlock(nn.Module):
def __init__(self, features: int, self_attention: MultiHeadAttentionBlock, cross_attention: MultiHeadAttentionBlock, feed_forward: FeedForwardBlock, dropout: float) -> None:
super().__init__()
self.self_attention = self_attention
self.cross_attention = cross_attention
self.feed_forward = feed_forward
self.residuals = nn.ModuleList([ResidualConnection(features, dropout) for _ in range(3)])
def forward(self, x, encoder_output, src_mask, tgt_mask):
x = self.residuals[0](x, lambda x: self.self_attention(x, x, x, tgt_mask))
x = self.residuals[1](x, lambda x: self.cross_attention(x, encoder_output, encoder_output, src_mask))
x = self.residuals[2](x, self.feed_forward)
return x
class Decoder(nn.Module):
def __init__(self, features: int, layers: nn.ModuleList) -> None:
super().__init__()
self.layers = layers
self.norm = LayerNormalization(features)
def forward(self, x, encoder_output, src_mask, tgt_mask):
for layer in self.layers:
x = layer(x, encoder_output, src_mask, tgt_mask)
return self.norm(x)
class ProjectionLayer(nn.Module):
def __init__(self, d_model, vocab_size) -> None:
super().__init__()
self.proj = nn.Linear(d_model, vocab_size)
def forward(self, x) -> None:
return self.proj(x)
class Transformer(nn.Module):
def __init__(self, encoder: Encoder, decoder: Decoder, src_embed: InputEmbeddings, tgt_embed: InputEmbeddings, src_pos: PositionalEncoding, tgt_pos: PositionalEncoding, projection_layer: ProjectionLayer) -> None:
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.src_embed = src_embed
self.tgt_embed = tgt_embed
self.src_pos = src_pos
self.tgt_pos = tgt_pos
self.projection_layer = projection_layer
def encode(self, src, src_mask):
src = self.src_embed(src)
src = self.src_pos(src)
return self.encoder(src, src_mask)
def decode(self, encoder_output: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor):
tgt = self.tgt_embed(tgt)
tgt = self.tgt_pos(tgt)
return self.decoder(tgt, encoder_output, src_mask, tgt_mask)
def project(self, x):
return self.projection_layer(x)
def build_transformer(src_vocab_size: int, tgt_vocab_size: int, src_seq_len: int, tgt_seq_len: int, d_model: int = 512, num_layers: int = 6, num_heads: int = 8, dropout: float = 0.1, d_ff: int = 2048) -> Transformer:
src_embed = InputEmbeddings(d_model, src_vocab_size)
tgt_embed = InputEmbeddings(d_model, tgt_vocab_size)
src_pos = PositionalEncoding(d_model, src_seq_len, dropout)
tgt_pos = PositionalEncoding(d_model, tgt_seq_len, dropout)
encoder_blocks = []
for _ in range(num_layers):
self_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
feed_forward = FeedForwardBlock(d_model, d_ff, dropout)
encoder_block = EncoderBlock(d_model, self_attention, feed_forward, dropout)
encoder_blocks.append(encoder_block)
decoder_blocks = []
for _ in range(num_layers):
self_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
cross_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
feed_forward = FeedForwardBlock(d_model, d_ff, dropout)
decoder_block = DecoderBlock(d_model, self_attention, cross_attention, feed_forward, dropout)
decoder_blocks.append(decoder_block)
encoder = Encoder(d_model, nn.ModuleList(encoder_blocks))
decoder = Decoder(d_model, nn.ModuleList(decoder_blocks))
projection_layer = ProjectionLayer(d_model, tgt_vocab_size)
transformer = Transformer(encoder, decoder, src_embed, tgt_embed, src_pos, tgt_pos, projection_layer)
for p in transformer.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return transformer
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