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model.py
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model.py
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import torch
import torch.nn as nn
import math
class InputEmbedding(nn.Module):
def __init__(self, d_model:int, vocab_size:int):
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 PE(nn.Module):
def __init__(self, d_model: int, seq_len: int, dropout: float) -> None:
super.__init__()
self.d_model = d_model
self.seq_len = seq_len
self.dropout = nn.Dropout(dropout)
# crrate matrix of shape (seq_len, d_model)
pe = torch.zeros(seq_len, d_model)
# use log for simplify the computation and make it more stable
# create a vector of shape (seq_len, 1)
position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1)
# denominator
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
# apply sin to even pos
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0) # (1, seq_len, d_model)
# register tesnor in buffer of model, saved in the model file
self.register_buffer('pe', pe)
def forward(self, x):
x = x + (self.pe[:, :x.shape[1], :]).requires_grad_(False)
return self.dropout(x)
class LayerNorm(nn.Module):
def __init__(self, eps: float= 10**-6 ) -> None:
super().__init__()
self.eps = eps
self.alpha = nn.Parameter(torch.ones(1)) # multiplied
self.bias = nn.Parameter(torch.zeros(1)) # added
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 FFBlock(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) # W1 b1
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_model) # W2 b2
def forward(self, x):
# (b, seq_len, d_model) -> (b, seq_len, d_ff) -> (b, seq_len, d_model)
return self.linear_2(self.dropout(torch.relu(self.linear_1(x))))
class MultiHeadAttentionBlock(nn.Module):
def __init__(self, d_model:int, h: int, dropout: float) -> None:
super().__init__()
self.d_model = d_model
self.h = h
assert d_model % h == 0, 'd_model is not divisible by h'
self.d_k = d_model // h
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]
# (b, h, seq_len, d_k) -> (b, h, seq_len, seq_len)
attention_scores = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
attention_scores.masked_fill(mask == 0 , -1e9)
attention_scores = attention_scores.softmax(dim = -1) # (b, h, seq_len, seq_len)
if dropout is not None:
attention_scores = dropout(attention_scores)
return (attention_scores @ value), attention_scores
def forward(self, q, k, v, mask):
query = self.w_q(q) # (b, seq_len, d_model) -> (b, seq_len, d_model)
key = self.w_k(k)
value = self.w_v(v)
# (b, seq_len, d_model) -> (b, seq_len h, d_k) -> (b, h, seq_len, d_k)
query = query.view(query.shape[0], query.shape[1], self.h, self.d_k).transpose(1,2)
key = key.view(key.shape[0], key.shape[1], self.h, self.d_k).transpose(1,2)
value = value.view(value.shape[0], value.shape[1], self.h, self.d_k).transpose(1,2)
x, self.attention_scores = self.attention(query, key, value, mask, self.dropout)
# (b, h, seq_len, d_k) -> (b, seq_len, h, d_k) -> (b, seq_len, d_model)
x = x.transpose(1,2).contiguous().view(x.shape[0], -1, self.h * self.d_k)
# (b, seq_len, d_mdoel) - > (b, seq_len, d_model)
return self.w_o(x)
class ResidualConnection(nn.Module):
def __init__(self, dropout: float) -> None:
super().__init__()
self.dropout = nn.Dropout(dropout)
self.norm = LayerNorm()
def forward(self, x, sublayer):
return x + self.dropout(sublayer(self.norm(x)))
class EncoderBlock(nn.Module):
def __init__(self, self_attention_block:MultiHeadAttentionBlock, ff_block: FFBlock, dropout: float) -> None:
super.__init__()
self.self_attention_block = self_attention_block
self.ff_block = ff_block
self.residudal_connections = nn.ModuleList([ResidualConnection(dropout=dropout) for _ in range(2)])
def forward(self, x, src_mask):
x = self.residudal_connections[0](x, lambda x: self.self_attention_block(x, x, x, src_mask))
x = self.residudal_connections[1](x, self.ff_block)
return x
class Encoder(nn.Module):
def __init__(self, layers: nn.ModuleList) -> None:
super.__init__()
self.layers = layers
self.norm = LayerNorm()
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, self_attention_block: MultiHeadAttentionBlock, cross_attention_block: MultiHeadAttentionBlock,
ff_block: FFBlock, dropout: float) -> None:
super.__init__()
self.self_attention_block = self_attention_block
self.cross_attention_block = cross_attention_block
self.ff_block = ff_block
self.residual_connections = nn.ModuleList([ResidualConnection(dropout=dropout) for _ in range(3)])
def forward(self, x, encoder_output, src_mask, tgt_mask):
x = self.residual_connections[0](x, lambda x: self.self_attention_block(x,x,x,tgt_mask))
x = self.residual_connections[1](x, lambda x: self.cross_attention_block(x,encoder_output,encoder_output,src_mask))
x = self.residual_connections[2](x, lambda x: self.ff_block)
return x
class Decoder(nn.Module):
def __init__(self, layers: nn.ModuleList) -> None:
super.__init__()
self.layers = layers
self.norm = LayerNorm()
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 ProjLayer(nn.Module):
def __init__(self, d_model: int, vocab_size: int) -> None:
super.__init__()
self.proj = nn.Linear(d_model, vocab_size)
def forward(self, x):
# (b, seq_len, d_model) -> (b, seq_len, vocab_size)
return torch.log_softmax(self.proj(x), dim=-1)
class Transformer(nn.Module):
def __init__(self, encoder: Encoder, decoder: Decoder,
src_emb: InputEmbedding, tgt_emb: InputEmbedding,
src_pos: PE, tgt_pos: PE,
proj_layer: ProjLayer) -> None:
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.src_emb = src_emb
self.tgt_emb = tgt_emb
self.src_pos = src_pos
self.tgt_pos = tgt_pos
self.proj_layer = proj_layer
def encode(self, src, src_mask):
src = self.src_emb(src)
src = self.src_pos(src)
return self.encoder(src, src_mask)
def decide(self, encoder_output, src_mask, tgt, tgt_mask):
tgt = self.tgt_emb(tgt)
tgt = self.tgt_pos(tgt)
return self.decoder(tgt, encoder_output, src_mask, tgt_mask)
def project(self, x):
return self.proj_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, N: int=6,
h:int=8, dropout: float=0.1, d_ff:int=2048):
src_emb = InputEmbedding(d_model, src_vocab_size)
tgt_emb = InputEmbedding(d_model, tgt_vocab_size)
src_pos = PE(d_model, src_seq_len, dropout=dropout)
tgt_pos = PE(d_model, tgt_seq_len, dropout=dropout)
encoder_blocks = []
for _ in range(N):
encoder_self_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
ff_block = FFBlock(d_model, d_ff, dropout)
encoder_block = EncoderBlock(encoder_self_attention_block, ff_block, dropout)
encoder_blocks.append(encoder_block)
decoder_blocks = []
for _ in range(N):
decoder_self_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
decoder_cross_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
ff_block = FFBlock(d_model, d_ff, dropout)
decoder_block = DecoderBlock(decoder_self_attention_block, decoder_cross_attention_block, ff_block, dropout)
decoder_blocks.append(decoder_block)
encoder = Encoder(nn.ModuleList(encoder_blocks))
decoder = Decoder(nn.ModuleList(decoder_blocks))
proj_layer = ProjLayer(d_model, tgt_vocab_size)
transformer = Transformer(encoder, decoder, src_emb, tgt_emb, src_pos, tgt_pos, proj_layer)
# init params
for p in transformer.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return transformer