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from torch import nn
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hid_dim, n_heads, dropout, device):
super().__init__()
assert hid_dim % n_heads == 0
self.hid_dim = hid_dim
self.n_heads = n_heads
self.head_dim = hid_dim // n_heads
self.fc_q = nn.Linear(hid_dim, hid_dim)
self.fc_k = nn.Linear(hid_dim, hid_dim)
self.fc_v = nn.Linear(hid_dim, hid_dim)
self.fc_o = nn.Linear(hid_dim, hid_dim)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)
def forward(self, query, key, value, mask = None):
batch_size = query.shape[0]
#query = [batch size, query len, hid dim]
#key = [batch size, key len, hid dim]
#value = [batch size, value len, hid dim]
Q = self.fc_q(query)
K = self.fc_k(key)
V = self.fc_v(value)
#Q = [batch size, query len, hid dim]
#K = [batch size, key len, hid dim]
#V = [batch size, value len, hid dim]
Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
#Q = [batch size, n heads, query len, head dim]
#K = [batch size, n heads, key len, head dim]
#V = [batch size, n heads, value len, head dim]
energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale
#energy = [batch size, n heads, query len, key len]
if mask is not None:
energy = energy.masked_fill(mask == 0, -1e10)
attention = torch.softmax(energy, dim = -1)
#attention = [batch size, n heads, query len, key len]
x = torch.matmul(self.dropout(attention), V)
#x = [batch size, n heads, query len, head dim]
x = x.permute(0, 2, 1, 3).contiguous()
#x = [batch size, query len, n heads, head dim]
x = x.view(batch_size, -1, self.hid_dim)
#x = [batch size, query len, hid dim]
x = self.fc_o(x)
#x = [batch size, query len, hid dim]
return x, attention
class PositionwiseFeedforwardLayer(nn.Module):
def __init__(self, hid_dim, pf_dim, dropout):
super().__init__()
self.fc_1 = nn.Linear(hid_dim, pf_dim)
self.fc_2 = nn.Linear(pf_dim, hid_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
#x = [batch size, seq len, hid dim]
x = self.dropout(torch.relu(self.fc_1(x)))
#x = [batch size, seq len, pf dim]
x = self.fc_2(x)
#x = [batch size, seq len, hid dim]
return x
class EncoderLayer(nn.Module):
def __init__(self,
hid_dim,
n_heads,
pf_dim,
dropout,
device):
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
self.ff_layer_norm = nn.LayerNorm(hid_dim)
self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim,
pf_dim,
dropout)
self.dropout = nn.Dropout(dropout)
def forward(self, src, src_mask):
#src = [batch size, src len, hid dim]
#src_mask = [batch size, 1, 1, src len]
#self attention
_src, _ = self.self_attention(src, src, src, src_mask)
#dropout, residual connection and layer norm
src = self.self_attn_layer_norm(src + self.dropout(_src))
#src = [batch size, src len, hid dim]
#positionwise feedforward
_src = self.positionwise_feedforward(src)
#dropout, residual and layer norm
src = self.ff_layer_norm(src + self.dropout(_src))
#src = [batch size, src len, hid dim]
return src
class Encoder(nn.Module):
def __init__(self,
input_dim,
hid_dim,
n_layers,
n_heads,
pf_dim,
dropout,
device,
max_length = 1024):
super().__init__()
self.device = device
self.tok_embedding = nn.Embedding(input_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_length, hid_dim)
self.layers = nn.ModuleList([EncoderLayer(hid_dim,
n_heads,
pf_dim,
dropout,
device)
for _ in range(n_layers)])
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)
def forward(self, src, src_mask):
#src = [batch size, src len]
#src_mask = [batch size, 1, 1, src len]
batch_size = src.shape[0]
src_len = src.shape[1]
pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
#pos = [batch size, src len]
src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
#src = [batch size, src len, hid dim]
for layer in self.layers:
src = layer(src, src_mask)
#src = [batch size, src len, hid dim]
return src
class BuSTv2(nn.Module):
def __init__(self,
encoder,
src_pad_idx,
d_model,
device,
num_classes=2, dropout=0.3):
super().__init__()
self.encoder = encoder
self.src_pad_idx = src_pad_idx
self.device = device
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(d_model * 2, num_classes)
self.sigmoid = nn.Sigmoid()
def make_src_mask(self, src):
#src = [batch size, src len]
src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
#src_mask = [batch size, 1, 1, src len]
return src_mask
def forward(self, src, trg):
#src = [batch size, src len]
#trg = [batch size, trg len]
src_mask = self.make_src_mask(src)
trg_mask = self.make_src_mask(trg)
#src_mask = [batch size, 1, 1, src len]
#trg_mask = [batch size, 1, 1, trg len]
enc_src = self.encoder(src, src_mask) # [batch size, src len, d_model]
enc_trg = self.encoder(trg, trg_mask) # [batch size, trg len, d_model]
enc_src_pooled = enc_src.mean(dim=1) # [batch size, d_model]
enc_trg_pooled = enc_trg.mean(dim=1) # [batch size, d_model]
combined = torch.cat((enc_src_pooled, enc_trg_pooled), dim=1) # [batch size, d_model * 2]
logits = self.classifier(combined) # [batch size, num_classes]
# probs = self.sigmoid(logits)
return logits
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