bust_model.py

import torch
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