prompt_model.py

import torch
import torch.nn as nn


class Encoder(nn.Module):
    def __init__(self,
                 input_dim,
                 hid_dim,
                 n_layers,
                 n_heads,
                 pf_dim,
                 dropout,
                 device,
                 max_length=1000):
        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):
        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)
        src = self.dropout(
            (self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
        for layer in self.layers:
            src = layer(src, src_mask)
        return src


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, _ = self.self_attention(src, src, src, src_mask)
        src = self.self_attn_layer_norm(src + self.dropout(_src))
        _src = self.positionwise_feedforward(src)
        src = self.ff_layer_norm(src + self.dropout(_src))
        return src


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 = self.dropout(torch.relu(self.fc_1(x)))
        x = self.fc_2(x)
        return x


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]
        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)
        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)
        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale
        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)
        attention = torch.softmax(energy, dim=-1)
        x = torch.matmul(self.dropout(attention), V)
        x = x.permute(0, 2, 1, 3).contiguous()
        x = x.view(batch_size, -1, self.hid_dim)
        x = self.fc_o(x)
        return x, attention


class Decoder(nn.Module):
    def __init__(self,
                 output_dim,
                 hid_dim,
                 n_layers,
                 n_heads,
                 pf_dim,
                 dropout,
                 device,
                 max_length=10000):
        super().__init__()
        self.device = device
        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(max_length, hid_dim)

        self.layers = nn.ModuleList([DecoderLayer(hid_dim,
                                                  n_heads,
                                                  pf_dim,
                                                  dropout,
                                                  device)
                                     for _ in range(n_layers)])
        self.fc_out = nn.Linear(hid_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        batch_size = trg.shape[0]
        trg_len = trg.shape[1]
        pos = torch.arange(0, trg_len).unsqueeze(
            0).repeat(batch_size, 1).to(self.device)
        trg = self.dropout(
            (self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))
        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)
        output = self.fc_out(trg)
        return output, attention


class DecoderLayer(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.enc_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.encoder_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, trg, enc_src, trg_mask, src_mask):
        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)
        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))
        _trg, attention = self.encoder_attention(
            trg, enc_src, enc_src, src_mask)
        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))
        _trg = self.positionwise_feedforward(trg)
        trg = self.ff_layer_norm(trg + self.dropout(_trg))

        return trg, attention


class Seq2Seq(nn.Module):
    def __init__(self,
                 encoder,
                 decoder,
                 src_pad_idx,
                 trg_pad_idx,
                 device):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        self.device = device

    def make_src_mask(self, src):
        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
        return src_mask

    def make_trg_mask(self, trg):
        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)
        trg_len = trg.shape[1]
        trg_sub_mask = torch.tril(torch.ones(
            (trg_len, trg_len), device=self.device)).bool()
        trg_mask = trg_pad_mask & trg_sub_mask
        return trg_mask

    def forward(self, src, trg):
        src_mask = self.make_src_mask(src)
        trg_mask = self.make_trg_mask(trg)
        enc_src = self.encoder(src, src_mask)
        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)
        return output, attention