transformer.py

import torch.nn as nn
import torch
import torch.optim as optim
import torch.utils.data as data
import math
import copy

# implementation taken from PyTorch Documentation / Datacamp
# https://www.datacamp.com/tutorial/building-a-transformer-with-py-torch

class PositionalEncoding(nn.Module):

    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)

        position = torch.arange(max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
        pe = torch.zeros(max_len, 1, d_model)
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe)

    def forward(self, x):
        """
        Arguments:
            x: Tensor, shape ``[seq_len, batch_size, embedding_dim]``
        """
        x = x + self.pe[:x.size(0)]
        return x
        #return self.dropout(x)

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model,num_heads):
        super(MultiHeadAttention, self).__init__()
        assert d_model % num_heads == 0, "d_model must be div by num_heads"

        self.d_model = d_model
        self.num_heads = num_heads
        self.d_k = d_model // num_heads  # dim of each heads k, q, v

        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)

    def scaled_dot_product_attention(self, Q, K, V, mask=None):
        attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        
        if mask is not None:
            #print(mask.size())
            #mask = mask.unsqueeze(1).unsqueeze(2)
            attn_scores = attn_scores.masked_fill(mask == 0, -1e9)
        
        attn_probs = torch.softmax(attn_scores, dim=-1)
        output = torch.matmul(attn_probs, V)
        return output 

    def split_heads(self, x):
        batch_size, seq_length, d_model = x.size()
        return x.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
    
    def combine_heads(self, x):
        batch_size, _, seq_length, d_k = x.size()
        return x.transpose(1, 2).contiguous().view(batch_size,seq_length, self.d_model)

    def forward(self, Q, K, V, mask=None):
        Q = self.split_heads(self.W_q(Q))    
        K = self.split_heads(self.W_k(K))   
        V = self.split_heads(self.W_v(V))   

        attn_output = self.scaled_dot_product_attention(Q, K, V, mask)
        output = self.W_o(self.combine_heads(attn_output))

        return output
    
class PositionWiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super(PositionWiseFeedForward, self).__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.fc2 = nn.Linear(d_ff, d_model)

        self.relu= nn.ReLU()

    def forward(self, x):
        return self.fc2(self.relu(self.fc1(x)))

class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout=0.2):
        super(DecoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, num_heads)
        self.cross_attn = nn.MultiheadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    # def forward(self, x, enc_output, src_mask, tgt_mask):
    #     attn_output = self.self_attn(x, x, x, tgt_mask)
    #     x = self.norm1(x + self.dropout(attn_output))
    #     attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
    #     x = self.norm2(x + self.dropout(attn_output))
    #     ff_output = self.ffn(x)
    #     x = self.norm3(x + self.dropout(ff_output))
    #     return x


    def forward(self, x, enc_output, src_padding_mask, tgt_padding_mask, tgt_causal_mask):
        # Self-attention with causal and padding masks
        attn_output, _ = self.self_attn(
            x, x, x, attn_mask=tgt_causal_mask, key_padding_mask=~tgt_padding_mask
        )
        x = self.norm1(x + self.dropout(attn_output))

        # Cross-attention with src padding mask
        attn_output, _ = self.cross_attn(
            x, enc_output, enc_output, key_padding_mask=~src_padding_mask
        )
        x = self.norm2(x + self.dropout(attn_output))

        # Feed-forward
        ff_output = self.feed_forward(x)
        x = self.norm3(x + self.dropout(ff_output))
        return x

class EncoderLayer(nn.Module):
    ''' Transform input tokens into contextualized representations '''
    def __init__(self, d_model, num_heads, d_ff, dropout=0.2,):
        super(EncoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask):
        attn_output = self.self_attn(x, x, x, mask)
        x = self.norm1(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm2(x + self.dropout(ff_output))
        return x

class Transformer(nn.Module):

    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, 
                 num_layers, d_ff, max_seq_length, dropout=0.2):
        super(Transformer, self).__init__()
        self.encoder_embedding = nn.Embedding(src_vocab_size, d_model)
        self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model, dropout, max_seq_length)

        self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])

        self.fc = nn.Linear(d_model, tgt_vocab_size)
        self.dropout = nn.Dropout(dropout)
        self.num_heads = num_heads

    def generate_mask(self, src, tgt):
        src_mask = (src != 0)
        #tgt_mask = (tgt != 0)

        #print(f'SRC_MASK og {src_mask.size()}')
        #print(f'TGT_MASK og {tgt_mask.size()}')

        tgt_padding_mask = (tgt != 0)

        src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
        tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(2)
        #tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(3) # OG

        #print(f'SRC_MASK unsqueeze {src_mask.size()}')
        #print(f'TGT_MASK unsqueeze {tgt_mask.size()}')

        seq_length = tgt.size(1)
        nopeak_mask = (1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)).bool()
       
        #print(f'NOPEAK_MASK {nopeak_mask.size()}')
       
        #tgt_mask = tgt_mask & nopeak_mask
        #tgt_mask = nopeak_mask  # Use this for attn_mask

        #attn_mask = nopeak_mask.unsqueeze(0).repeat(self.num_heads, 1, 1)
        attn_mask = nopeak_mask.unsqueeze(0).expand(self.num_heads, -1, -1)
        return src_mask, tgt_padding_mask, attn_mask

        #print(f'TGT_MASK & NOPEAK_MASK {tgt_mask.size()}')

        return src_mask, tgt_mask

    def forward(self, src, tgt):
        #src_mask, tgt_mask = self.generate_mask(src, tgt)

        src_mask, tgt_padding_mask, tgt_causal_mask = self.generate_mask(src, tgt)

        #src_mask_dec = src_mask.squeeze(1).squeeze(1)
        #tgt_mask_dec = tgt_mask.squeeze(1).squeeze(1)

        #print(f"transformer forward src_mask : {src_mask.size()}")
        #print(f"transformer forward tgt_mask : {tgt_mask.size()}")

        src_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(src)))
        tgt_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(tgt)))
        
        enc_output = src_embedded
        for enc_layer in self.encoder_layers:
            print(f'Adding encoder layer...')
            enc_output = enc_layer(enc_output, src_mask)

        dec_output = tgt_embedded
        for dec_layer in self.decoder_layers:
            print(f'Adding decoder layer...')
            #print(f'Adding decoder layer... src_mask {src_mask_dec.size()}, tgt_mask {tgt_mask_dec.size()}')
            #dec_output = dec_layer(dec_output, enc_output,src_mask_dec, tgt_mask_dec)
            #dec_output = dec_layer(dec_output, enc_output,src_mask, tgt_mask)
            dec_output = dec_layer(dec_output, enc_output,src_mask, tgt_padding_mask, tgt_causal_mask)

        output = self.fc(dec_output)   
        return output

def test_transformer():
    src_vocab_size = 5000
    tgt_vocab_size = 5000
    d_model = 512
    num_heads = 8
    num_layers = 6
    d_ff = 2048
    max_seq_length = 100
    dropout = 0.1

    transformer = Transformer(src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length, dropout)

    # Generate random sample data
    src_data = torch.randint(1, src_vocab_size, (64, max_seq_length))  # (batch_size, seq_length)
    tgt_data = torch.randint(1, tgt_vocab_size, (64, max_seq_length))  # (batch_size, seq_length)

    criterion = nn.CrossEntropyLoss(ignore_index=0)
    optimizer = optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)

    transformer.train()

    for epoch in range(100):
        optimizer.zero_grad()
        output = transformer(src_data, tgt_data[:, :-1])
        loss = criterion(output.contiguous().view(-1, tgt_vocab_size), tgt_data[:, 1:].contiguous().view(-1))
        loss.backward()
        optimizer.step()
        print(f"Epoch: {epoch+1}, Loss: {loss.item()}")

if __name__ == "__main__":
    test_transformer()