import torch import torch.nn as nn class SelfAttention(nn.Module): def __init__(self, embed_size, heads): super(SelfAttention, self).__init__() self.embed_size = embed_size self.heads = heads self.head_dim = embed_size // heads assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads" self.values = nn.Linear(self.head_dim, self.head_dim, bias=False) self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False) self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False) self.fc_out = nn.Linear(heads*self.head_dim, self.embed_size) def forward(self, values, keys, query, mask): N = query.shape[0] value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1] # Split embedding into self.heads pieces values = values.reshape(N, value_len, self.heads, self.head_dim) keys = keys.reshape(N, key_len, self.heads, self.head_dim) query = query.reshape(N, query_len, self.heads, self.head_dim) values = self.values(values) keys = self.keys(keys) query = self.queries(query) # einsum is black magic i guess energy = torch.einsum("nqhd,nkhd->nhqk", [query, keys]) if mask is not None: energy = energy.masked_fill(mask==0, float("-1e20")) attention = torch.softmax(energy / (self.embed_size**(1/2)), dim=3) # once again, we say the magic words, and then we flatten the einsum out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape( N, query_len, self.heads*self.head_dim ) out = self.fc_out(out) return out class TransformerBlock(nn.Module): def __init__(self, embed_size, heads, dropout, forward_expansion): super(TransformerBlock, self).__init__() self.attention = SelfAttention(embed_size, heads) self.norm1 = nn.LayerNorm(embed_size) self.norm2 = nn.LayerNorm(embed_size) self.feed_forward = nn.Sequential( nn.Linear(embed_size, forward_expansion*embed_size), nn.ReLU(), nn.Linear(forward_expansion*embed_size, embed_size) ) self.dropout = nn.Dropout(dropout) def forward(self, value, key, query, mask): attention = self.attention(value, key, query, mask) x = self.dropout(self.norm1(attention+query)) forward = self.feed_forward(x) out = self.dropout(self.norm2(forward + x)) return out class Encoder(nn.Module): def __init__(self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length): super(Encoder, self).__init__() self.embed_size = embed_size self.device = device self.word_embedding = nn.Embedding(src_vocab_size, embed_size) self.position_embedding = nn.Embedding(max_length, embed_size) self.layers = nn.ModuleList( [ TransformerBlock( embed_size, heads, dropout=dropout, forward_expansion=forward_expansion ) for _ in range(num_layers) ] ) self.dropout = nn.Dropout(dropout) def forward(self, x, mask): N, seq_len = x.shape positions = torch.arange(0, seq_len).expand(N, seq_len).to(self.device) out = self.dropout(self.word_embedding(x)+self.position_embedding(positions)) for layer in self.layers: out = layer(out, out, out, mask) return out class DecoderBlock(nn.Module): def __init__(self, embed_size, heads, forward_expansion, dropout, device): super(DecoderBlock, self).__init__() self.attention = SelfAttention(embed_size, heads) self.norm = nn.LayerNorm(embed_size) self.transformer_block = TransformerBlock( embed_size, heads, dropout, forward_expansion ) self.dropout = nn.Dropout(dropout) def forward(self, x, value, key, src_mask, trg_mask): attention = self.attention(x, x, x, trg_mask) query = self.dropout(self.norm(attention+x)) out = self.transformer_block(value, key, query, src_mask) return out class Decoder(nn.Module): def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length): super(Decoder, self).__init__() self.device = device self.word_embedding = nn.Embedding(trg_vocab_size, embed_size) self.position_embedding = nn.Embedding(max_length, embed_size) self.layers = nn.ModuleList( [ DecoderBlock( embed_size, heads, forward_expansion, dropout, device ) for _ in range(num_layers) ] ) self.fc_out = nn.Linear(embed_size, trg_vocab_size) self.dropout = nn.Dropout(dropout) def forward(self, x, enc_out, src_mask, trg_mask): N, seq_len = x.shape positions = torch.arange(0, seq_len).expand(N, seq_len).to(self.device) x = self.dropout(self.word_embedding(x)+self.position_embedding(positions)) for layer in self.layers: x = layer(x, enc_out, enc_out, src_mask, trg_mask) out = self.fc_out(x) return out class Transformer(nn.Module): def __init__(self, src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, embed_size=256, num_layers=6, forward_expansion=4, heads=8, dropout=0, device="cuda", max_length=50000): super(Transformer, self).__init__() self.encoder = Encoder( src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length ) self.decoder = Decoder( trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length ) 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.to(self.device) def make_trg_mask(self, trg): N, trg_len = trg.shape trg_mask = torch.tril(torch.ones(trg_len, trg_len)).expand( N, 1, trg_len, trg_len ) return trg_mask.to(self.device) 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) out = self.decoder(trg, enc_src, src_mask, trg_mask) return out if __name__ == "__main__": device = torch.device("cuda" if torch.cuda.is_available() else "cpu") x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0], [1, 8, 7, 3, 4, 5, 6, 7 ,2]]).to(device) trg = torch.tensor([[1, 7, 4, 3, 5, 9, 2, 0], [1, 5, 6, 2, 4, 7, 6, 2]]).to(device) src_pad_idx = 0 trg_pad_idx = 0 src_vocab_size = 10 trg_vocab_size = 10 model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device).to(device) out = model(x, trg[:, :-1]) print(out.shape)