pytorch_transformers_from_scratch.py

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 div 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, embed_size)

    def forward(self, values, keys, queries, mask):
        N = queries.shape[0]
        value_len, key_len, query_len = values.shape[1], keys.shape[1], queries.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)
        queries = queries.reshape(N, query_len, self.heads, self.head_dim)

        values, keys, queries = (
            self.values(values),
            self.keys(keys),
            self.queries(queries),
        )

        energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])
        # queries shape: (N, query_len, heads, heads_dim)
        # keys shape: (N, key_len, heads, heads_dim)
        # energy shape: (N, heads, query_len, key_len)

        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))

        attention = torch.softmax(energy / (self.embed_size ** (1 / 2)), dim=3)
        out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(
            N, query_len, self.heads * self.head_dim
        )
        # attention shape: (N, heads, query_len, key_len)
        # values shape: (N, value_len, heads, heads_dim)
        # out shape: (N, query_len, heads, head_dim) then flatten last two dims

        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.feedforward = 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.feedforward(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, forward_expansion)
                for _ in range(num_layers)
            ]
        )
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).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="cpu",
        max_length=100,
    ):
        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).to(
        device
    )
    out = model(x, trg[:, :-1])
    print(out.shape)