catt/transformer.py

"""
@author : Hyunwoong
@when : 2019-12-18
@homepage : https://github.com/gusdnd852
"""

import math
import torch
import torch.nn as nn


class EncoderLayer(nn.Module):

    def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
        super(EncoderLayer, self).__init__()
        self.attention = MultiHeadAttention(d_model=d_model, n_head=n_head)
        self.norm1 = LayerNorm(d_model=d_model)
        self.dropout1 = nn.Dropout(p=drop_prob)

        self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob)
        self.norm2 = LayerNorm(d_model=d_model)
        self.dropout2 = nn.Dropout(p=drop_prob)

    def forward(self, x, s_mask):
        # 1. compute self attention
        _x = x
        x = self.attention(q=x, k=x, v=x, mask=s_mask)
        
        # 2. add and norm
        x = self.dropout1(x)
        x = self.norm1(x + _x)
        
        # 3. positionwise feed forward network
        _x = x
        x = self.ffn(x)
      
        # 4. add and norm
        x = self.dropout2(x)
        x = self.norm2(x + _x)
        return x


class DecoderLayer(nn.Module):

    def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
        super(DecoderLayer, self).__init__()
        self.self_attention = MultiHeadAttention(d_model=d_model, n_head=n_head)
        self.norm1 = LayerNorm(d_model=d_model)
        self.dropout1 = nn.Dropout(p=drop_prob)

        self.enc_dec_attention = MultiHeadAttention(d_model=d_model, n_head=n_head)
        self.norm2 = LayerNorm(d_model=d_model)
        self.dropout2 = nn.Dropout(p=drop_prob)

        self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob)
        self.norm3 = LayerNorm(d_model=d_model)
        self.dropout3 = nn.Dropout(p=drop_prob)

    def forward(self, dec, enc, t_mask, s_mask):
        # 1. compute self attention
        _x = dec
        x = self.self_attention(q=dec, k=dec, v=dec, mask=t_mask)
        
        # 2. add and norm
        x = self.dropout1(x)
        x = self.norm1(x + _x)

        if enc is not None:
            # 3. compute encoder - decoder attention
            _x = x
            x = self.enc_dec_attention(q=x, k=enc, v=enc, mask=s_mask)
            
            # 4. add and norm
            x = self.dropout2(x)
            x = self.norm2(x + _x)

        # 5. positionwise feed forward network
        _x = x
        x = self.ffn(x)
        
        # 6. add and norm
        x = self.dropout3(x)
        x = self.norm3(x + _x)
        return x


class ScaleDotProductAttention(nn.Module):
    """
    compute scale dot product attention

    Query : given sentence that we focused on (decoder)
    Key : every sentence to check relationship with Qeury(encoder)
    Value : every sentence same with Key (encoder)
    """

    def __init__(self):
        super(ScaleDotProductAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, q, k, v, mask=None, e=1e-12):
        # input is 4 dimension tensor
        # [batch_size, head, length, d_tensor]
        batch_size, head, length, d_tensor = k.size()

        # 1. dot product Query with Key^T to compute similarity
        k_t = k.transpose(2, 3)  # transpose
        score = (q @ k_t) / math.sqrt(d_tensor)  # scaled dot product

        # 2. apply masking (opt)
        if mask is not None:
            score = score.masked_fill(mask == 0, -10000)

        # 3. pass them softmax to make [0, 1] range
        score = self.softmax(score)

        # 4. multiply with Value
        v = score @ v

        return v, score


class PositionwiseFeedForward(nn.Module):

    def __init__(self, d_model, hidden, drop_prob=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.linear1 = nn.Linear(d_model, hidden)
        self.linear2 = nn.Linear(hidden, d_model)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=drop_prob)

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x


class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, n_head):
        super(MultiHeadAttention, self).__init__()
        self.n_head = n_head
        self.attention = ScaleDotProductAttention()
        self.w_q = nn.Linear(d_model, d_model, bias=False)
        self.w_k = nn.Linear(d_model, d_model, bias=False)
        self.w_v = nn.Linear(d_model, d_model, bias=False)
        self.w_concat = nn.Linear(d_model, d_model, bias=False)

    def forward(self, q, k, v, mask=None):
        # 1. dot product with weight matrices
        q, k, v = self.w_q(q), self.w_k(k), self.w_v(v)

        # 2. split tensor by number of heads
        q, k, v = self.split(q), self.split(k), self.split(v)

        # 3. do scale dot product to compute similarity
        out, attention = self.attention(q, k, v, mask=mask)

        # 4. concat and pass to linear layer
        out = self.concat(out)
        out = self.w_concat(out)

        # 5. visualize attention map
        # TODO : we should implement visualization

        return out

    def split(self, tensor):
        """
        split tensor by number of head

        :param tensor: [batch_size, length, d_model]
        :return: [batch_size, head, length, d_tensor]
        """
        batch_size, length, d_model = tensor.size()

        d_tensor = d_model // self.n_head
        tensor = tensor.view(batch_size, length, self.n_head, d_tensor).transpose(1, 2)
        # it is similar with group convolution (split by number of heads)

        return tensor

    def concat(self, tensor):
        """
        inverse function of self.split(tensor : torch.Tensor)

        :param tensor: [batch_size, head, length, d_tensor]
        :return: [batch_size, length, d_model]
        """
        batch_size, head, length, d_tensor = tensor.size()
        d_model = head * d_tensor

        tensor = tensor.transpose(1, 2).contiguous().view(batch_size, length, d_model)
        return tensor


class LayerNorm(nn.Module):
    def __init__(self, d_model, eps=1e-12):
        super(LayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.ones(d_model))
        self.beta = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        var = x.var(-1, unbiased=False, keepdim=True)
        # '-1' means last dimension. 

        out = (x - mean) / torch.sqrt(var + self.eps)
        out = self.gamma * out + self.beta
        return out


class TransformerEmbedding(nn.Module):
    """
    token embedding + positional encoding (sinusoid)
    positional encoding can give positional information to network
    """

    def __init__(self, vocab_size, d_model, max_len, drop_prob, padding_idx, learnable_pos_emb=True):
        """
        class for word embedding that included positional information

        :param vocab_size: size of vocabulary
        :param d_model: dimensions of model
        """
        super(TransformerEmbedding, self).__init__()
        self.tok_emb = TokenEmbedding(vocab_size, d_model, padding_idx)
        if learnable_pos_emb:
            self.pos_emb = LearnablePositionalEncoding(d_model, max_len)
        else:
            self.pos_emb = SinusoidalPositionalEncoding(d_model, max_len)
        self.drop_out = nn.Dropout(p=drop_prob)

    def forward(self, x):
        tok_emb = self.tok_emb(x)
        pos_emb = self.pos_emb(x).to(tok_emb.device)
        return self.drop_out(tok_emb + pos_emb)


class TokenEmbedding(nn.Embedding):
    """
    Token Embedding using torch.nn
    they will dense representation of word using weighted matrix
    """

    def __init__(self, vocab_size, d_model, padding_idx):
        """
        class for token embedding that included positional information

        :param vocab_size: size of vocabulary
        :param d_model: dimensions of model
        """
        super(TokenEmbedding, self).__init__(vocab_size, d_model, padding_idx=padding_idx)


class SinusoidalPositionalEncoding(nn.Module):
    """
    compute sinusoid encoding.
    """

    def __init__(self, d_model, max_len):
        """
        constructor of sinusoid encoding class

        :param d_model: dimension of model
        :param max_len: max sequence length

        """
        super(SinusoidalPositionalEncoding, self).__init__()

        # same size with input matrix (for adding with input matrix)
        self.encoding = torch.zeros(max_len, d_model)
        self.encoding.requires_grad = False  # we don't need to compute gradient

        pos = torch.arange(0, max_len)
        pos = pos.float().unsqueeze(dim=1)
        # 1D => 2D unsqueeze to represent word's position

        _2i = torch.arange(0, d_model, step=2).float()
        # 'i' means index of d_model (e.g. embedding size = 50, 'i' = [0,50])
        # "step=2" means 'i' multiplied with two (same with 2 * i)

        self.encoding[:, 0::2] = torch.sin(pos / (10000 ** (_2i / d_model)))
        self.encoding[:, 1::2] = torch.cos(pos / (10000 ** (_2i / d_model)))
        # compute positional encoding to consider positional information of words

    def forward(self, x):
        # self.encoding
        # [max_len = 512, d_model = 512]

        batch_size, seq_len = x.size()
        # [batch_size = 128, seq_len = 30]

        return self.encoding[:seq_len, :]
        # [seq_len = 30, d_model = 512]
        # it will add with tok_emb : [128, 30, 512]


class LearnablePositionalEncoding(nn.Module):
    """
    compute sinusoid encoding.
    """

    def __init__(self, d_model, max_seq_len):
        """
        constructor of learnable positonal encoding class

        :param d_model: dimension of model
        :param max_seq_len: max sequence length

        """
        super(LearnablePositionalEncoding, self).__init__()
        self.max_seq_len = max_seq_len
        self.wpe = nn.Embedding(max_seq_len, d_model)

    def forward(self, x):
        # self.encoding
        # [max_len = 512, d_model = 512]
        device = x.device
        batch_size, seq_len = x.size()
        assert seq_len <= self.max_seq_len, f"Cannot forward sequence of length {seq_len}, max_seq_len is {self.max_seq_len}"
        pos = torch.arange(0, seq_len, dtype=torch.long, device=device) # shape (seq_len)
        pos_emb = self.wpe(pos) # position embeddings of shape (seq_len, d_model)

        return pos_emb
        # [seq_len = 30, d_model = 512]
        # it will add with tok_emb : [128, 30, 512]


class Encoder(nn.Module):

    def __init__(self, enc_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, padding_idx, learnable_pos_emb=True):
        super().__init__()
        self.emb = TransformerEmbedding(d_model=d_model,
                                        max_len=max_len,
                                        vocab_size=enc_voc_size,
                                        drop_prob=drop_prob,
                                        padding_idx=padding_idx,
                                        learnable_pos_emb=learnable_pos_emb
                                        )

        self.layers = nn.ModuleList([EncoderLayer(d_model=d_model,
                                                  ffn_hidden=ffn_hidden,
                                                  n_head=n_head,
                                                  drop_prob=drop_prob)
                                     for _ in range(n_layers)])

    def forward(self, x, s_mask):
        x = self.emb(x)

        for layer in self.layers:
            x = layer(x, s_mask)

        return x

class Decoder(nn.Module):
    def __init__(self, dec_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, padding_idx, learnable_pos_emb=True):
        super().__init__()
        self.emb = TransformerEmbedding(d_model=d_model,
                                        drop_prob=drop_prob,
                                        max_len=max_len,
                                        vocab_size=dec_voc_size,
                                        padding_idx=padding_idx,
                                        learnable_pos_emb=learnable_pos_emb
                                        )

        self.layers = nn.ModuleList([DecoderLayer(d_model=d_model,
                                                  ffn_hidden=ffn_hidden,
                                                  n_head=n_head,
                                                  drop_prob=drop_prob)
                                     for _ in range(n_layers)])

        self.linear = nn.Linear(d_model, dec_voc_size)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        trg = self.emb(trg)

        for layer in self.layers:
            trg = layer(trg, enc_src, trg_mask, src_mask)

        # pass to LM head
        output = self.linear(trg)
        return output

class Transformer(nn.Module):

    def __init__(self, src_pad_idx, trg_pad_idx, enc_voc_size, dec_voc_size, d_model, n_head, max_len,
                 ffn_hidden, n_layers, drop_prob, learnable_pos_emb=True):
        super().__init__()
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        self.encoder = Encoder(d_model=d_model,
                               n_head=n_head,
                               max_len=max_len,
                               ffn_hidden=ffn_hidden,
                               enc_voc_size=enc_voc_size,
                               drop_prob=drop_prob,
                               n_layers=n_layers,
                               padding_idx=src_pad_idx,
                               learnable_pos_emb=learnable_pos_emb)

        self.decoder = Decoder(d_model=d_model,
                               n_head=n_head,
                               max_len=max_len,
                               ffn_hidden=ffn_hidden,
                               dec_voc_size=dec_voc_size,
                               drop_prob=drop_prob,
                               n_layers=n_layers,
                               padding_idx=trg_pad_idx,
                               learnable_pos_emb=learnable_pos_emb)

    def get_device(self):
        return next(self.parameters()).device

    def forward(self, src, trg):
        device = self.get_device()
        src_mask = self.make_pad_mask(src, src, self.src_pad_idx, self.src_pad_idx).to(device)
        src_trg_mask = self.make_pad_mask(trg, src, self.trg_pad_idx, self.src_pad_idx).to(device)
        trg_mask = self.make_pad_mask(trg, trg, self.trg_pad_idx, self.trg_pad_idx).to(device) * \
                   self.make_no_peak_mask(trg, trg).to(device)

        #print(src_mask)
        #print('-'*100)
        #print(trg_mask)
        enc_src = self.encoder(src, src_mask)
        output = self.decoder(trg, enc_src, trg_mask, src_trg_mask)
        return output

    def make_pad_mask(self, q, k, q_pad_idx, k_pad_idx):
        len_q, len_k = q.size(1), k.size(1)

        # batch_size x 1 x 1 x len_k
        k = k.ne(k_pad_idx).unsqueeze(1).unsqueeze(2)
        # batch_size x 1 x len_q x len_k
        k = k.repeat(1, 1, len_q, 1)

        # batch_size x 1 x len_q x 1
        q = q.ne(q_pad_idx).unsqueeze(1).unsqueeze(3)
        # batch_size x 1 x len_q x len_k
        q = q.repeat(1, 1, 1, len_k)

        mask = k & q
        return mask

    def make_no_peak_mask(self, q, k):
        len_q, len_k = q.size(1), k.size(1)

        # len_q x len_k
        mask = torch.tril(torch.ones(len_q, len_k)).type(torch.BoolTensor)

        return mask


def make_pad_mask(x, pad_idx):
    q = k = x
    q_pad_idx = k_pad_idx = pad_idx
    len_q, len_k = q.size(1), k.size(1)

    # batch_size x 1 x 1 x len_k
    k = k.ne(k_pad_idx).unsqueeze(1).unsqueeze(2)
    # batch_size x 1 x len_q x len_k
    k = k.repeat(1, 1, len_q, 1)

    # batch_size x 1 x len_q x 1
    q = q.ne(q_pad_idx).unsqueeze(1).unsqueeze(3)
    # batch_size x 1 x len_q x len_k
    q = q.repeat(1, 1, 1, len_k)

    mask = k & q
    return mask


from torch.nn.utils.rnn import pad_sequence
# x_list is a list of tensors of shape TxH where T is the seqlen and H is the feats dim
def pad_seq_v2(sequences, batch_first=True, padding_value=0.0, prepadding=True):
    lens = [i.shape[0]for i in sequences]
    padded_sequences = pad_sequence(sequences, batch_first=True, padding_value=padding_value) # NxTxH
    if prepadding:
        for i in range(len(lens)):
            padded_sequences[i] = padded_sequences[i].roll(-lens[i])
    if not batch_first:
        padded_sequences = padded_sequences.transpose(0, 1) # TxNxH
    return padded_sequences



if __name__ == '__main__':
    import torch
    import random
    import numpy as np

    rand_seed = 10

    device = 'cpu'

    # model parameter setting
    batch_size = 128
    max_len = 256
    d_model = 512
    n_layers = 3
    n_heads = 16
    ffn_hidden = 2048
    drop_prob = 0.1

    # optimizer parameter setting
    init_lr = 1e-5
    factor = 0.9
    adam_eps = 5e-9
    patience = 10
    warmup = 100
    epoch = 1000
    clip = 1.0
    weight_decay = 5e-4
    inf = float('inf')
    
    src_pad_idx = 2
    trg_pad_idx = 3
    
    enc_voc_size = 37
    dec_voc_size = 15
    model = Transformer(src_pad_idx=src_pad_idx,
                        trg_pad_idx=trg_pad_idx,
                        d_model=d_model,
                        enc_voc_size=enc_voc_size,
                        dec_voc_size=dec_voc_size,
                        max_len=max_len,
                        ffn_hidden=ffn_hidden,
                        n_head=n_heads,
                        n_layers=n_layers,
                        drop_prob=drop_prob
                        ).to(device)

    random.seed(rand_seed)
    # Set the seed to 0 for reproducible results
    np.random.seed(rand_seed)
    torch.manual_seed(rand_seed)

    x_list = [
        torch.tensor([[1, 1]]).transpose(0, 1), # 2
        torch.tensor([[1, 1, 1, 1, 1, 1, 1]]).transpose(0, 1),  # 7
        torch.tensor([[1, 1, 1]]).transpose(0, 1) # 3
    ]


    src_pad_idx = model.src_pad_idx
    trg_pad_idx = model.trg_pad_idx

    src = pad_seq_v2(x_list, padding_value=src_pad_idx, prepadding=False).squeeze(2)
    trg = pad_seq_v2(x_list, padding_value=trg_pad_idx, prepadding=False).squeeze(2)
    out = model(src, trg)