AlainConnor / transformer

Implementation of "Attention Is All You Need" using pytorch

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Transformer

My own implementation Transformer model (Attention is All You Need - Google Brain, 2017)

model

1. Implementations

1.1 Positional Encoding

model

class PositionalEncoding(nn.Module):
    """
    compute sinusoid encoding.
    """
    def __init__(self, d_model, max_len, device):
        """
        constructor of sinusoid encoding class

        :param d_model: dimension of model
        :param max_len: max sequence length
        :param device: hardware device setting
        """
        super(PositionalEncoding, self).__init__()

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

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

        _2i = torch.arange(0, d_model, step=2, device=device).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]         



1.2 Multi-Head Attention

model

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)
        self.w_k = nn.Linear(d_model, d_model)
        self.w_v = nn.Linear(d_model, d_model)
        self.w_concat = nn.Linear(d_model, d_model)

    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, self.n_head, length, d_tensor)
        # 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.view(batch_size, length, d_model)
        return tensor



1.3 Scale Dot Product Attention

model

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()

    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.view(batch_size, head, d_tensor, length)  # 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, -e)

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

        # 4. multiply with Value
        v = score @ v

        return v, score



1.4 Layer Norm

model

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)
        std = x.std(-1, keepdim=True)
        # '-1' means last dimension. 

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



1.5 Positionwise Feed Forward

model

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



1.6 Encoder & Decoder Structure

model

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

class Encoder(nn.Module):

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

        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, src_mask):
        x = self.emb(x)

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

        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, trg_mask, src_mask):    
        # 1. compute self attention
        _x = dec
        x = self.self_attention(q=dec, k=dec, v=dec, mask=trg_mask)
        
        # 2. add and norm
        x = self.norm1(x + _x)
        x = self.dropout1(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=src_mask)
            
            # 4. add and norm
            x = self.norm2(x + _x)
            x = self.dropout2(x)

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

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

        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, src, trg_mask, src_mask):
        trg = self.emb(trg)

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

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



2. Experiments

I use Multi30K Dataset to train and evaluate model
You can check detail of dataset here
I follow original paper's parameter settings. (below)

conf

2.1 Model Specification

  • total parameters = 55,207,087
  • model size = 215.7MB
  • lr scheduling : ReduceLROnPlateau

2.1.1 configuration

  • batch_size = 128
  • max_len = 256
  • d_model = 512
  • n_layers = 6
  • n_heads = 8
  • ffn_hidden = 2048
  • drop_prob = 0.1
  • init_lr = 0.1
  • factor = 0.9
  • patience = 10
  • warmup = 100
  • adam_eps = 5e-9
  • epoch = 1000
  • clip = 1
  • weight_decay = 5e-4

2.2 Training Result

image

  • Minimum Training Loss = 2.852672759656864
  • Minimum Validation Loss = 3.2048025131225586

Model Dataset BLEU Score
Original Paper's WMT14 EN-DE 25.8
My Implementation Multi30K EN-DE 26.4



3. Reference



4. Licence

Copyright 2019 Hyunwoong Ko.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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Implementation of "Attention Is All You Need" using pytorch


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