# transformer

**Repository Path**: txh2022/transformer

## Basic Information

- **Project Name**: transformer
- **Description**: fork from https://github.com/hyunwoongko/transformer
- **Primary Language**: Python
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 4
- **Created**: 2024-09-11
- **Last Updated**: 2024-09-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

### WARNING
This code was written in 2019, and I was not very familiar with transformer model in that time.
So don't trust this code too much. Currently I am not managing this code well, so please open pull requests if you find bugs in the code and want to fix.

# Transformer
My own implementation Transformer model (Attention is All You Need - Google Brain, 2017)
<br><br>
![model](image/model.png)
<br><br>

## 1. Implementations

### 1.1 Positional Encoding

![model](image/positional_encoding.jpg)
   
    
```python
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]         
```
<br><br>

### 1.2 Multi-Head Attention


![model](image/multi_head_attention.jpg)

```python
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, 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
```
<br><br>

### 1.3 Scale Dot Product Attention

![model](image/scale_dot_product_attention.jpg)

```python
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
```
<br><br>

### 1.4 Layer Norm

![model](image/layer_norm.jpg)
    
```python
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

```
<br><br>

### 1.5 Positionwise Feed Forward

![model](image/positionwise_feed_forward.jpg)
    
```python

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
```
<br><br>

### 1.6 Encoder & Decoder Structure

![model](image/enc_dec.jpg)
    
```python
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.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
```
<br>

```python
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
```
<br>

```python
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.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=src_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
```
<br>

```python        
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
```
<br><br>

## 2. Experiments

I use Multi30K Dataset to train and evaluate model <br>
You can check detail of dataset [here](https://arxiv.org/abs/1605.00459) <br>
I follow original paper's parameter settings. (below) <br>

![conf](image/transformer-model-size.jpg)
### 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
<br><br>

### 2.2 Training Result

![image](saved/transformer-base/train_result.jpg)
* Minimum Training Loss = 2.852672759656864
* Minimum Validation Loss = 3.2048025131225586 
<br><br>

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

<br><br>


## 3. Reference
- [Attention is All You Need, 2017 - Google](https://arxiv.org/abs/1706.03762)
- [The Illustrated Transformer - Jay Alammar](http://jalammar.github.io/illustrated-transformer/)
- [Data & Optimization Code Reference - Bentrevett](https://github.com/bentrevett/pytorch-seq2seq/)

<br><br>

## 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.