transformer.py

import torch.nn as nn
import torch.nn.functional as F
import torch
import math
from torch.autograd import Variable

class TransformerBlock(nn.Module):
    """
    Bidirectional Encoder = Transformer (self-attention)
    Transformer = MultiHead_Attention + Feed_Forward with sublayer connection
    """

    def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout):
        """
        :param hidden: hidden size of transformer
        :param attn_heads: head sizes of multi-head attention
        :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size
        :param dropout: dropout rate
        """

        super().__init__()
        self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden)
        self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout)
        self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.dropout = nn.Dropout(p=dropout)
        self.hidden = hidden




    def forward(self, x, mask):
        self.position = PositionalEmbedding(self.hidden, x.size()[1])

        x = x + self.position(x)
        x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask))
        x = self.output_sublayer(x, self.feed_forward)
        return self.dropout(x)


class PositionalEmbedding(nn.Module):

    def __init__(self, d_model, max_len=512):
        super().__init__()

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]


class MultiHeadedAttention(nn.Module):
    """
    Take in model size and number of heads.
    """

    def __init__(self, h, d_model, dropout=0.1):
        super().__init__()
        assert d_model % h == 0

        # We assume d_v always equals d_k
        self.d_k = d_model // h
        self.h = h

        self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
        self.output_linear = nn.Linear(d_model, d_model)
        self.attention = Attention()

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)

        # 1) Do all the linear projections in batch from d_model => h x d_k
        query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
                             for l, x in zip(self.linear_layers, (query, key, value))]

        # 2) Apply attention on all the projected vectors in batch.
        x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout)

        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k)

        return self.output_linear(x)



class Attention(nn.Module):
    """
    Compute 'Scaled Dot Product Attention
    """

    def forward(self, query, key, value, mask=None, dropout=None):
        scores = torch.matmul(query, key.transpose(-2, -1)) \
                 / math.sqrt(query.size(-1))

        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)

        p_attn = F.softmax(scores, dim=-1)

        if dropout is not None:
            p_attn = dropout(p_attn)

        return torch.matmul(p_attn, value), p_attn

class PositionwiseFeedForward(nn.Module):
    "Implements FFN equation."

    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = GELU()

    def forward(self, x):
        return self.w_2(self.dropout(self.activation(self.w_1(x))))

class SublayerConnection(nn.Module):
    """
    A residual connection followed by a layer norm.
    Note for code simplicity the norm is first as opposed to last.
    """

    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        "Apply residual connection to any sublayer with the same size."
        return x + self.dropout(sublayer(self.norm(x)))

class LayerNorm(nn.Module):
    "Construct a layernorm module (See citation for details)."

    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    """

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

def train(datas,hidden,sample_size=1,max_len=1,attn_heads = 4,dropout = 0.1,epochtime = 500):
    
    net = TransformerBlock(hidden, attn_heads, hidden * 4, dropout)
    criterion = nn.MSELoss()
    optimizer = torch.optim.Adam(net.parameters(),lr=0.01)
    loss_list = []
    datas = datas.reshape(sample_size, max_len, hidden)
    datas = torch.Tensor(datas)
    for e in range(epochtime):
        out = net(datas,mask=None)
        loss = criterion(out, datas)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        loss_list.append(loss.item())
        if (e + 1) % 50 == 0:
            print('Epoch: {}, Loss: {:.5f}'.format(e + 1, loss.item()))
    
    net = net.eval()
    var_data = Variable(datas)
    pred_test = net(var_data,mask=None)
    pred_test = pred_test.view(-1).data.numpy().reshape(hidden)
    return pred_test,loss_list