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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| import numpy as np | |
| import math | |
| import os | |
| import copy | |
| def clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) | |
| class Encoder(nn.Module): | |
| def __init__(self, layer, N): | |
| super(Encoder, self).__init__() | |
| self.layers = clones(layer, N) | |
| self.norm = LayerNorm(layer.size) | |
| def forward(self, x, mask): | |
| for layer in self.layers: | |
| x = layer(x, mask) | |
| return x | |
| class LayerNorm(nn.Module): | |
| 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 | |
| def attention(query, key, value, mask=None, dropout=None): | |
| d_k = query.size(-1) | |
| scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) | |
| 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 SublayerConnection(nn.Module): | |
| def __init__(self, size, dropout): | |
| super(SublayerConnection, self).__init__() | |
| self.norm = LayerNorm(size) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x, sublayer): | |
| return x + self.dropout(sublayer(self.norm(x))) | |
| class EncoderLayer(nn.Module): | |
| def __init__(self, size, self_attn, feed_forward, dropout): | |
| super(EncoderLayer, self).__init__() | |
| self.self_attn = self_attn | |
| self.feed_forward = feed_forward | |
| self.sublayer = clones(SublayerConnection(size, dropout), 2) | |
| self.size = size | |
| def forward(self, x, mask): | |
| x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask)) | |
| return self.sublayer[1](x, self.feed_forward) | |
| class MultiHeadedAttention(nn.Module): | |
| def __init__(self, h, d_model, dropout=0.1): | |
| super(MultiHeadedAttention, self).__init__() | |
| assert d_model % h == 0 | |
| self.d_k = d_model // h | |
| self.h = h | |
| self.linears = clones(nn.Linear(d_model, d_model), 4) | |
| self.attn = None | |
| self.dropout = nn.Dropout(p=dropout) | |
| def forward(self, query, key, value, mask=None): | |
| if mask is not None: | |
| mask = mask.unsqueeze(1) | |
| nbatches = query.size(0) | |
| query, key, value = \ | |
| [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) | |
| for l, x in zip(self.linears, (query, key, value))] | |
| x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout) | |
| x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k) | |
| return self.linears[-1](x) | |
| class PositionwiseFeedForward(nn.Module): | |
| 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.gelu = nn.ReLU() | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| return self.w_2(self.dropout(self.gelu(self.w_1(x)))) | |
| class Transformer(nn.Module): | |
| def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27): | |
| super(Transformer, self).__init__() | |
| self.pos_embedding = nn.Parameter(torch.randn(1, length, d_model)) | |
| self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout) | |
| def forward(self, x, mask=None): | |
| x += self.pos_embedding | |
| x = self.model(x, mask) | |
| return x | |
| def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1): | |
| c = copy.deepcopy | |
| attn = MultiHeadedAttention(h, d_model) | |
| ff = PositionwiseFeedForward(d_model, d_ff, dropout) | |
| model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N) | |
| return model | |