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| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| # Transformer (encoder) https://github.com/jadore801120/attention-is-all-you-need-pytorch | |
| # Original Copyright 2017 Victor Huang | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| class ScaledDotProductAttention(nn.Module): | |
| """ | |
| Scaled Dot-Product Attention | |
| """ | |
| def __init__(self, temperature, attn_dropout=0.1): | |
| super().__init__() | |
| self.temperature = temperature | |
| self.dropout = nn.Dropout(attn_dropout) | |
| def forward(self, q, k, v, mask=None): | |
| attn = torch.matmul(q / self.temperature, k.transpose(2, 3)) | |
| if mask is not None: | |
| attn = attn.masked_fill(mask == 0, -1e9) | |
| attn = self.dropout(F.softmax(attn, dim=-1)) | |
| output = torch.matmul(attn, v) | |
| return output, attn | |
| class MultiHeadAttention(nn.Module): | |
| """ | |
| Multi-Head Attention module | |
| """ | |
| def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): | |
| super().__init__() | |
| self.n_head = n_head | |
| self.d_k = d_k | |
| self.d_v = d_v | |
| self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) | |
| self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) | |
| self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) | |
| self.fc = nn.Linear(n_head * d_v, d_model, bias=False) | |
| self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5) | |
| self.dropout = nn.Dropout(dropout) | |
| self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) | |
| def forward(self, q, k, v, mask=None): | |
| d_k, d_v, n_head = self.d_k, self.d_v, self.n_head | |
| sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) | |
| residual = q | |
| # Pass through the pre-attention projection: b x lq x (n*dv) | |
| # Separate different heads: b x lq x n x dv | |
| q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) | |
| k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) | |
| v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) | |
| # Transpose for attention dot product: b x n x lq x dv | |
| q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) | |
| if mask is not None: | |
| mask = mask.unsqueeze(1) # For head axis broadcasting. | |
| q, attn = self.attention(q, k, v, mask=mask) | |
| # Transpose to move the head dimension back: b x lq x n x dv | |
| # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) | |
| q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) | |
| q = self.dropout(self.fc(q)) | |
| q += residual | |
| q = self.layer_norm(q) | |
| return q, attn | |
| class PositionwiseFeedForward(nn.Module): | |
| """ | |
| A two-feed-forward-layer module | |
| """ | |
| def __init__(self, d_in, d_hid, dropout=0.1): | |
| super().__init__() | |
| self.w_1 = nn.Linear(d_in, d_hid) # position-wise | |
| self.w_2 = nn.Linear(d_hid, d_in) # position-wise | |
| self.layer_norm = nn.LayerNorm(d_in, eps=1e-6) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| residual = x | |
| x = self.w_2(F.relu(self.w_1(x))) | |
| x = self.dropout(x) | |
| x += residual | |
| x = self.layer_norm(x) | |
| return x | |
| def get_subsequent_mask(seq): | |
| """ | |
| For masking out the subsequent info. | |
| """ | |
| sz_b, len_s = seq.size() | |
| subsequent_mask = (1 - torch.triu( | |
| torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool() | |
| return subsequent_mask | |
| class PositionalEncoding(nn.Module): | |
| def __init__(self, d_hid, n_position=200): | |
| super(PositionalEncoding, self).__init__() | |
| # Not a parameter | |
| self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid)) | |
| def _get_sinusoid_encoding_table(self, n_position, d_hid): | |
| """ | |
| Sinusoid position encoding table | |
| """ | |
| # TODO: make it with torch instead of numpy | |
| def get_position_angle_vec(position): | |
| return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] | |
| sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) | |
| sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i | |
| sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 | |
| return torch.FloatTensor(sinusoid_table).unsqueeze(0) | |
| def forward(self, x): | |
| return x + self.pos_table[:, :x.size(1)].clone().detach() | |
| class EncoderLayer(nn.Module): | |
| """ | |
| Compose with two layers | |
| """ | |
| def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.0): | |
| super(EncoderLayer, self).__init__() | |
| self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout) | |
| self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout) | |
| def forward(self, enc_input, slf_attn_mask=None): | |
| enc_output, enc_slf_attn = self.slf_attn( | |
| enc_input, enc_input, enc_input, mask=slf_attn_mask) | |
| enc_output = self.pos_ffn(enc_output) | |
| return enc_output, enc_slf_attn | |
| class TransformerEncoder(nn.Module): | |
| """ | |
| A encoder model with self attention mechanism. | |
| """ | |
| def __init__( | |
| self, d_word_vec=512, n_layers=2, n_head=8, d_k=64, d_v=64, | |
| d_model=512, d_inner=2048, dropout=0.1, n_position=624, scale_emb=False): | |
| super().__init__() | |
| # self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=pad_idx) | |
| if n_position > 0: | |
| self.position_enc = PositionalEncoding(d_word_vec, n_position=n_position) | |
| else: | |
| self.position_enc = lambda x: x | |
| self.dropout = nn.Dropout(p=dropout) | |
| self.layer_stack = nn.ModuleList([ | |
| EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout) | |
| for _ in range(n_layers)]) | |
| self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) | |
| self.scale_emb = scale_emb | |
| self.d_model = d_model | |
| def forward(self, src_seq, src_mask, return_attns=False): | |
| enc_slf_attn_list = [] | |
| # -- Forward | |
| # enc_output = self.src_word_emb(src_seq) | |
| enc_output = src_seq | |
| if self.scale_emb: | |
| enc_output *= self.d_model ** 0.5 | |
| enc_output = self.dropout(self.position_enc(enc_output)) | |
| enc_output = self.layer_norm(enc_output) | |
| for enc_layer in self.layer_stack: | |
| enc_output, enc_slf_attn = enc_layer(enc_output, slf_attn_mask=src_mask) | |
| enc_slf_attn_list += [enc_slf_attn] if return_attns else [] | |
| if return_attns: | |
| return enc_output, enc_slf_attn_list | |
| return enc_output | |
| if __name__ == '__main__': | |
| pass | |