| from einops import rearrange |
| from torchvision import models |
| import math |
| import torch |
| from torch import nn |
|
|
| class LanguageTransformer(nn.Module): |
| def __init__(self, vocab_size, |
| d_model, nhead, |
| num_encoder_layers, num_decoder_layers, |
| dim_feedforward, max_seq_length, |
| pos_dropout, trans_dropout): |
| super().__init__() |
| |
| self.d_model = d_model |
| self.embed_tgt = nn.Embedding(vocab_size, d_model) |
| self.pos_enc = PositionalEncoding(d_model, pos_dropout, max_seq_length) |
| |
|
|
| self.transformer = nn.Transformer(d_model, nhead, |
| num_encoder_layers, num_decoder_layers, |
| dim_feedforward, trans_dropout) |
| |
| self.fc = nn.Linear(d_model, vocab_size) |
| |
| def forward(self, src, tgt, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None): |
| """ |
| Shape: |
| - src: (W, N, C) |
| - tgt: (T, N) |
| - src_key_padding_mask: (N, S) |
| - tgt_key_padding_mask: (N, T) |
| - memory_key_padding_mask: (N, S) |
| - output: (N, T, E) |
| |
| """ |
| tgt_mask = self.gen_nopeek_mask(tgt.shape[0]).to(src.device) |
| |
| src = self.pos_enc(src*math.sqrt(self.d_model)) |
| |
|
|
| tgt = self.pos_enc(self.embed_tgt(tgt) * math.sqrt(self.d_model)) |
| |
| output = self.transformer(src, tgt, tgt_mask=tgt_mask, src_key_padding_mask=src_key_padding_mask, |
| tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask) |
| |
| output = output.transpose(0, 1) |
| return self.fc(output) |
|
|
| def gen_nopeek_mask(self, length): |
| mask = (torch.triu(torch.ones(length, length)) == 1).transpose(0, 1) |
| mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) |
|
|
| return mask |
| |
| def forward_encoder(self, src): |
| src = self.pos_enc(src*math.sqrt(self.d_model)) |
| memory = self.transformer.encoder(src) |
| return memory |
| |
| def forward_decoder(self, tgt, memory): |
| tgt_mask = self.gen_nopeek_mask(tgt.shape[0]).to(tgt.device) |
| tgt = self.pos_enc(self.embed_tgt(tgt) * math.sqrt(self.d_model)) |
| |
| output = self.transformer.decoder(tgt, memory, tgt_mask=tgt_mask) |
| |
| output = output.transpose(0, 1) |
|
|
| return self.fc(output), memory |
| |
| def expand_memory(self, memory, beam_size): |
| memory = memory.repeat(1, beam_size, 1) |
| return memory |
| |
| def get_memory(self, memory, i): |
| memory = memory[:, [i], :] |
| return memory |
|
|
| class PositionalEncoding(nn.Module): |
| def __init__(self, d_model, dropout=0.1, max_len=100): |
| super(PositionalEncoding, self).__init__() |
| self.dropout = nn.Dropout(p=dropout) |
|
|
| pe = torch.zeros(max_len, d_model) |
| position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) |
| div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0).transpose(0, 1) |
| self.register_buffer('pe', pe) |
|
|
| def forward(self, x): |
| x = x + self.pe[:x.size(0), :] |
|
|
| return self.dropout(x) |
| |
| class LearnedPositionalEncoding(nn.Module): |
| def __init__(self, d_model, dropout=0.1, max_len=100): |
| super(LearnedPositionalEncoding, self).__init__() |
| self.dropout = nn.Dropout(p=dropout) |
|
|
| self.pos_embed = nn.Embedding(max_len, d_model) |
| self.layernorm = LayerNorm(d_model) |
|
|
| def forward(self, x): |
| seq_len = x.size(0) |
| pos = torch.arange(seq_len, dtype=torch.long, device=x.device) |
| pos = pos.unsqueeze(-1).expand(x.size()[:2]) |
| x = x + self.pos_embed(pos) |
| return self.dropout(self.layernorm(x)) |
|
|
| class LayerNorm(nn.Module): |
| "A layernorm module in the TF style (epsilon inside the square root)." |
| def __init__(self, d_model, variance_epsilon=1e-12): |
| super().__init__() |
| self.gamma = nn.Parameter(torch.ones(d_model)) |
| self.beta = nn.Parameter(torch.zeros(d_model)) |
| self.variance_epsilon = variance_epsilon |
|
|
| def forward(self, x): |
| u = x.mean(-1, keepdim=True) |
| s = (x - u).pow(2).mean(-1, keepdim=True) |
| x = (x - u) / torch.sqrt(s + self.variance_epsilon) |
| return self.gamma * x + self.beta |
|
|
