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