|
|
|
|
|
import torch |
|
|
import torch.nn as nn |
|
|
import math |
|
|
|
|
|
class PositionalEncoding(nn.Module): |
|
|
def __init__(self, d_model, max_len=128): |
|
|
super().__init__() |
|
|
pe = torch.zeros(max_len, d_model) |
|
|
position = torch.arange(0, max_len).unsqueeze(1) |
|
|
div_term = torch.exp(torch.arange(0, d_model, 2) * (-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) |
|
|
self.register_buffer('pe', pe) |
|
|
|
|
|
def forward(self, x): |
|
|
return x + self.pe[:, :x.size(1)] |
|
|
|
|
|
class EvoDecoderModel(nn.Module): |
|
|
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_ff=2048, max_len=128): |
|
|
super().__init__() |
|
|
self.token_embed = nn.Embedding(vocab_size, d_model) |
|
|
self.pos_encoder = PositionalEncoding(d_model, max_len) |
|
|
|
|
|
decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_ff, batch_first=True) |
|
|
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers) |
|
|
|
|
|
self.lm_head = nn.Linear(d_model, vocab_size) |
|
|
|
|
|
def generate_square_subsequent_mask(self, sz): |
|
|
return torch.triu(torch.full((sz, sz), float('-inf')), diagonal=1) |
|
|
|
|
|
def forward(self, input_ids): |
|
|
x = self.token_embed(input_ids) |
|
|
x = self.pos_encoder(x) |
|
|
|
|
|
tgt_mask = self.generate_square_subsequent_mask(x.size(1)).to(x.device) |
|
|
x = self.decoder(x, x, tgt_mask=tgt_mask) |
|
|
|
|
|
return self.lm_head(x) |
|
|
|
