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import torch |
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import torch.nn as nn |
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import math |
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class PositionalEncoding(nn.Module): |
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def __init__(self, d_model, max_len=512): |
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super().__init__() |
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pe = torch.zeros(max_len, d_model) |
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position = torch.arange(0, max_len, dtype=torch.float32).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) |
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self.register_buffer('pe', pe) |
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def forward(self, x): |
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return x + self.pe[:, :x.size(1)] |
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class EvoDecoderModel(nn.Module): |
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def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048, dropout=0.1): |
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super().__init__() |
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self.embedding = nn.Embedding(vocab_size, d_model) |
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self.pos_encoder = PositionalEncoding(d_model) |
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decoder_layer = nn.TransformerDecoderLayer( |
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d_model=d_model, |
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nhead=nhead, |
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dim_feedforward=dim_feedforward, |
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dropout=dropout, |
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batch_first=True |
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) |
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self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers) |
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self.linear = nn.Linear(d_model, vocab_size) |
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def forward(self, input_ids): |
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embedded = self.embedding(input_ids) |
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embedded = self.pos_encoder(embedded) |
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seq_len = embedded.size(1) |
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mask = torch.triu(torch.ones(seq_len, seq_len, device=embedded.device), diagonal=1).bool() |
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output = self.decoder(embedded, embedded, tgt_mask=mask) |
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logits = self.linear(output) |
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return logits |
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