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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=128): |
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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).unsqueeze(1) |
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div_term = torch.exp(torch.arange(0, d_model, 2) * (-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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self.max_len = max_len |
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def forward(self, x): |
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seq_len = x.size(1) |
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if seq_len > self.max_len: |
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raise ValueError(f"Input length {seq_len} exceeds max_len {self.max_len}") |
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return x + self.pe[:, :seq_len] |
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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.token_embed = nn.Embedding(vocab_size, d_model) |
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self.pos_encoder = PositionalEncoding(d_model) |
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decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True) |
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self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers) |
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self.lm_head = nn.Linear(d_model, vocab_size) |
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def forward(self, input_ids): |
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x = self.token_embed(input_ids) |
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x = self.pos_encoder(x) |
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seq_len = x.size(1) |
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mask = torch.triu(torch.ones(seq_len, seq_len, device=x.device), diagonal=1).bool() |
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x = self.decoder(x, x, tgt_mask=mask) |
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return self.lm_head(x) |
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