Update evo_model.py

evo_model.py

@@ -0,0 +1,41 @@
+# evo_model.py — Defines EvoDecoderModel used in inference and training
+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)  # [1, max_len, d_model]
+        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)