Update evo_model.py

evo_model.py

@@ -1,39 +1,14 @@
-# evo_model.py
-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)
-        self.max_len = max_len
-
-    def forward(self, x):
-        seq_len = x.size(1)
-        if seq_len > self.max_len:
-            raise ValueError(f"Input length {seq_len} exceeds max_len {self.max_len}")
-        return x + self.pe[:, :seq_len]
 
 class EvoDecoderModel(nn.Module):
     def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048, dropout=0.1):
-        super().__init__()
-        self.token_embed = nn.Embedding(vocab_size, d_model)
-        self.pos_encoder = PositionalEncoding(d_model)
-        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True)
-        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
-        self.lm_head = nn.Linear(d_model, vocab_size)
-
-    def forward(self, input_ids):
-        x = self.token_embed(input_ids)
-        x = self.pos_encoder(x)
-        seq_len = x.size(1)
-        mask = torch.triu(torch.ones(seq_len, seq_len, device=x.device), diagonal=1).bool()
-        x = self.decoder(x, x, tgt_mask=mask)
-        return self.lm_head(x)
+        super(EvoDecoderModel, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, d_model)
+        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
+        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers)
+        self.output_layer = nn.Linear(d_model, vocab_size)
+    
+    def forward(self, tgt, memory):
+        embedded = self.embedding(tgt)
+        output = self.transformer_decoder(embedded, memory)
+        return self.output_layer(output)