Update super-large-language-model.py

super-large-language-model.py

@@ -0,0 +1,39 @@
+import math
+import torch
+import torch.nn as nn
+
+class TransformerModel(nn.Module):
+    def __init__(self, vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):
+        super(TransformerModel, self).__init__()
+        self.model_type = 'Transformer'
+        self.src_mask = None
+        self.pos_encoder = PositionalEncoding(d_model, dropout)
+        self.encoder = nn.Embedding(vocab_size, d_model)
+        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)
+        self.decoder = nn.Linear(d_model, vocab_size)
+
+    def forward(self, src, tgt, src_mask=None, tgt_mask=None):
+        src = self.encoder(src) * math.sqrt(self.d_model)
+        src = self.pos_encoder(src)
+        tgt = self.encoder(tgt) * math.sqrt(self.d_model)
+        tgt = self.pos_encoder(tgt)
+        output = self.transformer(src, tgt, src_mask, tgt_mask)
+        output = self.decoder(output)
+        return output
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout=0.1, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-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).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:x.size(0), :]
+        return self.dropout(x)