make more accurate the way in which we count parameters. previous count incorrectly included the positional encoding params, when typically only the number of weight parameters is reported for these models

model.py

@@ -152,8 +152,19 @@ class GPT(nn.Module):
                 torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
 
         # report number of parameters
+        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
         n_params = sum(p.numel() for p in self.parameters())
-        print("number of parameters: %.2fM" % (n_params/1e6,))
+        if non_embedding:
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
 
     def _init_weights(self, module):
         if isinstance(module, nn.Linear):