tie the weights of lm_head.weight and transformer.wte.weight, i.e. th…

…e last linear layer of decoder and the token embeddings.

model.py

@@ -115,9 +115,10 @@ def __init__(self, config):
             ln_f = nn.LayerNorm(config.n_embd),
         ))
         self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.lm_head.weight = self.transformer.wte.weight # https://paperswithcode.com/method/weight-tying
 
-        # report number of parameters (note we don't count the decoder parameters in lm_head)
-        n_params = sum(p.numel() for p in self.transformer.parameters())
+        # report number of parameters
+        n_params = sum(p.numel() for p in self.parameters())
         print("number of parameters: %.2fM" % (n_params/1e6,))
 
     def forward(self, idx, targets=None):
@@ -156,8 +157,9 @@ def crop_block_size(self, block_size):
             block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
 
     @classmethod
-    def from_pretrained(cls, model_type, override_args):
+    def from_pretrained(cls, model_type, override_args=None):
         assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
+        override_args = override_args or {} # default to empty dict
         # only dropout can be overridden see more notes below
         assert all(k == 'dropout' for k in override_args)
         from transformers import GPT2LMHeadModel
@@ -235,6 +237,14 @@ def configure_optimizers(self, weight_decay, learning_rate, betas):
                     # weights of blacklist modules will NOT be weight decayed
                     no_decay.add(fpn)
 
+        # subtle: 'transformer.wte.weight' and 'lm_head.weight' are tied, so they
+        # will appear in the no_decay and decay sets respectively after the above.
+        # In addition, because named_parameters() doesn't return duplicates, it
+        # will only return the first occurence, key'd by 'transformer.wte.weight', below.
+        # so let's manually remove 'lm_head.weight' from decay set. This will include
+        # this tensor into optimization via transformer.wte.weight only, and not decayed.
+        decay.remove('lm_head.weight')
+
         # validate that we considered every parameter
         param_dict = {pn: p for pn, p in self.named_parameters()}
         inter_params = decay & no_decay