Fix layer reference loss + previous attempted fix

pytorch_transformers/modeling_bert.py

@@ -762,7 +762,7 @@ class BertForPreTraining(BertPreTrainedModel):
         if self.config.torchscript:
             self.cls.predictions.decoder.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.cls.predictions.decoder = self.bert.embeddings.word_embeddings  # Tied weights
+            self.cls.predictions.decoder.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
                 next_sentence_label=None, head_mask=None):
@@ -868,7 +868,7 @@ class BertForMaskedLM(BertPreTrainedModel):
         if self.config.torchscript:
             self.cls.predictions.decoder.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.cls.predictions.decoder = self.bert.embeddings.word_embeddings  # Tied weights
+            self.cls.predictions.decoder.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
         """

pytorch_transformers/modeling_gpt2.py

@@ -566,7 +566,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         if self.config.torchscript:
             self.lm_head.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.lm_head = self.transformer.wte  # Tied weights
+            self.lm_head.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, past=None, head_mask=None):
         """
@@ -662,7 +662,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         if self.config.torchscript:
             self.lm_head.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.lm_head = self.transformer.wte  # Tied weights
+            self.lm_head.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
                 position_ids=None, past=None, head_mask=None):

pytorch_transformers/modeling_openai.py

@@ -587,7 +587,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
         if self.config.torchscript:
             self.lm_head.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.lm_head = self.transformer.tokens_embed  # Tied weights
+            self.lm_head.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, head_mask=None):
         """
@@ -700,7 +700,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         if self.config.torchscript:
             self.lm_head.weight = nn.Parameter(input_embeddings.clone())
         else:
-            self.lm_head = self.transformer.tokens_embed  # Tied weights
+            self.lm_head.weight = input_embeddings  # Tied weights
 
     def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
                 position_ids=None, head_mask=None):

pytorch_transformers/tests/modeling_common_test.py

@@ -541,8 +541,8 @@ class ModelUtilsTest(unittest.TestCase):
             model.resize_token_embeddings(config.vocab_size + 10)
             decoding.weight.data.mul_(20)
             # Check that the embedding layer and decoding layer are the same in size and in value
-            self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
-            self.assertTrue(check_same_values(embeddings, decoding))
+            self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
+            self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
 
 
 if __name__ == "__main__":