Add mean_resizing for every VLMs' resizing_token_embeddings()

examples/modular-transformers/modeling_new_task_model.py

@@ -455,8 +455,9 @@ def resize_token_embeddings(
         self,
         new_num_tokens: Optional[int] = None,
         pad_to_multiple_of=None,
+        mean_resizing=True
     ) -> nn.Embedding:
-        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
 
         # Update vocab size
         self.config.text_config.vocab_size = model_embeds.num_embeddings

examples/modular-transformers/modular_new_task_model.py

@@ -73,8 +73,9 @@ def resize_token_embeddings(
         self,
         new_num_tokens: Optional[int] = None,
         pad_to_multiple_of=None,
+        mean_resizing=True
     ) -> nn.Embedding:
-        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
 
         # Update vocab size
         self.config.text_config.vocab_size = model_embeds.num_embeddings

src/transformers/models/bark/modeling_bark.py

@@ -1189,11 +1189,11 @@ def set_output_embeddings(self, new_output_embeddings):
         # one lm_head for each codebook
         self.lm_heads = new_output_embeddings
 
-    def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
+    def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None, mean_resizing=True):
         old_embeddings_list = self.get_input_embeddings()
         new_embeddings_list = nn.ModuleList(
             [
-                self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
+                self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of, mean_resizing)
                 for old_embeddings in old_embeddings_list
             ]
         )
@@ -1211,7 +1211,10 @@ def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
         return self.get_input_embeddings()
 
     def resize_token_embeddings(
-        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
+        self,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+        mean_resizing: bool = True,
     ) -> nn.Embedding:
         """
         Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`.
@@ -1230,11 +1233,19 @@ def resize_token_embeddings(
                 `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more
                 details about this, or help on choosing the correct value for resizing, refer to this guide:
                 https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
+            mean_resizing (`bool`):
+                Whether to initialize the added embeddings from a multivariate normal distribution that has old embeddings' mean and
+                covariance or to initialize them with a normal distribution that has a mean of zero and std equals `config.initializer_range`.
+
+                Setting `mean_resizing` to `True` is useful when increasing the size of the embeddings of causal language models,
+                where the generated tokens' probabilities won't be affected by the added embeddings because initializing the new embeddings with the
+                old embeddings' mean will reduce the kl-divergence between the next token probability before and after adding the new embeddings.
+                Refer to this article for more information: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
 
         Return:
             `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model.
         """
-        model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         if new_num_tokens is None and pad_to_multiple_of is None:
             return model_embeds
 

src/transformers/models/bart/modeling_bart.py

@@ -1577,8 +1577,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2457,8 +2457,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1232,8 +1232,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1184,8 +1184,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py

@@ -1295,8 +1295,10 @@ def prepare_inputs_for_generation(
     def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
         return self._shift_right(labels)
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/led/modeling_led.py

@@ -2319,8 +2319,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.led.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/lxmert/modeling_lxmert.py

@@ -1072,9 +1072,11 @@ def __init__(self, config):
             }
         self.visual_losses = visual_losses
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
         # Adding the following steps to resize bias to match the shape of resized embeddings
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self.cls.predictions.bias = self._resize_bias(self.cls.predictions.bias, new_num_tokens)
         return new_embeddings
 

src/transformers/models/marian/modeling_marian.py

@@ -1252,8 +1252,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         if self.config.share_encoder_decoder_embeddings:
             self._resize_final_logits_bias(new_num_tokens)
         return new_embeddings

src/transformers/models/mbart/modeling_mbart.py

@@ -1546,8 +1546,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/mvp/modeling_mvp.py

@@ -1370,8 +1370,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_num_tokens)
         return new_embeddings
 

src/transformers/models/omdet_turbo/modeling_omdet_turbo.py

@@ -1658,9 +1658,11 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, value):
         self.language_backbone.model.set_input_embeddings(value)
 
-    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
+    def resize_token_embeddings(
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing: bool = True
+    ) -> nn.Embedding:
         model_embeds = self.language_backbone.model.resize_token_embeddings(
-            new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of
+            new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of, mean_resizing=mean_resizing
         )
         self.config.text_config.vocab_size = model_embeds.num_embeddings
         self.vocab_size = model_embeds.num_embeddings

src/transformers/models/pegasus/modeling_pegasus.py

@@ -1265,8 +1265,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 

src/transformers/models/plbart/modeling_plbart.py

@@ -1274,8 +1274,10 @@ def get_encoder(self):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(
+        self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+    ) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings