[TF models] Common attributes as per #1721

transformers/modeling_tf_bert.py

@@ -460,6 +460,9 @@ class TFBertMainLayer(tf.keras.layers.Layer):
         self.encoder = TFBertEncoder(config, name='encoder')
         self.pooler = TFBertPooler(config, name='pooler')
 
+    def get_input_embeddings(self):
+        return self.embeddings
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -702,6 +705,9 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
         self.nsp = TFBertNSPHead(config, name='nsp___cls')
         self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
 
+    def get_output_embeddings(self):
+        return self.bert.embeddings
+
     def call(self, inputs, **kwargs):
         outputs = self.bert(inputs, **kwargs)
 
@@ -747,6 +753,9 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
         self.bert = TFBertMainLayer(config, name='bert')
         self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
 
+    def get_output_embeddings(self):
+        return self.bert.embeddings
+
     def call(self, inputs, **kwargs):
         outputs = self.bert(inputs, **kwargs)
 

transformers/modeling_tf_ctrl.py

@@ -192,6 +192,9 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
                                  name='h_._{}'.format(i)) for i in range(config.n_layer)]
         self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")
 
+    def get_input_embeddings(self):
+        return self.w
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -480,6 +483,9 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
 
         self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head")
 
+    def get_output_embeddings(self):
+        return self.lm_head.input_embeddings
+
     def call(self, inputs, **kwargs):
         transformer_outputs = self.transformer(inputs, **kwargs)
         hidden_states = transformer_outputs[0]

transformers/modeling_tf_distilbert.py

@@ -398,6 +398,9 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
         self.embeddings = TFEmbeddings(config, name="embeddings")   # Embeddings
         self.transformer = TFTransformer(config, name="transformer") # Encoder
 
+    def get_input_embeddings(self):
+        return self.embeddings
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -613,6 +616,9 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
         self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
         self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
 
+    def get_output_embeddings(self):
+        return self.vocab_projector.input_embeddings
+
     def call(self, inputs, **kwargs):
         distilbert_output = self.distilbert(inputs, **kwargs)
 

transformers/modeling_tf_gpt2.py

@@ -219,6 +219,9 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
                           name='h_._{}'.format(i)) for i in range(config.n_layer)]
         self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln_f')
 
+    def get_input_embeddings(self):
+        return self.wte
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -490,6 +493,9 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
         super(TFGPT2LMHeadModel, self).__init__(config, *inputs, **kwargs)
         self.transformer = TFGPT2MainLayer(config, name='transformer')
 
+    def get_output_embeddings(self):
+        return self.transformer.wte
+
     def call(self, inputs, **kwargs):
         transformer_outputs = self.transformer(inputs, **kwargs)
         hidden_states = transformer_outputs[0]
@@ -560,6 +566,9 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
         self.transformer = TFGPT2MainLayer(config, name='transformer')
         self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 
+    def get_output_embeddings(self):
+        return self.transformer.wte
+
     def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
         if isinstance(inputs, (tuple, list)):
             input_ids = inputs[0]

transformers/modeling_tf_openai.py

@@ -217,6 +217,9 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
                           scale=True,
                           name='h_._{}'.format(i)) for i in range(config.n_layer)]
 
+    def get_input_embeddings(self):
+        return self.tokens_embed
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -462,6 +465,9 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
         super(TFOpenAIGPTLMHeadModel, self).__init__(config, *inputs, **kwargs)
         self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
 
+    def get_output_embeddings(self):
+        return self.transformer.tokens_embed
+
     def call(self, inputs, **kwargs):
         transformer_outputs = self.transformer(inputs, **kwargs)
         hidden_states = transformer_outputs[0]
@@ -524,6 +530,9 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
         self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
         self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 
+    def get_output_embeddings(self):
+        return self.transformer.tokens_embed
+
     def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
         if isinstance(inputs, (tuple, list)):
             input_ids = inputs[0]

transformers/modeling_tf_roberta.py

@@ -65,6 +65,9 @@ class TFRobertaMainLayer(TFBertMainLayer):
         super(TFRobertaMainLayer, self).__init__(config, **kwargs)
         self.embeddings = TFRobertaEmbeddings(config, name='embeddings')
 
+    def get_input_embeddings(self):
+        return self.embeddings
+
 
 class TFRobertaPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
@@ -280,6 +283,9 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
         self.roberta = TFRobertaMainLayer(config, name="roberta")
         self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
 
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
     def call(self, inputs, **kwargs):
         outputs = self.roberta(inputs, **kwargs)
 

transformers/modeling_tf_transfo_xl.py

@@ -413,6 +413,9 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
                                             name='r_r_bias')
         super(TFTransfoXLMainLayer, self).build(input_shape)
 
+    def get_input_embeddings(self):
+        return self.word_emb
+
     def _resize_token_embeddings(self, new_num_tokens):
         return self.word_emb
 

transformers/modeling_tf_utils.py

@@ -65,6 +65,21 @@ class TFPreTrainedModel(tf.keras.Model):
         # Save config in model
         self.config = config
 
+    def get_input_embeddings(self):
+        """ Get model's input embeddings
+        """
+        base_model = getattr(self, self.base_model_prefix, self)
+        if base_model is not self:
+            return base_model.get_input_embeddings()
+        else:
+            raise NotImplementedError
+
+    def get_output_embeddings(self):
+        """ Get model's output embeddings
+            Return None if the model doesn't have output embeddings
+        """
+        return None  # Overwrite for models with output embeddings
+
     def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
         """ Build a resized Embedding Variable from a provided token Embedding Module.
             Increasing the size will add newly initialized vectors at the end

transformers/modeling_tf_xlm.py

@@ -277,6 +277,9 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
                     self.prune_heads({int(layer): list(map(int, heads))})
 
 
+    def get_input_embeddings(self):
+        return self.embeddings
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -641,6 +644,8 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
         self.transformer = TFXLMMainLayer(config, name='transformer')
         self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name='pred_layer_._proj')
 
+    def get_output_embeddings(self):
+        return self.pred_layer.input_embeddings
 
     def call(self, inputs, **kwargs):
         transformer_outputs = self.transformer(inputs, **kwargs)

transformers/modeling_tf_xlnet.py

@@ -371,6 +371,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         self.layer = [TFXLNetLayer(config, name='layer_._{}'.format(i)) for i in range(config.n_layer)]
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_input_embeddings(self):
+        return self.word_embedding
+
     def build(self, input_shape):
         initializer = get_initializer(self.initializer_range)
         self.mask_emb = self.add_weight(shape=(1, 1, self.d_model),
@@ -854,6 +857,9 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
         self.transformer = TFXLNetMainLayer(config, name='transformer')
         self.lm_loss = TFXLNetLMHead(config, self.transformer.word_embedding, name='lm_loss')
 
+    def get_output_embeddings(self):
+        return self.lm_loss.input_embeddings
+
     def call(self, inputs, **kwargs):
         transformer_outputs = self.transformer(inputs, **kwargs)
         hidden_state = transformer_outputs[0]

transformers/tests/modeling_tf_common_test.py

@@ -360,6 +360,16 @@ class TFCommonTestCases:
             #     self.assertTrue(models_equal)
 
 
+        def test_model_common_attributes(self):
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            for model_class in self.all_model_classes:
+                model = model_class(config)
+                assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
+                x = model.get_output_embeddings()
+                assert x is None or instanceof(x, tf.keras.layers.Layer)
+
+
         def test_tie_model_weights(self):
             pass
             # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()