Full rework of the TF input/output embeddings and bias resizing

src/transformers/models/albert/modeling_tf_albert.py

@@ -470,6 +470,21 @@ def build(self, input_shape):
 
         super().build(input_shape)
 
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, value):
+        self.decoder.word_embeddings = value
+        self.decoder.vocab_size = shape_list(value)[0]
+
+    def get_bias(self):
+        return {"bias": self.bias, "decoder_bias": self.decoder_bias}
+
+    def set_bias(self, value):
+        self.bias = value["bias"]
+        self.decoder_bias = value["decoder_bias"]
+        self.vocab_size = shape_list(value["bias"])[0]
+
     def call(self, hidden_states):
         hidden_states = self.dense(hidden_states)
         hidden_states = self.activation(hidden_states)
@@ -505,10 +520,7 @@ def get_input_embeddings(self):
 
     def set_input_embeddings(self, value):
         self.embeddings.word_embeddings = value
-        self.embeddings.vocab_size = value.shape[0]
-
-    def _resize_token_embeddings(self, new_num_tokens):
-        raise NotImplementedError
+        self.embeddings.vocab_size = shape_list(value)[0]
 
     def _prune_heads(self, heads_to_prune):
         """
@@ -834,34 +846,8 @@ def __init__(self, config, *inputs, **kwargs):
         self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
         self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier")
 
-    def get_output_embeddings(self):
-        return self.albert.embeddings
-
-    def resize_token_embeddings(self, new_num_tokens):
-        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
-
-        # ALBERT is a special case where there are two bias to update
-        # even though self.bias is not used anywhere and is here
-        # just to make the loading weights from a PT model happy
-        if new_num_tokens is not None:
-            num_tokens_to_copy = min(self.predictions.bias.shape[0], new_num_tokens)
-            self.predictions.vocab_size = num_tokens_to_copy
-            init_bias = tf.zeros((new_num_tokens,))
-            init_bias[:num_tokens_to_copy] = self.predictions.bias.value()[:num_tokens_to_copy]
-            name = self.name + "/" + self.predictions.name + "/bias"
-            self.predictions.bias = self.add_weight(
-                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
-            )
-            self.predictions.bias.assign(init_bias)
-
-            init_decoder_bias = tf.zeros((new_num_tokens,))
-            init_decoder_bias[:num_tokens_to_copy] = self.predictions.decoder_bias.value()[:num_tokens_to_copy]
-            name = self.name + "/" + self.predictions.name + "/decoder_bias"
-            self.predictions.decoder_bias = self.add_weight(
-                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
-            )
-
-            self.predictions.decoder_bias.assign(init_decoder_bias)
+    def get_lm_head(self):
+        return self.predictions
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -979,34 +965,8 @@ def __init__(self, config, *inputs, **kwargs):
         self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
         self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
 
-    def get_output_embeddings(self):
-        return self.albert.embeddings
-
-    def resize_token_embeddings(self, new_num_tokens):
-        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
-
-        # ALBERT is a special case where there are two bias to update
-        # even though self.bias is not used anywhere and is here
-        # just to make the loading weights from a PT model happy
-        if new_num_tokens is not None:
-            num_tokens_to_copy = min(self.predictions.bias.shape[0], new_num_tokens)
-            self.predictions.vocab_size = num_tokens_to_copy
-            init_bias = tf.zeros((new_num_tokens,))
-            init_bias[:num_tokens_to_copy] = self.predictions.bias.value()[:num_tokens_to_copy]
-            name = self.name + "/" + self.predictions.name + "/bias"
-            self.predictions.bias = self.add_weight(
-                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
-            )
-            self.predictions.bias.assign(init_bias)
-
-            init_decoder_bias = tf.zeros((new_num_tokens,))
-            init_decoder_bias[:num_tokens_to_copy] = self.predictions.decoder_bias.value()[:num_tokens_to_copy]
-            name = self.name + "/" + self.predictions.name + "/decoder_bias"
-            self.predictions.decoder_bias = self.add_weight(
-                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
-            )
-
-            self.predictions.decoder_bias.assign(init_decoder_bias)
+    def get_lm_head(self):
+        return self.predictions
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/bart/modeling_tf_bart.py

@@ -481,6 +481,29 @@ def dummy_inputs(self):
         }
         return dummy_inputs
 
+    def get_input_embeddings(self):
+        base_model = getattr(self, self.base_model_prefix, self)
+
+        return base_model.shared
+
+    def set_input_embeddings(self, value):
+        base_model = getattr(self, self.base_model_prefix, self)
+
+        try:
+            base_model.shared.weight = value
+        except AttributeError:
+            self(self.dummy_inputs)
+            base_model.shared.weight = value
+
+        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
+
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+
+        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
+        base_model.encoder.set_embed_tokens(embed_tokens)
+        base_model.decoder.set_embed_tokens(embed_tokens)
+
     @tf.function(
         input_signature=[
             {
@@ -634,6 +657,9 @@ def __init__(self, config: BartConfig, embed_tokens: Optional[TFSharedEmbeddings
             else None
         )
 
+    def set_embed_tokens(self, embed_tokens):
+        self.embed_tokens = embed_tokens
+
     def call(
         self,
         input_ids=None,
@@ -791,6 +817,9 @@ def __init__(self, config: BartConfig, embed_tokens: Optional[TFSharedEmbeddings
         self.dropout = tf.keras.layers.Dropout(config.dropout)
         self.do_blenderbot_90_layernorm = config.do_blenderbot_90_layernorm
 
+    def set_embed_tokens(self, embed_tokens):
+        self.embed_tokens = embed_tokens
+
     def call(
         self,
         input_ids=None,
@@ -1009,6 +1038,9 @@ def __init__(self, config: BartConfig, *inputs, **kwargs):
         self.encoder = TFBartEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFBartDecoder(config, embed_tokens, name="decoder")
 
+    def get_encoder(self):
+        return self.encoder
+
     def get_decoder(self):
         return self.decoder
 
@@ -1134,15 +1166,6 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, value):
-        self.shared = value
-
-    def get_output_embeddings(self):
-        return self.shared
-
 
 @add_start_docstrings(
     "The BART Model with a language modeling head. Can be used for summarization.",
@@ -1166,22 +1189,20 @@ def __init__(self, config, *inputs, **kwargs):
     def get_decoder(self):
         return self.model.decoder
 
-    def resize_token_embeddings(self, new_num_tokens):
-        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
-
-        # BART is a special case where the bias has two dimensions
-        # and not named just `bias`
-        if new_num_tokens is not None:
-            num_tokens_to_copy = min(self.final_logits_bias.shape[0], new_num_tokens)
-            init_bias = tf.zeros((new_num_tokens,))
-            init_bias[:num_tokens_to_copy] = self.final_logits_bias.value()[:num_tokens_to_copy]
-            self.final_logits_bias = self.add_weight(
-                shape=(1, new_num_tokens),
-                initializer="zeros",
-                trainable=False,
-                name="final_logits_bias",
-            )
-            self.final_logits_bias.assign(init_bias)
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_output_embeddings(self):
+        return self.get_input_embeddings()
+
+    def set_output_embeddings(self, value):
+        self.set_input_embeddings(value)
+
+    def get_bias(self):
+        return {"final_logits_bias": self.final_logits_bias}
+
+    def set_bias(self, value):
+        self.final_logits_bias = value["final_logits_bias"]
 
     @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@@ -1356,12 +1377,6 @@ def adjust_logits_during_generation(self, logits, cur_len, max_length):
         else:
             return logits
 
-    def get_output_embeddings(self):
-        return self.model.shared
-
-    def get_encoder(self):
-        return self.model.encoder
-
     def compute_loss(self, labels, logits):
         """CrossEntropyLoss that ignores pad tokens"""
         loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(

src/transformers/models/bert/modeling_tf_bert.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 """ TF 2.0 BERT model. """
 
+import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -526,6 +527,20 @@ def build(self, input_shape):
 
         super().build(input_shape)
 
+    def get_output_embeddings(self):
+        return self.input_embeddings
+
+    def set_output_embeddings(self, value):
+        self.input_embeddings.word_embeddings = value
+        self.input_embeddings.vocab_size = shape_list(value)[0]
+
+    def get_bias(self):
+        return {"bias": self.bias}
+
+    def set_bias(self, value):
+        self.bias = value["bias"]
+        self.vocab_size = shape_list(value["bias"])[0]
+
     def call(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.input_embeddings(hidden_states, mode="linear")
@@ -582,7 +597,7 @@ def get_input_embeddings(self):
 
     def set_input_embeddings(self, value):
         self.embeddings.word_embeddings = value
-        self.embeddings.vocab_size = value.shape[0]
+        self.embeddings.vocab_size = shape_list(value)[0]
 
     def _prune_heads(self, heads_to_prune):
         """
@@ -918,13 +933,11 @@ def __init__(self, config, *inputs, **kwargs):
         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 get_output_layer_with_bias(self):
+    def get_lm_head(self):
         return self.mlm.predictions
 
     def get_prefix_bias_name(self):
+        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
         return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1044,13 +1057,11 @@ def __init__(self, config, *inputs, **kwargs):
         self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
         self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
 
-    def get_output_embeddings(self):
-        return self.bert.embeddings
-
-    def get_output_layer_with_bias(self):
+    def get_lm_head(self):
         return self.mlm.predictions
 
     def get_prefix_bias_name(self):
+        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
         return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1153,13 +1164,11 @@ def __init__(self, config, *inputs, **kwargs):
         self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
         self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
 
-    def get_output_embeddings(self):
-        return self.bert.embeddings
-
-    def get_output_layer_with_bias(self):
+    def get_lm_head(self):
         return self.mlm.predictions
 
     def get_prefix_bias_name(self):
+        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
         return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
 
     @add_code_sample_docstrings(

src/transformers/models/ctrl/modeling_tf_ctrl.py

@@ -15,6 +15,8 @@
 # limitations under the License.
 """ TF 2.0 CTRL model."""
 
+import warnings
+
 import numpy as np
 import tensorflow as tf
 
@@ -242,10 +244,7 @@ def get_input_embeddings(self):
 
     def set_input_embeddings(self, value):
         self.w.weight = value
-        self.w.vocab_size = value.shape[0]
-
-    def _resize_token_embeddings(self, new_num_tokens):
-        raise NotImplementedError
+        self.w.vocab_size = shape_list(value)[0]
 
     def _prune_heads(self, heads_to_prune):
         """
@@ -620,6 +619,20 @@ def build(self, input_shape):
         self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
         super().build(input_shape)
 
+    def get_output_embeddings(self):
+        return self.input_embeddings
+
+    def set_output_embeddings(self, value):
+        self.input_embeddings.weight = value
+        self.input_embeddings.vocab_size = shape_list(value)[0]
+
+    def get_bias(self):
+        return {"bias": self.bias}
+
+    def set_bias(self, value):
+        self.bias = value["bias"]
+        self.vocab_size = shape_list(value["bias"])[0]
+
     def call(self, hidden_states):
         hidden_states = self.input_embeddings(hidden_states, mode="linear")
         hidden_states = hidden_states + self.bias
@@ -640,13 +653,11 @@ def __init__(self, config, *inputs, **kwargs):
 
         self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head")
 
-    def get_output_embeddings(self):
-        return self.lm_head.input_embeddings
-
-    def get_output_layer_with_bias(self):
+    def get_lm_head(self):
         return self.lm_head
 
     def get_prefix_bias_name(self):
+        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
         return self.name + "/" + self.lm_head.name
 
     def prepare_inputs_for_generation(self, inputs, past, **kwargs):