Bugfix: Removal of padding_idx in BartLearnedPositionalEmbedding (#10200)

* Assumption of padding_idx <2 might not stand

* Use offset instead of 2

* Fix with black

* Change behavior to warning instead for backward compatibility.

* Fix with black

* Remove warning

* Make padding_idx non-required

* padding_idx fix for blenderbot

* padding_idx fix for blenderbot_small

* padding_idx fix for led

* padding_idx fix for mbart

* Remove extra whitespaces

* padding_idx fix for template

* Fix padding_idx passed to nn.Embedding mistake

* Fixed padding_idx passed to positional embedding in template

* Remove padding_idx from pytorch learned positional embeddings

* Remove accidentally added quotes

* Remove padding_idx from tf learned positional embeddings

* Remove zeroing of weights in __init__
Co-authored-by: Wang Ming Rui <mingrui.wang@C02CJTUYMD6M.local>

src/transformers/models/bart/modeling_bart.py

@@ -108,12 +108,11 @@ class BartLearnedPositionalEmbedding(nn.Embedding):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
+    def __init__(self, num_embeddings: int, embedding_dim: int):
         # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
         # and adjust num_embeddings appropriately. Other models dont have this hack
         self.offset = 2
-        super().__init__(num_embeddings + self.offset, embedding_dim, padding_idx=padding_idx)
+        super().__init__(num_embeddings + self.offset, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -673,7 +672,6 @@ class BartEncoder(BartPretrainedModel):
         self.embed_positions = BartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BartEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
@@ -836,7 +834,6 @@ class BartDecoder(BartPretrainedModel):
         self.embed_positions = BartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BartDecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)

src/transformers/models/bart/modeling_tf_bart.py

@@ -113,8 +113,7 @@ class TFBartLearnedPositionalEmbedding(TFSharedEmbeddings):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
         # and adjust num_embeddings appropriately. Other models dont have this hack
         self.offset = 2
@@ -632,7 +631,6 @@ class TFBartEncoder(tf.keras.layers.Layer):
         self.embed_positions = TFBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -793,7 +791,6 @@ class TFBartDecoder(tf.keras.layers.Layer):
         self.embed_positions = TFBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -112,9 +112,8 @@ class BlenderbotLearnedPositionalEmbedding(nn.Embedding):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
-        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        super().__init__(num_embeddings, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -635,7 +634,6 @@ class BlenderbotEncoder(BlenderbotPreTrainedModel):
         self.embed_positions = BlenderbotLearnedPositionalEmbedding(
             config.max_position_embeddings,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BlenderbotEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layer_norm = nn.LayerNorm(config.d_model)
@@ -800,7 +798,6 @@ class BlenderbotDecoder(BlenderbotPreTrainedModel):
         self.embed_positions = BlenderbotLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BlenderbotDecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layer_norm = nn.LayerNorm(config.d_model)

src/transformers/models/blenderbot/modeling_tf_blenderbot.py

@@ -118,8 +118,7 @@ class TFBlenderbotLearnedPositionalEmbedding(TFSharedEmbeddings):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         super().__init__(num_embeddings, embedding_dim, **kwargs)
 
     def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
@@ -629,7 +628,6 @@ class TFBlenderbotEncoder(tf.keras.layers.Layer):
         self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -797,7 +795,6 @@ class TFBlenderbotDecoder(tf.keras.layers.Layer):
         self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -110,9 +110,8 @@ class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
-        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        super().__init__(num_embeddings, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -636,7 +635,6 @@ class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
         self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
             config.max_position_embeddings,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
@@ -800,7 +798,6 @@ class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
         self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([BlenderbotSmallDecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -117,8 +117,7 @@ class TFBlenderbotSmallLearnedPositionalEmbedding(TFSharedEmbeddings):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         super().__init__(num_embeddings, embedding_dim, **kwargs)
 
     def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
@@ -634,7 +633,6 @@ class TFBlenderbotSmallEncoder(tf.keras.layers.Layer):
         self.embed_positions = TFBlenderbotSmallLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFBlenderbotSmallEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -802,7 +800,6 @@ class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
         self.embed_positions = TFBlenderbotSmallLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0

src/transformers/models/led/modeling_led.py

@@ -112,9 +112,8 @@ class LEDLearnedPositionalEmbedding(nn.Embedding):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
-        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        super().__init__(num_embeddings, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -1622,7 +1621,6 @@ class LEDEncoder(LEDPreTrainedModel):
         self.embed_positions = LEDLearnedPositionalEmbedding(
             self.max_source_positions,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([LEDEncoderLayer(config, i) for i in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
@@ -1891,7 +1889,6 @@ class LEDDecoder(LEDPreTrainedModel):
         self.embed_positions = LEDLearnedPositionalEmbedding(
             self.max_target_positions,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([LEDDecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)

src/transformers/models/led/modeling_tf_led.py

@@ -108,8 +108,7 @@ class TFLEDLearnedPositionalEmbedding(TFSharedEmbeddings):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         super().__init__(num_embeddings, embedding_dim, **kwargs)
 
     def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
@@ -1612,7 +1611,6 @@ class TFLEDEncoder(tf.keras.layers.Layer):
         self.embed_positions = TFLEDLearnedPositionalEmbedding(
             config.max_encoder_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFLEDEncoderLayer(config, i, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -1865,7 +1863,6 @@ class TFLEDDecoder(tf.keras.layers.Layer):
         self.embed_positions = TFLEDLearnedPositionalEmbedding(
             config.max_decoder_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFLEDDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]

src/transformers/models/mbart/modeling_mbart.py

@@ -114,12 +114,11 @@ class MBartLearnedPositionalEmbedding(nn.Embedding):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
+    def __init__(self, num_embeddings: int, embedding_dim: int):
         # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
         # and adjust num_embeddings appropriately. Other models dont have this hack
         self.offset = 2
-        super().__init__(num_embeddings + self.offset, embedding_dim, padding_idx=padding_idx)
+        super().__init__(num_embeddings + self.offset, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -678,7 +677,6 @@ class MBartEncoder(MBartPreTrainedModel):
         self.embed_positions = MBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([MBartEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
@@ -844,7 +842,6 @@ class MBartDecoder(MBartPreTrainedModel):
         self.embed_positions = MBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([MBartDecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)

src/transformers/models/mbart/modeling_tf_mbart.py

@@ -115,8 +115,7 @@ class TFMBartLearnedPositionalEmbedding(TFSharedEmbeddings):
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
         # and adjust num_embeddings appropriately. Other models dont have this hack
         self.offset = 2
@@ -636,7 +635,6 @@ class TFMBartEncoder(tf.keras.layers.Layer):
         self.embed_positions = TFMBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TFMBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -806,7 +804,6 @@ class TFMBartDecoder(tf.keras.layers.Layer):
         self.embed_positions = TFMBartLearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -777,7 +777,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
         )
 
         return outputs
-    
+
     # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
     def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -800,7 +800,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
 
         self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
         self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
-    
+
     def get_lm_head(self) -> tf.keras.layers.Layer:
         return self.mlm.predictions
 
@@ -876,7 +876,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
     def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -975,7 +975,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
     def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -1015,7 +1015,7 @@ class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.L
 
 
 @add_start_docstrings(
-    """{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top 
+    """{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top
     e.g., for GLUE tasks. """,
     {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
 )
@@ -1098,7 +1098,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
     def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -1239,7 +1239,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     @tf.function(input_signature=[{
         "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
         "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
@@ -1250,7 +1250,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
         output = self.call(input_ids=inputs)
 
         return self.serving_output(output)
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
     def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -1347,7 +1347,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
     def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -1458,7 +1458,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-    
+
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
     def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
@@ -1565,8 +1565,7 @@ class TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(TFSharedE
     This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, **kwargs):
-        assert padding_idx is not None, "padding_idx cannot be None"
+    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
         super().__init__(num_embeddings, embedding_dim, **kwargs)
 
     def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
@@ -1876,7 +1875,7 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
             "input_ids": input_ids,
         }
         return dummy_inputs
-    
+
     @tf.function(
         input_signature=[
             {
@@ -2017,7 +2016,6 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
         self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
@@ -2028,7 +2026,7 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
 
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
-    
+
     def call(
         self,
         input_ids=None,
@@ -2160,7 +2158,6 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
         self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
             name="embed_positions",
         )
         self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
@@ -2328,7 +2325,7 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
 
         if inputs["output_hidden_states"]:
             all_hidden_states += (hidden_states,)
-        
+
         if inputs["output_attentions"]:
             all_self_attns = list(all_self_attns)
 
@@ -2344,7 +2341,7 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
                 hidden_states=all_hidden_states,
                 attentions=all_self_attns,
             )
-    
+
     @tf.function
     def compute_combined_attns_mask(self, inputs, input_shape, past_key_values_length):
         # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
@@ -2393,7 +2390,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
 
         self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, embed_tokens, name="encoder")
         self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, embed_tokens, name="decoder")
-    
+
     def get_input_embeddings(self):
         return self.shared
 
@@ -2407,7 +2404,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
         embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
         self.encoder.set_embed_tokens(embed_tokens)
         self.decoder.set_embed_tokens(embed_tokens)
-    
+
     def call(
         self,
         input_ids=None,
@@ -2503,7 +2500,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
 class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
     def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        
+
         self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
 
     def get_encoder(self):
@@ -2572,7 +2569,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
         )
 
         return outputs
-    
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
         pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
@@ -2580,7 +2577,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
         enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
-        
+
         return TFSeq2SeqModelOutput(
             last_hidden_state=output.last_hidden_state,
             past_key_values=pkv,
@@ -2614,7 +2611,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
 
     def get_decoder(self):
         return self.model.decoder
-    
+
     def get_encoder(self):
         return self.model.encoder
 
@@ -2623,7 +2620,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
 
     def set_bias(self, value):
         self.final_logits_bias = value["final_logits_bias"]
-    
+
     def get_output_embeddings(self):
         return self.get_input_embeddings()
 
@@ -2725,7 +2722,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
             encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
             encoder_attentions=outputs.encoder_attentions,  # 2 of e out
         )
-    
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
         pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
@@ -2733,7 +2730,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
         enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
-        
+
         return TFSeq2SeqLMOutput(
             logits=output.logits,
             past_key_values=pkv,

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -690,18 +690,18 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
             `What are input IDs? <../glossary.html#input-ids>`__
         attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
             Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
-            
+
             - 1 for tokens that are **not masked**,
             - 0 for tokens that are **masked**.
-            
+
             `What are attention masks? <../glossary.html#attention-mask>`__
         token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
             Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
             1]``:
-            
+
             - 0 corresponds to a `sentence A` token,
             - 1 corresponds to a `sentence B` token.
-            
+
             `What are token type IDs? <../glossary.html#token-type-ids>`_
         position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
             Indices of positions of each input sequence tokens in the position embeddings.
@@ -710,10 +710,10 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
             `What are position IDs? <../glossary.html#position-ids>`_
         head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
             Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
-            
+
             - 1 indicates the head is **not masked**,
             - 0 indicates the head is **masked**.
-            
+
         inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
             Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
             This is useful if you want more control over how to convert `input_ids` indices into associated vectors
@@ -1613,12 +1613,11 @@ def _expand_mask(
 
 class {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(nn.Embedding):
     """
-    This module learns positional embeddings up to a fixed maximum size. 
+    This module learns positional embeddings up to a fixed maximum size.
     """
 
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
-        assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
-        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        super().__init__(num_embeddings, embedding_dim)
 
     def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
         """`input_ids_shape` is expected to be [bsz x seqlen]."""
@@ -2172,7 +2171,6 @@ class {{cookiecutter.camelcase_modelname}}Encoder({{cookiecutter.camelcase_model
         self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
             config.max_position_embeddings,
             embed_dim,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}EncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
@@ -2258,7 +2256,7 @@ class {{cookiecutter.camelcase_modelname}}Encoder({{cookiecutter.camelcase_model
 
         encoder_states = () if output_hidden_states else None
         all_attentions = () if output_attentions else None
-        
+
         # check if head_mask has a correct number of layers specified if desired
         if head_mask is not None:
             assert head_mask.size()[0] == (
@@ -2335,7 +2333,6 @@ class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_model
         self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
             config.max_position_embeddings,
             config.d_model,
-            self.padding_idx,
         )
         self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}DecoderLayer(config) for _ in range(config.decoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)