Revert low cpu mem tie weights

src/transformers/models/bert/modeling_bert.py

@@ -692,9 +692,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1065,7 +1062,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -1175,7 +1171,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
@@ -1329,7 +1324,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/big_bird/modeling_big_bird.py

@@ -1707,9 +1707,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -2269,7 +2266,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=BigBirdForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -2382,7 +2378,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
@@ -2524,7 +2519,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/blip/modeling_blip_text.py

@@ -523,9 +523,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -820,7 +817,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     def forward(
         self,

src/transformers/models/ernie/modeling_ernie.py

@@ -608,9 +608,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -998,7 +995,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=ErnieForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -1113,7 +1109,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
@@ -1274,7 +1269,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/layoutlm/modeling_layoutlm.py

@@ -589,9 +589,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -872,7 +869,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)

src/transformers/models/markuplm/modeling_markuplm.py

@@ -318,9 +318,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)

src/transformers/models/megatron_bert/modeling_megatron_bert.py

@@ -659,9 +659,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1026,7 +1023,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MegatronBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -1136,7 +1132,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
@@ -1295,7 +1290,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/mpnet/modeling_mpnet.py

@@ -587,7 +587,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.lm_head.decoder = new_embeddings
-        self.lm_head.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
@@ -660,9 +659,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, features, **kwargs):
         x = self.dense(features)
         x = gelu(x)

src/transformers/models/mra/modeling_mra.py

@@ -820,9 +820,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1056,7 +1053,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/nezha/modeling_nezha.py

@@ -679,9 +679,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1047,7 +1044,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=NezhaForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -1156,7 +1152,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/nystromformer/modeling_nystromformer.py

@@ -428,9 +428,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -669,7 +666,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/qdqbert/modeling_qdqbert.py

@@ -683,9 +683,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1027,7 +1024,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
@@ -1194,7 +1190,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/roc_bert/modeling_roc_bert.py

@@ -744,9 +744,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1093,7 +1090,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
@@ -1286,7 +1282,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     def forward(
@@ -1424,7 +1419,6 @@ def get_output_embeddings(self):
     # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)

src/transformers/models/tapas/modeling_tapas.py

@@ -729,9 +729,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -1011,7 +1008,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)

src/transformers/models/vilt/modeling_vilt.py

@@ -896,7 +896,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.mlm_score.decoder = new_embeddings
-        self.mlm_score.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
@@ -1043,9 +1042,6 @@ def __init__(self, config, weight=None):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, x):
         x = self.transform(x)
         x = self.decoder(x)

src/transformers/models/visual_bert/modeling_visual_bert.py

@@ -499,9 +499,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -882,7 +879,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)

src/transformers/models/yoso/modeling_yoso.py

@@ -619,9 +619,6 @@ def __init__(self, config):
         # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
         self.decoder.bias = self.bias
 
-    def _tie_weights(self):
-        self.decoder.bias = self.bias
-
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -860,7 +857,6 @@ def get_output_embeddings(self):
 
     def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
-        self.cls.predictions.bias = new_embeddings.bias
 
     @add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(