Adding _tie_weights() to prediction heads to support low_cpu_mem_usage=True (#29024)

* Adding _tie_weights() to prediction heads to support low_cpu_mem_usage=True

* Testing for the non-safe-tensors case, since the default is safe-tensors already

* Running fixup/fix-copies

* Adding accelerate annotations to tests

src/transformers/models/qdqbert/modeling_qdqbert.py

@@ -681,6 +681,9 @@ class QDQBertLMPredictionHead(nn.Module):
         # 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)
@@ -1022,6 +1025,7 @@ class QDQBertLMHeadModel(QDQBertPreTrainedModel):
 
     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)
@@ -1188,6 +1192,7 @@ class QDQBertForMaskedLM(QDQBertPreTrainedModel):
 
     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/realm/modeling_realm.py

@@ -797,6 +797,9 @@ class RealmLMPredictionHead(nn.Module):
         # 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)
@@ -1391,6 +1394,7 @@ class RealmKnowledgeAugEncoder(RealmPreTrainedModel):
 
     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(
         REALM_INPUTS_DOCSTRING.format("batch_size, num_candidates, sequence_length")

src/transformers/models/reformer/modeling_reformer.py

@@ -1768,7 +1768,11 @@ class ReformerOnlyLMHead(nn.Module):
         hidden_states = self.decoder(hidden_states)
         return hidden_states
 
-    def _tie_weights(self):
+    def _tie_weights(self) -> None:
+        # For accelerate compatibility and to not break backward compatibility
+        if self.decoder.bias.device.type == "meta":
+            self.decoder.bias = self.bias
+        else:
             # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
             self.bias = self.decoder.bias
 
@@ -2208,6 +2212,7 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
 
     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(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
@@ -2328,6 +2333,7 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
 
     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(REFORMER_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)

src/transformers/models/roc_bert/modeling_roc_bert.py

@@ -749,6 +749,9 @@ class RoCBertLMPredictionHead(nn.Module):
         # 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)
@@ -1094,6 +1097,7 @@ class RoCBertForPreTraining(RoCBertPreTrainedModel):
     # 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)
@@ -1286,6 +1290,7 @@ class RoCBertForMaskedLM(RoCBertPreTrainedModel):
     # 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(
@@ -1423,6 +1428,7 @@ class RoCBertForCausalLM(RoCBertPreTrainedModel):
     # 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/roformer/modeling_roformer.py

@@ -657,6 +657,9 @@ class RoFormerLMPredictionHead(nn.Module):
         # 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) -> None:
+        self.decoder.bias = self.bias
+
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -954,6 +957,7 @@ class RoFormerForMaskedLM(RoFormerPreTrainedModel):
 
     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(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
@@ -1053,6 +1057,7 @@ class RoFormerForCausalLM(RoFormerPreTrainedModel):
 
     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(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)

src/transformers/models/squeezebert/modeling_squeezebert.py

@@ -400,6 +400,9 @@ class SqueezeBertLMPredictionHead(nn.Module):
         # 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) -> None:
+        self.decoder.bias = self.bias
+
     def forward(self, hidden_states):
         hidden_states = self.transform(hidden_states)
         hidden_states = self.decoder(hidden_states)
@@ -658,6 +661,7 @@ class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
 
     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(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/tapas/modeling_tapas.py

@@ -699,6 +699,9 @@ class TapasLMPredictionHead(nn.Module):
         # 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)
@@ -978,6 +981,7 @@ class TapasForMaskedLM(TapasPreTrainedModel):
 
     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

@@ -894,6 +894,7 @@ class ViltForMaskedLM(ViltPreTrainedModel):
 
     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)
@@ -1040,6 +1041,9 @@ class ViltMLMHead(nn.Module):
         # 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

@@ -489,6 +489,9 @@ class VisualBertLMPredictionHead(nn.Module):
         # 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)
@@ -869,6 +872,7 @@ class VisualBertForPreTraining(VisualBertPreTrainedModel):
 
     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/xlm_roberta_xl/modeling_xlm_roberta_xl.py

@@ -852,6 +852,7 @@ class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel):
 
     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(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
@@ -1011,6 +1012,7 @@ class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel):
 
     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(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
@@ -1099,7 +1101,11 @@ class XLMRobertaXLLMHead(nn.Module):
 
         return x
 
-    def _tie_weights(self):
+    def _tie_weights(self) -> None:
+        # For accelerate compatibility and to not break backward compatibility
+        if self.decoder.bias.device.type == "meta":
+            self.decoder.bias = self.bias
+        else:
             # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
             self.bias = self.decoder.bias
 

src/transformers/models/yoso/modeling_yoso.py

@@ -627,6 +627,9 @@ class YosoLMPredictionHead(nn.Module):
         # 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)
@@ -861,6 +864,7 @@ class YosoForMaskedLM(YosoPreTrainedModel):
 
     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(

tests/models/deformable_detr/test_modeling_deformable_detr.py

@@ -578,6 +578,18 @@ class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
                         msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                     )
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     def test_two_stage_training(self):
         model_class = DeformableDetrForObjectDetection
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

tests/models/deta/test_modeling_deta.py

@@ -528,6 +528,18 @@ class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
                         msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                     )
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     # Inspired by tests.test_modeling_common.ModelTesterMixin.test_tied_weights_keys
     def test_tied_weights_keys(self):
         for model_class in self.all_model_classes:

tests/models/encodec/test_modeling_encodec.py

@@ -325,6 +325,18 @@ class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
     def test_hidden_states_output(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     def test_determinism(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 

tests/models/lxmert/test_modeling_lxmert.py

@@ -766,6 +766,18 @@ class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
         return tf_inputs_dict
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
 
 @require_torch
 class LxmertModelIntegrationTest(unittest.TestCase):

tests/models/marian/test_modeling_marian.py

@@ -372,6 +372,18 @@ class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
     def test_training_gradient_checkpointing_use_reentrant_false(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
 
 def assert_tensors_close(a, b, atol=1e-12, prefix=""):
     """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""

tests/models/musicgen/test_modeling_musicgen.py

@@ -1273,6 +1273,18 @@ class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
     def test_tied_weights_keys(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     # override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work
     def test_retain_grad_hidden_states_attentions(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

tests/models/musicgen_melody/test_modeling_musicgen_melody.py

@@ -1258,6 +1258,18 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
     def test_tied_weights_keys(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     # override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work
     # Ignore copy
     def test_retain_grad_hidden_states_attentions(self):

tests/models/sew/test_modeling_sew.py

@@ -356,6 +356,18 @@ class SEWModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     def test_model_common_attributes(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     def test_retain_grad_hidden_states_attentions(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.output_hidden_states = True

tests/models/sew_d/test_modeling_sew_d.py

@@ -460,6 +460,18 @@ class SEWDModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     def test_feed_forward_chunking(self):
         pass
 
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_checkpoints(self):
+        pass
+
+    @unittest.skip("No support for low_cpu_mem_usage=True.")
+    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k")