Fix 29807, sinusoidal positional encodings overwritten by post_init() (#29813)

* Check for requires_grad when initing weights

* Add unit test

* Move sinusoidal positional encoding generation after post_init()

* Add modules to skip init list

* Move create_sinusoidal_embeddings to _init_weights

src/transformers/models/distilbert/modeling_distilbert.py

@@ -106,10 +106,6 @@ class Embeddings(nn.Module):
         super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
-        if config.sinusoidal_pos_embds:
-            create_sinusoidal_embeddings(
-                n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
-            )
 
         self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
         self.dropout = nn.Dropout(config.dropout)
@@ -634,6 +630,10 @@ class DistilBertPreTrainedModel(PreTrainedModel):
         elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
+        elif isinstance(module, Embeddings) and self.config.sinusoidal_pos_embds:
+            create_sinusoidal_embeddings(
+                self.config.max_position_embeddings, self.config.dim, module.position_embeddings.weight
+            )
 
 
 DISTILBERT_START_DOCSTRING = r"""

tests/models/distilbert/test_modeling_distilbert.py

@@ -37,6 +37,7 @@ if is_torch_available():
         DistilBertForTokenClassification,
         DistilBertModel,
     )
+    from transformers.models.distilbert.modeling_distilbert import _create_sinusoidal_embeddings
 
 
 class DistilBertModelTester(object):
@@ -238,6 +239,15 @@ class DistilBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_model(*config_and_inputs)
 
+    def test_distilbert_model_with_sinusoidal_encodings(self):
+        config = DistilBertConfig(sinusoidal_pos_embds=True)
+        model = DistilBertModel(config=config)
+        sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.dim), dtype=torch.float32)
+        _create_sinusoidal_embeddings(config.max_position_embeddings, config.dim, sinusoidal_pos_embds)
+        self.model_tester.parent.assertTrue(
+            torch.equal(model.embeddings.position_embeddings.weight, sinusoidal_pos_embds)
+        )
+
     def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_masked_lm(*config_and_inputs)