finish (#13593)

src/transformers/models/distilbert/modeling_distilbert.py

@@ -73,6 +73,17 @@ DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
 
 
 def create_sinusoidal_embeddings(n_pos, dim, out):
+    if is_deepspeed_zero3_enabled():
+        import deepspeed
+
+        with deepspeed.zero.GatheredParameters(out, modifier_rank=0):
+            if torch.distributed.get_rank() == 0:
+                _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)
+    else:
+        _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)
+
+
+def _create_sinusoidal_embeddings(n_pos, dim, out):
     position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
     out.requires_grad = False
     out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
@@ -86,16 +97,6 @@ class Embeddings(nn.Module):
         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:
-
-            if is_deepspeed_zero3_enabled():
-                import deepspeed
-
-                with deepspeed.zero.GatheredParameters(self.position_embeddings.weight, modifier_rank=0):
-                    if torch.distributed.get_rank() == 0:
-                        create_sinusoidal_embeddings(
-                            n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
-                        )
-            else:
             create_sinusoidal_embeddings(
                 n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
             )
@@ -475,22 +476,8 @@ class DistilBertModel(DistilBertPreTrainedModel):
         self.embeddings.position_embeddings = nn.Embedding(self.config.max_position_embeddings, self.config.dim)
 
         if self.config.sinusoidal_pos_embds:
-
-            if is_deepspeed_zero3_enabled():
-                import deepspeed
-
-                with deepspeed.zero.GatheredParameters(self.embeddings.position_embeddings.weight, modifier_rank=0):
-                    if torch.distributed.get_rank() == 0:
-                        create_sinusoidal_embeddings(
-                            n_pos=self.config.max_position_embeddings,
-                            dim=self.config.dim,
-                            out=self.embeddings.position_embeddings.weight,
-                        )
-            else:
             create_sinusoidal_embeddings(
-                    n_pos=self.config.max_position_embeddings,
-                    dim=self.config.dim,
-                    out=self.embeddings.position_embeddings.weight,
+                n_pos=self.config.max_position_embeddings, dim=self.config.dim, out=self.position_embeddings.weight
             )
         else:
             with torch.no_grad():
@@ -502,6 +489,8 @@ class DistilBertModel(DistilBertPreTrainedModel):
                     self.embeddings.position_embeddings.weight = nn.Parameter(
                         old_position_embeddings_weight[:num_position_embeds_diff]
                     )
+        # move position_embeddings to correct device
+        self.embeddings.position_embeddings.to(self.device)
 
     def get_input_embeddings(self):
         return self.embeddings.word_embeddings

src/transformers/models/pegasus/modeling_pegasus.py

@@ -668,6 +668,7 @@ class PegasusEncoder(PegasusPreTrainedModel):
             self.config.d_model,
             self.padding_idx,
         )
+        self.embed_positions.to(self.device)
 
     def get_position_embeddings(self) -> nn.Embedding:
         """
@@ -886,6 +887,7 @@ class PegasusDecoder(PegasusPreTrainedModel):
             self.config.d_model,
             self.padding_idx,
         )
+        self.embed_positions.to(self.device)
 
     def get_position_embeddings(self) -> nn.Embedding:
         """