Replace legacy tensor.Tensor with torch.tensor/torch.empty (#12027)

* Replace legacy torch.Tensor constructor with torch.{tensor, empty}

* Remove torch.Tensor in examples

examples/research_projects/lxmert/modeling_frcnn.py

@@ -1426,7 +1426,7 @@ class AnchorGenerator(nn.Module):
                 h = aspect_ratio * w
                 x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0
                 anchors.append([x0, y0, x1, y1])
-        return nn.Parameter(torch.Tensor(anchors))
+        return nn.Parameter(torch.tensor(anchors))
 
     def forward(self, features):
         """

examples/research_projects/lxmert/utils.py

@@ -532,7 +532,7 @@ def load_frcnn_pkl_from_url(url):
     for k, v in model.items():
         new[k] = torch.from_numpy(v)
         if "running_var" in k:
-            zero = torch.Tensor([0])
+            zero = torch.tensor([0])
             k2 = k.replace("running_var", "num_batches_tracked")
             new[k2] = zero
     return new

examples/research_projects/movement-pruning/emmental/modules/masked_nn.py

@@ -72,7 +72,7 @@ class MaskedLinear(nn.Linear):
         if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
             self.mask_scale = mask_scale
             self.mask_init = mask_init
-            self.mask_scores = nn.Parameter(torch.Tensor(self.weight.size()))
+            self.mask_scores = nn.Parameter(torch.empty(self.weight.size()))
             self.init_mask()
 
     def init_mask(self):

examples/research_projects/rag-end2end-retriever/finetune_rag.py

@@ -223,7 +223,7 @@ class GenerativeQAModule(BaseTransformer):
                 decoder_start_token_id = generator.config.decoder_start_token_id
                 decoder_input_ids = (
                     torch.cat(
-                        [torch.Tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
+                        [torch.tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
                         dim=1,
                     )
                     if target_ids.shape[0] < self.target_lens["train"]

examples/research_projects/rag/finetune_rag.py

@@ -222,7 +222,7 @@ class GenerativeQAModule(BaseTransformer):
                 decoder_start_token_id = generator.config.decoder_start_token_id
                 decoder_input_ids = (
                     torch.cat(
-                        [torch.Tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
+                        [torch.tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
                         dim=1,
                     )
                     if target_ids.shape[0] < self.target_lens["train"]