diff --git a/colossalai/nn/_ops/embedding.py b/colossalai/nn/_ops/embedding.py
index 1e392c04d..3f7787694 100644
--- a/colossalai/nn/_ops/embedding.py
+++ b/colossalai/nn/_ops/embedding.py
@@ -52,7 +52,7 @@ def colo_embedding_1Drow(input_tensor: ColoTensor,
     input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.tensor_spec.get_process_group()))
 
     tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
-    num_embeddings_per_partition = weight.size_base(0)
+    num_embeddings_per_partition = weight.size_local(0)
     vocab_start_index = tensor_parallel_rank * num_embeddings_per_partition
     vocab_end_index = vocab_start_index + num_embeddings_per_partition
 
diff --git a/colossalai/tensor/colo_parameter.py b/colossalai/tensor/colo_parameter.py
index 960c26474..0414c7d07 100644
--- a/colossalai/tensor/colo_parameter.py
+++ b/colossalai/tensor/colo_parameter.py
@@ -101,13 +101,3 @@ class ColoParameter(ColoTensor, torch.nn.Parameter):
         # TODO(jzy) we don't support object reflection now.
         # distspec cannot be pickled or rebuilt because it's tightly connected to runtime attribute `process_group`.
         raise NotImplementedError
-
-    #### the ColoParameter should use the torch.Tensor's builtin methodes ###
-
-    def view(self, *args) -> 'ColoTensor':
-        return super().view_base(*args)
-
-    def size(self, *args, **kwargs) -> torch.Size:
-        # import inspect
-        # print(*['{:40}| {}:{}\n'.format(x.function, x.filename, x.lineno) for x in inspect.stack()])
-        return super().size_base(*args, **kwargs)
diff --git a/colossalai/tensor/colo_tensor.py b/colossalai/tensor/colo_tensor.py
index bd7859dee..f36c18313 100644
--- a/colossalai/tensor/colo_tensor.py
+++ b/colossalai/tensor/colo_tensor.py
@@ -147,13 +147,13 @@ class ColoTensor(torch.Tensor):
 
     ##### override builtin functions which must use tensor in replicate placement ####
 
-    def view_base(self, *args) -> 'ColoTensor':
+    def view_local(self, *args) -> 'ColoTensor':
         return super().view(*args)
 
-    def size_base(self, *args, **kwargs) -> torch.Size:
+    def size_local(self, *args, **kwargs) -> torch.Size:
         return super().size(*args, **kwargs)
 
-    def view(self, *args) -> 'ColoTensor':
+    def view_global(self, *args) -> 'ColoTensor':
         """override the torch buildin view()
         the args passed in must be in a replicate placement.
         Returns:
@@ -167,7 +167,7 @@ class ColoTensor(torch.Tensor):
         self._tensor_spec.dist_spec = distspec.replicate()
         return super().view(*args)
 
-    def size(self, args: Optional[int] = None):
+    def size_global(self, args: Optional[int] = None):
         """override the torch buildin size()
         the shape passed in must be in a replicate placement.
         Returns:
diff --git a/tests/test_tensor/test_tensor.py b/tests/test_tensor/test_tensor.py
index 836d7c16e..b04f14f57 100644
--- a/tests/test_tensor/test_tensor.py
+++ b/tests/test_tensor/test_tensor.py
@@ -67,14 +67,14 @@ def _run_view(world_size):
         TensorSpec(distspec.shard(process_group=gpc.get_group(ParallelMode.DATA), dims=[0],
                                   num_partitions=[world_size])))
 
-    assert t.size()[0] == 4 * world_size
-    assert t.size(1) == 5
-    assert t.size() == torch.Size([4 * world_size, 5])
+    assert t.size_global()[0] == 4 * world_size
+    assert t.size_global(1) == 5
+    assert t.size_global() == torch.Size([4 * world_size, 5])
 
-    t.view_base(4 * 5)
+    t.view_local(4 * 5)
     assert t.tensor_spec.dist_spec.placement.value == 's'
 
-    t = t.view(4 * 5 * world_size)
+    t = t.view_global(4 * 5 * world_size)
     assert t.tensor_spec.dist_spec.placement.value == 'r'
     assert t.shape == torch.Size([4 * 5 * world_size])