diff --git a/colossalai/tensor/_ops/linear.py b/colossalai/tensor/_ops/linear.py
index a57599e6e..824ce702c 100644
--- a/colossalai/tensor/_ops/linear.py
+++ b/colossalai/tensor/_ops/linear.py
@@ -8,6 +8,7 @@ from colossalai.core import global_context as gpc
 from packaging import version
 from colossalai.utils.cuda import get_current_device
 
+
 @colo_op_impl(torch.nn.functional.linear)
 def colo_linear(types, args, kwargs, pg):
     """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
@@ -34,13 +35,13 @@ def colo_linear(types, args, kwargs, pg):
         elif weight.shard_spec == '1Drow':
             # Input:S[1] x Weight:S[0] = Output:P
             # All-Reduce(Output) + bias = res
-            assert divide(input_tensor.shape[-1], gpc.tensor_parallel_size) == weight.size[-1], \
+            assert divide(input_tensor.shape[-1], gpc.tensor_parallel_size) == weight.size(-1), \
             'Invalid shapes in 1Drow forward: input={}, weight={}. Expected last dim of input {}.'.format(
                 input_tensor.shape, weight.size, weight.size[-1] * gpc.tensor_parallel_size)
             # Input:S[1]
             input_per_partition = split_forward_gather_backward(input_tensor, ParallelMode.PARALLEL_1D, dim=-1)
             # Output:P
-            device = get_current_device() # TODO where to put to(deivce)?
+            device = get_current_device()    # TODO where to put to(deivce)?
             weight_ = weight.torch_tensor().to(device)
             partial_output = torch.nn.functional.linear(input_per_partition, weight_)
             # Reduce(Output)
@@ -50,7 +51,7 @@ def colo_linear(types, args, kwargs, pg):
                 bias_ = bias.to(device)
                 output = output + bias_
             return output
-            
+
         else:
             raise NotImplementedError
     else: