[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

colossalai/fx/tracer/meta_patch/patched_module/pooling.py

@@ -8,7 +8,7 @@ from ...registry import meta_patched_module
 @meta_patched_module.register(torch.nn.AvgPool1d)
 def torch_nn_avgpool1d(self, input):
     num_dim = input.dim()
-    assert num_dim in [2, 3], f'expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [2, 3], f"expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions"
 
     l_in = input.shape[-1]
 
@@ -25,13 +25,13 @@ def torch_nn_avgpool1d(self, input):
     l_out = math.floor((l_in + 2 * padding[0] - kernel_size[0]) / stride[0] + 1)
 
     result_shape = tuple(input.shape[:-1]) + (l_out,)
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.AvgPool2d)
 def torch_nn_avgpool2d(self, input):
     num_dim = input.dim()
-    assert num_dim in [3, 4], f'expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [3, 4], f"expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions"
 
     h_in, w_in = input.shape[-2:]
 
@@ -52,13 +52,13 @@ def torch_nn_avgpool2d(self, input):
         h_out,
         w_out,
     )
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.AvgPool3d)
 def torch_nn_avgpool3d(self, input):
     num_dim = input.dim()
-    assert num_dim in [4, 5], f'expected the input to have 4 or 5 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [4, 5], f"expected the input to have 4 or 5 dimensions, but got {num_dim} dimensions"
 
     d_in, h_in, w_in = input.shape[-3:]
 
@@ -81,13 +81,13 @@ def torch_nn_avgpool3d(self, input):
         h_out,
         w_out,
     )
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.MaxPool1d)
 def torch_nn_maxpool1d(self, input):
     num_dim = input.dim()
-    assert num_dim in [2, 3], f'expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [2, 3], f"expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions"
 
     l_in = input.shape[-1]
 
@@ -105,13 +105,13 @@ def torch_nn_maxpool1d(self, input):
     l_out = math.floor((l_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
 
     result_shape = tuple(input.shape[:-1]) + (l_out,)
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.MaxPool2d)
 def torch_nn_maxpool2d(self, input):
     num_dim = input.dim()
-    assert num_dim in [3, 4], f'expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [3, 4], f"expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions"
 
     h_in, w_in = input.shape[-2:]
 
@@ -133,13 +133,13 @@ def torch_nn_maxpool2d(self, input):
         h_out,
         w_out,
     )
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.MaxPool3d)
 def torch_nn_maxpool3d(self, input):
     num_dim = input.dim()
-    assert num_dim in [4, 5], f'expected the input to have 4 or 5 dimensions, but got {num_dim} dimensions'
+    assert num_dim in [4, 5], f"expected the input to have 4 or 5 dimensions, but got {num_dim} dimensions"
 
     d_in, h_in, w_in = input.shape[-3:]
 
@@ -163,7 +163,7 @@ def torch_nn_maxpool3d(self, input):
         h_out,
         w_out,
     )
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.AdaptiveAvgPool1d)
@@ -175,7 +175,7 @@ def torch_nn_adapative_pooling_1d(self, input):
     else:
         output_size = self.output_size
     result_shape = tuple(input.shape[:-1]) + output_size
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.AdaptiveAvgPool2d)
@@ -187,7 +187,7 @@ def torch_nn_adapative_pooling_2d(self, input):
     else:
         output_size = self.output_size
     result_shape = tuple(input.shape[:-2]) + output_size
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")
 
 
 @meta_patched_module.register(torch.nn.AdaptiveAvgPool3d)
@@ -199,4 +199,4 @@ def torch_nn_adapative_pooling_3d(self, input):
     else:
         output_size = self.output_size
     result_shape = tuple(input.shape[:-3]) + output_size
-    return torch.empty(result_shape, device='meta')
+    return torch.empty(result_shape, device="meta")