[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/tensor/d_tensor/api.py

@@ -44,7 +44,7 @@ def is_sharded(dtensor: torch.Tensor) -> bool:
     Returns:
         bool: True if the tensor is sharded, False otherwise.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     return list(dtensor.shape) == list(dtensor.dist_layout.global_shape)
 
 
@@ -77,8 +77,10 @@ def _hijack_detach_and_clone(dtensor: torch.Tensor) -> torch.Tensor:
     return dtensor
 
 
-def _construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec:
-    '''
+def _construct_default_sharding_spec(
+    tensor: torch.Tensor,
+) -> ShardingSpec:
+    """
     Construct the default sharding specification for the tensor.
 
     Args:
@@ -86,14 +88,14 @@ def _construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec:
 
     Returns:
         A `ShardingSpec` object without any sharding specified.
-    '''
+    """
     return ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={})
 
 
 def _apply_layout(tensor, layout):
-    '''
+    """
     Apply the layout to the local tensor during initializing process.
-    '''
+    """
     # layout converter requires a source and target laytout
     # we construct the source layer for an unsharded tensor
     # and use self.dist_layer as the targer layout for the sharded tensor
@@ -115,7 +117,7 @@ def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_sp
     Returns:
         torch.Tensor: The distributed tensor.
     """
-    assert not is_distributed_tensor(tensor), 'The input tensor is already a distributed tensor.'
+    assert not is_distributed_tensor(tensor), "The input tensor is already a distributed tensor."
     dist_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=tensor.shape)
 
     # shard tensor
@@ -128,7 +130,7 @@ def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_sp
 
 
 def redistribute(dtensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> None:
-    '''
+    """
     Convert the layout of the tensor from source_spec to target_spec.
     This will update the `local_tensor` and `dist_layout` in place.
 
@@ -136,13 +138,13 @@ def redistribute(dtensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec:
         dtensor (torch.Tensor): the distributed tensor to be converted.
         device_mesh (DeviceMesh): the device mesh for abstraction of the compute devices.
         target_layout (Layout): the target layout specification.
-    '''
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    """
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     global_shape = get_global_shape(dtensor)
     target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape)
-    resharded_tensor = layout_converter.apply(tensor=dtensor,
-                                              source_layout=dtensor.dist_layout,
-                                              target_layout=target_layout)
+    resharded_tensor = layout_converter.apply(
+        tensor=dtensor, source_layout=dtensor.dist_layout, target_layout=target_layout
+    )
     return resharded_tensor
 
 
@@ -157,7 +159,7 @@ def to_global(dtensor: torch.Tensor) -> torch.Tensor:
     Returns:
         torch.Tensor: the global tensor.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     layout_converter = LayoutConverter()
 
     global_sharding_spec = ShardingSpec(dtensor.dim(), {})
@@ -193,7 +195,7 @@ def shard_rowwise(
     if isinstance(group_or_device_mesh, ProcessGroup):
         device_mesh = DeviceMesh.from_process_group(group_or_device_mesh)
     else:
-        assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.'
+        assert len(group_or_device_mesh.shape) == 1, "Only 1D DeviceMesh is accepted for row-wise sharding."
         device_mesh = group_or_device_mesh
 
     sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]})
@@ -222,7 +224,7 @@ def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup
     if isinstance(group_or_device_mesh, ProcessGroup):
         device_mesh = DeviceMesh.from_process_group(group_or_device_mesh)
     else:
-        assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.'
+        assert len(group_or_device_mesh.shape) == 1, "Only 1D DeviceMesh is accepted for row-wise sharding."
         device_mesh = group_or_device_mesh
     sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={-1: [0]})
 
@@ -230,7 +232,7 @@ def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup
 
 
 def sharded_tensor_to_param(dtensor: torch.Tensor, requires_grad: bool = True):
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     param = torch.nn.Parameter(dtensor, requires_grad=requires_grad)
 
     # make it distributed as well
@@ -241,7 +243,7 @@ def sharded_tensor_to_param(dtensor: torch.Tensor, requires_grad: bool = True):
 
 
 def sharded_tensor_to_existing_param(dtensor: torch.Tensor, param: torch.nn.Parameter) -> None:
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     param.data = dtensor
     # make it distributed as well
     param.dist_layout = dtensor.dist_layout
@@ -258,7 +260,7 @@ def compute_global_numel(dtensor: torch.Tensor) -> int:
     Returns:
         int: The global number of elements in the distributed tensor.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     numel = reduce(operator.mul, dtensor.dist_layout.global_shape)
     return numel
 
@@ -274,7 +276,7 @@ def get_layout(dtensor: torch.Tensor) -> Layout:
         Layout: The layout of the distributed tensor.
 
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     return dtensor.dist_layout
 
 
@@ -288,7 +290,7 @@ def get_global_shape(dtensor: torch.Tensor) -> torch.Size:
     Returns:
         torch.Size: The global shape of the distributed tensor.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     return dtensor.dist_layout.global_shape
 
 
@@ -302,7 +304,7 @@ def get_device_mesh(dtensor: torch.Tensor) -> DeviceMesh:
     Returns:
         DeviceMesh: The device mesh of the distributed tensor.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     return dtensor.dist_layout.device_mesh
 
 
@@ -316,7 +318,7 @@ def get_sharding_spec(dtensor: torch.Tensor) -> ShardingSpec:
     Returns:
         ShardingSpec: The sharding spec of the distributed tensor.
     """
-    assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.'
+    assert is_distributed_tensor(dtensor), "The input tensor is not a distributed tensor."
     return dtensor.dist_layout.sharding_spec
 
 
@@ -335,7 +337,7 @@ def is_customized_distributed_tensor(tensor: torch.Tensor):
     Returns:
         bool: Whether the given tensor is a customized distributed tensor.
     """
-    return hasattr(tensor, 'shard_fn') and hasattr(tensor, 'gather_fn')
+    return hasattr(tensor, "shard_fn") and hasattr(tensor, "gather_fn")
 
 
 def _hijack_detach_and_clone_for_customized_distributed_tensor(dtensor: torch.Tensor) -> torch.Tensor:
@@ -402,9 +404,9 @@ def distribute_tensor_with_customization(tensor: torch.Tensor, shard_fn, gather_
     Returns:
         torch.Tensor: The distributed tensor.
     """
-    assert callable(shard_fn), 'The shard_fn must be callable.'
-    assert callable(gather_fn), 'The gather_fn must be callable.'
-    assert not is_distributed_tensor(tensor), 'The input tensor is already a distributed tensor.'
+    assert callable(shard_fn), "The shard_fn must be callable."
+    assert callable(gather_fn), "The gather_fn must be callable."
+    assert not is_distributed_tensor(tensor), "The input tensor is already a distributed tensor."
 
     sharded_tensor = shard_fn(tensor)
 
@@ -428,7 +430,7 @@ def to_global_for_customized_distributed_tensor(dtensor: torch.Tensor) -> torch.
     Returns:
         torch.Tensor: The global tensor.
     """
-    assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.'
+    assert is_customized_distributed_tensor(dtensor), "The input tensor is not a customized distributed tensor."
     return dtensor.gather_fn(dtensor)
 
 
@@ -436,7 +438,7 @@ def customized_distributed_tensor_to_param(dtensor: torch.Tensor, requires_grad:
     """
     Convert the given customized distributed tensor to a parameter.
     """
-    assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.'
+    assert is_customized_distributed_tensor(dtensor), "The input tensor is not a customized distributed tensor."
 
     param = torch.nn.Parameter(dtensor, requires_grad=requires_grad)
 
@@ -451,7 +453,7 @@ def customized_distributed_tensor_to_existing_param(dtensor: torch.Tensor, param
     """
     Convert the given customized distributed tensor to an existing parameter.
     """
-    assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.'
+    assert is_customized_distributed_tensor(dtensor), "The input tensor is not a customized distributed tensor."
 
     param.data = dtensor.data
     param.shard_fn = dtensor.shard_fn