[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/legacy/pipeline/utils.py

@@ -38,8 +38,7 @@ def _binary_partition(weights: List, start: int, end: int):
 
 
 def _heap_addition(weights: List, intervals: int, add_cnt: int):
-    """
-    """
+    """ """
 
     def _heap_push(heap, st, ed):
         value = weights[ed - 1]
@@ -113,8 +112,9 @@ def _binary_search(weights, num):
 
 
 def partition_uniform(num_items, pipeline_parallel_size, num_chunks):
-    assert num_items % num_chunks == 0, \
-        "Layer length should be divided by the number of chunks, otherwise parameter method is recommended"
+    assert (
+        num_items % num_chunks == 0
+    ), "Layer length should be divided by the number of chunks, otherwise parameter method is recommended"
 
     logger = get_dist_logger()
     parts = [[] for _ in range(pipeline_parallel_size)]
@@ -162,7 +162,7 @@ def build_kwargs_for_module(function, input_tensor, kw_dict):
     elif isinstance(input_tensor, torch.Tensor):
         kwargs_offset = 1
     elif isinstance(input_tensor, (tuple, OrderedDict)):
-        #assert isinstance(input_tensor, tuple), f'input_tensor should be a torch.Tensor or a tuple object.'
+        # assert isinstance(input_tensor, tuple), f'input_tensor should be a torch.Tensor or a tuple object.'
         # Huggingface will take their own structures based on OrderedDict as the output
         # between layers so we've to close this check.
         kwargs_offset = len(input_tensor)
@@ -204,21 +204,21 @@ def exec_func_with_kwargs(func, kw_dict, input_tensor, kwargs):
                 kwargs[k] = rst
         return input_tensor
     if isinstance(input_tensor, tuple):
-        assert len(input_tensor) > 0, f'input_tensor should not be empty, when kw_dict is None.'
+        assert len(input_tensor) > 0, f"input_tensor should not be empty, when kw_dict is None."
         sig = inspect.signature(func)
         func_args_num = len(sig.parameters)
         assert func_args_num <= len(
-            input_tensor), f'func requires {func_args_num} arguments, but input_tensors only have {len(input_tensor)}.'
+            input_tensor
+        ), f"func requires {func_args_num} arguments, but input_tensors only have {len(input_tensor)}."
         if func_args_num < len(input_tensor):
             return func(*input_tensor[:func_args_num])
         else:
             return func(*input_tensor)
-    assert isinstance(input_tensor, torch.Tensor), 'input_tensor should be a type of torch.Tensor or tuple.'
+    assert isinstance(input_tensor, torch.Tensor), "input_tensor should be a type of torch.Tensor or tuple."
     return func(input_tensor)
 
 
 def exec_funcs_with_kwargs(func_dict, func_key, input_tensor, kwargs):
-
     assert func_key in func_dict, f"{func_key} is not in the function_dict."
     funcs_to_exec = func_dict[func_key]
     if isinstance(funcs_to_exec, list):
@@ -243,7 +243,7 @@ def call_module(module, args=None, kwargs=None):
         forward_func = module.forward
     sig = inspect.signature(forward_func)
     param_nums = len(sig.parameters)
-    feed_nums = len(args) + len(kwargs)
+    len(args) + len(kwargs)
     args_needed_nums = param_nums - len(kwargs)
     args_needed = args[:args_needed_nums]
     if isinstance(module, CheckpointModule):
@@ -256,17 +256,17 @@ def call_module(module, args=None, kwargs=None):
 
 
 def customized_partition(exec_seq):
-    '''
+    """
     This function will analyze the exec_seq. In the exec_seq, users will use 'SPLIT_NODE' as an
     annotation to note the partition point.
-    '''
+    """
     customized_parts = {}
     start = 0
     stop = 0
     rank = 0
     for element in exec_seq:
         if isinstance(element, str):
-            if element == 'SPLIT_NODE':
+            if element == "SPLIT_NODE":
                 customized_parts[rank] = [(start, stop)]
                 start = stop
                 rank += 1