[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

examples/community/roberta/pretraining/utils/exp_util.py

@@ -12,8 +12,8 @@ def logging(s, log_path, print_=True, log_=True):
     if print_:
         print(s)
     if log_:
-        with open(log_path, 'a+') as f_log:
-            f_log.write(s + '\n')
+        with open(log_path, "a+") as f_log:
+            f_log.write(s + "\n")
 
 
 def get_logger(log_path, **kwargs):
@@ -22,22 +22,22 @@ def get_logger(log_path, **kwargs):
 
 def create_exp_dir(dir_path, scripts_to_save=None, debug=False):
     if debug:
-        print('Debug Mode : no experiment dir created')
+        print("Debug Mode : no experiment dir created")
         return functools.partial(logging, log_path=None, log_=False)
 
     if not os.path.exists(dir_path):
         os.makedirs(dir_path)
 
-    print('Experiment dir : {}'.format(dir_path))
+    print("Experiment dir : {}".format(dir_path))
     if scripts_to_save is not None:
-        script_path = os.path.join(dir_path, 'scripts')
+        script_path = os.path.join(dir_path, "scripts")
         if not os.path.exists(script_path):
             os.makedirs(script_path)
         for script in scripts_to_save:
-            dst_file = os.path.join(dir_path, 'scripts', os.path.basename(script))
+            dst_file = os.path.join(dir_path, "scripts", os.path.basename(script))
             shutil.copyfile(script, dst_file)
 
-    return get_logger(log_path=os.path.join(dir_path, 'log.txt'))
+    return get_logger(log_path=os.path.join(dir_path, "log.txt"))
 
 
 def get_cpu_mem():
@@ -48,8 +48,8 @@ def get_gpu_mem():
     return torch.cuda.memory_allocated() / 1024**2
 
 
-def get_mem_info(prefix=''):
-    return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'
+def get_mem_info(prefix=""):
+    return f"{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB"
 
 
 def get_tflops(model_numel, batch_size, seq_len, step_time):
@@ -59,11 +59,12 @@ def get_tflops(model_numel, batch_size, seq_len, step_time):
 def get_parameters_in_billions(model, world_size=1):
     gpus_per_model = world_size
 
-    approx_parameters_in_billions = sum([
-        sum([p.ds_numel if hasattr(p, 'ds_id') else p.nelement()
-             for p in model_module.parameters()])
-        for model_module in model
-    ])
+    approx_parameters_in_billions = sum(
+        [
+            sum([p.ds_numel if hasattr(p, "ds_id") else p.nelement() for p in model_module.parameters()])
+            for model_module in model
+        ]
+    )
 
     return approx_parameters_in_billions * gpus_per_model / (1e9)
 
@@ -71,13 +72,13 @@ def get_parameters_in_billions(model, world_size=1):
 def throughput_calculator(numel, args, config, iteration_time, total_iterations, world_size=1):
     gpus_per_model = 1
     batch_size = args.train_micro_batch_size_per_gpu
-    samples_per_model = batch_size * args.max_seq_length
-    model_replica_count = world_size / gpus_per_model
+    batch_size * args.max_seq_length
+    world_size / gpus_per_model
     approx_parameters_in_billions = numel
     elapsed_time_per_iter = iteration_time / total_iterations
     samples_per_second = batch_size / elapsed_time_per_iter
 
-    #flops calculator
+    # flops calculator
     hidden_size = config.hidden_size
     num_layers = config.num_hidden_layers
     vocab_size = config.vocab_size
@@ -87,9 +88,9 @@ def throughput_calculator(numel, args, config, iteration_time, total_iterations,
     # The factor of 4 is when used with activation check-pointing,
     # otherwise it will be 3.
     checkpoint_activations_factor = 4 if args.checkpoint_activations else 3
-    flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers *
-                           (hidden_size**2)) * (1. + (args.max_seq_length / (6. * hidden_size)) +
-                                                (vocab_size / (16. * num_layers * hidden_size)))
+    flops_per_iteration = (
+        24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers * (hidden_size**2)
+    ) * (1.0 + (args.max_seq_length / (6.0 * hidden_size)) + (vocab_size / (16.0 * num_layers * hidden_size)))
     tflops = flops_per_iteration / (elapsed_time_per_iter * (10**12))
     return samples_per_second, tflops, approx_parameters_in_billions
 
@@ -106,9 +107,9 @@ def synchronize():
 
 
 def log_args(logger, args):
-    logger.info('--------args----------')
-    message = '\n'.join([f'{k:<30}: {v}' for k, v in vars(args).items()])
-    message += '\n'
-    message += '\n'.join([f'{k:<30}: {v}' for k, v in gpc.config.items()])
+    logger.info("--------args----------")
+    message = "\n".join([f"{k:<30}: {v}" for k, v in vars(args).items()])
+    message += "\n"
+    message += "\n".join([f"{k:<30}: {v}" for k, v in gpc.config.items()])
     logger.info(message)
-    logger.info('--------args----------\n')
+    logger.info("--------args----------\n")