[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/tutorial/auto_parallel/auto_ckpt_solver_test.py

@@ -1,4 +1,3 @@
-import time
 from argparse import ArgumentParser
 from functools import partial
 
@@ -8,7 +7,6 @@ import torchvision.models as tm
 from bench_utils import GPTLMLoss, bench_rotor, data_gen_gpt2, data_gen_resnet, gpt2_medium
 
 import colossalai
-from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
 from colossalai.fx import metainfo_trace, symbolic_trace
 from colossalai.testing import spawn
 
@@ -19,37 +17,33 @@ def _benchmark(rank, world_size, port, args):
     The benchmark will sample in a range of memory budget for each model and output the benchmark summary and
     data visualization of peak memory vs. budget memory and relative step time vs. peak memory.
     """
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    if args.model == 'resnet50':
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    if args.model == "resnet50":
         model = tm.resnet50()
         data_gen = partial(data_gen_resnet, batch_size=128, shape=(3, 224, 224))
         gm = symbolic_trace(model)
-        gm = metainfo_trace(gm, torch.empty(128, 3, 224, 224, device='meta'))
+        gm = metainfo_trace(gm, torch.empty(128, 3, 224, 224, device="meta"))
         loss = torch.nn.CrossEntropyLoss()
     else:
         model = gpt2_medium()
         data_gen = partial(data_gen_gpt2, batch_size=8, seq_len=1024, vocab_size=50257)
-        data, mask = data_gen(device='meta')[0]
-        gm = symbolic_trace(model, meta_args={'input_ids': data, 'attention_mask': mask})
+        data, mask = data_gen(device="meta")[0]
+        gm = symbolic_trace(model, meta_args={"input_ids": data, "attention_mask": mask})
         gm = metainfo_trace(gm, data, mask)
         loss = GPTLMLoss()
 
-    free_memory = 11000 * 1024**2 if args.model == 'resnet50' else 56000 * 1024**2
-    start_factor = 4 if args.model == 'resnet50' else 10
+    free_memory = 11000 * 1024**2 if args.model == "resnet50" else 56000 * 1024**2
+    start_factor = 4 if args.model == "resnet50" else 10
 
     # trace and benchmark
-    budgets, peak_hist, step_hist = bench_rotor(gm,
-                                                loss,
-                                                data_gen,
-                                                num_steps=5,
-                                                sample_points=15,
-                                                free_memory=free_memory,
-                                                start_factor=start_factor)
+    budgets, peak_hist, step_hist = bench_rotor(
+        gm, loss, data_gen, num_steps=5, sample_points=15, free_memory=free_memory, start_factor=start_factor
+    )
 
     # print summary
     print("==============benchmark summary==============")
     for budget, peak, step in zip(budgets, peak_hist, step_hist):
-        print(f'memory budget: {budget:.3f} MB, peak memory: {peak:.3f} MB, step time: {step:.3f} MS')
+        print(f"memory budget: {budget:.3f} MB, peak memory: {peak:.3f} MB, step time: {step:.3f} MS")
 
     # plot valid results
     fig, axs = plt.subplots(1, 2, figsize=(16, 8))
@@ -57,14 +51,14 @@ def _benchmark(rank, world_size, port, args):
 
     # plot peak memory vs. budget memory
     axs[0].plot(budgets[valid_idx:], peak_hist[valid_idx:])
-    axs[0].plot([budgets[valid_idx], budgets[-1]], [budgets[valid_idx], budgets[-1]], linestyle='--')
+    axs[0].plot([budgets[valid_idx], budgets[-1]], [budgets[valid_idx], budgets[-1]], linestyle="--")
     axs[0].set_xlabel("Budget Memory (MB)")
     axs[0].set_ylabel("Peak Memory (MB)")
     axs[0].set_title("Peak Memory vs. Budget Memory")
 
     # plot relative step time vs. budget memory
     axs[1].plot(peak_hist[valid_idx:], [step_time / step_hist[-1] for step_time in step_hist[valid_idx:]])
-    axs[1].plot([peak_hist[valid_idx], peak_hist[-1]], [1.0, 1.0], linestyle='--')
+    axs[1].plot([peak_hist[valid_idx], peak_hist[-1]], [1.0, 1.0], linestyle="--")
     axs[1].set_xlabel("Peak Memory (MB)")
     axs[1].set_ylabel("Relative Step Time")
     axs[1].set_title("Step Time vs. Peak Memory")
@@ -81,7 +75,7 @@ def auto_activation_checkpoint_benchmark(args):
 
 if __name__ == "__main__":
     parser = ArgumentParser("Auto Activation Checkpoint Solver Benchmark")
-    parser.add_argument("--model", type=str, default='gpt2', choices=['gpt2', 'resnet50'])
+    parser.add_argument("--model", type=str, default="gpt2", choices=["gpt2", "resnet50"])
     args = parser.parse_args()
 
     auto_activation_checkpoint_benchmark(args)