[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/bench_utils.py

@@ -1,22 +1,19 @@
 import time
 from copy import deepcopy
-from functools import partial
 from typing import Callable, Tuple
 
 import numpy as np
 import torch
 import torch.nn as nn
-import torchvision.models as tm
 from transformers import GPT2Config, GPT2LMHeadModel
 
 from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
 from colossalai.fx import metainfo_trace
 
 
-def bench(gm: torch.fx.GraphModule,
-          criterion: torch.nn.Module,
-          data_gen: Callable,
-          num_steps: int = 5) -> Tuple[int, int]:
+def bench(
+    gm: torch.fx.GraphModule, criterion: torch.nn.Module, data_gen: Callable, num_steps: int = 5
+) -> Tuple[int, int]:
     """Benchmarking a given graph module
     Args:
         gm (torch.fx.GraphModule): The graph module to benchmark.
@@ -28,7 +25,7 @@ def bench(gm: torch.fx.GraphModule,
     """
     gm.train()
     gm.cuda()
-    step_time = float('inf')
+    step_time = float("inf")
     torch.cuda.synchronize()
     torch.cuda.empty_cache()
     torch.cuda.reset_peak_memory_stats()
@@ -58,13 +55,15 @@ def bench(gm: torch.fx.GraphModule,
     return peak_mem, step_time * 1.0e3
 
 
-def bench_rotor(gm: torch.fx.GraphModule,
-                criterion: torch.nn.Module,
-                data_gen: Callable,
-                num_steps: int = 5,
-                sample_points: int = 20,
-                free_memory: int = torch.cuda.mem_get_info()[0],
-                start_factor: int = 4) -> Tuple[np.array, list, list]:
+def bench_rotor(
+    gm: torch.fx.GraphModule,
+    criterion: torch.nn.Module,
+    data_gen: Callable,
+    num_steps: int = 5,
+    sample_points: int = 20,
+    free_memory: int = torch.cuda.mem_get_info()[0],
+    start_factor: int = 4,
+) -> Tuple[np.array, list, list]:
     """Auto Checkpoint Rotor Algorithm benchmarking
     Benchmarks the Auto Checkpoint Rotor Algorithm for a given graph module and data.
     Args:
@@ -88,7 +87,7 @@ def bench_rotor(gm: torch.fx.GraphModule,
             gm.graph = solver.solve(verbose=False)
             peak_memory, step_time = bench(gm, criterion, data_gen, num_steps=num_steps)
         except:
-            peak_memory, step_time = budget / 1024**2, float('inf')
+            peak_memory, step_time = budget / 1024**2, float("inf")
         peak_hist.append(peak_memory)
         step_hist.append(step_time)
         gm.graph = deepcopy(raw_graph)
@@ -100,22 +99,27 @@ class GPTLMModel(nn.Module):
     GPT Model
     """
 
-    def __init__(self,
-                 hidden_size=768,
-                 num_layers=12,
-                 num_attention_heads=12,
-                 max_seq_len=1024,
-                 vocab_size=50257,
-                 checkpoint=False):
+    def __init__(
+        self,
+        hidden_size=768,
+        num_layers=12,
+        num_attention_heads=12,
+        max_seq_len=1024,
+        vocab_size=50257,
+        checkpoint=False,
+    ):
         super().__init__()
         self.checkpoint = checkpoint
         self.model = GPT2LMHeadModel(
-            GPT2Config(n_embd=hidden_size,
-                       n_layer=num_layers,
-                       n_head=num_attention_heads,
-                       n_positions=max_seq_len,
-                       n_ctx=max_seq_len,
-                       vocab_size=vocab_size))
+            GPT2Config(
+                n_embd=hidden_size,
+                n_layer=num_layers,
+                n_head=num_attention_heads,
+                n_positions=max_seq_len,
+                n_ctx=max_seq_len,
+                vocab_size=vocab_size,
+            )
+        )
         if checkpoint:
             self.model.gradient_checkpointing_enable()
 
@@ -152,7 +156,7 @@ def gpt2_6b(checkpoint=False):
     return GPTLMModel(hidden_size=4096, num_layers=30, num_attention_heads=16, checkpoint=checkpoint)
 
 
-def data_gen_gpt2(batch_size, seq_len, vocab_size, device='cuda:0'):
+def data_gen_gpt2(batch_size, seq_len, vocab_size, device="cuda:0"):
     """
     Generate random data for gpt2 benchmarking
     """
@@ -161,7 +165,7 @@ def data_gen_gpt2(batch_size, seq_len, vocab_size, device='cuda:0'):
     return (input_ids, attention_mask), attention_mask
 
 
-def data_gen_resnet(batch_size, shape, device='cuda:0'):
+def data_gen_resnet(batch_size, shape, device="cuda:0"):
     """
     Generate random data for resnet benchmarking
     """