[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/language/gpt/experiments/auto_offload/model_zoo.py

@@ -2,22 +2,20 @@ import torch
 import torch.nn as nn
 from transformers import GPT2Config, GPT2LMHeadModel
 
-class GPTLMModel(nn.Module):
 
-    def __init__(self,
-                 hidden_size=768,
-                 num_layers=12,
-                 num_attention_heads=12,
-                 max_seq_len=1024,
-                 vocab_size=50257):
+class GPTLMModel(nn.Module):
+    def __init__(self, hidden_size=768, num_layers=12, num_attention_heads=12, max_seq_len=1024, vocab_size=50257):
         super().__init__()
         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,
+            )
+        )
 
     def forward(self, input_ids, attention_mask):
         # Only return lm_logits
@@ -25,7 +23,6 @@ class GPTLMModel(nn.Module):
 
 
 class GPTLMLoss(nn.Module):
-
     def __init__(self):
         super().__init__()
         self.loss_fn = nn.CrossEntropyLoss()
@@ -36,6 +33,7 @@ class GPTLMLoss(nn.Module):
         # Flatten the tokens
         return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
 
+
 def get_gpt2_components(model_type: str, batch_size: int):
     vocab_size = 1024
     seq_len = 8
@@ -62,4 +60,4 @@ def get_gpt2_components(model_type: str, batch_size: int):
         kwargs = dict(input_ids=input_ids, attention_mask=attention_mask)
         return kwargs
 
-    return gpt2_model_builder, gpt2_data_gen
+    return gpt2_model_builder, gpt2_data_gen

examples/language/gpt/experiments/auto_offload/requirements.txt

@@ -1,2 +1,2 @@
 colossalai >= 0.1.12
-torch >= 1.8.1
+torch >= 1.8.1

examples/language/gpt/experiments/auto_offload/train_gpt_offload.py

@@ -3,7 +3,6 @@ import time
 
 import pytest
 import torch
-from model_zoo import GPTLMLoss, get_gpt2_components
 from torch.utils._pytree import tree_map
 
 import colossalai
@@ -14,18 +13,19 @@ from colossalai.fx.profiler import parameter_size
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.testing import spawn
 from colossalai.utils import get_current_device
+from model_zoo import GPTLMLoss, get_gpt2_components
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model_type', type=str, default="gpt2_medium")
-    parser.add_argument('--batch_size', type=int, default=64)
-    parser.add_argument('--solver_type', type=str, default='asyn')
-    parser.add_argument('--memory_budget', type=float, default=16)
+    parser.add_argument("--model_type", type=str, default="gpt2_medium")
+    parser.add_argument("--batch_size", type=int, default=64)
+    parser.add_argument("--solver_type", type=str, default="asyn")
+    parser.add_argument("--memory_budget", type=float, default=16)
     return parser.parse_args()
 
 
-@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed')
+@pytest.mark.skipif(NOT_NVML, reason="pynvml is not installed")
 def train_gpt(args):
     memory_budget = args.memory_budget * 1024 * 1024 * 1024
     solver_type = args.solver_type
@@ -34,10 +34,15 @@ def train_gpt(args):
 
     # build model
     model_builder, data_gen = get_gpt2_components(model_type=model_type, batch_size=batch_size)
-    label = torch.randint(low=0, high=128, size=(
-        64,
-        8,
-    ), device=get_current_device())
+    label = torch.randint(
+        low=0,
+        high=128,
+        size=(
+            64,
+            8,
+        ),
+        device=get_current_device(),
+    )
     criterion = GPTLMLoss()
 
     start_time = time.time()
@@ -80,18 +85,20 @@ def train_gpt(args):
     exec_time = sum(sorted(time_list)[:5]) / 5
     runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2
     runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2
-    print(f'solver_type: {solver_type} | model_type: {model_type}')
-    print(f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB '
-          f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|')
+    print(f"solver_type: {solver_type} | model_type: {model_type}")
+    print(
+        f"| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB "
+        f"| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|"
+    )
     print(time_list)
 
 
 def run(rank, world_size, port, args):
     config = {}
-    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     train_gpt(args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     args = parse_args()
     spawn(run, 1, args=args)