fixed mkdir conflict and align yapf config with flake (#220)

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ repos:
     rev: v0.32.0
     hooks:
     - id: yapf
-      args: ['--style=google', '--parallel', '--in-place']
+      args: ['--style=.style.yapf', '--parallel', '--in-place']
   - repo: https://github.com/pycqa/flake8
     rev: '4.0.1'
     hooks:

.style.yapf

+[style]
+based_on_style = google
+spaces_before_comment = 4
+split_before_logical_operator = true
+column_limit = 120

colossalai/logging/logging.py

@@ -8,7 +8,6 @@ from typing import Union
 
 from colossalai.context.parallel_mode import ParallelMode
 
-
 _FORMAT = 'colossalai - %(name)s - %(asctime)s %(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=_FORMAT)
 
@@ -39,7 +38,8 @@ class DistributedLogger:
 
     def __init__(self, name):
         if name in DistributedLogger.__instances:
-            raise Exception('Logger with the same name has been created, you should use colossalai.logging.get_dist_logger')
+            raise Exception(
+                'Logger with the same name has been created, you should use colossalai.logging.get_dist_logger')
         else:
             self._name = name
             self._logger = logging.getLogger(name)
@@ -58,11 +58,7 @@ class DistributedLogger:
         self._check_valid_logging_level(level)
         self._logger.setLevel(getattr(logging, level))
 
-    def log_to_file(self,
-                    path: Union[str, Path],
-                    mode: str = 'a',
-                    level: str = 'INFO',
-                    suffix: str = None):
+    def log_to_file(self, path: Union[str, Path], mode: str = 'a', level: str = 'INFO', suffix: str = None):
         """Save the logs to file
 
         :param path: The file to save the log
@@ -77,9 +73,13 @@ class DistributedLogger:
         assert isinstance(path, (str, Path)), \
             f'expected argument path to be type str or Path, but got {type(path)}'
         self._check_valid_logging_level(level)
+
         if isinstance(path, str):
             path = Path(path)
 
+        # create log directory
+        path.mkdir(parents=True, exist_ok=True)
+
         # set the default file name if path is a directory
         if not colossalai.core.global_context.is_initialized(ParallelMode.GLOBAL):
             rank = 0

colossalai/utils/checkpointing.py

@@ -2,6 +2,7 @@ import os
 import os.path as osp
 import re
 from typing import Tuple
+from pathlib import Path
 
 import torch
 
@@ -10,10 +11,7 @@ from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 
 __all__ = [
-    'get_checkpoint_path',
-    'get_latest_checkpoint_path',
-    'get_latest_checkpoint_pattern',
-    'save_checkpoint',
+    'get_checkpoint_path', 'get_latest_checkpoint_path', 'get_latest_checkpoint_pattern', 'save_checkpoint',
     'load_checkpoint'
 ]
 
@@ -70,9 +68,9 @@ def get_checkpoint_path(checkpoint_dir: str, epoch: int, suffix: str = ''):
 
 def _ensure_directory_exists(filename: str):
     # ensure the directory exists
-    dir = os.path.dirname(filename)
-    if not os.path.exists(dir):
-        os.makedirs(dir)
+    dirpath = os.path.dirname(filename)
+    if not os.path.exists(dirpath):
+        Path(dirpath).mkdir(parents=True, exist_ok=True)
 
 
 def get_latest_checkpoint_pattern(suffix: str = ''):
@@ -84,7 +82,8 @@ def get_latest_checkpoint_pattern(suffix: str = ''):
     :rtype: regular expression
     """
     ranks_name = _get_ranks_name()
-    ckpt_pattern = re.compile(f'epoch(\d+)-{ranks_name}{suffix}\.pt')
+    pattern = r'epoch(\d+)-{}{}\.pt'.format(ranks_name, suffix)
+    ckpt_pattern = re.compile(pattern)
     return ckpt_pattern
 
 
@@ -127,7 +126,8 @@ def save_checkpoint(checkpoint_path: str,
                     optimizer: torch.optim.Optimizer,
                     lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
                     **kwargs):
-    """Given a directory to store the checkpoints, saves all the training components' parameters or buffers, such as model, optimizer, lr_scheduler and etc. into a checkpoint dictionary.
+    """Given a directory to store the checkpoints, saves all the training components' parameters or buffers, such as model,
+     optimizer, lr_scheduler and etc. into a checkpoint dictionary.
 
     This method can be used for both colosalai nn.BaseModel and normal pytorch nn.Module.
 
@@ -150,12 +150,7 @@ def save_checkpoint(checkpoint_path: str,
         model_sd = model.state_dict()
 
     # ckpt container
-    checkpoint = {
-        'epoch': epoch,
-        'model': model_sd,
-        'optimizer': optimizer.state_dict(),
-        **kwargs
-    }
+    checkpoint = {'epoch': epoch, 'model': model_sd, 'optimizer': optimizer.state_dict(), **kwargs}
     if lr_scheduler is not None:
         checkpoint['lr_scheduler'] = lr_scheduler.state_dict()
 
@@ -171,9 +166,11 @@ def load_checkpoint(checkpoint_path: str,
                     strict: bool = True) -> Tuple:
     """Loads the checkpoint file.
     If finetune is False, then we intend to continue/resume the training process from the checkpoint given.
-    So we copy parameters and buffers from state_dict into these modules(model, optimizer,lr_scheduler) and its descendants.
+    So we copy parameters and buffers from state_dict into these modules(model, optimizer,lr_scheduler)
+     and its descendants.
     If finetune is True, then only the weights and buffers of model should be reload.
-    If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.
+    If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s
+     state_dict() function.
 
     :param checkpoint_path: The exact and matched checkpoint_path directory to retrieve appropriate state_dict
     :type checkpoint_path: str