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examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py

@@ -42,11 +42,11 @@
 from typing import Any, Optional
 
 import torch
+from lightning_fabric.utilities.cloud_io import _load as pl_load
 from megatron.core import parallel_state
 from pytorch_lightning.core.saving import _load_state as ptl_load_state
 from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml
 from pytorch_lightning.trainer.trainer import Trainer
-from lightning_fabric.utilities.cloud_io import _load as pl_load
 from pytorch_lightning.utilities.migration import pl_legacy_patch
 
 from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel

nemo/collections/nlp/models/nlp_model.py

@@ -18,12 +18,12 @@
 import os
 from typing import Any, Optional
 
+from lightning_fabric.utilities.cloud_io import _load as pl_load
 from omegaconf import DictConfig, OmegaConf
 from pytorch_lightning import Trainer
 from pytorch_lightning.core.saving import _load_state as ptl_load_state
 from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml
 from pytorch_lightning.utilities import rank_zero_only
-from lightning_fabric.utilities.cloud_io import _load as pl_load
 from pytorch_lightning.utilities.migration import pl_legacy_patch
 from transformers import TRANSFORMERS_CACHE
 

nemo/collections/nlp/parts/nlp_overrides.py

@@ -24,14 +24,14 @@
 import pytorch_lightning as pl
 import torch
 from omegaconf import OmegaConf
+from pytorch_lightning.loops.fetchers import _DataFetcher
 from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
 from pytorch_lightning.plugins import ClusterEnvironment
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
 from pytorch_lightning.strategies.ddp import DDPStrategy
 from pytorch_lightning.trainer.trainer import Trainer
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.loops.fetchers import _DataFetcher
 from torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks import noop_hook
 from torch.nn.parallel import DistributedDataParallel
 

nemo/core/classes/modelPT.py

@@ -122,7 +122,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
         self._optimizer_param_groups = None
         self._optimizer = None
         self._scheduler = None
-        #TODO: Add a list for validation_step_outputs and test_step_outputs to support multiple dataloaders 
+        # TODO: Add a list for validation_step_outputs and test_step_outputs to support multiple dataloaders
         # by retrieving if there are more than 1 dataloaders from trainer ??
         self.validation_step_outputs = []
         self.test_step_outputs = []
@@ -885,7 +885,7 @@ def on_validation_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]
             if output_dict is not None and 'log' in output_dict:
                 self.log_dict(output_dict.pop('log'), on_epoch=True)
 
-            self.validation_step_outputs.clear() # free memory
+            self.validation_step_outputs.clear()  # free memory
             return output_dict
 
         else:  # Case where we provide more than 1 data loader
@@ -944,7 +944,7 @@ def on_validation_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]
             if 'log' in output_dict:
                 self.log_dict(output_dict.pop('log'), on_epoch=True)
 
-            self.validation_step_outputs.clear() # free memory
+            self.validation_step_outputs.clear()  # free memory
             # return everything else
             return output_dict
 
@@ -980,7 +980,7 @@ def on_test_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]:
             if output_dict is not None and 'log' in output_dict:
                 self.log_dict(output_dict.pop('log'), on_epoch=True)
 
-            self.test_step_outputs.clear() # free memory
+            self.test_step_outputs.clear()  # free memory
             return output_dict
 
         else:  # Case where we provide more than 1 data loader
@@ -1037,7 +1037,7 @@ def on_test_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]:
             if 'log' in output_dict:
                 self.log_dict(output_dict.pop('log'), on_epoch=True)
 
-            self.test_step_outputs.clear() # free memory
+            self.test_step_outputs.clear()  # free memory
             # return everything else
             return output_dict
 

nemo/utils/exp_manager.py

@@ -598,7 +598,7 @@ def check_resume(
         logging.info(f"Resuming from {last_checkpoints[0]}")
         checkpoint = last_checkpoints[0]
 
-    #PTL 2.0 supports ckpt_path instead of resume_from_checkpoint as the trainer flag
+    # PTL 2.0 supports ckpt_path instead of resume_from_checkpoint as the trainer flag
     trainer.ckpt_path = str(checkpoint)
 
     if is_global_rank_zero():

tests/collections/common/test_ema.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os.path
-from typing import Any, Dict, Union, Optional
+from typing import Any, Dict, Optional, Union
 
 import pytest
 import pytorch_lightning as pl

tests/core/test_exp_manager.py

@@ -330,17 +330,13 @@ def test_resume(self, tmp_path):
             {"resume_if_exists": True, "explicit_log_dir": str(tmp_path / "test_resume" / "default" / "version_0")},
         )
         checkpoint = Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--last.ckpt")
-        assert (
-            Path(test_trainer._checkpoint_connector._ckpt_path).resolve() == checkpoint.resolve()
-        )
+        assert Path(test_trainer._checkpoint_connector._ckpt_path).resolve() == checkpoint.resolve()
 
         # Succeed again and make sure that run_0 exists and previous log files were moved
         test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False)
         exp_manager(test_trainer, {"resume_if_exists": True, "explicit_log_dir": str(log_dir)})
         checkpoint = Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--last.ckpt")
-        assert (
-            Path(test_trainer._checkpoint_connector._ckpt_path).resolve() == checkpoint.resolve()
-        )
+        assert Path(test_trainer._checkpoint_connector._ckpt_path).resolve() == checkpoint.resolve()
         prev_run_dir = Path(tmp_path / "test_resume" / "default" / "version_0" / "run_0")
         assert prev_run_dir.exists()
         prev_log = Path(tmp_path / "test_resume" / "default" / "version_0" / "run_0" / "lightning_logs.txt")
@@ -373,10 +369,7 @@ def test_resume(self, tmp_path):
                 "explicit_log_dir": str(dirpath_log_dir),
             },
         )
-        assert (
-            Path(test_trainer._checkpoint_connector._ckpt_path).resolve()
-            == dirpath_checkpoint.resolve()
-        )
+        assert Path(test_trainer._checkpoint_connector._ckpt_path).resolve() == dirpath_checkpoint.resolve()
 
     @pytest.mark.unit
     def test_nemo_checkpoint_save_best_model_1(self, tmp_path):