[pre-commit.ci] auto fixes from pre-commit.com hooks

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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
 

tests/core/test_exp_manager.py

@@ -329,17 +329,13 @@ 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")
@@ -372,10 +368,7 @@ 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):