Remove running validating after finetuning (NVIDIA#10560)

* remove running validating after finetuning

* Apply isort and black reformatting

Signed-off-by: huvunvidia <huvunvidia@users.noreply.github.com>

---------

Signed-off-by: huvunvidia <huvunvidia@users.noreply.github.com>
Co-authored-by: Huy Vu2 <huvu@login-eos01.eos.clusters.nvidia.com>
Co-authored-by: huvunvidia <huvunvidia@users.noreply.github.com>

examples/nlp/language_modeling/megatron_t5_seq2seq_finetune.py

@@ -133,7 +133,11 @@ def load_from_checkpoint_dir(cls, cfg, trainer, modify_confg_fn):
     t5_cfg = modify_confg_fn(hparams_file.cfg, cfg, add_cfg_to_tree=True)
     with tempfile.NamedTemporaryFile(suffix='.yaml') as f:
         OmegaConf.save(config=t5_cfg, f=f.name)
-        model = cls.load_from_checkpoint(checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name,)
+        model = cls.load_from_checkpoint(
+            checkpoint_path=checkpoint_path,
+            trainer=trainer,
+            hparams_file=f.name,
+        )
         return model
 
 
@@ -162,7 +166,7 @@ def main(cfg) -> None:
         scaler = None
         if cfg.trainer.precision in [16, '16', '16-mixed']:
             scaler = GradScaler(
-                init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32),
+                init_scale=cfg.model.get('native_amp_init_scale', 2**32),
                 growth_interval=cfg.model.get('native_amp_growth_interval', 1000),
                 hysteresis=cfg.model.get('hysteresis', 2),
             )
@@ -223,7 +227,6 @@ def main(cfg) -> None:
             model = load_from_checkpoint_dir(MegatronT5SFTModel, cfg, trainer, modify_confg_fn=_modify_config)
 
     trainer.fit(model)
-    trainer.validate(model)
     if hasattr(cfg.model.data, 'test_ds'):
         trainer.test(model)