adding early stop callback to ptuning

examples/nlp/language_modeling/conf/megatron_gpt_prompt_learning_config.yaml

@@ -15,6 +15,7 @@ trainer:
   gradient_clip_val: 1.0
   resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
   benchmark: False
+
 
 
 exp_manager:
@@ -36,6 +37,14 @@ exp_manager:
     filename: 'megatron_gpt_prompt_tune--{val_loss:.3f}-{step}'
     model_parallel_size: ${model.tensor_model_parallel_size}
     save_best_model: True
+  create_early_stopping_callback: True
+  early_stopping_callback_params:
+    monitor: "val_loss"
+    mode: "min"
+    min_delta: 0.001
+    patience: 10
+    verbose: True
+
 
 model:
   seed: 1234

examples/nlp/language_modeling/conf/megatron_t5_prompt_learning.yaml

@@ -34,6 +34,13 @@ exp_manager:
     filename: "megatron_t5_prompt_tune--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}"
     model_parallel_size: ${model.tensor_model_parallel_size}
     save_best_model: True
+  create_early_stopping_callback: True
+  early_stopping_callback_params:
+    monitor: "val_loss"
+    mode: "min"
+    min_delta: 0.001
+    patience: 10
+    verbose: True
 
 model:
   seed: 1234

nemo/utils/exp_manager.py

@@ -32,6 +32,7 @@
 from hydra.utils import get_original_cwd
 from omegaconf import DictConfig, OmegaConf, open_dict
 from pytorch_lightning.callbacks import Callback, ModelCheckpoint
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 from pytorch_lightning.callbacks.timer import Interval, Timer
 from pytorch_lightning.loggers import MLFlowLogger, TensorBoardLogger, WandbLogger
 from pytorch_lightning.loops import TrainingEpochLoop
@@ -69,6 +70,21 @@ class CheckpointMisconfigurationError(NeMoBaseException):
     """ Raised when a mismatch between trainer.callbacks and exp_manager occurs"""
 
 
+@dataclass
+class EarlyStoppingParams:
+    monitor: str = "val_loss"  # The metric that early stopping should consider.
+    mode: str = "min"  # inform early stopping whether to look for increase or decrease in monitor.
+    min_delta: float = 0.001  # smallest change to consider as improvement.
+    patience: int = 10  # how many (continuous) validation cycles to wait with no improvement and stopping training.
+    verbose: bool = True
+    strict: bool = True
+    check_finite: bool = True
+    stopping_threshold: Optional[float] = None
+    divergence_threshold: Optional[float] = None
+    check_on_train_epoch_end: Optional[bool] = None
+    log_rank_zero_only: bool = False
+
+
 @dataclass
 class CallbackParams:
     filepath: Optional[str] = None  # Deprecated
@@ -153,6 +169,8 @@ class ExpManagerConfig:
     # Checkpointing parameters
     create_checkpoint_callback: Optional[bool] = True
     checkpoint_callback_params: Optional[CallbackParams] = CallbackParams()
+    create_early_stopping_callback: Optional[bool] = False
+    early_stopping_callback_params: Optional[EarlyStoppingParams] = EarlyStoppingParams()
     # Additional exp_manager arguments
     files_to_copy: Optional[List[str]] = None
     # logs timing of train/val/test steps
@@ -272,6 +290,8 @@ def exp_manager(trainer: 'pytorch_lightning.Trainer', cfg: Optional[Union[DictCo
                 pytorch lightning trainer. The ModelCheckpoint saves the top 3 models with the best "val_loss", the most
                 recent checkpoint under ``*last.ckpt``, and the final checkpoint after training completes under ``*end.ckpt``.
                 Defaults to True.
+            - create_early_stopping_callback (bool): Flag to decide if early stopping should be used to stop training. Default is False.
+             See EarlyStoppingParams dataclass above.
             - files_to_copy (list): A list of files to copy to the experiment logging directory. Defaults to None which
                 copies no files.
             - log_local_rank_0_only (bool): Whether to only create log files for local rank 0. Defaults to False.
@@ -420,6 +440,10 @@ def exp_manager(trainer: 'pytorch_lightning.Trainer', cfg: Optional[Union[DictCo
         )
         trainer.callbacks.append(ema_callback)
 
+    if cfg.create_early_stopping_callback:
+        early_stop_callback = EarlyStopping(**cfg.early_stopping_callback_params)
+        trainer.callbacks.append(early_stop_callback)
+
     if cfg.create_checkpoint_callback:
         configure_checkpointing(
             trainer, log_dir, checkpoint_name, cfg.resume_if_exists, cfg.checkpoint_callback_params

tests/core/test_config_utils.py

@@ -17,9 +17,11 @@
 
 import pytest
 import pytorch_lightning as ptl
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 
 from nemo.core.config.pytorch_lightning import TrainerConfig
 from nemo.utils import config_utils
+from nemo.utils.exp_manager import EarlyStoppingParams
 
 
 @pytest.fixture()
@@ -126,3 +128,12 @@ def test_ptl_config(self):
         assert signatures_match
         assert cls_subset is None
         assert dataclass_subset is None
+
+    @pytest.mark.unit
+    def test_early_stopping_config(self,):
+        result = config_utils.assert_dataclass_signature_match(EarlyStopping, EarlyStoppingParams)
+        signatures_match, cls_subset, dataclass_subset = result
+
+        assert signatures_match
+        assert cls_subset is None
+        assert dataclass_subset is None