[NeMo-UX] Use custom BatchProgress class which does not restore sta…

…tes (NVIDIA#10383)

* [WIP] fix batch sampler to match megatron dataloaders

Signed-off-by: ashors1 <ashors@nvidia.com>

* make batchprogress configurable

Signed-off-by: ashors1 <ashors@nvidia.com>

* Apply isort and black reformatting

Signed-off-by: ashors1 <ashors1@users.noreply.github.com>

---------

Signed-off-by: ashors1 <ashors@nvidia.com>
Signed-off-by: ashors1 <ashors1@users.noreply.github.com>
Co-authored-by: ashors1 <ashors1@users.noreply.github.com>
Co-authored-by: Shriya Rishab <69161273+ShriyaPalsamudram@users.noreply.github.com>
Co-authored-by: Pablo Garay <palenq@gmail.com>

nemo/lightning/pytorch/strategies/fsdp_strategy.py

@@ -35,6 +35,7 @@
 
 from nemo.lightning import io
 from nemo.lightning.pytorch.strategies.utils import (
+    _MegatronBatchProgress,
     ckpt_to_dir,
     create_checkpoint_io,
     fix_progress_bar,
@@ -73,13 +74,15 @@ def __init__(
         ckpt_load_optimizer: bool = True,
         ckpt_save_optimizer: bool = True,
         data_sampler=None,
+        overwrite_batch_progress: bool = True,
         **kwargs,
     ):
         super().__init__(auto_wrap_policy=auto_wrap_policy, state_dict_type=state_dict_type, **kwargs)
 
         self.data_sampler = data_sampler
         self.ckpt_load_optimizer = ckpt_load_optimizer
         self.ckpt_save_optimizer = ckpt_save_optimizer
+        self.overwrite_batch_progress = overwrite_batch_progress
 
     @override
     def setup_environment(self) -> None:
@@ -92,6 +95,11 @@ def setup(self, trainer: pl.Trainer) -> None:
         self.trainer = trainer
         setup_data_sampler(self.trainer)
         fix_progress_bar(trainer)
+
+        trainer_fn = trainer.state.fn
+        if trainer_fn == TrainerFn.FITTING and self.overwrite_batch_progress:
+            trainer.fit_loop.epoch_loop.batch_progress = _MegatronBatchProgress()
+
         super().setup(trainer)
 
     def _get_loss_reduction(self, step_type: str):

nemo/lightning/pytorch/strategies/megatron_strategy.py

@@ -61,6 +61,7 @@
 from nemo.lightning.pytorch.callbacks import ModelTransform
 from nemo.lightning.pytorch.strategies.utils import (
     RestoreConfig,
+    _MegatronBatchProgress,
     ckpt_to_dir,
     create_checkpoint_io,
     fix_progress_bar,
@@ -152,6 +153,8 @@ class MegatronStrategy(DDPStrategy, io.IOMixin):
             that prints the metrics to stdout. Suitable for non-interactive settings.
         progress_interval (int): How frequently to print progress to stdout. Only used when
             replace_progress_bar is True.
+        overwrite_batch_progress (bool): Whether to overwrite _BatchProgress class used in PTL by default with
+            _MegatronBatchProgress. This should be True whenever you're using a Megatron-based dataset.
         **kwargs: Additional keyword arguments.
 
     Note:
@@ -194,6 +197,7 @@ def __init__(
         replace_progress_bar: bool = True,
         progress_interval: int = 1,
         restore_config: Optional[RestoreConfig] = None,
+        overwrite_batch_progress: bool = True,
         **kwargs,
     ) -> None:
         super().__init__(
@@ -234,6 +238,7 @@ def __init__(
 
         self.replace_progress_bar = replace_progress_bar
         self.progress_interval = progress_interval
+        self.overwrite_batch_progress = overwrite_batch_progress
 
         self.restore_config = restore_config
 
@@ -331,6 +336,8 @@ def setup(self, trainer: pl.Trainer) -> None:
             self.configure_ddp()
 
             trainer.fit_loop.epoch_loop.automatic_optimization = _MegatronAutomaticOptimization(trainer)
+            if self.overwrite_batch_progress:
+                trainer.fit_loop.epoch_loop.batch_progress = _MegatronBatchProgress()
 
             import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
 

nemo/lightning/pytorch/strategies/utils.py

@@ -25,10 +25,12 @@
 from megatron.core.dist_checkpointing.strategies.torch import sharded_tensor_to_torch_sharded_tensor
 from megatron.core.transformer.utils import _get_extra_state_offsets
 from pytorch_lightning.callbacks import TQDMProgressBar
+from pytorch_lightning.loops.progress import _BatchProgress
 from pytorch_lightning.plugins.io.wrapper import _WrappingCheckpointIO
 from torch.distributed._sharded_tensor import ShardedTensor as TorchShardedTensor
 from torch.distributed._tensor import DTensor, Replicate, Shard
 from torch.distributed.device_mesh import DeviceMesh
+from typing_extensions import override
 
 from nemo.lightning import _strategy_lib
 from nemo.lightning.io.pl import MegatronCheckpointIO
@@ -46,6 +48,14 @@ class RestoreConfig:
     load_artifacts: bool = True
 
 
+class _MegatronBatchProgress(_BatchProgress):
+    @override
+    def load_state_dict(self, state_dict: dict) -> None:
+        ## in megatron, we want to start the batch progress over when
+        ## restoring from a checkpoint
+        return
+
+
 def setup_parallel_ranks(strategy: pl.strategies.Strategy):
     from megatron.core.model_parallel_config import ModelParallelConfig