Update BaseMegatronSampler for compatibility with PTL's _BatchProgress

nemo/lightning/data.py

@@ -286,17 +286,16 @@ def __init__(
         )
 
     def __len__(self):
-        num_available_samples: int = self.total_samples - self.consumed_samples
         if self.global_batch_size is not None:
             if self.drop_last:
-                num_global_batches = num_available_samples // self.global_batch_size
+                num_global_batches = self.total_samples // self.global_batch_size
             else:
-                num_global_batches = (num_available_samples + self.global_batch_size - 1) // self.global_batch_size
+                num_global_batches = (self.total_samples + self.global_batch_size - 1) // self.global_batch_size
             # return len of dataloader in terms of micro batches to avoid discrepancy between len of dataloader and
             # num of batches fetched (as training step fetches in terms of micro batches)
             return num_global_batches * (self.global_batch_size // self.micro_batch_times_data_parallel_size)
         else:
-            return (num_available_samples - 1) // self.micro_batch_times_data_parallel_size + 1
+            return (self.total_samples - 1) // self.micro_batch_times_data_parallel_size + 1
 
     @abc.abstractmethod
     def __iter__(self): ...

nemo/lightning/pytorch/strategies/fsdp_strategy.py

@@ -35,7 +35,6 @@
 
 from nemo.lightning import io
 from nemo.lightning.pytorch.strategies.utils import (
-    _MegatronBatchProgress,
     ckpt_to_dir,
     create_checkpoint_io,
     fix_progress_bar,
@@ -74,15 +73,13 @@ 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:
@@ -95,11 +92,6 @@ 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

@@ -67,7 +67,6 @@
 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,
@@ -160,8 +159,6 @@ 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:
@@ -204,7 +201,6 @@ 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__(
@@ -245,7 +241,6 @@ 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
 
@@ -345,8 +340,6 @@ 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

@@ -25,12 +25,10 @@
 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
@@ -48,14 +46,6 @@ 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