Revert changes to batch sampler, swap to pretrained sampler

Signed-off-by: SeanNaren <[email protected]>

nemo/collections/nlp/data/language_modeling/megatron/megatron_batch_samplers.py

@@ -140,24 +140,23 @@ def __iter__(self):
 
 class MegatronPretrainingBatchSampler(BaseMegatronBatchSampler):
     def get_start_end_idx(self) -> Tuple[int, int]:
-        start_idx = self.data_parallel_rank * self.micro_batch_size
-        end_idx = start_idx + self.micro_batch_size
+        start_idx = self.data_parallel_rank * self._global_batch_size_on_this_data_parallel_rank
+        end_idx = start_idx + self._global_batch_size_on_this_data_parallel_rank
         return start_idx, end_idx
 
     def __iter__(self):
         batch = []
-
-        data_parallel_micro_batch_size = self.data_parallel_size * self.micro_batch_size
         # Last batch will be dropped if drop_last is not set False
         for idx in range(self.consumed_samples, self.total_samples):
             batch.append(idx)
-            if len(batch) == data_parallel_micro_batch_size:
+            if len(batch) == self._global_batch_size:
+                # start_idx, end_idx = self.get_start_end_idx()
                 indices = [
-                    batch[i]
-                    for i in range(self.data_parallel_rank, data_parallel_micro_batch_size, self.data_parallel_size)
+                    batch[i] for i in range(self.data_parallel_rank, self._global_batch_size, self.data_parallel_size,)
                 ]
-                assert len(indices) == self.micro_batch_size
+                assert len(indices) == self._global_batch_size_on_this_data_parallel_rank
                 yield indices
+                # yield batch[start_idx:end_idx]
                 batch = []
 
         # Check the last partial batch and see drop_last is set

nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py

@@ -23,9 +23,9 @@
 from pytorch_lightning.accelerators import CPUAccelerator
 from pytorch_lightning.trainer.trainer import Trainer
 
-from nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers import (
-    MegatronPretrainingBatchSampler,
-    MegatronPretrainingRandomBatchSampler,
+from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import (
+    MegatronPretrainingRandomSampler,
+    MegatronPretrainingSampler,
 )
 from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
 from nemo.collections.nlp.modules.common.megatron.build_model import build_model
@@ -803,7 +803,7 @@ def build_pretraining_data_loader(self, dataset, consumed_samples, num_workers):
         # Megatron sampler
         if hasattr(self._cfg.data, 'dataloader_type') and self._cfg.data.dataloader_type is not None:
             if self._cfg.data.dataloader_type == 'single':
-                batch_sampler = MegatronPretrainingBatchSampler(
+                batch_sampler = MegatronPretrainingSampler(
                     total_samples=len(dataset),
                     consumed_samples=consumed_samples,
                     micro_batch_size=self._cfg.micro_batch_size,
@@ -813,11 +813,11 @@ def build_pretraining_data_loader(self, dataset, consumed_samples, num_workers):
                     drop_last=self._cfg.get('drop_last', True),
                 )
             elif self._cfg.data.dataloader_type == 'cyclic':
-                batch_sampler = MegatronPretrainingRandomBatchSampler(
+                batch_sampler = MegatronPretrainingRandomSampler(
                     total_samples=len(dataset),
                     consumed_samples=consumed_samples,
                     micro_batch_size=self._cfg.micro_batch_size,
-                    global_batch_size=self._cffg.global_batch_size,
+                    global_batch_size=self._cfg.global_batch_size,
                     data_parallel_rank=parallel_state.get_data_parallel_rank(),
                     data_parallel_size=parallel_state.get_data_parallel_world_size(),
                     drop_last=self._cfg.get('drop_last', True),