Fix memory leak at loss func (#8868)

* PR #8803: Update embedding init prototype to match mc

Signed-off-by: Jaemin Choi <jaeminc@nvidia.com>

* PR #8810: Fix import of get_gpt_layer_ammo_spec

Signed-off-by: Jaemin Choi <jaeminc@nvidia.com>

* PR #8853: Fix memory leak at loss func

Signed-off-by: Jaemin Choi <jaeminc@nvidia.com>

---------

Signed-off-by: Jaemin Choi <jaeminc@nvidia.com>
Signed-off-by: Shriya Palsamudram <69161273+ShriyaPalsamudram@users.noreply.github.com>
Co-authored-by: Jaemin Choi <jaeminc@nvidia.com>
Co-authored-by: Eric Harper <complex451@gmail.com>
Co-authored-by: Shriya Palsamudram <69161273+ShriyaPalsamudram@users.noreply.github.com>
Co-authored-by: Pablo Garay <palenq@gmail.com>
Signed-off-by: Ao Tang <aot@nvidia.com>

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

@@ -367,6 +367,9 @@ def __init__(self, cfg: DictConfig, trainer: Trainer):
         self.log_train_loss = bool(int(os.getenv("NEMO_LOG_TRAIN_LOSS", 1)))
         self.log_memory_usage = bool(int(os.getenv("NEMO_LOG_MEMORY_USAGE", 0)))
         self.loss_broadcast_src_rank = None
+        self.return_output_tensors = cfg.data.get('return_output_tensors', False)
+        self.validation_drop_last = cfg.data.get('validation_drop_last', True)
+        self.sample_weight = cfg.data.get('sample_weight', 'token')
         self.validation_param_sync_overlap = self.cfg.get('validation_param_sync_overlap', False)
 
         self.inference_params = None
@@ -621,7 +624,7 @@ def fwd_bwd_step(self, dataloader_iter, forward_only, first_val_step=None):
 
         # only the last stages of the pipeline return losses
         if losses_reduced_per_micro_batch:
-            if (not forward_only) or self.cfg.data.get('validation_drop_last', True):
+            if (not forward_only) or self.validation_drop_last:
                 # average loss across micro batches
                 loss_tensors_list = [loss_reduced['avg'] for loss_reduced in losses_reduced_per_micro_batch]
                 loss_tensor = torch.concat(loss_tensors_list)
@@ -1136,10 +1139,9 @@ def fwd_output_and_loss_func(dataloader_iter, model, checkpoint_activations_all_
             def loss_func(output_tensor):
                 # Loss for a micro-batch (ub)
                 loss_for_ub = self.loss_func(batch['loss_mask'], batch['num_valid_tokens_in_ub'], output_tensor)
-                cp_size = self.cfg.get('context_parallel_size', 1)
-                if self.cfg.data.get(
-                    "return_output_tensors", False
-                ):  # TODO: need a better way to check if loss_func is returning more stuff than just loss... (@adithyare)
+                cp_size = parallel_state.get_context_parallel_world_size()
+                if self.return_output_tensors:
+                    # TODO: need a better way to check if loss_func is returning more stuff than just loss... (@adithyare)
                     loss_for_ub, q_hs, d_hs, pos_cs, neg_cs, diff_cs = loss_for_ub
                     reduced_loss = average_losses_across_data_parallel_group([loss_for_ub])
                     pos_cs = average_losses_across_data_parallel_group([pos_cs])
@@ -1156,15 +1158,14 @@ def loss_func(output_tensor):
                             'diff_cs': diff_cs,
                         },
                     )
-                elif validation_step and not self.cfg.data.get('validation_drop_last', True):
-                    sample_weight = self.cfg.data.get('sample_weight', 'token')
+                elif validation_step and not self.validation_drop_last:
                     num_valid_tokens_in_ub = batch['num_valid_tokens_in_ub']
                     if loss_for_ub.isnan():
                         assert batch['loss_mask'].count_nonzero() == 0, 'Got NaN loss with non-empty input'
                         loss_sum_for_ub = torch.zeros_like(loss_for_ub)
                         num_valid_tokens_in_ub = 0
                     else:
-                        if sample_weight == 'constant':
+                        if self.sample_weight == 'constant':
                             num_valid_tokens_in_ub = 1
                         loss_sum_for_ub = num_valid_tokens_in_ub * loss_for_ub
 
@@ -1296,7 +1297,7 @@ def validation_step(self, dataloader_iter, dataloader_idx=0):
     def on_validation_epoch_end(self):
         if parallel_state.is_pipeline_last_stage():
             # only the last pipeline parallel stages return loss with their batch size
-            if self.cfg.data.get('validation_drop_last', True):
+            if self.validation_drop_last:
                 averaged_loss = torch.stack(self.validation_step_outputs).mean()
             else:
                 # Compute the avg loss by total_loss across all samples / total number of samples
@@ -1534,7 +1535,7 @@ def setup_validation_data(self, cfg):
             )
 
             drop_last = True
-            if not self.cfg.data.get('validation_drop_last', True):
+            if not self.validation_drop_last:
                 logging.info(f'Drop last in validation dataset is set to False')
                 drop_last = False
             pad_samples_to_global_batch_size = False