[NeMo-UX] Use single instance of loss reductions in GPTModel (#9801) (#…

…9861)

* Use single instance of loss reductions



* Apply isort and black reformatting



* Refactor



---------

Signed-off-by: Hemil Desai <hemild@nvidia.com>
Signed-off-by: hemildesai <hemildesai@users.noreply.github.com>
Co-authored-by: Hemil Desai <hemild@nvidia.com>
Co-authored-by: hemildesai <hemildesai@users.noreply.github.com>

nemo/collections/llm/gpt/model/base.py

@@ -160,6 +160,8 @@ def __init__(
         self.optim = optim or MegatronOptimizerModule(config=OptimizerConfig(lr=1e-4, use_distributed_optimizer=True))
         self.optim.connect(self)  # This will bind the `configure_optimizers` method
         self.model_transform = model_transform
+        self._training_loss_reduction = None
+        self._validation_loss_reduction = None
 
     def configure_model(self) -> None:
         if not hasattr(self, "module"):
@@ -200,11 +202,19 @@ def validation_step(self, batch, batch_idx=None) -> torch.Tensor:
 
         return self.forward_step(batch)
 
+    @property
     def training_loss_reduction(self) -> MaskedTokenLossReduction:
-        return MaskedTokenLossReduction()
+        if not self._training_loss_reduction:
+            self._training_loss_reduction = MaskedTokenLossReduction()
 
+        return self._training_loss_reduction
+
+    @property
     def validation_loss_reduction(self) -> MaskedTokenLossReduction:
-        return MaskedTokenLossReduction(validation_step=True)
+        if not self._validation_loss_reduction:
+            self._validation_loss_reduction = MaskedTokenLossReduction(validation_step=True)
+
+        return self._validation_loss_reduction
 
 
 def get_batch_on_this_context_parallel_rank(batch):