fix pretrianing data sizes and weights (#9627)

Signed-off-by: Chen Cui <chcui@nvidia.com>

nemo/collections/llm/gpt/data/pre_training.py

@@ -42,6 +42,9 @@ def __init__(
             paths = [paths]
         if weights is not None:
             assert len(weights) == len(paths)
+            if len(weights) == 1:
+                # weights must be None if there is only one dataset
+                weights = None
 
         self.paths = paths
         self.weights = weights
@@ -90,7 +93,7 @@ def setup(self, stage: str = "") -> None:
 
         if self.trainer.limit_val_batches <= 1.0 and isinstance(self.trainer.limit_val_batches, float):
             # This is to make sure we only have one epoch on every validation iteration
-            num_val_samples = None
+            num_val_samples = None if self.weights is None else 1
 
         train_valid_test_num_samples = [num_train_samples, num_val_samples, num_test_samples]
         self._train_ds, self._validation_ds, self._test_ds = BlendedMegatronDatasetBuilder(