sperate train and val dataset.

wit/lit_train.py

@@ -26,7 +26,6 @@ dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki"]
 dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki/20230198/58.jsonl.gz"]
 train_batch_size = 256
 val_batch_size = 16
-limit_val_batches = 128
 num_proc = 8
 max_epochs = 1000
 strategy = "fsdp"
@@ -35,10 +34,10 @@ seed = 42
 
 
 class SpecialDataset(Dataset):
-    def __init__(self, size=65536):
+    def __init__(self, start, end, size=65536):
         self.size = size
         self.features = []
-        a = torch.randint(0, 1024, [size])
+        a = torch.randint(start, end, [size])
         self.data = torch.stack([a, a * 2, a * 3, a * 4]).permute(1, 0)
 
     def __len__(self):
@@ -144,8 +143,8 @@ if __name__ == "__main__":
     train_dataset = ConcatDataset(train_dataset_list)
     val_dataset = ConcatDataset(val_dataset_list)
 
-    train_dataset = SpecialDataset()
-    val_dataset = SpecialDataset()
+    train_dataset = SpecialDataset(0, 1000, 65536)
+    val_dataset = SpecialDataset(1000, 1024, 1024)
 
     train_dataloader = DataLoader(
         train_dataset,
@@ -166,13 +165,7 @@ if __name__ == "__main__":
 
     torch.set_float32_matmul_precision("medium")
     precision = precision
-    lit_trainer = pl.Trainer(
-        accelerator="gpu",
-        precision=precision,
-        strategy=strategy,
-        max_epochs=max_epochs,
-        limit_val_batches=limit_val_batches,
-    )
+    lit_trainer = pl.Trainer(accelerator="gpu", precision=precision, strategy=strategy, max_epochs=max_epochs)
     lit_trainer.fit(
         lit_module,
         train_dataloaders=train_dataloader,