set use local dataset.

lit_module.py

@@ -8,9 +8,8 @@ import torchmetrics
 from utils import init_model
 from custom_models.gpt2.modeling_gpt2 import GPT2LMHeadModel
 
-from transformers import (
-    AutoConfig
-)
+from transformers import AutoConfig
+
 
 class LitModule(pl.LightningModule):
     def __init__(
@@ -33,7 +32,7 @@ class LitModule(pl.LightningModule):
         self.use_tril_attention_mask = use_tril_attention_mask
         self.metric_loss = torchmetrics.MeanMetric()
         self.metric_accuracy = torchmetrics.Accuracy(
-            task='multiclass',
+            task="multiclass",
             num_classes=self.llm.config.vocab_size,
         )
 
@@ -45,17 +44,17 @@ class LitModule(pl.LightningModule):
         return matrix
 
     def register_core_module(self, module: torch.nn.Module) -> torch.nn.Module:
-        object.__setattr__(self, '__core_module__', module)
+        object.__setattr__(self, "__core_module__", module)
         return module
 
     def training_step(self, batch: Dict[str, torch.Tensor], batch_idx):
-        batch_size, block_size = batch['input_ids'].shape
+        batch_size, block_size = batch["input_ids"].shape
         if self.use_tril_attention_mask:
-            batch['attention_mask'] = self.get_batch_tril_matrix(block_size, batch_size=batch_size).to(self.device)
+            batch["attention_mask"] = self.get_batch_tril_matrix(block_size, batch_size=batch_size).to(self.device)
         outputs = self.llm(**batch, return_dict=True)
         loss = outputs.loss
 
-        self.log('train_loss', loss, rank_zero_only=True)
+        self.log("train_loss", loss, rank_zero_only=True)
 
         return loss
 
@@ -63,7 +62,7 @@ class LitModule(pl.LightningModule):
         outputs = self.llm(**batch, return_dict=True)
         loss = outputs.loss
         logits = outputs.logits[..., :-1, :]
-        labels = batch['labels'][..., 1:]
+        labels = batch["labels"][..., 1:]
 
         self.metric_loss.update(loss)
 
@@ -71,8 +70,8 @@ class LitModule(pl.LightningModule):
         self.metric_accuracy.update(logits[label_mask], labels[label_mask])
 
     def on_validation_epoch_end(self) -> None:
-        self.log('val_loss', self.metric_loss, rank_zero_only=True)
-        self.log('accuracy', self.metric_accuracy, rank_zero_only=True)
+        self.log("val_loss", self.metric_loss, rank_zero_only=True)
+        self.log("accuracy", self.metric_accuracy, rank_zero_only=True)
 
     def configure_optimizers(self):
         strategy = self.trainer.strategy
@@ -92,15 +91,15 @@ class LitModule(pl.LightningModule):
 
     def configure_callbacks(self):
         checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            monitor='accuracy',
-            mode='max',
-            filename='{epoch:02d}-{accuracy:.4f}',
+            monitor="accuracy",
+            mode="max",
+            filename="{epoch:02d}-{accuracy:.4f}",
         )
         early_stop_callback = pl.callbacks.EarlyStopping(
-            monitor='accuracy',
+            monitor="accuracy",
             min_delta=0.001,
             patience=3,
-            mode='max',
+            mode="max",
             stopping_threshold=1,
         )
         return [checkpoint_callback, early_stop_callback]

lit_train.py

@@ -187,9 +187,7 @@ if __name__ == "__main__":
     val_dataset_list = []
     for dataset_name in args.dataset_name:
         dataset_args = dataset_name.split(":")
-        raw_dataset = datasets.load_dataset(
-            "json", data_files="/home/colin/develop/dataset/liwu/MNBVC/wiki/20230197/0.jsonl.gz"
-        )
+        raw_dataset = datasets.load_dataset("json", data_files="./dataset/58.jsonl.gz")
         # raw_dataset = datasets.load_dataset(*dataset_args)
         train_dataset, val_dataset = split_raw_dataset(raw_dataset)
         train_dataset = process_dataset(train_dataset, tokenizer)