[tutorial] removed huggingface model warning (#1925)

examples/tutorial/opt/opt/run_clm.py

@@ -30,24 +30,13 @@ from itertools import chain
 import datasets
 import torch
 import torch.distributed as dist
+import transformers
 from accelerate.utils import set_seed
 from context import barrier_context
 from datasets import load_dataset
 from packaging import version
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
-
-import colossalai
-import transformers
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.nn.optimizer import HybridAdam
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.tensor import ProcessGroup
-from colossalai.utils import get_current_device, get_dataloader
-from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.zero import ZeroOptimizer
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -61,6 +50,17 @@ from transformers import (
 )
 from transformers.utils.versions import require_version
 
+import colossalai
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.tensor import ProcessGroup
+from colossalai.utils import get_current_device, get_dataloader
+from colossalai.utils.model.colo_init_context import ColoInitContext
+from colossalai.zero import ZeroOptimizer
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
 MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
@@ -544,7 +544,7 @@ def main():
         model.train()
         for step, batch in enumerate(train_dataloader):
             batch = {k: v.cuda() for k, v in batch.items()}
-            outputs = model(**batch)
+            outputs = model(use_cache=False, **batch)
             loss = outputs['loss']
             optimizer.backward(loss)