[hotfix]fit to refactored dataloader api, and remove tensor shape in config file.

image/vision_transformer/hybrid_parallel/configs/vit_1d_tp2_pp2.py

@@ -34,4 +34,3 @@
 
 # pipeline config
 NUM_MICRO_BATCHES = parallel['pipeline']
-TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LENGTH, HIDDEN_SIZE)

image/vision_transformer/hybrid_parallel/train_with_engine.py

@@ -71,8 +71,8 @@ def train_imagenet():
 
     # create dataloaders
     root = os.environ['DATA']
-    train_dataloader, test_dataloader = build_dali_imagenet(root, rand_augment=False)
-
+    train_dataloader, test_dataloader = build_dali_imagenet(root, train_batch_size=gpc.config.BATCH_SIZE, \
+                                                            test_batch_size=gpc.config.BATCH_SIZE)
     # create loss function
     criterion = CrossEntropyLoss(label_smoothing=0.1)
 
@@ -92,7 +92,6 @@ def train_imagenet():
                                                                          test_dataloader=test_dataloader)
 
     logger.info("Engine is built", ranks=[0])
-
     # create schedule
     schedule = None
     tensor_shape = getattr(gpc.config, 'TENSOR_SHAPE', None)

image/vision_transformer/hybrid_parallel/train_with_trainer.py

@@ -66,7 +66,8 @@ def train_imagenet():
 
     # create dataloader
     root = os.environ['DATA']
-    train_dataloader, test_dataloader = build_dali_imagenet(root, rand_augment=False)
+    train_dataloader, test_dataloader = build_dali_imagenet(root, train_batch_size=gpc.config.BATCH_SIZE, \
+                                                            test_batch_size=gpc.config.BATCH_SIZE)
 
     # create loss function
     criterion = CrossEntropyLoss(label_smoothing=0.1)

language/gpt/gpt2_configs/gpt2_pp1d.py

@@ -5,7 +5,6 @@
 from colossalai.amp import AMP_TYPE
 import torch
 
-
 BATCH_SIZE = 8
 NUM_EPOCHS = 60
 SEQ_LEN = 1024
@@ -14,17 +13,11 @@
 HIDDEN_SIZE = 768
 PIPELINE = 2
 TENSOR_PARALLEL = 2
-MODE  = '1d'
-TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LEN, HIDDEN_SIZE)
+MODE = '1d'
 
-fp16 = dict(
-    mode=AMP_TYPE.NAIVE
-)
+fp16 = dict(mode=AMP_TYPE.NAIVE)
 
-parallel = dict(
-    pipeline=PIPELINE,
-    tensor=dict(mode=MODE, size=TENSOR_PARALLEL)
-)
+parallel = dict(pipeline=PIPELINE, tensor=dict(mode=MODE, size=TENSOR_PARALLEL))
 
 optimizer = dict(
     type=Adam,

language/gpt/train_gpt.py

@@ -141,8 +141,7 @@ def mask_function(attention_mask=None):
                 test_interval=1,
                 hooks=hook_list,
                 display_progress=True,
-                return_output_label=False,
-                max_steps=5)
+                return_output_label=False)
 
 
 if __name__ == '__main__':