fix bert unit test

tests/components_to_test/bert.py

@@ -1,9 +1,9 @@
 import torch
 import transformers
-from transformers import BertConfig, BertForSequenceClassification
 from packaging import version
-
 from torch.utils.data import SequentialSampler
+from transformers import BertConfig, BertForSequenceClassification
+
 from .registry import non_distributed_component_funcs
 
 
@@ -39,14 +39,14 @@ def get_training_components():
     num_layer = 2
 
     def bert_model_builder(checkpoint):
-        config = BertConfig(
-            gradient_checkpointing=checkpoint,
+        config = BertConfig(gradient_checkpointing=checkpoint,
                             hidden_size=hidden_dim,
                             intermediate_size=hidden_dim * 4,
                             num_attention_heads=num_head,
                             max_position_embeddings=sequence_length,
                             num_hidden_layers=num_layer,
-        )
+                            hidden_dropout_prob=0.,
+                            attention_probs_dropout_prob=0.)
         print('building BertForSequenceClassification model')
 
         # adapting huggingface BertForSequenceClassification for single unitest calling interface

tests/test_zero_data_parallel/test_shard_model_v2.py

@@ -13,6 +13,7 @@ from colossalai.utils import free_port
 from colossalai.zero.shard_utils.tensor_shard_strategy import \
     TensorShardStrategy
 from colossalai.zero.sharded_model import ShardedModelV2
+from colossalai.zero.sharded_model._zero3_utils import cast_tensor_to_fp16
 from tests.components_to_test.registry import non_distributed_component_funcs
 from torch.nn.parallel import DistributedDataParallel as DDP
 
@@ -45,8 +46,7 @@ def run_fwd_bwd_no_criterion(model, data, label, enable_autocast=False):
 def run_dist(rank, world_size, port):
     colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
-    test_models = ['bert']
-    # repeated_computed_layers resnet18
+    test_models = ['repeated_computed_layers', 'resnet18', 'bert']
     shard_strategy = TensorShardStrategy()
     for model_name in test_models:
         get_components_func = non_distributed_component_funcs.get_callable(model_name)
@@ -65,8 +65,7 @@ def run_dist(rank, world_size, port):
                 run_fwd_bwd_no_criterion(model, data, label, False)
                 run_fwd_bwd_no_criterion(zero_model, data, label, False)
             else:
-                # FIXME() data can be interger!
-                data, label = data.half().cuda(), label.cuda()
+                data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
                 run_fwd_bwd(model, data, label, criterion, False)
                 run_fwd_bwd(zero_model, data, label, criterion, False)
 
@@ -76,7 +75,6 @@ def run_dist(rank, world_size, port):
                 check_grads(model, zero_model, loose=True)
 
 
-@pytest.mark.skip(reason="Under development")
 @pytest.mark.dist
 @pytest.mark.parametrize("world_size", [1, 2, 4])
 def test_shard_model_v2(world_size):