1. fix bugs of DataSet.from_dataset 2. fix bugs of some tests 3. add to OneflowDriver.load_model

fastNLP/core/dataset/dataset.py

@@ -1048,7 +1048,7 @@ class DataSet:
         :param dataset 为实例化好的 huggingface Dataset 对象
         """
         from datasets import Dataset
-        if not isinstance(dataset, DataSet):
+        if not isinstance(dataset, Dataset):
             raise ValueError(f"Support huggingface dataset, but is {type(dataset)}!")
 
         data_dict = dataset.to_dict()

fastNLP/core/drivers/oneflow_driver/oneflow_driver.py

@@ -192,7 +192,8 @@ class OneflowDriver(Driver):
                                      f"`only_state_dict=False`")
         if not isinstance(res, dict):
             res = res.state_dict()
-        model.load_state_dict(res)
+        _strict = kwargs.get("strict")
+        model.load_state_dict(res, _strict)
 
     @rank_zero_call
     def save_checkpoint(self, folder: Path, states: Dict, dataloader, only_state_dict: bool = True, should_save_model: bool = True, **kwargs):

tests/core/collators/test_pakcer_unpacker.py

@@ -1,5 +1,5 @@
 
-from fastNLP.core.collators.packer_unpacker import *
+from fastNLP.core.collators.packer_unpacker import MappingPackerUnpacker, NestedMappingPackerUnpacker, SequencePackerUnpacker
 
 
 def test_unpack_batch_mapping():

tests/core/drivers/torch_driver/test_deepspeed.py

@@ -97,6 +97,7 @@ def dataloader_with_randomsampler(dataset, batch_size, shuffle, drop_last, seed=
 #     if dist.is_initialized():
 #         dist.destroy_process_group()
 
+@pytest.mark.deepspeed
 @magic_argv_env_context
 def test_multi_optimizers():
     torch_model = TorchNormalModel_Classification_1(10, 10)