TypeError: Object of type method is not JSON serializable
XumingYE opened this issue · comments
When I inference this model based on the code in Hugging Face. It shows that TypeError: Object of type method is not JSON serializable
The whole error output is as the follows:
[5](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4) from PIL import Image
[7](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6) pretrained_ckpt = '/mnt/raid5/sum/MSMO/original/models/mplug-owl-llama-7b'
----> [8](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7) model = MplugOwlForConditionalGeneration.from_pretrained(
[9](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8) pretrained_ckpt,
[10](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9) torch_dtype=torch.bfloat16,
[11](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10) )
[12](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11) image_processor = MplugOwlImageProcessor.from_pretrained(pretrained_ckpt)
[13](vscode-notebook-cell://ssh-remote%2B10.122.7.49/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/infer.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=12) tokenizer = MplugOwlTokenizer.from_pretrained(pretrained_ckpt)
File [~/anaconda3/envs/sent_train/lib/python3.8/site-packages/transformers/modeling_utils.py:2325](https://vscode-remote+ssh-002dremote-002b10-002e122-002e7-002e49.vscode-resource.vscode-cdn.net/mnt/raid5/sum/chenlin/FITE/mPLUG-Owl/~/anaconda3/envs/sent_train/lib/python3.8/site-packages/transformers/modeling_utils.py:2325), in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
2323 if not isinstance(config, PretrainedConfig):
2324 config_path = config if config is not None else pretrained_model_name_or_path
-> 2325 config, model_kwargs = cls.config_class.from_pretrained(
2326 config_path,
2327 cache_dir=cache_dir,
2328 return_unused_kwargs=True,
2329 force_download=force_download,
2330 resume_download=resume_download,
2331 proxies=proxies,
2332 local_files_only=local_files_only,
2333 token=token,
2334 revision=revision,
...
178 """
--> 179 raise TypeError(f'Object of type {o.__class__.__name__} '
180 f'is not JSON serializable')
TypeError: Object of type method is not JSON serializableI use the instruction on the hugging face.
Why did this happen? And Are there any other ways to infer this model? Thanks a lot.
I had this problem, this is how I fixed it. In configuration_mplug_owl.py add the following method to the MPlugOwlConfig class:
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = PretrainedConfig().to_dict()
# get class specific config dict
class_config_dict = self.__class__().to_dict() if not self.is_composition else {}
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if (
isinstance(getattr(self, key, None), PretrainedConfig)
and key in class_config_dict
and isinstance(class_config_dict[key], dict)
):
# For nested configs we need to clean the diff recursively
diff = recursive_diff_dict(value, class_config_dict[key], config_obj=getattr(self, key, None))
if "model_type" in value:
# Needs to be set even if it's not in the diff
diff["model_type"] = value["model_type"]
if len(diff) > 0:
serializable_config_dict[key] = diff
elif callable(value):
# We ignore function parameters
continue
elif (
key not in default_config_dict
or key == "transformers_version"
or value != default_config_dict[key]
or (key in class_config_dict and value != class_config_dict[key])
):
serializable_config_dict[key] = value
if hasattr(self, "quantization_config"):
serializable_config_dict["quantization_config"] = (
self.quantization_config.to_dict()
if not isinstance(self.quantization_config, dict)
else self.quantization_config
)
self.dict_torch_dtype_to_str(serializable_config_dict)
if "_flash_attn_2_enabled" in serializable_config_dict:
del serializable_config_dict["_flash_attn_2_enabled"]
return serializable_config_dict
I had this problem, this is how I fixed it. In
configuration_mplug_owl.pyadd the following method to theMPlugOwlConfigclass:def to_diff_dict(self) -> Dict[str, Any]: """ Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance, """ config_dict = self.to_dict() # get the default config dict default_config_dict = PretrainedConfig().to_dict() # get class specific config dict class_config_dict = self.__class__().to_dict() if not self.is_composition else {} serializable_config_dict = {} # only serialize values that differ from the default config for key, value in config_dict.items(): if ( isinstance(getattr(self, key, None), PretrainedConfig) and key in class_config_dict and isinstance(class_config_dict[key], dict) ): # For nested configs we need to clean the diff recursively diff = recursive_diff_dict(value, class_config_dict[key], config_obj=getattr(self, key, None)) if "model_type" in value: # Needs to be set even if it's not in the diff diff["model_type"] = value["model_type"] if len(diff) > 0: serializable_config_dict[key] = diff elif callable(value): # We ignore function parameters continue elif ( key not in default_config_dict or key == "transformers_version" or value != default_config_dict[key] or (key in class_config_dict and value != class_config_dict[key]) ): serializable_config_dict[key] = value if hasattr(self, "quantization_config"): serializable_config_dict["quantization_config"] = ( self.quantization_config.to_dict() if not isinstance(self.quantization_config, dict) else self.quantization_config ) self.dict_torch_dtype_to_str(serializable_config_dict) if "_flash_attn_2_enabled" in serializable_config_dict: del serializable_config_dict["_flash_attn_2_enabled"] return serializable_config_dict
where is recursive_diff_dict function？
The reason for this error is that your transformers package is not the 4.29.1 version used by the author.
try:
pip uninstall transformers pip install transformers==4.29.1
The method given by the friend above is equivalent to a patch for transformers, so it is better to use the correct version as much as possible.
As for the ‘’recursive_diff_dict‘’ function I also give it here to help people who want to use the patch method:
def recursive_diff_dict(self, dict_a, dict_b, config_obj=None): """ Helper function to recursively take the diff between two nested dictionaries. The resulting diff only contains the values fromdict_athat are different from values indict_b. """ diff = {} default = config_obj.__class__().to_dict() if config_obj is not None else {} for key, value in dict_a.items(): obj_value = getattr(config_obj, str(key), None) if isinstance(obj_value, PretrainedConfig) and key in dict_b and isinstance(dict_b[key], dict): diff_value = self.recursive_diff_dict(value, dict_b[key], config_obj=obj_value) if len(diff_value) > 0: diff[key] = diff_value elif key not in dict_b or value != dict_b[key] or key not in default or value != default[key]: diff[key] = value return diff
Likewise, you need to put this function in class MplugOwlConfig and, where the function is used, add "self." before the function name.