Hi, I am trying to convert my model to onnx format with the help of this notebook this,
Now how can I create a config.json file for this?
nielsr
October 4, 2021, 9:18am
#2
Normally, if you save your model using the .save_pretrained() method, it will save both the model weights and a config.json file in the specified directory.
Yes, but this is a custom model that I have saved in pytorch style, since it consists of additional layers, is there anyway to generate confg.json file?
You need to subclass it to have the save_pretrained methods available. So instead of
class Mean_Pooling_Model(nn.Module):
use
from transformers.modeling_utils import PreTrainedModel
class Mean_Pooling_Model(PreTrainedModel):
It will add extra functionality on top of nn.Module.
exclude_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to count embedding and softmax operations.
Returns:
:obj:`int`: The number of floating-point operations.
"""
return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin):
r"""
Base class for all models.
:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods
for loading, downloading and saving models as well as a few methods common to all models to:
* resize the input embeddings,
* prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
Thank you I will try this!
Is it possible to generate the configuration file for already trained model , i.e weights stored in normal pytorch model.bin
1 Like
Use model.config.to_json() method to generate config.json
Did you end up finding a solution to getting a config.json from an already trained model?
Nope, I was not able to find a proper solution, I ended up writing the config.json manually
You should be able to just call
model.config.to_json_file("config.json")
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
is serialized to JSON file.
"""
cc @seanbenhur
That only works for models that are transformer native and not nn.Module/pytorch native, sadly.
What is your use-case that you are using Transformers but not Transformers models? If you want to use the HF Trainer alongside with your own PyTorch model, I recommended to subclass the relevant classes, similar to PretrainedModel
exclude_embeddings (`bool`, *optional*, defaults to `True`):
Whether or not to count embedding and softmax operations.
Returns:
`int`: The number of floating-point operations.
"""
return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin):
r"""
Base class for all models.
[`PreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common to all models to:
- resize the input embeddings,
- prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
And to use your own PretrainedConfig alongside of it.
I’m currently struggling with the same problem 