Unable to serialize/save TF2.0 RobertaSequenceClassification model to saved model format
# 🐛 Bug
I am getting an error while trying to serialize/save TF2.0 RobertaSequenceClassification Keras model to saved model format. I do not see this issue with Bert or Albert model architecture. Please see below for my test script that can be used to reproduce this issue.
## Information

Model I am using (Bert, XLNet ...):  Roberta

Language I am using the model on (English, Chinese ...): English

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

```python
import tensorflow as tf
from transformers import *
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForSequenceClassification.from_pretrained('roberta-base')

##########Uncomment the following 2 lines for testing with BERT ############
#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
#model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')

input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  
outputs = model(input_ids)
logits = outputs[0]
tf_saved_model_path= "/tmp/saved_model/"
tf.keras.models.save_model(model, tf_saved_model_path, overwrite=True, include_optimizer=False, save_format='tf')
```

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)
I need to export/serialize a TF Keras model to TF saved model format 

## To reproduce

Steps to reproduce the behavior:

1. Run the script pasted above to reproduce the issue with Roberta
2. Uncomment the 2 lines as mentioned in the script for using Bert (no error seen with Bert)

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***Stack Trace for Roberta***
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-87e63ee0b3ac> in <module>
     15 
     16 tf_saved_model_path= "/tmp/saved_model/"
---> 17 tf.keras.models.save_model(model, tf_saved_model_path, overwrite=True, include_optimizer=False, save_format='tf')

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    136   else:
    137     saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 138                           signatures, options)
    139 
    140 

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
     76     # we use the default replica context here.
     77     with distribution_strategy_context._get_default_replica_context():  # pylint: disable=protected-access
---> 78       save_lib.save(model, filepath, signatures, options)
     79 
     80   if not include_optimizer:

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in save(obj, export_dir, signatures, options)
    949 
    950   _, exported_graph, object_saver, asset_info = _build_meta_graph(
--> 951       obj, export_dir, signatures, options, meta_graph_def)
    952   saved_model.saved_model_schema_version = constants.SAVED_MODEL_SCHEMA_VERSION
    953 

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _build_meta_graph(obj, export_dir, signatures, options, meta_graph_def)
   1035 
   1036   object_graph_proto = _serialize_object_graph(saveable_view,
-> 1037                                                asset_info.asset_index)
   1038   meta_graph_def.object_graph_def.CopyFrom(object_graph_proto)
   1039 

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _serialize_object_graph(saveable_view, asset_file_def_index)
    695   for obj, obj_proto in zip(saveable_view.nodes, proto.nodes):
    696     _write_object_proto(obj, obj_proto, asset_file_def_index,
--> 697                         saveable_view.function_name_map)
    698   return proto
    699 

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _write_object_proto(obj, proto, asset_file_def_index, function_name_map)
    735           version=versions_pb2.VersionDef(
    736               producer=1, min_consumer=1, bad_consumers=[]),
--> 737           metadata=obj._tracking_metadata)
    738       # pylint:enable=protected-access
    739     proto.user_object.CopyFrom(registered_type_proto)

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _tracking_metadata(self)
   2727   @property
   2728   def _tracking_metadata(self):
-> 2729     return self._trackable_saved_model_saver.tracking_metadata
   2730 
   2731   def _list_extra_dependencies_for_serialization(self, serialization_cache):

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py in tracking_metadata(self)
     52     # TODO(kathywu): check that serialized JSON can be loaded (e.g., if an
     53     # object is in the python property)
---> 54     return json_utils.Encoder().encode(self.python_properties)
     55 
     56   def list_extra_dependencies_for_serialization(self, serialization_cache):

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/json_utils.py in encode(self, obj)
     42 
     43   def encode(self, obj):
---> 44     return super(Encoder, self).encode(_encode_tuple(obj))
     45 
     46 

/usr/local/opt/pyenv/versions/3.6.7/lib/python3.6/json/encoder.py in encode(self, o)
    197         # exceptions aren't as detailed.  The list call should be roughly
    198         # equivalent to the PySequence_Fast that ''.join() would do.
--> 199         chunks = self.iterencode(o, _one_shot=True)
    200         if not isinstance(chunks, (list, tuple)):
    201             chunks = list(chunks)

/usr/local/opt/pyenv/versions/3.6.7/lib/python3.6/json/encoder.py in iterencode(self, o, _one_shot)
    255                 self.key_separator, self.item_separator, self.sort_keys,
    256                 self.skipkeys, _one_shot)
--> 257         return _iterencode(o, 0)
    258 
    259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/json_utils.py in default(self, obj)
     39       items = obj.as_list() if obj.rank is not None else None
     40       return {'class_name': 'TensorShape', 'items': items}
---> 41     return serialization.get_json_type(obj)
     42 
     43   def encode(self, obj):

~/huggingface/transformers/env/lib/python3.6/site-packages/tensorflow/python/util/serialization.py in get_json_type(obj)
     74     return obj.__wrapped__
     75 
---> 76   raise TypeError('Not JSON Serializable:', obj)

TypeError: ('Not JSON Serializable:', RobertaConfig {
  "_num_labels": 2,
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bad_words_ids": null,
  "bos_token_id": 0,
  "decoder_start_token_id": null,
  "do_sample": false,
  "early_stopping": false,
  "eos_token_id": 2,
  "finetuning_task": null,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "is_decoder": false,
  "is_encoder_decoder": false,
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1
  },
  "layer_norm_eps": 1e-05,
  "length_penalty": 1.0,
  "max_length": 20,
  "max_position_embeddings": 514,
  "min_length": 0,
  "model_type": "roberta",
  "no_repeat_ngram_size": 0,
  "num_attention_heads": 12,
  "num_beams": 1,
  "num_hidden_layers": 12,
  "num_return_sequences": 1,
  "output_attentions": false,
  "output_hidden_states": false,
  "output_past": true,
  "pad_token_id": 1,
  "prefix": null,
  "pruned_heads": {},
  "repetition_penalty": 1.0,
  "task_specific_params": null,
  "temperature": 1.0,
  "top_k": 50,
  "top_p": 1.0,
  "torchscript": false,
  "type_vocab_size": 1,
  "use_bfloat16": false,
  "vocab_size": 50265
}
)


## Expected behavior
There should be no error when saving/serializing the TF Keras Model for Roberta. I do not see any error with Bert or Albert.
<!-- A clear and concise description of what you would expect to happen. -->

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.7.0
- Platform: Darwin-19.2.0-x86_64-i386-64bit
- Python version: 3.6.7
- PyTorch version (GPU?): 1.4.0 (False)
- Tensorflow version (GPU?): 2.2.0-rc1 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

I also see the same issue with TF 2.1.0.


do you solve it ?<|||||>> do you solve it ?

Not yet. Let me know if you are able to find the fix.<|||||>yes, I have the same issue. I inspected the tensorboard graph and there is no config operation or anything of that sort. I also tried to save it with a manually defined signature, which didn't work either.

Workaround for now is to use the `save_pretrained`. Is there a way to convert pretrained to TF2.0 saved_model?<|||||>We also see a similar issue in transformers 2.9.1.

Also curious if people have a workaround or solution to use with TF model serving?<|||||>FYI: The cause for this issue is documented in #4709. The current workaround/fix is to remove `config` from the call to this function:

https://github.com/huggingface/transformers/blob/d6a677b14bcfd56b22fafeb212a27c6068886e07/src/transformers/modeling_tf_roberta.py#L331 

This prevents `trainable` from being set to `config` in the initialization function of `tf.keras.layers.Layer`. Then the model will be serialized correctly instead of failing to serialize the `trainable` value later.<|||||>Hello!

The saving in saved model format is not implemented yet, but it is planned to work on it :) I will reply here once there will be something about this. Sorry for the inconvenience.<|||||>I think this issue can be closed now due to PR #4884.

The sample code in the issue runs successfully in `master`.

<img width="1010" alt="Screenshot 2020-06-10 at 12 09 12" src="https://user-images.githubusercontent.com/5602332/84255742-5363d000-ab13-11ea-822c-72da88399995.png">
<|||||>Great, thanks for solving the issue @harkous <|||||>@harkous  Thanks for the work.
I have still the issue of the author even with your code and upgrade the last version of transformer with pip install transformers --upgrade , is it still working with you ?<|||||>You have to install transformers from the master branch. The fix has not been released yet.<|||||>Hello ! 

It seems that I have a similar issue with a model based on Camembert when trying to save my model with : 

`model.save("model",save_format='tf')`

Give me : 

```
TypeError: ('Not JSON Serializable:', CamembertConfig {
  "architectures": [
    "CamembertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 5,
  "eos_token_id": 6,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "camembert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "output_past": true,
  "pad_token_id": 1,
  "type_vocab_size": 1,
  "vocab_size": 32005
}
)
```

At first with transformers 2.11.0 but also after upgrading to 3.3 (with TF 2.3)

I can give a code snippet to reproduce if necessary and Custom Model construction can be found here : https://github.com/MAIF/melusine/blob/master/melusine/models/neural_architectures.py#L312


**Complete Stack Trace**
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-32-ae508742561b> in <module>
----> 1 model.model.save("test4",save_format='tf')

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1977     """
   1978     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-> 1979                     signatures, options)
   1980 
   1981   def save_weights(self,

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    132   else:
    133     saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 134                           signatures, options)
    135 
    136 

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
     78     # we use the default replica context here.
     79     with distribution_strategy_context._get_default_replica_context():  # pylint: disable=protected-access
---> 80       save_lib.save(model, filepath, signatures, options)
     81 
     82   if not include_optimizer:

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in save(obj, export_dir, signatures, options)
    974 
    975   _, exported_graph, object_saver, asset_info = _build_meta_graph(
--> 976       obj, export_dir, signatures, options, meta_graph_def)
    977   saved_model.saved_model_schema_version = constants.SAVED_MODEL_SCHEMA_VERSION
    978 

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _build_meta_graph(obj, export_dir, signatures, options, meta_graph_def)
   1074 
   1075   object_graph_proto = _serialize_object_graph(saveable_view,
-> 1076                                                asset_info.asset_index)
   1077   meta_graph_def.object_graph_def.CopyFrom(object_graph_proto)
   1078 

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _serialize_object_graph(saveable_view, asset_file_def_index)
    719   for obj, obj_proto in zip(saveable_view.nodes, proto.nodes):
    720     _write_object_proto(obj, obj_proto, asset_file_def_index,
--> 721                         saveable_view.function_name_map)
    722   return proto
    723 

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/saved_model/save.py in _write_object_proto(obj, proto, asset_file_def_index, function_name_map)
    759           version=versions_pb2.VersionDef(
    760               producer=1, min_consumer=1, bad_consumers=[]),
--> 761           metadata=obj._tracking_metadata)
    762       # pylint:enable=protected-access
    763     proto.user_object.CopyFrom(registered_type_proto)

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _tracking_metadata(self)
   3009   @property
   3010   def _tracking_metadata(self):
-> 3011     return self._trackable_saved_model_saver.tracking_metadata
   3012 
   3013   def _list_extra_dependencies_for_serialization(self, serialization_cache):

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py in tracking_metadata(self)
     52     # TODO(kathywu): check that serialized JSON can be loaded (e.g., if an
     53     # object is in the python property)
---> 54     return json_utils.Encoder().encode(self.python_properties)
     55 
     56   def list_extra_dependencies_for_serialization(self, serialization_cache):

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/json_utils.py in encode(self, obj)
     42 
     43   def encode(self, obj):
---> 44     return super(Encoder, self).encode(_encode_tuple(obj))
     45 
     46 

~/.conda/envs/emails_maif_vie/lib/python3.6/json/encoder.py in encode(self, o)
    197         # exceptions aren't as detailed.  The list call should be roughly
    198         # equivalent to the PySequence_Fast that ''.join() would do.
--> 199         chunks = self.iterencode(o, _one_shot=True)
    200         if not isinstance(chunks, (list, tuple)):
    201             chunks = list(chunks)

~/.conda/envs/emails_maif_vie/lib/python3.6/json/encoder.py in iterencode(self, o, _one_shot)
    255                 self.key_separator, self.item_separator, self.sort_keys,
    256                 self.skipkeys, _one_shot)
--> 257         return _iterencode(o, 0)
    258 
    259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/json_utils.py in default(self, obj)
     39       items = obj.as_list() if obj.rank is not None else None
     40       return {'class_name': 'TensorShape', 'items': items}
---> 41     return serialization.get_json_type(obj)
     42 
     43   def encode(self, obj):

~/.conda/envs/emails_maif_vie/lib/python3.6/site-packages/tensorflow/python/util/serialization.py in get_json_type(obj)
     70     return obj.__wrapped__
     71 
---> 72   raise TypeError('Not JSON Serializable:', obj)

TypeError: ('Not JSON Serializable:', CamembertConfig {
  "architectures": [
    "CamembertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 5,
  "eos_token_id": 6,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "camembert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "output_past": true,
  "pad_token_id": 1,
  "type_vocab_size": 1,
  "vocab_size": 32005
}
)
```<|||||>Please open another issue with a code snippet to make us able to reproduce your problem.