Unable to serialize/save TF2.0 Bert model
Author: Douboo
Pull request: no
Labels: name: TensorFlow

name: Should Fix

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
https://colab.research.google.com/gist/Douboo/fccd6bcda2e098b10b1c7490f2d8bbf3/untitled3.ipynb#scrollTo=yW4t3uQxjhy1
- `transformers` version: 3.1.0
- Platform: colab
- Python version: 3.7.0
- Tensorflow version (GPU?): 2.3.0
- Using GPU in script?: yes

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
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 Speed and Memory Benchmarks: @patrickvonplaten
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 Translation: @sshleifer
 Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
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 Longformer/Reformer: @patrickvonplaten
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 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->
@jplu

## Information

Model I am using (Bert):

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce
```python
# define model via functional api
def build_model(item_dim, num_layers, num_heads, max_len):
    config = BertConfig(hidden_size=item_dim, num_hidden_layers=num_layers,
                        num_attention_heads=num_heads, intermediate_size=item_dim*4,
                        max_position_embeddings=max_len)
    bert = TFBertMainLayer(config=config)
    inputs_embeds = Input(shape=(max_len, item_dim), dtype=tf.float32, name='inputs')
    inputs = {"inputs_embeds": inputs_embeds}
    # pre-training vectors to bert
    seq_emb = bert(inputs)[0]
    last_token_emb = seq_emb[:, -1, :]
    outputs = Dense(1, activation='sigmoid')(last_token_emb)
    model = Model(inputs=inputs, outputs=outputs)
    return model

model = build_model(item_dim, num_layers, num_heads, max_len)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])

model.fit(X, y, 
          epochs=2, batch_size=128,
          verbose=2
         )

model.save('./my_model')
```
Errors with
`TypeError: ('Not JSON Serializable:', BertConfig {
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 32,
  "initializer_range": 0.02,
  "intermediate_size": 128,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 9,
  "model_type": "bert",
  "num_attention_heads": 1,
  "num_hidden_layers": 1,
  "pad_token_id": 0,
  "type_vocab_size": 2,
  "vocab_size": 30522
}
)`

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## Expected behavior
* Successfully saved keras model.
<!-- A clear and concise description of what you would expect to happen. -->


Comments:
Hello!

I cannot reproduce your issue, can you try on the master version please?

> Hello!
> 
> I cannot reproduce your issue, can you try on the master version please?

I install transformers by `pip install git+https://github.com/huggingface/transformers`. I think it is master version by default. This is the complete [code](https://colab.research.google.com/gist/Douboo/fccd6bcda2e098b10b1c7490f2d8bbf3/untitled3.ipynb#scrollTo=wjN-uwz3LzJf)

> Hello!
> 
> I cannot reproduce your issue, can you try on the master version please?

The master version still reports the same bug. Install transformers by `pip install git+https://github.com/huggingface/transformers@master`.

Thanks a lot for the colab link. I will check this ASAP and will let you know here.

I can confirm the bug. As a workaround you can use `model.save_weights` instead. I cannot give you a specific date for the fix, but I will update the thread.

> I can confirm the bug. As a workaround you can use `model.save_weights` instead. I cannot give you a specific date for the fix, but I will update the thread.

ok, thanks!