Missing `missing_keys` when loading from saved base model checkpoint
# 🐛 Bug

## Information

If a base model (e.g. `BertModel`, `DistilBertModel`, ...) is saved using `save_pretrained` and a model with an additional head (e.g. `BertForSequenceClassification`, `DistilBertForQuestionAnswering`, ...) is loaded from that checkpoint, it will not detect that it is missing layers.

## To reproduce

Steps to reproduce the behavior:

1. Instantiate base model from configuration or from `from_pretrained`
2. Save model using `save_pretrained`
3. Load checkpoint in model with head
4. No warning is output. Furthermore, if `output_loading_info=True` in step 3), will output `{'missing_keys': [], 'unexpected_keys': [], 'error_msgs': []}`

Here's a reproducible example:

```py
from transformers import BertForSequenceClassification, BertModel, BertConfig

config = BertConfig()
base_model = BertModel(config)
base_model.save_pretrained(directory)

model, loading_info = BertForSequenceClassification.from_pretrained(directory, output_loading_info=True)
print(loading_info)

# {'missing_keys': [], 'unexpected_keys': [], 'error_msgs': []}
# Should output {'missing_keys': ['classifier.weight', 'classifier.bias'], 'unexpected_keys': [], 'error_msgs': []}

```

## Expected behavior

Should detect the missing keys, as it does when loading from a full checkpoint:

```py
from transformers import BertForSequenceClassification

model, loading_info = BertForSequenceClassification.from_pretrained("bert-base-cased", output_loading_info=True)
print(loading_info)

# {'missing_keys': ['classifier.weight', 'classifier.bias'], 'unexpected_keys': ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias'], 'error_msgs': []}

```

## 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: master branch
- Platform: Linux-5.5.7-arch1-1-x86_64-with-arch
- Python version: 3.6.10
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
