

TITLE
Problem loading a dynamic quantized distilbert model.

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Hello and thanks for your awesome library,
## Environment info
- `transformers` version: 3.0.2
- Platform: Linux-4.15.0-117-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.6.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help 
@VictorSanh
@stefan-it

## Information
I'm trying to optimize a fine-tuned (for token classification, NER) distilBert model through Dynamic Quantization.
I use this line: 
```python
quantized_model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
```
The model size goes from: 540 MB to 411 MB.

The quantized model works fine when I use it straight away in the script to make predictions, however I'm having trouble saving it and reloading it. 
I tried few things, first using save_pretrained: 
```python
quantized_model.save_pretrained(quantized_output_dir)
```
And then loading it using:
```python
model = AutoModelForTokenClassification.from_pretrained(quantized_output_dir)
```
When I use it to make predictions, I get the warning: 
``
Some weights of the model checkpoint at data/model3/quantized3/ were not used when initializing DistilBertForTokenClassification: ['distilbert.transformer.layer.0.attention.q_lin.scale', 'distilbert.transformer.layer.0.attention.q_lin.zero_point', 'distilbert.transformer.layer.0.attention.q_lin._packed_params.dtype', 'distilbert.transformer.layer.0.attention.q_lin._packed_params._packed_params', 'distilbert.transformer.layer.0.attention.k_lin.scale', 'distilbert.transformer.layer.0.attention.k_lin.zero_point', 'distilbert.transformer.layer.0.attention.k_lin._packed_params.dtype', 'distilbert.transformer.layer.0.attention.k_lin._packed_params._packed_params', 'distilbert.transformer.layer.0.attention.v_lin.scale', 'distilbert.transformer.layer.0.attention.v_lin.zero_point', 'distilbert.transformer.layer.0.attention.v_lin._packed_params.dtype', 'distilbert.transformer.layer.0.attention.v_lin._packed_params._packed_params', 'distilbert.transformer.layer.0.attention.out_lin.scale', 'distilbert.transformer.layer.0.attention.out_lin.zero_point', 'distilbert.transformer.layer.0.attention.out_lin._packed_params.dtype', 'distilbert.transformer.layer.0.attention.out_lin._packed_params._packed_params', 'distilbert.transformer.layer.0.ffn.lin1.scale', 'distilbert.transformer.layer.0.ffn.lin1.zero_point', 'distilbert.transformer.layer.0.ffn.lin1._packed_params.dtype', 'distilbert.transformer.layer.0.ffn.lin1._packed_params._packed_params', 'distilbert.transformer.layer.0.ffn.lin2.scale', 'distilbert.transformer.layer.0.ffn.lin2.zero_point', 'distilbert.transformer.layer.0.ffn.lin2._packed_params.dtype', 'distilbert.transformer.layer.0.ffn.lin2._packed_params._packed_params', 'distilbert.transformer.layer.1.attention.q_lin.scale', 
``

For all the layers.
And of course I got wrong predictions because it's as if the model isn't fine-tuned.

I tried saving it using:
```python
torch.save(quantized_model.state_dict(), path)
```
loading it using:
 ```python
config = DistilBertConfig.from_pretrained("distilbert-base-multilingual-cased", num_labels=5)
model = DistilBertForTokenClassification.from_pretrained("distilbert-base-multilingual-cased", config=config)
model.load_state_dict(torch.load(path))
```
and I got this runtime error:
``
RuntimeError: Error(s) in loading state_dict for DistilBertForTokenClassification:
	Missing key(s) in state_dict: "distilbert.transformer.layer.0.attention.q_lin.weight", "distilbert.transformer.layer.0.attention.q_lin.bias", "distilbert.transformer.layer.0.attention.k_lin.weight", "distilbert.transformer.layer.0.attention.k_lin.bias", "distilbert.transformer.layer.0.attention.v_lin.weight", "distilbert.transformer.layer.0.attention.v_lin.bias", "distilbert.transformer.layer.0.attention.out_lin.weight", "distilbert.transformer.layer.0.attention.out_lin.bias", "distilbert.transformer.layer.0.ffn.lin1.weight", "distilbert.transformer.layer.0.ffn.lin1.bias", "distilbert.transformer.layer.0.ffn.lin2.weight", "distilbert.transformer.layer.0.ffn.lin2.bias", "distilbert.transformer.layer.1.attention.q_lin.weight", 
	Unexpected key(s) in state_dict: "distilbert.transformer.layer.0.attention.q_lin.scale", "distilbert.transformer.layer.0.attention.q_lin.zero_point", "distilbert.transformer.layer.0.attention.q_lin._packed_params.dtype", "distilbert.transformer.layer.0.attention.q_lin._packed_params._packed_params", "distilbert.transformer.layer.0.attention.k_lin.scale", "distilbert.transformer.layer.0.attention.k_lin.zero_point", "distilbert.transformer.layer.0.attention.k_lin._packed_params.dtype", "distilbert.transformer.layer.0.attention.k_lin._packed_params._packed_params", "distilbert.transformer.layer.0.attention.v_lin.scale", "distilbert.transformer.layer.0.attention.v_lin.zero_point", "distilbert.transformer.layer.0.attention.v_lin._packed_params.dtype", "distilbert.transformer.layer.0.attention.v_lin._packed_params._packed_params", "distilbert.transformer.layer.0.attention.out_lin.scale", "distilbert.transformer.layer.0.attention.out_lin.zero_point", "distilbert.transformer.layer.0.attention.out_lin._packed_params.dtype", "distilbert.transformer.layer.0.attention.out_lin._packed_params._packed_params", "distilbert.transformer.layer.0.ffn.lin1.scale", "distilbert.transformer.layer.0.ffn.lin1.zero_point", "distilbert.transformer.layer.0.ffn.lin1._packed_params.dtype", "distilbert.transformer.layer.0.ffn.lin1._packed_params._packed_params", "distilbert.transformer.layer.0.ffn.lin2.scale", "distilbert.transformer.layer.0.ffn.lin2.zero_point", "distilbert.transformer.layer.0.ffn.lin2._packed_params.dtype", "distilbert.transformer.layer.0.ffn.lin2._packed_params._packed_params", "distilbert.transformer.layer.1.attention.q_lin.scale", "classifier._packed_params.dtype", "classifier._packed_params._packed_params". 
``
For all the layers also (didn't put it all to shorten the text).

Here is the text when printing the quantized model:
```JSON
DistilBertForTokenClassification(
  (distilbert): DistilBertModel(
    (embeddings): Embeddings(
      (word_embeddings): Embedding(119547, 768, padding_idx=0)
      (position_embeddings): Embedding(512, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (transformer): Transformer(
      (layer): ModuleList(
        (0): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
        (1): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
        (2): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
        (3): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
        (4): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
        (5): TransformerBlock(
          (attention): MultiHeadSelfAttention(
            (dropout): Dropout(p=0.1, inplace=False)
            (q_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (k_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (v_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (out_lin): DynamicQuantizedLinear(in_features=768, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (ffn): FFN(
            (dropout): Dropout(p=0.1, inplace=False)
            (lin1): DynamicQuantizedLinear(in_features=768, out_features=3072, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
            (lin2): DynamicQuantizedLinear(in_features=3072, out_features=768, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
          )
          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        )
      )
    )
  )
  (dropout): Dropout(p=0.1, inplace=False)
  (classifier): DynamicQuantizedLinear(in_features=768, out_features=5, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
)
```
## Expected behavior
You can successfully load the quantized fine-tuned model to make predictions.
Can be the "DynamicQuantizedLinear" instead of "Linear" be causing this problem ?

Thanks in advance for your help. 