[WIP, TF] replace keras dense by keras.layers.DenseEinsum
Fixes #6771.

This PR might speed up TF runtime on GPU.
Note that the change requires TF 2.3.0
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=h1) Report
> Merging [#6877](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a32d85f0d405be53117b96075eef2875d2185892?el=desc) will **increase** coverage by `0.16%`.
> The diff coverage is `100.00%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/6877/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #6877      +/-   ##
==========================================
+ Coverage   80.48%   80.65%   +0.16%     
==========================================
  Files         157      157              
  Lines       28794    28796       +2     
==========================================
+ Hits        23175    23224      +49     
+ Misses       5619     5572      -47     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_tf\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `98.38% <100.00%> (+<0.01%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |
| [src/transformers/modeling\_tf\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |
| [src/transformers/modeling\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |
| [src/transformers/modeling\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `77.63% <0.00%> (-6.21%)` | :arrow_down: |
| [src/transformers/data/data\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `86.63% <0.00%> (-5.27%)` | :arrow_down: |
| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |
| [src/transformers/tokenization\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |
| [src/transformers/configuration\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.97% <0.00%> (-0.68%)` | :arrow_down: |
| [src/transformers/modeling\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.50% <0.00%> (-0.56%)` | :arrow_down: |
| ... and [11 more](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree-more) | |

------

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> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=footer). Last update [a32d85f...ddbccd8](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>As a comparision. When running this line on current `master`:
```
TF_CPP_MIN_LOG_LEVEL=3 python examples/benchmarking/run_benchmark_tf.py --models bert-base-uncased --no_memory --batch_sizes 1 --sequence_lengths 128 256 512
```

one gets the following results: 

```
====================       INFERENCE - SPEED - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length     Time in s   
--------------------------------------------------------------------------------
      bert-base-uncased              1              128            0.006     
      bert-base-uncased              1              256            0.009     
      bert-base-uncased              1              512            0.017     
--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================
- transformers_version: 3.0.2
- framework: TensorFlow
- eager_mode: False
- use_xla: False
- framework_version: 2.3.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-09-01
- time: 11:23:30.836691
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```

for a TITAN RTX GPU.

When running the above line on this branch, one gets the following results:

```
====================       INFERENCE - SPEED - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length     Time in s   
--------------------------------------------------------------------------------
      bert-base-uncased              1              128            0.006     
      bert-base-uncased              1              256            0.008     
      bert-base-uncased              1              512            0.016     
--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================
- transformers_version: 3.0.2
- framework: TensorFlow
- eager_mode: False
- use_xla: False
- framework_version: 2.3.0
- python_version: 3.6.10
- system: Linux
- cpu: x86_64
- architecture: 64bit
- date: 2020-09-01
- time: 11:28:12.021389
- fp16: False
- use_multiprocessing: True
- only_pretrain_model: False
- cpu_ram_mb: 32088
- use_gpu: True
- num_gpus: 1
- gpu: TITAN RTX
- gpu_ram_mb: 24217
- gpu_power_watts: 280.0
- gpu_performance_state: 2
- use_tpu: False
```

So, I cannot see a real difference here :-/ @jlei2<|||||>I will see whether the benchmark results are better on a GPU-V100 GPU. 
@jlei2 - could you post the code you used to benchmark HF Bert vs. Google Bert ? This would help a lot for reproducability.<|||||>oops didn't see it was [WIP]<|||||>@patrickvonplaten can you update your code with the version given by @jlei2 [here](https://github.com/jlei2/transformers/pull/2) when you have time please. Thanks a lot!