GitLab

**Description**
Fix bug of duration feature for model benchmarks in distributed mode.

**Major Revision**
- Add all_reduce to sync the result of is_finished(the function to judge whether the model benchmark should be stopped) in each step 
  - to avoid inconsistency between different ranks to determine duration end (some rank may enter one more step and can never finish)
- Add torch.cuda.synchronize() before and after step time measuring in train_step() for all model benchmarks
  - some operations in train_step() maybe async resulting incorrect step time records (for example, lstm) 

**Description**
Remove fp16 samples type converting time for training cnn and lstm inference.

**Description**
Remove fp16  samples type converting time for cnn and lstm models.

**Description**
The BatchNorm operator is not numerically stable in fp16.  PyTorch documentation recommends to keep the BN op in fp32 for fp16 AMP models.  Refer to https://pytorch.org/docs/stable/amp.html#ops-that-can-autocast-to-float32.  Preserving BN in fp32 for superbench more accurately reflects real workloads.

Fix bugs and refine log in single GPU benchmarks:

* Fix none framework issue
* Fix empty parameter bug
* Remove missed mobilenet_v3 models
* Change benchmark registration log to debug level
* Add pid in logging
* Add missing benchmarks in default config
* Fix deprecated logging warn

* Benchmarks: Add Benchmark - Add CNN model benchmarks.

Benchmarks: Code Revision - Fix some issue for BERT benchmark. (#58)

Benchmarks: Add Benchmark - Add Pytorch BERT benchmarks, including bert-base and bert-large.   (#20)

* add pytorch bert benchmarks.

* revise code

* fix issue

* revise code.
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>