GitLab

Added MoE model using MixtralConfig. 

1. Added 8x7b and 8x22b variants 
2. Requires high VRAM as all experts are loaded in memory. Thus,
disabled training due to memory constraint on test worker.

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Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

Added llama benchmark - training and inference in accordance with the
existing pytorch models implementation like gpt2, lstm etc.

- added llama fp8 unit test for better code coverage, to reduce memory
required
- updated transformers version >= 4.28.0 for LLamaConfig
- set tokenizers version <= 0.20.3 to avoid 0.20.4 version
[issues](https://github.com/huggingface/tokenizers/issues/1691

) with
py3.8
- added llama2 to tensorrt
- llama2 tests not added to test_tensorrt_inference_performance.py due
to large memory requirement for worker gpu. tests validated separately
on gh200

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Co-authored-by: dpatlolla <dpatlolla@microsoft.com>

**Description**
Megatron-LM/Megatron-Deepspeed GPT pretrain benchmark

* Benchmarks: Add Benchmark - Add LSTM model benchmarks.

* Benchmarks: Add Benchmark - Add CNN model benchmarks.

* Benchmarks: Add Benchmark - Add GPT2 model benchmark.

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>

* move benchmarks registration from registry.py to __init__.py

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

* benchmarks init.
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>