Add Baichuan2 support
jenkins jenkins jenkins jenkins
# What does this PR do ?

Add a Chinese open-source LLM Baichuan2 (7B/13B) support.

**Collection**: NLP

# Changelog 
- Add Baichuan2 model configuration.
- Add Baichuan2 hf-to-nemo conversion.
- Add Baichuan2 nemo-to-hf conversion.

# Usage
```python
# Model conversion from huggingface to nemo
python ./scripts/nlp_language_modeling/convert_hf_baichuan2_to_nemo.py --in-file '/mount/data/Baichuan2-7B-Base' --out-file '/mount/data/Baichuan2-7B-Base/baichuan2.nemo'
```
```python
# Model conversion from nemo to huggingface
python ./scripts/nlp_language_modeling/convert_nemo_baichuan2_to_hf.py \
    --in-file /mount/data/Baichuan2-7B-Base/baichuan2.nemo \
    --out-file /mount/data/Baichuan2-7B-Base/pytorch_model.bin \
    --hf-in-path /mount/data/Baichuan2-7B-Base \
    --hf-out-path /mount/data/Baichuan2-7B-Base-convert
```
```python
# Inference
python ./examples/nlp/language_modeling/megatron_gpt_eval.py \
    --config-name=megatron_baichuan2_inference \
    gpt_model_file=${BAICHUAN2_NEMO_MODEL} \
    trainer.devices=1 \
    trainer.num_nodes=1 \
    tensor_model_parallel_size=1 pipeline_model_parallel_size=1 
```
```python
# Pretrain
python3 -u examples/nlp/language_modeling/megatron_gpt_pretraining.py \
  --config-name=megatron_baichuan2_config \
  trainer.devices=8 \
  trainer.num_nodes=1 \
  trainer.val_check_interval=100 \
  trainer.precision=bf16 \
  trainer.max_steps=1000 \
  trainer.max_epochs=1 \
  model.megatron_amp_O2=True \
  model.sequence_parallel=True \
  model.tensor_model_parallel_size=2 \
  model.pipeline_model_parallel_size=1 \
  model.global_batch_size=16 \
  model.micro_batch_size=2 
```
```python
# Parameter efficient fine-tuning
python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
    trainer.max_steps=200 \
    trainer.devices=2 \
    trainer.val_check_interval=200 \
    exp_manager.exp_dir=exp \
    exp_manager.name=test \
    model.tensor_model_parallel_size=1 \
    model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
    model.global_batch_size=32 \
    model.micro_batch_size=4 \
    model.data.train_ds.file_names=${TRAIN} \
    model.data.validation_ds.file_names=${VALID} \
    model.answer_only_loss=True \
    model.peft.peft_scheme="lora" \
    model.data.train_ds.concat_sampling_probabilities=[1.0] 2>&1|tee /mount/data/test.log
```
```python
# SFT
python ./examples/nlp/language_modeling/tuning/megatron_gpt_sft.py \
    trainer.devices=8 \
    trainer.val_check_interval=200 \
    model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
    model.tensor_model_parallel_size=${TP_SIZE} \
    model.pipeline_model_parallel_size=${PP_SIZE} \
    model.answer_only_loss=True \
    model.global_batch_size=16 \
    model.micro_batch_size=2 \
    model.data.train_ds.file_names=${TRAIN} \
    model.data.train_ds.concat_sampling_probabilities=[1.0] \
    model.data.validation_ds.file_names=${VALID} \
    model.data.validation_ds.global_batch_size=64 \
    model.data.validation_ds.micro_batch_size=8 \
    model.megatron_amp_O2=True \
    exp_manager.exp_dir=exp \
    exp_manager.name=test \
    trainer.precision=bf16-mixed 2>&1|tee /mount/data/log/test.log
```
```python
# peft model evaluation
CUDA_VISIBLE_DEVICES='0,1' python ./examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \
        trainer.devices=2 \
        trainer.num_nodes=1 \
        trainer.precision=32 \
        model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
        model.peft.restore_from_path=${LORA_MODEL} \
        model.data.test_ds.file_names=["path/to/your/dataset.jsonl"] \
        model.data.test_ds.global_batch_size=32 \
        model.data.test_ds.micro_batch_size=16 \
        model.tensor_model_parallel_size=1 \
        model.pipeline_model_parallel_size=1 \
        inference.greedy=True \
        inference.outfile_path=..
```

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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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