vLLM Export Support
# What does this PR do ?

Implements export of NeMo checkpoints to vLLM 0.5.0 and deployment of vLLM engines on the Triton server.

**Collection**: NLP

# Changelog 
- Added `nemo.export.vllm` package that implements the export and deployment functionality, primarily with the `nemo.export.vllm.Exporter` class.
- Added script `scripts/deploy/nlp/deploy_triton_vllm.py` to use the new Exporter.
- Removed the shortcut `nemo.export.TensorRTLLM` to avoid initializing TRT-LLM when anything is accessed in the `nemo.export` package, use `nemo.export.tensorrt_llm.TensorRTLLM` instead.
- Moved the `sentencepiece_tokenizer.py` module from `nemo.export.tensorrt_llm.nemo` into `nemo.export` for the same reason.

# Supported Models
- LLAMA2
- Mistral
- Mixtral (should work but I couldn't test it due to GPU OOM)
- Gemma
- StarCoder2

# Usage
```sh
deploy_triton_vllm.py --nemo_checkpoint <path/to/checkpoint.nemo> --model_type llama --triton_model_name LLAMA --num_gpus 1
```

See `nemo/export/vllm/exporter.py` for inline documentation on the Exporter class.

# PR Type
- [X] New Feature

# Known issues
- Currently, vLLM and TRT-LLM cannot be installed in the same container because they require conflicting versions of various packages. Also, installing vLLM breaks something in the `transformer_engine` module, requiring it to be uninstalled.
- Fine-tuning is not supported currently, neither P-tuning nor LoRA