can't run sft with gpt-2b-001 using FP8, model initialization parameters mismatch error
**Describe the bug**
Hi,

I want to compare the SFT training convergence between bf16 and fp8.

So I am trying to do SFT training in NeMo using fp8 on a H100 GPU.

However, it reports an error during model initialization.

The error log file is in the attachment.
[sft_fp8.log](https://github.com/NVIDIA/NeMo/files/12340953/sft_fp8.log)


It seems that the NeMo fp8 layer names are different from bf16 model.


**Steps/Code to reproduce bug**

```shell
EXE=./nemo/sft/nemo_sft.fp8.py
NEMO_PATH=./GPT-2B-001_bf16_tp1.nemo
FP8=True
   python ${EXE} --config-path=${CONFIG_DIR}/conf --config-name=megatron_gpt_sft.yaml \
        trainer.num_nodes=1 \
        trainer.devices=[${GPU}] \
        trainer.max_steps=8 \
        exp_manager.create_checkpoint_callback=false \
        exp_manager.checkpoint_callback_params.every_n_train_steps=128 \
        exp_manager.exp_dir=${EXP_DIR} \
        exp_manager.name=nemo-fp8-2b \
        exp_manager.create_wandb_logger=${USE_WANDB} \
        exp_manager.wandb_logger_kwargs.project=SFT-FP8 \
        exp_manager.wandb_logger_kwargs.name=gpt-2b \
	model.transformer_engine=${FP8} \
	model.fp8=${FP8} \
        model.restore_from_path=${NEMO_PATH} \
        model.tensor_model_parallel_size=1 \
        model.optim.name=fused_adam \
        model.optim.lr=1e-5 \
        model.data.train_ds.max_seq_length=1024 \
        model.data.train_ds.micro_batch_size=2 \
        model.data.train_ds.global_batch_size=32 \
        model.data.train_ds.file_names=[${DATAFILE}]"
```



**Additional context**
Model: https://huggingface.co/nvidia/GPT-2B-001
Image: nvcr.io/ea-bignlp/nemofw-training:23.07-py3