update sampler and use LoRA finetune LLM

[chengxin0913]

I'm trying to use finetune_lora.sh to finetune InternLM-XComposer2. Except for using LoRA to finetune the part of LLM, I want to know whether can I also update the parameter of sampler. Changing the --fix_sampler False is the only thing I have to do? Or I should change somwthing else?
For the state of saving updated model, I notice that they only save the part of LoRA. Maybe I need to do some change for safe_save_model_for_hf_trainer or get_peft_state_maybe_zero_3?

note: LoRA finetune LLM and update sampler(projection layer)

If anyone can give some advice I would be very grateful.

#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
DIR=pwd

export MODEL="internlm/internlm-xcomposer2d5-7b"
export DATA="data.txt"

GPUS_PER_NODE=8
NNODES=1
NODE_RANK=0
MASTER_ADDR=localhost
MASTER_PORT=6001

DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE
--nnodes $NNODES
--node_rank $NODE_RANK
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
"

torchrun $DISTRIBUTED_ARGS finetune.py
--model_name_or_path $MODEL
--data_path $DATA
--given_num True
--bf16 True
--fix_vit True
*--fix_sampler False *
--use_lora True
--hd_num 18
--output_dir output/finetune_lora
--num_train_epochs 1
--batch_size 2
--per_device_train_batch_size 1
--per_device_eval_batch_size 1
--gradient_accumulation_steps 8
--evaluation_strategy "no"
--save_strategy "epoch"
--save_total_limit 1
--learning_rate 5e-5
--weight_decay 0.1
--adam_beta2 0.95
--warmup_ratio 0.01
--lr_scheduler_type "cosine"
--logging_steps 1
--report_to "none"
--max_length 16384
--deepspeed ds_config_zero2.json
--gradient_checkpointing True