export CUDA_VISIBLE_DEVICES=0,1,2,3
WAND_PROJECT='Ego-R1-SFT-RL'
date_time=$(date "+%Y%m%d_%H%M%S")

# export DATA_DIR='data/nq_search'
export DATA_DIR='data'

# parameters
export EPOCHS=3
export BATCH_SIZE=4

export BASE_MODEL="Qwen/Qwen2.5-3B-Instruct"
export EXPERIMENT_NAME=ego-r1-grpo-qwen2.5-3b-it

# set -x
export VLLM_ATTENTION_BACKEND=XFORMERS # vllm + qwen2-7b with flash_attn has some issues

prompt_length=8192 # 2048

PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \
    data.train_files=$DATA_DIR/train_rl.parquet \
    data.val_files=$DATA_DIR/test.parquet \
    data.train_data_num=null \
    data.val_data_num=null \
    data.train_batch_size=$BATCH_SIZE \
    data.val_batch_size=$(($BATCH_SIZE/2)) \
    data.max_prompt_length=$prompt_length \
    data.max_response_length=4096 \
    data.max_start_length=$prompt_length \
    data.max_obs_length=2048 \
    data.shuffle_train_dataloader=True \
    algorithm.adv_estimator=grpo \
    actor_rollout_ref.model.path=$BASE_MODEL \
    actor_rollout_ref.model.enable_gradient_checkpointing=true \
    actor_rollout_ref.model.use_remove_padding=false \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.285 \
    actor_rollout_ref.actor.use_kl_loss=true \
    actor_rollout_ref.actor.ppo_mini_batch_size=16 \
    actor_rollout_ref.actor.ppo_micro_batch_size=4 \
    actor_rollout_ref.actor.fsdp_config.param_offload=false \
    actor_rollout_ref.actor.fsdp_config.grad_offload=false \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=false \
    actor_rollout_ref.rollout.log_prob_micro_batch_size=8 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
    actor_rollout_ref.ref.log_prob_micro_batch_size=8 \
    actor_rollout_ref.ref.fsdp_config.param_offload=true \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    algorithm.no_think_rl=false \
    actor_rollout_ref.rollout.n_agent=3 \
    actor_rollout_ref.rollout.temperature=1 \
    actor_rollout_ref.actor.state_masking=true \
    trainer.logger=['console','wandb'] \
    +trainer.val_only=false \
    +trainer.val_before_train=false \
    trainer.default_hdfs_dir=null \
    trainer.n_gpus_per_node=4 \
    trainer.nnodes=1 \
    trainer.save_freq=5 \
    trainer.test_freq=5 \
    trainer.project_name=$WAND_PROJECT \
    trainer.experiment_name=$EXPERIMENT_NAME \
    trainer.total_epochs=$EPOCHS \
    trainer.total_training_steps=1005 \
    trainer.default_hdfs_dir=null \
    trainer.default_local_dir=verl_checkpoints/$EXPERIMENT_NAME \
    max_turns=9 \
    2>&1 | tee log_files/${EXPERIMENT_NAME}_${date_time}.log