set -x

# Environment variables
export VLLM_ATTENTION_BACKEND=XFORMERS
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export MKL_THREADING_LAYER=GNU
export TORCH_CUDA_ALLOW_TF32=1
export HYDRA_FULL_ERROR=1
export HF_ENDPOINT=https://hf-mirror.com
export CUDA_VISIBLE_DEVICES=0,1,2,3

# wandb configuration
export WANDB_MODE=online
export WANDB_API_KEY=c2fe654fd0527d4fad92e03cdc1e3f59b9a20595
export WANDB_BASE_URL=https://api.wandb.ai

# Default parameters
BASE_MODEL="Qwen/Qwen2.5-3B-Instruct"
EXPERIMENT_NAME="Qwen3BInstruct-KKlogic345ppl-REINFORCE++-4RTX4090-$(date +%Y%m%d%H%M)"
PROJECT_NAME="Logic-RL-Lite"
N_GPUS=4
ROLLOUT_TP_SIZE=2

# Curriculum learning dataset PPL values
PPL_VALUES="3 4 5"

# Initial model path
MODEL_PATH=$BASE_MODEL

# Clear GPU memory cache
python -c "import torch; torch.cuda.empty_cache()"

# Start iterative training
for ppl in $PPL_VALUES; do
    echo "Starting training for ${ppl}ppl dataset"

    TRAIN_FILE="./data/kklogic/${ppl}ppl/train.parquet"
    VAL_FILE="./data/kklogic/${ppl}ppl/test.parquet"

    # Define the experiment name for the current stage
    CURRENT_EXPERIMENT_NAME="${EXPERIMENT_NAME}-${ppl}ppl"

    # Construct the training command
    COMMAND="python3 -m verl.trainer.main_ppo \
        algorithm.adv_estimator=reinforce_plus_plus \
        data.train_files=${TRAIN_FILE} \
        data.val_files=${VAL_FILE} \
        data.train_batch_size=4 \
        data.val_batch_size=16 \
        data.max_prompt_length=512 \
        data.max_response_length=4096 \
        actor_rollout_ref.model.path='\"${MODEL_PATH}\"' \
        actor_rollout_ref.actor.optim.lr=1e-6 \
        actor_rollout_ref.actor.ppo_mini_batch_size=8 \
        actor_rollout_ref.actor.ppo_micro_batch_size=4 \
        actor_rollout_ref.actor.use_kl_loss=True \
        actor_rollout_ref.actor.kl_loss_coef=0.001 \
        actor_rollout_ref.actor.kl_loss_type=low_var_kl \
        actor_rollout_ref.model.use_remove_padding=True \
        actor_rollout_ref.model.enable_gradient_checkpointing=True \
        actor_rollout_ref.actor.fsdp_config.param_offload=False \
        actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
        actor_rollout_ref.rollout.log_prob_micro_batch_size=8 \
        actor_rollout_ref.rollout.tensor_model_parallel_size=${ROLLOUT_TP_SIZE} \
        actor_rollout_ref.rollout.name=vllm \
        actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
        actor_rollout_ref.rollout.n=8 \
        actor_rollout_ref.rollout.temperature=0.7 \
        +actor_rollout_ref.rollout.val_temperature=0.7 \
        actor_rollout_ref.ref.log_prob_micro_batch_size=8 \
        actor_rollout_ref.ref.fsdp_config.param_offload=True \
        algorithm.kl_ctrl.kl_coef=0.001 \
        trainer.critic_warmup=0 \
        trainer.logger=['wandb'] \
        trainer.project_name=${PROJECT_NAME} \
        trainer.experiment_name=${CURRENT_EXPERIMENT_NAME} \
        trainer.n_gpus_per_node=${N_GPUS} \
        trainer.nnodes=1 \
        trainer.save_freq=200 \
        trainer.test_freq=200 \
        trainer.default_hdfs_dir=./saved_models \
        trainer.total_epochs=5 \
        +trainer.val_before_train=True"

    echo "Executing command: ${COMMAND}"
    eval ${COMMAND}

    # Update the model path to the current stage's checkpoint
    MODEL_PATH="./saved_models/${CURRENT_EXPERIMENT_NAME}/actor" # Assuming the model is saved in this path
done

echo "Curriculum learning completed!"