#!/bin/bash
set -x

# Warning: Export VLLM_ATTENTION_BACKEND on every machine before starting Ray cluster.
# vLLM without XFORMERS will results in CUDA errors.
N_NODE=1
EXPERIMENT_NAME=qwen3-1.7b-stage1
DATA=parquet/stage1/polaris-data-53K.parquet

# Parse command line arguments
while [[ $# -gt 0 ]]; do
    case $1 in
        --model)
            MODEL_PATH="$2"
            shift 2
            ;;
        --data_path)
            DATA="$2"
            shift 2
            ;;
        --n_node)
            N_NODE="$2"
            shift 2
            ;;
        --experiment_name)
            EXPERIMENT_NAME="$2"
            shift 2
            ;;
        *)
            break
            ;;
    esac
done


# Train over a single node, 8 H800-80GB GPUs.
python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=${DATA} \
    data.val_files=evaluation/benchmarks/aime24.parquet \
    data.train_batch_size=128 \
    data.val_batch_size=512 \
    data.max_prompt_length=1024 \
    data.max_response_length=39936 \
    actor_rollout_ref.model.path=$MODEL_PATH  \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.actor.ppo_mini_batch_size=128 \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=40960 \
    actor_rollout_ref.rollout.max_num_batched_tokens=40960 \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.actor.kl_loss_coef=0 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.clip_ratio_low=0.2 \
    actor_rollout_ref.actor.clip_ratio_high=0.28 \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=True \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.temperature=1.5 \
    actor_rollout_ref.rollout.top_p=1.0 \
    actor_rollout_ref.rollout.top_k=-1 \
    actor_rollout_ref.rollout.enable_chunked_prefill=True \
    actor_rollout_ref.rollout.n=8 \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
    actor_rollout_ref.rollout.val_kwargs.temperature=1.2 \
    actor_rollout_ref.rollout.val_kwargs.top_p=1.0 \
    actor_rollout_ref.rollout.val_kwargs.n=8 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.kl_ctrl.kl_coef=0.001 \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='Polaris-Reproduce-1.7B' \
    trainer.experiment_name=$EXPERIMENT_NAME \
    trainer.val_before_train=False \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=$N_NODE \
    trainer.debug=False \
    trainer.dyn_sampling_polaris=True \
    trainer.save_freq=10 \
    trainer.test_freq=10 \
    trainer.default_hdfs_dir=null \
    trainer.total_epochs=30 "${@:1}"



# Echo the command for verification
echo "Command to be executed:"
echo "$CMD"

# Execute the command
eval "$CMD"