set -x

export VLLM_USE_V1=1

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=gae \
    data.train_files=$HOME/data/dapo-math-17k.parquet \
    data.val_files=$HOME/data/dapo-math-17k.parquet \
    data.train_batch_size=256 \
    data.max_prompt_length=2000 \
    data.max_response_length=12000 \
    data.shuffle=False \
    actor_rollout_ref.model.path=Qwen/Qwen3-8B \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.actor.fsdp_config.param_offload=True \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.use_torch_compile=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \
    actor_rollout_ref.rollout.max_num_batched_tokens=14000 \
    actor_rollout_ref.rollout.max_num_seqs=64 \
    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \
    actor_rollout_ref.rollout.enable_chunked_prefill=True \
    actor_rollout_ref.rollout.enforce_eager=False \
    critic.optim.lr=1e-5 \
    critic.model.use_remove_padding=True \
    critic.model.path=Qwen/Qwen3-8B \
    critic.model.enable_gradient_checkpointing=True \
    critic.ppo_micro_batch_size_per_gpu=1 \
    critic.ulysses_sequence_parallel_size=2 \
    critic.model.fsdp_config.param_offload=True \
    critic.model.fsdp_config.optimizer_offload=True \
    critic.use_dynamic_bsz=True \
    trainer.critic_warmup=0 \
    trainer.logger=console \
    trainer.project_name='verl_example_dapo_math_17k' \
    trainer.experiment_name='qwen3_8b_fsdp' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=20 \
    trainer.test_freq=-1 \
    trainer.val_before_train=False \
    trainer.device=npu \
    trainer.max_actor_ckpt_to_keep=1 \
    trainer.max_critic_ckpt_to_keep=1 \
    trainer.total_training_steps=100 $@