set -x

export VLLM_ATTENTION_BACKEND=XFORMERS

python3 -m verl.trainer.main_ppo \
    data.train_files=$HOME/data/gsm8k/train.parquet \
    data.val_files=$HOME/data/gsm8k/test.parquet \
    data.train_batch_size=1024 \
    data.val_batch_size=1312 \
    data.max_prompt_length=512 \
    data.max_response_length=512 \
    data.return_raw_chat=True \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.1 \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size=32 \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=12000 \
    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=128 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=12000 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=12000 \
    critic.optim.lr=1e-5 \
    critic.model.use_remove_padding=True \
    critic.optim.lr_warmup_steps_ratio=0.05 \
    critic.model.path=Qwen/Qwen2.5-0.5B \
    critic.model.enable_gradient_checkpointing=False \
    critic.ppo_micro_batch_size=32 \
    critic.use_dynamic_bsz=True \
    critic.ppo_max_token_len_per_gpu=98304 \
    critic.model.fsdp_config.param_offload=False \
    critic.model.fsdp_config.grad_offload=False \
    critic.model.fsdp_config.optimizer_offload=False \
    reward_model.enable=True \
    reward_model.model.path=Qwen/Qwen2.5-0.5B\
    reward_model.model.use_remove_padding=True \
    reward_model.model.fsdp_config.param_offload=True \
    reward_model.micro_batch_size=16 \
    reward_model.use_dynamic_bsz=True \
    reward_model.forward_max_token_len_per_gpu=98304 \
    algorithm.kl_ctrl.kl_coef=0.001 \
    trainer.critic_warmup=0 \
    trainer.logger=['console'] \
    +trainer.val_before_train=False \
    trainer.project_name='verl_example' \
    trainer.experiment_name='Qwen2.5-0.5B-ci_hybrid_rm_seq_balance' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=1 \
    trainer.save_freq=-1 \
    trainer.total_training_steps=1 $@
