set -x

# profiling configuration
PROFILE_STEPS="[2,4]"
PROFILE_RANKS_ALL=False
DISCRETE=True  

# profiling NPU options
SAVE_PATH="$HOME/profile_data"
LEVEL="level1"
WITH_MEMORY=False
RECORD_SHAPES=False
WITH_NPU=True
WITH_CPU=True
WITH_MODULE=False
WITH_STACK=False
ANALYSIS=True

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=$HOME/data/gsm8k/train.parquet \
    data.val_files=$HOME/data/gsm8k/test.parquet \
    data.train_batch_size=1024 \
    data.max_prompt_length=1024 \
    data.max_response_length=1024 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \
    actor_rollout_ref.actor.optim.lr=5e-8 \
    actor_rollout_ref.model.use_remove_padding=False \
    actor_rollout_ref.actor.ppo_mini_batch_size=32 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.profiler.ranks=[1,2] \
    actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \
    actor_rollout_ref.actor.profiler.discrete=$DISCRETE \
    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_per_gpu=2 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \
    actor_rollout_ref.rollout.n=5 \
    actor_rollout_ref.rollout.enable_chunked_prefill=False \
    actor_rollout_ref.rollout.profiler.ranks=[1,2] \
    actor_rollout_ref.rollout.profiler.all_ranks=$PROFILE_RANKS_ALL \
    actor_rollout_ref.rollout.profiler.discrete=$DISCRETE \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    actor_rollout_ref.ref.profiler.ranks=[4,8] \
    actor_rollout_ref.ref.profiler.all_ranks=$PROFILE_RANKS_ALL \
    actor_rollout_ref.ref.profiler.discrete=$DISCRETE \
    algorithm.use_kl_in_reward=False \
    trainer.npu_profile.options.save_path=$SAVE_PATH \
    trainer.npu_profile.options.level=$LEVEL \
    trainer.npu_profile.options.with_memory=$WITH_MEMORY \
    trainer.npu_profile.options.record_shapes=$RECORD_SHAPES \
    trainer.npu_profile.options.with_npu=$WITH_NPU \
    trainer.npu_profile.options.with_cpu=$WITH_CPU \
    trainer.npu_profile.options.with_module=$WITH_MODULE \
    trainer.npu_profile.options.with_stack=$WITH_STACK \
    trainer.npu_profile.options.analysis=$ANALYSIS \
    trainer.critic_warmup=0 \
    trainer.logger=['console'] \
    trainer.project_name='verl_grpo_example_gsm8k' \
    trainer.experiment_name='qwen2_5_7b_function_rm' \
    trainer.n_gpus_per_node=16 \
    trainer.nnodes=1 \
    trainer.save_freq=-1 \
    trainer.test_freq=5 \
    trainer.total_epochs=5 \
    trainer.profile_steps=$PROFILE_STEPS \
    trainer.device=npu $@