# -*- coding: utf-8 -*-
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
NOW=$(date +%Y%m%d)
export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-32b-${NOW}
export WANDB_PROJECT=${WANDB_DIR}
export WANDB_EXP=32b-${NOW}
MODEL_PATH=Qwen/Qwen2.5-32B-Instruct

set -x
export VLLM_ATTENTION_BACKEND=XFORMERS
# If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs:
# export VLLM_ATTENTION_BACKEND=XFORMERS
nproc_per_gpu=45 # 32√ → 64× → 48× → 40√ → 44√ → 46× → 45×
nnodes=1
ngpu_per_node=4
total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node ))
mini_batch_size=$(( total_procs ))

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=data/gsm8k/train.parquet \
    data.val_files=data/gsm8k/test.parquet \
    data.train_batch_size=${total_procs} \
    data.val_batch_size=${total_procs} \
    data.max_prompt_length=512 \
    data.max_response_length=1024 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.shuffle=False \
    actor_rollout_ref.model.path=$MODEL_PATH  \
    actor_rollout_ref.model.use_shm=True  \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.model.lora_rank=32 \
    actor_rollout_ref.model.lora_alpha=32 \
    actor_rollout_ref.model.target_modules=all-linear \
    actor_rollout_ref.actor.optim.lr=3e-5 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \
    actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=True \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
    actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \
    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.max_num_seqs=512 \
    actor_rollout_ref.rollout.max_model_len=1536 \
    actor_rollout_ref.rollout.max_num_batched_tokens=1536 \
    actor_rollout_ref.rollout.enable_chunked_prefill=False \
    actor_rollout_ref.rollout.load_format=safetensors \
    actor_rollout_ref.rollout.layered_summon=True \
    actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \
    actor_rollout_ref.actor.entropy_coeff=0.001 \
    algorithm.kl_ctrl.kl_coef=0.001 \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name=${WANDB_PROJECT} \
    trainer.experiment_name=${WANDB_EXP} \
    trainer.n_gpus_per_node=4 \
    trainer.nnodes=1 \
    trainer.save_freq=20 \
    trainer.test_freq=5 \
    trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log
