| set -x | |
| ENGINE=${1:-vllm} | |
| export VLLM_ATTENTION_BACKEND=XFORMERS | |
| python3 -m verl.trainer.main_ppo \ | |
| algorithm.adv_estimator=grpo \ | |
| data.train_files=$HOME/data/geo3k/train.parquet \ | |
| data.val_files=$HOME/data/geo3k/test.parquet \ | |
| data.train_batch_size=512 \ | |
| data.max_prompt_length=1024 \ | |
| data.max_response_length=2048 \ | |
| data.filter_overlong_prompts=True \ | |
| data.truncation='error' \ | |
| data.image_key=images \ | |
| actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ | |
| actor_rollout_ref.actor.optim.lr=1e-6 \ | |
| actor_rollout_ref.model.use_remove_padding=True \ | |
| actor_rollout_ref.actor.ppo_mini_batch_size=128 \ | |
| actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ | |
| actor_rollout_ref.actor.use_kl_loss=True \ | |
| actor_rollout_ref.actor.kl_loss_coef=0.01 \ | |
| actor_rollout_ref.actor.kl_loss_type=low_var_kl \ | |
| actor_rollout_ref.actor.entropy_coeff=0 \ | |
| actor_rollout_ref.model.enable_gradient_checkpointing=True \ | |
| actor_rollout_ref.actor.fsdp_config.param_offload=False \ | |
| actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ | |
| actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ | |
| actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ | |
| actor_rollout_ref.rollout.name=$ENGINE \ | |
| actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ | |
| actor_rollout_ref.rollout.enable_chunked_prefill=False \ | |
| actor_rollout_ref.rollout.enforce_eager=False \ | |
| actor_rollout_ref.rollout.free_cache_engine=False \ | |
| actor_rollout_ref.rollout.n=5 \ | |
| actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ | |
| actor_rollout_ref.ref.fsdp_config.param_offload=True \ | |
| algorithm.use_kl_in_reward=False \ | |
| trainer.critic_warmup=0 \ | |
| trainer.logger=['console','wandb'] \ | |
| trainer.project_name='verl_grpo_example_geo3k' \ | |
| trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ | |
| trainer.n_gpus_per_node=8 \ | |
| trainer.nnodes=1 \ | |
| trainer.save_freq=-1 \ | |
| trainer.test_freq=5 \ | |
| trainer.total_epochs=15 $@ |