| set -x | |
| export VLLM_ATTENTION_BACKEND=XFORMERS | |
| gsm8k_train_path=$HOME/data/gsm8k/train.parquet | |
| gsm8k_test_path=$HOME/data/gsm8k/test.parquet | |
| math_train_path=$HOME/data/math/train.parquet | |
| math_test_path=$HOME/data/math/test.parquet | |
| train_files="['$gsm8k_train_path', '$math_train_path']" | |
| test_files="['$gsm8k_test_path', '$math_test_path']" | |
| python3 -m verl.trainer.main_ppo --config-path=config \ | |
| --config-name='ppo_megatron_trainer.yaml'\ | |
| algorithm.adv_estimator=grpo \ | |
| data.train_files="$train_files" \ | |
| data.val_files="$test_files" \ | |
| 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=deepseek-ai/deepseek-llm-7b-chat \ | |
| actor_rollout_ref.actor.optim.lr=1e-6 \ | |
| actor_rollout_ref.actor.ppo_mini_batch_size=256 \ | |
| actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ | |
| actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ | |
| actor_rollout_ref.actor.megatron.tensor_model_parallel_size=4 \ | |
| 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.entropy_coeff=0 \ | |
| actor_rollout_ref.model.enable_gradient_checkpointing=True \ | |
| actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ | |
| actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ | |
| actor_rollout_ref.rollout.name=vllm \ | |
| actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ | |
| actor_rollout_ref.rollout.n=5 \ | |
| actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ | |
| algorithm.use_kl_in_reward=False \ | |
| trainer.critic_warmup=0 \ | |
| trainer.logger=['console','wandb'] \ | |
| trainer.project_name='verl_grpo_example_gsm8k' \ | |
| trainer.experiment_name='deepseek_llm_7b_function_rm_math_megatron' \ | |
| trainer.n_gpus_per_node=16 \ | |
| trainer.nnodes=1 \ | |
| trainer.save_freq=-1 \ | |
| trainer.test_freq=5 \ | |
| trainer.total_epochs=15 $@ |