File size: 3,329 Bytes
f338eb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | data_name=nq_hotpotqa_train
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export DATA_DIR=data/${data_name} # first download the data from https://huggingface.co/datasets/PeterJinGo/nq_hotpotqa_train
WAND_PROJECT="Search-R1"
RAY_DASHBOARD_ADDRESS="http://xx.xx.xx.xx:8265" # your head node address
N_NODES=4
export BASE_MODEL='Qwen/Qwen2.5-32B'
export EXPERIMENT_NAME=${train_data}-${test_data}-search-r1-ppo-qwen2.5-32b-em-multinode-${N_NODES}
# set -x
export VLLM_ATTENTION_BACKEND=XFORMERS
ulimit -n 65535
ray job submit --address=$RAY_DASHBOARD_ADDRESS \
--runtime-env=verl/trainer/runtime_env.yaml \
--no-wait \
-- \
python3 -m verl.trainer.main_ppo \
data.train_files=$DATA_DIR/train.parquet \
data.val_files=$DATA_DIR/test.parquet \
data.train_data_num=null \
data.val_data_num=null \
data.train_batch_size=512 \
data.val_batch_size=256 \
data.max_prompt_length=4096 \
data.max_response_length=500 \
data.max_start_length=2048 \
data.max_obs_length=500 \
data.shuffle_train_dataloader=True \
algorithm.adv_estimator=gae \
actor_rollout_ref.model.path=$BASE_MODEL \
actor_rollout_ref.actor.optim.lr=2e-7 \
actor_rollout_ref.model.enable_gradient_checkpointing=true \
actor_rollout_ref.model.use_remove_padding=True \
actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.285 \
actor_rollout_ref.actor.ppo_mini_batch_size=256 \
actor_rollout_ref.actor.ppo_micro_batch_size=32 \
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=True \
actor_rollout_ref.rollout.log_prob_micro_batch_size=32 \
actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
actor_rollout_ref.rollout.name=vllm \
actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \
actor_rollout_ref.ref.log_prob_micro_batch_size=32 \
actor_rollout_ref.ref.fsdp_config.param_offload=False \
actor_rollout_ref.rollout.n_agent=1 \
actor_rollout_ref.rollout.temperature=1 \
actor_rollout_ref.rollout.top_p=1.0 \
actor_rollout_ref.actor.state_masking=true \
critic.optim.lr=1e-5 \
critic.model.use_remove_padding=True \
critic.optim.lr_warmup_steps_ratio=0.015 \
critic.model.path=$BASE_MODEL \
critic.model.enable_gradient_checkpointing=true \
critic.ppo_micro_batch_size=32 \
critic.model.fsdp_config.param_offload=False \
critic.model.fsdp_config.grad_offload=False \
critic.model.fsdp_config.optimizer_offload=True \
algorithm.kl_ctrl.kl_coef=0.001 \
algorithm.no_think_rl=false \
trainer.critic_warmup=0 \
trainer.logger=['wandb'] \
+trainer.val_only=false \
+trainer.val_before_train=true \
trainer.default_hdfs_dir=null \
trainer.n_gpus_per_node=8 \
trainer.nnodes=$N_NODES \
trainer.save_freq=100 \
trainer.test_freq=100 \
trainer.project_name=$WAND_PROJECT \
trainer.experiment_name=$EXPERIMENT_NAME \
trainer.total_epochs=15 \
trainer.total_training_steps=1005 \
trainer.default_hdfs_dir=null \
trainer.default_local_dir=verl_checkpoints/$EXPERIMENT_NAME \
max_turns=4 \
retriever.url="http://127.0.0.1:8000/retrieve" \
retriever.topk=3 \
2>&1 | tee $EXPERIMENT_NAME.log
|