78 lines
3.2 KiB
Bash
78 lines
3.2 KiB
Bash
data_name=nq_hotpotqa_train
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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export DATA_DIR=data/${data_name} # first download the data from https://huggingface.co/datasets/PeterJinGo/nq_hotpotqa_train
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WAND_PROJECT="Search-R1"
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RAY_DASHBOARD_ADDRESS="http://xx.xx.xx.xx:8265" # your head node address
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N_NODES=4
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export BASE_MODEL='Qwen/Qwen2.5-32B'
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export EXPERIMENT_NAME=${train_data}-${test_data}-search-r1-grpo-qwen2.5-32b-em-multinode-${N_NODES}
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# set -x
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export VLLM_ATTENTION_BACKEND=XFORMERS # vllm + qwen2-7b with flash_attn has some issues
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# max_prompt_length = (config['training']['max_start_length'] + config['training']['max_response_length'] * (config['training']['max_turns'] - 1) + config['training']['max_obs_length'] * config['training']['max_turns'])
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ulimit -n 65535
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ray job submit --address=$RAY_DASHBOARD_ADDRESS \
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--runtime-env=verl/trainer/runtime_env.yaml \
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--no-wait \
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-- \
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python3 -m verl.trainer.main_ppo \
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data.train_files=$DATA_DIR/train.parquet \
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data.val_files=$DATA_DIR/test.parquet \
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data.train_data_num=null \
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data.val_data_num=null \
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data.train_batch_size=512 \
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data.val_batch_size=256 \
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data.max_prompt_length=4096 \
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data.max_response_length=500 \
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data.max_start_length=2048 \
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data.max_obs_length=500 \
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data.shuffle_train_dataloader=True \
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algorithm.adv_estimator=grpo \
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actor_rollout_ref.model.path=$BASE_MODEL \
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actor_rollout_ref.model.enable_gradient_checkpointing=True \
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actor_rollout_ref.model.use_remove_padding=True \
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actor_rollout_ref.actor.optim.lr=2e-7 \
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actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.285 \
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actor_rollout_ref.actor.use_kl_loss=True \
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actor_rollout_ref.actor.ppo_mini_batch_size=256 \
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actor_rollout_ref.actor.ppo_micro_batch_size=64 \
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actor_rollout_ref.actor.fsdp_config.param_offload=false \
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actor_rollout_ref.actor.fsdp_config.grad_offload=false \
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actor_rollout_ref.actor.fsdp_config.optimizer_offload=false \
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actor_rollout_ref.rollout.log_prob_micro_batch_size=128 \
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actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \
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actor_rollout_ref.ref.log_prob_micro_batch_size=128 \
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actor_rollout_ref.ref.fsdp_config.param_offload=false \
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actor_rollout_ref.actor.kl_loss_coef=0.001 \
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actor_rollout_ref.actor.kl_loss_type=low_var_kl \
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algorithm.no_think_rl=false \
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actor_rollout_ref.rollout.n_agent=5 \
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actor_rollout_ref.rollout.temperature=1 \
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actor_rollout_ref.actor.state_masking=True \
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trainer.logger=['wandb'] \
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+trainer.val_only=false \
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+trainer.val_before_train=false \
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trainer.default_hdfs_dir=null \
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trainer.n_gpus_per_node=8 \
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trainer.nnodes=$N_NODES \
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trainer.save_freq=100 \
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trainer.test_freq=100 \
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trainer.project_name=$WAND_PROJECT \
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trainer.experiment_name=$EXPERIMENT_NAME \
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trainer.total_epochs=15 \
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trainer.total_training_steps=1005 \
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trainer.default_hdfs_dir=null \
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trainer.default_local_dir=verl_checkpoints/$EXPERIMENT_NAME \
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max_turns=4 \
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retriever.url="http://127.0.0.1:8000/retrieve" \
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retriever.topk=3 \
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2>&1 | tee $EXPERIMENT_NAME.log
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