135 lines
5.4 KiB
Markdown
135 lines
5.4 KiB
Markdown
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## Multinode Training
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Our codebase supports multi-node training for large-scale language models. The implementation is mainly based on [Ray](https://github.com/ray-project/ray).
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There are two types of nodes when doing Ray multi-node training: (1) head node and (2) worker nodes.
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There is only one head node where you will start the ray cluster and submit the job.
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The other nodes are worker nodes, where you only need to start and register to the ray cluster.
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### Step 1: Set up multinode ray cluster (from [link](https://verl.readthedocs.io/en/latest/start/multinode.html#set-up-multinode-ray-cluster))
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a. Start **head** node with ```ray start --head --dashboard-host=0.0.0.0```, there’re 2 address you should care about:
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- GCS address: ```ray start --address=<address>```, where **worker** node should connect to.
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- Dashboard address: ```<address>:8265```, where you should submit job to the cluster.
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b. Start **worker node** and register it to the ray cluster with ```ray start --address=<address>``` you get above.
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c. Check the cluster status with ```ray status```.
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For example, if you have two nodes (each with 8 GPUs) in the cluster, you should see something like this:
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### Step 2: Launch the retrieval server on every node.
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We would recommend launch the **same** retrieval server on every nodes (including both head and worker nodes) for the stable RL training. Detailed information on how to launch different retrievers can be found as follows: [doc](https://github.com/PeterGriffinJin/Search-R1/blob/main/docs/retriever.md) and [scripts](https://github.com/PeterGriffinJin/Search-R1/tree/main/example/retriever).
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For example, if you want to launch the local dense retriever with flat indexing, run the following command on **every** nodes:
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```
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bash retrieval_launch.sh
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```
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### Step 3: Start the job
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After the retrievers are launched, you can start the training job. You only need to start the job on the ***head*** node.
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An example script is shown as below. Change ```RAY_DASHBOARD_ADDRESS``` and ```N_NODES``` to your dashboard address found in step 1 and the number of nodes respectively.
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More script examples can be found [here](https://github.com/PeterGriffinJin/Search-R1/tree/main/example/multinode).
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```bash
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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export DATA_DIR='data/nq_search'
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WAND_PROJECT="Search-R1-release"
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RAY_DASHBOARD_ADDRESS="<address>:8265"
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N_NODES=2
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export BASE_MODEL='Qwen/Qwen2.5-7B'
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export EXPERIMENT_NAME=${train_data}-${test_data}-search-r1-ppo-qwen2.5-7b-em-multinode-$N_NODES
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# set -x
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export VLLM_ATTENTION_BACKEND=XFORMERS
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ulimit -n 65535
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ray job submit --address=$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=gae \
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actor_rollout_ref.model.path=$BASE_MODEL \
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actor_rollout_ref.actor.optim.lr=1e-6 \
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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_warmup_steps_ratio=0.285 \
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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=1 \
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actor_rollout_ref.rollout.name=vllm \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
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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.rollout.n_agent=1 \
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actor_rollout_ref.rollout.temperature=1 \
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actor_rollout_ref.rollout.top_p=1.0 \
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actor_rollout_ref.actor.state_masking=true \
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critic.optim.lr=1e-5 \
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critic.model.use_remove_padding=True \
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critic.optim.lr_warmup_steps_ratio=0.015 \
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critic.model.path=$BASE_MODEL \
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critic.model.enable_gradient_checkpointing=true \
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critic.ppo_micro_batch_size=16 \
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critic.model.fsdp_config.param_offload=False \
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critic.model.fsdp_config.grad_offload=False \
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critic.model.fsdp_config.optimizer_offload=False \
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algorithm.kl_ctrl.kl_coef=0.001 \
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algorithm.no_think_rl=false \
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trainer.critic_warmup=0 \
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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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```
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