add multinode support

docs/multinode.md

@@ -0,0 +1,134 @@
+
+## Multinode Training
+
+Our codebase supports multi-node training for large-scale language models. The implementation is mainly based on [Ray](https://github.com/ray-project/ray).
+
+There are two types of nodes when doing Ray multi-node training: (1) head node and (2) worker nodes.
+There is only one head node where you will start the ray cluster and submit the job.
+The other nodes are worker nodes, where you only need to start and register to the ray cluster.
+
+### Step 1: Set up multinode ray cluster (from [link](https://verl.readthedocs.io/en/latest/start/multinode.html#set-up-multinode-ray-cluster))
+
+a. Start **head** node with ```ray start --head --dashboard-host=0.0.0.0```, there’re 2 address you should care about:
+
+- GCS address: ```ray start --address=<address>```, where **worker** node should connect to.
+
+- Dashboard address: ```<address>:8265```, where you should submit job to the cluster.
+
+![head](../public/head.png)
+
+b. Start **worker node** and register it to the ray cluster with ```ray start --address=<address>``` you get above.
+
+![worker](../public/worker.png)
+
+c. Check the cluster status with ```ray status```.
+
+For example, if you have two nodes (each with 8 GPUs) in the cluster, you should see something like this:
+
+![status](../public/status.png)
+
+
+### Step 2: Launch the retrieval server on every node.
+
+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).
+
+For example, if you want to launch the local dense retriever with flat indexing, run the following command on **every** nodes:
+
+```
+bash retrieval_launch.sh
+```
+
+
+### Step 3: Start the job
+
+After the retrievers are launched, you can start the training job. You only need to start the job on the ***head*** node.
+
+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.
+
+More script examples can be found [here](https://github.com/PeterGriffinJin/Search-R1/tree/main/example/multinode).
+
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export DATA_DIR=/home/peterjin/mnt/Search-R1/data/nq_search
+
+WAND_PROJECT="Search-R1-release"
+RAY_DASHBOARD_ADDRESS="<address>:8265"
+N_NODES=2
+
+export BASE_MODEL='Qwen/Qwen2.5-7B'
+export EXPERIMENT_NAME=${train_data}-${test_data}-search-r1-ppo-qwen2.5-7b-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=$BASE_MODEL \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.enable_gradient_checkpointing=true \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.285 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
+    actor_rollout_ref.actor.ppo_micro_batch_size=64 \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.grad_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size=128 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
+    actor_rollout_ref.ref.log_prob_micro_batch_size=128 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=False \
+    actor_rollout_ref.rollout.n_agent=1 \
+    actor_rollout_ref.rollout.temperature=1 \
+    actor_rollout_ref.rollout.top_p=1.0 \
+    actor_rollout_ref.actor.state_masking=true \
+    critic.optim.lr=1e-5 \
+    critic.model.use_remove_padding=True \
+    critic.optim.lr_warmup_steps_ratio=0.015 \
+    critic.model.path=$BASE_MODEL \
+    critic.model.enable_gradient_checkpointing=true \
+    critic.ppo_micro_batch_size=16 \
+    critic.model.fsdp_config.param_offload=False \
+    critic.model.fsdp_config.grad_offload=False \
+    critic.model.fsdp_config.optimizer_offload=False \
+    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=false \
+    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
+```

verl/trainer/ppo/ray_trainer.py

@@ -448,7 +448,7 @@ class RayPPOTrainer(object):
             max_prompt_length=self.config.data.max_prompt_length,
             max_response_length=self.config.data.max_response_length,
             max_obs_length=self.config.data.max_obs_length,
-            num_gpus=self.config.trainer.n_gpus_per_node,
+            num_gpus=self.config.trainer.n_gpus_per_node * self.config.trainer.nnodes,
             no_think_rl=self.config.algorithm.no_think_rl,
             search_url = self.config.retriever.url,
             topk = self.config.retriever.topk,
@@ -679,7 +679,7 @@ class RayPPOTrainer(object):
             max_prompt_length=self.config.data.max_prompt_length,
             max_response_length=self.config.data.max_response_length,
             max_obs_length=self.config.data.max_obs_length,
-            num_gpus=self.config.trainer.n_gpus_per_node,
+            num_gpus=self.config.trainer.n_gpus_per_node * self.config.trainer.nnodes,
             no_think_rl=self.config.algorithm.no_think_rl,
             search_url = self.config.retriever.url,
             topk = self.config.retriever.topk,