[Feature] Multi-node Ray support for GRPO sota-implementation

sota-implementations/grpo/README.md

@@ -134,6 +134,20 @@ The async mode offers better performance by:
 - Better throughput
 - More flexible buffer management
 
+### Running GRPO on More Than One Node with SLURM
+
+GRPO can be run across more than one node using SLURM, enabling distributed training for moderately scaled workloads.
+
+Two scripts are provided for launching multi-node runs:
+
+- `grpo-sync-multi-node.sbatch`: SLURM job script that launches sync GRPO across multiple nodes using Ray.
+- `grpo-async-multi-node.sbatch`: SLURM job script that launches async GRPO across multiple nodes using Ray.
+
+Example Usage:
+
+```bash
+sbatch sota-implementations/grpo/grpo-sync-multi-node.sbatch
+
 ### KL Divergences in PPO: Reference vs Inference
 
 KL divergence is a key regularization term in policy optimization algorithms like PPO and in LLM post-training. It measures how much the updated policy diverges from a baseline or reference policy, helping to prevent the new policy from drifting too far and ensuring stable learning.

sota-implementations/grpo/grpo-async-multi-node.sbatch

@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --job-name=grpo-async-multi-node
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=96
+#SBATCH --exclusive
+#SBATCH --output=logs/%x.job%j.out
+#SBATCH --time=24:00:00
+
+# Exit on any error
+set -euo pipefail
+
+# Ensure logs directory exists
+mkdir -p logs
+
+# Environment variables
+export LIST_TO_STACK=1
+export VLLM_USE_V1=0
+export RAY_CLUSTER_MANAGED_EXTERNALLY=1
+
+# Run command in Ray cluster
+CMD="python grpo-async.py mode=async train_model.num_devices=8 ref_model.num_devices=4 inference_model.num_devices=4"
+srun bash run_in_ray_cluster.sh "$CMD"
+
+echo "Job completed"

sota-implementations/grpo/grpo-async.py

@@ -159,8 +159,11 @@ def train(
     model_metadata = vLLMUpdater.get_model_metadata(policy_training)
 
     # Create weight updater with remote LLM
+    ray_managed_externally = os.environ.get("RAY_CLUSTER_MANAGED_EXTERNALLY")
     weight_updater: vLLMUpdater = make_weight_updater(
-        master_address="localhost",  # Since we're running locally
+        master_address="localhost"
+        if not ray_managed_externally
+        else ray.util.get_node_ip_address(),
         master_port=None,  # Will auto-assign an open port
         model_metadata=model_metadata,
         vllm_tp_size=cfg.inference_model.num_devices
@@ -422,7 +425,11 @@ def main(cfg):
                 ray_init_config["runtime_env"]["env_vars"]
             )
         torchrl_logger.info(f"Ray init config: {ray_init_config=}")
-        ray.init(**ray_init_config)
+        ray_managed_externally = os.environ.get("RAY_CLUSTER_MANAGED_EXTERNALLY")
+        if ray_managed_externally:
+            ray.init(address="auto")
+        else:
+            ray.init(**ray_init_config)
 
     # Check if num_devices is set
     if cfg.inference_model.num_devices is None:

sota-implementations/grpo/grpo-sync-multi-node.sbatch

@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --job-name=grpo-sync-multi-node
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=96
+#SBATCH --exclusive
+#SBATCH --output=logs/%x.job%j.out
+#SBATCH --time=24:00:00
+
+# Exit on any error
+set -euo pipefail
+
+# Ensure logs directory exists
+mkdir -p logs
+
+# Environment variables
+export LIST_TO_STACK=1
+export VLLM_USE_V1=0
+export RAY_CLUSTER_MANAGED_EXTERNALLY=1
+
+# Run command in Ray cluster
+CMD="python grpo-sync.py mode=sync train_model.num_devices=8 ref_model.num_devices=4 inference_model.num_devices=4"
+srun bash run_in_ray_cluster.sh "$CMD"
+
+echo "Job completed"

sota-implementations/grpo/grpo-sync.py

@@ -160,8 +160,11 @@ def train(
     model_metadata = vLLMUpdater.get_model_metadata(policy_training)
 
     # Create weight updater with remote LLM
+    ray_managed_externally = os.environ.get("RAY_CLUSTER_MANAGED_EXTERNALLY")
     weight_updater: vLLMUpdater = make_weight_updater(
-        master_address="localhost",  # Since we're running locally
+        master_address="localhost"
+        if not ray_managed_externally
+        else ray.util.get_node_ip_address(),
         master_port=None,  # Will auto-assign an open port
         model_metadata=model_metadata,
         vllm_tp_size=cfg.inference_model.num_devices
@@ -436,7 +439,11 @@ def main(cfg):
                 ray_init_config["runtime_env"]["env_vars"]
             )
         torchrl_logger.info(f"Ray init config: {ray_init_config=}")
-        ray.init(**ray_init_config)
+        ray_managed_externally = os.environ.get("RAY_CLUSTER_MANAGED_EXTERNALLY")
+        if ray_managed_externally:
+            ray.init(address="auto")
+        else:
+            ray.init(**ray_init_config)
 
     # Check if num_devices is set
     if cfg.inference_model.num_devices is None:

sota-implementations/grpo/run_in_ray_cluster.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+
+set -euo pipefail
+
+# Get command from argument
+CMD="$1"
+
+# Set up Ray cluster configuration
+HEAD_NODE=$(scontrol show hostname "$SLURM_NODELIST" | head -n 1)
+RAY_PORT=6379
+
+# Get current node name 
+CURRENT_NODE=$(hostname | cut -d. -f1)
+
+# Get HEAD_NODE_IP
+if [ "$SLURM_NODEID" -eq 0 ]; then
+    # We're on the head node, get our own IP
+    HEAD_NODE_IP=$(hostname -I | awk '{print $1}')
+else
+    # We're on a worker, resolve the head node's IP using DNS
+    HEAD_NODE_IP=$(getent hosts "$HEAD_NODE" | awk '{print $1}')
+fi
+
+# Set up cleanup function
+cleanup() {
+    if command -v ray &>/dev/null; then
+        echo "Stopping Ray on node $CURRENT_NODE"
+        ray stop || true
+    fi
+}
+trap cleanup EXIT
+
+# Start Ray based on node role
+if [ "$SLURM_NODEID" -eq 0 ]; then
+    echo "Starting Ray head node on $CURRENT_NODE"
+    ray start --head --disable-usage-stats --port=$RAY_PORT
+    echo "Ray head node started at $HEAD_NODE_IP:$RAY_PORT"
+else
+    echo "Waiting for head node to be ready..."
+    sleep 10
+    echo "Starting Ray worker on node $CURRENT_NODE (ID: $SLURM_NODEID)"
+    ray start --disable-usage-stats --address="$HEAD_NODE_IP:$RAY_PORT"
+fi
+
+# Ensure Ray cluster is ready
+sleep 2
+
+# Only head node runs the training command
+if [ "$SLURM_NODEID" -eq 0 ]; then
+    echo "Starting training process on head node $CURRENT_NODE"
+    bash -c "$CMD"
+else
+    # Worker nodes just wait for the head to finish
+    while ray status --address="$HEAD_NODE_IP:$RAY_PORT" &>/dev/null; do
+        sleep 10
+    done
+fi
+
+echo "Node $CURRENT_NODE: Done"