chore: import upstream snapshot with attribution

examples/train/grpo/multi_node/server_multi_node.sh

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+# NOTE: Requires NCCL connectivity between the training master node and rollout nodes
+# This script demonstrates multi-node rollout and multi-node training with swift.
+# node1 and node2: multi-node rollout servers
+# node3 and node4: distributed training nodes
+
+# --- Rollout Section ---
+# For rollout, you can launch any number of servers on different nodes
+# Start rollout server on node1:
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift rollout \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --vllm_tensor_parallel_size 2 \
+    --vllm_data_parallel_size 2 \
+    --port <node1_port>
+
+# Start rollout server on node2:
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift rollout \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --vllm_tensor_parallel_size 2 \
+    --vllm_data_parallel_size 2 \
+    --port <node2_port>
+
+# --- Training Section ---
+# node3: Master training node (rank 0)
+NNODES=2 \
+NODE_RANK=0 \
+MASTER_ADDR=127.0.0.1 \
+MASTER_PORT=29500 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=4 \
+swift rlhf \
+    --rlhf_type grpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --reward_funcs accuracy \
+    --use_vllm true \
+    --vllm_mode server \
+    --vllm_server_host <node1_ip> <node2_ip> \
+    --vllm_server_port <node1_port> <node2_port> \
+    --dataset AI-MO/NuminaMath-TIR#1000 \
+    --load_from_cache_file true \
+    --max_completion_length 2048 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 1 \
+    --learning_rate 1e-6 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_generations 4 \
+    --deepspeed zero2 \
+    --log_completions true \
+
+# node4: Secondary training node (rank 1)
+NNODES=2 \
+NODE_RANK=1 \
+MASTER_ADDR=<node3_ip> \
+MASTER_PORT=29500 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=4 \
+swift rlhf \
+    --rlhf_type grpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --reward_funcs accuracy \
+    --use_vllm true \
+    --vllm_mode server \
+    --vllm_server_host <node1_ip> <node2_ip> \
+    --vllm_server_port <node1_port> <node2_port> \
+    --dataset AI-MO/NuminaMath-TIR#1000 \
+    --load_from_cache_file true \
+    --max_completion_length 2048 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 1 \
+    --learning_rate 1e-6 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_generations 4 \
+    --deepspeed zero2 \
+    --log_completions true \