Decouple vLLM engine and GRPOTrainer.

docs/source/Instruction/GRPO.md

@@ -16,13 +16,34 @@ pip install -U trl
 
 ![](../../resources/grpo.png)
 
-SWIFT的GRPO训练中，训练模型尽量使用可见显卡的前部分，而rollout尽量使用可见显卡的后部分。这意味着
+GRPO 训练框架支持集成高性能推理引擎（如 vLLM）来加速采样过程，提供以下两种部署模式：
 
-- 如果命令中NPROC_PER_NODE和num_infer_workers相等，都是可见显卡数量，训练和推理放在了相同显卡上，这时需要配置sleep_level
-- 如果命令中NPROC_PER_NODE加上num_infer_workers等于可见显卡数量，则训练使用前部分卡，rollout使用后部分卡，这时可以配置async_generate
+### 1. 内部集成模式 (Internal)
+
+- 在Trainer内部直接启动推理服务
+- 提供两种资源分配策略：
+  - **协同模式 (Colocate)**: 训练与推理共享GPU资源
+  - **异步模式 (Async)**: 训练与推理使用独立GPU资源
+
+### GRPO训练资源配置方案
+| 配置场景                 | NPROC_PER_NODE | num_infer_workers | 资源分配说明             |
+|--------------------------|----------------|------------------|------------------------|
+| **Colocate**   | =总GPU数      | =总GPU数          | 训练和推理共享全部GPU资源              |
+| **Async**      | =训练卡数      | =推理卡数         | 必须满足：训练卡数 + 推理卡数 = 总GPU数 |
+
+**注：**
+1. 在Colocate模式下推荐设置`sleep_level=1`, 在模型训练时释放vLLM占用显存
+2. 总GPU数指可见的GPU设备总数
+
+### 2. 外部服务模式 (External)
+连接外部的 vLLM 推理服务器
+使用时，使用以下参数配置外部 vLLM 服务器
+```bash
+--vllm_server_host <服务器IP> \
+--vllm_server_port <服务端口> \
+--vllm_server_timeout <超时时间> \
+```
 
-> async_generate实际上使用了step-1的policy model，因此`clip`操作实际上不生效。如果训练中不稳定或者无法收敛，可以尝试关掉此参数。
-> 在我们的实际实验中，即使开启async_generate后不稳定或不收敛的情况较少出现。
 
 ## 奖励函数
 ### 自定义奖励函数
@@ -122,8 +143,15 @@ A conversation between User and Assistant. The user asks a question, and the Ass
   - 提示：若没有设置`--report_to wandb`，则会在checkpoint中创建`completions.jsonl`来存储生成内容
 - use_vllm: 是否使用vLLM作为采样的生成后端，默认为False，建议使用加快训练速度
 - vllm_device: 设置vLLM部署的设备，默认为`auto`, 即未被使用的第一张显卡，使用`cuda:x`来设置特定的卡。
-- vllm_gpu_memory_utilization: vLLM透传参数
-- vllm_max_model_len: vLLM透传参数
+- vllm_gpu_memory_utilization: vllm透传参数，默认为0.9
+- vllm_max_model_len: vllm透传参数，默认为None
+- vllm_max_num_seqs: vllm透传参数，默认为256
+- vllm_enforce_eager: vllm透传参数，默认为False
+- vllm_limit_mm_per_prompt: vllm透传参数，默认为None
+- vllm_enable_prefix_caching: vllm透传参数，默认为True
+- vllm_server_host：vLLM server host地址，默认为None，使用外部vLLM server时使用
+- vllm_server_port vLLM server 服务端口，默认为8000
+- vllm_server_timeout 连接vLLM server的超时时间，默认为120s
 - reward_model: 同model, 使用奖励模型作为奖励函数，与reward_funcs至少需要指定一个
 - num_iterations: 每个批次代更新次数，默认为1.
 - epsilon: clip 系数，默认为0.2.
@@ -140,6 +168,10 @@ A conversation between User and Assistant. The user asks a question, and the Ass
 - dynamic_sample：筛除group内奖励标准差为0的数据，额外采样新数据，默认为False。
 - max_resample_times：dynamic_sample设置下限制重采样次数，默认3次。
 - overlong_filter：跳过超长截断的样本，不参与loss计算，默认为False。
+- vllm_server_host：vLLM server host地址，默认为None，使用外部vLLM server时使用 \
+- vllm_server_port vLLM server 服务端口，默认为8000 \
+- vllm_server_timeout 连接vLLM server的超时时间，默认为120s \
+
 
 奖励函数参数，见[内置奖励函数](#内置奖励函数)
 
@@ -299,6 +331,12 @@ swift rlhf \
     --system 'examples/train/grpo/prompt.txt' \
     --log_completions true
 ```
+多机训练参考[这里](../../../examples/train/grpo/multi_node/)
+
+注：内部集成模式下，需要不同节点的GPU配置以及训练参数相同
+
+
+
 
 ## DAPO
 [Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO)](https://arxiv.org/abs/2503.14476)在GRPO的基础上设置了几种trick，分别是

docs/source/Instruction/命令行参数.md

@@ -401,6 +401,9 @@ reward模型参数将在PPO、GRPO中使用。
 - vllm_enforce_eager: vllm透传参数，默认为False
 - vllm_limit_mm_per_prompt: vllm透传参数，默认为None
 - vllm_enable_prefix_caching: vllm透传参数，默认为True
+- vllm_server_host：vLLM server host地址，默认为None，使用外部vLLM server时使用
+- vllm_server_port vLLM server 服务端口，默认为8000
+- vllm_server_timeout 连接vLLM server的超时时间，默认为120s
 - top_k: 默认为50
 - top_p: 默认为0.9
 - repetition_penalty: 重复惩罚项。默认为1.
@@ -468,7 +471,7 @@ soft overlong 奖励参数
   - 注意：在`swift app`或者`swift eval`时，默认为False
 - log_interval: tokens/s统计值打印间隔，默认20秒。设置为-1则不打印
 - max_logprobs: 最多返回客户端的logprobs数量，默认为20
-
+- use_async_engine: vLLM backend下是否使用async engine，默认为True
 
 ### Web-UI参数
 - server_name: web-ui的host，默认为'0.0.0.0'

docs/source_en/Instruction/Command-line-parameters.md

@@ -412,6 +412,9 @@ The meanings of the following parameters can be referenced [here](https://huggin
 - vllm_enforce_eager: vLLM passthrough parameter, default is False.
 - vllm_limit_mm_per_prompt: vLLM passthrough parameter, default is None.
 - vllm_enable_prefix_caching: vLLM passthrough parameter, default is True.
+- vllm_server_host: The host address of the vLLM server. Default is None. This is used when connecting to an external vLLM server.
+- vllm_server_port: The service port of the vLLM server. Default is 8000.
+- vllm_server_timeout: The connection timeout for the vLLM server. Default is 120 seconds.
 - top_k: Default is 50.
 - top_p: Default is 0.9.
 - repetition_penalty: Repetition penalty term. Default is 1.
@@ -482,6 +485,8 @@ Deployment Arguments inherit from the [inference arguments](#inference-arguments
   - Note: In `swift app` or `swift eval`, the default is False.
 - log_interval: Interval for printing tokens/s statistics, default is 20 seconds. If set to -1, it will not be printed.
 - max_logprobs: Maximum number of logprobs returned to the client, with a default value of 20.
+- use_async_engine: Whether to use the async engine under the vLLM backend. Default is True.
+
 
 ### Web-UI Arguments
 - server_name: Host for the web UI, default is '0.0.0.0'.

docs/source_en/Instruction/GRPO.md

@@ -17,14 +17,36 @@ pip install -U trl
 
 ![](../../resources/grpo.png)
 
-In SWIFT's GRPO training, the training model preferentially uses the front portion of the available GPUs, while the rollout process utilizes the rear portion of the available GPUs. This means:
+The GRPO training framework supports the integration of high-performance inference engines (such as vLLM) to accelerate the sampling process, offering the following two deployment modes:
 
-- **If both `NPROC_PER_NODE` and `num_infer_workers` in the command are equal to the number of available GPUs**, training and inference are assigned to the same GPUs. In this case, you need to configure `sleep_level`.
-- **If the sum of `NPROC_PER_NODE` and `num_infer_workers` equals the total number of available GPUs**, training will use the front GPUs and rollout will use the rear GPUs. In this scenario, you can configure `async_generate`.
+### 1. Internal Integration Mode
 
-> Note: async_generate uses the policy model and responses of current_step-1, so in fact the `clip` method will be ignored
-> If you encountered unstable in training, turn off this argument.
-> In our experiments, unstable cases is not frequently occurring when async_generate is true.
+- Launch the inference service directly within the Trainer.
+- Provides two resource allocation strategies:
+  - **Colocate Mode**: Training and inference share GPU resources.
+  - **Async Mode**: Training and inference use separate GPU resources.
+
+### GRPO Training Resource Allocation Scheme
+
+| Configuration Scenario  | NPROC_PER_NODE | num_infer_workers | Resource Allocation Description       |
+|-------------------------|----------------|-------------------|---------------------------------------|
+| **Colocate**            | = Total GPUs   | = Total GPUs      | Training and inference share all GPU resources. |
+| **Async**               | = Training GPUs| = Inference GPUs  | Must satisfy: Training GPUs + Inference GPUs = Total GPUs. |
+
+**Note:**
+1. In Colocate mode, it is recommended to set `sleep_level=1` to release the GPU memory occupied by vLLM during model training.
+2. Total GPUs refers to the total number of visible GPU devices.
+
+### 2. External Service Mode
+
+Connect to an external vLLM inference server.
+When using this mode, configure the external vLLM server with the following parameters:
+
+```bash
+--vllm_server_host <Server IP> \
+--vllm_server_port <Server Port> \
+--vllm_server_timeout <Timeout> \
+```
 
 ## Reward Functions
 ### Custom Reward Functions
@@ -125,8 +147,15 @@ Arguments
   - Note: If `--report_to wandb` is not set, a `completions.jsonl` will be created in the checkpoint to store the generated content.
 - use_vllm: Whether to use vLLM as the back-end for sampling generation; default is False, using it is recommended to speed up training.
 - vllm_device: Device for deploying vLLM, default is auto, meaning the first unused GPU. Use cuda:x to specify a particular card.
-- vllm_gpu_memory_utilization: vLLM pass-through parameter.
-- vllm_max_model_len: vLLM pass-through parameter.
+- vllm_gpu_memory_utilization: vLLM passthrough parameter, default is 0.9.
+- vllm_max_model_len: vLLM passthrough parameter, default is None.
+- vllm_max_num_seqs: vLLM passthrough parameter, default is 256.
+- vllm_enforce_eager: vLLM passthrough parameter, default is False.
+- vllm_limit_mm_per_prompt: vLLM passthrough parameter, default is None.
+- vllm_enable_prefix_caching: vLLM passthrough parameter, default is True.
+- vllm_server_host: The host address of the vLLM server. Default is None. This is used when connecting to an external vLLM server.
+- vllm_server_port: The service port of the vLLM server. Default is 8000.
+- vllm_server_timeout: The connection timeout for the vLLM server. Default is 120 seconds.
 - reward_model: Same as the model, using a reward model as a reward function. At least one of reward_funcs and reward_model needs to be specified.
 - num_iterations: number of iterations per batch. Default is 1.
 - epsilon: epsilon value for clipping. Default is 0.2.
@@ -302,6 +331,10 @@ swift rlhf \
     --log_completions true
 ```
 
+For multi-node training, refer to [here](../../../examples/train/grpo/multi_node/) .
+
+Note : In the internal integration mode, the GPU configurations and training parameters must be identical across different nodes.
+
 ## DAPO
 Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) introduces several tricks based on GRPO, which are:
 - Clip Higher

examples/train/grpo/multi_node/Qwen2_5_32B_full.sh

@@ -0,0 +1,50 @@
+# External vLLM
+
+# Assume we have two nodes, one with 8 GPUs of 80GB each (880G) and another with 2 GPUs of 80GB each (2 80G).
+#   NODE1. The node with 2*80G will be used to deploy the vLLM server.
+#   NODE2. The node with 8*80G will be used for full-parameter fine-tuning of the 32B model.
+
+# Note : Use beta=0 to disable the reference model; otherwise, it may lead to Out-of-Memory (OOM) errors.
+
+# NODE1 for vLLM Server
+CUDA_VISIBLE_DEVICES=0,1 \
+swift deploy \
+    --model Qwen/Qwen2.5-32B-Instruct \
+    --infer_backend vllm \
+    --use_async_engine false \
+    --tensor_parallel_size 2
+
+# NODE2 for Training
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+NPROC_PER_NODE=8 \
+swift rlhf \
+    --rlhf_type grpo \
+    --model Qwen/Qwen2.5-32B-Instruct \
+    --reward_funcs accuracy \
+    --use_vllm true \
+    --vllm_server_host xxx \
+    --vllm_server_port 8000 \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --dataset AI-MO/NuminaMath-TIR#1000 \
+    --max_completion_length 2048 \
+    --num_train_epochs 3 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-6 \
+    --gradient_accumulation_steps 1 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_generations 8 \
+    --temperature 1.0 \
+    --top_p 0.9 \
+    --top_k 50 \
+    --deepspeed zero3 \
+    --log_completions true \
+    --num_iterations 1 \
+    --num_infer_workers 1 \
+    --report_to tensorboard wandb \
+    --beta 0.0

examples/train/grpo/multi_node/multi_node1.sh

@@ -1,5 +1,8 @@
+# Internal vLLM
+
 # pip install math_verify # reward function
 # pip install -U trl
+# note: Note: The parameters of each node need to be consistent.
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 export NNODES=2
 export NODE_RANK=0

examples/train/grpo/multi_node/train_dlc.sh

@@ -1,3 +1,5 @@
+# This script is used in DLC (Deep Learning Containers)
+# For more information, visit:https://www.aliyun.com/activity/bigdata/pai-dlc
 NNODES=$WORLD_SIZE \
 NODE_RANK=$RANK \
 PYTHONPATH=. \

swift/llm/argument/infer_args.py

@@ -127,6 +127,8 @@ class InferArguments(MergeArguments, VllmArguments, LmdeployArguments, BaseArgum
     # only for inference
     val_dataset_sample: Optional[int] = None
 
+    use_async_engine: bool = True
+
     def _get_result_path(self, folder_name: str) -> str:
         result_dir = self.ckpt_dir or f'result/{self.model_suffix}'
         os.makedirs(result_dir, exist_ok=True)

swift/llm/argument/rlhf_args.py

@@ -5,7 +5,7 @@
 
 from swift.llm import MODEL_MAPPING
 from swift.trainers.arguments import GRPOArgumentsMixin
-from swift.utils import get_logger, set_default_ddp_config
+from swift.utils import get_logger, is_master, set_default_ddp_config
 from .train_args import TrainArguments
 
 logger = get_logger()
@@ -109,9 +109,11 @@ def __post_init__(self):
         self._init_simpo()
         self._init_ppo()
         self._set_default()
+        self._init_external_vllm()
         super().__post_init__()
         self._check_rlhf()
         self._check_grpo()
+        self._external_vllm_warning()
 
         if self.loss_scale is None:
             if self.rlhf_type == 'orpo' and not self.model_meta.is_multimodal:
@@ -185,6 +187,14 @@ def _init_rm(self):
             self.task_type = 'seq_cls'
             self.num_labels = 1
 
+    def _init_external_vllm(self):
+        if self.rlhf_type != 'grpo' or self.vllm_server_host is None:
+            return
+        from swift.trainers.rlhf_trainer.vllm_client import VLLMClient
+        if is_master():
+            self.vllm_client = VLLMClient(
+                self.vllm_server_host, self.vllm_server_port, connection_timeout=self.vllm_server_timeout)
+
     def _set_default(self):
         if self.beta is None:
             self.beta = 0.1
@@ -206,7 +216,7 @@ def _check_grpo(self):
         _, _, _, local_world_size = get_dist_setting()
         num_infer_workers = self.num_infer_workers
         fast_infer = self.use_vllm or self.use_lmdeploy
-        if fast_infer:
+        if fast_infer and self.vllm_server_host is None:
             is_colocate_mode = (device_count == num_infer_workers)
 
             if is_colocate_mode:
@@ -241,3 +251,23 @@ def _check_grpo(self):
         if self.mini_batch_size:
             assert self.per_device_train_batch_size % self.mini_batch_size == 0,\
                 'per_device_train_batch_size needs be divisible by mini_batch_size'
+
+    def _external_vllm_warning(self):
+        if self.rlhf_type != 'grpo' or not self.vllm_server_host:
+            return
+
+        if self.vllm_device != 'auto':
+            logger.warning("Configuration conflict: External vLLM engine detected, but 'vllm_device' is set to '%s'. ",
+                           self.vllm_device)
+
+        if self.num_infer_workers != 1:
+            logger.warning(
+                "Auto-adjustment: Changing 'num_infer_workers' from %s to 1 because external vLLM engine is detected",
+                self.num_infer_workers)
+            self.num_infer_workers = 1
+
+        if self.vllm_max_model_len is not None:
+            logger.warning(
+                "Configuration conflict: 'vllm_max_model_len=%s' is ignored for external vLLM. "
+                'Please specify it when launching the inference service: '
+                '`swift deploy --max_model_len <value>`', self.vllm_max_model_len)