[BREAKING][rollout,cfg] refactor: get rid of actor_rollout_ref config from rollout

tests/experimental/agent_loop/agent_utils.py

@@ -79,7 +79,7 @@ def init_agent_loop_manager(config: DictConfig) -> AgentLoopManager | RayWorkerG
         config=config,
         rm_resource_pool=rm_resource_pool,
     )
-    agent_loop_manager = AgentLoopManager(
+    agent_loop_manager = AgentLoopManager.create(
         config=config,
         worker_group=actor_rollout_wg,
         reward_loop_worker_handles=reward_loop_manager.reward_loop_workers,

tests/experimental/agent_loop/test_agent_loop_extra_fields_schema_on_cpu.py

@@ -147,7 +147,10 @@ async def test_agent_loop_extra_fields_schema_stable_for_training_concat_on_cpu(
     # Minimal config surface used by the agent loops.
     config = OmegaConf.create(
         {
-            "actor_rollout_ref": {"rollout": {"prompt_length": 16, "response_length": 16}},
+            "actor_rollout_ref": {
+                "rollout": {"prompt_length": 16, "response_length": 16, "multi_turn": {"tool_config_path": None}},
+                "model": {},
+            },
             "data": {
                 "tool_config_path": None,
                 "apply_chat_template_kwargs": {},
@@ -160,23 +163,23 @@ async def test_agent_loop_extra_fields_schema_stable_for_training_concat_on_cpu(
     processor = None
 
     trainer_config = DictConfigWrap(config)
-    dataset_config = DictConfigWrap(config.data)
+    data_config = DictConfigWrap(config.data)
 
     single_turn = SingleTurnAgentLoop(
         trainer_config=trainer_config,
         server_manager=server_manager,
         tokenizer=tokenizer,
         processor=processor,
         dataset_cls=RLHFDataset,
-        dataset_config=dataset_config,
+        data_config=data_config,
     )
     partial_single_turn = PartialSingleTurnAgentLoop(
         trainer_config=trainer_config,
         server_manager=server_manager,
         tokenizer=tokenizer,
         processor=processor,
         dataset_cls=RLHFDataset,
-        dataset_config=dataset_config,
+        data_config=data_config,
     )
 
     raw_prompt = [{"role": "user", "content": "hi"}]

tests/experimental/reward_loop/test_agent_reward_loop_colocate.py

@@ -98,7 +98,10 @@ def test_agent_reward_loop_standalone():
     )
     actor_rollout_wg.init_model()
 
-    agent_loop_manager = AgentLoopManager(config, worker_group=actor_rollout_wg)
+    agent_loop_manager = AgentLoopManager.create(
+        config=config,
+        worker_group=actor_rollout_wg,
+    )
     # sleep rollout replicas
     checkpoint_manager = CheckpointEngineManager(
         config=omega_conf_to_dataclass(config.actor_rollout_ref.rollout.checkpoint_engine),

tests/experimental/reward_loop/test_agent_reward_loop_standalone.py

@@ -56,6 +56,7 @@ def test_agent_reward_loop_standalone():
     config.actor_rollout_ref.rollout.prompt_length = 1024
     config.actor_rollout_ref.rollout.response_length = 4096
     config.actor_rollout_ref.rollout.skip_tokenizer_init = True
+    config.actor_rollout_ref.rollout.nnodes = 1
     config.trainer.n_gpus_per_node = 4
     config.trainer.nnodes = 1
 
@@ -76,8 +77,9 @@ def test_agent_reward_loop_standalone():
 
     # 1. init reward model manager
     reward_loop_manager = RewardLoopManager(config)
-    agent_loop_manager = AgentLoopManager(
-        config=config, reward_loop_worker_handles=reward_loop_manager.reward_loop_workers
+    agent_loop_manager = AgentLoopManager.create(
+        config=config,
+        reward_loop_worker_handles=reward_loop_manager.reward_loop_workers,
     )
 
     # 2. init test data

verl/experimental/agent_loop/single_turn_agent_loop.py

@@ -30,10 +30,10 @@ class SingleTurnAgentLoop(AgentLoopBase):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.prompt_length = self.config.actor_rollout_ref.rollout.prompt_length
-        self.response_length = self.config.actor_rollout_ref.rollout.response_length
+        self.prompt_length = self.rollout_config.prompt_length
+        self.response_length = self.rollout_config.response_length
 
-        tool_config_path = self.config.data.tool_config_path
+        tool_config_path = self.rollout_config.multi_turn.tool_config_path
         tool_list = initialize_tools_from_config(tool_config_path) if tool_config_path else []
         self.tool_schemas = [tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True) for tool in tool_list]
 

verl/experimental/agent_loop/tool_agent_loop.py

@@ -21,13 +21,10 @@
 
 import torch
 from PIL import Image
-from transformers import AutoProcessor, AutoTokenizer
 
 from verl.experimental.agent_loop.agent_loop import (
     AgentLoopBase,
     AgentLoopOutput,
-    AsyncLLMServerManager,
-    DictConfigWrap,
     register,
 )
 from verl.experimental.agent_loop.tool_parser import FunctionCall, ToolParser
@@ -96,37 +93,27 @@ def __init__(
 
 @register("tool_agent")
 class ToolAgentLoop(AgentLoopBase):
-    def __init__(
-        self,
-        trainer_config: DictConfigWrap,
-        server_manager: AsyncLLMServerManager,
-        tokenizer: AutoTokenizer,
-        processor: AutoProcessor,
-        **kwargs,
-    ):
-        super().__init__(trainer_config, server_manager, tokenizer, processor, **kwargs)
-        config = trainer_config.config
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
         # Initialize tools from config file
-        self.max_user_turns = config.actor_rollout_ref.rollout.multi_turn.max_user_turns
-        self.max_assistant_turns = config.actor_rollout_ref.rollout.multi_turn.max_assistant_turns
-        self.max_parallel_calls = config.actor_rollout_ref.rollout.multi_turn.max_parallel_calls
-        self.max_tool_response_length = config.actor_rollout_ref.rollout.multi_turn.max_tool_response_length
-        self.tool_response_truncate_side = config.actor_rollout_ref.rollout.multi_turn.tool_response_truncate_side
-        tool_config_path = config.actor_rollout_ref.rollout.multi_turn.tool_config_path
+        self.max_user_turns = self.rollout_config.multi_turn.max_user_turns
+        self.max_assistant_turns = self.rollout_config.multi_turn.max_assistant_turns
+        self.max_parallel_calls = self.rollout_config.multi_turn.max_parallel_calls
+        self.max_tool_response_length = self.rollout_config.multi_turn.max_tool_response_length
+        self.tool_response_truncate_side = self.rollout_config.multi_turn.tool_response_truncate_side
+        tool_config_path = self.rollout_config.multi_turn.tool_config_path
         tool_list = initialize_tools_from_config(tool_config_path) if tool_config_path else []
         self.tools = {tool.name: tool for tool in tool_list}
         self.tool_schemas = [tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True) for tool in tool_list]
-        self.tool_parser = ToolParser.get_tool_parser(
-            config.actor_rollout_ref.rollout.multi_turn.format, self.tokenizer
-        )
-        self.tool_parser_name = config.actor_rollout_ref.rollout.multi_turn.format
+        self.tool_parser = ToolParser.get_tool_parser(self.rollout_config.multi_turn.format, self.tokenizer)
+        self.tool_parser_name = self.rollout_config.multi_turn.format
 
-        self.prompt_length = config.actor_rollout_ref.rollout.prompt_length
-        self.response_length = config.actor_rollout_ref.rollout.response_length
+        self.prompt_length = self.rollout_config.prompt_length
+        self.response_length = self.rollout_config.response_length
 
         # Initialize interactions from config file
-        self.interaction_config_file = config.actor_rollout_ref.rollout.multi_turn.interaction_config_path
+        self.interaction_config_file = self.rollout_config.multi_turn.interaction_config_path
         if self.interaction_config_file:
             self.interaction_map: dict[str, BaseInteraction] = self._initialize_interactions(
                 self.interaction_config_file

verl/experimental/fully_async_policy/agent_loop/agent_loop.py

@@ -28,11 +28,11 @@
     AsyncLLMServerManager,
     DictConfigWrap,
     _agent_loop_registry,
+    _get_rollout_and_model_config,
     get_trajectory_info,
 )
-from verl.experimental.agent_loop.prometheus_utils import update_prometheus_config
 from verl.protocol import DataProto
-from verl.single_controller.ray import RayWorkerGroup
+from verl.single_controller.ray import RayResourcePool, RayWorkerGroup
 from verl.utils.rollout_trace import (
     rollout_trace_attr,
     rollout_trace_op,
@@ -102,7 +102,7 @@ async def generate_sequences_no_post(
         Returns:
             list[AgentLoopOutput]: List of agent loop outputs, one per sample in the batch.
         """
-        config = self.config.actor_rollout_ref.rollout
+        config = self.rollout_config
         sampling_params = dict(
             temperature=config.temperature,
             top_p=config.top_p,
@@ -191,7 +191,7 @@ async def _partial_run_agent_loop(
                     tokenizer=self.tokenizer,
                     processor=self.processor,
                     dataset_cls=self.dataset_cls,
-                    dataset_config=DictConfigWrap(config=self.config.data),
+                    data_config=DictConfigWrap(config=self.config.data),
                 )
                 output: AgentLoopOutput = await agent_loop.run(
                     sampling_params, cancellation_event=self.cancellation_event, **kwargs
@@ -219,15 +219,17 @@ def __init__(
         self,
         config: DictConfig,
         worker_group: RayWorkerGroup = None,
+        rollout_resource_pool: RayResourcePool = None,
         reward_loop_worker_handles: list[ray.actor.ActorHandle] = None,
     ):
         self.config = config
+        self.rollout_config, self.model_config = _get_rollout_and_model_config(config)
         self.worker_group = worker_group
         self.reward_loop_worker_handles = reward_loop_worker_handles
         self.agent_loop_workers_class = FullyAsyncAgentLoopWorker
 
         # Select rollout replica class based on rollout name
-        rollout_name = config.actor_rollout_ref.rollout.name
+        rollout_name = self.rollout_config.name
         if rollout_name == "sglang":
             from verl.experimental.fully_async_policy.sglang_rollout.sglang_async_server import FullyAsyncSGLangReplica
 
@@ -246,63 +248,6 @@ def __init__(
         self.server_addresses = None
         self.agent_loop_workers = None
 
-    @classmethod
-    async def create(
-        cls,
-        config: DictConfig,
-        worker_group: RayWorkerGroup = None,
-        reward_loop_worker_handles: list[ray.actor.ActorHandle] = None,
-    ):
-        instance = cls(config, worker_group, reward_loop_worker_handles)
-        await instance._async_init()
-        return instance
-
-    async def _async_init(self):
-        await self._initialize_llm_servers_async()
-        self._init_agent_loop_workers()
-
-    async def _initialize_llm_servers_async(self):
-        rollout_world_size = (
-            self.config.actor_rollout_ref.rollout.tensor_model_parallel_size
-            * self.config.actor_rollout_ref.rollout.data_parallel_size
-            * self.config.actor_rollout_ref.rollout.pipeline_model_parallel_size
-        )
-        world_size = (
-            self.worker_group.world_size
-            if self.worker_group
-            else self.config.rollout.n_gpus_per_node * self.config.rollout.nnodes
-        )
-        num_replicas = world_size // rollout_world_size
-
-        rollout_config = self.config.actor_rollout_ref.rollout
-        model_config = self.config.actor_rollout_ref.model
-        self.rollout_replicas = [
-            self.rollout_replica_class(
-                replica_rank=replica_rank,
-                config=rollout_config,
-                model_config=model_config,
-                gpus_per_node=self.config.rollout.n_gpus_per_node,
-            )
-            for replica_rank in range(num_replicas)
-        ]
-
-        if self.worker_group:
-            await asyncio.gather(*[server.init_hybrid(self.worker_group) for server in self.rollout_replicas])
-        else:
-            await asyncio.gather(*[server.init_standalone() for server in self.rollout_replicas])
-
-        self.server_handles = [server._server_handle for server in self.rollout_replicas]
-        self.server_addresses = [server._server_address for server in self.rollout_replicas]
-
-        print(f"AgentLoopManager: {self.server_addresses}")
-        # Update Prometheus configuration with server addresses
-        if rollout_config.prometheus.enable:
-            if rollout_config.disable_log_stats:
-                raise ValueError("PROMETHEUS needs disable_log_stats==False, but it is currently True.")
-            await asyncio.to_thread(
-                update_prometheus_config, rollout_config.prometheus, self.server_addresses, rollout_config.name
-            )
-
     async def generate_single_sample_async(
         self,
         sample: DataProto,

verl/experimental/fully_async_policy/agent_loop/partial_single_turn_agent_loop.py

@@ -30,9 +30,9 @@ class PartialSingleTurnAgentLoop(AgentLoopBase):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.prompt_length = self.config.actor_rollout_ref.rollout.prompt_length
-        self.response_length = self.config.actor_rollout_ref.rollout.response_length
-        self.apply_chat_template_kwargs = self.config.data.get("apply_chat_template_kwargs", {})
+        self.prompt_length = self.rollout_config.prompt_length
+        self.response_length = self.rollout_config.response_length
+        self.apply_chat_template_kwargs = self.data_config.get("apply_chat_template_kwargs", {})
 
     async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput:
         output: Optional[AgentLoopOutput] = kwargs.get("output", None)

verl/experimental/fully_async_policy/agent_loop/partial_tool_agent_loop.py

@@ -33,9 +33,9 @@ class AsyncPartialToolAgentLoop(ToolAgentLoop):
 
     """
 
-    def __init__(self, trainer_config, **kwargs):
-        super().__init__(trainer_config, **kwargs)
-        self.enable_partial_rollout = trainer_config.config.async_training.get("partial_rollout", False)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.enable_partial_rollout = self.config.async_training.get("partial_rollout", False)
 
     # async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput:
     async def run(

verl/experimental/fully_async_policy/fully_async_main.py

@@ -287,6 +287,9 @@ def main(config):
     from time import time
 
     start_time = time()
+    # TODO: unify rollout config with actor_rollout_ref
+    config.actor_rollout_ref.rollout.nnodes = config.rollout.nnodes
+    config.actor_rollout_ref.rollout.n_gpus_per_node = config.rollout.n_gpus_per_node
     run_ppo(config, task_runner_class=FullyAsyncTaskRunner)
     print(f"total time: {time() - start_time:.2f} seconds")
 

verl/experimental/one_step_off_policy/agent_loop/agent_loop.py

@@ -18,9 +18,7 @@
 import ray
 
 from verl.experimental.agent_loop.agent_loop import AgentLoopManager
-from verl.experimental.agent_loop.prometheus_utils import update_prometheus_config
 from verl.protocol import DataProto
-from verl.single_controller.ray import RayResourcePool
 
 logger = logging.getLogger(__file__)
 logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN"))
@@ -56,54 +54,6 @@ async def generate_sequences_async(self, prompts: DataProto) -> DataProto:
         output.meta_info = {"timing": timing, **outputs[0].meta_info}
         return output
 
-    def _initialize_llm_servers(self, rollout_resource_pool: RayResourcePool):
-        rollout_world_size = (
-            self.config.actor_rollout_ref.rollout.tensor_model_parallel_size
-            * self.config.actor_rollout_ref.rollout.data_parallel_size
-            * self.config.actor_rollout_ref.rollout.pipeline_model_parallel_size
-        )
-        world_size = (
-            self.worker_group.world_size
-            if self.worker_group
-            else self.config.rollout.n_gpus_per_node * self.config.rollout.nnodes
-        )
-        num_replicas = world_size // rollout_world_size
-
-        rollout_config = self.config.actor_rollout_ref.rollout
-        model_config = self.config.actor_rollout_ref.model
-        self.rollout_replicas = [
-            self.rollout_replica_class(
-                replica_rank=replica_rank,
-                config=rollout_config,
-                model_config=model_config,
-                gpus_per_node=self.config.rollout.n_gpus_per_node,
-            )
-            for replica_rank in range(num_replicas)
-        ]
-
-        if self.worker_group and rollout_config.name != "trtllm":
-            self._run_all([server.init_hybrid(self.worker_group) for server in self.rollout_replicas])
-        elif self.worker_group and rollout_config.name == "trtllm":
-            self._run_all(
-                [
-                    server.init_hybrid_colocated(self.worker_group, rollout_resource_pool)
-                    for server in self.rollout_replicas
-                ]
-            )
-        else:
-            self._run_all([server.init_standalone() for server in self.rollout_replicas])
-
-        self.server_handles = [server._server_handle for server in self.rollout_replicas]
-        self.server_addresses = [server._server_address for server in self.rollout_replicas]
-
-        print(f"AgentLoopManager: {self.server_addresses}")
-
-        # Update Prometheus configuration with server addresses
-        if rollout_config.prometheus.enable:
-            if rollout_config.disable_log_stats:
-                raise ValueError("PROMETHEUS needs disable_log_stats==False, but it is currently True.")
-            update_prometheus_config(rollout_config.prometheus, self.server_addresses, rollout_config.name)
-
     async def wake_up(self):
         await asyncio.gather(*[replica.wake_up() for replica in self.rollout_replicas])
 

verl/experimental/one_step_off_policy/main_ppo.py

@@ -182,6 +182,10 @@ def main(config):
     # Automatically set `config.trainer.device = npu` when running on Ascend NPU.
     auto_set_device(config)
 
+    # TODO: unify rollout config with actor_rollout_ref
+    config.actor_rollout_ref.rollout.nnodes = config.rollout.nnodes
+    config.actor_rollout_ref.rollout.n_gpus_per_node = config.rollout.n_gpus_per_node
+
     run_ppo(config, task_runner_class=OneStepTaskRunner)
     print(f"total time: {time() - start_time:.2f} seconds")
 

verl/experimental/one_step_off_policy/ray_trainer.py

@@ -182,7 +182,7 @@ def _init_async_rollout_manager(self):
         from verl.experimental.one_step_off_policy.agent_loop import OneStepOffAgentLoopManager
 
         self.async_rollout_mode = True
-        self.async_rollout_manager = OneStepOffAgentLoopManager(
+        self.async_rollout_manager = OneStepOffAgentLoopManager.create(
             config=self.config, reward_loop_worker_handles=reward_loop_worker_handles
         )