add rollout controller

impl rollout controller

Author: chucai.dzq

areal/api/controller_api.py

@@ -315,7 +315,7 @@ def set_version(self, version: int):
         """
         raise NotImplementedError()
 
-    def get_version(self) -> int:
+    def get_version(self) -> List[int]:
         """Get the current weight version in the training engine.
 
         Returns
@@ -359,7 +359,7 @@ def train_batch(
         input_: DistributedBatch,
         loss_fn: Callable[[torch.Tensor, Dict[str, Any]], torch.Tensor],
         loss_weight_fn: Callable[[Dict[str, Any]], torch.Tensor],
-    ) -> Dict[str, float]:
+    ) -> List[Dict[str, float]]:
         """Update the model with a batch of data and a loss function.
 
         Note
@@ -382,7 +382,7 @@ def train_batch(
 
         Returns
         -------
-        Dict[str, float]
+        List[Dict[str, float]]
             Scalar statistics after training, e.g., the current learning rate,
             gradient norm, etc.
         """
@@ -394,7 +394,7 @@ def eval_batch(
         input_: DistributedBatch,
         loss_fn: Callable[[torch.Tensor, Dict[str, Any]], torch.Tensor],
         loss_weight_fn: Callable[[Dict[str, Any]], torch.Tensor],
-    ) -> torch.Tensor | None:
+    ) -> List[torch.Tensor]:
         """Evaluate the model using the forward pass and loss function.
 
         Note
@@ -458,7 +458,6 @@ def forward(
         """
         raise NotImplementedError()
 
-
 class RolloutController(abc.ABC):
     """A centralized controller that manages multiple distributed InferenceEngine workers for rollout generation.
 
@@ -508,21 +507,6 @@ def destroy(self):
         """Destroy the engine and release GPU memory for the local inference engine."""
         raise NotImplementedError()
 
-    async def agenerate(self, req: ModelRequest) -> ModelResponse:
-        """Asynchronously generate a response for the given request.
-
-        Parameters
-        ----------
-        req : ModelRequest
-            The model request containing input data and generation parameters
-
-        Returns
-        -------
-        ModelResponse
-            The generated response from the model
-        """
-        raise NotImplementedError()
-
     def update_weights(self, meta: WeightUpdateMeta) -> Future:
         """Update weights in the inference engine in a non-blocking manner.
 
@@ -571,7 +555,7 @@ def get_version(self) -> int:
 
     def submit(
         self,
-        data: Dict[str, Any],
+        data: DistributedBatch,
         workflow: Optional["RolloutWorkflow"] = None,
         workflow_builder: Optional[Callable] = None,
         should_accept: Callable | None = None,
@@ -623,7 +607,7 @@ def wait(self, count: int, timeout: float | None = None) -> DistributedBatch:
 
     def rollout_batch(
         self,
-        data: List[Dict[str, Any]],
+        data: DistributedBatch,
         workflow: Optional["RolloutWorkflow"] = None,
         workflow_builder: Optional[Callable] = None,
         should_accept: Callable | None = None,
@@ -652,7 +636,7 @@ def rollout_batch(
 
     def prepare_batch(
         self,
-        dataloader: StatefulDataLoader,
+        dataloader: DistributedBatch,
         workflow: Optional["RolloutWorkflow"] = None,
         workflow_builder: Optional[Callable] = None,
         should_accept: Callable | None = None,
@@ -688,31 +672,4 @@ def pause(self):
 
     def resume(self):
         """Resume request submission for async rollout."""
-        raise NotImplementedError()
-
-    def register_callback_to_all_worker(
-        self, method: str, callback: Callable, **kwargs
-    ):
-        """Register a callback function for the specified method across all workers.
-
-        Partial rollout API. After successful registration, the controller will poll
-        and call the specified method in a background thread. When the return value
-        is obtained, it will be used as a parameter to call the `callback` function.
-
-        Parameters
-        ----------
-        method : str
-            The name of the method to register the callback for
-        callback : Callable
-            The callback function to be called with the method's return value
-        **kwargs
-            Additional keyword arguments for the callback registration
-        """
-        raise NotImplementedError()
-
-    def abort_all_requests(self) -> None:
-        """Abort all ongoing requests in the inference engine.
-
-        Partial rollout API for canceling all queued and in-progress requests.
-        """
-        raise NotImplementedError()
+        raise NotImplementedError()

areal/api/engine_api.py

@@ -25,6 +25,8 @@ class Scheduling:
     cpu: int
     gpu: int
     mem: int
+    port_count: int
+    cmd: str | None = None
     nodelist: str | None = None
     exclude: str | None = None
     partition: str | None = None
@@ -138,7 +140,7 @@ def parallelism_group(self) -> dist.ProcessGroup:
         """
         raise NotImplementedError()
 
-    def get_scheduling_config(self) -> Scheduling:
+    def get_scheduling_config(self) -> List[Scheduling]:
         """Get the scheduling configuration for the engine.
 
         This includes configuration such as container image, CPU/GPU/memory size.
@@ -553,3 +555,15 @@ def pause(self):
     def resume(self):
         """Resume request submission for async rollout."""
         raise NotImplementedError()
+
+    def get_scheduling_config(self) -> List[Scheduling]:
+        """Get the scheduling configuration for the engine.
+
+        This includes configuration such as container image, CPU/GPU/memory size.
+
+        Returns
+        -------
+        Scheduling
+            The scheduling configuration for the engine
+        """
+        raise NotImplementedError()

areal/api/scheduler_api.py

@@ -1,48 +1,40 @@
 import abc
 from dataclasses import dataclass, field
-from typing import Dict, List
+from typing import List, Literal
+
+from areal.api.engine_api import Scheduling
 
 
 @dataclass
 class Worker:
     id: str
     ip: str
-    ports: List[str] = field(default_factory=list)
-
-
-@dataclass
-class ContainerSpec:
-    cpu: int = 0
-    gpu: int = 0
-    mem: int = 0
-    container_image: str = ""
-    cmd: str = ""
-    env_vars: Dict[str, str] = field(default_factory=dict)
-    port_count: int = 2
+    serve_port: str
+    extra_ports: List[str] = field(default_factory=list)
 
 
 @dataclass
 class ScheduleStrategy:
-    type: str = ""
-    uid: str = ""
+    type: Literal["colocation", "separation", ""] = ""
+    target: str = ""
 
 
 @dataclass
-class SchedulingConfig:
+class Job:
     replicas: int = 0
-    specs: List[ContainerSpec] = field(default_factory=list)
+    tasks: List[Scheduling] = field(default_factory=list)
     schedule_strategy: ScheduleStrategy | None = None
     role: str = ""
 
 
 class Scheduler(abc.ABC):
     def create_workers(self, worker_key, scheduler_config, *args, **kwargs) -> None:
         """
-        Start workers, return job id
+        Start workers
         """
         raise NotImplementedError()
 
-    def get_workers(self, worker_key, timeout=None) -> List[Worker]:
+    def get_workers(self, role: str, timeout=None) -> List[Worker]:
         """
         Wait and return worker list, including scheduling results such as ip and engine ports
         (worker id, ip, ports)

areal/api/workflow_api.py

@@ -521,7 +521,7 @@ def rollout_batch(
 
     def prepare_batch(
         self,
-        dataloader: StatefulDataLoader,
+        dataloader: StatefulDataLoader | List[Dict[str, Any]],
         workflow: "RolloutWorkflow" | None = None,
         workflow_builder: Callable | None = None,
         should_accept: Callable | None = None,
@@ -530,28 +530,62 @@ def prepare_batch(
 
         See :meth:`~areal.api.engine_api.InferenceEngine.prepare_batch` for detailed documentation.
         """
-        if not hasattr(self, "data_generator"):
-            self.data_generator = cycle_dataloader(dataloader)
-        assert dataloader.batch_size is not None
-        while True:
-            # Submit at least two batches to allow maximum overlap
-            if (
-                self.get_capacity() + dataloader.batch_size > 0
-                and self.input_queue.qsize() + dataloader.batch_size
-                < self.input_queue.maxsize
-            ):
-                data = next(self.data_generator)
-                for item in data:
+        if isinstance(dataloader, StatefulDataLoader):
+            # 处理StatefulDataLoader类型 - 保持原有逻辑不变
+            if not hasattr(self, "data_generator"):
+                self.data_generator = cycle_dataloader(dataloader)
+            assert dataloader.batch_size is not None
+            batch_size = dataloader.batch_size
+            
+            while True:
+                # Submit at least two batches to allow maximum overlap
+                if (
+                    self.get_capacity() + batch_size > 0
+                    and self.input_queue.qsize() + batch_size
+                    < self.input_queue.maxsize
+                ):
+                    data = next(self.data_generator)
+                    for item in data:
+                        self.submit(
+                            item,
+                            workflow=workflow,
+                            workflow_builder=workflow_builder,
+                            should_accept=should_accept,
+                        )
+                try:
+                    return self.wait(batch_size, timeout=1)
+                except TimeoutError:
+                    pass
+        else:
+            self.data_list_index = 0
+            
+            # 对于List类型，使用固定的batch_size=1
+            batch_size = 1
+            
+            while True:
+                # Submit at least two batches to allow maximum overlap
+                if (
+                    self.get_capacity() + batch_size > 0
+                    and self.input_queue.qsize() + batch_size
+                    < self.input_queue.maxsize
+                ):
+                    # 从List中获取数据，支持循环访问
+                    if self.data_list_index >= len(dataloader):
+                        self.data_list_index = 0  # 循环访问
+                    
+                    item = dataloader[self.data_list_index]
+                    self.data_list_index += 1
+                    
                     self.submit(
                         item,
                         workflow=workflow,
                         workflow_builder=workflow_builder,
                         should_accept=should_accept,
                     )
-            try:
-                return self.wait(dataloader.batch_size, timeout=1)
-            except TimeoutError:
-                pass
+                try:
+                    return self.wait(batch_size, timeout=1)
+                except TimeoutError:
+                    pass
 
     def pause(self):
         """Pause request submission for async rollout.