.

arealite/README.md

@@ -92,7 +92,7 @@ def main_grpo():
             future.result()
 
             # synchronous rollout
-            rollout_batch = rollout.rollout(batch, workflow=MyRolloutWorkflow(rollout_config.workflow))
+            rollout_batch = rollout.rollout_batch(batch, workflow=MyRolloutWorkflow(rollout_config.workflow))
             # or asynchronous rollout with filtering and off-policyness control
             # rollout_batch = rollout.prepare_batch(batch, 
             #                                       workflow=MyRolloutWorkflow(rollout_config.workflow),
@@ -697,7 +697,7 @@ reward = TrainController(Critic())
 rollout_controller = RolloutController(...)
 for _ in range(epochs):
     for _ in range(steps_per_epoch):
-        data = rollout_controller.rollout(prompt)
+        data = rollout_controller.rollout_batch(prompt)
         data['reward'] = reward.compute_values(data)
         ...
 ```

arealite/api/cli_args.py

@@ -339,7 +339,6 @@ class SGLangConfig:
     model_path: str = ""
     random_seed: int = 1
     skip_tokenizer_init: bool = False
-
     disable_cuda_graph: bool = False
     disable_radix_cache: bool = False
     disable_cuda_graph_padding: bool = False
@@ -375,10 +374,8 @@ class SGLangConfig:
     schedule_policy: str = "lpm"
     schedule_conservativeness: float = 1.0
     cpu_offload_gb: int = 0
-
     dtype: str = "float16"
     kv_cache_dtype: str = "auto"
-
     # logging
     log_level: str = "warning"
     log_level_http: Optional[str] = "warning"
@@ -439,7 +436,6 @@ class SGLangConfig:
             raise ValueError(
                 "A installed SGLang package or a specific SGLang version should be provided to build SGLang server cmd."
             )
-
         if version_less_than_0_4_4:
             args.pop("log_requests_level")
         if version_less_than_0_4_3:
@@ -449,7 +445,6 @@ class SGLangConfig:
             args.pop("enable_memory_saver")
             args.pop("allow_auto_truncate")
             args.pop("file_storage_path")
-
         flags = []
         for k, v in args.items():
             if v is None or v is False or v == "":
@@ -506,11 +501,6 @@ class InferenceEngineConfig:
     )
 
 
-@dataclass
-class SGLangEngineConfig:
-    pass
-
-
 @dataclass
 class _Timer:
     experiment_name: str = MISSING
@@ -807,7 +797,6 @@ def parse_cli_args(argv: List[str]):
         "--config", help="The path of the main configuration file", required=True
     )
     args, overrides = parser.parse_known_args(argv)
-
     # Initialize hydra config
     config_file = Path(args.config).absolute()
     assert config_file.exists()
@@ -834,7 +823,6 @@ def load_expr_config(argv: List[str], config_cls):
     cfg = to_structured_cfg(cfg, config_cls=config_cls)
     cfg = OmegaConf.to_object(cfg)
     assert isinstance(cfg, BaseExperimentConfig)
-
     # Setup environment
     from realhf.base import constants, name_resolve, names
 

arealite/api/engine_api.py

@@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
 
 import torch
 from tensordict import TensorDict
+from torchdata.stateful_dataloader import StatefulDataLoader
 
 from arealite.api.io_struct import (
     FinetuneSpec,
@@ -78,8 +79,8 @@ class TrainEngine(abc.ABC):
     def train_batch(
         self,
         input_: TensorDict,
-        loss_fn: Callable[[torch.Tensor, Dict], torch.Tensor],
-        loss_weight_fn: Callable[[Dict], float],
+        loss_fn: Callable[[torch.Tensor, TensorDict], torch.Tensor],
+        loss_weight_fn: Callable[[TensorDict], float],
     ) -> Dict[str, float]:
         """Update the model with a batch of data and a loss function."""
         raise NotImplementedError()
@@ -88,8 +89,8 @@ class TrainEngine(abc.ABC):
     def eval_batch(
         self,
         input_: TensorDict,
-        loss_fn: Callable[[torch.Tensor, Dict], torch.Tensor],
-        loss_weight_fn: Callable[[Dict], float],
+        loss_fn: Callable[[torch.Tensor, TensorDict], torch.Tensor],
+        loss_weight_fn: Callable[[TensorDict], float],
     ) -> torch.Tensor | None:
         """Evaluate the model using the forward pass and loss function."""
         raise NotImplementedError()
@@ -99,7 +100,7 @@ class TrainEngine(abc.ABC):
         self,
         input_: TensorDict,
         output_seqlens: List[List[int]] | None = None,
-        post_hook: Callable[[torch.Tensor, Dict], Any] | None = None,
+        post_hook: Callable[[torch.Tensor, TensorDict], Any] | None = None,
         aggregate_fn: Callable[[List[Any]], Any] = torch.cat,
     ) -> Any | None:
         """Run the forward pass or inference on the model. Note that it is gradient-free."""
@@ -136,6 +137,20 @@ class InferenceEngine(abc.ABC):
         """Wait for a specified number of requests to complete, with a timeout."""
         raise NotImplementedError()
 
+    def rollout_batch(
+        self, data: List[Dict[str, Any]], workflow: "RolloutWorkflow"
+    ) -> TensorDict:
+        """Submit a batch of requests to the inference engine and wait for the results."""
+        raise NotImplementedError()
+
+    def prepare_batch(
+        self,
+        dataloader: StatefulDataLoader,
+        workflow: "RolloutWorkflow",
+    ):
+        """Asynchronously submit and wait until a full batch is ready."""
+        raise NotImplementedError()
+
     def pause(self):
         """Pause request submission for async rollout. Used during evaluation to prevent data over generation."""
         raise NotImplementedError()
@@ -143,9 +158,3 @@ class InferenceEngine(abc.ABC):
     def resume(self):
         """Resume request submission for async rollout."""
         raise NotImplementedError()
-
-    def rollout(
-        self, data: List[Dict[str, Any]], workflow: "RolloutWorkflow"
-    ) -> TensorDict:
-        """Submit a batch of requests to the inference engine and wait for the results."""
-        raise NotImplementedError()

arealite/engine/sglang_remote.py

@@ -481,7 +481,7 @@ class RemoteSGLangEngine(InferenceEngine):
         )
         return concat_padded_tensors(results)
 
-    def rollout(
+    def rollout_batch(
         self, data: List[Dict[str, Any]], workflow: "RolloutWorkflow"
     ) -> TensorDict:
         """Submit a batch of requests to the inference engine and wait for the results."""

arealite/tests/test_sglang_engine.py

@@ -108,7 +108,7 @@ def test_remote_sglang_rollout(sglang_server, n_samples):
     data = {
         "messages": [{"role": "user", "content": "Hello, how are you?"}],
     }
-    result = engine.rollout([data] * 2, workflow=workflow)
+    result = engine.rollout_batch([data] * 2, workflow=workflow)
     assert isinstance(result, TensorDict)
     bs = result.batch_size
     assert bs == torch.Size([2 * n_samples])