RPC support for OmniLLM

vllm_omni/entrypoints/omni_stage.py

@@ -15,7 +15,8 @@
 import logging
 import multiprocessing as mp
 import os
-from typing import Any, Optional, Union
+import uuid
+from typing import Any, Callable, Optional, TypeVar, Union
 
 from vllm.inputs import TextPrompt
 from vllm.inputs.preprocess import InputPreprocessor
@@ -38,6 +39,8 @@
 
 logger = init_logger(__name__)
 
+_R = TypeVar("_R")
+
 
 class OmniStage:
     """Stage manager for orchestrating a single stage in the omni pipeline.
@@ -320,6 +323,64 @@ def process_engine_inputs(
                 stage_list, engine_input_source, prompt, self.requires_multimodal_data
             )
 
+    def collective_rpc(
+        self,
+        method: str | Callable[..., _R],
+        timeout: float | None = None,
+        args: tuple = (),
+        kwargs: dict[str, Any] | None = None,
+    ) -> list[_R]:
+        """Execute an RPC call on all workers via the stage engine.
+
+        Args:
+            method: Name of the worker method to execute, or a callable that
+                is serialized and sent to all workers to execute.
+
+                If the method is a callable, it should accept an additional
+                `self` argument, in addition to the arguments passed in `args`
+                and `kwargs`. The `self` argument will be the worker object.
+            timeout: Maximum time in seconds to wait for execution. Raises a
+                [`TimeoutError`][] on timeout. `None` means wait indefinitely.
+            args: Positional arguments to pass to the worker method.
+            kwargs: Keyword arguments to pass to the worker method.
+
+        Returns:
+            A list containing the results from each worker.
+
+        Note:
+            It is recommended to use this API to only pass control messages,
+            and set up data-plane communication to pass data.
+        """
+        assert self._in_q is not None and self._out_q is not None, "Queues must be attached before collective_rpc"
+
+        # Submit collective_rpc task to worker
+        rpc_id = str(uuid.uuid4())
+        self._in_q.put({
+            "type": "collective_rpc",
+            "rpc_id": rpc_id,
+            "method": method,
+            "timeout": timeout,
+            "args": args,
+            "kwargs": kwargs,
+        })
+
+        # Wait for result from worker
+        import time
+        start_time = time.time()
+        while True:
+            if timeout is not None and (time.time() - start_time) > timeout:
+                raise TimeoutError(f"collective_rpc timed out after {timeout} seconds")
+
+            result = self.try_collect()
+            if result is not None:
+                if result.get("type") == "collective_rpc_result":
+                    if result.get("rpc_id") == rpc_id:
+                        if "error" in result:
+                            raise RuntimeError(f"collective_rpc failed: {result['error']}")
+                        return result["result"]
+
+            time.sleep(0.001)  # Small sleep to avoid busy waiting
+
 
 def _stage_worker(
     model: str,
@@ -448,12 +509,35 @@ def filter(self, record: _logging.LogRecord) -> bool:
 
     # Batch processing loop
     while True:
+
         task = in_q.get()
         _recv_dequeue_ts = _time.time()
         if task is None:
             _logging.getLogger(__name__).error("[Stage-%s] Received shutdown signal", stage_id)
             break
 
+        # Handle collective_rpc requests
+        if isinstance(task, dict) and task.get("type") == "collective_rpc":
+            rpc_id = task.get("rpc_id")
+            method = task.get("method")
+            timeout = task.get("timeout")
+            args = task.get("args")
+            kwargs = task.get("kwargs")
+            try:
+                result = stage_engine.collective_rpc(method, timeout, args, kwargs)
+                out_q.put({
+                    "type": "collective_rpc_result",
+                    "rpc_id": rpc_id,
+                    "result": result,
+                })
+            except Exception as e:
+                out_q.put({
+                    "type": "collective_rpc_result",
+                    "rpc_id": rpc_id,
+                    "error": str(e),
+                })
+            continue
+
         max_batch_size = int(runtime_cfg.get("max_batch_size", 1) or 1)
         print(f"[Stage-{stage_id}] Max batch size: {max_batch_size}")
         batch_tasks: list[dict[str, Any]] = [task]