Upgrade to vllm 0.17.0 corex v4.1 overlay

vllm/distributed/weight_transfer/base.py

@@ -3,7 +3,7 @@
 """Base class for weight transfer engines."""
 
 from abc import ABC, abstractmethod
-from collections.abc import Callable
+from collections.abc import Callable, Iterator
 from dataclasses import KW_ONLY, dataclass, field
 from typing import Any, Generic, TypeVar
 
@@ -156,3 +156,30 @@ class WeightTransferEngine(ABC, Generic[TInitInfo, TUpdateInfo]):
         This should be called when the worker is shutting down.
         """
         raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def trainer_send_weights(
+        iterator: Iterator[tuple[str, torch.Tensor]],
+        trainer_args: dict[str, Any] | Any,
+    ) -> None:
+        """
+        Send weights from trainer to inference workers.
+
+        This is a static method that can be called from the trainer process
+        to send weights to all inference workers.
+
+        Args:
+            iterator: Iterator of model parameters. Returns (name, tensor) tuples.
+                     The tensors should be on the appropriate device for the backend.
+            trainer_args: Dictionary containing backend-specific arguments needed
+                         to send weights. The structure depends on the backend:
+                         - NCCL: Contains 'group', 'src', 'packed', etc.
+                         - IPC: Contains 'mode' ('http' or 'ray'),
+                                'llm_handle' (for Ray), 'url' (for HTTP), etc.
+
+        Example:
+            >>> param_iter = ((n, p) for n, p in model.named_parameters())
+            >>> engine.trainer_send_weights(param_iter, trainer_args)
+        """
+        raise NotImplementedError