VLLMWeightReceiver

class torchrl.weight_update.llm.VLLMWeightReceiver(scheme: VLLMWeightSyncScheme, vllm_engine)

Receives weights in a vLLM worker using collective communication.

RPC + Collective Implementation

This class implements both layers:

1. RPC Layer: Currently uses Ray for coordination - init() in test uses Ray ray.get_actor() to find trainer - Fetches metadata via Ray remote call - Signals readiness to participate in collective

2. Collective Layer: Participates in NCCL broadcast - Receives weights via collective operations - vLLM engine applies weights internally during broadcast

Extending RPC Backends

To use a different RPC backend:

class TorchRPCVLLMReceiver(VLLMWeightReceiver):
    def init(self):
        # Custom RPC: Get metadata from trainer
        metadata = torch.distributed.rpc.rpc_sync(
            "trainer",
            lambda: get_metadata()
        )

        # Then init collective (unchanged)
        self.receiver.init_all_workers_group(metadata)

Note

The RPC and collective layers are loosely coupled. The RPC layer ensures all ranks are ready before the collective starts, but the actual data transfer is independent of the RPC mechanism.

apply_weights(weights: Any, inplace: bool = True) → None

Apply weights to vLLM engine.

Parameters:

• weights – The weights to apply.

• inplace – Whether to apply weights in place. Default is True.

Note: For vLLM, weights are applied automatically during the collective broadcast operation. This method is a no-op but kept for API consistency.

init_all_workers_group(model_metadata: dict[str, tuple[torch.dtype, torch.Size]])

Initialize the collective communication group.

Parameters:

model_metadata – Dict mapping param names to (dtype, shape) tuples.

poll_and_apply(timeout: float = 0.1) → bool

Poll for and apply weights.

Returns:

False - vLLM uses push-based updates via collectives, not polling.