vllm.distributed.device_communicators.xpu_communicator ¶

class XpuCommunicator(DeviceCommunicatorBase):
    def __init__(
        self,
        cpu_group: ProcessGroup,
        device: Optional[torch.device] = None,
        device_group: Optional[ProcessGroup] = None,
        unique_name: str = "",
    ):
        super().__init__(cpu_group, device, device_group, unique_name)
        if self.use_all2all:
            all2all_backend = envs.VLLM_ALL2ALL_BACKEND
            if all2all_backend != "naive":
                logger.warning(
                    "`%s` all2all manager is not supported on XPU."
                    "Falling back to `naive` all2all manager for XPU.",
                    all2all_backend,
                )
                all2all_backend = "naive"
            if all2all_backend == "naive":
                from .all2all import NaiveAll2AllManager

                self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
                logger.info("Using naive all2all manager.")

    def all_reduce(self, input_) -> torch.Tensor:
        dist.all_reduce(input_, group=self.device_group)
        return input_

    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
    ) -> Optional[torch.Tensor]:
        assert -input_.dim() <= dim < input_.dim(), (
            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()
        # For xpu path, gather doesn't work properly together with ray
        # cluster so we use all_gather instead for now.
        input_size = input_.size()
        # Allocate output tensor.
        output_tensor = torch.empty(
            (self.world_size,) + input_size, dtype=input_.dtype, device=input_.device
        )
        # All-gather.
        dist.all_gather_into_tensor(output_tensor, input_, group=self.device_group)
        if self.rank_in_group == dst:
            # Reshape
            output_tensor = output_tensor.movedim(0, dim)
            output_tensor = output_tensor.reshape(
                input_size[:dim]
                + (self.world_size * input_size[dim],)
                + input_size[dim + 1 :]
            )
        else:
            output_tensor = None
        return output_tensor

    def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
        dist.broadcast(input_, src=src, group=self.device_group)

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_sequence_parallel: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        assert self.all2all_manager is not None
        hidden_states, router_logits = self.all2all_manager.dispatch(
            hidden_states, router_logits, is_sequence_parallel
        )
        return hidden_states, router_logits

    def combine(
        self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
        assert self.all2all_manager is not None
        hidden_states = self.all2all_manager.combine(
            hidden_states, is_sequence_parallel
        )
        return hidden_states