[XPU] support data parallel for MoE models on XPU

vllm/distributed/device_communicators/xpu_communicator.py

@@ -7,8 +7,13 @@
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
 
+import vllm.envs as envs
+from vllm.logger import init_logger
+
 from .base_device_communicator import DeviceCommunicatorBase
 
+logger = init_logger(__name__)
+
 
 class XpuCommunicator(DeviceCommunicatorBase):
 
@@ -18,6 +23,12 @@ def __init__(self,
                  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":
+                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)

vllm/model_executor/layers/fused_moe/layer.py

@@ -655,6 +655,8 @@ def forward_tpu(
         forward_native = forward_tpu
     elif current_platform.is_cpu():
         forward_native = forward_cpu
+    elif current_platform.is_xpu():
+        forward_native = forward_xpu
     else:
         forward_native = forward_cuda