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[XPU] support data parallel for MoE models on XPU (#22887)

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Signed-off-by: chzhang <chaojun.zhang@intel.com>
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Chaojun Zhang 2025-08-29 09:23:04 +08:00 committed by GitHub
parent b668055a11
commit 235c9db8a7
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2 changed files with 13 additions and 0 deletions
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11
vllm/distributed/device_communicators/xpu_communicator.py
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@ -7,8 +7,13 @@ import torch
import torch.distributed as dist import torch.distributed as dist
from torch.distributed import ProcessGroup from torch.distributed import ProcessGroup
import vllm.envs as envs
from vllm.logger import init_logger
from .base_device_communicator import DeviceCommunicatorBase from .base_device_communicator import DeviceCommunicatorBase
logger = init_logger(__name__)
class XpuCommunicator(DeviceCommunicatorBase): class XpuCommunicator(DeviceCommunicatorBase):
@ -18,6 +23,12 @@ class XpuCommunicator(DeviceCommunicatorBase):
device_group: Optional[ProcessGroup] = None, device_group: Optional[ProcessGroup] = None,
unique_name: str = ""): unique_name: str = ""):
super().__init__(cpu_group, device, device_group, unique_name) 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: def all_reduce(self, input_) -> torch.Tensor:
dist.all_reduce(input_, group=self.device_group) dist.all_reduce(input_, group=self.device_group)

2
vllm/model_executor/layers/fused_moe/layer.py
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@ -655,6 +655,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
forward_native = forward_tpu forward_native = forward_tpu
elif current_platform.is_cpu(): elif current_platform.is_cpu():
forward_native = forward_cpu forward_native = forward_cpu
elif current_platform.is_xpu():
forward_native = forward_xpu
else: else:
forward_native = forward_cuda forward_native = forward_cuda