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[XPU] Fix MOE DP accuracy issue on XPU (#25465)

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Fanli Lin 2025-09-23 22:32:57 +08:00 committed by GitHub
parent da5e7e4329
commit 4c966e440e
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GPG Key ID: B5690EEEBB952194
2 changed files with 29 additions and 1 deletions
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11
examples/offline_inference/data_parallel.py
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@ -101,6 +101,13 @@ def parse_args():
"--quantization",
type=str,
)
parser.add_argument(
"--disable-expert-parallel",
dest="enable_expert_parallel",
action="store_false",
help="Disable expert parallel (default: enabled).",
)
parser.set_defaults(enable_expert_parallel=True)
return parser.parse_args()
@ -113,6 +120,7 @@ def main(
dp_master_port,
GPUs_per_dp_rank,
enforce_eager,
enable_expert_parallel,
trust_remote_code,
max_num_seqs,
max_model_len,
@ -168,7 +176,7 @@ def main(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=enforce_eager,
enable_expert_parallel=True,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
max_num_seqs=max_num_seqs,
max_model_len=max_model_len,
@ -229,6 +237,7 @@ if __name__ == "__main__":
dp_master_port,
tp_size,
args.enforce_eager,
args.enable_expert_parallel,
args.trust_remote_code,
args.max_num_seqs,
args.max_model_len,

19
vllm/distributed/device_communicators/xpu_communicator.py
View File

@ -25,6 +25,12 @@ class XpuCommunicator(DeviceCommunicatorBase):
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)
@ -67,3 +73,16 @@ class XpuCommunicator(DeviceCommunicatorBase):
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) -> tuple[torch.Tensor, torch.Tensor]:
assert self.all2all_manager is not None
hidden_states, router_logits = self.all2all_manager.dispatch(
hidden_states, router_logits)
return hidden_states, router_logits
def combine(self, hidden_states: torch.Tensor) -> torch.Tensor:
assert self.all2all_manager is not None
hidden_states = self.all2all_manager.combine(hidden_states)
return hidden_states