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[BugFix]Fix eplb problems when using dynamic eplb. (#3364)

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### What this PR does / why we need it?
When using dynamic eplb,it will be blocking by nz tensor.We fix these
prolems by clone src tensor and recv tensor.

### Does this PR introduce any user-facing change?

### How was this patch tested?
Qwen3_moe in A3.

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
This commit is contained in:
offline893
2025-10-11 14:04:02 +08:00
committed by GitHub
parent ca05f7d632
commit 82b6c846ca
8 changed files with 58 additions and 34 deletions
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21
vllm_ascend/torchair/ops/torchair_fused_moe.py
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@@ -29,6 +29,7 @@ from vllm.distributed import (GroupCoordinator, get_tensor_model_parallel_rank,
from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
get_tp_group)
from vllm.forward_context import get_forward_context
from vllm.logger import logger
from vllm.model_executor.layers.fused_moe.config import \
FusedMoEConfig # isort: skip
from vllm.model_executor.layers.fused_moe.config import \
@@ -1027,13 +1028,23 @@ class TorchairAscendFusedMoE(FusedMoE):
os.R_OK):
self.expert_load_balancer = ExpertLoadBalancer(
self.expert_map_path, self.global_num_experts)
self.local_num_experts, self.expert_map = (
self.expert_load_balancer.get_rank_placement_map(
self.moe_instance_id, self.ep_rank))
self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
self.moe_instance_id, self.ep_rank).npu()
self.global_redundant_expert_num = (
self.expert_load_balancer.get_global_redundant_expert_num())
try:
self.local_num_experts, self.expert_map = (
self.expert_load_balancer.get_rank_placement_map(
self.moe_instance_id, self.ep_rank))
self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
self.moe_instance_id, self.ep_rank).npu()
except Exception as e:
logger.warning(
f"Init expert map of mtp/eagle when using sample.{e}")
self.local_num_experts, self.expert_map = determine_default_expert_map(
self.global_num_experts, self.ep_size, self.ep_rank,
self.global_redundant_expert_num)
self.log2phy = determine_default_log2phy_map(
self.global_num_experts, self.ep_size, self.ep_rank,
self.global_redundant_expert_num).npu()
else:
# init moe.
self.local_num_experts, self.expert_map = determine_expert_map(