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[v0.11.0][Bugfix][MoE] enable force_load_balance in aclgraph (#4367)

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### What this PR does / why we need it?
Enable force_load_balance in aclgraph, solving OOM issues.
pick from https://github.com/vllm-project/vllm-ascend/pull/4366
### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
e2e & ut

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
This commit is contained in:
weichen
2025-11-25 09:16:57 +08:00
committed by GitHub
parent 75452abe1e
commit a3164ac372
1 changed files with 2 additions and 15 deletions
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17
vllm_ascend/ops/common_fused_moe.py
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@@ -19,7 +19,7 @@ from typing import Any, Callable, Optional
import torch import torch
import torch_npu import torch_npu
from vllm.config import CompilationLevel, get_current_vllm_config from vllm.config import get_current_vllm_config
from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group, from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group,
tensor_model_parallel_all_reduce) tensor_model_parallel_all_reduce)
from vllm.forward_context import get_forward_context from vllm.forward_context import get_forward_context
@@ -51,20 +51,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
def __init__(self, moe: FusedMoEConfig = None): def __init__(self, moe: FusedMoEConfig = None):
super().__init__(moe=moe) super().__init__(moe=moe)
# NOTE: Currently, this self.use_aclgraph is only used in
# UnquantizedFusedMoEMethod.forward_oot to decide whether to use in
# ops/fused_moe.py:568 to circumvent torch.randint_like not supported issue.
# Once torch.randint_like is supported or removed, this flag can be removed.
vllm_config = get_current_vllm_config()
ascend_config = get_ascend_config()
self.dynamic_eplb = get_ascend_config().dynamic_eplb self.dynamic_eplb = get_ascend_config().dynamic_eplb
if ascend_config.torchair_graph_config.enabled:
self.use_aclgraph = False
else:
self.use_aclgraph = (vllm_config.compilation_config.level
== CompilationLevel.PIECEWISE and
not vllm_config.model_config.enforce_eager)
self.transpose = True self.transpose = True
def process_weights_after_loading(self, layer): def process_weights_after_loading(self, layer):
@@ -133,7 +120,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
# this is a naive implementation for experts load balance so as # this is a naive implementation for experts load balance so as
# to avoid accumulating too much tokens on a single rank. # to avoid accumulating too much tokens on a single rank.
# currently it is only activated when doing profile runs. # currently it is only activated when doing profile runs.
if enable_force_load_balance and not self.use_aclgraph: if enable_force_load_balance:
topk_ids = torch.randint_like(topk_ids, 0, global_num_experts) topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
moe_comm_method = get_forward_context().moe_comm_method moe_comm_method = get_forward_context().moe_comm_method