[Refactor] Adjustments to moe_comm_method selection process (#3001)

### What this PR does / why we need it?
Fix issues mentioned in
https://github.com/vllm-project/vllm-ascend/pull/2791 and some minor
refactoring.
1. Use Enum instead of string.
2. Avoid setting a new property to forward_context in
AscendFusedMoE.forward().
3. Enabling TokenDispatcherWithMoge.
4. Remove redundant code.

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?

Qwen3-30B-A3B/Qwen3-30B-A3B-W8A8/DeepSeek-V3-W4A8-Pruing/deepseek-mtp/pangu-pro-moe-pruing:
1. Enable/Disable EP
2. Aclgraph & eager


- vLLM version: v0.10.2
- vLLM main:
9607d5eb44

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
This commit is contained in:
weichen
2025-09-22 19:12:58 +08:00
committed by GitHub
parent bb1f0d5a62
commit 37a0715eda
14 changed files with 170 additions and 351 deletions

View File

@@ -22,6 +22,13 @@ class FusedMoEState(Enum):
All2AllSeq = 5
class MoECommType(Enum):
ALLGATHER = 0
MC2 = 1
ALLTOALL = 2
NAIVE_MULTICAST = 3
# TODO(zzzzwwjj): add soc_version to choose branch
def _get_fused_moe_state(ep_size: int, with_prefill: bool,
is_deepseek_v3_r1: bool):
@@ -52,7 +59,7 @@ def set_ascend_forward_context(
with_prefill: bool = True,
in_profile_run: bool = False,
reserved_mc2_mask: Optional[torch.Tensor] = None,
moe_comm_method: str = "",
moe_comm_type: Optional[MoECommType] = None,
num_actual_tokens: Optional[int] = None,
aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
batch_descriptor: Optional[BatchDescriptor] = None,
@@ -72,7 +79,11 @@ def set_ascend_forward_context(
batch_descriptor=batch_descriptor,
):
forward_context = get_forward_context()
forward_context.moe_comm_method_name = moe_comm_method + "commimpl"
from vllm_ascend.ops.moe.moe_comm_method import get_moe_comm_method
forward_context.moe_comm_type = moe_comm_type
forward_context.moe_comm_method = get_moe_comm_method(moe_comm_type)
forward_context.with_prefill = with_prefill
tp_world_size = get_tensor_model_parallel_world_size()
ep_size = (get_ep_group().world_size if