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[Feature]Use DispatchGmmCombineDecode operator to replace MC2(Optional) (#5040)

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### What this PR does / why we need it?

This PR adds model-side integration for the previously introduced
experimental AscendC fused operator DispatchGmmCombineDecode, used in
MoE decoding.

The operator implementation itself was added in a prior PR[#4139
](https://github.com/vllm-project/vllm-ascend/pull/4139).
This change only adapts the model execution path to optionally use the
fused operator.

When the environment variable VLLM_ASCEND_ENABLE_FUSED_MC2=2 is set, the
original MC2 path composed of multiple operators (A8W8 dispatch → GMM →
SwiGLU → GMM → combine) might be replaced by the single fused operator
DispatchGmmCombineDecode.

By default, the existing multi-operator MC2 implementation is preserved.

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

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
This commit is contained in:
wangqiankun13
2025-12-21 15:23:59 +08:00
committed by GitHub
parent 67a0325cf2
commit 904c18f929
6 changed files with 51 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
vllm_ascend/ascend_forward_context.py
View File

@@ -253,12 +253,24 @@ def select_moe_comm_method(num_tokens: int,
ascend_config = get_ascend_config()
dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
# TODO: drop the EP-size guard when dispatch_ffn_combine supports larger EP sizes
fused_mc2_enable = envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 and quant_type == "w8a8_dynamic" and get_ep_group(
).world_size <= 16 and (not dynamic_eplb) and (not is_mtp_model)
# TODO: drop dynamic_eplb guard when dispatch_gmm_combine_decode supports tensor list inputs
# TODO: add guard for dispatch_gmm_combine_decode when mtp uses float while moe uses w8a8
fused_mc2_enable = envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 and quant_type == "w8a8_dynamic" and (
not dynamic_eplb)
if num_tokens <= mc2_tokens_capacity:
moe_comm_type = MoECommType.FUSED_MC2 if fused_mc2_enable else MoECommType.MC2
fused_decode_enable = fused_mc2_enable
if envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 1:
fused_decode_enable = fused_mc2_enable and get_ep_group(
).world_size <= 16 and (not is_mtp_model)
moe_comm_type = MoECommType.FUSED_MC2 if fused_decode_enable else MoECommType.MC2
else:
moe_comm_type = MoECommType.FUSED_MC2 if fused_mc2_enable else MoECommType.ALLTOALL
fused_prefill_enable = fused_mc2_enable
if envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 1:
fused_prefill_enable = fused_mc2_enable and get_ep_group(
).world_size <= 16 and (not is_mtp_model)
elif envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2:
fused_prefill_enable = False
moe_comm_type = MoECommType.FUSED_MC2 if fused_prefill_enable else MoECommType.ALLTOALL
else:
raise ValueError(f"Unsupported soc_version: {soc_version}")

8
vllm_ascend/envs.py
View File

@@ -135,7 +135,13 @@ env_variables: Dict[str, Callable[[], Any]] = {
# Whether to anbale dynamic EPLB
"DYNAMIC_EPLB":
lambda: os.getenv("DYNAMIC_EPLB", "false").lower(),
# Whether to anbale fused mc2(dispatch_gmm_combine_decode/dispatch_ffn_combine operator)
# Whether to enable fused mc2(`dispatch_gmm_combine_decode`/`dispatch_ffn_combine` operator)
# 0, or not set: default ALLTOALL and MC2 will be used.
# 1: ALLTOALL and MC2 might be replaced by `dispatch_ffn_combine` operator.
# `dispatch_ffn_combine` can be used only for moe layer with W8A8, EP<=16, non-mtp, non-dynamic-eplb.
# 2: MC2 might be replaced by `dispatch_gmm_combine_decode` operator.
# `dispatch_gmm_combine_decode` can be used only for **decode node** moe layer
# with W8A8, non-dynamic-eplb. And MTP layer must be W8A8.
"VLLM_ASCEND_ENABLE_FUSED_MC2":
lambda: int(os.getenv("VLLM_ASCEND_ENABLE_FUSED_MC2", '0')),
}

2
vllm_ascend/ops/fused_moe/fused_moe.py
View File

@@ -345,7 +345,7 @@ class AscendFusedMoE(FusedMoE):
shared_out = fc3_context.shared_experts(hidden_states)
# NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
moe_comm_type = forward_context.moe_comm_type
if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2} \
if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2, MoECommType.FUSED_MC2} \
and not shared_expert_dp_enabled():
shared_out = tensor_model_parallel_all_reduce(shared_out)
set_flash_common3_context(shared_out=shared_out)

18
vllm_ascend/ops/fused_moe/moe_comm_method.py
View File

@@ -291,9 +291,9 @@ class FusedMC2CommImpl(MoECommMethod):
assert not (
w1_scale is None or w2_scale is None
), "w1_scale and w2_scale cannot be None for FusedMC2CommImpl."
out = torch.empty_like(hidden_states)
if envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 1:
out = torch.empty_like(hidden_states)
torch.ops._C_ascend.dispatch_ffn_combine(
x=hidden_states,
weight1=w1[0],
@@ -307,7 +307,21 @@ class FusedMC2CommImpl(MoECommMethod):
out=out,
)
elif envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2:
raise NotImplementedError()
assert expert_map is not None, "expert_map cannot be None."
out, _ = torch.ops._C_ascend.dispatch_gmm_combine_decode(
x=hidden_states,
expert_ids=topk_ids,
gmm1_permuted_weight=w1[0],
gmm1_permuted_weight_scale=w1_scale[0],
gmm2_weight=w2[0],
gmm2_weight_scale=w2_scale[0],
expert_smooth_scales=None,
expert_scales=topk_weights.to(torch.float32),
group_ep=self.token_dispatcher.moe_all_to_all_group_name,
ep_rank_size=self.token_dispatcher.ep_world_size,
ep_rank_id=self.token_dispatcher.ep_rank_id,
moe_expert_num=len(expert_map),
global_bs=self.token_dispatcher.fused_global_bs)
else:
raise ValueError(
f"Wrong value of {envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2=}")

1
vllm_ascend/ops/fused_moe/token_dispatcher.py
View File

@@ -125,6 +125,7 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
max_num_tokens = min(max_num_reqs * uniform_decode_query_len, 512)
num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
self.global_bs = num_tokens_per_tp_rank * self.ep_world_size
self.fused_global_bs = max_num_tokens * self.ep_world_size
def get_dispatch_mc2_kwargs(
self,

11
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -231,6 +231,10 @@ class AscendW8A8DynamicFusedMoEMethod:
topk_weights = topk_weights.to(self.in_dtype)
moe_comm_method = get_forward_context().moe_comm_method
# When VLLM_ASCEND_ENABLE_FUSED_MC2 == 2, use dispatch_gmm_combine_decode, need fp32 scale
w2_weight_scale_fp32_flag = (
get_forward_context().moe_comm_type == MoECommType.FUSED_MC2
and envs_ascend.VLLM_ASCEND_ENABLE_FUSED_MC2 == 2)
if self.dynamic_eplb:
w1 = layer.w13_weight_list
w1_scale = layer.w13_weight_scale_fp32_list
@@ -240,7 +244,10 @@ class AscendW8A8DynamicFusedMoEMethod:
w1 = [layer.w13_weight]
w1_scale = [layer.w13_weight_scale_fp32]
w2 = [layer.w2_weight]
w2_scale = [layer.w2_weight_scale]
w2_scale = [
layer.w2_weight_scale_fp32
if w2_weight_scale_fp32_flag else layer.w2_weight_scale
]
fused_scale_flag = (get_forward_context().moe_comm_type
== MoECommType.FUSED_MC2
@@ -279,6 +286,8 @@ class AscendW8A8DynamicFusedMoEMethod:
layer.w13_weight_offset.data.shape[0], -1)
layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(
layer.w2_weight_scale.data.shape[0], -1)
layer.w2_weight_scale_fp32 = layer.w2_weight_scale.data.to(
torch.float32)
layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
layer.w2_weight_offset.data.shape[0], -1)