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[MoE] [Refactor] Combine common_fused_moe and fused_moe (#3176)

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### What this PR does / why we need it?
1. Move additional functionalities from fused_moe.py to
common_fused_moe.py and remove fused_moe.py
2. Remove unnecessary custom classes from qwen3_moe.py, and it will be
completely removed after we release vllm-ascend v0.11.0

### 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
3. Aclgraph & eager
4. SP


- vLLM version: v0.11.0

---------

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
This commit is contained in:
weichen
2025-10-09 14:12:46 +08:00
committed by GitHub
parent a36e3da78e
commit 94dd832815
17 changed files with 175 additions and 1110 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py
View File

@@ -26,6 +26,8 @@ from vllm.distributed.parallel_state import (
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoEConfig
from vllm_ascend.utils import get_rm_router_logits_state
class FusedMoEPrepareAndFinalize(ABC):
"""
@@ -41,13 +43,16 @@ class FusedMoEPrepareAndFinalize(ABC):
def __init__(self, moe_config: FusedMoEConfig):
self.moe_config = moe_config
is_deepseek_v3_r1 = self.moe_config.original_num_experts == 256
self.rm_router_logits = get_rm_router_logits_state(
self.moe_config.ep_size, self.moe_config.dp_size,
is_deepseek_v3_r1)
@abstractmethod
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -61,7 +66,6 @@ class FusedMoEPrepareAndFinalize(ABC):
hidden_states (torch.Tensor): Input features, shape [num_tokens, hidden_size]
router_logits (torch.Tensor): Router outputs, shape [num_tokens, num_experts]
enable_shared_expert_dp (bool): Skip DP communication for shared experts
rm_router_logits (bool): Discard input router_logits and recompute via gate
replace_allreduce (bool): Bypass default all-reduce behavior
gate (nn.Module, optional): Gate network to recompute router_logits if needed
@@ -116,7 +120,6 @@ class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -215,7 +218,6 @@ class FusedMoEPrepareAndFinalizeWithAll2All(FusedMoEPrepareAndFinalize):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -294,7 +296,6 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -302,7 +303,6 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
1. Fetch max token count across DP group from forward context.
2. Pad local tensors to that size.
3. All-gather across DP group to form global input tensor.
4. Optionally recompute router_logits using gate if `rm_router_logits=True`.
Returns:
Tuple of (global_hidden_states, global_router_logits, None)
@@ -318,14 +318,14 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
if not rm_router_logits:
if not self.rm_router_logits:
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
# All-gather across DP group
hidden_states = self.moe_config.dp_group.all_gather(
hidden_states, 0)
if rm_router_logits:
if self.rm_router_logits:
router_logits, _ = gate(hidden_states) # Recompute globally
else:
router_logits = self.moe_config.dp_group.all_gather(
@@ -399,14 +399,12 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
1. Fetch cumulative token boundaries from forward context.
2. Multicast hidden_states and router_logits to form global tensors.
3. Optionally recompute router_logits globally if `rm_router_logits=True`.
Returns:
Tuple of (global_hidden_states, global_router_logits, None)
@@ -418,7 +416,7 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
).dp_metadata.cu_tokens_across_sp(1)
hidden_states = self._naive_multicast(hidden_states,
self.cu_tokens_across_dp_cpu)
if rm_router_logits:
if self.rm_router_logits:
router_logits, _ = gate(hidden_states)
else:
router_logits = self._naive_multicast(

3
vllm_ascend/ops/moe/moe_comm_method.py
View File

@@ -67,12 +67,11 @@ class MoECommMethod(ABC):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor]:
hidden_states, router_logits, mc2_mask = self.fused_moe_prepare_finalize.prepare(
hidden_states, router_logits, enable_shared_expert_dp,
rm_router_logits, replace_allreduce, gate)
replace_allreduce, gate)
self.mc2_mask = mc2_mask
return hidden_states, router_logits

3
vllm_ascend/ops/moe/token_dispatcher.py
View File

@@ -468,9 +468,6 @@ class TokenDispatcherWithAll2AllV(MoETokenDispatcher):
super().__init__(**kwargs)
self.with_quant = False
self.num_local_experts = kwargs.get("num_local_experts", 0)
self.num_global_redundant_experts = kwargs.get(
"num_global_redundant_experts", 0)
self.num_experts = self.num_experts + self.num_global_redundant_experts
self.hidden_shape = None
self.topk_weights = None