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[Dist][EP] Remove ETP/EP maintained in vllm-ascend (#1681)

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### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced

This is a part of #1422 backport.

Fixes https://github.com/vllm-project/vllm-ascend/issues/1396
https://github.com/vllm-project/vllm-ascend/issues/1154

### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.

### How was this patch tested?
CI passed with new added and existing test.


- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a

Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2025-07-21 09:08:04 +08:00
committed by GitHub
parent a8b316ac5b
commit 8cfd257992
24 changed files with 66 additions and 548 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
vllm_ascend/ops/common_fused_moe.py
View File

@@ -88,6 +88,7 @@ def forward_oot(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
moe_parallel_config=self.moe.moe_parallel_config,
topk_weights=topk_weights,
topk_ids=topk_ids,
top_k=top_k,

37
vllm_ascend/ops/fused_moe.py
View File

@@ -26,7 +26,8 @@ from vllm.config import get_current_vllm_config
from vllm.distributed import (GroupCoordinator, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce)
from vllm.distributed.parallel_state import get_dp_group, get_tp_group
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.model_executor.layers.fused_moe.config import \
FusedMoEConfig # isort: skip
@@ -41,7 +42,6 @@ import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.communication_op import \
data_parallel_reduce_scatter
from vllm_ascend.distributed.parallel_state import get_ep_group, get_etp_group
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.utils import (FusedMoEState, dispose_tensor,
get_all_reduce_merge_state, get_fused_moe_state,
@@ -124,6 +124,7 @@ def fused_experts_with_mc2(
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
moe_parallel_config: FusedMoEParallelConfig,
expert_map: torch.Tensor = None,
moe_all_to_all_group_name: Optional[str] = None,
shared_experts: Optional[Any] = None
@@ -142,22 +143,20 @@ def fused_experts_with_mc2(
rank = torch.distributed.get_rank()
quant_mode = 0
ep_group = get_ep_group().device_group
local_rank = torch.distributed.get_rank(group=ep_group)
all_to_all_group_size = torch.distributed.get_world_size(ep_group)
ep_rank_id = moe_parallel_config.ep_rank
ep_world_size = moe_parallel_config.ep_size
tp_size = get_etp_group().world_size
tp_rank = rank % tp_size
tp_world_size = moe_parallel_config.tp_size
tp_rank = rank % tp_world_size
stage1_kwargs = {
"scales": None,
"quant_mode": quant_mode,
"group_ep": moe_all_to_all_group_name,
"ep_world_size": all_to_all_group_size,
"ep_rank_id": local_rank,
# "group_tp": self.moe_rs_group_name,
"ep_world_size": ep_world_size,
"ep_rank_id": ep_rank_id,
"group_tp": moe_all_to_all_group_name,
"tp_world_size": tp_size,
"tp_world_size": tp_world_size,
"tp_rank_id": tp_rank,
}
kwargs_mc2.update(stage1_kwargs)
@@ -217,12 +216,12 @@ def fused_experts_with_mc2(
stage3_kwargs = {
"ep_send_counts": ep_recv_counts,
"group_ep": moe_all_to_all_group_name,
"ep_world_size": all_to_all_group_size,
"ep_rank_id": local_rank,
"ep_world_size": ep_world_size,
"ep_rank_id": ep_rank_id,
"tp_send_counts": tp_recv_counts,
# "group_tp": self.moe_rs_group_name,
"group_tp": moe_all_to_all_group_name,
"tp_world_size": tp_size,
"tp_world_size": tp_world_size,
"tp_rank_id": tp_rank,
}
kwargs_mc2.update(stage3_kwargs)
@@ -560,6 +559,7 @@ def fused_experts_moge(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
moe_parallel_config: FusedMoEParallelConfig,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
@@ -581,7 +581,7 @@ def fused_experts_moge(
Returns:
hidden_states: Hidden states after routing.
"""
ep_size = get_ep_group().world_size
ep_size = moe_parallel_config.ep_size
local_num_experts = global_num_experts // ep_size
local_num_group = top_k // ep_size
@@ -982,7 +982,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
vllm_config = get_current_vllm_config()
self.ep_group = get_ep_group()
self.ep_size = self.ep_group.world_size
self.ep_size = self.moe.moe_parallel_config.ep_size
self.global_batch_size = vllm_config.scheduler_config.max_num_seqs
self.local_batch_size = self.global_batch_size // self.ep_size
self.max_model_len = vllm_config.model_config.max_model_len
@@ -1074,13 +1074,14 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
if enable_force_load_balance:
topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
fused_moe_state = get_fused_moe_state(self.ep_group.world_size,
is_prefill, is_deepseek_v3_r1)
fused_moe_state = get_fused_moe_state(self.ep_size, is_prefill,
is_deepseek_v3_r1)
if fused_moe_state == FusedMoEState.MC2:
return fused_experts_with_mc2(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
moe_parallel_config=self.moe.moe_parallel_config,
topk_weights=topk_weights,
topk_ids=topk_ids,
top_k=top_k,