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Support multistream of shared experts in FusedMoE (#997)

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Contains on #1111 for completeness.

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### What this PR does / why we need it?
Implement multi-stream parallelism for MoE layers with shared experts,
where computation of shared experts will be overlapped with expert token
dispatch and combine. Also, when multi-stream is enabled, weights of
shared experts will be force to replicate across all cards, regardless
of any tensor parallelism configurations, to avoid AllReduce operations.

With the expected overlaping being:
```
| shared gate_up | shared act |              | shared down |
|    dispatch    | routed gate_up, act, down |   combine   |
```

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### Does this PR introduce _any_ user-facing change?
No.

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### How was this patch tested?
Tested on 1x16 910 node, with tailored 2 layer DSKv2.
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---------

Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
This commit is contained in:
sdmyzlp
2025-06-11 09:18:38 +08:00
committed by GitHub
parent 04abfd8721
commit 7bdc606677
11 changed files with 296 additions and 308 deletions
Download Patch File Download Diff File Expand all files Collapse all files

77
vllm_ascend/ops/fused_moe.py
View File

@@ -16,7 +16,7 @@
# Adapted from vllm/tests/kernels/test_moe.py
import os
from typing import Callable, List, Optional
from typing import Any, Callable, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
@@ -36,6 +36,7 @@ import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
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 npu_stream_switch, npu_wait_tensor
VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
USING_LCCL_COM: bool = envs_ascend.USING_LCCL_COM
@@ -106,15 +107,17 @@ def process_topk_ids(topk_ids: torch.Tensor, expert_num: int, ep_size: int,
return topk_ids_pad, unpad_indices
def fused_experts_with_mc2(hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
expert_map: torch.Tensor = None,
moe_all_to_all_group_name: Optional[str] = None,
**kwargs) -> torch.Tensor:
def fused_experts_with_mc2(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
expert_map: torch.Tensor = None,
moe_all_to_all_group_name: Optional[str] = None,
shared_experts: Optional[Any] = None
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
global_bs = 0
moe_expert_num = len(expert_map)
kwargs_mc2 = {
@@ -154,6 +157,13 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts = output[
0:5]
if shared_experts is not None:
with npu_stream_switch("moe_secondary", 0):
npu_wait_tensor(hidden_states, topk_weights)
shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
npu_wait_tensor(shared_gate_up, expand_x)
shared_act = shared_experts.act_fn(shared_gate_up)
w1 = w1.transpose(1, 2)
group_list = expert_token_nums.to(torch.int64)
@@ -210,7 +220,13 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2)
return hidden_states
if shared_experts is None:
return hidden_states
else:
with npu_stream_switch("moe_secondary", 0):
npu_wait_tensor(shared_act, down_out_list)
shared_hidden_states, _ = shared_experts.down_proj(shared_act)
return hidden_states, shared_hidden_states
def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
@@ -875,6 +891,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
e_score_correction_bias: Optional[torch.Tensor] = None,
is_prefill: bool = False,
enable_force_load_balance: bool = False,
shared_experts: Optional[Any] = None,
**kwargs,
) -> torch.Tensor:
@@ -924,7 +941,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
top_k=top_k,
expert_map=expert_map,
moe_all_to_all_group_name=self.moe_all_to_all_group_name,
**kwargs)
shared_experts=shared_experts)
elif self.torchair_graph_enabled or get_ep_group().world_size == 1:
return fused_experts(hidden_states=x,
w1=layer.w13_weight,
@@ -1053,9 +1070,6 @@ class AscendFusedMoE(FusedMoE):
self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
# NOTE: multistream only effective when `VLLM_ENABLE_MC2` is on
self.enable_multistream_shared_expert = \
ascend_config.torchair_graph_config.enable_multistream_shared_expert and VLLM_ENABLE_MC2
if self.scoring_func != "softmax" and not self.use_grouped_topk:
raise ValueError("Only softmax scoring function is supported for "
@@ -1102,8 +1116,8 @@ class AscendFusedMoE(FusedMoE):
router_logits: torch.Tensor,
is_prefill: bool,
enable_force_load_balance: bool = False,
top_k=None,
**kwargs):
top_k: Optional[int] = None,
shared_experts: Optional[Any] = None):
assert self.quant_method is not None
if top_k:
@@ -1132,7 +1146,7 @@ class AscendFusedMoE(FusedMoE):
hidden_states, router_logits)
# Matrix multiply.
hidden_states = self.quant_method.apply(
e_hidden_states = self.quant_method.apply(
layer=self,
x=hidden_states,
router_logits=router_logits,
@@ -1150,36 +1164,39 @@ class AscendFusedMoE(FusedMoE):
enable_force_load_balance=enable_force_load_balance,
log2phy=self.log2phy,
global_redundant_expert_num=self.global_redundant_expert_num,
**kwargs)
shared_experts=shared_experts,
)
if self.enable_multistream_shared_expert and not is_prefill:
hidden_states, shared_output = hidden_states
if shared_experts is not None:
# Provide dummy implementation of "non-separated" shared experts.
if not isinstance(e_hidden_states, tuple):
return e_hidden_states, shared_experts(hidden_states)
else:
return e_hidden_states
if self.dp_size > 1:
if VLLM_ENABLE_MC2 and not is_prefill:
...
elif self.torchair_graph_enabled:
if USING_LCCL_COM: # type: ignore
hidden_states = dist._functional_collectives.reduce_scatter_tensor(
hidden_states,
e_hidden_states = dist._functional_collectives.reduce_scatter_tensor(
e_hidden_states,
"sum",
scatter_dim=0,
group=get_dp_group().device_group)
elif self.torchair_graph_enabled and not is_prefill:
hidden_states = dist._functional_collectives.reduce_scatter_tensor(
hidden_states,
e_hidden_states = dist._functional_collectives.reduce_scatter_tensor(
e_hidden_states,
"sum",
scatter_dim=0,
group=get_dp_group().device_group)
else:
hidden_states = get_ep_group().combine(hidden_states)
e_hidden_states = get_ep_group().combine(e_hidden_states)
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
hidden_states = tensor_model_parallel_all_reduce(hidden_states)
e_hidden_states = tensor_model_parallel_all_reduce(e_hidden_states)
if self.enable_multistream_shared_expert and not is_prefill:
return hidden_states, shared_output
return hidden_states
return e_hidden_states
# ----------------------------------------- TBO-related --------------------------------------------