eeedf7c503
### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.
This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.
### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.
### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
co-authored-by: shen-shanshan <467638484@qq.com>
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
599 lines
26 KiB
Python
599 lines
26 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from dataclasses import dataclass, field
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from functools import wraps
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from typing import Callable, Optional
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import torch
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import torch.nn.functional as F
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group,
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tensor_model_parallel_all_reduce)
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, UnquantizedFusedMoEMethod, get_compressed_expert_map)
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from vllm.model_executor.layers.fused_moe.shared_fused_moe import \
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SharedFusedMoE
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ascend_forward_context import MoECommType
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
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from vllm_ascend.flash_common3_context import (get_flash_common3_context,
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set_flash_common3_context)
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from vllm_ascend.ops.fused_moe.experts_selector import (select_experts,
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zero_experts_compute)
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from vllm_ascend.ops.fused_moe.moe_comm_method import (AllGatherCommImpl,
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FusedExpertsResult,
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setup_moe_comm_method)
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from vllm_ascend.ops.fused_moe.prepare_finalize import QuantType
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from vllm_ascend.utils import (AscendDeviceType, enable_sp,
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get_ascend_device_type, maybe_trans_nz,
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npu_stream_switch, shared_expert_dp_enabled,
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shared_experts_calculation_stream)
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@dataclass
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class FusedMoEResult:
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routed_out: torch.Tensor
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before_dispatch_evt: torch.npu.Event | None = None
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before_combine_evt: torch.npu.Event | None = None
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@dataclass
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class FusedMoEEvents:
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before_routed_experts: torch.npu.Event
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before_dispatch: torch.npu.Event | None = field(default=None)
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before_combine: torch.npu.Event | None = field(default=None)
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class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
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def __init__(self, moe: FusedMoEConfig = None):
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super().__init__(moe=moe)
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self.dynamic_eplb = get_ascend_config().eplb_config.dynamic_eplb
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def process_weights_after_loading(self, layer):
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super(UnquantizedFusedMoEMethod,
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self).process_weights_after_loading(layer)
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w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(
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1, 2).contiguous()
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layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False)
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w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(
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1, 2).contiguous()
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layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
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if get_ascend_device_type() != AscendDeviceType._310P:
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layer.w13_weight.data = maybe_trans_nz(layer.w13_weight.data)
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layer.w2_weight.data = maybe_trans_nz(layer.w2_weight.data)
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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: Optional[torch.Tensor] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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enable_force_load_balance: bool = False,
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log2phy: torch.Tensor = None,
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**kwargs) -> torch.Tensor:
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zero_expert_num = getattr(layer, "zero_expert_num", 0)
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zero_expert_type = getattr(layer, "zero_expert_type", None)
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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routed_scaling_factor=routed_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts)
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if zero_expert_num > 0 and zero_expert_type is not None:
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topk_ids, topk_weights, zero_expert_result = zero_experts_compute(
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expert_indices=topk_ids,
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expert_scales=topk_weights,
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num_experts=global_num_experts,
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zero_expert_type=zero_expert_type,
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hidden_states=x,
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)
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topk_weights = topk_weights.to(x.dtype)
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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance:
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random_matrix = torch.rand(topk_ids.size(0),
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global_num_experts,
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device=topk_ids.device)
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topk_ids = torch.argsort(
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random_matrix, dim=1)[:, :topk_ids.size(1)].to(topk_ids.dtype)
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moe_comm_method = get_forward_context().moe_comm_method
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final_hidden_states = moe_comm_method.fused_experts(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input,
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dynamic_eplb=self.dynamic_eplb,
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log2phy=log2phy,
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mc2_mask=kwargs.get("mc2_mask", None))
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if zero_expert_num > 0 and zero_expert_type is not None:
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final_hidden_states += zero_expert_result
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return final_hidden_states
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class AscendFusedMoE(FusedMoE):
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moe_counter = -1
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gate_stream: Optional[torch.npu.Stream] = None
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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num_experts = kwargs["num_experts"]
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intermediate_size = kwargs["intermediate_size"]
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AscendFusedMoE.moe_counter += 1
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self.moe_instance_id = AscendFusedMoE.moe_counter
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self._expert_map = None
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self.log2phy = None
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if self.quant_config is None:
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self.quant_method = AscendUnquantizedFusedMoEMethod(
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self.moe_config)
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else:
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self.quant_method = self.quant_config.get_quant_method(
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self, self.layer_name)
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assert self.quant_method is not None
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self.moe_config.tp_group = get_tp_group()
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self.moe_config.dp_group = get_dp_group()
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self.moe_config.ep_group = get_ep_group()
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self.moe_config.mc2_group = get_mc2_group()
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self.moe_config.supports_eplb = self.quant_method.supports_eplb
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ascend_config = get_ascend_config()
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# flashcommon3 gate stream
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self.multistream_overlap_gate = ascend_config.multistream_overlap_gate
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if self.multistream_overlap_gate and AscendFusedMoE.gate_stream is None:
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AscendFusedMoE.gate_stream = torch.npu.Stream()
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if self.custom_routing_function is None and self.e_score_correction_bias is not None:
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vllm_config = get_current_vllm_config()
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self.e_score_correction_bias.data = self.e_score_correction_bias.data.to(
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dtype=vllm_config.model_config.dtype)
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# init moe
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eplb_config = ascend_config.eplb_config
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self.global_expert_map, self._expert_map, self.log2phy, self.global_redundant_expert_num = init_eplb_config(
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eplb_config, self.moe_instance_id, self.moe_config)
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self.global_num_experts = num_experts + self.global_redundant_expert_num
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self.dynamic_eplb = eplb_config.dynamic_eplb and (self.log2phy
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is not None)
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self.local_num_experts = (torch.sum(
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self._expert_map != -1).item() if self._expert_map is not None else
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self.global_num_experts)
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if self._expert_map is not None:
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logger.info_once(
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"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
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" number of experts: %s/%s. Experts local to global index map:"
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" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
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self.global_num_experts,
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get_compressed_expert_map(self._expert_map))
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if self.dynamic_eplb:
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self.moe_load = torch.zeros(self.local_num_experts,
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dtype=torch.int64).npu()
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self.moe_config.num_experts = self.global_num_experts
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self.moe_config.num_local_experts = self.local_num_experts
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self.moe_config.global_redundant_expert_num = self.global_redundant_expert_num
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moe_quant_params = {
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"num_experts": self.local_num_experts,
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"hidden_size": self.hidden_size,
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"intermediate_size_per_partition":
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self.intermediate_size_per_partition,
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"params_dtype": self.params_dtype,
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"weight_loader": self.weight_loader,
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}
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# need full intermediate size pre-sharding for WNA16 act order
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if (self.quant_method.__class__.__name__
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in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
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moe_quant_params["intermediate_size_full"] = intermediate_size
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self.quant_method.create_weights(layer=self, **moe_quant_params)
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self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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setup_moe_comm_method(self.moe_config)
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self.quant_type = self._get_quant_type()
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def _get_quant_type(self) -> QuantType:
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quant_method = self.quant_method
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if not hasattr(quant_method,
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"quant_method") or quant_method.quant_method is None:
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return QuantType.NONE
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method = quant_method.quant_method
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if hasattr(method, "quant_type"):
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from vllm_ascend.quantization.methods.base import \
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QuantType as SchemeQuantType
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scheme_quant_type = method.quant_type
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if scheme_quant_type == SchemeQuantType.W8A8:
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return QuantType.W8A8
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elif scheme_quant_type == SchemeQuantType.W4A8:
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return QuantType.W4A8
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return QuantType.NONE
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def update_expert_map(self, new_expert_map):
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self._expert_map = new_expert_map
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def get_log2phy_map(self):
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return self.log2phy
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def clear_moe_load(self):
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if self.moe_load is not None:
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self.moe_load.zero_()
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def maybe_all_reduce_tensor_model_parallel(
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self, final_hidden_states: torch.Tensor):
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"""NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`,
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and `alltoallcommimpl`, we do not need to all-reduce the final outputs since
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the outputs are already aggregated across tensor parallel ranks in the
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`finalize` function. In `allgathercommimpl`, we still need to all-reduce the
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outputs since each rank only has partial outputs.
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"""
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return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states)
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def forward_impl( # type: ignore[override]
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self,
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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return_with_event: bool = False) -> torch.Tensor | FusedMoEResult:
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assert self.quant_method is not None
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forward_context = get_forward_context()
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# Load balancing for token distribution among experts in dummy_run
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# TODO: The community only considers load balancing when DP > 1.
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# This approach may overlook some extreme scenarios.
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enable_force_load_balance = forward_context.in_profile_run
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forward_context = get_forward_context()
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if self.multistream_overlap_gate:
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assert AscendFusedMoE.gate_stream is not None
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fc3_context = get_flash_common3_context()
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assert fc3_context is not None
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AscendFusedMoE.gate_stream.wait_stream(torch.npu.current_stream())
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with npu_stream_switch(AscendFusedMoE.gate_stream,
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enabled=self.multistream_overlap_gate):
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# share_expert
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assert fc3_context.shared_experts is not None
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shared_out = fc3_context.shared_experts(hidden_states)
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# NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
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moe_comm_type = forward_context.moe_comm_type
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if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2, MoECommType.FUSED_MC2} \
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and not shared_expert_dp_enabled():
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shared_out = tensor_model_parallel_all_reduce(shared_out)
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set_flash_common3_context(shared_out=shared_out)
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topk_weights, topk_ids = select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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use_grouped_topk=self.use_grouped_topk,
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renormalize=self.renormalize,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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custom_routing_function=self.custom_routing_function,
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scoring_func=self.scoring_func,
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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.e_score_correction_bias,
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global_num_experts=self.global_num_experts)
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if isinstance(forward_context.moe_comm_method,
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AllGatherCommImpl):
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topk_weights = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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topk_weights, True, True)
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topk_ids = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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topk_ids, True, True)
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set_flash_common3_context(topk_weights=topk_weights,
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topk_ids=topk_ids)
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hidden_states, router_logits, mc2_mask, context_metadata = forward_context.moe_comm_method.prepare(
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hidden_states=hidden_states,
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router_logits=router_logits,
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replace_allreduce=forward_context.sp_enabled,
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enable_shared_expert_dp=self.enable_shared_expert_dp,
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quant_type=self.quant_type)
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# Make sure the default stream waits for the gate stream to finish.
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if self.multistream_overlap_gate:
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torch.npu.current_stream().wait_stream(AscendFusedMoE.gate_stream)
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if isinstance(hidden_states, tuple):
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hidden_states, pertoken_scale = hidden_states
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else:
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pertoken_scale = None
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# Matrix multiply.
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fused_experts_results: FusedExpertsResult = self.quant_method.apply(
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layer=self,
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x=hidden_states,
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router_logits=router_logits,
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pertoken_scale=pertoken_scale,
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top_k=self.top_k,
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renormalize=self.renormalize,
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use_grouped_topk=self.use_grouped_topk,
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global_num_experts=self.global_num_experts,
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expert_map=self._expert_map,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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custom_routing_function=self.custom_routing_function,
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scoring_func=self.scoring_func,
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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.e_score_correction_bias,
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activation=self.activation,
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apply_router_weight_on_input=self.apply_router_weight_on_input,
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enable_force_load_balance=enable_force_load_balance,
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log2phy=self.log2phy,
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global_redundant_expert_num=self.global_redundant_expert_num,
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mc2_mask=mc2_mask)
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if self.dynamic_eplb:
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expert_tokens = fused_experts_results.expert_tokens
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group_list_type = fused_experts_results.group_list_type
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assert expert_tokens is not None and group_list_type is not None, \
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"expert_tokens and group_list_type should not be None when dynamic_eplb is enabled."
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local_load = expert_tokens if group_list_type == 1 else \
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torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
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self.moe_load.add_(local_load)
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routed_out = forward_context.moe_comm_method.finalize(
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|
hidden_states=fused_experts_results.routed_out,
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|
reduce_results=self.reduce_results,
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|
context_metadata=context_metadata)
|
|
|
|
if return_with_event:
|
|
return FusedMoEResult(
|
|
routed_out=routed_out,
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|
before_dispatch_evt=fused_experts_results.before_dispatch_evt,
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|
before_combine_evt=fused_experts_results.before_combine_evt)
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|
else:
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|
# The vLLM FusedMoE forward_impl does not return events.
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|
return routed_out
|
|
|
|
|
|
class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
|
|
|
|
def __init__(
|
|
self,
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|
shared_experts: torch.nn.Module,
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|
gate: Optional[torch.nn.Module] = None,
|
|
use_overlapped: bool = True,
|
|
**kwargs,
|
|
):
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|
AscendFusedMoE.__init__(self, **kwargs)
|
|
|
|
self._shared_experts = shared_experts
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|
self.use_overlapped = use_overlapped
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|
self.shared_expert_stream = None
|
|
ascend_config = get_ascend_config()
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|
self.multistream_overlap_shared_expert = \
|
|
ascend_config.multistream_overlap_shared_expert and \
|
|
self._shared_experts is not None
|
|
self.multistream_overlap_gate = \
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|
ascend_config.multistream_overlap_gate and \
|
|
self._shared_experts is not None
|
|
if enable_sp():
|
|
logger.info_once(
|
|
"Sequence parallelism is enabled, shared experts are replicated for best performance."
|
|
)
|
|
|
|
self._gate = gate
|
|
|
|
if self.multistream_overlap_shared_expert:
|
|
# Wrap the quant_method's process_weights_after_loading to validate that
|
|
# splitting shared expert computation (gate_up projection + activation,
|
|
# then down projection) yields identical results to integrated
|
|
# computation after weight loading.
|
|
original_process_weights = self.quant_method.process_weights_after_loading
|
|
|
|
@wraps(original_process_weights)
|
|
def wrapped_process_weights(*args, **kwargs):
|
|
result = original_process_weights(*args, **kwargs)
|
|
self._validate_shared_expert_consistency()
|
|
return result
|
|
|
|
self.quant_method.process_weights_after_loading = wrapped_process_weights # type: ignore
|
|
|
|
def _shared_experts_part1(self, hidden_states: torch.Tensor):
|
|
shared_gate_up, _ = self._shared_experts.gate_up_proj(
|
|
hidden_states) # type: ignore
|
|
return shared_gate_up
|
|
|
|
def _shared_experts_part2(self, hidden_states: torch.Tensor,
|
|
shared_gate_up: torch.Tensor):
|
|
shared_act = self._shared_experts.act_fn(
|
|
shared_gate_up) # type: ignore
|
|
shared_out, _ = self._shared_experts.down_proj(
|
|
shared_act) # type: ignore
|
|
|
|
# Qwen3-Next specific gating mechanism
|
|
if hasattr(self._shared_experts, "expert_gate") and \
|
|
self._shared_experts.expert_gate is not None:
|
|
gate_out, _ = self._shared_experts.expert_gate(hidden_states) # type: ignore
|
|
shared_out = F.sigmoid(gate_out) * shared_out
|
|
return shared_out
|
|
|
|
def _validate_shared_expert_consistency(self):
|
|
"""Validate that split shared expert computation matches integrated
|
|
computation."""
|
|
test_input = torch.rand(
|
|
10, self.hidden_size, device='npu', dtype=self.moe_config.in_dtype
|
|
) * 2 - 1 # Random input for testing, scoped to [-1, 1]
|
|
|
|
integrated_out = self._shared_experts(test_input)
|
|
part1_out = self._shared_experts_part1(test_input)
|
|
split_out = self._shared_experts_part2(test_input, part1_out)
|
|
|
|
if not torch.allclose(integrated_out, split_out):
|
|
diff = (integrated_out - split_out).abs()
|
|
logger.error(
|
|
"SharedFusedMoE shared experts split computation does not "
|
|
"match the integrated computation.")
|
|
logger.error(f"Max absolute difference: {diff.max().item()}")
|
|
logger.error("Integrated output - sum: %s, norm: %s",
|
|
integrated_out.sum().item(),
|
|
integrated_out.norm().item())
|
|
logger.error("Split output - sum: %s, norm: %s",
|
|
split_out.sum().item(),
|
|
split_out.norm().item())
|
|
raise ValueError(
|
|
"SharedFusedMoE shared experts split computation does not "
|
|
"match the integrated computation.")
|
|
logger.info_once(
|
|
"SharedFusedMoE shared experts split computation matches the "
|
|
"integrated computation.")
|
|
|
|
@property
|
|
def gate(self) -> Optional[torch.nn.Module]:
|
|
return self._gate if self.use_overlapped else None
|
|
|
|
@property
|
|
def is_internal_router(self) -> bool:
|
|
return False
|
|
|
|
@property
|
|
def use_dp_chunking(self) -> bool:
|
|
"""This func routes to the chunked forward path using the FlashInfer Cutlass kernel
|
|
only when data parallelism (DP) is enabled. Thus just returning False in vllm-ascend
|
|
"""
|
|
return False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
if self._shared_experts is None:
|
|
fused_out = AscendFusedMoE.forward(
|
|
self,
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
)
|
|
shared_out = None
|
|
return shared_out, fused_out
|
|
shared_out, fused_out = AscendFusedMoE.forward(
|
|
self,
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
)
|
|
return shared_out, fused_out
|
|
|
|
def _forward_shared_experts(self, hidden_states: torch.Tensor,
|
|
fused_moe_evts: FusedMoEEvents):
|
|
if self._shared_experts is None:
|
|
return None
|
|
|
|
def maybe_wait_event(evt: torch.npu.Event | None):
|
|
if evt is not None:
|
|
torch.npu.current_stream().wait_event(evt)
|
|
|
|
with npu_stream_switch(shared_experts_calculation_stream(),
|
|
enabled=self.multistream_overlap_shared_expert):
|
|
# Ensure the shared experts wait for hidden_states to be ready.
|
|
torch.npu.current_stream().wait_event(
|
|
fused_moe_evts.before_routed_experts)
|
|
# Execute the gate projection and activation concurrently with the
|
|
# dispatch communication.
|
|
maybe_wait_event(fused_moe_evts.before_dispatch)
|
|
part1_out = self._shared_experts_part1(hidden_states)
|
|
# Execute the down projection concurrently with the combine
|
|
# communication.
|
|
maybe_wait_event(fused_moe_evts.before_combine)
|
|
shared_out = self._shared_experts_part2(hidden_states, part1_out)
|
|
|
|
# Make sure the default stream waits for the shared experts stream to
|
|
# finish.
|
|
if self.multistream_overlap_shared_expert:
|
|
torch.npu.current_stream().wait_stream(
|
|
shared_experts_calculation_stream())
|
|
|
|
# NOTE: This is exactly the opposite of
|
|
# `maybe_all_reduce_tensor_model_parallel`
|
|
forward_context = get_forward_context()
|
|
moe_comm_type = forward_context.moe_comm_type
|
|
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)
|
|
return shared_out
|
|
|
|
def forward_impl( # type: ignore[override]
|
|
self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
|
|
if self.multistream_overlap_gate:
|
|
set_flash_common3_context(shared_experts=self._shared_experts)
|
|
|
|
before_routed_experts = torch.npu.current_stream().record_event()
|
|
fused_moe_results = AscendFusedMoE.forward_impl(
|
|
self,
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
return_with_event=True,
|
|
)
|
|
routed_out = fused_moe_results.routed_out
|
|
|
|
if self._shared_experts is None:
|
|
return routed_out
|
|
|
|
if self.multistream_overlap_gate:
|
|
fc3_context = get_flash_common3_context()
|
|
assert fc3_context is not None
|
|
shared_out = fc3_context.shared_out
|
|
else:
|
|
shared_out = self._forward_shared_experts(
|
|
hidden_states,
|
|
FusedMoEEvents(
|
|
before_routed_experts=before_routed_experts,
|
|
before_dispatch=fused_moe_results.before_dispatch_evt,
|
|
before_combine=fused_moe_results.before_combine_evt,
|
|
))
|
|
|
|
return shared_out, routed_out
|