2f1266d451
### What this PR does / why we need it? Support Pangu Pro MoE model (https://arxiv.org/abs/2505.21411) ### Does this PR introduce _any_ user-facing change? Yes, new model supported ### How was this patch tested? Test locally --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com>
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
from typing import Callable, Optional
|
|
|
|
import torch
|
|
from vllm.model_executor.layers.fused_moe.layer import \
|
|
UnquantizedFusedMoEMethod
|
|
|
|
from vllm_ascend.ops.fused_moe import fused_experts, select_experts
|
|
|
|
|
|
def forward_oot(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
use_grouped_topk: bool,
|
|
top_k: int,
|
|
router_logits: torch.Tensor,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
global_num_experts: Optional[int] = None,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
) -> torch.Tensor:
|
|
topk_weights, topk_ids = select_experts(
|
|
global_num_experts=global_num_experts,
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
top_k=top_k,
|
|
use_grouped_topk=use_grouped_topk,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
)
|
|
|
|
return fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input)
|
|
|
|
|
|
UnquantizedFusedMoEMethod.forward_oot = forward_oot
|