ee40d3d850
### What this PR does / why we need it?
The optimization solution for non-deepseek select_experts is to replace
gating_topk_softmax with softmax+topk+to, which is optimized from 37us
to 14us on bf16/fp16 of qwen3-235b
- vLLM version: v0.9.2
- vLLM main:
1a4f35e2ea
---------
Signed-off-by: ttanzhiqiang <389825161@qq.com>
113 lines
4.1 KiB
Python
113 lines
4.1 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 typing import Callable, Optional
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import torch
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from vllm.config import CompilationLevel, get_current_vllm_config
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from vllm.model_executor.layers.fused_moe.layer import \
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UnquantizedFusedMoEMethod
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ops.fused_moe import (fused_experts, fused_experts_moge,
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select_experts,
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select_gating_top_k_softmax_experts)
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from vllm_ascend.utils import is_310p
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SELECT_GATING_TOPK_SOTFMAX_EXPERTS: bool = envs_ascend.SELECT_GATING_TOPK_SOTFMAX_EXPERTS
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original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
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def unquantized_fused_moe_init_func(self, *args, **kwargs):
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original_unquantized_fused_moe_init_func(self, *args, **kwargs)
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vllm_config = get_current_vllm_config()
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self.max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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self.use_aclgraph = vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and not vllm_config.model_config.enforce_eager
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def forward_oot(
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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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e_score_correction_bias: Optional[torch.Tensor] = None,
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global_num_experts: Optional[int] = None,
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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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activation: str = "silu",
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) -> torch.Tensor:
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if SELECT_GATING_TOPK_SOTFMAX_EXPERTS:
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topk_weights, topk_ids = select_gating_top_k_softmax_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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renormalize=renormalize)
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else:
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topk_weights, topk_ids = select_experts(
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global_num_experts=global_num_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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e_score_correction_bias=e_score_correction_bias,
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)
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if topk_ids.shape[1] < top_k or is_310p():
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assert global_num_experts is not None
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return fused_experts_moge(
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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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top_k=top_k,
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global_num_experts=global_num_experts,
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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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# If use aclgraph, we need to set max_num_tokens to make
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# the input shape of `npu_moe_init_routing` fixed
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max_num_tokens = self.max_num_batched_tokens if self.use_aclgraph else None
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return 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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top_k=top_k,
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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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max_num_tokens=max_num_tokens)
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UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
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UnquantizedFusedMoEMethod.forward_oot = forward_oot
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