66b60c9440
### What this PR does / why we need it?
1. [Refact] Refact MLA/SFA weight prefetch to consist with moe weight
prefetch
2. Remove duplicated o_proj weight prefetch in forward for MLA/SFA
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
1) Performance result:
Perf test data:
*) MLA:
| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 11.9669 token/s | 12.0287 token/s |
11.9978 |
| o_proj no duplicate prefetch | 12.5594 token/s | 12.6216 token/s |
12.5905 | 4.94%| |
single layer performace improve: 5%~8%
*) SFA:
| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 13.0523 token/s | 13.1084 token/s |
13.08035 | |
| o_proj no duplicate prefetch | 13.9844 token/s | 14.1678 token/s |
14.0761 | 7.6% |
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
#
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# Copyright (c) 2026 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 collections.abc import Callable
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import torch
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from vllm_ascend.ops.fused_moe.experts_selector import _native_select_experts
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from vllm_ascend.utils import get_weight_prefetch_method
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def select_experts(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: torch.Tensor | None = None,
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global_num_experts: int = -1,
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):
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"""
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Fused experts with select experts.
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Args:
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router_logits: router logits of shape (num_tokens, hidden_size).
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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top_k: number of top k experts.
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use_grouped_topk: Whether to group experts before selecting top-k.
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renormalize: Whether to renormalize the routing weights.
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topk_group: Number of expert groups to select from.
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num_expert_group: Number of experts in each group.
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custom_routing_function: Custom routing function.
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scoring_func: Scoring function to use.
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e_score_correction_bias: Correction bias to apply to expert scores.
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global_num_experts: Global number of experts.
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Returns:
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topk_weights: router weights of shape (num_tokens, top_k).
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topk_ids: selected expert IDs of shape (num_tokens, top_k).
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"""
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# prefetch w1_w3_proj.weight preprocess
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weight_prefetch_method = get_weight_prefetch_method()
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weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
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topk_weights, topk_ids = _native_select_experts(
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hidden_states=hidden_states,
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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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global_num_experts=global_num_experts,
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)
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return topk_weights, topk_ids
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