[RL] support moe-topk use topk_reduce_func

[zoooo0820]

Motivation

• 由于不同模型在组网部分的moe-topk计算逻辑有少许差异，支持在组网时传入 topk_reduce_func保证数值的准确性，并在FD_USE_PHI_MOE_TOPK生效时，不在noaux_ac算子内部计算normalize和scaling，而是使用传入的topk_reduce_func在算子外部计算normalize和scaling

• 由于验证上述组合方式可以保证数值准确性，移除了此前数值对齐的moe_topk_select 散OP实现

Modifications

Usage or Command

Accuracy Tests

Checklist

• Add at least a tag in the PR title.
  • Tag list: [[FDConfig],[APIServer],[Engine], [Scheduler], [PD Disaggregation], [Executor], [Graph Optimization], [Speculative Decoding], [RL], [Models], [Quantization], [Loader], [OP], [KVCache], [DataProcessor], [BugFix], [Docs], [CI], [Optimization], [Feature], [Benchmark], [Others], [XPU], [HPU], [GCU], [DCU], [Iluvatar], [Metax]]
  • You can add new tags based on the PR content, but the semantics must be clear.
• Format your code, run pre-commit before commit.
• Add unit tests. Please write the reason in this PR if no unit tests.
• Provide accuracy results.
• If the current PR is submitting to the release branch, make sure the PR has been submitted to the develop branch, then cherry-pick it to the release branch with the [Cherry-Pick] PR tag.

[paddle-bot]

Thanks for your contribution!

[codecov-commenter]

Codecov Report

❌ Patch coverage is 23.07692% with 10 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (develop@8cb417e). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
fastdeploy/model_executor/layers/moe/moe.py	9.09%	8 Missing and 2 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             develop    #7218   +/-   ##
==========================================
  Coverage           ?   72.82%           
==========================================
  Files              ?      377           
  Lines              ?    53217           
  Branches           ?     8311           
==========================================
  Hits               ?    38755           
  Misses             ?    11739           
  Partials           ?     2723           

Flag	Coverage Δ
GPU	72.82% <23.07%> (?)

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[fastdeploy-bot]

📋 Review 摘要

PR 概述：支持 MoE TopK 通过 topk_reduce_func 进行自定义归一化，当 FD_USE_PHI_MOE_TOPK 生效时在算子外部计算 normalize 和 scaling，并移除了此前用于数值对齐的 moe_topk_select 函数实现。

变更范围：fastdeploy/model_executor/layers/moe/、fastdeploy/model_executor/models/

影响面 Tag：[RL] [OP]

📝 PR 规范检查

PR 标题包含 [RL] tag，符合规范。PR 描述填写了 Motivation 和 Modifications，但 Checklist 中未勾选 Accuracy Tests。

问题

级别	文件	概述
🟡 建议	fastdeploy/model_executor/layers/moe/moe.py:136	FD_USE_PHI_MOE_TOPK=True 且 renormalize=True 时，若 topk_reduce_func=None 则归一化不会执行
🟡 建议	fastdeploy/model_executor/models/glm4_moe.py:185	其他模型未传入 topk_reduce_func，可能在特定环境下产生数值问题

总体评价

代码变更逻辑清晰，通过支持 topk_reduce_func 参数实现了 MoE TopK 的自定义归一化机制。但存在以下需要注意的问题：

1. 行为不一致风险：当 FD_USE_PHI_MOE_TOPK=True 且 renormalize=True 时，只有当 topk_reduce_func 不为 None 时才会执行归一化。如果调用方未传入该参数，归一化操作会被静默跳过，可能导致数值结果与预期不符。

2. 模型兼容性问题：目前只有 glm4_moe.py 中传入了 topk_reduce_func，其他模型（如 deepseek_v3.py、gpt_oss.py、ernie4_5_moe.py 等）在使用 FusedMoE 时未传入该参数。如果这些模型在 FD_USE_PHI_MOE_TOPK=True 环境下运行且 renormalize=True，可能会遇到数值问题。

建议在 FD_USE_PHI_MOE_TOPK 环境变量启用时，对 topk_reduce_func 为 None 的情况添加明确的警告或错误处理，或者提供默认的归一化行为。