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Fix the issue where TTFT and TPOT have no data when running Kimi2.5 i…#153

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Fix the issue where TTFT and TPOT have no data when running Kimi2.5 i…#153
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@Li-8916 Li-8916 commented Feb 27, 2026

…n a PD separation scenario.

Thanks for your contribution; we appreciate it a lot. The following instructions will make your pull request healthier and help you get feedback more easily. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
感谢您的贡献,我们非常重视。以下说明将使您的拉取请求更健康,更易于获得反馈。如果您不理解某些项目,请不要担心,只需提交拉取请求并从维护人员那里寻求帮助即可。

PR Type / PR类型

  • [1] Feature(功能新增)
  • Bugfix(Bug 修复)
  • Docs(文档更新)
  • CI/CD(持续集成/持续部署)
  • Refactor(代码重构)
  • Perf(性能优化)
  • Dependency(依赖项更新)
  • Test-Cases(测试用例更新)
  • Other(其他)

Related Issue | 关联 Issue

🔍 Motivation / 变更动机

Fix the issue where TTFT and TPOT have no data when running Kimi2.5 on PD separation scenario
修复AIS_BENCH在跑kimi2.5 PD分离场景下,TTFT和TPOT没有数据的问题。

📝 Modification / 修改内容

ais_bench/benchmark/models/api_models/vllm_custom_api_chat.py文件中

1、函数async def parse_stream_response(self, json_content, output):
修改前:
if item["delta"].get("reasoning_content"):
output.reasoning_content += item["delta"]["reasoning_content"]
修改后支持kimi2.5类型:
if item["delta"].get("reasoning_content") or item["delta"].get("reasoning"):
output.reasoning_content += item["delta"].get("reasoning_content") or item["delta"].get("reasoning")
2、函数async def parse_text_response(self, json_content, output):
修改前:
if reasoning_content:=item["message"].get("reasoning_content"):
修改后支持kimi2.5类型:
if reasoning_content:=item["message"].get("reasoning_content") or item["message"].get("reasoning"):

📐 Associated Test Results / 关联测试结果

在Kimi2.5测试可以通过

⚠️ BC-breaking (Optional) / 向后不兼容变更(可选)

Does the modification introduce changes that break the backward compatibility of the downstream repositories? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
是否引入了会破坏下游存储库向后兼容性的更改?如果是,请描述它如何破坏兼容性,以及下游项目应该如何修改其代码以保持与此 PR 的兼容性。

⚠️ Performance degradation (Optional) / 性能下降(可选)

If the modification introduces performance degradation, please describe the impact of the performance degradation and the expected performance improvement.
如果引入了性能下降,请描述性能下降的影响和预期的性能改进。

🌟 Use cases (Optional) / 使用案例(可选)

If this PR introduces a new feature, it is better to list some use cases here and update the documentation.
如果此拉取请求引入了新功能,最好在此处列出一些用例并更新文档。

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Before PR:

  • Pre-commit or other linting tools are used to fix the potential lint issues. / 使用预提交或其他 linting 工具来修复潜在的 lint 问题。
  • Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests. / 修复的 Bug 已完全由单元测试覆盖,导致 Bug 的情况应在单元测试中添加。
  • The modification is covered by complete unit tests. If not, please add more unit tests to ensure the correctness. / 此拉取请求中的修改已完全由单元测试覆盖。如果不是,请添加更多单元测试以确保正确性。
  • All relevant documentation (API docs, docstrings, example tutorials) has been updated to reflect these changes. / 所有相关文档(API 文档、文档字符串、示例教程)已更新以反映这些更改。

After PR:

  • If the modification has potential influence on downstream or other related projects, this PR should be tested with those projects. / 如果此拉取请求对下游或其他相关项目有潜在影响,应在那些项目中测试此 PR。
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  • Relevant Module Owners / 相关模块负责人: @xxx
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/gemini review Performs a code review for the current pull request in its current state by Gemini. / 对当前拉取请求在当前状态下由 Gemini 执行代码审核。
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Summary of Changes

Hello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical issue where Time To First Token (TTFT) and Time Per Output Token (TPOT) data were not being recorded when running Kimi2.5 models in a PD separation scenario within the AIS_BENCH framework. The changes adapt the API response parsing logic to correctly identify and extract reasoning content, which was previously missed due to a difference in key names used by Kimi2.5, thereby ensuring complete data collection for benchmarking.

Highlights

  • Kimi2.5 Compatibility for Streamed Responses: Modified the parse_stream_response function to correctly extract reasoning_content by also checking for a reasoning field in the response delta, ensuring data is captured for Kimi2.5 models.
  • Kimi2.5 Compatibility for Text Responses: Enhanced the parse_text_response function to include the reasoning field when extracting reasoning_content from non-streamed responses, resolving data absence for Kimi2.5.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Changelog
  • ais_bench/benchmark/models/api_models/vllm_custom_api_chat.py
    • Updated parse_stream_response to check for both reasoning_content and reasoning keys when extracting reasoning data.
    • Updated parse_text_response to check for both reasoning_content and reasoning keys when extracting reasoning data.
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Code Review

感谢您的贡献。本次修改主要是为了在解析Kimi2.5的API响应时,兼容 reasoningreasoning_content 两种字段,解决了在特定场景下TTFT和TPOT无数据的问题。

代码修改方向正确,但我注意到在 parse_stream_response 函数中的实现存在一些冗余。我已在具体的代码行提出了修改建议,通过使用海象运算符来优化代码,使其更高效、更具可读性,并与 parse_text_response 中的实现风格保持一致。请查阅我的评论。

Comment on lines +144 to +145
if item["delta"].get("reasoning_content") or item["delta"].get("reasoning"):
output.reasoning_content += item["delta"].get("reasoning_content") or item["delta"].get("reasoning")
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medium

这部分代码存在重复。item["delta"].get("reasoning_content") or item["delta"].get("reasoning") 这个表达式在 if 条件和 if 代码块中被计算了两次。这不仅效率低下,也降低了代码的可读性。

建议使用海象运算符(:=)来避免重复计算,就像您在 parse_text_response 函数中所做的那样。这样可以使代码更简洁、高效,并与另一处修改保持风格一致。

Suggested change
if item["delta"].get("reasoning_content") or item["delta"].get("reasoning"):
output.reasoning_content += item["delta"].get("reasoning_content") or item["delta"].get("reasoning")
if reasoning := item["delta"].get("reasoning_content") or item["delta"].get("reasoning"):
output.reasoning_content += reasoning

output.content += item["delta"]["content"]
if item["delta"].get("reasoning_content"):
output.reasoning_content += item["delta"]["reasoning_content"]
if item["delta"].get("reasoning_content") or item["delta"].get("reasoning"):
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【review】所用的框架是什么,标准openai接口返回的应该都是reasoning_content,是否是框架差异导致

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