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Add end-to-end test for dataset preparation

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#175 0 条评论 0 个 reaction 已指派 0 人 在 GitHub 查看

还没有人认领这个 Issue。

评估

难度
3/5
预计耗时
1-2 天
新手友好度
35/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
停滞
技术栈
python
领域
data, testing

调研方向

The payload names only the gpt memap dataset preparation path and issue #172, which it describes as a bug that slipped through. Find the memap preparation entry point and read #172 to see what it broke. Write an end-to-end test that runs preparation on a small input and checks the output, so the #172 regression would fail it. Done means the test passes on the current code and would have caught #172.

由索引模型根据 Issue 内容生成。

描述

enhancement

🎯 Goal (What & Why)

We don't properly test dataset preparation (gpt memap), so bugs can easily slip int the code, as for example with #172.

🚀 Execution Plan

Make a test

📌 Acceptance Criteria (Must-Haves for Completion)

  • The feature must be functional and tested.
  • The implementation must be documented in practical terms.
  • The PR must include a performance/impact summary.
  • No refactors unless directly necessary for feature completion.

🛠️ Project Management

  • Assign the project to the Fast-LLM project.
  • Set the Estimate field (in days) in the GitHub project.
  • Use the Size field to categorize the PR size (Small/Medium/Large).
  • Assign an owner when opening the issue.
主要语言
Python
星标
337
派生
52
30 天内没有已合并 PR

环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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