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How to map RadixAttention from the course to SGLang source with Zero to Sglang

Compare the Chapter 8 prefix caching walkthrough with Part III caching chapters and the real SGLang code.

  • Research
  • Advanced
  • 3 hours
  • From the README
  • Any agent

Who it is forAn engineer optimizing repeated-prompt workloads, such as shared system prompts, who wants to know how prefix caching is implemented in SGLang.

The problemPrefix caching is easy to misunderstand when only the high-level idea is known.

The outcomeA clear mapping from your mini-sglang RadixAttention code to the real SGLang implementation, plus an understanding of hierarchical cache tiers.

What you need first

  • Python and basic software engineering habits
  • Familiarity with PyTorch and neural network basics
  • Git installed to clone the repository
  • A GPU for Part 0 deployment and full mini-sglang performance tests (cloud GPU is fine; Part I needs no GPU)

Steps

  1. git clone https://github.com/datawhalechina/zero-to-sglang.git
  2. cd zero-to-sglang
  3. Read course-material/eng/part2/Chapter8_RadixAttention-and-Prefix-Caching.md and its Code Walkthrough file.
  4. Read course-material/eng/part3/Chapter3_Hierarchical-Caching.md for L1/L2/L3 cache tiers and prefix matching.
  5. Clone https://github.com/sgl-project/sglang and locate the RadixAttention and cache code paths referenced in the chapters. (inferred, check the README)

Prompt for your agent

Using https://github.com/datawhalechina/zero-to-sglang, read the RadixAttention chapter and the Hierarchical Caching chapter, then find the matching functions in a local clone of https://github.com/sgl-project/sglang at <path>. Expected output: a table of course concept to source location. Verify each file path exists in the clone before listing it.

Caveat. Part III chapters 2, 4, and 5 are still planned, so this mapping covers attention backends and hierarchical caching only.

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