Project 8: vLLM-lite (PagedAttention Implementation)

Build a simplified PagedAttention system to understand memory paging for KV cache.

Quick Reference

Attribute Value
Difficulty Level 5: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites KV cache basics, memory management
Key Topics PagedAttention, memory paging, cache management

1. Learning Objectives

By completing this project, you will:

  1. Implement a paged KV cache.
  2. Manage page allocation and eviction.
  3. Simulate memory fragmentation.
  4. Compare memory usage vs standard KV cache.
  5. Profile latency impacts.

2. Theoretical Foundation

2.1 PagedAttention

PagedAttention stores KV cache in fixed-size pages to reduce fragmentation and improve utilization.

3. Project Specification

3.1 What You Will Build

A minimal PagedAttention system that manages KV cache pages and supports inference simulations.

3.2 Functional Requirements

  1. Page allocator for KV cache.
  2. Eviction policy for old pages.
  3. Page table mapping tokens to pages.
  4. Benchmark memory usage vs baseline.
  5. Visualization of page allocation.

3.3 Non-Functional Requirements

  • Deterministic simulations.
  • Clear metrics for fragmentation.
  • Configurable page size.

4. Solution Architecture

4.1 Components

Component Responsibility
Page Allocator Allocate/free pages
Page Table Map tokens to pages
Eviction Policy Decide which pages to free
Profiler Track memory use

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P08-pagedattention/
├── src/
│   ├── allocator.py
│   ├── pagetable.py
│   ├── eviction.py
│   ├── simulate.py
│   └── report.py

5.2 Implementation Phases

Phase 1: Page allocator (6-10h)

  • Allocate and free pages.
  • Checkpoint: page usage tracked.

Phase 2: Page table + eviction (6-10h)

  • Map tokens to pages, evict when full.
  • Checkpoint: eviction policy works.

Phase 3: Benchmarking (6-10h)

  • Compare memory usage vs baseline.
  • Checkpoint: report shows fragmentation reduction.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit allocator correct page counts
Integration paging token mapping correctness
Regression benchmark stable metrics

6.2 Critical Test Cases

  1. Page allocation respects page size.
  2. Eviction frees pages correctly.
  3. Fragmentation metrics decrease vs baseline.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Memory leaks pages never freed add eviction tests
Incorrect mapping wrong outputs validate page table
High overhead slow simulation optimize data structures

8. Extensions & Challenges

Beginner

  • Add LRU eviction.
  • Add page visualization.

Intermediate

  • Add multi-request paging.
  • Add cache reuse across sessions.

Advanced

  • Compare with vLLM metrics.
  • Implement prefetching strategies.

9. Real-World Connections

  • vLLM uses PagedAttention to scale inference.
  • Serving systems depend on efficient KV cache management.

10. Resources

  • vLLM papers and docs
  • KV cache optimization guides

11. Self-Assessment Checklist

  • I can implement paged KV cache.
  • I can measure fragmentation and memory savings.
  • I can compare to baseline cache usage.

12. Submission / Completion Criteria

Minimum Completion:

  • Paged KV cache simulator

Full Completion:

  • Benchmark report

Excellence:

  • Prefetching + vLLM comparisons

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.