# CLAUDE.md

This file provides guidance to Claude Code when working with this repository.

## Overview

Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline LLM inference. Supports Qwen3 models with CPU offload for long-context inference.

## Documentation Index

| Document | Purpose |
|----------|---------|
| [`docs/architecture_guide.md`](docs/architecture_guide.md) | Core components, CPU offload system design, ring buffer architecture, stream configuration |
| [`docs/sparse_policy_architecture.md`](docs/sparse_policy_architecture.md) | SparsePolicy abstraction: prefill/decode delegation, pipeline modes, policy implementations |
| [`docs/sparse_policy_implementation_guide.md`](docs/sparse_policy_implementation_guide.md) | How to implement custom SparsePolicy: required methods, hooks, ring buffer pipeline pattern |
| [`docs/sparse_attention_guide.md`](docs/sparse_attention_guide.md) | Block sparse attention methods (XAttention, FlexPrefill, MInference, AvgPool, Quest), computation flow, algorithms |
| [`docs/xattention_algorithm_guide.md`](docs/xattention_algorithm_guide.md) | XAttention 算法详解: stride reshape、Triton kernels、BSA 依赖、块选择算法 |
| [`docs/xattn_kernels_guide.md`](docs/xattn_kernels_guide.md) | XAttention Triton kernels: flat_group_gemm (反对角线求和)、softmax_fuse_block_sum (block 聚合) |
| [`docs/xattn_chunked_prefill.md`](docs/xattn_chunked_prefill.md) | XAttention chunked prefill: API、使用方式、一致性要求 |
| [`docs/xattn_bsa_policy_design.md`](docs/xattn_bsa_policy_design.md) | XAttention BSA Policy: 算法设计、性能基准(128K)、内存管理、density 统计 |
| [`docs/block_sparse_attn_interface.md`](docs/block_sparse_attn_interface.md) | BSA (Block Sparse Attention) 接口文档: 函数签名、使用示例、约束条件 |
| [`docs/debugging_guide.md`](docs/debugging_guide.md) | PyTorch hooks for debugging, hook positions, tensor comparison, memory profiling |
| [`docs/optimization_guide.md`](docs/optimization_guide.md) | Performance optimizations: sgDMA (15x), Triton merge (4.3x), N-way pipeline (2x) |
| [`docs/known_issues.md`](docs/known_issues.md) | Documented bugs and fixes: partial last block bug, block size 4096 race condition |
| [`docs/ruler_benchmark_results_32k.md`](docs/ruler_benchmark_results_32k.md) | RULER benchmark results (32K context): 13 tasks, 92.3% accuracy, CPU offload performance |
| [`docs/ruler_32k_chunked_offload_issue.md`](docs/ruler_32k_chunked_offload_issue.md) | ⚠️ OPEN ISSUE: 32K chunked offload accuracy problem (20% error rate in RULER) |
| [`docs/chunked_attention_solutions.md`](docs/chunked_attention_solutions.md) | 🔧 SOLUTIONS: Chunked attention 准确性问题的代码分析和解决方案 |
| [`docs/nsys_wrong_event_order_bug.md`](docs/nsys_wrong_event_order_bug.md) | 🐛 NSYS BUG: Ring buffer pipeline 触发 nsys 时间戳乱序问题的调试记录 |
| [`docs/cpu_scheduling_latency_analysis.md`](docs/cpu_scheduling_latency_analysis.md) | ⚡ PERF: CPU 调度延迟分析，kernel 间隙来源，GPU 利用率优化方向 |
| [`docs/bench_offload_results.md`](docs/bench_offload_results.md) | 📊 BENCH: CPU offload 性能测试结果，Full vs XAttention 对比 (32K/128K) |
| [`docs/cpu_offload_optimization_strategies.md`](docs/cpu_offload_optimization_strategies.md) | 🚀 OPT: CPU offload 优化策略：chunk size、CUDA Graph、前沿研究(InfiniGen/ShadowKV) |
| [`docs/gpu_only_xattn_guide.md`](docs/gpu_only_xattn_guide.md) | 🚀 GPU-Only XAttention: 内存预分配、性能分析 (32K +15%, 64K +41%)、CUDA Graph 限制 |
| [`docs/xattn_performance_analysis.md`](docs/xattn_performance_analysis.md) | 📊 XAttention 性能分析: NVTX 标记、block size 影响、estimate vs compute 耗时对比 |
| [`docs/observer_architecture.md`](docs/observer_architecture.md) | 📊 Observer 架构: InferenceObserver (TTFT/TPOT)、MemoryObserver (H2D/D2H/D2D) 设计 |
| [`docs/memory_communication_benchmark.md`](docs/memory_communication_benchmark.md) | 📊 通信量测试: Full vs XAttention 通信量对比 (32K/64K)、阶段分离统计 |
| [`docs/estimate_block_size_performance.md`](docs/estimate_block_size_performance.md) | 🔥 PERF: estimate 阶段 block_size 性能分析，softmax_fuse_block_sum 最优点 (512-1024)，当前 4096 慢 15x |
| [`docs/long_context_models_1m.md`](docs/long_context_models_1m.md) | 📚 REF: 1M+ 上下文长度模型列表 (Qwen/GLM/InternLM/Llama/VL)，≤10B 推荐模型 |

## Rules Index

| Rule | Purpose |
|------|---------|
| [`.claude/rules/multi-gpu-debugging.md`](.claude/rules/multi-gpu-debugging.md) | **Multi-GPU debugging**: GPU allocation (1-2 for validation, rest for exploration), single-task validation policy |
| [`.claude/rules/gpu-testing.md`](.claude/rules/gpu-testing.md) | GPU type detection, card assignment, needle test requirements |
| [`.claude/rules/sparse-policy.md`](.claude/rules/sparse-policy.md) | SparsePolicy implementation requirements |
| [`.claude/rules/planning-with-files.md`](.claude/rules/planning-with-files.md) | Planning file management for complex tasks |
| [`.claude/rules/gpu-monitor.md`](.claude/rules/gpu-monitor.md) | **GPU memory monitoring**: 必须使用 gpu-monitor agent，禁止手动 nvidia-smi 循环 |

## GPU Mutex for Multi-Instance Debugging

**IMPORTANT**: When running multiple Claude instances for parallel debugging, different rules apply based on script type:

### Benchmarks (`bench*.py`) - Exclusive GPU Access Required

Before running any `bench*.py` script, Claude MUST wait for exclusive GPU access:

```bash
# Check and wait for GPU to be free
while [ -n "$(nvidia-smi --query-compute-apps=pid --format=csv,noheader)" ]; do
  echo "GPU busy, waiting 10s..."
  sleep 10
done
```

### Other Scripts (tests, examples) - No Special Requirements

For non-benchmark scripts, exclusive GPU access is NOT required. Multiple nanovllm processes can run simultaneously on different GPUs - each process automatically selects a unique port for `torch.distributed` communication.

## Multi-Instance Development with PYTHONPATH

**IMPORTANT**: When running multiple Claude instances on different worktrees, do NOT use `pip install -e .` globally as it will affect other instances.

**Use PYTHONPATH directly** - no pip install needed:

```bash
# Set PYTHONPATH to point to the project root directory
PYTHONPATH=/path/to/your/worktree:$PYTHONPATH python <script.py>

# Example: running tests
PYTHONPATH=/home/zijie/Code/nano-vllm:$PYTHONPATH python tests/test_needle.py
```

**Benefits**:
- No `pip install` required
- Code changes take effect immediately (no reinstall needed)
- Each worktree is completely isolated

## Configuration

| Parameter | Default | Notes |
|-----------|---------|-------|
| `kvcache_block_size` | 1024 | Tokens per block (4096 now works after race condition fix) |
| `max_num_batched_tokens` | 16384 | Set = max_model_len for long context |
| `gpu_memory_utilization` | 0.9 | GPU memory fraction |
| `enable_cpu_offload` | False | Enable for long context |
| `enforce_eager` | False | Set True to disable CUDA graphs |

## Benchmarking

**Files**: `bench.py` (GPU), `bench_offload.py` (CPU offload), `bench_vllm.py` (comparison)

**Offload Mode Constraint**: When using `enable_cpu_offload=True`, only test with context length ≥ 32K. Shorter contexts don't exercise the chunked offload pipeline properly.

**Common Issues**:
1. `max_num_batched_tokens < max_model_len`: Set equal for long context
2. CUDA graph dimension mismatch: Ensure `input_len + output_len <= max_model_len`
3. RoPE out of bounds: Check model's `max_position_embeddings` in config.json

**Model Limits**:
- Qwen3-0.6B/4B: 40960 tokens
- Qwen2.5-7B-Instruct-1M: 1048576 tokens

**Performance (Qwen3-0.6B)**:
- GPU: ~18k tok/s (prefill), ~100 tok/s (decode)
- CPU Offload (16K): ~14k tok/s (prefill)
- CPU Offload (32K): ~13k tok/s (prefill)

---

**Author**: Zijie Tian