nano-vllm/CLAUDE.md

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, Llama-3, and GLM-4 models with CPU offload for long-context inference.

Documentation Index

Document	Purpose
docs/architecture_guide.md	Core components, CPU offload system design, ring buffer architecture, stream configuration
docs/sparse_policy_architecture.md	SparsePolicy abstraction: prefill/decode delegation, pipeline modes, policy implementations
docs/sparse_policy_implementation_guide.md	How to implement custom SparsePolicy: required methods, hooks, ring buffer pipeline pattern
docs/sparse_attention_guide.md	Block sparse attention methods (XAttention, FlexPrefill, MInference, AvgPool, Quest), computation flow, algorithms
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	XAttention Triton kernels: flat_group_gemm (反对角线求和)、softmax_fuse_block_sum (block 聚合)
docs/xattn_kv_chunking_kernels.md	XAttention KV Chunking: 三阶段 softmax、存储开销分析 (O(S) vs O(S²))、峰值显存优化 (8x)、Q/KV 独立分块
docs/xattn_chunked_prefill.md	XAttention chunked prefill: API、使用方式、一致性要求
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/xattn_density_benchmark.md	📊 XAttention Density Benchmark: 4K-32K context、stride 参数、per-layer density 分析
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	PyTorch hooks for debugging, hook positions, tensor comparison, memory profiling
docs/optimization_guide.md	Performance optimizations: sgDMA (15x), Triton merge (4.3x), N-way pipeline (2x)
docs/known_issues.md	Documented bugs and fixes: partial last block bug, block size 4096 race condition
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	⚠️ OPEN ISSUE: 32K chunked offload accuracy problem (20% error rate in RULER)
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	🐛 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	⚡ 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	📊 BENCH: CPU offload 性能测试结果，Full vs XAttention 对比 (32K/128K)
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	🚀 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	📊 XAttention 性能分析: NVTX 标记、block size 影响、estimate vs compute 耗时对比
docs/observer_architecture.md	📊 Observer 架构: InferenceObserver (TTFT/TPOT)、MemoryObserver (H2D/D2H/D2D) 设计
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	🔥 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	📚 REF: 1M+ 上下文长度模型列表 (Qwen/GLM/InternLM/Llama/VL)，≤10B 推荐模型
docs/new_model_integration_guide.md	🔧 GUIDE: 新模型整合指南 - 配置映射、RoPE变体、EOS处理、权重转换、验证清单
docs/xattn_density_alignment_analysis.md	📊 ANALYSIS: GPU-only vs Offload 模式 density 对齐分析，chunked softmax 边界效应，5-7% 差异根因
docs/xattn_kv_chunking_density_test.md	🧪 TEST: XAttention KV chunking density 验证，threshold=1.0 对齐，threshold<1.0 差异 10-13%
docs/gpuonly_density_alignment_test.md	✅ TEST: Density 对齐验证 (GPU-only + Offload, 4K-64K)，xattn_estimate vs KV chunking 完全一致
docs/xattn_memory_benchmark.md	📊 BENCH: XAttention 内存基准测试，Qwen3-0.6B 32K 在 24GB 显存可行 (gpu-util=0.28)
docs/xattn_offload_stream_sync_fix.md	🐛 FIX: XAttention Offload stream 同步 bug，Pass1/Pass2 K 数据不一致，compute_stream 包装
docs/xattn_density_types.md	📊 Compute vs Comm density: BSA block (128) vs CPU block (4096) 粒度，聚合效应导致 comm=100%
docs/xattn_density_alignment_verification.md	✅ VERIFIED: GPU-only vs Offload density 对齐验证 (32K 差异 0.37%, 64K 差异 0.09%)
docs/test_ruler_usage_guide.md	📖 GUIDE: test_ruler.py 使用指南，RULER benchmark 测试命令，已验证的命令示例

Rules Index

Rule	Purpose
.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	GPU type detection, card assignment, needle test requirements
.claude/rules/sparse-policy.md	SparsePolicy implementation requirements
.claude/rules/planning-with-files.md	Planning file management for complex tasks
.claude/rules/gpu-monitor.md	GPU memory monitoring: 必须使用 gpu-monitor agent，禁止手动 nvidia-smi 循环
.claude/rules/test-ruler.md	test_ruler.py 规则: 禁止 --help，必须查阅文档，含快速参考和命令模板

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:

# 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:

# 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)

GPU-only 测试模型选择:

GPU	显存	GPU-only 测试模型
RTX 3090	24GB	Qwen3-0.6B (必须，7B+ 模型会 OOM)
A100	40GB+	Qwen3-0.6B / 4B / 7B 均可

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)

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Author: Zijie Tian