nano-vllm/docs/xattn_density_alignment_verification.md

XAttention Density Alignment Verification

验证 GPU-only 和 Offload 模式的 density 对齐情况。

测试日期: 2026-02-05 测试模型: Llama-3.1-8B-Instruct 测试任务: RULER niah_single_1

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测试配置

参数	值
sparse_policy	XATTN_BSA
threshold	0.9
chunk_size	4096 (已对齐)
stride	8
BSA block_size	128

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测试结果

32K Context

模式	Layer 0 Density	Overall Density	准确率
GPU-only	0.502079	0.4012	100%
Offload	0.498421	0.4984	100%
差异	0.37%	-	-

64K Context

模式	Layer 0 Density	Overall Density	准确率
GPU-only	0.369972	0.2963	100%
Offload	0.369052	0.3691	100%
差异	0.09%	-	-

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关键修复

Commit 829b311 - chunk_size 对齐 + Stream 同步修复

问题: 之前 GPU-only 和 Offload 模式的 density 差异达 10-13%

根因:

1. GPU-only 使用 chunk_size=16384，Offload 使用 chunk_size=4096
2. Stream 同步 bug 导致 Pass 1/2 K 数据不一致

修复:

1. 将 XAttentionBSAPolicy.chunk_size 默认值从 16384 改为 4096
2. 所有 compute kernels 包装在 compute_stream context 中

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测试命令

GPU-only 模式

CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/path/to/nano-vllm:$PYTHONPATH \
    python tests/test_ruler.py \
    --model ~/models/Llama-3.1-8B-Instruct \
    --data-dir tests/data/ruler_32k \
    --datasets niah_single_1 \
    --num-samples 1 \
    --max-model-len 40960 \
    --sparse-policy XATTN_BSA \
    --sparse-threshold 0.9

Offload 模式

CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/path/to/nano-vllm:$PYTHONPATH \
    python tests/test_ruler.py \
    --model ~/models/Llama-3.1-8B-Instruct \
    --data-dir tests/data/ruler_32k \
    --datasets niah_single_1 \
    --num-samples 1 \
    --max-model-len 40960 \
    --enable-offload \
    --sparse-policy XATTN_BSA \
    --sparse-threshold 0.9

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详细日志

32K Offload 模式 Per-Chunk Density

Layer0 chunk: q_len=4096, k_len=4096,  density=0.6234
Layer0 chunk: q_len=4096, k_len=8192,  density=0.6239
Layer0 chunk: q_len=4096, k_len=12288, density=0.6026
Layer0 chunk: q_len=4096, k_len=16384, density=0.5695
Layer0 chunk: q_len=4096, k_len=20480, density=0.5285
Layer0 chunk: q_len=4096, k_len=24576, density=0.4891
Layer0 chunk: q_len=4096, k_len=28672, density=0.4514
Layer0 chunk: q_len=3813, k_len=32485, density=0.4208

64K Offload 模式 Per-Chunk Density

Layer0 chunk: q_len=4096, k_len=4096,  density=0.6234
Layer0 chunk: q_len=4096, k_len=8192,  density=0.6239
Layer0 chunk: q_len=4096, k_len=12288, density=0.6026
Layer0 chunk: q_len=4096, k_len=16384, density=0.5681
Layer0 chunk: q_len=4096, k_len=20480, density=0.5255
Layer0 chunk: q_len=4096, k_len=24576, density=0.4859
Layer0 chunk: q_len=4096, k_len=28672, density=0.4485
Layer0 chunk: q_len=4096, k_len=32768, density=0.4161
Layer0 chunk: q_len=4096, k_len=36864, density=0.3892
Layer0 chunk: q_len=4096, k_len=40960, density=0.3658
Layer0 chunk: q_len=4096, k_len=45056, density=0.3464
Layer0 chunk: q_len=4096, k_len=49152, density=0.3303
Layer0 chunk: q_len=4096, k_len=53248, density=0.3170
Layer0 chunk: q_len=4096, k_len=57344, density=0.3068
Layer0 chunk: q_len=4096, k_len=61440, density=0.2988
Layer0 chunk: q_len=3451, k_len=64891, density=0.2947

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结论

1. Density 对齐成功: 差异从 10-13% 降到 <0.5%
2. 准确率一致: 两种模式都达到 100% 准确率
3. Density 随 context 增长下降: 符合预期，更长的 context 稀疏性更高

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相关文档

• docs/xattn_offload_stream_sync_fix.md - Stream 同步修复详情
• docs/xattn_density_types.md - Compute vs Comm density
• docs/gpuonly_density_alignment_test.md - 早期对齐测试