📝 docs: add GPU-only density alignment test results

Document test results verifying XAttention density calculation in
GPU-only mode matches independent xattn_estimate calls.

Test results (Llama-3.1-8B-Instruct, threshold=0.9):
- 4k:  Layer 0 density 63.8%, verified ✅
- 8k:  Layer 0 density 65.0%, verified ✅
- 16k: Layer 0 density 61.6%, verified ✅
- 32k: Layer 0 density 50.2%, verified ✅
- 64k: Layer 0 density 37.0%, verified ✅

All tests show exact match (attn_sums diff=0, mask exact match).

Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

docs/gpuonly_density_alignment_test.md

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+# GPU-Only Density Alignment Test Results
+
+验证 GPU-only 模式下 `xattn_bsa.py` 的 density 计算与独立调用 `xattn_estimate` 的一致性。
+
+## 测试配置
+
+- **模型**: Llama-3.1-8B-Instruct (32 layers, 32 heads, 8 KV heads, head_dim=128)
+- **Threshold**: 0.9 (选择覆盖 90% attention 的 blocks)
+- **Block Size**: 128 tokens (BSA block)
+- **Stride**: 8
+- **数据集**: RULER niah_single_1 (各长度 1 sample)
+
+## 测试结果
+
+| Context | Tokens | Layer 0 Density | Compute Density | Min Layer | 验证结果 |
+|---------|--------|-----------------|-----------------|-----------|----------|
+| 4k | 3,692 | 63.8% | 52.9% | Layer 3 (31.3%) | ✅ PASSED |
+| 8k | 7,892 | 65.0% | 52.5% | Layer 5 (27.3%) | ✅ PASSED |
+| 16k | 15,689 | 61.6% | 47.8% | Layer 5 (23.5%) | ✅ PASSED |
+| 32k | 32,485 | 50.2% | 40.1% | Layer 5 (18.5%) | ✅ PASSED |
+| 64k | 64,891 | 37.0% | 29.6% | Layer 5 (12.4%) | ✅ PASSED |
+
+## 验证指标
+
+对于所有测试长度，验证脚本检查以下指标：
+
+| 指标 | 预期 | 实际结果 |
+|------|------|----------|
+| attn_sums max diff | 0 | 0.000000e+00 |
+| attn_sums mean diff | 0 | 0.000000e+00 |
+| mask exact match | True | True |
+| density diff | 0 | 0.000000 |
+
+## Density 计算公式
+
+### Total (分母)
+
+```python
+# Causal mask: Q block i 只能看到 K block 0 到 i
+causal_mask[i, j] = (j <= i + q_offset_blocks)
+
+# Total = causal 区域内的 block 数 × batch × heads
+total = causal_mask.sum() × batch × heads
+      = (n × (n+1) / 2) × 1 × 32  # n = valid_q_blocks
+```
+
+### Selected (分子)
+
+```python
+# 在 causal 区域内，被选中 (mask=True) 的 block 数量
+selected = (mask & causal_mask).sum()
+```
+
+### Density
+
+```python
+density = selected / total
+```
+
+## 观察
+
+1. **Density 随 context 增长而降低**: 4k (63.8%) → 64k (37.0%)，这是因为长序列中 attention 更加分散
+
+2. **Layer 5 通常是最稀疏的层**: 在所有长度测试中，Layer 5 的 density 最低
+
+3. **Layer 0 density 最高**: 第一层的 attention pattern 最密集，可能与 sink token 效应有关
+
+4. **Threshold=0.9 对应 ~50% density**: 在 32k context 下，threshold=0.9 意味着选择覆盖 90% attention 的 blocks，实际 density 约 50%
+
+## 使用方法
+
+### Step 1: 启用 debug 保存
+
+```python
+# nanovllm/kvcache/sparse/xattn_bsa.py
+_DEBUG_SAVE_MASK = True  # 改为 True
+```
+
+### Step 2: 运行 GPU-only 推理
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+### Step 3: 运行验证脚本
+
+```bash
+python tests/test_gpuonly_density_alignment.py
+```
+
+## 相关文件
+
+- `nanovllm/kvcache/sparse/xattn_bsa.py`: XAttention BSA Policy 实现
+- `nanovllm/ops/xattn.py`: xattn_estimate 函数
+- `tests/test_gpuonly_density_alignment.py`: 验证脚本