232fcf043e
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>
103 lines
2.9 KiB
Markdown
103 lines
2.9 KiB
Markdown
# GPU-Only Density Alignment Test Results
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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。
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## 测试配置
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- **模型**: Llama-3.1-8B-Instruct (32 layers, 32 heads, 8 KV heads, head_dim=128)
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- **Threshold**: 0.9 (选择覆盖 90% attention 的 blocks)
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- **Block Size**: 128 tokens (BSA block)
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- **Stride**: 8
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- **数据集**: RULER niah_single_1 (各长度 1 sample)
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## 测试结果
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| Context | Tokens | Layer 0 Density | Compute Density | Min Layer | 验证结果 |
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|---------|--------|-----------------|-----------------|-----------|----------|
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| 4k | 3,692 | 63.8% | 52.9% | Layer 3 (31.3%) | ✅ PASSED |
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| 8k | 7,892 | 65.0% | 52.5% | Layer 5 (27.3%) | ✅ PASSED |
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| 16k | 15,689 | 61.6% | 47.8% | Layer 5 (23.5%) | ✅ PASSED |
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| 32k | 32,485 | 50.2% | 40.1% | Layer 5 (18.5%) | ✅ PASSED |
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| 64k | 64,891 | 37.0% | 29.6% | Layer 5 (12.4%) | ✅ PASSED |
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## 验证指标
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对于所有测试长度，验证脚本检查以下指标：
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| 指标 | 预期 | 实际结果 |
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|------|------|----------|
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| attn_sums max diff | 0 | 0.000000e+00 |
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| attn_sums mean diff | 0 | 0.000000e+00 |
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| mask exact match | True | True |
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| density diff | 0 | 0.000000 |
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## Density 计算公式
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### Total (分母)
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```python
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# Causal mask: Q block i 只能看到 K block 0 到 i
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causal_mask[i, j] = (j <= i + q_offset_blocks)
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# Total = causal 区域内的 block 数 × batch × heads
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total = causal_mask.sum() × batch × heads
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= (n × (n+1) / 2) × 1 × 32 # n = valid_q_blocks
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```
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### Selected (分子)
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```python
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# 在 causal 区域内，被选中 (mask=True) 的 block 数量
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selected = (mask & causal_mask).sum()
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```
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### Density
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```python
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density = selected / total
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```
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## 观察
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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
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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
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3. **Layer 0 density 最高**: 第一层的 attention pattern 最密集，可能与 sink token 效应有关
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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%
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## 使用方法
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### Step 1: 启用 debug 保存
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```python
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# nanovllm/kvcache/sparse/xattn_bsa.py
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_DEBUG_SAVE_MASK = True # 改为 True
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```
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### Step 2: 运行 GPU-only 推理
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```bash
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CUDA_VISIBLE_DEVICES=0 python tests/test_ruler.py \
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--model ~/models/Llama-3.1-8B-Instruct \
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--data-dir tests/data/ruler_32k \
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--datasets niah_single_1 \
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--num-samples 1 \
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--max-model-len 40960 \
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--sparse-policy XATTN_BSA \
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--sparse-threshold 0.9
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```
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### Step 3: 运行验证脚本
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```bash
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python tests/test_gpuonly_density_alignment.py
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```
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## 相关文件
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- `nanovllm/kvcache/sparse/xattn_bsa.py`: XAttention BSA Policy 实现
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- `nanovllm/ops/xattn.py`: xattn_estimate 函数
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- `tests/test_gpuonly_density_alignment.py`: 验证脚本
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