📈 feat: add MemoryObserver for GPU-CPU communication tracking

Implement MemoryObserver to track memory transfers between GPU and CPU:
- H2D (Host to Device): CPU → GPU transfers
- D2H (Device to Host): GPU → CPU transfers
- D2D (Device to Device): GPU buffer copies
- Supports prefill/decode phase separation

Integration points in offload_engine.py:
- load_to_slot_layer: H2D with is_prefill parameter
- offload_slot_layer_to_cpu, offload_prefill_buffer_async: D2H
- write_to_prefill_buffer, write_to_decode_buffer: D2D
- load_block_sample_from_cpu, load_block_full_from_cpu: H2D

Add bench_offload.py integration for memory stats printing.

Benchmark results (Llama-3.1-8B, 64K context):
- Full Policy: Prefill H2D 262.13 GB
- XAttention: Prefill H2D 386.62 GB (1.48x)

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/memory_communication_benchmark.md

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+# Memory Communication Benchmark
+
+GPU-CPU 通信量测试结果，对比 Full Policy 和 XAttention BSA Policy。
+
+## 测试环境
+
+- **模型**: Llama-3.1-8B-Instruct
+- **GPU**: RTX 3090 (24GB)
+- **配置**: `num_gpu_blocks=4`, `block_size=1024`, `enable_cpu_offload=True`
+- **XAttention 参数**: `threshold=0.95`, `stride=8`
+
+## 32K 上下文测试结果
+
+| 指标 | Full Policy | XAttention | 比率 |
+|------|-------------|------------|------|
+| **Prefill H2D** | 66.57 GB | 111.12 GB | **1.67x** |
+| Prefill D2H | 4.29 GB | 4.29 GB | 1.00x |
+| TTFT | 8473 ms | 10367 ms | 1.22x |
+
+### XAttention Block Selection (32K)
+
+| 指标 | 数值 |
+|------|------|
+| 可用 blocks | 465 |
+| 选中 blocks | 374 |
+| 选择密度 | 80.4% |
+
+## 64K 上下文测试结果
+
+| 指标 | Full Policy | XAttention | 比率 |
+|------|-------------|------------|------|
+| **Prefill H2D** | 262.13 GB | 386.62 GB | **1.48x** |
+| Prefill D2H | 8.46 GB | 8.46 GB | 1.00x |
+| Decode H2D (32 tokens) | 262.13 GB | 262.13 GB | 1.00x |
+| TTFT | 27081 ms | 33634 ms | 1.24x |
+
+## 通信量比率对比
+
+| 上下文长度 | XAttn/Full Prefill H2D 比率 |
+|------------|----------------------------|
+| 32K | 1.67x |
+| 64K | 1.48x |
+
+### 分析
+
+1. **XAttention 通信量增加原因**：
+   - Estimate 阶段：加载 **100%** 历史 blocks（用于 attention score 估计）
+   - Compute 阶段：加载 **选中的** blocks（约 70-80%）
+   - 理论比率：`1 + selection_density`
+
+2. **64K 比率更低的原因**：
+   - 更长上下文时，attention 分布更稀疏
+   - XAttention 的 block 选择更有效（选中比例更低）
+   - First/last block 强制包含的影响相对减小
+
+3. **Decode 阶段通信量相同**：
+   - XAttention 仅支持 prefill 阶段
+   - Decode 阶段 fallback 到 Full Policy
+
+## 测试命令
+
+```bash
+# 32K Full Policy
+python bench_offload.py --max-len 32768 --input-len 32000
+
+# 32K XAttention
+python bench_offload.py --max-len 32768 --input-len 32000 --enable-xattn
+
+# 64K Full Policy
+python bench_offload.py --max-len 65536 --input-len 64000
+
+# 64K XAttention
+python bench_offload.py --max-len 65536 --input-len 64000 --enable-xattn
+
+# 包含 decode 测试
+python bench_offload.py --max-len 65536 --input-len 64000 --bench-decode --output-len 32
+```
+
+## 相关文档
+
+- [`observer_architecture.md`](observer_architecture.md) - Observer 架构设计
+- [`xattn_bsa_policy_design.md`](xattn_bsa_policy_design.md) - XAttention BSA 算法设计