📝 docs: add CUDA Graph optimization plan for offload mode decode

- Update task_plan.md with 6-phase segmented graph implementation plan
- Add findings.md documenting 7 key discoveries about current implementation
- Add progress.md for tracking implementation progress
- Add test_chunk_attention_graph_reuse.py validating 2-graph reuse strategy

Key architecture decision: Split transformer layer into 3 segments:
- PRE-ATTENTION GRAPH: norm → qkv_proj → rotary (1 graph, reused)
- CHUNKED ATTENTION: H2D (eager) + flash_attn (2 graphs) + merge (eager)
- POST-ATTENTION GRAPH: o_proj → norm → FFN (1 graph, reused)

Total: 4 graphs serving all layers via copy_() tensor updates.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

findings.md

@@ -0,0 +1,109 @@
+# Findings: CUDA Graph for Offload Mode
+
+## Discovery 1: 为什么 Offload Mode 不使用 CUDA Graph
+
+**位置**: `nanovllm/engine/model_runner.py:421`
+
+```python
+use_eager = is_prefill or self.enforce_eager or input_ids.size(0) > 512 or context.is_chunked_prefill
+```
+
+**原因**: `run_chunked_offload_decode()` 设置 `is_chunked_prefill=True`，强制使用 eager mode。
+
+---
+
+## Discovery 2: 当前 CUDA Graph 架构
+
+**文件**: `model_runner.py:682-717`
+
+```python
+def capture_cudagraph(self):
+    # 为不同 batch size 捕获完整 model forward
+    for bs in [1, 2, 4, 8, 16, ...]:
+        with torch.cuda.graph(graph):
+            outputs[:bs] = self.model(input_ids[:bs], positions[:bs])
+```
+
+**特点**:
+- 捕获完整的 `model()` 调用（包含所有层）
+- 使用 graph pool 共享内存
+- 只用于 decode（prefill 始终 eager）
+
+---
+
+## Discovery 3: Offload Decode 的 Attention 流程
+
+**文件**: `nanovllm/kvcache/sparse/full_policy.py:304-379`
+
+**Ring Buffer Pipeline**:
+```
+1. 预加载前 N 个 blocks 到 GPU slots
+2. 对每个 block:
+   a. wait_slot_layer()       # 等待 H2D
+   b. get_kv_for_slot()       # 获取 KV
+   c. flash_attn_with_lse()   # ⭐ 可 graph
+   d. record_slot_compute_done()
+   e. load_next_block()       # 启动下一个 H2D
+   f. merge_attention_outputs() # ⭐ 可 graph（但动态）
+```
+
+**关键**: H2D 传输不能 graph，但 attention 计算可以。
+
+---
+
+## Discovery 4: 验证 Graph 复用可行性
+
+**测试**: `tests/test_chunk_attention_graph_reuse.py`
+
+**结论**:
+- 只需 2 个 graph（causal + non-causal）
+- 通过 `copy_()` 更新 static tensors
+- 可复用于所有层和所有 chunk pairs
+
+**测试结果**:
+```
+Layer 0: max_diff=3.91e-03 ✅
+Layer 1: max_diff=7.81e-03 ✅
+Layer 2: max_diff=3.91e-03 ✅
+✅ PASSED
+```
+
+---
+
+## Discovery 5: Chunk Size 和 Block Size 关系
+
+**观察**:
+- Prefilled blocks 的 KV size = `block_size`
+- Decode buffer 的 KV size = `1` 到 `block_size`（动态）
+
+**Graph 策略**:
+- Prefilled blocks: 固定 size = block_size，适合 graph
+- Decode buffer: 动态 size，建议保持 eager
+
+---
+
+## Discovery 6: 使用的 Triton 算子
+
+**文件**: `nanovllm/ops/chunked_attention.py`
+
+| 算子 | 功能 | 可 Graph |
+|------|------|----------|
+| `flash_attn_with_lse()` | Attention + LSE | ✅ |
+| `merge_attention_outputs()` | 合并两个 attention 输出 | ✅ |
+
+这两个算子是纯 GPU 计算，可以被 CUDA Graph 捕获。
+
+---
+
+## Discovery 7: 数据依赖分析
+
+**Attention 输入**:
+- `q`: 来自当前层的 QKV projection，shape 固定
+- `k, v`: 来自 GPU slot（H2D 传输后），shape = [1, block_size, heads, dim]
+
+**依赖链**:
+```
+H2D(block) → wait() → get_kv() → copy_to_static() → graph.replay() → clone_output()
+```
+
+**关键**: Graph 只封装 attention 计算，不包含数据传输。

progress.md

@@ -0,0 +1,55 @@
+# Progress: CUDA Graph for Offload Mode
+
+## Session: 2026-01-22
+
+### 调研阶段 ✅ 完成
+
+**完成的调研**:
+
+1. ✅ 分析 `model_runner.py` 中的 CUDA Graph 实现
+   - `capture_cudagraph()`: 为不同 batch size 捕获完整 model forward
+   - `run_model()`: 通过 `is_chunked_prefill` 决定 eager/graph
+
+2. ✅ 分析 offload decode 流程
+   - `run_chunked_offload_decode()` 设置 `is_chunked_prefill=True`
+   - 导致永远使用 eager mode
+
+3. ✅ 分析 ring buffer pipeline
+   - `_decode_ring_buffer_pipeline()` 包含 H2D 传输 + attention 计算
+   - H2D 不能 graph，attention 可以 graph
+
+4. ✅ 验证 graph 复用策略
+   - 创建 `test_chunk_attention_graph_reuse.py`
+   - 确认 2 个 graph 可复用于所有层
+
+### 计划编写 ✅ 完成
+
+- ✅ 创建 `task_plan.md`
+- ✅ 创建 `findings.md`
+- ✅ 创建 `progress.md`
+
+### 下一步: 实现
+
+**Phase 1**: 添加 graph 捕获到 OffloadEngine
+- [ ] 在 `offload_engine.py` 添加 `capture_attention_graphs()`
+- [ ] 添加 `attention_graph_causal` 和 `attention_graph_non_causal` 属性
+
+**Phase 2**: 修改 ring buffer pipeline
+- [ ] 在 `_decode_ring_buffer_pipeline()` 使用 graph replay
+- [ ] 保持 H2D 和 merge 为 eager
+
+**Phase 3**: 测试
+- [ ] 运行 needle test 验证正确性
+- [ ] 对比性能
+
+---
+
+## 文件清单
+
+| 文件 | 状态 | 说明 |
+|------|------|------|
+| `tests/test_chunk_attention_graph.py` | ✅ 已提交 | 预分配 chunk pair graphs 测试 |
+| `tests/test_chunk_attention_graph_reuse.py` | 待提交 | Graph 复用验证 |
+| `task_plan.md` | ✅ 创建 | 实现计划 |
+| `findings.md` | ✅ 创建 | 调研发现 |
+| `progress.md` | ✅ 创建 | 进度日志 |

task_plan.md

@@ -1,90 +1,357 @@
-# Task Plan: XAttention BSA 集成到 nanovllm
+# Task Plan: CUDA Graph 优化 Offload Mode Decode
 
-## Goal
+## 目标
 
-使用 `--sparse-policy XATTN_BSA` 运行 `test_ruler.py`，通过 `niah_single_1` 的前 5 个 sample。
+为 nanovllm 的 CPU offload 模式添加 CUDA Graph 支持，加速 decode 阶段的计算。
 
-**验收标准**:
-```bash
-CUDA_VISIBLE_DEVICES=X PYTHONPATH=/home/zijie/Code/nano-vllm:$PYTHONPATH \
-    python tests/test_ruler.py \
-    --model ~/models/Llama-3.1-8B-Instruct \
-    --enable-offload \
-    --sparse-policy XATTN_BSA \
-    --task niah_single_1 \
-    --sample-ids 0,1,2,3,4
-# 期望: 5/5 PASS
+## 问题分析
+
+### Transformer 层的完整结构
+
+```
+Qwen3DecoderLayer.forward:
+├── input_layernorm (RMSNorm)           # ✅ 纯 GPU
+├── self_attn:
+│   ├── qkv_proj (Linear)               # ✅ 纯 GPU
+│   ├── q_norm, k_norm (RMSNorm)        # ✅ 纯 GPU
+│   ├── rotary_emb                      # ✅ 纯 GPU
+│   ├── attn._chunked_decode_attention: # ⚠️ 包含 CPU→GPU
+│   │   ├── H2D transfer                # ❌ 不能 graph
+│   │   ├── flash_attn_with_lse         # ✅ 可以 graph
+│   │   └── merge                       # ✅ 纯 GPU
+│   └── o_proj (Linear)                 # ✅ 纯 GPU
+├── post_attention_layernorm            # ✅ 纯 GPU
+└── mlp (FFN: gate, up, down)           # ✅ 纯 GPU
 ```
 
-## 当前状态
+**核心问题**：H2D 传输被嵌在 attention 中间，打断了整层的 graph 捕获。
 
-- `XAttentionBSAPolicy.compute_chunked_prefill` 实现 = `FullAttentionPolicy`（无 sparse）
-- `xattn_estimate_chunked` 已实现并验证
-- BSA kernel (`block_sparse_attn`) 可用
+### 可能的方案
 
-## Phases
+| 方案 | 描述 | 优点 | 缺点 |
+|------|------|------|------|
+| A. 分段 Graph | 将层拆分为 pre/post attention 两段 | 覆盖面广 | 改动大，需拆分层执行 |
+| B. 只 Graph Attention | 只优化 flash_attn_with_lse | 改动小 | 优化效果有限 |
+| C. 重构执行流程 | 完全重写 model forward | 最优效果 | 工作量巨大 |
 
-- [ ] Phase 1: 理解当前代码路径
-- [ ] Phase 2: 实现 sparse mask 估计
-- [ ] Phase 3: 实现 BSA sparse 计算
-- [ ] Phase 4: 测试验证
+### 推荐：方案 A（分段 Graph）
 
-## Phase 1: 理解当前代码路径
+将每层拆分为两个 graph：
+1. **pre_attention_graph**: `norm → qkv_proj → q/k_norm → rotary`
+2. **post_attention_graph**: `o_proj → norm → FFN`
 
-### 1.1 确认 XATTN_BSA policy 是否被正确加载
-- [ ] 检查 `test_ruler.py` 如何解析 `--sparse-policy XATTN_BSA`
-- [ ] 检查 `KVCacheManager` 如何实例化 sparse_policy
-- [ ] 运行 baseline 测试（`--sparse-policy FULL`）确认基础功能正常
+中间的 `_chunked_decode_attention` 保持 eager（包含 H2D），但内部的 `flash_attn_with_lse` 使用 graph。
 
-### 1.2 确认数据流
-- [ ] `compute_chunked_prefill` 的输入参数含义
-- [ ] `offload_engine` 提供的数据访问接口
-- [ ] 当前 chunk 的 K/V 如何获取
+---
 
-## Phase 2: 实现 sparse mask 估计
+## 当前状态分析
 
-### 2.1 调用 xattn_estimate_chunked
-- [ ] 在 `compute_chunked_prefill` 中加载历史 K
-- [ ] 拼接历史 K + 当前 K
-- [ ] 调用 `xattn_estimate_chunked(q, k_full, q_start_pos=...)`
-- [ ] 获取 block mask
+### 现有 CUDA Graph 实现
 
-### 2.2 处理参数对齐
-- [ ] BSA block_size = 128
-- [ ] chunk_size 与 kvcache_block_size 的关系
-- [ ] q_start_pos 计算
+**文件**: `nanovllm/engine/model_runner.py`
 
-## Phase 3: 实现 BSA sparse 计算
+| 方法 | 行号 | 功能 |
+|------|------|------|
+| `capture_cudagraph()` | 682-717 | 为不同 batch size 捕获完整 model forward |
+| `run_model()` | 415-436 | 决定使用 eager 还是 graph replay |
 
-### 3.1 方案选择
-- 选项 A: 历史 + 当前分开计算，然后 merge
-- 选项 B: 全部一起用 BSA 计算
+**关键逻辑** (`run_model`):
+```python
+use_eager = is_prefill or self.enforce_eager or input_ids.size(0) > 512 or context.is_chunked_prefill
+```
 
-### 3.2 实现
-- [ ] 构造 BSA 需要的输入格式
-- [ ] 调用 `block_sparse_attn_func`
-- [ ] 处理输出格式
+**问题**: `run_chunked_offload_decode` 设置 `is_chunked_prefill=True`，导致**永远使用 eager mode**。
 
-## Phase 4: 测试验证
+### Offload Decode 流程
 
-### 4.1 单元测试
-- [ ] 验证 sparse mask 与 `test_xattn_estimate_chunked.py` 一致
+**文件**: `nanovllm/kvcache/sparse/full_policy.py`
 
-### 4.2 集成测试
-- [ ] 运行验收命令
-- [ ] 5/5 PASS
+`_decode_ring_buffer_pipeline()` (L304-379):
+```
+for block in cpu_blocks:
+    1. wait_slot_layer(slot)           # 等待 H2D 完成
+    2. k, v = get_kv_for_slot(slot)    # 获取 KV
+    3. o, lse = flash_attn_with_lse()  # ⭐ 纯 GPU 计算
+    4. record_slot_compute_done(slot)  # 标记计算完成
+    5. load_next_block()               # 启动下一个 H2D
+    6. merge_attention_outputs()       # ⭐ 纯 GPU 计算
+```
 
-## Key Questions
+**可 Graph 化的部分**:
+- `flash_attn_with_lse()` - 纯 GPU 计算
+- 不可 Graph 化: H2D 传输、动态 merge
 
-1. 历史 K 如何高效加载？（全量 vs 按需）
-2. BSA causal mask 如何处理？（历史 non-causal + 当前 causal）
+## 验证结果
 
-## Status
+**测试文件**: `tests/test_chunk_attention_graph_reuse.py`
 
-**Currently in Phase 1** - 等待用户确认后开始
+| 测试 | 结果 |
+|------|------|
+| 2 个 Graph 复用于所有层和所有 chunk | ✅ PASSED |
+| copy_() 更新 static tensors | ✅ 有效 |
+| Eager merge | ✅ 用户已接受 |
 
-## 待讨论
+**结论**: 只需 2 个 graph（causal + non-causal），通过 copy_() 复用。
 
-请确认：
-1. 这个 goal 和验收标准是否正确？
-2. 我使用哪个 GPU 运行测试？
+---
+
+## 修改计划（方案 A：分段 Graph）
+
+### 架构设计
+
+```
+每层执行流程（Offload Decode）:
+┌─────────────────────────────────────────────────────────────┐
+│  PRE-ATTENTION GRAPH (可复用于所有层)                        │
+│  input_layernorm → qkv_proj → q/k_norm → rotary → split Q  │
+└─────────────────────────────────────────────────────────────┘
+                              ↓
+┌─────────────────────────────────────────────────────────────┐
+│  CHUNKED ATTENTION (Eager + 部分 Graph)                     │
+│  for block in cpu_blocks:                                   │
+│      H2D transfer (eager)                                   │
+│      flash_attn_with_lse (GRAPH - 2个可复用)                │
+│      merge (eager)                                          │
+│  decode_buffer attention (eager)                            │
+└─────────────────────────────────────────────────────────────┘
+                              ↓
+┌─────────────────────────────────────────────────────────────┐
+│  POST-ATTENTION GRAPH (可复用于所有层)                       │
+│  o_proj → post_layernorm → gate_proj → up_proj → SiLU      │
+│  → down_proj → residual                                     │
+└─────────────────────────────────────────────────────────────┘
+```
+
+**总共需要的 Graph 数量**:
+- 1 个 pre_attention_graph（所有层复用）
+- 2 个 attention_graph（causal + non-causal，所有层复用）
+- 1 个 post_attention_graph（所有层复用）
+- **总计: 4 个 graph**
+
+---
+
+### Phase 1: 拆分 DecoderLayer 执行
+
+**目标**: 将 `Qwen3DecoderLayer.forward` 拆分为可独立调用的三段
+
+**修改文件**: `nanovllm/models/qwen3.py`
+
+**新增方法**:
+```python
+class Qwen3DecoderLayer:
+    def forward_pre_attention(self, positions, hidden_states, residual):
+        """Pre-attention: norm → qkv → rotary → 返回 q, k, v"""
+        if residual is None:
+            hidden_states, residual = self.input_layernorm(hidden_states), hidden_states
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        qkv = self.self_attn.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q.view(-1, self.num_heads, self.head_dim)
+        k = k.view(-1, self.num_kv_heads, self.head_dim)
+        v = v.view(-1, self.num_kv_heads, self.head_dim)
+        q = self.self_attn.q_norm(q)
+        k = self.self_attn.k_norm(k)
+        q, k = self.self_attn.rotary_emb(positions, q, k)
+        return q, k, v, hidden_states, residual
+
+    def forward_post_attention(self, attn_output, hidden_states, residual):
+        """Post-attention: o_proj → norm → FFN"""
+        output = self.self_attn.o_proj(attn_output.flatten(1, -1))
+        hidden_states, residual = self.post_attention_layernorm(output, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+```
+
+**状态**: `pending`
+
+---
+
+### Phase 2: 捕获 Pre/Post Attention Graph
+
+**目标**: 捕获 pre_attention 和 post_attention 的 graph
+
+**修改文件**: `nanovllm/engine/model_runner.py`
+
+**新增方法**: `capture_offload_layer_graphs()`
+
+```python
+def capture_offload_layer_graphs(self):
+    """捕获 offload mode 的 layer graphs"""
+    # 获取任意一层作为模板（所有层结构相同）
+    layer = self.model.model.layers[0]
+
+    # Static tensors
+    static_hidden = torch.zeros(1, self.hidden_size, ...)
+    static_residual = torch.zeros(1, self.hidden_size, ...)
+    static_positions = torch.zeros(1, ...)
+
+    # Pre-attention graph
+    self.pre_attn_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(self.pre_attn_graph):
+        static_q, static_k, static_v, _, _ = layer.forward_pre_attention(
+            static_positions, static_hidden, static_residual
+        )
+
+    # Post-attention graph
+    self.post_attn_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(self.post_attn_graph):
+        _, _ = layer.forward_post_attention(
+            static_attn_output, static_hidden, static_residual
+        )
+```
+
+**状态**: `pending`
+
+---
+
+### Phase 3: 捕获 Attention Graph
+
+**目标**: 捕获 2 个 attention graph（causal + non-causal）
+
+**修改文件**: `nanovllm/kvcache/offload_engine.py`
+
+```python
+class OffloadEngine:
+    def capture_attention_graphs(self):
+        """捕获 attention graphs（复用于所有层）"""
+        self.attn_graph_causal = self._capture_attn_graph(causal=True)
+        self.attn_graph_non_causal = self._capture_attn_graph(causal=False)
+
+    def _capture_attn_graph(self, causal: bool):
+        static_q = torch.zeros(1, 1, num_heads, head_dim, ...)
+        static_k = torch.zeros(1, block_size, num_kv_heads, head_dim, ...)
+        static_v = torch.zeros(1, block_size, num_kv_heads, head_dim, ...)
+
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            output, lse = flash_attn_with_lse(static_q, static_k, static_v,
+                                              self.scale, causal)
+        return AttentionGraph(graph, static_q, static_k, static_v, output, lse)
+```
+
+**状态**: `pending`
+
+---
+
+### Phase 4: 修改 Offload Decode 执行流程
+
+**目标**: 使用 graph replay 执行 offload decode
+
+**修改文件**: `nanovllm/engine/model_runner.py`
+
+**修改方法**: `run_chunked_offload_decode()`
+
+```python
+def run_chunked_offload_decode_with_graph(self, seqs):
+    """使用 graph 加速的 offload decode"""
+    seq = seqs[0]
+
+    # 准备输入
+    input_ids = torch.tensor([seq.last_token], ...)
+    positions = torch.tensor([len(seq) - 1], ...)
+
+    # Embedding
+    hidden_states = self.model.model.embed_tokens(input_ids)
+    residual = None
+
+    for layer_id, layer in enumerate(self.model.model.layers):
+        # Phase 1: Pre-attention (GRAPH)
+        self.pre_attn_vars["hidden"].copy_(hidden_states)
+        self.pre_attn_vars["residual"].copy_(residual) if residual else None
+        self.pre_attn_vars["positions"].copy_(positions)
+        self.pre_attn_graph.replay()
+        q = self.pre_attn_vars["q"].clone()
+        k = self.pre_attn_vars["k"].clone()
+        v = self.pre_attn_vars["v"].clone()
+
+        # Phase 2: Chunked Attention (Eager + Graph)
+        attn_output = self._chunked_attention_with_graph(q, k, v, layer_id, ...)
+
+        # Phase 3: Post-attention (GRAPH)
+        self.post_attn_vars["attn_output"].copy_(attn_output)
+        self.post_attn_graph.replay()
+        hidden_states = self.post_attn_vars["hidden"].clone()
+        residual = self.post_attn_vars["residual"].clone()
+
+    # LM head
+    logits = self.model.compute_logits(hidden_states)
+    return logits
+```
+
+**状态**: `pending`
+
+---
+
+### Phase 5: 修改 Ring Buffer Pipeline
+
+**目标**: 在 attention 内部使用 graph
+
+**修改文件**: `nanovllm/kvcache/sparse/full_policy.py`
+
+**修改**: `_decode_ring_buffer_pipeline()` 中的 `flash_attn_with_lse` 调用
+
+```python
+# 当前：eager
+prev_o, prev_lse = flash_attn_with_lse(q, k, v, scale, causal=False)
+
+# 修改为：graph replay
+graph = offload_engine.attn_graph_non_causal
+graph.static_q.copy_(q)
+graph.static_k.copy_(k)
+graph.static_v.copy_(v)
+graph.graph.replay()
+prev_o = graph.static_output.clone()
+prev_lse = graph.static_lse.clone()
+```
+
+**状态**: `pending`
+
+---
+
+### Phase 6: 添加配置开关
+
+**修改文件**: `nanovllm/config.py`
+
+```python
+enable_offload_graph: bool = True  # 默认启用
+```
+
+**状态**: `pending`
+
+---
+
+## 文件修改清单
+
+| 文件 | 修改类型 | 说明 |
+|------|----------|------|
+| `nanovllm/engine/model_runner.py` | 新增方法 | `capture_offload_attention_graph()` |
+| `nanovllm/kvcache/offload_engine.py` | 新增属性+方法 | Graph 存储和访问 |
+| `nanovllm/kvcache/sparse/full_policy.py` | 修改方法 | 使用 graph replay |
+| `nanovllm/config.py` | 新增配置 | `enable_offload_graph` |
+
+---
+
+## 风险和注意事项
+
+1. **Graph 捕获时机**: 需要在 KV cache 分配后、第一次 decode 前捕获
+2. **Chunk size 匹配**: Graph 的 chunk_size 必须和 block_size 一致
+3. **多 GPU**: Graph 需要在每个 GPU 上分别捕获
+4. **内存**: 2 个 graph 的额外内存开销很小
+
+---
+
+## 测试计划
+
+1. **单元测试**: 验证 graph replay 结果正确
+2. **集成测试**: 运行 `test_needle.py --enable-offload --input-len 32768`
+3. **性能测试**: 对比 eager vs graph 的 decode 延迟
+
+---
+
+## 预期收益
+
+- Decode 阶段 attention 计算加速（减少 kernel launch overhead）
+- 与现有 ring buffer pipeline 兼容
+- 内存开销极小（只有 2 个额外 graph）

tests/test_chunk_attention_graph_reuse.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""
+Test: Reuse a single CUDA Graph across all layers and all chunk pairs.
+
+Key insight: LLM layers have identical computation structure.
+We only need 2 graphs (causal + non-causal), reused for all (layer, Q_i, K_j) combinations.
+
+Usage:
+    CUDA_VISIBLE_DEVICES=0 python tests/test_chunk_attention_graph_reuse.py
+"""
+
+from dataclasses import dataclass
+
+import torch
+
+from nanovllm.ops.chunked_attention import flash_attn_with_lse, merge_attention_outputs
+
+
+@dataclass
+class ReusableChunkGraph:
+    """A single graph that can be reused with copy_() updates."""
+    graph: torch.cuda.CUDAGraph
+    static_q: torch.Tensor
+    static_k: torch.Tensor
+    static_v: torch.Tensor
+    static_output: torch.Tensor
+    static_lse: torch.Tensor
+
+
+def capture_reusable_graph(
+    chunk_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    head_dim: int,
+    scale: float,
+    device: torch.device,
+    dtype: torch.dtype,
+    causal: bool,
+) -> ReusableChunkGraph:
+    """Capture ONE graph to be reused for all chunk pairs."""
+    static_q = torch.zeros(1, chunk_size, num_heads, head_dim, dtype=dtype, device=device)
+    static_k = torch.zeros(1, chunk_size, num_kv_heads, head_dim, dtype=dtype, device=device)
+    static_v = torch.zeros(1, chunk_size, num_kv_heads, head_dim, dtype=dtype, device=device)
+
+    static_q.normal_()
+    static_k.normal_()
+    static_v.normal_()
+
+    # Warmup
+    with torch.inference_mode():
+        for _ in range(3):
+            _ = flash_attn_with_lse(static_q, static_k, static_v, scale, causal)
+        torch.cuda.synchronize()
+
+    # Capture
+    graph = torch.cuda.CUDAGraph()
+    with torch.inference_mode():
+        with torch.cuda.graph(graph):
+            static_output, static_lse = flash_attn_with_lse(static_q, static_k, static_v, scale, causal)
+
+    torch.cuda.synchronize()
+
+    return ReusableChunkGraph(
+        graph=graph,
+        static_q=static_q,
+        static_k=static_k,
+        static_v=static_v,
+        static_output=static_output,
+        static_lse=static_lse,
+    )
+
+
+def replay_with_copy(graph: ReusableChunkGraph, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
+    """Replay graph after updating static tensors with copy_()."""
+    graph.static_q.copy_(q)
+    graph.static_k.copy_(k)
+    graph.static_v.copy_(v)
+    graph.graph.replay()
+    return graph.static_output.clone(), graph.static_lse.clone()
+
+
+def main():
+    device = torch.device("cuda")
+    dtype = torch.bfloat16
+
+    chunk_size = 64
+    num_chunks = 4
+    num_layers = 3  # Simulate multiple layers
+    num_heads = 8
+    num_kv_heads = 8
+    head_dim = 64
+    scale = 1.0 / (head_dim ** 0.5)
+    seq_len = chunk_size * num_chunks
+
+    print(f"Device: {torch.cuda.get_device_name()}")
+    print(f"Chunk size: {chunk_size}, Num chunks: {num_chunks}, Num layers: {num_layers}")
+    print(f"Only 2 graphs (causal + non-causal) for ALL layer × chunk combinations")
+
+    # Capture only 2 graphs
+    graph_causal = capture_reusable_graph(
+        chunk_size, num_heads, num_kv_heads, head_dim, scale, device, dtype, causal=True
+    )
+    graph_non_causal = capture_reusable_graph(
+        chunk_size, num_heads, num_kv_heads, head_dim, scale, device, dtype, causal=False
+    )
+    print("2 graphs captured (causal + non-causal)")
+
+    all_pass = True
+
+    for layer_id in range(num_layers):
+        # Different Q/K/V for each layer (simulating different layer outputs)
+        full_q = torch.randn(1, seq_len, num_heads, head_dim, dtype=dtype, device=device)
+        full_k = torch.randn(1, seq_len, num_kv_heads, head_dim, dtype=dtype, device=device)
+        full_v = torch.randn(1, seq_len, num_kv_heads, head_dim, dtype=dtype, device=device)
+
+        # Reference: full causal attention
+        with torch.inference_mode():
+            full_output, _ = flash_attn_with_lse(full_q, full_k, full_v, scale, causal=True)
+
+        # Chunked with graph reuse
+        chunked_output = torch.zeros_like(full_output)
+
+        for q_idx in range(num_chunks):
+            q_chunk = full_q[:, q_idx*chunk_size:(q_idx+1)*chunk_size]
+            acc_out, acc_lse = None, None
+
+            for k_idx in range(q_idx + 1):
+                k_chunk = full_k[:, k_idx*chunk_size:(k_idx+1)*chunk_size]
+                v_chunk = full_v[:, k_idx*chunk_size:(k_idx+1)*chunk_size]
+
+                # Reuse graph with copy_()
+                graph = graph_causal if k_idx == q_idx else graph_non_causal
+                out, lse = replay_with_copy(graph, q_chunk, k_chunk, v_chunk)
+
+                if acc_out is None:
+                    acc_out, acc_lse = out, lse
+                else:
+                    with torch.inference_mode():
+                        acc_out, acc_lse = merge_attention_outputs(acc_out, acc_lse, out, lse)
+
+            chunked_output[:, q_idx*chunk_size:(q_idx+1)*chunk_size] = acc_out
+
+        torch.cuda.synchronize()
+
+        # Compare
+        max_diff = (full_output - chunked_output).abs().max().item()
+        status = "✅" if max_diff < 1e-2 else "❌"
+        print(f"Layer {layer_id}: max_diff={max_diff:.2e} {status}")
+        if max_diff >= 1e-2:
+            all_pass = False
+
+    print("✅ PASSED - Single graph reuse across layers works!" if all_pass else "❌ FAILED")
+
+
+if __name__ == "__main__":
+    main()