📝 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>
This commit is contained in:
Zijie Tian
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task_plan.md
395
task_plan.md
@@ -1,90 +1,357 @@
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# Task Plan: XAttention BSA 集成到 nanovllm
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# Task Plan: CUDA Graph 优化 Offload Mode Decode
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## Goal
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## 目标
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使用 `--sparse-policy XATTN_BSA` 运行 `test_ruler.py`，通过 `niah_single_1` 的前 5 个 sample。
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为 nanovllm 的 CPU offload 模式添加 CUDA Graph 支持，加速 decode 阶段的计算。
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**验收标准**:
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```bash
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CUDA_VISIBLE_DEVICES=X PYTHONPATH=/home/zijie/Code/nano-vllm:$PYTHONPATH \
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python tests/test_ruler.py \
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--model ~/models/Llama-3.1-8B-Instruct \
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--enable-offload \
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--sparse-policy XATTN_BSA \
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--task niah_single_1 \
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--sample-ids 0,1,2,3,4
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# 期望: 5/5 PASS
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## 问题分析
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### Transformer 层的完整结构
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```
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Qwen3DecoderLayer.forward:
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├── input_layernorm (RMSNorm) # ✅ 纯 GPU
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├── self_attn:
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│ ├── qkv_proj (Linear) # ✅ 纯 GPU
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│ ├── q_norm, k_norm (RMSNorm) # ✅ 纯 GPU
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│ ├── rotary_emb # ✅ 纯 GPU
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│ ├── attn._chunked_decode_attention: # ⚠️ 包含 CPU→GPU
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│ │ ├── H2D transfer # ❌ 不能 graph
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│ │ ├── flash_attn_with_lse # ✅ 可以 graph
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│ │ └── merge # ✅ 纯 GPU
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│ └── o_proj (Linear) # ✅ 纯 GPU
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├── post_attention_layernorm # ✅ 纯 GPU
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└── mlp (FFN: gate, up, down) # ✅ 纯 GPU
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```
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## 当前状态
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**核心问题**：H2D 传输被嵌在 attention 中间，打断了整层的 graph 捕获。
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- `XAttentionBSAPolicy.compute_chunked_prefill` 实现 = `FullAttentionPolicy`（无 sparse）
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- `xattn_estimate_chunked` 已实现并验证
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- BSA kernel (`block_sparse_attn`) 可用
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### 可能的方案
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## Phases
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| 方案 | 描述 | 优点 | 缺点 |
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|------|------|------|------|
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| 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 |
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| B. 只 Graph Attention | 只优化 flash_attn_with_lse | 改动小 | 优化效果有限 |
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| C. 重构执行流程 | 完全重写 model forward | 最优效果 | 工作量巨大 |
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- [ ] Phase 1: 理解当前代码路径
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- [ ] Phase 2: 实现 sparse mask 估计
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- [ ] Phase 3: 实现 BSA sparse 计算
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- [ ] Phase 4: 测试验证
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### 推荐：方案 A（分段 Graph）
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## Phase 1: 理解当前代码路径
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将每层拆分为两个 graph：
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1. **pre_attention_graph**: `norm → qkv_proj → q/k_norm → rotary`
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2. **post_attention_graph**: `o_proj → norm → FFN`
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### 1.1 确认 XATTN_BSA policy 是否被正确加载
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- [ ] 检查 `test_ruler.py` 如何解析 `--sparse-policy XATTN_BSA`
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- [ ] 检查 `KVCacheManager` 如何实例化 sparse_policy
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- [ ] 运行 baseline 测试（`--sparse-policy FULL`）确认基础功能正常
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中间的 `_chunked_decode_attention` 保持 eager（包含 H2D），但内部的 `flash_attn_with_lse` 使用 graph。
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### 1.2 确认数据流
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- [ ] `compute_chunked_prefill` 的输入参数含义
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- [ ] `offload_engine` 提供的数据访问接口
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- [ ] 当前 chunk 的 K/V 如何获取
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---
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## Phase 2: 实现 sparse mask 估计
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## 当前状态分析
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### 2.1 调用 xattn_estimate_chunked
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- [ ] 在 `compute_chunked_prefill` 中加载历史 K
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- [ ] 拼接历史 K + 当前 K
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- [ ] 调用 `xattn_estimate_chunked(q, k_full, q_start_pos=...)`
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- [ ] 获取 block mask
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### 现有 CUDA Graph 实现
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### 2.2 处理参数对齐
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- [ ] BSA block_size = 128
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- [ ] chunk_size 与 kvcache_block_size 的关系
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- [ ] q_start_pos 计算
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**文件**: `nanovllm/engine/model_runner.py`
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## Phase 3: 实现 BSA sparse 计算
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| 方法 | 行号 | 功能 |
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|------|------|------|
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| `capture_cudagraph()` | 682-717 | 为不同 batch size 捕获完整 model forward |
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| `run_model()` | 415-436 | 决定使用 eager 还是 graph replay |
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### 3.1 方案选择
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- 选项 A: 历史 + 当前分开计算，然后 merge
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- 选项 B: 全部一起用 BSA 计算
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**关键逻辑** (`run_model`):
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```python
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use_eager = is_prefill or self.enforce_eager or input_ids.size(0) > 512 or context.is_chunked_prefill
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```
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### 3.2 实现
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- [ ] 构造 BSA 需要的输入格式
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- [ ] 调用 `block_sparse_attn_func`
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- [ ] 处理输出格式
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**问题**: `run_chunked_offload_decode` 设置 `is_chunked_prefill=True`，导致**永远使用 eager mode**。
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## Phase 4: 测试验证
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### Offload Decode 流程
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### 4.1 单元测试
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- [ ] 验证 sparse mask 与 `test_xattn_estimate_chunked.py` 一致
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**文件**: `nanovllm/kvcache/sparse/full_policy.py`
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### 4.2 集成测试
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- [ ] 运行验收命令
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- [ ] 5/5 PASS
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`_decode_ring_buffer_pipeline()` (L304-379):
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```
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for block in cpu_blocks:
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1. wait_slot_layer(slot) # 等待 H2D 完成
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2. k, v = get_kv_for_slot(slot) # 获取 KV
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3. o, lse = flash_attn_with_lse() # ⭐ 纯 GPU 计算
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4. record_slot_compute_done(slot) # 标记计算完成
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5. load_next_block() # 启动下一个 H2D
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6. merge_attention_outputs() # ⭐ 纯 GPU 计算
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```
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## Key Questions
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**可 Graph 化的部分**:
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- `flash_attn_with_lse()` - 纯 GPU 计算
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- 不可 Graph 化: H2D 传输、动态 merge
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1. 历史 K 如何高效加载？（全量 vs 按需）
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2. BSA causal mask 如何处理？（历史 non-causal + 当前 causal）
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## 验证结果
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## Status
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**测试文件**: `tests/test_chunk_attention_graph_reuse.py`
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**Currently in Phase 1** - 等待用户确认后开始
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| 测试 | 结果 |
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|------|------|
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| 2 个 Graph 复用于所有层和所有 chunk | ✅ PASSED |
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| copy_() 更新 static tensors | ✅ 有效 |
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| Eager merge | ✅ 用户已接受 |
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## 待讨论
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**结论**: 只需 2 个 graph（causal + non-causal），通过 copy_() 复用。
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请确认：
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1. 这个 goal 和验收标准是否正确？
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2. 我使用哪个 GPU 运行测试？
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---
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## 修改计划（方案 A：分段 Graph）
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### 架构设计
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```
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每层执行流程（Offload Decode）:
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┌─────────────────────────────────────────────────────────────┐
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│ PRE-ATTENTION GRAPH (可复用于所有层) │
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│ input_layernorm → qkv_proj → q/k_norm → rotary → split Q │
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└─────────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ CHUNKED ATTENTION (Eager + 部分 Graph) │
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│ for block in cpu_blocks: │
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│ H2D transfer (eager) │
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│ flash_attn_with_lse (GRAPH - 2个可复用) │
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│ merge (eager) │
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│ decode_buffer attention (eager) │
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└─────────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ POST-ATTENTION GRAPH (可复用于所有层) │
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│ o_proj → post_layernorm → gate_proj → up_proj → SiLU │
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│ → down_proj → residual │
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└─────────────────────────────────────────────────────────────┘
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```
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**总共需要的 Graph 数量**:
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- 1 个 pre_attention_graph（所有层复用）
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- 2 个 attention_graph（causal + non-causal，所有层复用）
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- 1 个 post_attention_graph（所有层复用）
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- **总计: 4 个 graph**
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---
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### Phase 1: 拆分 DecoderLayer 执行
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**目标**: 将 `Qwen3DecoderLayer.forward` 拆分为可独立调用的三段
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**修改文件**: `nanovllm/models/qwen3.py`
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**新增方法**:
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```python
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class Qwen3DecoderLayer:
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def forward_pre_attention(self, positions, hidden_states, residual):
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"""Pre-attention: norm → qkv → rotary → 返回 q, k, v"""
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if residual is None:
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hidden_states, residual = self.input_layernorm(hidden_states), hidden_states
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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qkv = self.self_attn.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q = q.view(-1, self.num_heads, self.head_dim)
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k = k.view(-1, self.num_kv_heads, self.head_dim)
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v = v.view(-1, self.num_kv_heads, self.head_dim)
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q = self.self_attn.q_norm(q)
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k = self.self_attn.k_norm(k)
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q, k = self.self_attn.rotary_emb(positions, q, k)
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return q, k, v, hidden_states, residual
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def forward_post_attention(self, attn_output, hidden_states, residual):
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"""Post-attention: o_proj → norm → FFN"""
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output = self.self_attn.o_proj(attn_output.flatten(1, -1))
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hidden_states, residual = self.post_attention_layernorm(output, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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```
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**状态**: `pending`
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---
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### Phase 2: 捕获 Pre/Post Attention Graph
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**目标**: 捕获 pre_attention 和 post_attention 的 graph
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**修改文件**: `nanovllm/engine/model_runner.py`
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**新增方法**: `capture_offload_layer_graphs()`
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```python
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def capture_offload_layer_graphs(self):
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"""捕获 offload mode 的 layer graphs"""
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# 获取任意一层作为模板（所有层结构相同）
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layer = self.model.model.layers[0]
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# Static tensors
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static_hidden = torch.zeros(1, self.hidden_size, ...)
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static_residual = torch.zeros(1, self.hidden_size, ...)
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static_positions = torch.zeros(1, ...)
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# Pre-attention graph
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self.pre_attn_graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(self.pre_attn_graph):
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static_q, static_k, static_v, _, _ = layer.forward_pre_attention(
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static_positions, static_hidden, static_residual
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)
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# Post-attention graph
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self.post_attn_graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(self.post_attn_graph):
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_, _ = layer.forward_post_attention(
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static_attn_output, static_hidden, static_residual
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)
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```
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**状态**: `pending`
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---
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### Phase 3: 捕获 Attention Graph
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**目标**: 捕获 2 个 attention graph（causal + non-causal）
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**修改文件**: `nanovllm/kvcache/offload_engine.py`
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```python
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class OffloadEngine:
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def capture_attention_graphs(self):
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"""捕获 attention graphs（复用于所有层）"""
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self.attn_graph_causal = self._capture_attn_graph(causal=True)
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self.attn_graph_non_causal = self._capture_attn_graph(causal=False)
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def _capture_attn_graph(self, causal: bool):
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static_q = torch.zeros(1, 1, num_heads, head_dim, ...)
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static_k = torch.zeros(1, block_size, num_kv_heads, head_dim, ...)
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static_v = torch.zeros(1, block_size, num_kv_heads, head_dim, ...)
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph):
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output, lse = flash_attn_with_lse(static_q, static_k, static_v,
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self.scale, causal)
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return AttentionGraph(graph, static_q, static_k, static_v, output, lse)
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```
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**状态**: `pending`
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---
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### Phase 4: 修改 Offload Decode 执行流程
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**目标**: 使用 graph replay 执行 offload decode
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**修改文件**: `nanovllm/engine/model_runner.py`
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**修改方法**: `run_chunked_offload_decode()`
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```python
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def run_chunked_offload_decode_with_graph(self, seqs):
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"""使用 graph 加速的 offload decode"""
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seq = seqs[0]
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# 准备输入
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input_ids = torch.tensor([seq.last_token], ...)
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positions = torch.tensor([len(seq) - 1], ...)
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# Embedding
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hidden_states = self.model.model.embed_tokens(input_ids)
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residual = None
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for layer_id, layer in enumerate(self.model.model.layers):
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# Phase 1: Pre-attention (GRAPH)
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self.pre_attn_vars["hidden"].copy_(hidden_states)
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self.pre_attn_vars["residual"].copy_(residual) if residual else None
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self.pre_attn_vars["positions"].copy_(positions)
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self.pre_attn_graph.replay()
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q = self.pre_attn_vars["q"].clone()
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k = self.pre_attn_vars["k"].clone()
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v = self.pre_attn_vars["v"].clone()
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# Phase 2: Chunked Attention (Eager + Graph)
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attn_output = self._chunked_attention_with_graph(q, k, v, layer_id, ...)
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# Phase 3: Post-attention (GRAPH)
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self.post_attn_vars["attn_output"].copy_(attn_output)
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self.post_attn_graph.replay()
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hidden_states = self.post_attn_vars["hidden"].clone()
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residual = self.post_attn_vars["residual"].clone()
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# LM head
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logits = self.model.compute_logits(hidden_states)
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return logits
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```
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**状态**: `pending`
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---
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### Phase 5: 修改 Ring Buffer Pipeline
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**目标**: 在 attention 内部使用 graph
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**修改文件**: `nanovllm/kvcache/sparse/full_policy.py`
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**修改**: `_decode_ring_buffer_pipeline()` 中的 `flash_attn_with_lse` 调用
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```python
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# 当前：eager
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prev_o, prev_lse = flash_attn_with_lse(q, k, v, scale, causal=False)
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# 修改为：graph replay
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graph = offload_engine.attn_graph_non_causal
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graph.static_q.copy_(q)
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graph.static_k.copy_(k)
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graph.static_v.copy_(v)
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graph.graph.replay()
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prev_o = graph.static_output.clone()
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prev_lse = graph.static_lse.clone()
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```
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**状态**: `pending`
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---
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### Phase 6: 添加配置开关
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**修改文件**: `nanovllm/config.py`
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```python
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enable_offload_graph: bool = True # 默认启用
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```
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**状态**: `pending`
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---
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## 文件修改清单
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| 文件 | 修改类型 | 说明 |
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|------|----------|------|
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| `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）
|
||||
|
||||
Reference in New Issue
Block a user