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[PD Disaggregation] Prefill and decode support cache storage (#6768)

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* Prefill and decode support cache storage

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* update docs and refine mooncake store

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[中文文档](../../zh/features/global_cache_pooling.md) | English
# Global Cache Pooling
This document describes how to use MooncakeStore as the KV Cache storage backend for FastDeploy, enabling **Global Cache Pooling** across multiple inference instances.
## Overview
### What is Global Cache Pooling?
Global Cache Pooling allows multiple FastDeploy instances to share KV Cache through a distributed storage layer. This enables:
- **Cross-instance cache reuse**: KV Cache computed by one instance can be reused by another
- **PD Disaggregation optimization**: Prefill and Decode instances can share cache seamlessly
- **Reduced computation**: Avoid redundant prefix computation across requests
### Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ Mooncake Master Server │
│ (Metadata & Coordination Service) │
└────────────────────────────┬────────────────────────────────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ FastDeploy │ │ FastDeploy │ │ FastDeploy │
│ Instance P │ │ Instance D │ │ Instance X │
│ (Prefill) │ │ (Decode) │ │ (Standalone) │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└───────────────────┼───────────────────┘
│
┌────────▼────────┐
│ MooncakeStore │
│ (Shared KV │
│ Cache Pool) │
└─────────────────┘
```
## Example Scripts
Ready-to-use example scripts are available in [examples/cache_storage/](../../../examples/cache_storage/).
| Script | Scenario | Description |
|--------|----------|-------------|
| `run.sh` | Multi-Instance | Two standalone instances sharing cache |
| `run_03b_pd_storage.sh` | PD Disaggregation | P+D instances with global cache pooling |
## Prerequisites
### Hardware Requirements
- NVIDIA GPU with CUDA support
- RDMA network (recommended for production) or TCP
### Software Requirements
- Python 3.8+
- CUDA 11.8+
- FastDeploy (see installation below)
## Installation
Refer to [NVIDIA CUDA GPU Installation](https://paddlepaddle.github.io/FastDeploy/get_started/installation/nvidia_gpu/) for FastDeploy installation.
```bash
# Option 1: Install from PyPI
pip install fastdeploy-gpu
# Option 2: Build from source
bash build.sh
pip install ./dist/fastdeploy*.whl
```
MooncakeStore is automatically installed when you install FastDeploy.
## Configuration
We support two ways to configure MooncakeStore: via configuration file `mooncake_config.json` or via environment variables.
### Mooncake Configuration File
Create a `mooncake_config.json` file:
```json
{
"metadata_server": "http://0.0.0.0:15002/metadata",
"master_server_addr": "0.0.0.0:15001",
"global_segment_size": 1000000000,
"local_buffer_size": 134217728,
"protocol": "rdma",
"rdma_devices": ""
}
```
Set the `MOONCAKE_CONFIG_PATH` environment variable to enable the configuration:
```bash
export MOONCAKE_CONFIG_PATH=path/to/mooncake_config.json
```
Configuration parameters:
| Parameter | Description | Default |
|-----------|-------------|---------|
| `metadata_server` | HTTP metadata server URL | Required |
| `master_server_addr` | Master server address | Required |
| `global_segment_size` | Memory space each TP process shares to global shared memory (bytes) | 1GB |
| `local_buffer_size` | Local buffer size for data transfer (bytes) | 128MB |
| `protocol` | Transfer protocol: `rdma` or `tcp` | `rdma` |
| `rdma_devices` | RDMA device names (comma-separated) | Auto-detect |
### Environment Variables
Mooncake can also be configured via environment variables:
| Variable | Description |
|----------|-------------|
| `MOONCAKE_MASTER_SERVER_ADDR` | Master server address (e.g., `10.0.0.1:15001`) |
| `MOONCAKE_METADATA_SERVER` | Metadata server URL |
| `MOONCAKE_GLOBAL_SEGMENT_SIZE` | Memory space each TP process shares to global shared memory (bytes) |
| `MOONCAKE_LOCAL_BUFFER_SIZE` | Local buffer size (bytes) |
| `MOONCAKE_PROTOCOL` | Transfer protocol (`rdma` or `tcp`) |
| `MOONCAKE_RDMA_DEVICES` | RDMA device names |
## Usage Scenarios
### Scenario 1: Multi-Instance Cache Sharing
Run multiple FastDeploy instances sharing a global KV Cache pool.
**Step 1: Start Mooncake Master**
```bash
mooncake_master \
--port=15001 \
--enable_http_metadata_server=true \
--http_metadata_server_host=0.0.0.0 \
--http_metadata_server_port=15002 \
--metrics_port=15003
```
**Step 2: Start FastDeploy Instances**
Instance 0:
```bash
export MOONCAKE_CONFIG_PATH="./mooncake_config.json"
export CUDA_VISIBLE_DEVICES=0
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52700 \
--max-model-len 32768 \
--max-num-seqs 32 \
--kvcache-storage-backend mooncake
```
Instance 1:
```bash
export MOONCAKE_CONFIG_PATH="./mooncake_config.json"
export CUDA_VISIBLE_DEVICES=1
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52800 \
--max-model-len 32768 \
--max-num-seqs 32 \
--kvcache-storage-backend mooncake
```
**Step 3: Test Cache Reuse**
Send the same prompt to both instances. The second instance should reuse the KV Cache computed by the first instance.
```bash
# Request to Instance 0
curl -X POST "http://0.0.0.0:52700/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello, world!"}], "max_tokens": 50}'
# Request to Instance 1 (should hit cached KV)
curl -X POST "http://0.0.0.0:52800/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello, world!"}], "max_tokens": 50}'
```
### Scenario 2: PD Disaggregation with Global Cache
This scenario combines **PD Disaggregation** with **Global Cache Pooling**, enabling:
- Prefill instances to read Decode instances' output cache
- Optimal multi-turn conversation performance
**Architecture:**
```
┌──────────────────────────────────────────┐
│ Router │
│ (Load Balancer) │
└─────────────────┬────────────────────────┘
│
┌───────────────┴───────────────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Prefill │ │ Decode │
│ Instance │◄───────────────►│ Instance │
│ │ KV Transfer │ │
└──────┬──────┘ └──────┬──────┘
│ │
└───────────────┬───────────────┘
│
┌────────▼────────┐
│ MooncakeStore │
│ (Global Cache) │
└─────────────────┘
```
**Step 1: Start Mooncake Master**
```bash
mooncake_master \
--port=15001 \
--enable_http_metadata_server=true \
--http_metadata_server_host=0.0.0.0 \
--http_metadata_server_port=15002
```
**Step 2: Start Router**
```bash
python -m fastdeploy.router.launch \
--port 52700 \
--splitwise
```
**Step 3: Start Prefill Instance**
```bash
export MOONCAKE_MASTER_SERVER_ADDR="127.0.0.1:15001"
export MOONCAKE_METADATA_SERVER="http://127.0.0.1:15002/metadata"
export MOONCAKE_PROTOCOL="rdma"
export CUDA_VISIBLE_DEVICES=0
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52400 \
--max-model-len 32768 \
--max-num-seqs 32 \
--splitwise-role prefill \
--cache-transfer-protocol rdma \
--router "0.0.0.0:52700" \
--kvcache-storage-backend mooncake
```
**Step 4: Start Decode Instance**
```bash
export MOONCAKE_MASTER_SERVER_ADDR="127.0.0.1:15001"
export MOONCAKE_METADATA_SERVER="http://127.0.0.1:15002/metadata"
export MOONCAKE_PROTOCOL="rdma"
export CUDA_VISIBLE_DEVICES=1
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52500 \
--max-model-len 32768 \
--max-num-seqs 32 \
--splitwise-role decode \
--cache-transfer-protocol rdma \
--router "0.0.0.0:52700" \
--enable-output-caching \
--kvcache-storage-backend mooncake
```
**Step 5: Send Requests via Router**
```bash
curl -X POST "http://0.0.0.0:52700/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 50}'
```
## FastDeploy Parameters for Mooncake
| Parameter | Description |
|-----------|-------------|
| `--kvcache-storage-backend mooncake` | Enable Mooncake as KV Cache storage backend |
| `--enable-output-caching` | Enable output token caching (Decode instance recommended) |
| `--cache-transfer-protocol rdma` | Use RDMA for KV transfer between P and D |
| `--splitwise-role prefill/decode` | Set instance role in PD disaggregation |
| `--router` | Router address for PD disaggregation |
## Verification
### Check Cache Hit
To verify cache hit in logs:
```bash
# For multi-instance scenario
grep -E "storage_cache_token_num" log_*/api_server.log
# For PD disaggregation scenario
grep -E "storage_cache_token_num" log_prefill/api_server.log
```
If `storage_cache_token_num > 0`, the instance successfully read cached KV blocks from the global pool.
### Monitor Mooncake Master
```bash
# Check master status
curl http://localhost:15002/metadata
# Check metrics (if metrics_port is configured)
curl http://localhost:15003/metrics
```
## Troubleshooting
### Common Issues
**1. Port Already in Use**
```bash
# Check port usage
ss -ltn | grep 15001
# Kill existing process
kill -9 $(lsof -t -i:15001)
```
**2. RDMA Connection Failed**
- Verify RDMA devices: `ibv_devices`
- Check RDMA network: `ibv_devinfo`
- Fallback to TCP: Set `MOONCAKE_PROTOCOL=tcp`
**3. Cache Not Being Shared**
- Verify all instances connect to the same Mooncake master
- Check metadata server URL is consistent
- Verify `global_segment_size` is large enough
**4. Permission Denied on /dev/shm**
```bash
# Clean up stale shared memory files
find /dev/shm -type f -print0 | xargs -0 rm -f
```
### Debug Mode
Enable debug logging:
```bash
export FD_DEBUG=1
```
## More Resources
- [Mooncake Official Documentation](https://github.com/kvcache-ai/Mooncake)
- [Mooncake Troubleshooting Guide](https://github.com/kvcache-ai/Mooncake/blob/main/docs/source/troubleshooting/troubleshooting.md)
- [FastDeploy Documentation](https://paddlepaddle.github.io/FastDeploy/)
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[English](../../features/global_cache_pooling.md) | 中文文档
# 全局缓存池化
本文档介绍如何将 MooncakeStore 作为 FastDeploy 的 KV Cache 存储后端，实现多推理实例间的**全局缓存池化**。
## 概述
### 什么是全局缓存池化？
全局缓存池化允许多个 FastDeploy 实例通过分布式存储层共享 KV Cache，具有以下优势：
- **跨实例缓存复用**：一个实例计算的 KV Cache 可被其他实例复用
- **PD 分离架构优化**：Prefill 和 Decode 实例可无缝共享缓存
- **减少重复计算**：避免跨请求的重复前缀计算
### 架构图
```
┌─────────────────────────────────────────────────────────────────┐
│ Mooncake Master 服务 │
│ (元数据与协调服务) │
└────────────────────────────┬────────────────────────────────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ FastDeploy │ │ FastDeploy │ │ FastDeploy │
│ Instance P │ │ Instance D │ │ Instance X │
│ (Prefill) │ │ (Decode) │ │ (Standalone) │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└───────────────────┼───────────────────┘
│
┌────────▼────────┐
│ MooncakeStore │
│ (共享 KV │
│ Cache 池) │
└─────────────────┘
```
## 示例脚本
开箱即用的示例脚本位于 [examples/cache_storage/](../../../examples/cache_storage/)。
| 脚本 | 场景 | 说明 |
|------|------|------|
| `run.sh` | 多实例缓存共享 | 两个独立实例共享缓存 |
| `run_03b_pd_storage.sh` | PD 分离 | P+D 实例配合全局缓存池 |
## 环境要求
### 硬件要求
- 支持 CUDA 的 NVIDIA GPU
- RDMA 网络（生产环境推荐）或 TCP 网络
### 软件要求
- Python 3.8+
- CUDA 11.8+
- FastDeploy（见下方安装说明）
## 安装步骤
参考 [NVIDIA CUDA GPU 安装指南](https://paddlepaddle.github.io/FastDeploy/get_started/installation/nvidia_gpu/) 安装 FastDeploy。
```bash
# 方式一：从 PyPI 安装
pip install fastdeploy-gpu
# 方式二：从源码编译
bash build.sh
pip install ./dist/fastdeploy*.whl
```
安装FastDeploy后自动安装了MooncakeStore。
## 配置说明
我们支持两种方式配置MooncakeStore，一是通过配置文件`mooncake_config.json`，二是通过环境变量进行配置。
### Mooncake 配置文件
创建 `mooncake_config.json` 配置文件：
```json
{
"metadata_server": "http://0.0.0.0:15002/metadata",
"master_server_addr": "0.0.0.0:15001",
"global_segment_size": 1000000000,
"local_buffer_size": 134217728,
"protocol": "rdma",
"rdma_devices": ""
}
```
设置MOONCAKE_CONFIG_PATH环境变量后，配置文件生效：
```bash
export MOONCAKE_CONFIG_PATH=path/to/mooncake_config.json
```
配置参数说明：
| 参数 | 说明 | 默认值 |
|------|------|--------|
| `metadata_server` | HTTP 元数据服务地址 | 必填 |
| `master_server_addr` | Master 服务地址 | 必填 |
| `global_segment_size` | 每个TP进程给全局共享内存共享的内存空间（字节） | 1GB |
| `local_buffer_size` | 数据传输本地缓冲区大小（字节） | 128MB |
| `protocol` | 传输协议：`rdma` 或 `tcp` | `rdma` |
| `rdma_devices` | RDMA 设备名称（逗号分隔） | 自动检测 |
### 环境变量配置
Mooncake 也支持通过环境变量进行配置：
| 环境变量 | 说明 |
|----------|------|
| `MOONCAKE_MASTER_SERVER_ADDR` | Master 服务地址（如 `10.0.0.1:15001`） |
| `MOONCAKE_METADATA_SERVER` | 元数据服务 URL |
| `MOONCAKE_GLOBAL_SEGMENT_SIZE` | 每个TP进程给全局共享内存共享的内存空间（字节） |
| `MOONCAKE_LOCAL_BUFFER_SIZE` | 本地缓冲区大小（字节） |
| `MOONCAKE_PROTOCOL` | 传输协议（`rdma` 或 `tcp`） |
| `MOONCAKE_RDMA_DEVICES` | RDMA 设备名称 |
## 使用场景
### 场景一：多实例缓存共享
运行多个 FastDeploy 实例，共享全局 KV Cache 池。
**步骤 1：启动 Mooncake Master**
```bash
mooncake_master \
--port=15001 \
--enable_http_metadata_server=true \
--http_metadata_server_host=0.0.0.0 \
--http_metadata_server_port=15002 \
--metrics_port=15003
```
**步骤 2：启动 FastDeploy 实例**
实例 0：
```bash
export MOONCAKE_CONFIG_PATH="./mooncake_config.json"
export CUDA_VISIBLE_DEVICES=0
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52700 \
--max-model-len 32768 \
--max-num-seqs 32 \
--kvcache-storage-backend mooncake
```
实例 1：
```bash
export MOONCAKE_CONFIG_PATH="./mooncake_config.json"
export CUDA_VISIBLE_DEVICES=1
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52800 \
--max-model-len 32768 \
--max-num-seqs 32 \
--kvcache-storage-backend mooncake
```
**步骤 3：测试缓存复用**
向两个实例发送相同的 prompt，第二个实例应能复用第一个实例计算的 KV Cache。
```bash
# 请求实例 0
curl -X POST "http://0.0.0.0:52700/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello, world!"}], "max_tokens": 50}'
# 请求实例 1（应命中缓存）
curl -X POST "http://0.0.0.0:52800/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello, world!"}], "max_tokens": 50}'
```
### 场景二：PD 分离 + 全局缓存池
此场景将 **PD 分离架构** 与 **全局缓存池化** 结合，实现：
- Prefill 实例可读取 Decode 实例的输出缓存
- 优化多轮对话性能
**架构图：**
```
┌──────────────────────────────────────────┐
│ Router │
│ (负载均衡器) │
└─────────────────┬────────────────────────┘
│
┌───────────────┴───────────────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Prefill │ │ Decode │
│ Instance │◄───────────────►│ Instance │
│ │ KV Transfer │ │
└──────┬──────┘ └──────┬──────┘
│ │
└───────────────┬───────────────┘
│
┌────────▼────────┐
│ MooncakeStore │
│ (全局缓存池) │
└─────────────────┘
```
**步骤 1：启动 Mooncake Master**
```bash
mooncake_master \
--port=15001 \
--enable_http_metadata_server=true \
--http_metadata_server_host=0.0.0.0 \
--http_metadata_server_port=15002
```
**步骤 2：启动 Router**
```bash
python -m fastdeploy.router.launch \
--port 52700 \
--splitwise
```
**步骤 3：启动 Prefill 实例**
```bash
export MOONCAKE_MASTER_SERVER_ADDR="127.0.0.1:15001"
export MOONCAKE_METADATA_SERVER="http://127.0.0.1:15002/metadata"
export MOONCAKE_PROTOCOL="rdma"
export CUDA_VISIBLE_DEVICES=0
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52400 \
--max-model-len 32768 \
--max-num-seqs 32 \
--splitwise-role prefill \
--cache-transfer-protocol rdma \
--router "0.0.0.0:52700" \
--kvcache-storage-backend mooncake
```
**步骤 4：启动 Decode 实例**
```bash
export MOONCAKE_MASTER_SERVER_ADDR="127.0.0.1:15001"
export MOONCAKE_METADATA_SERVER="http://127.0.0.1:15002/metadata"
export MOONCAKE_PROTOCOL="rdma"
export CUDA_VISIBLE_DEVICES=1
python -m fastdeploy.entrypoints.openai.api_server \
--model ${MODEL_NAME} \
--port 52500 \
--max-model-len 32768 \
--max-num-seqs 32 \
--splitwise-role decode \
--cache-transfer-protocol rdma \
--router "0.0.0.0:52700" \
--enable-output-caching \
--kvcache-storage-backend mooncake
```
**步骤 5：通过 Router 发送请求**
```bash
curl -X POST "http://0.0.0.0:52700/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 50}'
```
## FastDeploy Mooncake 相关参数
| 参数 | 说明 |
|------|------|
| `--kvcache-storage-backend mooncake` | 启用 Mooncake 作为 KV Cache 存储后端 |
| `--enable-output-caching` | 启用输出 token 缓存（推荐 Decode 实例开启） |
| `--cache-transfer-protocol rdma` | P 和 D 之间使用 RDMA 进行 KV 传输 |
| `--splitwise-role prefill/decode` | 设置实例在 PD 分离中的角色 |
| `--router` | PD 分离场景下的 Router 地址 |
## 验证方法
### 检查缓存命中
通过日志验证缓存命中情况：
```bash
# 多实例场景
grep -E "storage_cache_token_num" log_*/api_server.log
# PD 分离场景
grep -E "storage_cache_token_num" log_prefill/api_server.log
```
如果 `storage_cache_token_num > 0`，表示实例成功从全局池读取了缓存的 KV 块。
### 监控 Mooncake Master
```bash
# 检查 master 状态
curl http://localhost:15002/metadata
# 检查指标（如配置了 metrics_port）
curl http://localhost:15003/metrics
```
## 故障排查
### 常见问题
**1. 端口被占用**
```bash
# 检查端口使用情况
ss -ltn | grep 15001
# 终止占用进程
kill -9 $(lsof -t -i:15001)
```
**2. RDMA 连接失败**
- 检查 RDMA 设备：`ibv_devices`
- 检查 RDMA 网络：`ibv_devinfo`
- 降级使用 TCP：设置 `MOONCAKE_PROTOCOL=tcp`
**3. 缓存未共享**
- 确认所有实例连接到同一个 Mooncake master
- 检查元数据服务 URL 是否一致
- 确认 `global_segment_size` 足够大
**4. /dev/shm 权限不足**
```bash
# 清理残留的共享内存文件
find /dev/shm -type f -print0 | xargs -0 rm -f
```
### 调试模式
开启调试日志：
```bash
export FD_DEBUG=1
```
## 更多资源
- [Mooncake 官方文档](https://github.com/kvcache-ai/Mooncake)
- [Mooncake 故障排查指南](https://github.com/kvcache-ai/Mooncake/blob/main/docs/source/troubleshooting/troubleshooting.md)
- [FastDeploy 文档](https://paddlepaddle.github.io/FastDeploy/)