isagellm 0.5.3.4

sageLLM: Modular LLM inference engine with PD separation for domestic computing power

sageLLM

Protocol Compliance (Mandatory)

• MUST follow Protocol v0.1: https://github.com/intellistream/sagellm-docs/blob/main/docs/specs/protocol_v0.1.md
• Any globally shared definitions (fields, error codes, metrics, IDs, schemas) MUST be added to Protocol first.

🚀 Modular LLM Inference Engine for Domestic Computing Power

Ollama-like experience for Chinese hardware ecosystems (Huawei Ascend, NVIDIA)

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✨ Features

• 🎯 One-Click Install - pip install isagellm gets you started immediately
• 🧠 CPU-First - Default CPU engine, no GPU required
• 🇨🇳 Domestic Hardware - First-class support for Huawei Ascend NPU
• 📊 Observable - Built-in metrics (TTFT, TBT, throughput, KV usage)
• 🧩 Plugin System - Extend with custom backends and engines
• 🔄 Mixed Inference - Unified LLM + Embedding client (MixedInferenceClient)
• 🦙 Ollama Backend - Use a local Ollama server as an inference backend
• 📈 Performance Profiling - Load profiling data and interpolate TTFT/throughput

📦 Quick Install

# Install sageLLM (CPU-first, no GPU required)
pip install isagellm

# With Control Plane (request routing & scheduling)
pip install 'isagellm[control-plane]'

# With API Gateway (OpenAI-compatible REST API)
pip install 'isagellm[gateway]'

# Full server (Control Plane + Gateway)
pip install 'isagellm[server]'

# With KV Cache
pip install 'isagellm[kv-cache]'

# With communication layer
pip install 'isagellm[comm]'

# With compression (quantization, sparse)
pip install 'isagellm[compression]'

# All optional features
pip install 'isagellm[all]'

# Reproduce exactly-tested sub-package versions (recommended for production)
pip install isagellm -c https://raw.githubusercontent.com/intellistream/sagellm/main-dev/constraints.txt

🚀 国内加速安装 PyTorch（推荐）

由于 PyTorch CUDA 版本从官方源下载较慢（~800MB），我们在 GitHub Releases 提供预先下载的 wheels：

# 方法 1：使用 sagellm CLI (推荐，最简单)
pip install isagellm
sage-llm install cuda --github     # 从 GitHub 下载，快速
sage-llm install cuda              # 从官方源下载（默认）

# 方法 2：直接使用 pip --find-links
pip install torch==2.5.1+cu121 torchvision torchaudio \
  --find-links https://github.com/intellistream/sagellm-pytorch-wheels/releases/download/v2.5.1-cu121/ \
  --trusted-host github.com

其他支持的后端：

• sage-llm install ascend - 华为昇腾 NPU
• sage-llm install kunlun - 百度昆仑 XPU
• sage-llm install haiguang - 海光 DCU
• sage-llm install cpu - CPU-only（最小下载）

💡 为什么使用 GitHub 加速？

• ✅ 国内访问速度快（GitHub CDN）
• ✅ 无需配置镜像源
• ✅ 官方 wheels，100% 可信

📦 Wheels 仓库: https://github.com/intellistream/sagellm-pytorch-wheels

🚀 Quick Start

CLI 命令统一

• 统一主命令：sagellm
• 兼容别名：sage-llm（保留向后兼容，建议迁移到 sagellm）

CLI (像 vLLM/Ollama 一样简单)

# 一键启动（完整栈：Gateway + Engine）
pip install 'isagellm[gateway]'
sage-llm serve --model Qwen2-7B

# ✅ OpenAI API 自动可用
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen2-7B",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# 查看系统信息
sage-llm info

# 单次推理（不启动服务器）
sage-llm run -p "What is LLM inference?"

# 推荐生产启动（通过 Gateway + Control Plane）
sage-llm serve --backend cpu --model sshleifer/tiny-gpt2 --port 8888
sage-llm serve \
  --backend cpu \
  --model sshleifer/tiny-gpt2 \
  --port 8888 \
  --with-embedding \
  --embedding-model sentence-transformers/all-MiniLM-L6-v2

# 一键启动：Gateway + LLM + Embedding
sage-llm serve \
  --backend cpu \
  --model sshleifer/tiny-gpt2 \
  --port 8888 \
  --with-embedding \
  --embedding-model sentence-transformers/all-MiniLM-L6-v2

Python API (Control Plane - Recommended)

import asyncio

from sagellm import ControlPlaneManager, BackendConfig, EngineConfig

# Install with: pip install 'isagellm[control-plane]'
async def main() -> None:
    manager = ControlPlaneManager(
        backend_config=BackendConfig(kind="cpu", device="cpu"),
        engine_configs=[
            EngineConfig(
                kind="cpu",
                model="sshleifer/tiny-gpt2",
                model_path="sshleifer/tiny-gpt2"
            )
        ]
    )

    await manager.start()
    try:
        # Requests are automatically routed to available engines
        response = await manager.execute_request(
            prompt="Hello, world!",
            max_tokens=128
        )
        print(response.output_text)
        print(f"TTFT: {response.metrics.ttft_ms:.2f} ms")
        print(f"Throughput: {response.metrics.throughput_tps:.2f} tokens/s")
    finally:
        await manager.stop()


asyncio.run(main())

⚠️ Important: Direct engine creation (create_engine()) is not exported from the umbrella package. All production code must use ControlPlaneManager for proper request routing, scheduling, and lifecycle management.

Mixed Inference (LLM + Embedding)

from sagellm import MixedInferenceClient, MixedRequest, RequestKind

# Unified client for both LLM and embedding
client = MixedInferenceClient(
    llm_url="http://localhost:8000",
    embedding_url="http://localhost:8001",
)

# LLM completion
resp = client.complete("What is 2+2?")
print(resp["text"])

# Embedding
vecs = client.embed("Hello world")

# Mixed batch dispatch
results = client.dispatch([
    MixedRequest(kind=RequestKind.LLM, content="Tell me a joke"),
    MixedRequest(kind=RequestKind.EMBEDDING, content="The quick brown fox"),
])

Ollama Backend

# Use a local Ollama server as inference backend
sage-llm ollama status                       # check health
sage-llm ollama list                         # list models
sage-llm ollama run -m llama3 -p "Hello!"    # single completion
sage-llm ollama chat -p "Explain Python"     # chat

from sagellm import OllamaClient

client = OllamaClient(model="llama3")
resp = client.complete("What is 2+2?")
print(resp["text"])
models = client.list_models()

Performance Profiling & Interpolation

from sagellm.profiling import PerformanceInterpolator

# Load CSV: columns isl, ttft, itl, throughput
interp = PerformanceInterpolator.from_csv("profiles/qwen2_7b_a100.csv")

# Predict metrics for a given input sequence length
ttft = interp.predict_ttft(512)          # → seconds
itl  = interp.predict_itl(512)           # → seconds/token
tput = interp.predict_throughput(512)    # → tokens/second

# Reverse: find max ISL that satisfies a TTFT budget
max_isl = interp.reverse_ttft(target_ttft=0.3)
print(f"Max ISL for 300ms TTFT: {max_isl} tokens")

Configuration

# ~/.sagellm/config.yaml
backend:
  kind: cpu  # Options: cpu, pytorch-cuda, pytorch-ascend
  device: cpu

engine:
  kind: cpu
  model: sshleifer/tiny-gpt2

control_plane:
  endpoint: "localhost:8080"

📊 Metrics & Validation

sageLLM provides comprehensive performance metrics:

{
  "ttft_ms": 45.2,
  "tbt_ms": 12.5,
  "throughput_tps": 80.0,
  "peak_mem_mb": 24576,
  "kv_used_tokens": 4096,
  "prefix_hit_rate": 0.85
}

Run benchmarks:

sage-llm demo --workload year1 --output metrics.json

🏗️ Architecture

isagellm (umbrella package)
├── isagellm-protocol       # Protocol v0.1 types
│   └── Request, Response, Metrics, Error, StreamEvent
├── isagellm-backend        # Hardware abstraction (L1 - Foundation)
│   └── BackendProvider, CPUBackend, (CUDABackend, AscendBackend)
├── isagellm-comm           # Communication primitives (L2 - Infrastructure)
│   └── Topology, CollectiveOps (all_reduce/gather), P2P (send/recv), Overlap
├── isagellm-kv-cache       # KV cache management (L2 - Optional)
│   └── PrefixCache, MemoryPool, EvictionPolicies, Predictor, KV Transfer
├── isagellm-compression    # Inference acceleration (quantization, sparsity, etc.) (L2 - Optional)
│   └── Quantization, Sparsity, SpeculativeDecoding, Fusion
├── isagellm-core           # Engine core & runtime (L3)
│   └── Config, Engine, Factory, DemoRunner, Adapters (vLLM/LMDeploy)
├── isagellm-control-plane  # Request routing & scheduling (L4 - Optional)
│   └── ControlPlaneManager, Router, Policies, Lifecycle
└── isagellm-gateway        # OpenAI-compatible REST API (L5 - Optional)
    └── FastAPI server, /v1/chat/completions, Session management

🔧 Development

Quick Setup (Development Mode)

# Clone all repositories
./scripts/clone-all-repos.sh

# Install all packages in editable mode
./quickstart.sh

# Open all repos in VS Code Multi-root Workspace
code sagellm.code-workspace

📖 See WORKSPACE_GUIDE.md for Multi-root Workspace usage.

Testing

# Clone and setup
git clone https://github.com/IntelliStream/sagellm.git
cd sagellm
pip install -e ".[dev]"

# Run tests
pytest -v

# Format & lint
ruff format .
ruff check . --fix

# Type check
mypy src/sagellm/

# Verify dependency hierarchy
python scripts/verify_dependencies.py

📖 Development Resources

• DEPLOYMENT_GUIDE.md - 完整部署与配置指南
• TROUBLESHOOTING.md - 故障排查快速参考
• ENVIRONMENT_VARIABLES.md - 环境变量完整参考
• DEVELOPER_GUIDE.md - 开发者指南
• WORKSPACE_GUIDE.md - Multi-root Workspace 使用
• INFERENCE_FLOW.md - 推理流程详解
• PR_CHECKLIST.md - Pull Request 检查清单

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📚 Documentation Index

用户文档

• 快速开始 - 5 分钟上手
• 部署指南 - 生产环境部署
• 配置参考 - 完整配置选项
• 环境变量 - 环境变量参考
• 故障排查 - 常见问题解决

开发者文档

• 开发指南 - 贡献代码
• 架构设计 - 系统架构
• Workspace 使用 - Multi-root 工作区
• PR 检查清单 - 提交前检查

API 文档

• OpenAI 兼容 API - 参见 sagellm-gateway
• Python API - 参见 API_REFERENCE.md（待补充）

子包文档

• sagellm-protocol - 协议定义

• sagellm-backend - 后端抽象

• sagellm-core - 引擎核心

• sagellm-control-plane - 控制面

• sagellm-gateway - API 网关

• sagellm-benchmark - 基准测试

• DEVELOPER_GUIDE.md - 架构规范与开发指南

• PR_CHECKLIST.md - Pull Request 审查清单

• scripts/verify_dependencies.py - 依赖层次验证

� 贡献指南

工作流程（必须遵循）

在提交代码前，必须严格遵循以下步骤：

1️⃣ 创建 Issue

描述你要解决的问题、实现的功能或改进：

gh issue create \
  --title "[Category] 简短描述" \
  --label "bug,sagellm-core" \
  --body "详细描述..."

Issue 类型：

• [Bug] - Bug 修复
• [Feature] - 新功能
• [Performance] - 性能优化
• [Integration] - 与其他模块集成
• [Docs] - 文档改进

2️⃣ 在本地分支开发

创建开发分支并解决问题：

# 从 main-dev 创建分支（不是 main！）
git fetch origin main-dev
git checkout -b fix/#123-short-description origin/main-dev

# 进行开发
# ...

# 确保通过所有检查
ruff format .
ruff check . --fix
pytest -v

分支命名约定：

• Bug 修复：bugfix/#123-xxx
• 新功能：feature/#456-xxx
• 文档：docs/#789-xxx
• 性能：perf/#101-xxx

3️⃣ 发起 Pull Request

提交代码供审查：

git push origin fix/#123-short-description
gh pr create \
  --base main-dev \
  --head fix/#123-short-description \
  --title "Fix: [简短描述]" \
  --body "解决 #123

## 改动
- 改动 1
- 改动 2

## 测试
- 新增单元测试
- 所有测试通过 ✓"

PR 必须包含：

• 清晰的标题（Fix/Feature/Docs/Perf）
• 关联 issue 号：Closes #123
• 改动列表和测试说明
• 通过所有 CI 检查

4️⃣ 代码审查与合并

等待审批后合并到 main-dev：

# 在 GitHub 界面点击"Merge"按钮
# 合并到 main-dev（不是 main！）

合并前条件：

• ✅ 至少一名维护者审批
• ✅ CI 检查全部通过（pytest, ruff）
• ✅ 合并到 main-dev 分支

快速检查清单

在发起 PR 前检查：

• 从 main-dev 分支创建开发分支
• 更新了 CHANGELOG.md
• ruff format . 格式化代码
• ruff check . --fix 通过 lint
• pytest -v 通过所有测试
• 关联了相关 issue：Closes #123

反面例子 ❌

• ❌ 直接在 main 分支提交
• ❌ PR 中没有关联 issue
• ❌ 修改了代码但没有更新 CHANGELOG
• ❌ 代码没有通过 lint 检查
• ❌ 提交前没有运行测试

相关资源

• Issue Labels：bug, enhancement, documentation, sagellm-core, sagellm-backend 等
• GitHub CLI：gh issue create, gh pr create
• 更多信息：见 .github/copilot-instructions.md

�📚 Package Details

Package	PyPI Name	Import Name	Description
sagellm	isagellm	sagellm	Umbrella package (install this)
sagellm-protocol	isagellm-protocol	sagellm_protocol	Protocol v0.1 types
sagellm-core	isagellm-core	sagellm_core	Runtime & config
sagellm-backend	isagellm-backend	sagellm_backend	Hardware abstraction

📄 License

Proprietary - IntelliStream. Internal use only.

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_{Built with ❤️ by IntelliStream Team for domestic AI infrastructure}

# test