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sageLLM backend provider abstraction (CPU/CUDA/Ascend)

Project description

sagellm-backend

Protocol Compliance (Mandatory)

CI PyPI version Python Version License Code style: ruff

硬件抽象层 - 为 sageLLM 提供统一的硬件接口(CUDA/Ascend/Kunlun)

架构定位

┌─────────────────────────────────────────────────────────────┐
│  sagellm-core (引擎协调层)                                   │
│  • BaseEngine, EngineFactory                               │
│  • CPUEngine, HFCudaEngine                                 │
├─────────────────────────────────────────────────────────────┤
│  sagellm-backend (硬件抽象层) ← 本仓库                       │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  BackendProvider Interface                         │    │
│  │  • Stream/Event 异步流                              │    │
│  │  • KVBlock 内存管理                                 │    │
│  │  • Collective 操作(all_reduce/all_gather)        │    │
│  └─────────────────────────────────────────────────────┘    │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐                  │
│  │  CUDA    │  │  Ascend  │  │  Kunlun  │                  │
│  │ Provider │  │ Provider │  │ Provider │                  │
│  └──────────┘  └──────────┘  └──────────┘                  │
├─────────────────────────────────────────────────────────────┤
│  Hardware SDK Layer                                          │
│  CUDA/cuDNN/NCCL │ CANN/HCCL │ XPU SDK │ DCU SDK           │
└─────────────────────────────────────────────────────────────┘

职责分离(v0.2.0 重构)

  • 本仓库负责:硬件抽象、设备管理、内存原语
  • 不再包含:BaseEngine, EngineFactory(已移至 sagellm-core)
  • 🔗 被使用于:sagellm-core 中的引擎实现

Features

  • 统一硬件抽象:单一 API 支持多硬件后端
  • CPU Backend:无 GPU 环境的默认后端
  • CUDA Support:原生 CUDA 后端实现
  • CPU Support:CPU-only 后端实现
  • 能力发现:硬件能力查询与验证

Installation

pip install isagellm-backend

Quick Start

git clone git@github.com:intellistream/sagellm-backend.git
cd sagellm-backend
./quickstart.sh

# Run tests
pytest tests/ -v

Usage Examples

Basic Backend Usage

from sagellm_backend import CPUBackendProvider, DType

# Create backend
backend = CPUBackendProvider()

# Query capabilities
cap = backend.capability()
print(cap.supported_dtypes)

# Allocate KV block
block = backend.kv_block_alloc(128, DType.FP16)

Using with sagellm-core Engines

注意:引擎实现(如 HFCudaEngine)已移至 sagellm-core。Backend 现在专注于硬件抽象。

# 引擎现在位于 sagellm-core
from sagellm_core import HFCudaEngine, EngineFactory
from sagellm_backend import CPUBackendProvider

# 创建 backend
backend = CPUBackendProvider()

# 使用 EngineFactory(来自 core)
engine = EngineFactory.create(
    backend_type="cpu",
    config={
        "engine_id": "cpu-001",
        "model_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    },
    provider=backend,
)

Extending with New Backends

# Create provider in providers/ directory
class AscendBackendProvider:
    def capability(self) -> CapabilityDescriptor:
        return CapabilityDescriptor(
            supported_dtypes=[DType.FP16, DType.BF16, DType.INT8],
            # ...
        )

    # Implement other interface methods...

# Register via entry point in pyproject.toml
[project.entry-points."sagellm.backends"]
ascend_cann = "sagellm_backend.providers.ascend:create_ascend_backend"

Documentation

License

Proprietary

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