# ms_custom_ops

**Repository Path**: erpim/ms_custom_ops

## Basic Information

- **Project Name**: ms_custom_ops
- **Description**: MindSpore 自定义算子扩展（ms_custom_ops），是依托 MindSpore 原生的自定义算子与自定义 pass 能力构建而成的独立算子扩展插件包。
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 26
- **Created**: 2025-10-22
- **Last Updated**: 2025-11-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MindSpore Custom Operators Extension (ms_custom_ops)

[Chinese Version](README.md) | English

## Introduction

MindSpore Custom Operators Extension (ms_custom_ops) is an independent operator extension plugin package built upon MindSpore's native custom operator and custom pass capabilities. Its core positioning is to provide key high-performance operator support for cutting-edge AI domain models such as large models, helping models break through computational efficiency bottlenecks; in terms of operator integration, this plugin package has extremely high flexibility, comprehensively covering various types of operator technologies including third-party operator libraries, AscendC operators, Triton DSL, and other technical approaches, fully meeting operator integration requirements in different development scenarios, and providing underlying support for efficient iteration of AI models.

<div align="center">
  <img src="docs/arch.png" alt="Description" width="800" />
</div>

## Directory Structure

```text
ms_custom_ops/
├── CMakeLists.txt        # CMake build configuration
├── README.md             # Project documentation
├── README_EN.md          # English project documentation
├── OWNERS                # Project maintainers
├── LICENSE               # Open source license
├── requirements.txt      # Python dependencies
├── setup.py              # Python package configuration
├── version.txt           # Version information
├── 3rdparty/             # Third-party dependencies
├── cmake/                # CMake build scripts
├── docs/                 # Documentation resources
├── ops/                  # Custom operator kernel source code and integration code
│   ├── ascendc/          # AscendC operator implementation and integration code
│   ├── c_api/            # Operators integrated via pre-packaged API calls
│   ├── framework/        # Operator integration common code
│   └── dsl/              # DSL (Domain Specific Language) operator source code
├── pass/                 # Custom fusion passes
├── prebuild/             # Pre-compiled binary libraries
├── python/               # Python bindings and extensions
├── scripts/              # Build and utility scripts
└── tests/                # Test cases
```

## Quick Start

### Prerequisites

- **Python**: >= 3.9
- **MindSpore**: >= 2.7.1
- **Huawei Ascend Software**: CANN toolkit >= 8.3.RC1
- **Compiler**: GCC 7.3 or later
- **CMake**: 3.16 or later
- **Ninja**: 1.11 or later

### Environment Setup

1. **Install Huawei Ascend CANN toolkit**:
   Download and install the CANN toolkit from the [Huawei Ascend official website](https://www.hiascend.com/developer/download/community/result?module=cann)

2. **Set Ascend environment**:

   ```bash
   export ASCEND_HOME_PATH=${YOUR_INSTALL_PATH}$/ascend-toolkit/latest
   source ${ASCEND_HOME_PATH}/../set_env.sh
   ```

3. **Install MindSpore**:
   Download and install from [MindSpore official website](https://www.mindspore.cn/install)

### Build and Installation

1. **Clone the repository**:

   ```bash
   git clone https://gitee.com/mindspore/ms-custom-ops.git
   cd ms_custom_ops
   ```

2. **Install Python dependencies**:

   ```bash
   pip install -r requirements.txt
   ```

3. **Use the build.sh script (recommended)**:

   ```bash
   # View build options
   bash scripts/build.sh -h

   # Default build (Release mode)
   bash scripts/build.sh

   # Debug build
   bash scripts/build.sh -d

   # Build specified operators
   bash scripts/build.sh -p ${absolute_op_dir_path}

   # Build specified operators
   bash scripts/build.sh -p ${absolute_op_dir_path}
   eg. bash scripts/build.sh -p /home/ms_custom_ops/ccsrc/ops/ascendc/add,/home/ms_custom_ops/ccsrc/ops/ascendc/add
   ```

   After compilation, install the generated wheel package:

   ```bash
   pip install dist/*.whl
   ```

4. **Install using setup.py**

   ```bash
   # Install (automatically compiles custom operators)
   python setup.py install

   # Or build wheel package
   python setup.py bdist_wheel
   ```

## Basic Usage

### Using Custom Operators

   After installation, you can use the custom operations in your MindSpore code:

   ```python
   import mindspore as ms
   import ms_custom_ops

   # Example usage of a custom operation (actual API may vary)
   # result = ms_custom_ops.some_custom_operation(input_tensor)
   ```

### Using Custom Passes

   You can register and use custom fusion passes to optimize graph execution:

   ```python
   import mindspore as ms
   import ms_custom_ops

   # Register custom passes
   ms_custom_ops.register_custom_pass("AddRmsNormFusionPass", backend="ascend")
   ms_custom_ops.register_custom_pass("ReplaceAddNFusionPass", backend="cpu")

   # Configure MindSpore context
   ms.context.set_context(mode=ms.GRAPH_MODE, device_target="Ascend")

   # Your network will automatically apply the registered pass optimizations
   ```

   For detailed Pass mechanism, architecture design, and development guide, please refer to: [Pass Auto-Registration Documentation](docs/PASS_README_EN.md)

## Reference Documentation

### Internal Documentation

- [Pass Auto-Registration Mechanism](docs/PASS_README_EN.md) - Detailed guide on Pass architecture, development workflow, and best practices

### External Documentation

- [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/r2.7.0/index.html)
- [MindSpore Custom Programming](https://www.mindspore.cn/tutorials/en/r2.7.0/custom_program/op_custom.html)
- [AscendC Programming](https://www.hiascend.com/cann/ascend-c)

## Contributing

We welcome contributions to this project. Please see the [CONTRIBUTING.md](https://www.mindspore.cn/vllm_mindspore/docs/en/master/developer_guide/contributing.html) file for guidelines on how to contribute.
We welcome and value any form of contribution and collaboration. Please inform us via Issue of any bugs you encounter, or submit your feature requests, improvement suggestions, and technical proposals.