| # NeuroFlow C++ Core |
|
|
| 高性能C++底层实现，提供Python绑定接口。 |
|
|
| ## 特点 |
|
|
| - **轻量化**: 相比原Python版本减少80%+内存占用 |
| - **SIMD优化**: AVX2/ARM NEON加速矩阵运算 |
| - **量化支持**: INT8/FP8量化，4x内存压缩 |
| - **MLA技术**: DeepSeek KV压缩，87.5%+内存节省 |
| - **长记忆**: 分页记忆系统，支持磁盘溢出 |
| - **零依赖**: 单静态库，无外部依赖 |
|
|
| ## 构建 |
|
|
| ```bash |
| cd cpp_core |
| chmod +x build.sh |
| ./build.sh build # 构建核心库 |
| ./build.sh test # 构建并测试 |
| ./build.sh python # 构建Python绑定 |
| ``` |
|
|
| ## 目录结构 |
|
|
| ``` |
| cpp_core/ |
| ├── include/neuroflow/ |
| │ ├── tensor.hpp # 张量运算引擎 (SIMD) |
| │ ├── networks.hpp # ECN/DMN/SN网络 |
| │ ├── memory.hpp # MLA/记忆模块 |
| │ └── model.hpp # 主模型类 |
| ├── src/ |
| │ ├── tensor.cpp |
| │ └── model.cpp |
| ├── bindings/ |
| │ └── python_bindings.cpp # pybind11绑定 |
| ├── tests/ |
| │ ├── test_tensor.cpp |
| │ └── test_model.cpp |
| ├── CMakeLists.txt |
| └── build.sh |
| ``` |
|
|
| ## Python使用 |
|
|
| ```python |
| import neuroflow_cpp as nf |
| |
| # 创建模型 |
| config = nf.ModelConfig( |
| input_dim=512, |
| hidden_dim=256, |
| output_dim=10, |
| use_quantization=True |
| ) |
| model = nf.NeuroFlowModel(config) |
| |
| # 前向传播 |
| import numpy as np |
| x = np.random.randn(32, 512).astype(np.float32) |
| output = model.forward(x) |
| |
| print(output.output.shape) # (32, 10) |
| print(output.decision.shape) |
| print(output.manifold.shape) # 如果return_manifold=True |
| |
| # 性能对比 |
| stats = nf.benchmark() |
| print(f"Size reduction: {stats['size_reduction']*100:.1f}%") |
| ``` |
|
|
| ## 性能对比 |
|
|
| | 版本 | 参数量 | 内存 | 推理时间 | 相比原版 | |
| |------|--------|------|----------|----------| |
| | C++ Original | 1.25M | 5 MB | 2.5 ms | baseline | |
| | C++ Optimized | 171K | 0.7 MB | 1.2 ms | 2x加速 | |
| | C++ Lite | 79K | 0.3 MB | 0.8 ms | 3x加速 | |
| | C++ Quantized | 79K | 0.08 MB | 0.6 ms | 4x加速 | |
