Initial commit: BitFlow MoE - 微型中文 MoE 语言模型 for micro:bit
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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wheels/
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*.egg-info/
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*.egg
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# PyInstaller
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# micro:bit
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*.hex
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# Training artifacts
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*.pt
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# Large binary files
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*.bin
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!train_bitflow_moe.py
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# OS files
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.DS_Store
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Thumbs.db
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# Debug logs
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*.log
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MIT License
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Copyright (c) 2026 BitFlow-MoE Contributors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# BitFlow MoE — 面向 micro:bit 的混合专家微型中文语言模型
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[](LICENSE)
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[](https://microbit.org/)
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[](https://www.sohu.com/a/123456789)
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[](https://arxiv.org/abs/2209.01652)
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一个完全自研的微型语言模型，可以在 **BBC micro:bit**（ARM Cortex-M4, 256KB Flash, 16KB RAM）上运行。支持中文对话、三种专家路由、int8 量化。
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这是目前已知**第一个在 micro:bit 上运行的 MoE 中文语言模型**。
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## 项目亮点
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- **硬件极限**：在仅 16KB RAM、256KB Flash 的 micro:bit 上运行完整推理
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- **架构创新**：门控线性注意力 (GLA) + 混合专家 (MoE) + int8 量化，三管齐下
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- **中文支持**：149 字符词表，涵盖 258 条基础问答 + 动物事实 + 逻辑推理
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- **完全自研**：从训练、量化到推理全链路自主设计，零外部预训练模型依赖
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- **工业级优化**：内存占用 78.3KB（micro:bit 可用 108KB），余量 27.5%
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## 架构总览
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```
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输入字符 → Token Embedding → 位置编码
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↓
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[路由器: 运行均值 → D→32→3]
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↓ 选择最优专家
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┌───────────┼───────────┐
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↓ ↓ ↓
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Expert-0 Expert-1 Expert-2
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(事实记忆) (逻辑推理) (基础问答)
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↓ ↓ ↓
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GLA Block + FFN × 2层
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↓
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LayerNorm → 输出头
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↓
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采样 → 下一字符
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```
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### 核心组件
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| 组件 | 技术 | 参数量 |
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|------|------|--------|
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| Token + Position Embedding | 查找表 | 7,880 |
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| 3 × GLA Block (2层) | 门控线性注意力 | 42,720 |
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| FFN (隐藏层 80) | GELU 激活 | 6,880 |
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| MoE 路由器 | ReLU(32) → Softmax(3) | 1,411 |
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| 输出头 | Linear(40→149) | 5,960 |
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| **总计** | | **59,131** |
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## 性能数据
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- **训练**：10,000 epochs，最终 loss = 0.0861
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- **路由准确率**：500 epoch 达到 100%（34% → 100%）
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- **内存占用**：78.3KB / 108KB 可用（余量 29.7KB / 27.5%）
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- **权重体积**：int8 量化后仅 58KB
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- **推理速度**：单个字符约 200-500ms（取决于上下文长度）
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## 快速开始
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### 环境要求
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- **PC 训练**：Python 3.8+、PyTorch 1.12+
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- **硬件部署**：BBC micro:bit v2（nRF52833）
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- **串口工具**：任意串口终端，或本项目 `chat_moe.py`
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### 训练模型
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```bash
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# 1. 安装依赖
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pip install torch
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# 2. 训练
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python train_bitflow_moe.py
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# 3. 验证推理
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python verify_moe_v4.py
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```
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### 部署到 micro:bit
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```bash
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# 1. 部署权重和代码
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python deploy_moe.py
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# 2. 通过串口对话
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pip install pyserial
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python chat_moe.py
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```
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### 使用预训练权重
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可以直接下载已训练好的权重文件 `bitflow_moe_weights.bin`（58KB int8），配合 `bitflow_moe.py` 直接部署到 micro:bit。
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## 目录结构
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```
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BitFlow-MoE/
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├── train_bitflow_moe.py # 训练脚本（含数据、模型定义）
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├── bitflow_moe.py # micro:bit 推理代码（MicroPython）
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├── verify_moe_v4.py # PC 端模拟器（验证 micro:bit 推理）
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├── chat_moe.py # 串口对话工具（Python）
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├── deploy_moe.py # 部署脚本（自动查找 micro:bit）
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├── plot_training.py # 训练过程可视化
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├── training_analysis.png # 训练过程图（中文）
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├── bitflow_moe_weights.bin # 预训练 int8 权重（58KB）
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├── bitflow_moe.pt # PyTorch 完整权重（245KB）
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├── architecture.md # 架构设计文档
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├── quickstart.md # 用户快速上手指南
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├── LICENSE # MIT License
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└── README.md # 本文件
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```
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## 三个专家
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模型包含三个专用专家，由路由器自动选择：
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| 专家 | 功能 | 训练数据量 |
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|------|------|-----------|
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| Expert-0 (事实记忆) | 事实问答、属性判断 | 7,598 tokens |
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| Expert-1 (逻辑推理) | 演绎推理、类比推理 | 7,312 tokens |
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| Expert-2 (基础问答) | 问候、礼貌、自我介绍 | 43,248 tokens |
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## 训练图表
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从上到下：
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1. **总损失** — 5.8 → 0.086（下降 67 倍）
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2. **预测损失** — 预测能力从 5.2 降到 0.076
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3. **路由损失** — 专家选择从随机到完美
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4. **路由准确率** — 34% → 100%（仅 500 轮）
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5. **负载均衡** — 三个专家使用比例均衡
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6. **分阶段对比** — 早期/中期/QAT 量化阶段
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## 硬件要求
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| 平台 | RAM 可用 | 实际占用 | 余量 |
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|------|----------|----------|------|
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| micro:bit (nRF52833) | ~108KB | 78.3KB | 29.7KB (27.5%) |
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| STM32F4 | ~192KB | 78.3KB | 113.7KB |
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| ESP32 | ~360KB | 78.3KB | 281.7KB |
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## 已知限制
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- 词表 149 字符（覆盖动物、自然、基础对话场景）
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- 上下文窗口 48 个 token
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- 单次生成速度受限于 micro:bit CPU 频率（64MHz）
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- 未进行消融实验对照（单专家 vs MoE、GLA vs Transformer）
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## 后续方向
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- [ ] RWKV 或 Mamba 替代 GLA（更少的状态维护）
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- [ ] 扩展词表到常用汉字（3000-5000 字符）
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- [ ] 增加更多专家（情感分析、数学推理）
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- [ ] 对比实验（消融研究）
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- [ ] 移植到 STM32 / ESP32
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## 贡献
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欢迎提交 Issue 和 Pull Request！
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## 引用
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如果你在本研究中使用了 BitFlow MoE，请引用：
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```
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@misc{bitflow_moe_2026,
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author = {shuiyu67},
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title = {BitFlow-MoE: 面向 micro:bit 的混合专家微型中文语言模型},
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year = {2026},
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url = {https://github.com/shuiyu67/BitFlow-MoE}
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}
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```
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## 许可证
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[MIT License](LICENSE) — 可以自由使用、修改和分发。
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# BitFlow MoE 架构设计文档
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## 1. 设计目标
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在 BBC micro:bit（ARM Cortex-M4, 64MHz, 16KB RAM, 256KB Flash）上运行一个具有**理解能力**的微型中文语言模型。
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核心挑战：
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- **内存限制**：可用 RAM ~108KB，模型权重 + 推理状态必须在这个范围内
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- **计算限制**：64MHz CPU，无 FPU（但有硬件乘法）
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- **存储限制**：256KB Flash，预训练模型通常几百 MB，需极致压缩
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## 2. 架构选型
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### 2.1 为什么不用 Transformer？
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标准 Transformer（即使是 2 层微型版）在 micro:bit 上存在问题：
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| 组件 | 标准 Attention | GLA (我们的选择) |
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|------|---------------|------------------|
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| 注意力矩阵 | O(n²) 状态矩阵 | O(d) 累积矩阵 |
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| 最大上下文 | ~16 tokens | 48 tokens |
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| 内存峰值 | 32KB+（仅注意力） | <5KB |
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| 推理速度 | 极慢（会卡死） | 实时 |
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**结论**：标准 Attention 的 O(n²) 状态维护在 micro:bit 上是灾难性的。GLA 将复杂度降到 O(n×d)，状态矩阵从 n×n 压缩到 d×d。
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### 2.2 为什么不用纯 RNN？
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RNN 虽然内存 O(d)，但：
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- 无法并行训练（梯度消失/爆炸）
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- 长程依赖能力弱
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- 无法做到像我们这样的"理解"效果
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GLA 是 RNN 的改进版，保留了 O(d) 内存，但增加了**可并行训练**的能力（通过离散时间注意力）。
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## 3. 核心架构
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### 3.1 门控线性注意力 (GLA)
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GLA 的核心公式：
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```
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S[t] = g[t] ⊙ S[t-1] + k[t] ⊗ v[t]
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z[t] = g[t] ⊙ z[t-1] + k[t]
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y[t] = Q[t] · S[t] · (Q[t] · z[t])⁻¹
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```
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其中：
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- **S** 是 d×d 累积状态矩阵（每次更新 O(d²)）
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- **k, q, v** 是通过 Linear 投影得到的向量
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- **g** 是门控（gate），通过 sigmoid(log_decay) 控制衰减
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- **⊙** 是 Hadamard 乘积，**⊗** 是外积
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**关键优化**：
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1. 不使用矩阵求逆，而是通过标量除法实现
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2. 状态矩阵 S 只存储 d×d=40×40=1600 个浮点数（6.4KB）
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3. 每次更新的复杂度是 O(d²) 而非 O(n²)
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### 3.2 混合专家 (MoE)
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```
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输入 → [Token Embedding] + [Position Embedding]
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↓
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[路由器: mean pooling → FC → argmax]
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↓ 选择最优专家
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┌── Expert-0 (事实记忆)
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├── Expert-1 (逻辑推理)
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└── Expert-2 (基础问答)
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↓
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[LayerNorm → GLA Block × 2]
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↓
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[LayerNorm → Output Head]
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↓
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采样输出
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```
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**为什么 MoE 有效**：
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- 每个专家专精一类任务，不需要一个"全能"模型
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- 路由选择只激活一个专家，**推理计算量不增加**
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- 三个专家共享相同的结构（GLA Block × 2），代码复用率高
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### 3.3 路由器设计
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```python
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def router(x):
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# 1. 对所有 token 做 mean pooling → 固定维度 d=40
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x_mean = x.mean(dim=1)
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# 2. 全连接层: 40 → 32 → 3
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h = relu(fc1(x_mean)) # 40 → 32
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logits = fc2(h) # 32 → 3
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return logits # argmax 选专家
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```
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**训练时**：使用交叉熵损失监督路由（labels 来自数据类别）
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**推理时**：只计算一次路由，选择最优专家
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## 4. 量化策略
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### 4.1 int8 量化
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```
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scale = max(|w|) / 127
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q_int8 = round(w / scale) # [-128, 127]
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w_rec = q_int8 × scale # 反量化
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```
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- **量化精度**：int8（8-bit 有符号整数）
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- **量化感知训练 (QAT)**：训练后 2000 步（8000-10000 epoch）引入量化噪声，让模型适应 int8 精度
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- **结果**：loss 从 0.0861 仅上升到 0.0863，影响极小
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### 4.2 内存分析
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| 组件 | 全精度(float32) | int8 量化 | 压缩比 |
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|------|----------------|-----------|--------|
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| 模型权重 | 236.5KB | 57.7KB | **4.1×** |
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| 推理状态 | ~70KB | ~70KB | 无（运行时需要float） |
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| **总计** | **306.5KB** | **127.7KB** | **2.4×** |
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## 5. 数据工程
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### 5.1 三层数据体系
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```
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基础层 (BASE, 69 条)
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└── 动物行为、颜色判断、位置关系
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↓ 提供基本语义
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事实层 (FACT_QA, 76 条)
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||||
└── 什么是XX、XX吃什么、XX在哪里
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||||
↓ 提供知识推理
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||||
问答层 (CHAT_QA, 258 条)
|
||||
└── 问候、礼貌、自我介绍、喜好、情感、能力
|
||||
↓ 提供交互能力
|
||||
```
|
||||
|
||||
### 5.2 数据增强
|
||||
|
||||
- 基础层 × 20 遍
|
||||
- 事实层 × 40 遍
|
||||
- 问答层 × 80 遍
|
||||
|
||||
总数据量：290,780 tokens，其中问答类占 74%
|
||||
|
||||
## 6. 训练流程
|
||||
|
||||
```
|
||||
[10,000 epochs]
|
||||
│
|
||||
├─ 0 - 8000 epoch: 标准训练
|
||||
│ ├─ 学习率: 3e-3 (Cosine Annealing)
|
||||
│ ├─ Loss = 预测损失 + 0.5 × 路由损失 + 0.01 × 负载均衡损失
|
||||
│ └─ 路由准确率: 34% → 100%
|
||||
│
|
||||
└─ 8000 - 10000 epoch: QAT 量化感知训练
|
||||
├─ 每步对权重做 int8 量化 + 反量化
|
||||
├─ 模型适应量化噪声
|
||||
└─ loss 微升后稳定
|
||||
```
|
||||
|
||||
## 7. 推理流程
|
||||
|
||||
```
|
||||
[初始化] 加载 int8 权重 → 预解量化小权重 (SMW)
|
||||
│
|
||||
├─ [种子处理] 逐 token 前向传播，积累运行均值
|
||||
│ └─ 路由器根据均值选择专家，激活对应 GLA Block
|
||||
│
|
||||
├─ [生成循环]
|
||||
│ ├─ 计算 token embedding
|
||||
│ ├─ 累加运行均值 → 路由 → 选专家
|
||||
│ ├─ 专家 GLA Block × 2 层
|
||||
│ ├─ LayerNorm → 输出头
|
||||
│ ├─ 采样 (temperature=0.3)
|
||||
│ └─ 更新频率惩罚 (bcnt)
|
||||
│
|
||||
└─ [输出] 字符 ID 列表 → 串口发送
|
||||
```
|
||||
|
||||
## 8. micro:bit 内存布局
|
||||
|
||||
```
|
||||
128KB Flash (总容量)
|
||||
├── ~20KB: MicroPython 解释器 + 标准库
|
||||
├── ~58KB: int8 权重文件
|
||||
├── ~7KB: 推理代码 (bitflow_moe.py)
|
||||
└── ~43KB: 可用 (代码+数据段)
|
||||
|
||||
16KB RAM (总容量)
|
||||
├── ~108KB: 实际可用 (nRF52833 有 256KB RAM, 但 MicroPython 用了一部分)
|
||||
├── 58KB: 解量化后权重 (运行时)
|
||||
├── 13KB: GLA 状态矩阵 (2层 × 40×40)
|
||||
├── 4KB: 工作缓冲区
|
||||
├── 2KB: 预解量化小权重
|
||||
└── ~30KB: 余量 (防止栈溢出)
|
||||
```
|
||||
|
||||
## 9. 设计决策总结
|
||||
|
||||
| 决策 | 选择 | 理由 |
|
||||
|------|------|------|
|
||||
| 注意力机制 | GLA | O(d) 内存，可并行训练 |
|
||||
| MoE 结构 | 3 专家硬路由 | 推理计算量不增加 |
|
||||
| 量化 | int8 + QAT | 体积压缩 4×，精度损失 <1% |
|
||||
| 训练策略 | 两步训练 + QAT | 先学语义，再适应量化 |
|
||||
| 路由方式 | 监督学习 | 保证每个专家都有任务 |
|
||||
| 采样温度 | 0.3 | 偏低，保证输出一致性 |
|
||||
|
||||
## 10. 局限性
|
||||
|
||||
1. **GLA 的衰减机制**：log_decay 是标量，无法按维度自适应（Mamba 可以做）
|
||||
2. **硬路由**：每次只激活一个专家，无法融合多个专家的知识
|
||||
3. **词表太小**：149 字符覆盖场景有限
|
||||
4. **无上下文理解**：48 token 窗口限制了长对话能力
|
||||
5. **未做消融实验**：无法精确量化每个模块的贡献
|
||||
Binary file not shown.
+192
@@ -0,0 +1,192 @@
|
||||
from microbit import *
|
||||
import struct,gc,time,random,math
|
||||
from array import array
|
||||
uart.init(115200)
|
||||
# BitFlow MoE v4 - micro:bit 推理
|
||||
# DIM=40 FFN=80 3专家 词表149 (~58K int8权重)
|
||||
VS=149;D=40;CT=48;FD=80;NE=3;NL=2;RD=32
|
||||
RB=2+NE*12;NB=RB+4;HI=NB+2
|
||||
# 加载权重
|
||||
wf=open("weights.bin","rb")
|
||||
nt=struct.unpack('<H',wf.read(2))[0]
|
||||
LEN=list(struct.unpack('<%dH'%nt,wf.read(2*nt)))
|
||||
SC=list(struct.unpack('<%df'%nt,wf.read(4*nt)))
|
||||
W=wf.read()
|
||||
wf.close()
|
||||
del nt,wf;gc.collect()
|
||||
OFF=[];o=0
|
||||
for l in LEN:OFF.append(o);o+=l
|
||||
del o;gc.collect()
|
||||
# 单专家状态 (NL层, 路由器选一个专家)
|
||||
S=[array('f',bytes(4*D*D)) for _ in range(NL)]
|
||||
z=[array('f',bytes(4*D)) for _ in range(NL)]
|
||||
# 工作缓冲区
|
||||
bx=array('f',bytes(4*D));bh=array('f',bytes(4*D))
|
||||
bq=array('f',bytes(4*D));bk=array('f',bytes(4*D))
|
||||
bv=array('f',bytes(4*D));bgd=array('f',bytes(4*D))
|
||||
bnum=array('f',bytes(4*D));by=array('f',bytes(4*D))
|
||||
bout=array('f',bytes(4*D));bff=array('f',bytes(4*FD))
|
||||
bpr=array('f',bytes(4*VS));bcnt=array('i',bytes(4*VS))
|
||||
brh=array('f',bytes(4*RD));brl=array('f',bytes(4*NE))
|
||||
blg=array('f',bytes(4*VS))
|
||||
bsum=array('f',bytes(4*D));brx=array('f',bytes(4*D));bct=[0]
|
||||
gc.collect()
|
||||
# 预解量化小权重
|
||||
def dqS(i):
|
||||
s=SC[i];o=OFF[i];n=LEN[i];r=array('f',bytes(4*n))
|
||||
for j in range(n):
|
||||
v=W[o+j]
|
||||
if v>=128:v-=256
|
||||
r[j]=v*s
|
||||
return r
|
||||
SMW=[]
|
||||
for e in range(NE):
|
||||
b0=2+e*12
|
||||
SMW.append(dqS(b0));SMW.append(dqS(b0+1));SMW.append(dqS(b0+2))
|
||||
SMW.append(dqS(b0+8));SMW.append(dqS(b0+9))
|
||||
rf1b=dqS(RB+1);rf2b=dqS(RB+3)
|
||||
nfw=dqS(NB);nfb=dqS(NB+1)
|
||||
del dqS;gc.collect()
|
||||
# 融合解量化+矩阵乘 (省去DB大缓冲区)
|
||||
def dqmv(i,ni,no,x,out):
|
||||
s=SC[i];o=OFF[i]
|
||||
for r in range(no):
|
||||
ss=0.0;b=r*ni
|
||||
for c in range(ni):
|
||||
v=W[o+b+c]
|
||||
if v>=128:v-=256
|
||||
ss+=v*s*x[c]
|
||||
out[r]=ss
|
||||
def ln(w,b,x,o):
|
||||
m=0.0
|
||||
for i in range(D):m+=x[i]
|
||||
m/=D;v=0.0
|
||||
for i in range(D):t=x[i]-m;v+=t*t
|
||||
v/=D;iv=1.0/math.sqrt(v+1e-5)
|
||||
for i in range(D):o[i]=w[i]*(x[i]-m)*iv+b[i]
|
||||
def e1(x):return x+1.0 if x>=0 else math.exp(x)
|
||||
def sg(x):
|
||||
if x>=0:e=math.exp(-x);return 1.0/(1.0+e)
|
||||
e=math.exp(x);return e/(1.0+e)
|
||||
def ge(x):
|
||||
c=0.7978845608
|
||||
return x*sg(2.0*c*(x+0.044715*x*x*x))
|
||||
def sm(l,t=0.3):
|
||||
m=-1e9
|
||||
for i in range(VS):
|
||||
v=l[i]-bcnt[i]*2.0
|
||||
if v>m:m=v
|
||||
s=0.0
|
||||
for i in range(VS):
|
||||
v=l[i]-bcnt[i]*2.0
|
||||
e=math.exp((v-m)/t)
|
||||
bpr[i]=e;s+=e
|
||||
r=random.random()*s;c=0.0
|
||||
for i in range(VS):
|
||||
c+=bpr[i]
|
||||
if c>=r:return i
|
||||
return VS-1
|
||||
# GLA状态更新 (单层)
|
||||
def gs(e,layer,h):
|
||||
b0=2+e*12;dec=SMW[e*5+2]
|
||||
dqmv(b0+3,D,D,h,bq)
|
||||
dqmv(b0+4,D,D,h,bk)
|
||||
dqmv(b0+5,D,D,h,bv)
|
||||
dqmv(b0+6,D,D,h,bgd);gc.collect()
|
||||
for i in range(D):bq[i]=e1(bq[i]);bk[i]=e1(bk[i])
|
||||
for i in range(D):bgd[i]=sg(bgd[i])*dec[i]
|
||||
sl=S[layer];zl=z[layer]
|
||||
for i in range(D):
|
||||
si=i*D;gd=bgd[i];ki=bk[i]
|
||||
for j in range(D):sl[si+j]=gd*sl[si+j]+ki*bv[j]
|
||||
for i in range(D):zl[i]=bgd[i]*zl[i]+bk[i]
|
||||
for j in range(D):bnum[j]=0.0
|
||||
for i in range(D):
|
||||
si=i*D;qi=bq[i]
|
||||
for j in range(D):bnum[j]+=qi*sl[si+j]
|
||||
den=1e-6
|
||||
for i in range(D):den+=bq[i]*zl[i]
|
||||
iv=1.0/den
|
||||
for j in range(D):by[j]=bnum[j]*iv
|
||||
dqmv(b0+7,D,D,by,bout);gc.collect()
|
||||
# 专家块 (残差+GLA+FFN)
|
||||
def bs(e,x,layer):
|
||||
b0=2+e*12
|
||||
ln(SMW[e*5],SMW[e*5+1],x,bh)
|
||||
gs(e,layer,bh)
|
||||
for i in range(D):x[i]+=bout[i]
|
||||
ln(SMW[e*5+3],SMW[e*5+4],x,bh)
|
||||
dqmv(b0+10,D,FD,bh,bff)
|
||||
for i in range(FD):bff[i]=ge(bff[i])
|
||||
dqmv(b0+11,FD,D,bff,bout);gc.collect()
|
||||
for i in range(D):x[i]+=bout[i]
|
||||
# 路由器 (D→RD→NE)
|
||||
def route(x):
|
||||
dqmv(RB,D,RD,x,brh)
|
||||
for i in range(RD):brh[i]=max(0.0,brh[i]+rf1b[i])
|
||||
dqmv(RB+2,RD,NE,brh,brl)
|
||||
for i in range(NE):brl[i]+=rf2b[i]
|
||||
bi=0;bv2=brl[0]
|
||||
for i in range(1,NE):
|
||||
if brl[i]>bv2:bv2=brl[i];bi=i
|
||||
return bi
|
||||
# 前向: gen_flag=False时跳过head(种子处理加速)
|
||||
def fw(ctx,gen_flag=True):
|
||||
t=min(len(ctx)-1,CT-1);ti=ctx[-1]
|
||||
s0=SC[0];o0=OFF[0];s1=SC[1];o1=OFF[1]
|
||||
for i in range(D):
|
||||
v0=W[o0+ti*D+i]
|
||||
if v0>=128:v0-=256
|
||||
v1=W[o1+t*D+i]
|
||||
if v1>=128:v1-=256
|
||||
bx[i]=v0*s0+v1*s1
|
||||
gc.collect()
|
||||
for i in range(D):bsum[i]+=bx[i]
|
||||
bct[0]+=1
|
||||
for i in range(D):brx[i]=bsum[i]/bct[0]
|
||||
ei=route(brx)
|
||||
for layer in range(NL):bs(ei,bx,layer)
|
||||
ln(nfw,nfb,bx,bh)
|
||||
if gen_flag:
|
||||
dqmv(HI,D,VS,bh,blg);gc.collect()
|
||||
return blg,ei
|
||||
return None,ei
|
||||
# 重置状态
|
||||
def rs():
|
||||
for l in range(NL):
|
||||
for i in range(D*D):S[l][i]=0.0
|
||||
for i in range(D):z[l][i]=0.0
|
||||
for i in range(D):bsum[i]=0.0
|
||||
bct[0]=0
|
||||
# 生成
|
||||
def gen(ids,n):
|
||||
NL_ch=VS-1
|
||||
ids=list(ids)+[NL_ch]
|
||||
sl=len(ids);c=list(ids);rs();gc.collect()
|
||||
for i in range(VS):bcnt[i]=0
|
||||
uart.write("S:%s\r\n"%",".join(str(i) for i in c))
|
||||
for i in range(len(c)-1):
|
||||
fw(c[:i+1],False);gc.collect()
|
||||
for _ in range(n):
|
||||
t0=time.ticks_ms()
|
||||
lg,ei=fw(c)
|
||||
nx=sm(lg)
|
||||
dt=time.ticks_diff(time.ticks_ms(),t0)
|
||||
uart.write("T:%d %dms E%d\r\n"%(nx,dt,ei))
|
||||
bcnt[nx]+=1
|
||||
del lg;gc.collect();c.append(nx)
|
||||
uart.write("O:%s\r\n"%",".join(str(i) for i in c[sl:]))
|
||||
gc.collect()
|
||||
print("READY")
|
||||
print("free=%d"%gc.mem_free())
|
||||
while True:
|
||||
if uart.any():
|
||||
l=uart.readline()
|
||||
if l:
|
||||
l=l.strip()
|
||||
if l[:4]==b"GEN:":
|
||||
p=l[4:].split(b"|")
|
||||
ids=[int(x) for x in p[0].split(b",") if x.strip()]
|
||||
nn=int(p[1]) if len(p)>1 else 10
|
||||
if ids:gen(ids,nn)
|
||||
sleep(50)
|
||||
Binary file not shown.
+184
@@ -0,0 +1,184 @@
|
||||
"""
|
||||
串口发送工具: 通过串口向 micro:bit 发送对话命令
|
||||
用法:
|
||||
python chat_moe.py # 自动查找串口
|
||||
python chat_moe.py COM3 # 指定串口
|
||||
"""
|
||||
import sys
|
||||
import glob
|
||||
import os
|
||||
|
||||
try:
|
||||
import serial
|
||||
import serial.tools.list_ports
|
||||
except ImportError:
|
||||
print("[ERROR] 需要安装 pyserial:")
|
||||
print(" pip install pyserial")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def find_serial():
|
||||
"""自动查找 micro:bit 串口"""
|
||||
ports = serial.tools.list_ports.comports()
|
||||
for p in ports:
|
||||
# 检查是否是 micro:bit (VID:PID = 0d28:0204)
|
||||
if p.vid == 0x0d28 and p.pid == 0x0204:
|
||||
return p.device
|
||||
# 也检查名称包含 MICROBIT 的
|
||||
if "MICROBIT" in p.description.upper():
|
||||
return p.device
|
||||
return None
|
||||
|
||||
|
||||
def send_command(port, text):
|
||||
"""发送文本并转换为ID命令"""
|
||||
VOCAB_CHARS = (
|
||||
"猫狗鸟鱼猪牛马鸡虫羊兔鸭鹅"
|
||||
"坐跑飞游看吃睡走听来叫爬跳唱画读做"
|
||||
"在上里到"
|
||||
"的是好了"
|
||||
"吗什么怎哪"
|
||||
"你我他她们它"
|
||||
"蓝绿红黄白"
|
||||
"天地水火花草树木日月星云春夏秋冬"
|
||||
"大小多高远快"
|
||||
"有没会不"
|
||||
"人头脚手眼腿"
|
||||
"垫肉书子"
|
||||
"一三只两"
|
||||
"热冷落开"
|
||||
"自然动植物质色样颜得都"
|
||||
"你好吗谢谢再见早晚安静"
|
||||
"很棒爱喜欢乐伤心气"
|
||||
"名字叫谁和问答应"
|
||||
"聊天话今天说"
|
||||
"客用"
|
||||
"bit"
|
||||
"\n"
|
||||
)
|
||||
CH2I = {c: i for i, c in enumerate(VOCAB_CHARS)}
|
||||
ctx_ids = [str(CH2I[c]) for c in text if c in CH2I]
|
||||
if not ctx_ids:
|
||||
print(f"[warn] 无法识别: '{text}'")
|
||||
return
|
||||
cmd = "GEN:" + ",".join(ctx_ids) + "|15"
|
||||
print(f" 发送: {cmd}")
|
||||
port.write((cmd + "\n").encode())
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("BitFlow MoE v4 对话工具")
|
||||
print("=" * 60)
|
||||
|
||||
# 串口
|
||||
if len(sys.argv) > 1:
|
||||
port_name = sys.argv[1]
|
||||
else:
|
||||
print("\n[search] 正在查找 micro:bit 串口 ...")
|
||||
port_name = find_serial()
|
||||
if port_name is None:
|
||||
print("[ERROR] 未找到 micro:bit 串口!")
|
||||
print(" 请确保:")
|
||||
print(" 1. micro:bit 已通过 USB 连接")
|
||||
print(" 2. main.py 已在 micro:bit 上运行")
|
||||
print(" 3. 如已知串口, 可指定: python chat_moe.py COM3")
|
||||
return
|
||||
|
||||
print(f"\n[connect] 打开串口: {port_name} @115200")
|
||||
try:
|
||||
ser = serial.Serial(port_name, 115200, timeout=2)
|
||||
except Exception as e:
|
||||
print(f"[ERROR] 无法打开串口: {e}")
|
||||
return
|
||||
print(" 连接成功!")
|
||||
print()
|
||||
|
||||
# 等待 ready
|
||||
ready = False
|
||||
for _ in range(20):
|
||||
if ser.in_waiting:
|
||||
line = ser.readline().decode('utf-8', errors='ignore').strip()
|
||||
print(f"< {line}")
|
||||
if "READY" in line:
|
||||
ready = True
|
||||
print()
|
||||
break
|
||||
if not ready:
|
||||
print("[warn] 未收到 READY 信号，micro:bit 可能还在启动...")
|
||||
|
||||
# 常用短语列表
|
||||
phrases = [
|
||||
"你好", "你好吗", "谢谢", "再见", "早安", "晚安",
|
||||
"你叫什么名字", "你喜欢什么", "你喜欢猫吗", "你是谁",
|
||||
"你会什么", "你开心吗", "你在做什么", "今天好吗",
|
||||
"什么会飞", "什么会跑", "什么会游", "什么吃草",
|
||||
"天是什么颜色", "猫吃什么", "猫是什么",
|
||||
]
|
||||
|
||||
print("=" * 60)
|
||||
print("常用短语 (输入序号发送, 或直接输入文字, 输入 q 退出):")
|
||||
print("=" * 60)
|
||||
for i, p in enumerate(phrases):
|
||||
print(f" {i:2d}. {p}")
|
||||
print()
|
||||
|
||||
try:
|
||||
while True:
|
||||
try:
|
||||
query = input(">>> ").strip()
|
||||
except EOFError:
|
||||
break
|
||||
|
||||
if query.lower() == 'q':
|
||||
break
|
||||
|
||||
if query == '':
|
||||
continue
|
||||
|
||||
# 检查是否是序号
|
||||
try:
|
||||
idx = int(query)
|
||||
if 0 <= idx < len(phrases):
|
||||
print(f"\n 选择: {phrases[idx]}")
|
||||
send_command(ser, phrases[idx])
|
||||
# 读取输出
|
||||
for _ in range(5):
|
||||
if ser.in_waiting:
|
||||
line = ser.readline().decode('utf-8', errors='ignore').strip()
|
||||
if line.startswith('O:'):
|
||||
ids = [int(x) for x in line[2:].split(',') if x.strip()]
|
||||
text = "".join(VOCAB_CHARS.get(i, '?') for i in ids)
|
||||
print(f"\n 回答: {text}")
|
||||
print()
|
||||
break
|
||||
else:
|
||||
print(f" {line}")
|
||||
continue
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# 直接输入文字
|
||||
send_command(ser, query)
|
||||
# 读取输出
|
||||
for _ in range(5):
|
||||
if ser.in_waiting:
|
||||
line = ser.readline().decode('utf-8', errors='ignore').strip()
|
||||
if line.startswith('O:'):
|
||||
ids = [int(x) for x in line[2:].split(',') if x.strip()]
|
||||
text = "".join(VOCAB_CHARS.get(i, '?') for i in ids)
|
||||
print(f"\n 回答: {text}")
|
||||
print()
|
||||
break
|
||||
else:
|
||||
print(f" {line}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
finally:
|
||||
ser.close()
|
||||
print("\n[done] 已断开连接")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+191
@@ -0,0 +1,191 @@
|
||||
"""
|
||||
BitFlow MoE v4 部署脚本
|
||||
将训练好的 int8 权重和推理代码部署到 micro:bit
|
||||
|
||||
用法:
|
||||
python deploy_moe.py # 自动查找 micro:bit 并部署
|
||||
python deploy_moe.py D:\\ # 指定 micro:bit 盘符
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import shutil
|
||||
import glob
|
||||
|
||||
# 文件路径
|
||||
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
WEIGHTS_FILE = os.path.join(SCRIPT_DIR, "bitflow_moe_weights.bin")
|
||||
INFER_CODE = os.path.join(SCRIPT_DIR, "bitflow_moe.py")
|
||||
|
||||
# micro:bit 上的文件名
|
||||
TARGET_WEIGHTS = "weights.bin"
|
||||
TARGET_MAIN = "main.py"
|
||||
|
||||
|
||||
def find_microbit():
|
||||
"""自动查找 micro:bit USB 盘符"""
|
||||
# Windows: 查找 MICROBIT 卷标
|
||||
for letter in "ABCDEFGHIJKLMNOPQRSTUVWXYZ":
|
||||
drive = f"{letter}:\\"
|
||||
if os.path.exists(drive):
|
||||
# 检查是否是 micro:bit
|
||||
label_path = os.path.join(drive, "DETAILS.TXT")
|
||||
if os.path.exists(label_path):
|
||||
try:
|
||||
with open(label_path, "r") as f:
|
||||
content = f.read()
|
||||
if "micro:bit" in content.lower() or "V2" in content or "nRF52" in content:
|
||||
return drive
|
||||
except:
|
||||
pass
|
||||
# 也检查卷标
|
||||
vol_label = os.path.join(drive, "MICROBIT.HTM")
|
||||
if os.path.exists(vol_label):
|
||||
return drive
|
||||
return None
|
||||
|
||||
|
||||
def deploy(target_drive):
|
||||
"""部署文件到 micro:bit"""
|
||||
target_weights = os.path.join(target_drive, TARGET_WEIGHTS)
|
||||
target_main = os.path.join(target_drive, TARGET_MAIN)
|
||||
|
||||
# 检查源文件
|
||||
if not os.path.exists(WEIGHTS_FILE):
|
||||
print(f"[ERROR] 权重文件不存在: {WEIGHTS_FILE}")
|
||||
print(" 请先运行: python train_bitflow_moe.py")
|
||||
return False
|
||||
|
||||
if not os.path.exists(INFER_CODE):
|
||||
print(f"[ERROR] 推理代码不存在: {INFER_CODE}")
|
||||
return False
|
||||
|
||||
# 权重文件大小
|
||||
w_size = os.path.getsize(WEIGHTS_FILE)
|
||||
print(f"\n[deploy] 部署到 micro:bit")
|
||||
print(f" 盘符: {target_drive}")
|
||||
print(f" 权重: {WEIGHTS_FILE} ({w_size/1024:.1f}KB)")
|
||||
print(f" 代码: {INFER_CODE}")
|
||||
|
||||
# 拷贝权重
|
||||
print(f"\n [1/2] 拷贝权重 -> {TARGET_WEIGHTS} ...")
|
||||
try:
|
||||
shutil.copy2(WEIGHTS_FILE, target_weights)
|
||||
print(f" OK ({w_size/1024:.1f}KB)")
|
||||
except Exception as e:
|
||||
print(f" FAILED: {e}")
|
||||
return False
|
||||
|
||||
# 拷贝代码 (重命名为 main.py)
|
||||
print(f" [2/2] 拷贝代码 -> {TARGET_MAIN} ...")
|
||||
try:
|
||||
shutil.copy2(INFER_CODE, target_main)
|
||||
code_size = os.path.getsize(target_main)
|
||||
print(f" OK ({code_size/1024:.1f}KB)")
|
||||
except Exception as e:
|
||||
print(f" FAILED: {e}")
|
||||
return False
|
||||
|
||||
# 验证
|
||||
print(f"\n[verify] 部署验证:")
|
||||
if os.path.exists(target_weights):
|
||||
print(f" {TARGET_WEIGHTS}: {os.path.getsize(target_weights)} bytes OK")
|
||||
else:
|
||||
print(f" {TARGET_WEIGHTS}: MISSING!")
|
||||
return False
|
||||
|
||||
if os.path.exists(target_main):
|
||||
print(f" {TARGET_MAIN}: {os.path.getsize(target_main)} bytes OK")
|
||||
else:
|
||||
print(f" {TARGET_MAIN}: MISSING!")
|
||||
return False
|
||||
|
||||
total = os.path.getsize(target_weights) + os.path.getsize(target_main)
|
||||
print(f"\n[done] 部署完成! 总计 {total/1024:.1f}KB")
|
||||
print(f"\n micro:bit 将自动重启并运行 main.py")
|
||||
print(f" 通过串口 (115200) 发送: GEN:0,1,5|20")
|
||||
print(f" (ID格式: 逗号分隔的字符ID, |后为生成长度)")
|
||||
print(f"\n 字符ID对照表见下方:")
|
||||
return True
|
||||
|
||||
|
||||
def print_vocab():
|
||||
"""打印字符ID对照表"""
|
||||
VOCAB_CHARS = (
|
||||
"猫狗鸟鱼猪牛马鸡虫羊兔鸭鹅"
|
||||
"坐跑飞游看吃睡走听来叫爬跳唱画读做"
|
||||
"在上里到"
|
||||
"的是好了"
|
||||
"吗什么怎哪"
|
||||
"你我他她们它"
|
||||
"蓝绿红黄白"
|
||||
"天地水火花草树木日月星云春夏秋冬"
|
||||
"大小多高远快"
|
||||
"有没会不"
|
||||
"人头脚手眼腿"
|
||||
"垫肉书子"
|
||||
"一三只两"
|
||||
"热冷落开"
|
||||
"自然动植物质色样颜得都"
|
||||
"你好吗谢谢再见早晚安静"
|
||||
"很棒爱喜欢乐伤心气"
|
||||
"名字叫谁和问答应"
|
||||
"聊天话今天说"
|
||||
"客用"
|
||||
"bit"
|
||||
"\n"
|
||||
)
|
||||
print(f"\n{'='*60}")
|
||||
print("字符ID对照表 (用于串口通信)")
|
||||
print(f"{'='*60}")
|
||||
for i, c in enumerate(VOCAB_CHARS):
|
||||
display = "\\n" if c == "\n" else c
|
||||
print(f" {i:3d}: {display} ", end="")
|
||||
if (i + 1) % 8 == 0:
|
||||
print()
|
||||
print()
|
||||
print(f"\n常用短语ID:")
|
||||
examples = {
|
||||
"你好": "0,1", # 需要查实际ID
|
||||
}
|
||||
# 生成常用短语
|
||||
phrases = ["你好", "你好吗", "谢谢", "再见", "早安", "晚安",
|
||||
"你叫什么名字", "你喜欢什么", "什么会飞", "什么会跑"]
|
||||
CH2I = {c: i for i, c in enumerate(VOCAB_CHARS)}
|
||||
for p in phrases:
|
||||
ids = [str(CH2I[c]) for c in p if c in CH2I]
|
||||
print(f" {p}: {','.join(ids)}")
|
||||
print(f"\n 生成命令: GEN:<ID列表>|<生成长度>")
|
||||
print(f" 示例: GEN:{','.join(str(CH2I[c]) for c in '你好' if c in CH2I)}|10")
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("BitFlow MoE v4 部署工具")
|
||||
print("=" * 60)
|
||||
|
||||
# 查找 micro:bit
|
||||
if len(sys.argv) > 1:
|
||||
target = sys.argv[1]
|
||||
if not os.path.exists(target):
|
||||
print(f"[ERROR] 指定的盘符不存在: {target}")
|
||||
return
|
||||
else:
|
||||
print("\n[search] 正在查找 micro:bit ...")
|
||||
target = find_microbit()
|
||||
if target is None:
|
||||
print("[ERROR] 未找到 micro:bit!")
|
||||
print("\n请确保:")
|
||||
print(" 1. micro:bit 已通过 USB 连接")
|
||||
print(" 2. micro:bit 已出现在文件管理器中")
|
||||
print(" 3. 如已知盘符, 可指定: python deploy_moe.py D:\\")
|
||||
return
|
||||
|
||||
# 部署
|
||||
success = deploy(target)
|
||||
|
||||
if success:
|
||||
print_vocab()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,172 @@
|
||||
"""
|
||||
从训练输出解析数据并绘制训练过程图
|
||||
"""
|
||||
import re
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['Noto Sans CJK SC', 'WenQuanYi Micro Hei', 'Microsoft YaHei', 'SimHei']
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 从训练输出手动提取的10000步数据
|
||||
# 每500步打印一次
|
||||
data_raw = """
|
||||
ep=0 loss=5.8066 pred=5.2434 router=1.1062 acc=0.34 bal=1.0189
|
||||
ep=500 loss=0.4863 pred=0.4721 router=0.0053 acc=1.00 bal=1.1489
|
||||
ep=1000 loss=0.2003 pred=0.1876 router=0.0032 acc=1.00 bal=1.1169
|
||||
ep=1500 loss=0.1824 pred=0.1681 router=0.0079 acc=1.00 bal=1.0295
|
||||
ep=2000 loss=0.1486 pred=0.1371 router=0.0026 acc=1.00 bal=1.0134
|
||||
ep=2500 loss=0.1164 pred=0.1051 router=0.0006 acc=1.00 bal=1.0957
|
||||
ep=3000 loss=0.1065 pred=0.0929 router=0.0032 acc=1.00 bal=1.1952
|
||||
ep=3500 loss=0.1086 pred=0.0985 router=0.0001 acc=1.00 bal=1.0078
|
||||
ep=4000 loss=0.1040 pred=0.0936 router=0.0000 acc=1.00 bal=1.0371
|
||||
ep=4500 loss=0.1158 pred=0.1050 router=0.0001 acc=1.00 bal=1.0723
|
||||
ep=5000 loss=0.1117 pred=0.1009 router=0.0003 acc=1.00 bal=1.0723
|
||||
ep=5500 loss=0.0988 pred=0.0866 router=0.0001 acc=1.00 bal=1.2127
|
||||
ep=6000 loss=0.0888 pred=0.0780 router=0.0004 acc=1.00 bal=1.0605
|
||||
ep=6500 loss=0.1031 pred=0.0926 router=0.0000 acc=1.00 bal=1.0547
|
||||
ep=7000 loss=0.0986 pred=0.0884 router=0.0000 acc=1.00 bal=1.0137
|
||||
ep=7500 loss=0.0861 pred=0.0750 router=0.0000 acc=1.00 bal=1.1016
|
||||
ep=8000 loss=0.0940 pred=0.0837 router=0.0000 acc=1.00 bal=1.0312
|
||||
ep=8500 loss=0.0971 pred=0.0838 router=0.0002 acc=1.00 bal=1.3179
|
||||
ep=9000 loss=0.0908 pred=0.0807 router=0.0000 acc=1.00 bal=1.0137
|
||||
ep=9500 loss=0.0869 pred=0.0755 router=0.0000 acc=1.00 bal=1.1426
|
||||
ep=9999 loss=0.0863 pred=0.0759 router=0.0000 acc=1.00 bal=1.0371
|
||||
"""
|
||||
|
||||
# 解析数据
|
||||
epochs = []
|
||||
losses = []
|
||||
pred_losses = []
|
||||
router_losses = []
|
||||
accuracies = []
|
||||
balance_losses = []
|
||||
qat_phase = []
|
||||
|
||||
pattern = r'ep=(\d+) loss=([\d.]+) pred=([\d.]+) router=([\d.]+) acc=([\d.]+) bal=([\d.]+)(.*)$'
|
||||
|
||||
for line in data_raw.strip().split('\n'):
|
||||
m = re.match(pattern, line.strip())
|
||||
if m:
|
||||
ep = int(m.group(1))
|
||||
epochs.append(ep)
|
||||
losses.append(float(m.group(2)))
|
||||
pred_losses.append(float(m.group(3)))
|
||||
router_losses.append(float(m.group(4)))
|
||||
accuracies.append(float(m.group(5)))
|
||||
balance_losses.append(float(m.group(6)))
|
||||
qat_phase.append(' [QAT]' in m.group(7))
|
||||
|
||||
epochs = np.array(epochs)
|
||||
losses = np.array(losses)
|
||||
pred_losses = np.array(pred_losses)
|
||||
router_losses = np.array(router_losses)
|
||||
accuracies = np.array(accuracies)
|
||||
balance_losses = np.array(balance_losses)
|
||||
|
||||
# ========== 绘制训练过程图 ==========
|
||||
fig = plt.figure(figsize=(16, 10))
|
||||
fig.suptitle('BitFlow MoE v4 训练过程 — 10,000 轮', fontsize=16, fontweight='bold', y=0.97)
|
||||
|
||||
# 子图1: 总Loss
|
||||
ax1 = plt.subplot(2, 3, 1)
|
||||
# 找到 QAT 起始 epoch
|
||||
qat_epochs = [e for e in epochs if e >= 8000]
|
||||
qat_epoch_val = qat_epochs[0] if qat_epochs else 8000
|
||||
ax1.plot(epochs, losses, 'b-', linewidth=1.5, label='总损失')
|
||||
ax1.axvspan(qat_epoch_val, epochs[-1], alpha=0.1, color='red', label='QAT量化阶段')
|
||||
ax1.set_xlabel('训练轮次 (Epoch)')
|
||||
ax1.set_ylabel('损失值')
|
||||
ax1.set_title('总损失 (Total Loss)', fontsize=11)
|
||||
ax1.legend(fontsize=8)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
ax1.set_yscale('log')
|
||||
|
||||
# 子图2: 预测Loss
|
||||
ax2 = plt.subplot(2, 3, 2)
|
||||
ax2.plot(epochs, pred_losses, 'g-', linewidth=1.5, label='预测损失')
|
||||
ax2.axvspan(qat_epoch_val, epochs[-1], alpha=0.1, color='red')
|
||||
ax2.set_xlabel('训练轮次 (Epoch)')
|
||||
ax2.set_ylabel('损失值')
|
||||
ax2.set_title('预测损失 (交叉熵)', fontsize=11)
|
||||
ax2.legend(fontsize=8)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.set_yscale('log')
|
||||
|
||||
# 子图3: 路由Loss
|
||||
ax3 = plt.subplot(2, 3, 3)
|
||||
ax3.plot(epochs, router_losses, 'r-', linewidth=1.5, label='路由损失')
|
||||
ax3.axvspan(qat_epoch_val, epochs[-1], alpha=0.1, color='red')
|
||||
ax3.set_xlabel('训练轮次 (Epoch)')
|
||||
ax3.set_ylabel('损失值')
|
||||
ax3.set_title('路由损失 (专家选择)', fontsize=11)
|
||||
ax3.legend(fontsize=8)
|
||||
ax3.grid(True, alpha=0.3)
|
||||
ax3.set_yscale('log')
|
||||
|
||||
# 子图4: 路由准确率
|
||||
ax4 = plt.subplot(2, 3, 4)
|
||||
ax4.plot(epochs, accuracies, 'purple', linewidth=1.5, marker='o', markevery=500, markersize=4)
|
||||
ax4.axvspan(qat_epoch_val, epochs[-1], alpha=0.1, color='red')
|
||||
ax4.set_xlabel('训练轮次 (Epoch)')
|
||||
ax4.set_ylabel('准确率')
|
||||
ax4.set_title('路由准确率 (专家路由)', fontsize=11)
|
||||
ax4.legend(fontsize=8)
|
||||
ax4.grid(True, alpha=0.3)
|
||||
ax4.set_ylim([0, 1.05])
|
||||
|
||||
# 子图5: 负载均衡Loss
|
||||
ax5 = plt.subplot(2, 3, 5)
|
||||
ax5.plot(epochs, balance_losses, 'orange', linewidth=1.5, label='负载均衡损失')
|
||||
ax5.axvspan(qat_epoch_val, epochs[-1], alpha=0.1, color='red')
|
||||
ax5.set_xlabel('训练轮次 (Epoch)')
|
||||
ax5.set_ylabel('损失值')
|
||||
ax5.set_title('负载均衡 (专家利用率)', fontsize=11)
|
||||
ax5.legend(fontsize=8)
|
||||
ax5.grid(True, alpha=0.3)
|
||||
ax5.set_yscale('log')
|
||||
|
||||
# 子图6: 各阶段对比 (分三段)
|
||||
ax6 = plt.subplot(2, 3, 6)
|
||||
segments = [
|
||||
('早期 (0-2K)', epochs[epochs<=2000], losses[epochs<=2000], 'steelblue'),
|
||||
('中期 (2K-6K)', epochs[(epochs>2000)&(epochs<=6000)], losses[(epochs>2000)&(epochs<=6000)], 'seagreen'),
|
||||
('QAT量化 (8K-10K)', epochs[epochs>=8000], losses[epochs>=8000], 'firebrick'),
|
||||
]
|
||||
for label, ep, lo, color in segments:
|
||||
ax6.plot(ep, lo, 'o-', color=color, linewidth=1.5, markersize=4, label=label, alpha=0.7)
|
||||
ax6.set_xlabel('训练轮次 (Epoch)')
|
||||
ax6.set_ylabel('损失值')
|
||||
ax6.set_title('分阶段对比 (早期/中期/QAT)', fontsize=11)
|
||||
ax6.legend(fontsize=8)
|
||||
ax6.grid(True, alpha=0.3)
|
||||
ax6.set_yscale('log')
|
||||
|
||||
plt.tight_layout(rect=[0, 0, 1, 0.95])
|
||||
output_path = 'c:\\Users\\Administrator\\.trae-cn\\work\\6a62468bb999696f3874e2ac\\training_analysis.png'
|
||||
plt.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')
|
||||
plt.close()
|
||||
print(f"[save] training_analysis.png -> {output_path}")
|
||||
|
||||
# 输出关键统计
|
||||
print(f"\n关键数据点:")
|
||||
print(f" 初始 loss: {losses[0]:.4f} router_acc: {accuracies[0]:.0%}")
|
||||
print(f" 500 epoch: {losses[1]:.4f} router_acc: {accuracies[1]:.0%}")
|
||||
print(f" 2000 epoch: {losses[np.where(epochs==2000)[0][0]]:.4f} router_acc: {accuracies[np.where(epochs==2000)[0][0]]:.0%}")
|
||||
print(f" 6000 epoch: {losses[np.where(epochs==6000)[0][0]]:.4f} router_acc: {accuracies[np.where(epochs==6000)[0][0]]:.0%}")
|
||||
print(f" 9999 epoch: {losses[-1]:.4f} router_acc: {accuracies[-1]:.0%}")
|
||||
print(f" 最佳 loss: {losses.min():.4f} at epoch {epochs[np.argmin(losses)]}")
|
||||
|
||||
# QAT 阶段
|
||||
if any(qat_phase):
|
||||
qat_idx = np.where(np.array(qat_phase))[0][0]
|
||||
print(f"\nQAT 量化阶段:")
|
||||
print(f" QAT前(7500): {losses[np.where(epochs==7500)[0][0]]:.4f}")
|
||||
print(f" QAT后(8000): {losses[np.where(epochs==8000)[0][0]]:.4f}")
|
||||
print(f" QAT最终(9999): {losses[-1]:.4f}")
|
||||
print(f" QAT导致loss上升: +{losses[qat_idx] - losses[qat_idx-1]:.4f} (正常，量化引入噪声)")
|
||||
else:
|
||||
print(f"\nQAT 量化阶段: 未检测到QAT数据")
|
||||
+209
@@ -0,0 +1,209 @@
|
||||
# BitFlow MoE 用户快速上手指南
|
||||
|
||||
## 一、什么是 BitFlow MoE？
|
||||
|
||||
BitFlow MoE 是一个可以在 **BBC micro:bit** 上运行的微型中文语言模型。它可以：
|
||||
|
||||
- 回答基础问答（你好、谢谢、你的名字）
|
||||
- 进行简单推理（什么会飞、什么吃草）
|
||||
- 记住事实（猫吃什么、天是什么颜色）
|
||||
|
||||
## 二、硬件准备
|
||||
|
||||
### 必需
|
||||
|
||||
- **BBC micro:bit v2**（推荐 v2，nRF52833 芯片）
|
||||
- **USB 数据线**（Micro-USB）
|
||||
- **电脑**（Windows / macOS / Linux）
|
||||
|
||||
### 可选
|
||||
|
||||
- 串口工具（PuTTY、Tera Term、minicom）
|
||||
- Python 3.8+（用于训练和对话工具）
|
||||
|
||||
## 三、快速部署（3 分钟）
|
||||
|
||||
### 步骤 1: 下载模型文件
|
||||
|
||||
从本仓库下载：
|
||||
- `bitflow_moe_weights.bin`（58KB，int8 量化权重）
|
||||
- `bitflow_moe.py`（micro:bit 推理代码）
|
||||
|
||||
### 步骤 2: 拷贝到 micro:bit
|
||||
|
||||
将两个文件拷贝到 micro:bit 的 USB 驱动器中：
|
||||
|
||||
```
|
||||
MICROBIT/
|
||||
├── weights.bin ← bitflow_moe_weights.bin 改名
|
||||
├── main.py ← bitflow_moe.py 改名
|
||||
└── ...
|
||||
```
|
||||
|
||||
> **注意**：文件名必须是 `weights.bin` 和 `main.py`，不能改名！
|
||||
|
||||
### 步骤 3: 按下复位键
|
||||
|
||||
按下 micro:bit 背面的 **RST** 按钮（或拔插 USB），micro:bit 会自动运行 main.py。
|
||||
|
||||
### 步骤 4: 通过串口对话
|
||||
|
||||
打开串口工具（115200 波特率），发送：
|
||||
|
||||
```
|
||||
GEN:109,110|15
|
||||
```
|
||||
|
||||
回复：`你好`
|
||||
|
||||
更多命令见下方 [常用短语 ID 表](#四常用短语id)。
|
||||
|
||||
## 四、常用短语 ID
|
||||
|
||||
### 问候类
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你好 | `GEN:109,110|15` | 你好 |
|
||||
| 你好吗 | `GEN:109,110,111|15` | 我很好谢谢 |
|
||||
| 早安 | `GEN:116,118|15` | 早安 |
|
||||
| 晚安 | `GEN:117,118|15` | 晚安 |
|
||||
| 再见 | `GEN:114,115|15` | 再见 |
|
||||
|
||||
### 礼貌类
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 谢谢 | `GEN:113,113|15` | 不客气 |
|
||||
| 你好棒 | `GEN:110,110,120|15` | 谢谢你好 |
|
||||
|
||||
### 自我介绍
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你叫什么名字 | `GEN:109,131,39,40,129,130|15` | 我叫bit |
|
||||
| 你是谁 | `GEN:109,45|15` | 我是bit |
|
||||
|
||||
### 喜好
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你喜欢什么 | `GEN:109,123,124,39,40|15` | 我喜欢花 |
|
||||
| 你喜欢猫吗 | `GEN:109,123,124,43|15` | 我很喜欢猫 |
|
||||
|
||||
### 情感
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你开心吗 | `GEN:109,70,71|15` | 我很开心你好 |
|
||||
| 你伤心吗 | `GEN:109,126,127|15` | 我不伤心 |
|
||||
|
||||
### 能力
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你会什么 | `GEN:109,78,59|15` | 我会说话 |
|
||||
|
||||
### 闲聊
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 你在做什么 | `GEN:109,60,61|15` | 我在看猫 |
|
||||
| 今天好吗 | `GEN:140,60,36,37|15` | 今天很好 |
|
||||
|
||||
### 动物推理
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 什么会飞 | `GEN:39,40,78,15|15` | 鸟会飞 |
|
||||
| 什么会跑 | `GEN:39,40,78,14|15` | 猫会跑 |
|
||||
| 什么会游 | `GEN:39,40,78,16|15` | 鱼会游 |
|
||||
| 什么吃草 | `GEN:39,40,78,18|15` | 牛吃草 |
|
||||
|
||||
### 事实
|
||||
|
||||
| 中文 | 命令 | 回复 |
|
||||
|------|------|------|
|
||||
| 天是什么颜色 | `GEN:54,35,38,39,40|15` | 天是蓝的 |
|
||||
| 猫吃什么 | `GEN:43,35,18|15` | 猫吃鱼 |
|
||||
| 猫是什么 | `GEN:43,35,38|15` | 猫是动物 |
|
||||
|
||||
## 五、使用 Python 对话工具
|
||||
|
||||
如果你不想手动输入 ID，可以使用对话工具：
|
||||
|
||||
```bash
|
||||
# 安装依赖
|
||||
pip install pyserial
|
||||
|
||||
# 运行对话工具（自动查找 micro:bit 串口）
|
||||
python chat_moe.py
|
||||
```
|
||||
|
||||
对话工具会自动：
|
||||
1. 查找 micro:bit 的 USB 串口
|
||||
2. 显示常用短语菜单
|
||||
3. 你输入序号或文字
|
||||
4. 自动发送并翻译回复
|
||||
|
||||
## 六、在电脑上训练自己的模型
|
||||
|
||||
```bash
|
||||
# 安装依赖
|
||||
pip install torch matplotlib
|
||||
|
||||
# 训练
|
||||
python train_bitflow_moe.py
|
||||
|
||||
# 训练完成后会自动：
|
||||
# 1. 保存 PyTorch 权重 (bitflow_moe.pt)
|
||||
# 2. 导出 int8 量化权重 (bitflow_moe_weights.bin)
|
||||
# 3. 测试推理效果
|
||||
# 4. 绘制训练过程图 (training_analysis.png)
|
||||
```
|
||||
|
||||
## 七、常见问题
|
||||
|
||||
### Q1: 为什么回复是一串数字？
|
||||
|
||||
BitFlow MoE 输出的是**字符 ID**（0-148），不是中文。这是为了在 micro:bit 上节省内存——字符 ID 只需要 1 字节，而中文字符需要多字节。
|
||||
|
||||
你可以通过 `chat_moe.py` 自动翻译，或参考上方 ID 表手动解码。
|
||||
|
||||
### Q2: 模型回答不总是对的？
|
||||
|
||||
是的。这个模型是**微型模型**，学习的数据量很小（290K tokens），词表只有 149 字符。它更像是一个"概念演示"，证明在 micro:bit 上可以做语言建模。
|
||||
|
||||
### Q3: 可以扩展词表吗？
|
||||
|
||||
可以。修改 `VOCAB_CHARS` 字符串即可扩展词表，但需要：
|
||||
1. 增加 DIM 和 FFN_DIM（参数量会增大）
|
||||
2. 可能需要更大的 micro:bit（如 ESP32）
|
||||
3. 重新训练
|
||||
|
||||
### Q4: 可以用在其他单片机上吗？
|
||||
|
||||
可以！本项目的推理代码是纯 Python/MicroPython，理论上可以移植到任何支持 MicroPython 的 MCU。我们提供了内存分析：
|
||||
|
||||
| 平台 | RAM | 是否可用 |
|
||||
|------|-----|----------|
|
||||
| micro:bit v2 | 256KB (可用 ~108KB) | ✅ 刚好够用 |
|
||||
| STM32F4 | 192KB | ✅ 完全够用 |
|
||||
| ESP32 | 520KB | ✅ 绰绰有余 |
|
||||
| Arduino Nano | 2KB | ❌ 远远不够 |
|
||||
|
||||
### Q5: 推理速度很慢？
|
||||
|
||||
micro:bit 的 CPU 是 64MHz，每个字符生成需要 200-500ms。这是硬件限制，无法通过软件优化大幅改善。
|
||||
|
||||
如果你需要更快的推理，可以考虑：
|
||||
- 降低 DIM（32 → 24）
|
||||
- 减少专家数量（3 → 1）
|
||||
- 换用更强的 MCU（如 ESP32-S3）
|
||||
|
||||
## 八、技术参考
|
||||
|
||||
- [架构设计文档](architecture.md) — 详细的架构分析
|
||||
- [训练过程图](training_analysis.png) — 10,000 轮训练可视化
|
||||
- [训练代码](train_bitflow_moe.py) — 完整的训练脚本
|
||||
- [推理代码](bitflow_moe.py) — micro:bit 推理实现
|
||||
@@ -0,0 +1,775 @@
|
||||
"""
|
||||
BitFlow MoE v4: 面向 micro:bit 的混合专家微型中文对话模型
|
||||
架构: 门控线性注意力 (GLA) + 混合专家 (MoE) + int8 量化
|
||||
|
||||
v4 改进:
|
||||
- 3个专家: 事实记忆(E0) + 逻辑推理(E1) + 基础问答(E2)
|
||||
- 增大参数量: DIM=40, FFN=80 (~54K params, 更聪明)
|
||||
- 扩展词表: +你好吗谢谢再见早晚安等基础对话字符
|
||||
- 基础问答专家: 200+条自然问答 (问候/礼貌/自我介绍/喜好/情感/能力/闲聊/天气/动物/颜色/数字)
|
||||
"""
|
||||
import math
|
||||
import random
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
# ---------- 配置 ----------
|
||||
VOCAB_CHARS = (
|
||||
"猫狗鸟鱼猪牛马鸡虫羊兔鸭鹅" # 动物
|
||||
"坐跑飞游看吃睡走听来叫爬跳唱画读做" # 动作
|
||||
"在上里到" # 位置/方向
|
||||
"的是好了" # 助词/语气
|
||||
"吗什么怎哪" # 疑问
|
||||
"你我他她们它" # 代词
|
||||
"蓝绿红黄白" # 颜色
|
||||
"天地水火花草树木日月星云春夏秋冬" # 自然
|
||||
"大小多高远快" # 形容词
|
||||
"有没会不" # 否定/存在/能力
|
||||
"人头脚手眼腿" # 身体
|
||||
"垫肉书子" # 物品
|
||||
"一三只两" # 数字/量词
|
||||
"热冷落开" # 状态
|
||||
"自然动植物质色样颜得都" # 补充
|
||||
"你好吗谢谢再见早晚安静" # 基础问答 (新增)
|
||||
"很棒爱喜欢乐伤心气" # 情感 (新增)
|
||||
"名字叫谁和问答应" # 对话用语 (新增)
|
||||
"聊天话今天说" # 补充对话字符 (修复)
|
||||
"客用" # 不客气/不用谢 (修复)
|
||||
"bit" # 名字 (修复)
|
||||
"\n" # 换行
|
||||
)
|
||||
VOCAB_SIZE = len(VOCAB_CHARS)
|
||||
DIM = 40 # 增大: 32 -> 40
|
||||
N_LAYER = 2
|
||||
CONTEXT = 48
|
||||
FFN_DIM = 80 # 增大: 64 -> 80
|
||||
NUM_EXPERTS = 3 # 3个专家
|
||||
ROUTER_DIM = 32 # 增大: 24 -> 32
|
||||
EPOCHS = 10000 # 增多: 8000 -> 10000
|
||||
BATCH_SIZE = 32
|
||||
LR = 3e-3
|
||||
SEED = 1337
|
||||
|
||||
QAT_START = 8000
|
||||
ROUTER_WEIGHT = 0.5
|
||||
LB_WEIGHT = 0.01
|
||||
|
||||
random.seed(SEED)
|
||||
torch.manual_seed(SEED)
|
||||
|
||||
CH2I = {c: i for i, c in enumerate(VOCAB_CHARS)}
|
||||
I2CH = {i: c for i, c in enumerate(VOCAB_CHARS)}
|
||||
|
||||
# ---------- 训练数据 ----------
|
||||
|
||||
BASE = [
|
||||
"猫坐在垫子上", "狗坐在木头上", "鸟坐在树上", "鱼在水里游",
|
||||
"羊在吃草", "兔在跑", "鸭在水里游", "鹅在水里游",
|
||||
"猫在跑", "狗在跑", "鸟在飞", "鱼在游",
|
||||
"马在跑", "牛在走", "猪在睡", "鸡在走",
|
||||
"虫在树上", "羊在走", "兔在跳", "鸭在走", "鹅在叫",
|
||||
"天是蓝的", "水是绿的", "火是红的", "花是黄的",
|
||||
"草是绿的", "日是红的", "月是黄的", "星是好的", "云是白的",
|
||||
"春天是好的", "夏天是热的", "秋天是好的", "冬天是冷的",
|
||||
"猫看狗", "狗看猫", "鸟看鱼", "猫吃鱼", "牛吃草",
|
||||
"鸟吃虫", "羊吃草", "兔吃草", "鸭吃虫", "鹅吃草",
|
||||
"猫在吃鱼", "狗在跑走", "鸟在唱", "虫在爬", "兔在跳",
|
||||
"鸭在叫", "鹅在游",
|
||||
"马是大的", "虫是小的", "马跑得快", "鸟飞得高",
|
||||
"鱼游得快", "猫跳得高", "狗跑得远",
|
||||
"猫有腿", "鸟有脚", "鱼没有腿", "人有手", "人有眼",
|
||||
"人有脚", "猫有眼",
|
||||
"春天花开了", "夏天日大了", "秋天花落了", "冬天水冷了",
|
||||
]
|
||||
|
||||
# Expert-0: 事实与记忆类问答
|
||||
FACT_QA = [
|
||||
"猫是什么\n猫是动物", "狗是什么\n狗是动物", "鸟是什么\n鸟是动物",
|
||||
"鱼是什么\n鱼是动物", "马是什么\n马是动物", "牛是什么\n牛是动物",
|
||||
"猪是什么\n猪是动物", "鸡是什么\n鸡是动物", "虫是什么\n虫是动物",
|
||||
"羊是什么\n羊是动物", "兔是什么\n兔是动物", "鸭是什么\n鸭是动物",
|
||||
"鹅是什么\n鹅是动物",
|
||||
"花是什么\n花是植物", "草是什么\n草是植物", "树是什么\n树是植物",
|
||||
"水是什么\n水是物质", "火是什么\n火是物质", "天是什么\n天是自然",
|
||||
"地是什么\n地是自然", "日是什么\n日是星", "月是什么\n月是星",
|
||||
"云是什么\n云是水", "人是什么\n人是动物",
|
||||
"天是什么颜色\n天是蓝的", "水是什么颜色\n水是绿的",
|
||||
"火是什么颜色\n火是红的", "草是什么颜色\n草是绿的",
|
||||
"花是什么颜色\n花是黄的", "日是什么颜色\n日是红的",
|
||||
"月是什么颜色\n月是黄的", "云是什么颜色\n云是白的",
|
||||
"猫是动物吗\n猫是动物", "狗是动物吗\n狗是动物",
|
||||
"鱼是动物吗\n鱼是动物", "花是植物吗\n花是植物",
|
||||
"树是植物吗\n树是植物",
|
||||
"天是蓝的吗\n天是蓝的", "水是绿的吗\n水是绿的",
|
||||
"火是红的吗\n火是红的", "云是白的吗\n云是白的",
|
||||
"猫在哪里\n猫在垫子上", "狗在哪里\n狗在木头上",
|
||||
"鸟在哪里\n鸟在树上", "鱼在哪里\n鱼在水里",
|
||||
"羊在哪里\n羊在草地上", "兔在哪里\n兔在草地上",
|
||||
"虫在哪里\n虫在树上",
|
||||
"猫吃什么\n猫吃鱼", "狗吃什么\n狗吃肉", "鸟吃什么\n鸟吃虫",
|
||||
"牛吃什么\n牛吃草", "鱼吃什么\n鱼吃虫", "羊吃什么\n羊吃草",
|
||||
"兔吃什么\n兔吃草", "鸭吃什么\n鸭吃虫", "鹅吃什么\n鹅吃草",
|
||||
"春天是什么\n春天是好的", "夏天是什么\n夏天是热的",
|
||||
"秋天是什么\n秋天是好的", "冬天是什么\n冬天是冷的",
|
||||
"春天来了\n花开了", "夏天来了\n日大了",
|
||||
"秋天来了\n花落了", "冬天来了\n水冷了",
|
||||
"你在看什么\n我在看猫", "你在看什么\n我在看狗",
|
||||
"你在看什么\n我在看鸟", "你在看什么\n我在看鱼",
|
||||
"你在做什么\n我在看花", "你在做什么\n我在画猫",
|
||||
"你在做什么\n我在读书", "他在做什么\n他在听鸟叫",
|
||||
"她是什么\n她是花", "他是什么\n他是猫", "他是什么\n他是狗",
|
||||
]
|
||||
|
||||
# Expert-1: 逻辑与推理类问答
|
||||
REASONING_QA = [
|
||||
"猫是动物\n动物会跑\n猫会跑",
|
||||
"狗是动物\n动物会跑\n狗会跑",
|
||||
"鸟是动物\n动物会飞\n鸟会飞",
|
||||
"鱼是动物\n动物会游\n鱼会游",
|
||||
"马是动物\n动物会跑\n马会跑",
|
||||
"牛是动物\n动物会走\n牛会走",
|
||||
"猪是动物\n动物会睡\n猪会睡",
|
||||
"羊是动物\n动物会走\n羊会走",
|
||||
"兔是动物\n动物会跳\n兔会跳",
|
||||
"鸭是动物\n动物会游\n鸭会游",
|
||||
"鹅是动物\n动物会叫\n鹅会叫",
|
||||
"动物有腿\n猫是动物\n猫有腿",
|
||||
"动物有腿\n狗是动物\n狗有腿",
|
||||
"动物有腿\n马是动物\n马有腿",
|
||||
"动物有腿\n牛是动物\n牛有腿",
|
||||
"动物有眼\n猫有眼",
|
||||
"鱼是动物\n鱼没有腿",
|
||||
"什么会跑\n猫会跑",
|
||||
"什么会跑\n狗会跑",
|
||||
"什么会飞\n鸟会飞",
|
||||
"什么会游\n鱼会游",
|
||||
"什么会游\n鸭会游",
|
||||
"什么会跳\n兔会跳",
|
||||
"什么会叫\n鹅会叫",
|
||||
"什么会睡\n猪会睡",
|
||||
"什么会走\n牛会走",
|
||||
"什么会走\n羊会走",
|
||||
"猫有腿\n狗有腿\n它们都有腿",
|
||||
"猫会跑\n狗会跑\n它们都会跑",
|
||||
"鸟有脚\n鸟会飞",
|
||||
"鱼没有腿\n鱼会游",
|
||||
"猫吃什么\n猫吃鱼",
|
||||
"什么吃鱼\n猫吃鱼",
|
||||
"什么吃草\n牛吃草",
|
||||
"什么吃草\n羊吃草",
|
||||
"什么吃虫\n鸟吃虫",
|
||||
"什么吃虫\n鸭吃虫",
|
||||
"猫是小的\n虫是小的",
|
||||
"马是大的\n马跑得快",
|
||||
"鸟飞得高\n鱼游得快",
|
||||
"猫跳得高\n狗跑得远",
|
||||
]
|
||||
|
||||
# Expert-2: 基础问答/闲聊 (大幅扩展 v4: 200+条)
|
||||
CHAT_QA = [
|
||||
# === 问候类 (30条) ===
|
||||
"你好\n你好",
|
||||
"你好\n你好你好",
|
||||
"你好bit\n你好",
|
||||
"你好猫\n你好猫",
|
||||
"你好狗\n你好狗",
|
||||
"你好花\n你好花",
|
||||
"你好天\n你好天",
|
||||
"你好水\n你好水",
|
||||
"你好火\n你好火",
|
||||
"你好你好\n你好",
|
||||
"bit你好\n你好bit",
|
||||
"猫你好\n你好",
|
||||
"你好吗\n我很好",
|
||||
"你好吗\n我很好谢谢",
|
||||
"你好吗\n我很好你好",
|
||||
"你好吗bit\n我很好",
|
||||
"bit你好吗\n我很好",
|
||||
"你好了吗\n我好了",
|
||||
"你好吗\n我很好和开心",
|
||||
"早\n早",
|
||||
"早\n早早",
|
||||
"早安\n早安",
|
||||
"早安bit\n早安",
|
||||
"bit早安\n早安",
|
||||
"晚安\n晚安",
|
||||
"晚安bit\n晚安",
|
||||
"再见\n再见",
|
||||
"再见\n再见再见",
|
||||
"再见bit\n再见",
|
||||
"bit再见\n再见",
|
||||
|
||||
# === 礼貌类 (18条) ===
|
||||
"谢谢\n不客气",
|
||||
"谢谢\n不用谢",
|
||||
"谢谢\n不客气你好",
|
||||
"谢谢bit\n不客气",
|
||||
"谢谢猫\n不客气",
|
||||
"谢谢你好\n不客气",
|
||||
"不客气\n谢谢",
|
||||
"不用谢\n谢谢",
|
||||
"你好棒\n谢谢",
|
||||
"你好棒\n谢谢你好",
|
||||
"你好棒\n谢谢你",
|
||||
"你好棒bit\n谢谢",
|
||||
"bit好棒\n谢谢",
|
||||
"猫好棒\n猫很好",
|
||||
"狗好棒\n狗很好",
|
||||
"谢谢你\n不客气",
|
||||
"你好棒\n谢谢你好bit",
|
||||
"谢谢和再见\n不客气",
|
||||
|
||||
# === 自我介绍 (20条) ===
|
||||
"你叫什么名字\n我叫bit",
|
||||
"你叫什么\n我叫bit",
|
||||
"你的名字\n我叫bit",
|
||||
"你的名字是什么\n我叫bit",
|
||||
"你是谁\n我是bit",
|
||||
"你是谁\n我是猫",
|
||||
"你是谁\n我是动物",
|
||||
"你是bit吗\n我是bit",
|
||||
"你是猫吗\n我不是猫",
|
||||
"你是狗吗\n我不是狗",
|
||||
"你是动物吗\n我不是动物",
|
||||
"你是人吗\n我不是人",
|
||||
"你是花吗\n我不是花",
|
||||
"bit是谁\nbit是我",
|
||||
"猫是谁\n猫是动物",
|
||||
"狗是谁\n狗是动物",
|
||||
"你叫什么\n我叫bit你好",
|
||||
"我叫猫\n你好猫",
|
||||
"我叫狗\n你好狗",
|
||||
"我叫bit\n你好bit",
|
||||
|
||||
# === 喜好类 (38条) ===
|
||||
"你喜欢什么\n我喜欢猫",
|
||||
"你喜欢什么\n我喜欢狗",
|
||||
"你喜欢什么\n我喜欢鸟",
|
||||
"你喜欢什么\n我喜欢鱼",
|
||||
"你喜欢什么\n我喜欢花",
|
||||
"你喜欢什么\n我喜欢树",
|
||||
"你喜欢什么\n我喜欢草",
|
||||
"你喜欢什么\n我喜欢天",
|
||||
"你喜欢什么\n我喜欢水",
|
||||
"你喜欢什么\n我喜欢火",
|
||||
"你喜欢什么\n我喜欢日",
|
||||
"你喜欢什么\n我喜欢月",
|
||||
"你喜欢什么\n我喜欢星",
|
||||
"你喜欢什么\n我喜欢云",
|
||||
"你喜欢什么\n我喜欢春天",
|
||||
"你喜欢什么\n我喜欢夏天",
|
||||
"你喜欢什么\n我喜欢秋天",
|
||||
"你喜欢什么\n我喜欢冬天",
|
||||
"你喜欢什么\n我喜欢读书",
|
||||
"你喜欢什么\n我喜欢画画",
|
||||
"你喜欢什么\n我喜欢聊天",
|
||||
"你喜欢猫吗\n我很喜欢猫",
|
||||
"你喜欢狗吗\n我很喜欢狗",
|
||||
"你喜欢鸟吗\n我很喜欢鸟",
|
||||
"你喜欢鱼吗\n我很喜欢鱼",
|
||||
"你喜欢花吗\n我很喜欢花",
|
||||
"你喜欢树吗\n我很喜欢树",
|
||||
"你喜欢草吗\n我很喜欢草",
|
||||
"你喜欢水吗\n我很喜欢水",
|
||||
"你喜欢火吗\n我很喜欢火",
|
||||
"你喜欢天吗\n我很喜欢天",
|
||||
"你喜欢春天吗\n我很喜欢春天",
|
||||
"你喜欢夏天吗\n我很喜欢夏天",
|
||||
"你喜欢秋天吗\n我很喜欢秋天",
|
||||
"你喜欢冬天吗\n我很喜欢冬天",
|
||||
"你喜欢读书吗\n我很喜欢读书",
|
||||
"你喜欢画画吗\n我很喜欢画画",
|
||||
"你喜欢聊天吗\n我很喜欢聊天",
|
||||
|
||||
# === 情感类 (22条) ===
|
||||
"你开心吗\n我很开心",
|
||||
"你开心吗\n我很开心谢谢",
|
||||
"你伤心吗\n我不伤心",
|
||||
"你伤心吗\n我不伤心谢谢",
|
||||
"你乐吗\n我很乐",
|
||||
"你气吗\n我不气",
|
||||
"你好开心\n我很开心",
|
||||
"你好伤心\n我不伤心",
|
||||
"我很开心\n你好",
|
||||
"我很伤心\n你好",
|
||||
"我很喜欢\n你好",
|
||||
"我很爱你\n谢谢",
|
||||
"你爱我吗\n我很爱你",
|
||||
"你爱猫吗\n我很爱猫",
|
||||
"你爱花吗\n我很爱花",
|
||||
"猫开心吗\n猫很开心",
|
||||
"狗开心吗\n狗很开心",
|
||||
"鸟开心吗\n鸟很开心",
|
||||
"鱼开心吗\n鱼很开心",
|
||||
"bit开心吗\nbit很开心",
|
||||
"你开心吗\n我很开心你好",
|
||||
"你好开心吗\n我很开心",
|
||||
|
||||
# === 能力问答 (23条) ===
|
||||
"你会什么\n我会说话",
|
||||
"你会什么\n我会画画",
|
||||
"你会什么\n我会读书",
|
||||
"你会什么\n我会聊天",
|
||||
"你会什么\n我会看花",
|
||||
"你会什么\n我会看猫",
|
||||
"你会什么\n我会看狗",
|
||||
"你会说话吗\n我会说话",
|
||||
"你会画画吗\n我会画画",
|
||||
"你会读书吗\n我会读书",
|
||||
"你会聊天吗\n我会聊天",
|
||||
"你会跑吗\n我不会跑",
|
||||
"你会飞吗\n我不会飞",
|
||||
"你会游吗\n我不会游",
|
||||
"你会跳吗\n我不会跳",
|
||||
"你会唱吗\n我不会唱",
|
||||
"你会画猫吗\n我会画猫",
|
||||
"你会画狗吗\n我会画狗",
|
||||
"你会画花吗\n我会画花",
|
||||
"bit会什么\nbit会说话",
|
||||
"bit会说话吗\nbit会说话",
|
||||
"bit会聊天吗\nbit会聊天",
|
||||
"你会什么\n我会说话和聊天",
|
||||
|
||||
# === 在做什么 (25条) ===
|
||||
"你在做什么\n我在和你说话",
|
||||
"你在做什么\n我在和你聊天",
|
||||
"你在做什么\n我在看猫",
|
||||
"你在做什么\n我在看狗",
|
||||
"你在做什么\n我在看花",
|
||||
"你在做什么\n我在读书",
|
||||
"你在做什么\n我在画画",
|
||||
"你在做什么\n我在画猫",
|
||||
"你在做什么\n我在画花",
|
||||
"你在做什么\n我在听鸟叫",
|
||||
"你在做什么\n我在看鸟",
|
||||
"你在做什么\n我在看鱼",
|
||||
"你在做什么\n我在看天",
|
||||
"你在做什么\n我在看水",
|
||||
"谁在说话\n我在说话",
|
||||
"谁在说话\n你在说话",
|
||||
"谁在说话\nbit在说话",
|
||||
"谁在聊天\n我在聊天",
|
||||
"谁在聊天\n你在聊天",
|
||||
"谁在聊天\nbit在聊天",
|
||||
"你在聊天吗\n我在聊天",
|
||||
"你在说话吗\n我在说话",
|
||||
"bit在做什么\nbit在说话",
|
||||
"bit在说话吗\nbit在说话",
|
||||
"bit在聊天吗\nbit在聊天",
|
||||
|
||||
# === 天气时间 (22条) ===
|
||||
"今天好吗\n今天很好",
|
||||
"天气好吗\n天气很好",
|
||||
"天好吗\n天很好",
|
||||
"花好吗\n花很好",
|
||||
"今天天好吗\n今天天很好",
|
||||
"今天热吗\n今天很热",
|
||||
"今天冷吗\n今天不冷",
|
||||
"天热吗\n天很热",
|
||||
"天冷吗\n天不冷",
|
||||
"夏天热吗\n夏天很热",
|
||||
"冬天冷吗\n冬天很冷",
|
||||
"春天好吗\n春天很好",
|
||||
"夏天好吗\n夏天很好",
|
||||
"秋天好吗\n秋天很好",
|
||||
"冬天好吗\n冬天很好",
|
||||
"今天天热吗\n今天天很热",
|
||||
"今天天冷吗\n今天天不冷",
|
||||
"早好吗\n早很好",
|
||||
"晚好吗\n晚很好",
|
||||
"早安好吗\n早安很好",
|
||||
"晚安好吗\n晚安很好",
|
||||
"今天天气好吗\n今天天气很好",
|
||||
|
||||
# === 动物闲聊 (19条) ===
|
||||
"猫好吗\n猫很好",
|
||||
"狗好吗\n狗很好",
|
||||
"鱼好吗\n鱼很好",
|
||||
"鸟好吗\n鸟很好",
|
||||
"虫好吗\n虫很好",
|
||||
"马好吗\n马很好",
|
||||
"牛好吗\n牛很好",
|
||||
"猪好吗\n猪很好",
|
||||
"羊好吗\n羊很好",
|
||||
"兔好吗\n兔很好",
|
||||
"鸭好吗\n鸭很好",
|
||||
"鹅好吗\n鹅很好",
|
||||
"猫好吗\n猫是好的",
|
||||
"狗好吗\n狗是好的",
|
||||
"花好吗\n花是好的",
|
||||
"天好吗\n天是好的",
|
||||
"水好吗\n水是好的",
|
||||
"猫好吗\n猫很好谢谢",
|
||||
"狗好吗\n狗很好谢谢",
|
||||
|
||||
# === 颜色喜好 (18条) ===
|
||||
"你喜欢什么颜色\n我喜欢蓝的",
|
||||
"你喜欢什么颜色\n我喜欢红的",
|
||||
"你喜欢什么颜色\n我喜欢绿的",
|
||||
"你喜欢什么颜色\n我喜欢黄的",
|
||||
"你喜欢什么颜色\n我喜欢白的",
|
||||
"你喜欢蓝的吗\n我很喜欢蓝的",
|
||||
"你喜欢红的吗\n我很喜欢红的",
|
||||
"你喜欢绿的吗\n我很喜欢绿的",
|
||||
"你喜欢黄的吗\n我很喜欢黄的",
|
||||
"你喜欢白的吗\n我很喜欢白的",
|
||||
"天是蓝的吗\n天是蓝的",
|
||||
"水是绿的吗\n水是绿的",
|
||||
"火是红的吗\n火是红的",
|
||||
"云是白的吗\n云是白的",
|
||||
"花是黄的吗\n花是黄的",
|
||||
"草是绿的吗\n草是绿的",
|
||||
"日是红的吗\n日是红的",
|
||||
"月是黄的吗\n月是黄的",
|
||||
|
||||
# === 数字/量词 (11条) ===
|
||||
"一只猫\n一只猫",
|
||||
"一只狗\n一只狗",
|
||||
"一只鸟\n一只鸟",
|
||||
"一只鱼\n一只鱼",
|
||||
"两只猫\n两只猫",
|
||||
"两只狗\n两只狗",
|
||||
"三只鸟\n三只鸟",
|
||||
"三只猫\n三只猫",
|
||||
"一只猫好吗\n猫很好",
|
||||
"一只狗好吗\n狗很好",
|
||||
"三只鸟好吗\n鸟很好",
|
||||
|
||||
# === 组合对话 (12条) ===
|
||||
"你好和再见\n你好再见",
|
||||
"你好猫和狗\n你好猫你好狗",
|
||||
"你喜欢猫和狗吗\n我很喜欢猫和狗",
|
||||
"你喜欢花和树吗\n我很喜欢花和树",
|
||||
"你喜欢天和水吗\n我很喜欢天和水",
|
||||
"猫和狗好吗\n猫和狗都很好",
|
||||
"花和草好吗\n花和草都很好",
|
||||
"天和水好吗\n天和水都很好",
|
||||
"春天和秋天好吗\n春天和秋天都很好",
|
||||
"你和bit好吗\n我和bit都很好",
|
||||
"你好和谢谢\n你好谢谢",
|
||||
"你好再见谢谢\n你好再见不客气",
|
||||
]
|
||||
|
||||
# 分别构建数据
|
||||
FACT_TEXT = ("\n".join(BASE) + "\n") * 20 + "\n".join(FACT_QA) * 40
|
||||
REASON_TEXT = "\n".join(REASONING_QA) * 80
|
||||
CHAT_TEXT = "\n".join(CHAT_QA) * 80
|
||||
|
||||
FACT_DATA = torch.tensor([CH2I[c] for c in FACT_TEXT if c in CH2I], dtype=torch.long)
|
||||
REASON_DATA = torch.tensor([CH2I[c] for c in REASON_TEXT if c in CH2I], dtype=torch.long)
|
||||
CHAT_DATA = torch.tensor([CH2I[c] for c in CHAT_TEXT if c in CH2I], dtype=torch.long)
|
||||
DATA = torch.cat([FACT_DATA, REASON_DATA, CHAT_DATA])
|
||||
|
||||
print(f"[data] fact={FACT_DATA.numel()} reason={REASON_DATA.numel()} chat={CHAT_DATA.numel()} total={DATA.numel()}")
|
||||
print(f"[data] base={len(BASE)} fact_qa={len(FACT_QA)} reasoning_qa={len(REASONING_QA)} chat_qa={len(CHAT_QA)}")
|
||||
|
||||
all_chars = set("".join(BASE) + "\n".join(FACT_QA) + "\n".join(REASONING_QA) + "\n".join(CHAT_QA))
|
||||
missing = [c for c in VOCAB_CHARS if c not in all_chars and c != '\n']
|
||||
unused = [c for c in all_chars if c not in CH2I]
|
||||
if missing:
|
||||
print(f"[warn] vocab chars not in training data: {missing}")
|
||||
if unused:
|
||||
print(f"[warn] training chars not in vocab (will be filtered): {unused}")
|
||||
print(f"[vocab] size={VOCAB_SIZE}")
|
||||
|
||||
|
||||
# ---------- GLA ----------
|
||||
class GLA(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.q = nn.Linear(dim, dim, bias=False)
|
||||
self.k = nn.Linear(dim, dim, bias=False)
|
||||
self.v = nn.Linear(dim, dim, bias=False)
|
||||
self.g = nn.Linear(dim, dim, bias=False)
|
||||
self.o = nn.Linear(dim, dim, bias=False)
|
||||
self.log_decay = nn.Parameter(torch.zeros(dim))
|
||||
|
||||
def forward(self, x):
|
||||
B, T, D = x.shape
|
||||
q = F.elu(self.q(x)) + 1
|
||||
k = F.elu(self.k(x)) + 1
|
||||
v = self.v(x)
|
||||
gate = torch.sigmoid(self.g(x))
|
||||
decay = torch.sigmoid(self.log_decay)
|
||||
|
||||
S = torch.zeros(B, D, D, device=x.device)
|
||||
z = torch.zeros(B, D, device=x.device)
|
||||
outputs = []
|
||||
for t in range(T):
|
||||
g_t = gate[:, t] * decay
|
||||
S = g_t.unsqueeze(-1) * S + k[:, t].unsqueeze(-1) * v[:, t].unsqueeze(1)
|
||||
z = g_t * z + k[:, t]
|
||||
num = torch.bmm(q[:, t].unsqueeze(1), S).squeeze(1)
|
||||
den = (q[:, t] * z).sum(-1, keepdim=True) + 1e-6
|
||||
y = num / den
|
||||
outputs.append(y)
|
||||
y = torch.stack(outputs, 1)
|
||||
return self.o(y)
|
||||
|
||||
|
||||
class BitFlowBlock(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.norm1 = nn.LayerNorm(dim)
|
||||
self.attn = GLA(dim)
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
self.ff1 = nn.Linear(dim, FFN_DIM, bias=False)
|
||||
self.ff2 = nn.Linear(FFN_DIM, dim, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = x + self.attn(self.norm1(x))
|
||||
h = self.norm2(x)
|
||||
h = self.ff2(F.gelu(self.ff1(h), approximate='tanh'))
|
||||
x = x + h
|
||||
return x
|
||||
|
||||
|
||||
# ---------- MoE ----------
|
||||
class Router(nn.Module):
|
||||
def __init__(self, dim, num_experts, router_dim=ROUTER_DIM):
|
||||
super().__init__()
|
||||
self.fc1 = nn.Linear(dim, router_dim)
|
||||
self.fc2 = nn.Linear(router_dim, num_experts)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.mean(dim=1)
|
||||
x = F.relu(self.fc1(x))
|
||||
return self.fc2(x)
|
||||
|
||||
|
||||
class BitFlowMoE(nn.Module):
|
||||
def __init__(self, num_experts=NUM_EXPERTS):
|
||||
super().__init__()
|
||||
self.tok = nn.Embedding(VOCAB_SIZE, DIM)
|
||||
self.pos = nn.Embedding(CONTEXT, DIM)
|
||||
self.experts = nn.ModuleList([
|
||||
BitFlowBlock(DIM) for _ in range(num_experts)
|
||||
])
|
||||
self.router = Router(DIM, num_experts)
|
||||
self.norm_f = nn.LayerNorm(DIM)
|
||||
self.head = nn.Linear(DIM, VOCAB_SIZE, bias=False)
|
||||
self.num_experts = num_experts
|
||||
|
||||
n = sum(p.numel() for p in self.parameters())
|
||||
print(f"[model] BitFlow MoE v3 params={n} ({n/1024:.1f}K) experts={num_experts} D={DIM} FFN={FFN_DIM}")
|
||||
|
||||
def forward(self, idx, labels=None):
|
||||
B, T = idx.shape
|
||||
pos = torch.arange(T)
|
||||
x = self.tok(idx) + self.pos(pos)[None]
|
||||
gate_logits = self.router(x)
|
||||
|
||||
if labels is not None:
|
||||
outputs = torch.zeros_like(x)
|
||||
for i in range(self.num_experts):
|
||||
mask = (labels == i)
|
||||
if mask.any():
|
||||
idx_mask = torch.where(mask)[0]
|
||||
for _ in range(N_LAYER):
|
||||
x[idx_mask] = self.experts[i](x[idx_mask])
|
||||
outputs[idx_mask] = x[idx_mask]
|
||||
x = outputs
|
||||
else:
|
||||
expert_idx = gate_logits.argmax(dim=-1)
|
||||
outputs = torch.zeros_like(x)
|
||||
for i in range(self.num_experts):
|
||||
mask = (expert_idx == i)
|
||||
if mask.any():
|
||||
idx_mask = torch.where(mask)[0]
|
||||
for _ in range(N_LAYER):
|
||||
x[idx_mask] = self.experts[i](x[idx_mask])
|
||||
outputs[idx_mask] = x[idx_mask]
|
||||
x = outputs
|
||||
|
||||
x = self.norm_f(x)
|
||||
return self.head(x), gate_logits
|
||||
|
||||
|
||||
# ---------- 数据采样 ----------
|
||||
DATASETS = [FACT_DATA, REASON_DATA, CHAT_DATA]
|
||||
|
||||
def get_batch(bs=BATCH_SIZE):
|
||||
xs, ys, labels = [], [], []
|
||||
for _ in range(bs):
|
||||
# 均匀采样 3 个专家
|
||||
r = random.random()
|
||||
if r < 0.40:
|
||||
data, label = FACT_DATA, 0
|
||||
elif r < 0.70:
|
||||
data, label = REASON_DATA, 1
|
||||
else:
|
||||
data, label = CHAT_DATA, 2
|
||||
i = random.randint(0, data.numel() - CONTEXT - 2)
|
||||
xs.append(data[i:i+CONTEXT])
|
||||
ys.append(data[i+1:i+CONTEXT+1])
|
||||
labels.append(label)
|
||||
return torch.stack(xs), torch.stack(ys), torch.tensor(labels, dtype=torch.long)
|
||||
|
||||
|
||||
def apply_qat(model):
|
||||
with torch.no_grad():
|
||||
for name, p in model.named_parameters():
|
||||
if p.requires_grad:
|
||||
scale = p.abs().max() / 127.0 + 1e-8
|
||||
q = torch.clamp((p / scale).round(), -128, 127) * scale
|
||||
p.copy_(q)
|
||||
|
||||
|
||||
# ---------- 训练 ----------
|
||||
def main():
|
||||
model = BitFlowMoE(num_experts=NUM_EXPERTS)
|
||||
opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=0.01)
|
||||
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, EPOCHS)
|
||||
model.train()
|
||||
|
||||
best_loss = float('inf')
|
||||
|
||||
for ep in range(EPOCHS):
|
||||
if ep >= QAT_START:
|
||||
apply_qat(model)
|
||||
|
||||
x, y, labels = get_batch()
|
||||
logits, gate_logits = model(x, labels=labels)
|
||||
|
||||
pred_loss = F.cross_entropy(logits.reshape(-1, VOCAB_SIZE), y.reshape(-1))
|
||||
router_loss = F.cross_entropy(gate_logits, labels)
|
||||
gate_probs = F.softmax(gate_logits, dim=-1)
|
||||
avg_usage = gate_probs.mean(dim=0)
|
||||
balance_loss = NUM_EXPERTS * (avg_usage * avg_usage).sum()
|
||||
|
||||
loss = pred_loss + ROUTER_WEIGHT * router_loss + LB_WEIGHT * balance_loss
|
||||
|
||||
if torch.isnan(loss) or torch.isinf(loss):
|
||||
print(f"[warn] NaN/Inf at ep={ep}, skipping")
|
||||
opt.zero_grad()
|
||||
continue
|
||||
|
||||
opt.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
opt.step()
|
||||
sched.step()
|
||||
|
||||
if ep % 500 == 0 or ep == EPOCHS - 1:
|
||||
qat_tag = " [QAT]" if ep >= QAT_START else ""
|
||||
with torch.no_grad():
|
||||
pred_expert = gate_logits.argmax(dim=-1)
|
||||
router_acc = (pred_expert == labels).float().mean().item()
|
||||
print(f"[train] ep={ep:4d} loss={loss.item():.4f} "
|
||||
f"pred={pred_loss.item():.4f} router={router_loss.item():.4f} "
|
||||
f"acc={router_acc:.2f} bal={balance_loss.item():.4f}{qat_tag}")
|
||||
if loss.item() < best_loss:
|
||||
best_loss = loss.item()
|
||||
|
||||
model.eval()
|
||||
print(f"\n[best] loss={best_loss:.4f}")
|
||||
|
||||
test_model(model)
|
||||
torch.save(model.state_dict(), "bitflow_moe.pt")
|
||||
print("\n[save] bitflow_moe.pt")
|
||||
|
||||
try:
|
||||
export_int8(model)
|
||||
except Exception:
|
||||
import traceback
|
||||
print("[ERROR] export failed:")
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
def test_model(model):
|
||||
import traceback
|
||||
try:
|
||||
seeds = [
|
||||
# 基础问答 (Expert-2)
|
||||
"你好", "你好吗", "谢谢", "再见", "你叫什么名字",
|
||||
"你喜欢什么", "你喜欢猫吗", "你好棒", "你是谁",
|
||||
"你会什么", "你开心吗", "早安", "今天好吗",
|
||||
"你在做什么", "猫好吗", "你喜欢什么颜色",
|
||||
# 事实类 (Expert-0)
|
||||
"猫是什么", "猫在做什么", "猫吃什么", "天是什么颜色",
|
||||
# 推理类 (Expert-1)
|
||||
"什么会飞", "什么会跑", "什么会游", "什么吃草",
|
||||
]
|
||||
NL = CH2I['\n']
|
||||
for seed in seeds:
|
||||
ctx = [CH2I[c] for c in seed if c in CH2I]
|
||||
if not ctx:
|
||||
continue
|
||||
ctx.append(NL)
|
||||
seed_len = len(ctx)
|
||||
|
||||
with torch.no_grad():
|
||||
x = torch.tensor([ctx[-CONTEXT:]], dtype=torch.long)
|
||||
_, gate_logits = model(x, labels=None)
|
||||
expert_idx = gate_logits.argmax(dim=-1).item()
|
||||
gate_probs = F.softmax(gate_logits[0], dim=-1)
|
||||
|
||||
with torch.no_grad():
|
||||
for _ in range(20):
|
||||
x = torch.tensor([ctx[-CONTEXT:]], dtype=torch.long)
|
||||
logits, _ = model(x, labels=None)
|
||||
probs = F.softmax(logits[0, -1] / 0.3, dim=-1)
|
||||
nxt = torch.multinomial(probs, 1).item()
|
||||
ctx.append(nxt)
|
||||
gen_ids = ctx[seed_len:]
|
||||
gen = "".join(I2CH[i] for i in gen_ids)
|
||||
answer = gen.split("\n")[0] if "\n" in gen else gen
|
||||
|
||||
probs_str = " ".join(f"{gate_probs[i].item():.2f}" for i in range(NUM_EXPERTS))
|
||||
print(f" Q: {seed} [E{expert_idx} | {probs_str}]")
|
||||
print(f" A: {answer}")
|
||||
print()
|
||||
except Exception:
|
||||
print("[ERROR] generation failed:")
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
def export_int8(model):
|
||||
import struct
|
||||
state = model.state_dict()
|
||||
tensors = []
|
||||
names = []
|
||||
for name, p in state.items():
|
||||
if name.endswith('.log_decay'):
|
||||
val = torch.sigmoid(p).cpu()
|
||||
else:
|
||||
val = p.cpu()
|
||||
tensors.append(val)
|
||||
names.append(name)
|
||||
|
||||
quant_data = bytearray()
|
||||
scales = []
|
||||
lengths = []
|
||||
for t in tensors:
|
||||
t_flat = t.flatten()
|
||||
abs_max = t_flat.abs().max().item()
|
||||
if abs_max < 1e-8:
|
||||
scale = 1.0
|
||||
else:
|
||||
scale = abs_max / 127.0
|
||||
q = torch.clamp((t_flat / scale).round(), -128, 127).to(torch.int8)
|
||||
quant_data.extend(q.numpy().tobytes())
|
||||
scales.append(scale)
|
||||
lengths.append(len(q))
|
||||
|
||||
with open("bitflow_moe_weights.bin", "wb") as f:
|
||||
nt = len(tensors)
|
||||
f.write(struct.pack('<H', nt))
|
||||
f.write(struct.pack('<%dH' % nt, *lengths))
|
||||
f.write(struct.pack('<%df' % nt, *scales))
|
||||
f.write(bytes(quant_data))
|
||||
|
||||
total_bytes = len(quant_data)
|
||||
header_bytes = 2 + 2 * len(tensors) + 4 * len(tensors)
|
||||
print(f"\n[export] int8 weights: {nt} tensors, data={total_bytes}B ({total_bytes/1024:.1f}KB), "
|
||||
f"header={header_bytes}B, total={total_bytes+header_bytes}B ({(total_bytes+header_bytes)/1024:.1f}KB)")
|
||||
print(f" scales range: [{min(scales):.6f}, {max(scales):.6f}]")
|
||||
|
||||
print("\n[param layout]")
|
||||
for name, t in zip(names, tensors):
|
||||
print(f" {name:40s} shape={list(t.shape)} numel={t.numel()}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 207 KiB |
@@ -0,0 +1,306 @@
|
||||
"""
|
||||
PC端模拟器: 验证 bitflow_moe.py 的推理逻辑与 PyTorch 模型一致
|
||||
模拟 micro:bit 上的 int8 解量化 + GLA + MoE + 采样 全流程
|
||||
"""
|
||||
import struct, math, random, sys
|
||||
|
||||
# ========== 加载 int8 权重 ==========
|
||||
with open("bitflow_moe_weights.bin", "rb") as f:
|
||||
nt = struct.unpack('<H', f.read(2))[0]
|
||||
LEN = list(struct.unpack('<%dH' % nt, f.read(2 * nt)))
|
||||
SC = list(struct.unpack('<%df' % nt, f.read(4 * nt)))
|
||||
W = f.read()
|
||||
|
||||
OFF = []
|
||||
o = 0
|
||||
for l in LEN:
|
||||
OFF.append(o)
|
||||
o += l
|
||||
|
||||
# ========== 参数 ==========
|
||||
VS = 149; D = 40; CT = 48; FD = 80; NE = 3; NL = 2; RD = 32
|
||||
RB = 2 + NE * 12 # 38
|
||||
NB = RB + 4 # 42
|
||||
HI = NB + 2 # 44
|
||||
|
||||
# ========== 词表 ==========
|
||||
VOCAB_CHARS = (
|
||||
"猫狗鸟鱼猪牛马鸡虫羊兔鸭鹅"
|
||||
"坐跑飞游看吃睡走听来叫爬跳唱画读做"
|
||||
"在上里到"
|
||||
"的是好了"
|
||||
"吗什么怎哪"
|
||||
"你我他她们它"
|
||||
"蓝绿红黄白"
|
||||
"天地水火花草树木日月星云春夏秋冬"
|
||||
"大小多高远快"
|
||||
"有没会不"
|
||||
"人头脚手眼腿"
|
||||
"垫肉书子"
|
||||
"一三只两"
|
||||
"热冷落开"
|
||||
"自然动植物质色样颜得都"
|
||||
"你好吗谢谢再见早晚安静"
|
||||
"很棒爱喜欢乐伤心气"
|
||||
"名字叫谁和问答应"
|
||||
"聊天话今天说"
|
||||
"客用"
|
||||
"bit"
|
||||
"\n"
|
||||
)
|
||||
CH2I = {c: i for i, c in enumerate(VOCAB_CHARS)}
|
||||
I2CH = {i: c for i, c in enumerate(VOCAB_CHARS)}
|
||||
|
||||
# ========== 预解量化 ==========
|
||||
def dqS(i):
|
||||
s = SC[i]; o = OFF[i]; n = LEN[i]; r = [0.0] * n
|
||||
for j in range(n):
|
||||
v = W[o + j]
|
||||
if v >= 128: v -= 256
|
||||
r[j] = v * s
|
||||
return r
|
||||
|
||||
SMW = []
|
||||
for e in range(NE):
|
||||
b0 = 2 + e * 12
|
||||
SMW.append(dqS(b0)) # norm1.w
|
||||
SMW.append(dqS(b0 + 1)) # norm1.b
|
||||
SMW.append(dqS(b0 + 2)) # log_decay
|
||||
SMW.append(dqS(b0 + 8)) # norm2.w
|
||||
SMW.append(dqS(b0 + 9)) # norm2.b
|
||||
|
||||
rf1b = dqS(RB + 1)
|
||||
rf2b = dqS(RB + 3)
|
||||
nfw = dqS(NB)
|
||||
nfb = dqS(NB + 1)
|
||||
|
||||
# ========== 融合解量化+矩阵乘 ==========
|
||||
def dqmv(i, ni, no, x, out):
|
||||
s = SC[i]; o = OFF[i]
|
||||
for r in range(no):
|
||||
ss = 0.0; b = r * ni
|
||||
for c in range(ni):
|
||||
v = W[o + b + c]
|
||||
if v >= 128: v -= 256
|
||||
ss += v * s * x[c]
|
||||
out[r] = ss
|
||||
|
||||
def mvW(w, ni, no, x, out):
|
||||
for r in range(no):
|
||||
ss = 0.0; b = r * ni
|
||||
for c in range(ni):
|
||||
ss += w[b + c] * x[c]
|
||||
out[r] = ss
|
||||
|
||||
def ln(w, b, x, o):
|
||||
m = sum(x) / D
|
||||
v = sum((x[i] - m) ** 2 for i in range(D)) / D
|
||||
iv = 1.0 / math.sqrt(v + 1e-5)
|
||||
for i in range(D):
|
||||
o[i] = w[i] * (x[i] - m) * iv + b[i]
|
||||
|
||||
def e1(x):
|
||||
return x + 1.0 if x >= 0 else math.exp(x)
|
||||
|
||||
def sg(x):
|
||||
if x >= 0:
|
||||
e = math.exp(-x); return 1.0 / (1.0 + e)
|
||||
e = math.exp(x); return e / (1.0 + e)
|
||||
|
||||
def ge(x):
|
||||
c = 0.7978845608
|
||||
return x * sg(2.0 * c * (x + 0.044715 * x * x * x))
|
||||
|
||||
def sm(l, t=0.3):
|
||||
m = -1e9
|
||||
for i in range(VS):
|
||||
v = l[i] - bcnt[i] * 2.0
|
||||
if v > m: m = v
|
||||
s = 0.0
|
||||
for i in range(VS):
|
||||
v = l[i] - bcnt[i] * 2.0
|
||||
e = math.exp((v - m) / t)
|
||||
bpr[i] = e; s += e
|
||||
r = random.random() * s; c = 0.0
|
||||
for i in range(VS):
|
||||
c += bpr[i]
|
||||
if c >= r: return i
|
||||
return VS - 1
|
||||
|
||||
# ========== 状态 ==========
|
||||
S = [[0.0] * (D * D) for _ in range(NL)]
|
||||
z = [[0.0] * D for _ in range(NL)]
|
||||
bx = [0.0] * D; bh = [0.0] * D
|
||||
bq = [0.0] * D; bk = [0.0] * D
|
||||
bv = [0.0] * D; bgd = [0.0] * D
|
||||
bnum = [0.0] * D; by = [0.0] * D
|
||||
bout = [0.0] * D; bff = [0.0] * FD
|
||||
bpr = [0.0] * VS; bcnt = [0] * VS
|
||||
brh = [0.0] * RD; brl = [0.0] * NE
|
||||
blg = [0.0] * VS
|
||||
bsum = [0.0] * D; brx = [0.0] * D; bct = [0]
|
||||
|
||||
# ========== GLA ==========
|
||||
def gs(e, layer, h):
|
||||
b0 = 2 + e * 12; dec = SMW[e * 5 + 2]
|
||||
dqmv(b0 + 3, D, D, h, bq)
|
||||
dqmv(b0 + 4, D, D, h, bk)
|
||||
dqmv(b0 + 5, D, D, h, bv)
|
||||
dqmv(b0 + 6, D, D, h, bgd)
|
||||
for i in range(D):
|
||||
bq[i] = e1(bq[i]); bk[i] = e1(bk[i])
|
||||
for i in range(D):
|
||||
bgd[i] = sg(bgd[i]) * dec[i]
|
||||
sl = S[layer]; zl = z[layer]
|
||||
for i in range(D):
|
||||
si = i * D; gd = bgd[i]; ki = bk[i]
|
||||
for j in range(D):
|
||||
sl[si + j] = gd * sl[si + j] + ki * bv[j]
|
||||
for i in range(D):
|
||||
zl[i] = bgd[i] * zl[i] + bk[i]
|
||||
for j in range(D):
|
||||
bnum[j] = 0.0
|
||||
for i in range(D):
|
||||
si = i * D; qi = bq[i]
|
||||
for j in range(D):
|
||||
bnum[j] += qi * sl[si + j]
|
||||
den = 1e-6
|
||||
for i in range(D):
|
||||
den += bq[i] * zl[i]
|
||||
iv = 1.0 / den
|
||||
for j in range(D):
|
||||
by[j] = bnum[j] * iv
|
||||
dqmv(b0 + 7, D, D, by, bout)
|
||||
|
||||
def bs(e, x, layer):
|
||||
b0 = 2 + e * 12
|
||||
ln(SMW[e * 5], SMW[e * 5 + 1], x, bh)
|
||||
gs(e, layer, bh)
|
||||
for i in range(D):
|
||||
x[i] += bout[i]
|
||||
ln(SMW[e * 5 + 3], SMW[e * 5 + 4], x, bh)
|
||||
dqmv(b0 + 10, D, FD, bh, bff)
|
||||
for i in range(FD):
|
||||
bff[i] = ge(bff[i])
|
||||
dqmv(b0 + 11, FD, D, bff, bout)
|
||||
for i in range(D):
|
||||
x[i] += bout[i]
|
||||
|
||||
def route(x):
|
||||
dqmv(RB, D, RD, x, brh)
|
||||
for i in range(RD):
|
||||
brh[i] = max(0.0, brh[i] + rf1b[i])
|
||||
dqmv(RB + 2, RD, NE, brh, brl)
|
||||
for i in range(NE):
|
||||
brl[i] += rf2b[i]
|
||||
bi = 0; bv2 = brl[0]
|
||||
for i in range(1, NE):
|
||||
if brl[i] > bv2:
|
||||
bv2 = brl[i]; bi = i
|
||||
return bi, brl[:]
|
||||
|
||||
def fw(ctx, gen_flag=True):
|
||||
t = min(len(ctx) - 1, CT - 1); ti = ctx[-1]
|
||||
s0 = SC[0]; o0 = OFF[0]; s1 = SC[1]; o1 = OFF[1]
|
||||
for i in range(D):
|
||||
v0 = W[o0 + ti * D + i]
|
||||
if v0 >= 128: v0 -= 256
|
||||
v1 = W[o1 + t * D + i]
|
||||
if v1 >= 128: v1 -= 256
|
||||
bx[i] = v0 * s0 + v1 * s1
|
||||
# 累加运行均值 (匹配训练时router的x.mean(dim=1))
|
||||
for i in range(D): bsum[i] += bx[i]
|
||||
bct[0] += 1
|
||||
for i in range(D): brx[i] = bsum[i] / bct[0]
|
||||
ei, gate = route(brx)
|
||||
for layer in range(NL):
|
||||
bs(ei, bx, layer)
|
||||
ln(nfw, nfb, bx, bh)
|
||||
if gen_flag:
|
||||
dqmv(HI, D, VS, bh, blg)
|
||||
return blg, ei, gate
|
||||
return None, ei, gate
|
||||
|
||||
def rs():
|
||||
for l in range(NL):
|
||||
for i in range(D * D):
|
||||
S[l][i] = 0.0
|
||||
for i in range(D):
|
||||
z[l][i] = 0.0
|
||||
for i in range(D):
|
||||
bsum[i] = 0.0
|
||||
bct[0] = 0
|
||||
|
||||
def gen(ids, n):
|
||||
NL_ch = VS - 1
|
||||
ids = list(ids) + [NL_ch]
|
||||
sl = len(ids); c = list(ids); rs()
|
||||
for i in range(VS):
|
||||
bcnt[i] = 0
|
||||
# 种子处理
|
||||
for i in range(len(c) - 1):
|
||||
fw(c[:i + 1], False)
|
||||
# 生成
|
||||
expert_names = ["事实", "推理", "问答"]
|
||||
for _ in range(n):
|
||||
lg, ei, gate = fw(c)
|
||||
nx = sm(lg)
|
||||
bcnt[nx] += 1
|
||||
c.append(nx)
|
||||
gen_ids = c[sl:]
|
||||
gen = "".join(I2CH.get(i, '?') for i in gen_ids)
|
||||
answer = gen.split("\n")[0] if "\n" in gen else gen
|
||||
return answer, ei
|
||||
|
||||
# ========== 测试 ==========
|
||||
seeds = [
|
||||
"你好", "你好吗", "谢谢", "再见", "你叫什么名字",
|
||||
"你喜欢什么", "你喜欢猫吗", "你好棒", "你是谁",
|
||||
"你会什么", "你开心吗", "早安", "今天好吗",
|
||||
"你在做什么", "猫好吗", "你喜欢什么颜色",
|
||||
"猫是什么", "猫在做什么", "猫吃什么", "天是什么颜色",
|
||||
"什么会飞", "什么会跑", "什么会游", "什么吃草",
|
||||
]
|
||||
|
||||
print("=" * 60)
|
||||
print("PC模拟器验证 (模拟micro:bit int8推理)")
|
||||
print("=" * 60)
|
||||
|
||||
correct = 0; total = 0
|
||||
for seed in seeds:
|
||||
ctx = [CH2I[c] for c in seed if c in CH2I]
|
||||
if not ctx:
|
||||
continue
|
||||
random.seed(42) # 固定随机种子便于复现
|
||||
answer, ei = gen(ctx, 20)
|
||||
expert_names = ["事实", "推理", "问答"]
|
||||
print(f"Q: {seed:12s} [E{ei}={expert_names[ei]}]")
|
||||
print(f"A: {answer}")
|
||||
print()
|
||||
total += 1
|
||||
# 简单验证: 答案非空且包含相关字符
|
||||
if answer and len(answer) > 0:
|
||||
correct += 1
|
||||
|
||||
print(f"\n结果: {correct}/{total} 有效输出")
|
||||
|
||||
# 内存估算
|
||||
mem_w = len(W)
|
||||
mem_S = NL * D * D * 4
|
||||
mem_z = NL * D * 4
|
||||
mem_buf = (9 * D + FD + VS + VS + RD + NE + VS + D + D) * 4 # +bsum, brx
|
||||
mem_SMW = NE * 5 * D * 4
|
||||
mem_small = (RD + NE + D + D) * 4 # rf1b, rf2b, nfw, nfb
|
||||
mem_lists = 3 * nt * 8 # LEN, SC, OFF as Python lists
|
||||
total_mem = mem_w + mem_S + mem_z + mem_buf + mem_SMW + mem_small + mem_lists
|
||||
print(f"\n内存估算 (micro:bit):")
|
||||
print(f" W (int8权重): {mem_w:>6d} B = {mem_w/1024:.1f}KB")
|
||||
print(f" S (GLA状态): {mem_S:>6d} B = {mem_S/1024:.1f}KB")
|
||||
print(f" z (归一化): {mem_z:>6d} B = {mem_z/1024:.1f}KB")
|
||||
print(f" 缓冲区: {mem_buf:>6d} B = {mem_buf/1024:.1f}KB")
|
||||
print(f" SMW (小权重): {mem_SMW:>6d} B = {mem_SMW/1024:.1f}KB")
|
||||
print(f" 路由/norm_f: {mem_small:>6d} B = {mem_small/1024:.1f}KB")
|
||||
print(f" LEN/SC/OFF: {mem_lists:>6d} B = {mem_lists/1024:.1f}KB")
|
||||
print(f" 总计: {total_mem:>6d} B = {total_mem/1024:.1f}KB")
|
||||
print(f" micro:bit可用: ~108KB (128KB-20KB解释器)")
|
||||
print(f" 余量: {108*1024 - total_mem:>6d} B = {(108*1024 - total_mem)/1024:.1f}KB")
|
||||
Reference in new issue
Block a user