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324 lines
8.6 KiB
Markdown
324 lines
8.6 KiB
Markdown
# LNP-ML + SELFIES-TED 快速上手指南
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适用于：拿到代码包、从未运行过任何代码的新组员。
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论文：IBM, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language" (2024-2025)
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模型：ibm/materials.selfies-ted（358M 参数，BART 架构，无需手动下载，训练时自动从 HuggingFace 拉取）
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---
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## ⚠️ 与前几个模型的三个关键区别
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1. **必须用 GPU 运行时**（模型 358M，CPU 会非常慢）。开始前先确认硬件加速器选了 GPU。
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2. **需要额外安装 selfies 库**（用于 SMILES → SELFIES 转换）。
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3. **hidden size 是 1024**（前几个模型是 768 或 300），这已经在 encoder 代码里处理好了。
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---
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## 前提条件（开始前确认）
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- Google Drive 的 `MyDrive/lnp_project_selfiested/` 下已有：
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- `lnp_ml/`：项目代码（从 BioT5 版本复制来的，已修改）
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- SELFIES-TED 权重无需提前下载，训练时自动拉取
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如果文件夹不存在，先看文末的【从零开始】章节。
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---
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## 第一步：确认 GPU
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菜单栏 → 代码执行程序 → 更改运行时类型 → 硬件加速器选 **GPU** → 保存。
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然后运行：
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```python
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import torch
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print(f"GPU 可用: {torch.cuda.is_available()}")
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```
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显示 `True` 再继续。如果是 `False`，按上面的步骤切换 GPU。
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---
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## 每次重新打开 Colab 都需要运行
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### Cell 1：挂载 Drive
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```python
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from google.colab import drive
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drive.mount('/content/drive')
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```
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> 弹出授权页面时，确认选择有 `lnp_project_selfiested` 的 Google 账号。
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---
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### Cell 2：安装依赖（注意多了 selfies）
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```python
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!pip install rdkit loguru transformers sentencepiece optuna selfies -q
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```
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---
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### Cell 3：配置路径
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```python
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import os, sys
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LNP_PATH = "/content/drive/MyDrive/lnp_project_selfiested/lnp_ml"
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sys.path.insert(0, LNP_PATH)
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os.chdir(LNP_PATH)
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import torch
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print(f"路径配置完成 ✓ | GPU 可用: {torch.cuda.is_available()}")
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```
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---
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### Cell 4：确认文件完整
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```python
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checks = [
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f"{LNP_PATH}/lnp_ml/modeling/models.py",
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f"{LNP_PATH}/lnp_ml/modeling/encoders/selfiested_encoder.py",
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f"{LNP_PATH}/lnp_ml/modeling/encoders/__init__.py",
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f"{LNP_PATH}/data/processed/train.parquet",
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f"{LNP_PATH}/data/processed/val.parquet",
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f"{LNP_PATH}/data/processed/test.parquet",
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f"{LNP_PATH}/data/processed/train_pretrain.parquet",
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f"{LNP_PATH}/data/processed/val_pretrain.parquet",
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]
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for f in checks:
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print(f"{'✓' if os.path.exists(f) else '✗ 缺失'} {f}")
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```
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**所有文件都显示 ✓ 再继续。**
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---
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## 正式流程
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### Cell 5：验证 SELFIES-TED 模块
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```python
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!PYTHONPATH={LNP_PATH} \
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python {LNP_PATH}/verify_llm_encoder.py \
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--model_path "ibm/materials.selfies-ted" \
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--lnp_repo_path "{LNP_PATH}"
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```
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**正常输出：**
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```
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TEST 1 PASSED ✓
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TEST 2 PASSED ✓
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TEST 3 PASSED ✓
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TEST 4 PASSED ✓
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ALL TESTS PASSED ✓
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```
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---
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### Cell 6：预训练（约 10 分钟）
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```python
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!PYTHONPATH={LNP_PATH} \
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python -m lnp_ml.modeling.pretrain main \
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--train-path data/processed/train_pretrain.parquet \
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--val-path data/processed/val_pretrain.parquet \
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--epochs 50 \
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--lr 1e-4 \
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--device cuda
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```
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完成后确认权重存在：
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```python
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print("✓ 预训练权重存在" if os.path.exists(f"{LNP_PATH}/models/pretrain_delivery.pt")
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else "✗ 预训练权重未生成")
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```
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参考结果：`Best val_loss: 0.5659`
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---
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### Cell 7：正式训练（约 25-30 分钟，比前几个模型略慢）
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```python
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!PYTHONPATH={LNP_PATH} \
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python lnp_ml/modeling/final_train_optuna_cv.py \
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--init-from-pretrain models/pretrain_delivery.pt \
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--use-llm \
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--llm-model-path "ibm/materials.selfies-ted" \
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--llm-device cuda \
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--n-trials 20 \
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--epochs-per-trial 30 \
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--seed 42 \
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--device cuda \
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--output-dir models/final
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```
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完成后确认：
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```python
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print("✓ 模型权重存在" if os.path.exists(f"{LNP_PATH}/models/final/model.pt")
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else "✗ 模型未生成")
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```
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---
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### Cell 8：测试评估
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```python
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!PYTHONPATH={LNP_PATH} \
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python lnp_ml/modeling/predict.py test \
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--test-path data/processed/test.parquet \
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--model-path models/final/model.pt \
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--output-path models/final/test_results.json
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import json
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with open(f'{LNP_PATH}/models/final/test_results.json') as f:
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results = json.load(f)
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print("=== 分类任务 ===")
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for task in ['pdi', 'ee', 'toxic']:
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m = results['detailed_metrics'][task]
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print(f" {task}: acc={m['accuracy']:.4f}, f1={m['f1']:.4f}")
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print("\n=== 回归任务 ===")
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for task in ['size', 'delivery']:
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m = results['detailed_metrics'][task]
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print(f" {task}: R²={m['r2']:.4f}, RMSE={m['rmse']:.4f}")
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print("\n=== 分布任务 ===")
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m = results['detailed_metrics']['biodist']
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print(f" biodist: KL={m['kl_divergence']:.4f}, JS={m['js_divergence']:.4f}")
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```
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**参考指标：**
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| 任务 | 指标 | 参考值 |
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| delivery | R² | ~0.62 |
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| size | R² | ~0.38 |
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| pdi | acc | ~0.66 |
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| ee | acc | ~0.68 |
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| toxic | acc | ~0.94 |
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| biodist | KL | ~0.64 |
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---
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### Cell 9：备份模型
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```python
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!cp -r {LNP_PATH}/models/final \
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{LNP_PATH}/models/final_backup
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print("备份完成 ✓")
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```
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---
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## 如果已有训练好的模型，直接从 Cell 8 开始
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只需运行 Cell 1 → Cell 2 → Cell 3 → Cell 8，跳过 Cell 4-7。
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---
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## 常见问题
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| 报错 | 原因 | 解决方法 |
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| `Device: cpu` 或训练极慢 | 没选 GPU 运行时 | 菜单 → 更改运行时类型 → 选 GPU |
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| `No module named 'selfies'` | selfies 库未装 | 重新运行 Cell 2 |
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| `No module named 'rdkit'` | 依赖未安装 | 重新运行 Cell 2 |
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| `No module named 'lnp_ml'` | 路径未配置 | 重新运行 Cell 3 |
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| `'NoneType' has no attribute 'Study'` | optuna 未安装 | 重新运行 Cell 2 |
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| `Mountpoint must not already contain files` | Drive 缓存混乱 | 菜单 → 运行时 → 重新启动运行时，再从 Cell 1 开始 |
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| Colab 断开后重连 | 环境变量丢失 | 从 Cell 1 重跑，Cell 6/7 已有权重可跳过 |
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---
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## 【从零开始】如果 lnp_project_selfiested 不存在
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### Step A：从 BioT5 版本复制代码
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```python
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import os
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os.makedirs("/content/drive/MyDrive/lnp_project_selfiested", exist_ok=True)
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!cp -r "/content/drive/MyDrive/lnp_project_biot5-plus/lnp_ml" \
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"/content/drive/MyDrive/lnp_project_selfiested/lnp_ml"
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print("代码复制完成 ✓")
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```
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### Step B：上传 selfiested_encoder.py
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```python
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from google.colab import files
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import shutil
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uploaded = files.upload() # 选择 selfiested_encoder.py
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shutil.copy(
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'selfiested_encoder.py',
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'/content/drive/MyDrive/lnp_project_selfiested/lnp_ml/lnp_ml/modeling/encoders/selfiested_encoder.py'
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)
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print("上传完成 ✓")
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```
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### Step C：修改代码文件
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```python
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# Step C-1：更新 __init__.py
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path = "/content/drive/MyDrive/lnp_project_selfiested/lnp_ml/lnp_ml/modeling/encoders/__init__.py"
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with open(path, 'w') as f:
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f.write("""from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder
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from lnp_ml.modeling.encoders.mpnn_encoder import CachedMPNNEncoder
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from .llm_encoder import LLMEncoder
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from .selfiested_encoder import SELFIESTEDEncoder
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__all__ = ["CachedRDKitEncoder", "CachedMPNNEncoder", "LLMEncoder", "SELFIESTEDEncoder"]
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""")
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print("__init__.py ✓")
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```
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```python
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# Step C-2：更新 models.py
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path = "/content/drive/MyDrive/lnp_project_selfiested/lnp_ml/lnp_ml/modeling/models.py"
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with open(path, 'r') as f:
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content = f.read()
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content = content.replace(
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"from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder, LLMEncoder",
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"from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder, LLMEncoder, SELFIESTEDEncoder"
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)
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content = content.replace(
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"self.llm_encoder = LLMEncoder(",
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"self.llm_encoder = SELFIESTEDEncoder("
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)
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with open(path, 'w') as f:
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f.write(content)
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print("models.py ✓")
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```
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```python
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# Step C-3：修复 verify 脚本（属性名是 _model）
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path = "/content/drive/MyDrive/lnp_project_selfiested/lnp_ml/verify_llm_encoder.py"
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with open(path, 'r') as f:
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content = f.read()
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content = content.replace(
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"from lnp_ml.modeling.encoders.llm_encoder import LLMEncoder",
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"from lnp_ml.modeling.encoders.selfiested_encoder import SELFIESTEDEncoder as LLMEncoder"
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)
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content = content.replace(
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"encoder._llm.named_parameters()",
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"encoder._model.named_parameters()"
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)
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content = content.replace(
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"model.llm_encoder._llm.parameters()",
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"model.llm_encoder._model.parameters()"
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)
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with open(path, 'w') as f:
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f.write(content)
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print("verify_llm_encoder.py ✓")
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```
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完成后从【第一步：确认 GPU】开始正常运行。
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