diff --git a/.github/workflows/property_tools_tests.yml b/.github/workflows/property_tools_tests.yml new file mode 100644 index 0000000000..0179b3af4c --- /dev/null +++ b/.github/workflows/property_tools_tests.yml @@ -0,0 +1,34 @@ +name: DeePMD Property Tools Tests + +on: + push: + paths: + - "deepmd/deepmd_property_tools/**" + - ".github/workflows/property_tools_tests.yml" + pull_request: + paths: + - "deepmd/deepmd_property_tools/**" + - ".github/workflows/property_tools_tests.yml" + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install lightweight test dependencies + run: | + python -m pip install --upgrade pip + python -m pip install numpy pytest + + - name: Run unit tests + env: + PYTHONPATH: deepmd/deepmd_property_tools + run: | + python -m pytest deepmd/deepmd_property_tools/tests -v diff --git 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13 15 2 0 + 12 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 19 1 0 + 16 20 2 0 + 6 21 1 0 + 21 22 1 0 + 21 23 2 0 + 11 7 1 0 + 4 24 1 0 + 4 25 1 0 + 5 26 1 0 + 5 27 1 0 + 5 28 1 0 + 18 29 1 0 + 18 30 1 0 + 19 31 1 0 + 19 32 1 0 + 19 33 1 0 +M CHG 4 13 1 14 -1 21 1 22 -1 +M END diff --git a/deepmd/deepmd_property_tools/DPA3_finetune_hyperparameters.md b/deepmd/deepmd_property_tools/DPA3_finetune_hyperparameters.md new file mode 100644 index 0000000000..15d632ab52 --- /dev/null +++ b/deepmd/deepmd_property_tools/DPA3_finetune_hyperparameters.md @@ -0,0 +1,480 @@ +# DPA3 预训练微调参数说明 + +本文说明使用 `DPA-3.2-5M.pt` 这类 DPA3 预训练模型做分子性质微调时，哪些参数应与预训练模型保持一致，哪些参数可以根据新任务自行设置。 + +## 1. 总体原则 + +预训练微调可以理解为： + +```text +DPA3 descriptor 使用预训练模型权重 +property fitting net / property head 面向新性质重新训练 +``` + +因此参数可以分成两类： + +```text +模型结构参数：应尽量和预训练模型一致，否则权重加载会失败 +训练任务参数：可以按当前数据和任务重新设置 +``` + +在当前 `deepmd_property_tools` 中，推荐使用： + +```python +PropertyTrain( + ..., + finetune=PRETRAINED_MODEL, + use_pretrain_script=True, +) +``` + +其中 `use_pretrain_script=True` 会让 DeePMD-kit 根据预训练模型里的 `model_params` 自动修正当前 `input.json` 中的模型结构，使其更容易和 `DPA-3.2-5M.pt` 对齐。 + +______________________________________________________________________ + +## 2. 应与预训练模型保持一致的参数 + +这些参数通常决定模型权重张量的形状或模型 forward 逻辑。如果和预训练模型不一致，容易出现： + +```text +size mismatch +missing key +unexpected key +``` + +### 2.1 `model.type_map` + +示例： + +```json +"type_map": ["H", "C", "N", "O"] +``` + +微调数据中的元素类型应被预训练模型支持。当前 20 条 demo 数据自动生成： + +```json +[ + "H", + "C", + "N", + "O" +] +``` + +如果使用全量数据且包含 `I`，则可能生成： + +```json +[ + "H", + "C", + "N", + "O", + "I" +] +``` + +需要确认预训练模型支持这些元素。 + +### 2.2 `model.descriptor.type` + +必须是： + +```json +"type": "dpa3" +``` + +因为微调目标是继承 DPA3 descriptor。 + +### 2.3 DPA3 repflow 维度参数 + +这些参数应与预训练模型一致： + +```json +"n_dim": 128, +"e_dim": 64, +"a_dim": 32 +``` + +含义： + +- `n_dim`：节点表示维度 +- `e_dim`：边表示维度 +- `a_dim`：角表示维度 + +这些参数改变后，descriptor 内部权重矩阵形状会改变。 + +### 2.4 DPA3 层数 + +```json +"nlayers": 24 +``` + +注意：当前工具原始 `input.json` 模板中可能是： + +```json +"nlayers": 16 +``` + +但使用 `DPA-3.2-5M.pt` 并开启 `use_pretrain_script=True` 后，DeePMD-kit 会在 `input_v2_compat.json` / `out.json` 中把它改成预训练模型实际使用的层数，例如： + +```json +"nlayers": 24 +``` + +这类结构参数应以预训练模型为准。 + +### 2.5 cutoff 和 neighbor selection 参数 + +这些参数建议和预训练模型一致： + +```json +"e_rcut": 6.0, +"e_rcut_smth": 5.3, +"e_sel": 1200, +"a_rcut": 4.0, +"a_rcut_smth": 3.5, +"a_sel": 300, +"axis_neuron": 4 +``` + +含义： + +- `e_rcut` / `e_rcut_smth`：边距离 cutoff 与平滑区间 +- `e_sel`：边邻居选择数量 +- `a_rcut` / `a_rcut_smth`：角相关 cutoff 与平滑区间 +- `a_sel`：角邻居选择数量 +- `axis_neuron`：descriptor 内部投影维度相关参数 + +### 2.6 activation 和其他 descriptor 开关 + +预训练兼容后的配置中可能包含： + +```json +"activation_function": "custom_silu:3.0", +"precision": "float32", +"use_tebd_bias": false, +"concat_output_tebd": false, +"use_loc_mapping": true, +"skip_stat": true, +"edge_init_use_dist": true, +"use_exp_switch": true, +"n_multi_edge_message": 1, +"optim_update": true +``` + +这些参数有些会影响模型结构，有些会影响模型计算逻辑。做预训练微调时，不建议手动随意修改。 + +______________________________________________________________________ + +## 3. 可以根据当前任务设置的参数 + +这些参数主要控制当前微调任务，不需要和预训练模型完全一致。 + +### 3.1 训练数据路径 + +例如： + +```json +"training_data": { + "systems": [ + "prepared_data/train/10", + "prepared_data/train/15" + ] +} +``` + +这些应使用当前任务生成的数据路径。 + +### 3.2 验证数据路径 + +例如： + +```json +"validation_data": { + "systems": [ + "prepared_data/valid/22" + ] +} +``` + +同样由当前任务数据决定。 + +### 3.3 训练步数 + +可以自行设置： + +```python +numb_steps = 10 +``` + +或正式训练时设置更大： + +```python +numb_steps = 10000 +numb_steps = 50000 +numb_steps = 200000 +``` + +当前 20 条 demo 数据只用于 smoke test，`10` steps 只是验证流程。 + +### 3.4 batch size + +可以根据数据量和显存调整： + +```python +batch_size = 1 +``` + +或使用 DeePMD 支持的自动 batch： + +```python +batch_size = "auto:512" +``` + +当前 20 条 demo 数据中很多 system 只有 1-2 个样本，如果设置： + +```python +batch_size = 1024 +``` + +会出现 warning： + +```text +required batch size is larger than the size of the dataset +``` + +这不是致命错误，但小数据测试时 `batch_size=1` 更自然。 + +### 3.5 learning rate + +微调通常使用比从头训练更小的学习率。 + +从头训练常见： + +```json +"start_lr": 1e-3 +``` + +预训练微调可用： + +```json +"start_lr": 1e-4, +"stop_lr": 1e-6 +``` + +在 `train_property_20.py` 中可通过 `input_updates` 设置： + +```python +input_updates = { + "learning_rate": { + "type": "exp", + "decay_steps": 1000, + "start_lr": 1e-4, + "stop_lr": 1e-6, + } +} +``` + +### 3.6 loss + +性质预测任务使用： + +```json +"loss": { + "type": "property", + "metric": ["mae", "rmse"], + "loss_func": "smooth_mae", + "beta": 1.0 +} +``` + +这个由新任务决定，不需要和预训练模型原任务一致。 + +### 3.7 property name / property column + +例如： + +```python +property_name = "Property" +property_col = "Property" +``` + +含义： + +- `property_col`：CSV 中读取哪一列作为标签 +- `property_name`：写入 DeePMD 数据和 fitting net 的性质名 + +如果以后换性质，只需要对应修改这两个参数。 + +### 3.8 property fitting net + +例如： + +```json +"fitting_net": { + "type": "property", + "property_name": "Property", + "intensive": true, + "task_dim": 1, + "neuron": [240, 240, 240] +} +``` + +对于新性质任务，fitting net 通常会重新初始化并训练。日志中出现： + +```text +The fitting net will be re-init instead of using that in the pretrained model! +``` + +表示当前任务使用了新的 property head。 + +初期建议保持默认结构，确认流程稳定后再调 `neuron`、`task_dim` 等参数。 + +### 3.9 freeze + +这是 `deepmd_property_tools` 的工具层参数： + +```python +freeze = False +``` + +它控制训练结束后是否自动导出 `frozen_model.pth`。 + +当前 DPA3 预训练模型的 `custom_silu` 在 TorchScript freeze 阶段可能报错，因此当前 demo 中使用： + +```python +freeze = False +``` + +先保存 checkpoint： + +```text +model.ckpt-10.pt +``` + +并直接用 checkpoint 做预测。 + +### 3.10 `nproc_per_node` + +这是 `deepmd_property_tools` 的训练启动参数，用于控制单节点启动多少个训练进程： + +```python +nproc_per_node = 1 +``` + +默认值是 `1`，表示单进程训练。单进程时，工具会直接调用 DeePMD-kit 的 Python 训练入口。 + +如果设置为大于 1，例如： + +```python +nproc_per_node = 2 +``` + +工具会改用 `torchrun` 启动多进程训练，等价于： + +```bash +torchrun --nproc_per_node=2 --no-python dp --pt train input.json +``` + +通常含义是单节点 2 张 GPU / 2 个训练进程。8 卡训练可以设置： + +```python +nproc_per_node = 8 +``` + +注意：`nproc_per_node` 不是 CPU 线程数。如果只是在 CPU 上想使用更多线程，应通过环境变量控制，例如： + +```bash +export OMP_NUM_THREADS=4 +export DP_INTRA_OP_PARALLELISM_THREADS=4 +export DP_INTER_OP_PARALLELISM_THREADS=2 +python train_property_20.py +``` + +______________________________________________________________________ + +## 4. 当前推荐配置示例 + +```python +trainer = PropertyTrain( + task="regression", + data_type="molecule", + property_name="Property", + property_col="Property", + save_path=ROOT / "exp_property_20", + numb_steps=10, + batch_size=1024, + model_name="dpa3", + model_size="5m", + freeze=False, + nproc_per_node=1, + finetune=ROOT / "DPA-3.2-5M.pt", + use_pretrain_script=True, + input_updates={ + "learning_rate": { + "type": "exp", + "decay_steps": 1000, + "start_lr": 1e-4, + "stop_lr": 1e-6, + } + }, +) +``` + +对于更正式的训练，可以优先调整： + +```text +numb_steps +batch_size +learning_rate +train_ratio +nproc_per_node +property_name / property_col +``` + +不建议优先手动修改： + +```text +model.descriptor.repflow.* +activation_function +precision +DPA3 结构开关 +``` + +这些应由 `use_pretrain_script=True` 自动继承预训练模型配置。 + +______________________________________________________________________ + +## 5. 简要总结 + +应继承预训练模型的主要是： + +```text +DPA3 descriptor 结构参数 +repflow 维度、层数、cutoff、sel +activation_function +precision +与 type_map 兼容的元素设置 +``` + +可以自行设置的是： + +```text +训练/验证数据 +batch_size +numb_steps +learning_rate +loss +property_name / property_col +property fitting head +是否 freeze +nproc_per_node +``` + +当前工具推荐让 DeePMD-kit 通过： + +```python +use_pretrain_script = True +``` + +自动继承预训练模型结构，而用户主要调当前任务相关的训练超参。 diff --git a/deepmd/deepmd_property_tools/MANIFEST.in b/deepmd/deepmd_property_tools/MANIFEST.in new file mode 100644 index 0000000000..f78b0137fb --- /dev/null +++ b/deepmd/deepmd_property_tools/MANIFEST.in @@ -0,0 +1 @@ +recursive-include deepmd_property_tools/config *.json diff --git a/deepmd/deepmd_property_tools/README.md b/deepmd/deepmd_property_tools/README.md new file mode 100644 index 0000000000..197f4ad78a --- /dev/null +++ b/deepmd/deepmd_property_tools/README.md @@ -0,0 +1,94 @@ +# DeePMD Property Tools + +`deepmd_property_tools` is a Uni-Mol-tools-like interface for DeePMD-kit molecular property training and prediction. + +It wraps DeePMD-kit data generation, DPA3 property training, fine-tuning, freezing, and `DeepProperty` inference behind a small API: + +## Installation + +Install the package from this directory: + +```bash +pip install . +``` + +For local development with tests: + +```bash +pip install ".[test]" +python -m pytest tests -v +``` + +```python +from deepmd_property_tools import PropertyTrain, PropertyPredict + +clf = PropertyTrain( + task="regression", + property_name="Property", + property_col="Property", + save_path="./exp", + finetune="DPA-3.2-5M", +) +clf.fit({"dataset": "DATA/dataset_demo.csv", "mol_dir": "DATA/mol_convert"}) + +predictor = PropertyPredict(load_model="./exp/model.ckpt-10.pt") +y_pred = predictor.predict( + {"dataset": "DATA/dataset_demo.csv", "mol_dir": "DATA/mol_convert"}, + save_path="./pred", +) +``` + +## Data format + +For CSV + MOL workflows, row `i` in the CSV maps to `mol_convert/id{i}.mol` by default. The selected property column is converted to a DeePMD property fitting target. + +```text +DATA/ + dataset_demo.csv + mol_convert/ + id0.mol + id1.mol +``` + +Direct coordinate data is also supported: + +```python +clf.fit( + { + "atoms": [["C", "H", "H", "H", "H"], ["O", "H", "H"]], + "coordinates": [coords0, coords1], + "target": [0.1, 0.2], + } +) +``` + +## Command Line + +The package exposes an entry point after installation: + +```bash +deepmd-property-tools --help +``` + +Train from CSV + MOL inputs: + +```bash +deepmd-property-tools train \ + --dataset DATA/dataset_demo.csv \ + --mol-dir DATA/mol_convert \ + --save-path exp_property +``` + +Predict with a checkpoint file or an experiment directory: + +```bash +deepmd-property-tools predict \ + --model exp_property \ + --dataset DATA/dataset_demo.csv \ + --mol-dir DATA/mol_convert \ + --save-path pred_property +``` + +## Notes + +This package does not reimplement DeePMD models. It is a convenience layer that calls DeePMD-kit training and inference APIs internally. diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/__init__.py new file mode 100644 index 0000000000..296cd549c8 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/__init__.py @@ -0,0 +1,11 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Uni-Mol-tools-like helpers for DeePMD property tasks.""" + +from .predict import ( + PropertyPredict, +) +from .train import ( + PropertyTrain, +) + +__all__ = ["PropertyPredict", "PropertyTrain"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/cli.py b/deepmd/deepmd_property_tools/deepmd_property_tools/cli.py new file mode 100644 index 0000000000..27d7c84167 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/cli.py @@ -0,0 +1,124 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Command line interface for DeePMD property tools.""" + +from __future__ import ( + annotations, +) + +import argparse +from pathlib import ( + Path, +) +from collections.abc import Sequence + +from deepmd_property_tools import ( + PropertyPredict, + PropertyTrain, +) + + +def build_parser() -> argparse.ArgumentParser: + """Build the command line parser. + + Returns + ------- + argparse.ArgumentParser + Parser containing training and prediction subcommands. + """ + parser = argparse.ArgumentParser( + prog="deepmd-property-tools", + description="DeePMD molecular property training and prediction helpers.", + ) + subparsers = parser.add_subparsers(dest="command") + + train_parser = subparsers.add_parser("train", help="Train a property model") + train_parser.add_argument( + "--dataset", required=True, type=Path, help="CSV dataset path" + ) + train_parser.add_argument( + "--mol-dir", required=True, type=Path, help="MOL directory path" + ) + train_parser.add_argument( + "--save-path", required=True, type=Path, help="Experiment output directory" + ) + train_parser.add_argument( + "--property-col", default="Property", help="CSV property column" + ) + train_parser.add_argument( + "--property-name", default="Property", help="DeePMD property name" + ) + train_parser.add_argument( + "--finetune", default=None, help="Pretrained model name or path" + ) + train_parser.add_argument( + "--numb-steps", type=int, default=None, help="Number of training steps" + ) + train_parser.add_argument( + "--batch-size", type=int, default=None, help="Training batch size" + ) + train_parser.set_defaults(func=_run_train) + + predict_parser = subparsers.add_parser("predict", help="Predict properties") + predict_parser.add_argument( + "--model", required=True, type=Path, help="Model file or experiment directory" + ) + predict_parser.add_argument( + "--dataset", required=True, type=Path, help="CSV dataset path" + ) + predict_parser.add_argument( + "--mol-dir", required=True, type=Path, help="MOL directory path" + ) + predict_parser.add_argument( + "--save-path", default=None, type=Path, help="Prediction output directory" + ) + predict_parser.set_defaults(func=_run_predict) + + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + """Run the command line interface. + + Parameters + ---------- + argv + Optional argument list. When omitted, arguments are read from the command + line. + + Returns + ------- + int + Process exit code. + """ + parser = build_parser() + args = parser.parse_args(argv) + if not hasattr(args, "func"): + parser.print_help() + return 0 + args.func(args) + return 0 + + +def _run_train(args: argparse.Namespace) -> None: + trainer = PropertyTrain( + property_name=args.property_name, + property_col=args.property_col, + save_path=args.save_path, + numb_steps=args.numb_steps, + batch_size=args.batch_size, + finetune=args.finetune, + ) + trainer.fit({"dataset": args.dataset, "mol_dir": args.mol_dir}) + + +def _run_predict(args: argparse.Namespace) -> None: + predictor = PropertyPredict(load_model=args.model) + y_pred = predictor.predict( + {"dataset": args.dataset, "mol_dir": args.mol_dir}, + save_path=args.save_path, + ) + print(y_pred) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/config/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/config/__init__.py new file mode 100644 index 0000000000..d403b861f1 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/config/__init__.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Configuration helpers for deepmd_property_tools.""" + +from .config_handler import ( + ConfigHandler, +) + +__all__ = ["ConfigHandler"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/config/config_handler.py b/deepmd/deepmd_property_tools/deepmd_property_tools/config/config_handler.py new file mode 100644 index 0000000000..21c649832e --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/config/config_handler.py @@ -0,0 +1,47 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""JSON config handler.""" + +from __future__ import ( + annotations, +) + +import copy +import json +from pathlib import ( + Path, +) +from typing import ( + Any, +) + + +class ConfigHandler: + def __init__(self, config_path: str | Path | None = None) -> None: + self.config_path = ( + Path(config_path) + if config_path + else Path(__file__).with_name("default.json") + ) + + def read(self) -> dict[str, Any]: + return json.loads(self.config_path.read_text(encoding="utf-8")) + + def write(self, data: dict[str, Any], out_file_path: str | Path) -> None: + Path(out_file_path).write_text( + json.dumps(data, indent=2) + "\n", encoding="utf-8" + ) + + @staticmethod + def merge(base: dict[str, Any], updates: dict[str, Any] | None) -> dict[str, Any]: + result = copy.deepcopy(base) + if updates: + _deep_update(result, updates) + return result + + +def _deep_update(target: dict[str, Any], updates: dict[str, Any]) -> None: + for key, value in updates.items(): + if isinstance(value, dict) and isinstance(target.get(key), dict): + _deep_update(target[key], value) + else: + target[key] = value diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/config/default.json b/deepmd/deepmd_property_tools/deepmd_property_tools/config/default.json new file mode 100644 index 0000000000..be41673fa7 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/config/default.json @@ -0,0 +1,83 @@ +{ + "model": { + "type_map": [], + "descriptor": { + "type": "dpa3", + "repflow": { + "n_dim": 128, + "e_dim": 64, + "a_dim": 32, + "nlayers": 16, + "e_rcut": 6.0, + "e_rcut_smth": 5.3, + "e_sel": 1200, + "a_rcut": 4.0, + "a_rcut_smth": 3.5, + "a_sel": 300, + "axis_neuron": 4, + "fix_stat_std": 0.3, + "a_compress_rate": 1, + "a_compress_e_rate": 2, + "a_compress_use_split": true, + "update_angle": true, + "smooth_edge_update": true, + "use_dynamic_sel": true, + "sel_reduce_factor": 10.0, + "use_exp_switch": true, + "update_style": "res_residual", + "update_residual": 0.1, + "update_residual_init": "const" + }, + "activation_function": "silut:3.0", + "use_tebd_bias": false, + "precision": "float32", + "concat_output_tebd": false + }, + "fitting_net": { + "type": "property", + "property_name": "Property", + "intensive": true, + "task_dim": 1, + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 + } + }, + "loss": { + "type": "property", + "metric": [ + "mae", + "rmse" + ], + "loss_func": "smooth_mae", + "beta": 1.0 + }, + "learning_rate": { + "type": "exp", + "decay_steps": 1000, + "start_lr": 0.001, + "stop_lr": 1e-5, + "warmup_steps": 0 + }, + "training": { + "training_data": { + "systems": [], + "batch_size": "auto:512" + }, + "validation_data": { + "systems": [], + "batch_size": 1 + }, + "numb_steps": 1000000, + "gradient_max_norm": 5.0, + "max_ckpt_keep": 1000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 200, + "save_freq": 1000 + } +} diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/data/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/data/__init__.py new file mode 100644 index 0000000000..7f1cea5bb8 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/data/__init__.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Data helpers.""" + +from .converter import ( + PropertyDataResult, + build_frame, + default_input, + prepare_property_data, + register_extra_dtypes, +) +from .datahub import ( + DataHub, +) +from .mol import ( + build_used_type_map, + parse_property_value, + predict_records_from_data, + read_mol_coords, +) + +__all__ = [ + "DataHub", + "PropertyDataResult", + "build_frame", + "build_used_type_map", + "default_input", + "parse_property_value", + "predict_records_from_data", + "prepare_property_data", + "read_mol_coords", + "register_extra_dtypes", +] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/data/converter.py b/deepmd/deepmd_property_tools/deepmd_property_tools/data/converter.py new file mode 100644 index 0000000000..345c9760f3 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/data/converter.py @@ -0,0 +1,258 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""DeepMD mixed-npy conversion for property labels.""" + +from __future__ import ( + annotations, +) + +import csv +import json +import os +import random +import shutil +from dataclasses import ( + dataclass, +) +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +import numpy as np +from deepmd_property_tools.config import ( + ConfigHandler, +) + +from .mol import ( + build_used_type_map, + records_from_csv_mol, + records_from_direct_data, +) + + +@dataclass +class PropertyDataResult: + input_path: Path + output_dir: Path + train_systems: list[str] + valid_systems: list[str] + type_map: list[str] + failed_rows: list[tuple[int, str, str]] + samples_used: int + skipped_zero: int + skipped_overlap: int + raw_data: list[dict[str, Any]] + + +def register_extra_dtypes(property_name: str) -> None: + import dpdata + from dpdata.data_type import ( + Axis, + DataType, + ) + + datatypes = [ + DataType(property_name, np.ndarray, shape=(Axis.NFRAMES, 1), required=False), + DataType("stru_id", np.ndarray, shape=(Axis.NFRAMES, 1), required=False), + ] + for dtype in datatypes: + dpdata.System.register_data_type(dtype) + dpdata.LabeledSystem.register_data_type(dtype) + + +def to_relative_path(path: Path, base: Path) -> str: + path_abs = path.resolve() + base_abs = base.resolve() + try: + return str(path_abs.relative_to(base_abs)) + except ValueError: + return os.path.relpath(path_abs, base_abs) + + +def build_frame( + *, + symbols: list[str], + coords: np.ndarray, + property_value: float, + stru_id: int, + property_name: str, + type_map: list[str], + type_index: dict[str, int], +) -> dict[str, Any]: + natoms = len(symbols) + if coords.shape != (natoms, 3): + raise ValueError(f"coords shape mismatch for stru_id={stru_id}: {coords.shape}") + + atom_types = np.array([type_index[s] for s in symbols], dtype=np.int32) + atom_numbs = np.zeros(len(type_map), dtype=np.int32) + for idx in atom_types: + atom_numbs[idx] += 1 + + return { + "orig": np.array([0, 0, 0], dtype=np.int32), + "atom_names": type_map, + "atom_numbs": atom_numbs.tolist(), + "atom_types": atom_types, + "cells": np.array([[[100.0, 0.0, 0.0], [0.0, 100.0, 0.0], [0.0, 0.0, 100.0]]]), + "nopbc": True, + "coords": coords[np.newaxis, :, :].astype(np.float32), + "energies": np.zeros((1,), dtype=np.float32), + "forces": np.zeros((1, natoms, 3), dtype=np.float32), + property_name: np.array([[property_value]], dtype=np.float32), + "stru_id": np.array([[stru_id]], dtype=np.int64), + } + + +def default_input( + *, + property_name: str, + train_systems: list[str], + valid_systems: list[str], + type_map: list[str], + numb_steps: int = 1000000, + input_updates: dict[str, Any] | None = None, +) -> dict[str, Any]: + config = ConfigHandler().read() + config["model"]["type_map"] = type_map + config["model"]["fitting_net"]["property_name"] = property_name + config["training"]["training_data"]["systems"] = train_systems + config["training"]["validation_data"]["systems"] = valid_systems + config["training"]["numb_steps"] = numb_steps + return ConfigHandler.merge(config, input_updates) + + +def prepare_property_data( + data: dict[str, Any] | str | Path, + *, + output_dir: str | Path, + input_out: str | Path, + property_name: str = "Property", + property_col: str = "Property", + train_ratio: float = 0.9, + mol_dir: str | Path | None = None, + mol_template: str = "id{row}.mol", + overlap_tol: float = 1e-6, + seed: int = 42, + overwrite: bool = False, + numb_steps: int = 1000000, + input_updates: dict[str, Any] | None = None, +) -> PropertyDataResult: + if not (0.0 < train_ratio < 1.0): + raise ValueError("train_ratio must be in (0, 1)") + + import dpdata + + register_extra_dtypes(property_name) + + failed_rows: list[tuple[int, str, str]] = [] + skipped_zero = 0 + skipped_overlap = 0 + if isinstance(data, (str, Path)) or (isinstance(data, dict) and "dataset" in data): + dataset = Path(data if isinstance(data, (str, Path)) else data["dataset"]) + mol_dir_value = mol_dir if mol_dir is not None else data.get("mol_dir") + if mol_dir_value is None: + raise ValueError("mol_dir is required for CSV/MOL data") + records, failed_rows, skipped_zero, skipped_overlap, raw_data = ( + records_from_csv_mol( + dataset=dataset, + mol_dir=mol_dir_value, + property_col=property_col, + mol_template=mol_template, + overlap_tol=overlap_tol, + ) + ) + else: + records, raw_data = records_from_direct_data(data) + + used_elements = {symbol for symbols, _, _, _ in records for symbol in symbols} + type_map = build_used_type_map(used_elements) + if not type_map: + raise RuntimeError("No usable elements found after filtering.") + type_index = {el: i for i, el in enumerate(type_map)} + + systems: list[dpdata.LabeledSystem] = [] + for symbols, coords, property_value, row_idx in records: + frame_data = build_frame( + symbols=symbols, + coords=coords, + property_value=property_value, + stru_id=row_idx, + property_name=property_name, + type_map=type_map, + type_index=type_index, + ) + systems.append(dpdata.LabeledSystem(data=frame_data, type_map=type_map)) + + n_total = len(systems) + if n_total < 2: + raise RuntimeError(f"Not enough usable samples: {n_total}") + + output_path = Path(output_dir).resolve() + train_dir = output_path / "train" + valid_dir = output_path / "valid" + if overwrite and output_path.exists(): + shutil.rmtree(output_path) + output_path.mkdir(parents=True, exist_ok=True) + + rng = random.Random(seed) + indices = list(range(n_total)) + rng.shuffle(indices) + train_count = int(n_total * train_ratio) + train_count = max(1, min(train_count, n_total - 1)) + + ms_train = dpdata.MultiSystems() + ms_valid = dpdata.MultiSystems() + for idx in indices[:train_count]: + ms_train.append(systems[idx]) + for idx in indices[train_count:]: + ms_valid.append(systems[idx]) + + ms_train.to_deepmd_npy_mixed(str(train_dir)) + ms_valid.to_deepmd_npy_mixed(str(valid_dir)) + + input_path = Path(input_out).resolve() + path_base = input_path.parent + train_systems = sorted( + to_relative_path(path, path_base) + for path in train_dir.iterdir() + if path.is_dir() + ) + valid_systems = sorted( + to_relative_path(path, path_base) + for path in valid_dir.iterdir() + if path.is_dir() + ) + if not train_systems or not valid_systems: + raise RuntimeError("Generated system directories are empty.") + + input_dict = default_input( + property_name=property_name, + train_systems=train_systems, + valid_systems=valid_systems, + type_map=type_map, + numb_steps=numb_steps, + input_updates=input_updates, + ) + input_path.parent.mkdir(parents=True, exist_ok=True) + input_path.write_text(json.dumps(input_dict, indent=2) + "\n", encoding="utf-8") + + fail_csv = output_path / "failed_rows.csv" + with fail_csv.open("w", encoding="utf-8", newline="") as fp: + writer = csv.writer(fp) + writer.writerow(["row_index", "mol_path", "error"]) + writer.writerows(failed_rows) + + return PropertyDataResult( + input_path=input_path, + output_dir=output_path, + train_systems=train_systems, + valid_systems=valid_systems, + type_map=type_map, + failed_rows=failed_rows, + samples_used=n_total, + skipped_zero=skipped_zero, + skipped_overlap=skipped_overlap, + raw_data=raw_data, + ) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/data/datahub.py b/deepmd/deepmd_property_tools/deepmd_property_tools/data/datahub.py new file mode 100644 index 0000000000..9558f26cb0 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/data/datahub.py @@ -0,0 +1,72 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Uni-Mol-style data hub for DeePMD property workflows.""" + +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +from .converter import ( + PropertyDataResult, + prepare_property_data, +) +from .mol import ( + predict_records_from_data, +) + + +class DataHub: + def __init__( + self, + data: dict[str, Any] | str | Path, + *, + is_train: bool, + save_path: str | Path, + property_name: str = "Property", + property_col: str | None = "Property", + train_ratio: float = 0.9, + mol_dir: str | Path | None = None, + mol_template: str = "id{row}.mol", + overlap_tol: float = 1e-6, + seed: int = 42, + overwrite: bool = False, + numb_steps: int = 1000000, + input_updates: dict[str, Any] | None = None, + ) -> None: + self.data_input = data + self.is_train = is_train + self.save_path = Path(save_path) + self.property_name = property_name + self.property_col = property_col + if is_train: + self.result: PropertyDataResult | None = prepare_property_data( + data, + output_dir=self.save_path / "prepared_data", + input_out=self.save_path / "input.json", + property_name=property_name, + property_col=property_col, + train_ratio=train_ratio, + mol_dir=mol_dir, + mol_template=mol_template, + overlap_tol=overlap_tol, + seed=seed, + overwrite=overwrite, + numb_steps=numb_steps, + input_updates=input_updates, + ) + self.type_map = self.result.type_map + self.raw_data = self.result.raw_data + else: + self.result = None + self.atoms, self.coordinates, self.raw_data = predict_records_from_data( + data, + property_col=property_col, + mol_dir=mol_dir, + mol_template=mol_template, + ) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/data/mol.py b/deepmd/deepmd_property_tools/deepmd_property_tools/data/mol.py new file mode 100644 index 0000000000..9207a1cfc9 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/data/mol.py @@ -0,0 +1,322 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""MOL and direct-coordinate data helpers.""" + +from __future__ import ( + annotations, +) + +import csv +import re +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +import numpy as np + +ELEMENTS = np.array( + [ + "H", + "He", + "Li", + "Be", + "B", + "C", + "N", + "O", + "F", + "Ne", + "Na", + "Mg", + "Al", + "Si", + "P", + "S", + "Cl", + "Ar", + "K", + "Ca", + "Sc", + "Ti", + "V", + "Cr", + "Mn", + "Fe", + "Co", + "Ni", + "Cu", + "Zn", + "Ga", + "Ge", + "As", + "Se", + "Br", + "Kr", + "Rb", + "Sr", + "Y", + "Zr", + "Nb", + "Mo", + "Tc", + "Ru", + "Rh", + "Pd", + "Ag", + "Cd", + "In", + "Sn", + "Sb", + "Te", + "I", + "Xe", + "Cs", + "Ba", + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + "Hf", + "Ta", + "W", + "Re", + "Os", + "Ir", + "Pt", + "Au", + "Hg", + "Tl", + "Pb", + "Bi", + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + "Rf", + "Db", + "Sg", + "Bh", + "Hs", + "Mt", + "Ds", + "Rg", + "Cn", + "Nh", + "Fl", + "Mc", + "Lv", + "Ts", + "Og", + ] +) +ELEMENT_INDEX = {name: i for i, name in enumerate(ELEMENTS)} + + +def find_column(columns: list[str], choices: list[str]) -> str: + lower_map = {col.lower(): col for col in columns} + for choice in choices: + if choice.lower() in lower_map: + return lower_map[choice.lower()] + raise KeyError(f"None of columns {choices} found in {columns}") + + +def parse_property_value(raw_value: object) -> float: + if isinstance(raw_value, (int, float)): + return float(raw_value) + text = str(raw_value).strip() + try: + return float(text) + except ValueError: + match = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", text) + if match: + return float(match.group(0)) + raise + + +def read_mol_coords(path: str | Path) -> tuple[list[str], np.ndarray]: + mol_path = Path(path) + lines = mol_path.read_text(encoding="utf-8", errors="ignore").splitlines() + if len(lines) < 4: + raise ValueError(f"Bad MOL file (too short): {mol_path}") + + counts = lines[3] + try: + natoms = int(counts[0:3]) + except ValueError: + parts = counts.split() + if not parts: + raise ValueError(f"Bad MOL counts line: {mol_path}") from None + natoms = int(parts[0]) + + atom_lines = lines[4 : 4 + natoms] + if len(atom_lines) != natoms: + raise ValueError(f"Bad MOL atom block length: {mol_path}") + + symbols: list[str] = [] + coords: list[list[float]] = [] + for atom_line in atom_lines: + if len(atom_line) >= 34: + x = float(atom_line[0:10]) + y = float(atom_line[10:20]) + z = float(atom_line[20:30]) + symbol = atom_line[31:34].strip() + else: + parts = atom_line.split() + if len(parts) < 4: + raise ValueError(f"Bad MOL atom line: {mol_path}") + x, y, z = float(parts[0]), float(parts[1]), float(parts[2]) + symbol = parts[3] + + if symbol not in ELEMENT_INDEX: + raise ValueError(f"Unknown element {symbol!r} in {mol_path}") + symbols.append(symbol) + coords.append([x, y, z]) + + return symbols, np.asarray(coords, dtype=np.float32) + + +def has_overlapping_atoms(coords: np.ndarray, tol: float) -> bool: + if coords.shape[0] < 2: + return False + diff = coords[:, np.newaxis, :] - coords[np.newaxis, :, :] + dist2 = np.sum(diff * diff, axis=-1) + np.fill_diagonal(dist2, np.inf) + return float(np.min(dist2)) < tol * tol + + +def build_used_type_map(used_elements: set[str]) -> list[str]: + return [el for el in ELEMENTS.tolist() if el in used_elements] + + +def records_from_csv_mol( + *, + dataset: str | Path, + mol_dir: str | Path, + property_col: str, + mol_template: str = "id{row}.mol", + overlap_tol: float = 1e-6, +) -> tuple[ + list[tuple[list[str], np.ndarray, float, int]], + list[tuple[int, str, str]], + int, + int, + list[dict[str, Any]], +]: + with Path(dataset).open("r", encoding="utf-8") as fp: + rows = list(csv.DictReader(fp)) + if not rows: + raise ValueError(f"No rows found in dataset: {dataset}") + prop_col = find_column(list(rows[0].keys()), [property_col, "Property", "property"]) + + records: list[tuple[list[str], np.ndarray, float, int]] = [] + failed_rows: list[tuple[int, str, str]] = [] + skipped_zero = 0 + skipped_overlap = 0 + kept_rows: list[dict[str, Any]] = [] + for row_idx, row in enumerate(rows): + mol_path = (Path(mol_dir) / mol_template.format(row=row_idx)).resolve() + try: + symbols, coords = read_mol_coords(mol_path) + if np.allclose(coords, 0.0): + skipped_zero += 1 + continue + if has_overlapping_atoms(coords, overlap_tol): + skipped_overlap += 1 + continue + records.append( + (symbols, coords, parse_property_value(row[prop_col]), row_idx) + ) + kept_rows.append(dict(row)) + except Exception as exc: + failed_rows.append((row_idx, str(mol_path), str(exc))) + return records, failed_rows, skipped_zero, skipped_overlap, kept_rows + + +def records_from_direct_data( + data: dict[str, Any], +) -> tuple[list[tuple[list[str], np.ndarray, float, int]], list[dict[str, Any]]]: + atoms = data.get("atoms") + coordinates = data.get("coordinates") + targets = data.get("target", data.get("targets")) + if atoms is None or coordinates is None or targets is None: + raise ValueError("Direct training data requires atoms, coordinates, and target") + if not (len(atoms) == len(coordinates) == len(targets)): + raise ValueError("atoms, coordinates, and target must have the same length") + records = [] + rows = [] + for idx, (symbols, coords, target) in enumerate(zip(atoms, coordinates, targets)): + records.append( + (list(symbols), np.asarray(coords, dtype=np.float32), float(target), idx) + ) + rows.append({"sample_id": idx, "target": float(target)}) + return records, rows + + +def predict_records_from_data( + data: dict[str, Any] | str | Path, + *, + property_col: str | None = "Property", + mol_dir: str | Path | None = None, + mol_template: str = "id{row}.mol", +) -> tuple[list[list[str]], list[np.ndarray], list[dict[str, Any]]]: + if isinstance(data, (str, Path)) or (isinstance(data, dict) and "dataset" in data): + dataset = Path(data if isinstance(data, (str, Path)) else data["dataset"]) + mol_dir_value = mol_dir if mol_dir is not None else data.get("mol_dir") + if mol_dir_value is None: + raise ValueError("mol_dir is required for CSV/MOL data") + resolved_mol_dir = Path(mol_dir_value) + with dataset.open("r", encoding="utf-8") as fp: + rows = list(csv.DictReader(fp)) + if rows and property_col is not None: + find_column(list(rows[0].keys()), [property_col, "Property", "property"]) + atoms: list[list[str]] = [] + coords: list[np.ndarray] = [] + kept_rows: list[dict[str, Any]] = [] + for row_idx, row in enumerate(rows): + symbols, coord = read_mol_coords( + resolved_mol_dir / mol_template.format(row=row_idx) + ) + atoms.append(symbols) + coords.append(coord) + kept_rows.append(dict(row)) + return atoms, coords, kept_rows + + atoms_raw = data.get("atoms") + coords_raw = data.get("coordinates") + if atoms_raw is None or coords_raw is None: + raise ValueError("Prediction data requires atoms and coordinates") + atoms = [list(symbols) for symbols in atoms_raw] + coords = [np.asarray(coord, dtype=np.float32) for coord in coords_raw] + if len(atoms) != len(coords): + raise ValueError("atoms and coordinates must have the same length") + rows = [{"sample_id": idx} for idx in range(len(atoms))] + return atoms, coords, rows diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/models/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/models/__init__.py new file mode 100644 index 0000000000..3a2797c769 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/models/__init__.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Model wrappers.""" + +from .property_model import ( + PropertyModel, +) + +__all__ = ["PropertyModel"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/models/property_model.py b/deepmd/deepmd_property_tools/deepmd_property_tools/models/property_model.py new file mode 100644 index 0000000000..2ff256b137 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/models/property_model.py @@ -0,0 +1,22 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Property inference model wrapper.""" + +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) + + +class PropertyModel: + def __init__(self, model_path: str | Path) -> None: + from deepmd.infer.deep_property import ( + DeepProperty, + ) + + self.model = DeepProperty(str(model_path), no_jit=True) + + def eval(self, *args: object, **kwargs: object) -> object: + return self.model.eval(*args, **kwargs) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/predict.py b/deepmd/deepmd_property_tools/deepmd_property_tools/predict.py new file mode 100644 index 0000000000..fa4ad3cacc --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/predict.py @@ -0,0 +1,105 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""High-level property prediction interface.""" + +from __future__ import ( + annotations, +) + +import json +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +import numpy as np +from deepmd_property_tools.data import ( + DataHub, +) +from deepmd_property_tools.predictor import ( + Predictor, +) + + +class PropertyPredict: + def __init__( + self, + load_model: str | Path, + type_map: list[str] | None = None, + property_name: str | None = None, + ) -> None: + if not load_model: + raise ValueError("load_model is empty") + load_model_path = Path(load_model) + if load_model_path.is_dir(): + self.model_dir = load_model_path + frozen_model = load_model_path / "frozen_model.pth" + self.load_model = ( + frozen_model + if frozen_model.exists() + else self._latest_checkpoint(load_model_path) + ) + else: + self.load_model = load_model_path + self.model_dir = load_model_path.parent + config = self._load_config() + self.type_map = type_map or config.get("type_map") + if self.type_map is None: + raise ValueError( + "type_map is required when property_tools_config.json is absent" + ) + self.property_name = property_name or config.get("property_name", "Property") + self.datahub: DataHub | None = None + + def predict( + self, + data: dict[str, Any] | str | Path, + save_path: str | Path | None = None, + metrics: str = "none", + ) -> np.ndarray: + del metrics + self.datahub = DataHub( + data=data, + is_train=False, + save_path=self.load_model.parent, + property_name=self.property_name, + property_col=None, + ) + prefix = Path(data).stem if isinstance(data, (str, Path)) else "test" + predictor = Predictor( + model_path=self.load_model, + type_map=self.type_map, + property_name=self.property_name, + ) + return predictor.predict( + self.datahub.atoms, + self.datahub.coordinates, + self.datahub.raw_data, + save_path=save_path, + prefix=prefix, + ) + + def _load_config(self) -> dict[str, Any]: + candidates = [ + self.model_dir / "property_tools_config.json", + ] + for path in candidates: + if path.exists(): + return json.loads(path.read_text(encoding="utf-8")) + return {} + + @staticmethod + def _latest_checkpoint(model_dir: Path) -> Path: + candidates = sorted( + model_dir.glob("model.ckpt-*.pt"), + key=lambda path: path.stat().st_mtime, + reverse=True, + ) + candidates.append(model_dir / "model.ckpt.pt") + for checkpoint in candidates: + if checkpoint.exists(): + return checkpoint + raise FileNotFoundError( + f"No frozen_model.pth or model.ckpt*.pt checkpoint found in {model_dir}" + ) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/predictor.py b/deepmd/deepmd_property_tools/deepmd_property_tools/predictor.py new file mode 100644 index 0000000000..04ed836ad1 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/predictor.py @@ -0,0 +1,100 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Prediction pipeline implementation.""" + +from __future__ import ( + annotations, +) + +import csv +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +import numpy as np +from deepmd_property_tools.models import ( + PropertyModel, +) + + +class Predictor: + def __init__( + self, *, model_path: str | Path, type_map: list[str], property_name: str + ) -> None: + self.model_path = Path(model_path) + self.type_map = type_map + self.type_index = {element: idx for idx, element in enumerate(type_map)} + self.property_name = property_name + + def predict( + self, + atoms: list[list[str]], + coordinates: list[np.ndarray], + rows: list[dict[str, Any]], + save_path: str | Path | None = None, + prefix: str = "test", + ) -> np.ndarray: + coords, atom_types = self.standardize(atoms, coordinates) + y_pred = PropertyModel(self.model_path).eval( + coords, None, atom_types, mixed_type=True + )[0] + if save_path is not None: + self.save_predict(rows, y_pred, Path(save_path), prefix) + return y_pred + + def standardize( + self, atoms: list[list[str]], coordinates: list[np.ndarray] + ) -> tuple[np.ndarray, np.ndarray]: + if not atoms: + raise ValueError("No samples to predict") + max_natoms = max(len(symbols) for symbols in atoms) + coords = np.zeros((len(atoms), max_natoms, 3), dtype=np.float32) + atom_types = np.full((len(atoms), max_natoms), -1, dtype=np.int32) + for frame_idx, (symbols, coord) in enumerate(zip(atoms, coordinates)): + if coord.shape != (len(symbols), 3): + raise ValueError( + f"coordinates shape mismatch at sample {frame_idx}: {coord.shape}" + ) + for atom_idx, symbol in enumerate(symbols): + if symbol not in self.type_index: + raise ValueError( + f"Element {symbol!r} is not present in type_map {self.type_map}" + ) + atom_types[frame_idx, atom_idx] = self.type_index[symbol] + coords[frame_idx, : len(symbols), :] = coord + return coords, atom_types + + def save_predict( + self, + rows: list[dict[str, Any]], + y_pred: np.ndarray, + save_path: Path, + prefix: str, + ) -> Path: + save_path.mkdir(parents=True, exist_ok=True) + out_path = save_path / f"{prefix}.predict.0.csv" + run_id = 0 + while out_path.exists(): + run_id += 1 + out_path = save_path / f"{prefix}.predict.{run_id}.csv" + + predict_cols = [f"predict_{self.property_name}"] + if y_pred.shape[1] > 1: + predict_cols = [ + f"predict_{self.property_name}_{idx}" for idx in range(y_pred.shape[1]) + ] + fieldnames = list(rows[0].keys()) if rows else [] + for col in predict_cols: + if col not in fieldnames: + fieldnames.append(col) + with out_path.open("w", encoding="utf-8", newline="") as fp: + writer = csv.DictWriter(fp, fieldnames=fieldnames) + writer.writeheader() + for row, pred in zip(rows, y_pred): + out_row = dict(row) + for col, value in zip(predict_cols, pred): + out_row[col] = float(value) + writer.writerow(out_row) + return out_path diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/__init__.py new file mode 100644 index 0000000000..920246abf6 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/__init__.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Task wrappers.""" + +from .trainer import ( + Trainer, +) + +__all__ = ["Trainer"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/trainer.py b/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/trainer.py new file mode 100644 index 0000000000..9ffec2bc73 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/tasks/trainer.py @@ -0,0 +1,118 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Training task wrapper.""" + +from __future__ import ( + annotations, +) + +import os +import subprocess +from pathlib import ( + Path, +) + + +class Trainer: + def __init__( + self, + *, + save_path: str | Path, + finetune: str | None = None, + nproc_per_node: int = 1, + freeze: bool = False, + use_pretrain_script: bool = False, + skip_neighbor_stat: bool = False, + force_load: bool = False, + model_branch: str = "", + ) -> None: + self.save_path = Path(save_path) + self.finetune = finetune + self.nproc_per_node = nproc_per_node + self.freeze_model = freeze + self.use_pretrain_script = use_pretrain_script + self.skip_neighbor_stat = skip_neighbor_stat + self.force_load = force_load + self.model_branch = model_branch + + def run(self, input_path: str | Path) -> None: + input_path = Path(input_path) + if self.nproc_per_node == 1: + from deepmd.pt.entrypoints.main import ( + train, + ) + + old_cwd = os.getcwd() + try: + os.chdir(self.save_path) + train( + input_file=str(input_path), + init_model=None, + restart=None, + finetune=self.finetune, + init_frz_model=None, + model_branch=self.model_branch, + skip_neighbor_stat=self.skip_neighbor_stat, + use_pretrain_script=self.use_pretrain_script, + force_load=self.force_load, + output=str(self.save_path / "out.json"), + ) + finally: + os.chdir(old_cwd) + else: + self._run_torchrun(input_path) + if self.freeze_model: + self.freeze() + + def _run_torchrun(self, input_path: Path) -> None: + cmd = [ + "torchrun", + f"--nproc_per_node={self.nproc_per_node}", + "--no-python", + "dp", + "--pt", + "train", + str(input_path), + "--output", + str(self.save_path / "out.json"), + ] + if self.finetune is not None: + cmd.extend(["--finetune", self.finetune]) + if self.model_branch: + cmd.extend(["--model-branch", self.model_branch]) + if self.skip_neighbor_stat: + cmd.append("--skip-neighbor-stat") + if self.use_pretrain_script: + cmd.append("--use-pretrain-script") + if self.force_load: + cmd.append("--force-load") + subprocess.run(cmd, check=True, cwd=self.save_path) + + def freeze(self) -> None: + from deepmd.pt.entrypoints.main import ( + freeze, + ) + + checkpoint = self.latest_checkpoint() + try: + freeze( + model=str(checkpoint), + output=str(self.save_path / "frozen_model.pth"), + head=None, + ) + except RuntimeError as exc: + raise RuntimeError( + "Training finished, but DeePMD failed to freeze the checkpoint with TorchScript. " + f"Use the checkpoint directly instead: {checkpoint}" + ) from exc + + def latest_checkpoint(self) -> Path: + candidates = sorted( + self.save_path.glob("model.ckpt-*.pt"), + key=lambda path: path.stat().st_mtime, + reverse=True, + ) + candidates.append(self.save_path / "model.ckpt.pt") + for checkpoint in candidates: + if checkpoint.exists(): + return checkpoint + raise FileNotFoundError(f"No model checkpoint found in {self.save_path}") diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/train.py b/deepmd/deepmd_property_tools/deepmd_property_tools/train.py new file mode 100644 index 0000000000..fef98a2415 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/train.py @@ -0,0 +1,174 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""High-level property training interface.""" + +from __future__ import ( + annotations, +) + +import json +from pathlib import ( + Path, +) +from typing import ( + Any, +) + +from deepmd_property_tools.config import ( + ConfigHandler, +) +from deepmd_property_tools.data import ( + DataHub, +) +from deepmd_property_tools.tasks import ( + Trainer, +) +from deepmd_property_tools.weights import ( + WeightHub, +) + + +class PropertyTrain: + def __init__( + self, + task: str = "regression", + property_name: str = "Property", + property_col: str = "Property", + save_path: str | Path = "./exp_property", + epochs: int | None = None, + batch_size: int | str | None = None, + metrics: str | list[str] | None = None, + data_type: str = "molecule", + model_name: str = "dpa3", + model_size: str = "5m", + numb_steps: int | None = None, + finetune: str | Path | None = None, + nproc_per_node: int = 1, + train_ratio: float = 0.9, + mol_template: str = "id{row}.mol", + overlap_tol: float = 1e-6, + seed: int = 42, + overwrite: bool = True, + freeze: bool = False, + use_pretrain_script: bool = False, + skip_neighbor_stat: bool = False, + force_load: bool = False, + model_branch: str = "", + input_updates: dict[str, Any] | None = None, + **params: Any, + ) -> None: + if params: + names = ", ".join(sorted(params)) + raise TypeError(f"Unexpected PropertyTrain argument(s): {names}") + if task != "regression": + raise ValueError( + "DeePMD property tools currently support task='regression'" + ) + if data_type != "molecule": + raise ValueError( + "DeePMD property tools currently support data_type='molecule'" + ) + if model_name != "dpa3": + raise ValueError( + "DeePMD property tools currently support model_name='dpa3'" + ) + self.task = task + self.data_type = data_type + self.model_name = model_name + self.model_size = model_size + self.epochs = epochs + self.batch_size = batch_size + self.metrics = metrics + self.property_name = property_name + self.property_col = property_col + self.save_path = Path(save_path) + self.numb_steps = ( + numb_steps if numb_steps is not None else self._epochs_to_steps(epochs) + ) + self.finetune = ( + None + if finetune is None + else WeightHub(root=self.save_path.parent).get(finetune) + ) + self.nproc_per_node = nproc_per_node + self.train_ratio = train_ratio + self.mol_template = mol_template + self.overlap_tol = overlap_tol + self.seed = seed + self.overwrite = overwrite + self.freeze_model = freeze + self.use_pretrain_script = use_pretrain_script + self.skip_neighbor_stat = skip_neighbor_stat + self.force_load = force_load + self.model_branch = model_branch + if input_updates is None: + input_updates = {} + if batch_size is not None: + input_updates = ConfigHandler.merge( + input_updates, + {"training": {"training_data": {"batch_size": batch_size}}}, + ) + if metrics is not None: + metric_list = [metrics] if isinstance(metrics, str) else list(metrics) + input_updates = ConfigHandler.merge( + input_updates, {"loss": {"metric": metric_list}} + ) + self.input_updates = input_updates + self.datahub: DataHub | None = None + + def fit(self, data: dict[str, Any] | str | Path) -> None: + self.save_path.mkdir(parents=True, exist_ok=True) + self.datahub = DataHub( + data=data, + is_train=True, + save_path=self.save_path, + property_name=self.property_name, + property_col=self.property_col, + train_ratio=self.train_ratio, + mol_template=self.mol_template, + overlap_tol=self.overlap_tol, + seed=self.seed, + overwrite=self.overwrite, + numb_steps=self.numb_steps, + input_updates=self.input_updates, + ) + self._save_config() + trainer = Trainer( + save_path=self.save_path, + finetune=self.finetune, + nproc_per_node=self.nproc_per_node, + freeze=self.freeze_model, + use_pretrain_script=self.use_pretrain_script, + skip_neighbor_stat=self.skip_neighbor_stat, + force_load=self.force_load, + model_branch=self.model_branch, + ) + trainer.run(self.datahub.result.input_path) + + def _save_config(self) -> None: + if self.datahub is None or self.datahub.result is None: + return + config = { + "task": self.task, + "data_type": self.data_type, + "model_name": self.model_name, + "model_size": self.model_size, + "epochs": self.epochs, + "batch_size": self.batch_size, + "metrics": self.metrics, + "property_name": self.property_name, + "property_col": self.property_col, + "type_map": self.datahub.result.type_map, + "input_path": str(self.datahub.result.input_path), + "prepared_data": str(self.datahub.result.output_dir), + "frozen_model": str(self.save_path / "frozen_model.pth"), + "checkpoint": str(self.save_path / "model.ckpt.pt"), + } + (self.save_path / "property_tools_config.json").write_text( + json.dumps(config, indent=2) + "\n", encoding="utf-8" + ) + + @staticmethod + def _epochs_to_steps(epochs: int | None) -> int: + if epochs is None: + return 1000000 + return max(1, int(epochs)) * 1000 diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/utils/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/__init__.py new file mode 100644 index 0000000000..3da0b1a4a9 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/__init__.py @@ -0,0 +1,14 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Utility helpers.""" + +from .base_logger import ( + logger, +) +from .metrics import ( + regression_metrics, +) +from .util import ( + ensure_dir, +) + +__all__ = ["ensure_dir", "logger", "regression_metrics"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/utils/base_logger.py b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/base_logger.py new file mode 100644 index 0000000000..4cb8e9c88c --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/base_logger.py @@ -0,0 +1,11 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Logging helpers.""" + +import logging + +logger = logging.getLogger("deepmd_property_tools") +if not logger.handlers: + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter("%(asctime)s | %(levelname)s | %(message)s")) + logger.addHandler(handler) +logger.setLevel(logging.INFO) diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/utils/metrics.py b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/metrics.py new file mode 100644 index 0000000000..48a5fbad86 --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/metrics.py @@ -0,0 +1,16 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Simple regression metrics.""" + +from __future__ import ( + annotations, +) + +import numpy as np + + +def regression_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> dict[str, float]: + diff = np.asarray(y_pred, dtype=float) - np.asarray(y_true, dtype=float) + return { + "mae": float(np.mean(np.abs(diff))), + "rmse": float(np.sqrt(np.mean(diff * diff))), + } diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/utils/util.py b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/util.py new file mode 100644 index 0000000000..9e3b7cba1e --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/utils/util.py @@ -0,0 +1,12 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""General utilities.""" + +from pathlib import ( + Path, +) + + +def ensure_dir(path: str | Path) -> Path: + out = Path(path) + out.mkdir(parents=True, exist_ok=True) + return out diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/weights/__init__.py b/deepmd/deepmd_property_tools/deepmd_property_tools/weights/__init__.py new file mode 100644 index 0000000000..785b2ddedd --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/weights/__init__.py @@ -0,0 +1,8 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Weight helpers.""" + +from .weighthub import ( + WeightHub, +) + +__all__ = ["WeightHub"] diff --git a/deepmd/deepmd_property_tools/deepmd_property_tools/weights/weighthub.py b/deepmd/deepmd_property_tools/deepmd_property_tools/weights/weighthub.py new file mode 100644 index 0000000000..3a3ada59fa --- /dev/null +++ b/deepmd/deepmd_property_tools/deepmd_property_tools/weights/weighthub.py @@ -0,0 +1,74 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Local pretrained-weight path helper.""" + +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) +from typing import ( + Any, +) + + +class WeightHub: + def __init__( + self, root: str | Path = ".", cache_dir: str | Path | None = None + ) -> None: + self.root = Path(root) + self.cache_dir = ( + Path(cache_dir) + if cache_dir is not None + else self.root / "pretrained_models" + ) + + def get(self, name_or_path: str | Path) -> str: + path = Path(name_or_path) + if path.exists(): + print(f"Using local pretrained model: {path.resolve()}") + return str(path) + candidate = self.root / path + if candidate.exists(): + print(f"Using local pretrained model: {candidate.resolve()}") + return str(candidate) + model_registry = self._model_registry() + model_name = self._resolve_model_name(path, model_registry) + if model_name is not None: + from deepmd.pretrained.download import ( + resolve_model_path, + ) + + filename = str(model_registry[model_name]["filename"]) + expected_path = self.cache_dir / filename + was_cached = expected_path.exists() + resolved_path = resolve_model_path(model_name, cache_dir=self.cache_dir) + action = "Using cached" if was_cached else "Downloaded" + print(f"{action} pretrained model: {resolved_path}") + return str(resolved_path) + available = ", ".join(sorted(model_registry)) + raise FileNotFoundError( + f"Pretrained model not found: {name_or_path}. Available built-in models: {available}" + ) + + @staticmethod + def _model_registry() -> dict[str, dict[str, Any]]: + from deepmd.pretrained.registry import ( + MODEL_REGISTRY, + ) + + return MODEL_REGISTRY + + @staticmethod + def _resolve_model_name( + path: Path, model_registry: dict[str, dict[str, Any]] + ) -> str | None: + alias = path.name + if alias in model_registry: + return alias + lowered = alias.lower() + for model_name, model_info in model_registry.items(): + if lowered in {model_name.lower(), str(model_info["filename"]).lower()}: + return model_name + return None diff --git a/deepmd/deepmd_property_tools/predict_property_20.py b/deepmd/deepmd_property_tools/predict_property_20.py new file mode 100644 index 0000000000..7bfff1ac4d --- /dev/null +++ b/deepmd/deepmd_property_tools/predict_property_20.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 +# SPDX-License-Identifier: LGPL-3.0-or-later +from pathlib import ( + Path, +) + +from deepmd_property_tools import ( + PropertyPredict, +) + +ROOT = Path(__file__).resolve().parent +DATA_DIR = ROOT / "DATA" +MODEL_PATH = ROOT / "exp_property_20" / "model.ckpt-10.pt" + +if not MODEL_PATH.exists(): + raise FileNotFoundError(f"Train first; checkpoint not found: {MODEL_PATH}") + +predictor = PropertyPredict(load_model=MODEL_PATH) + +y_pred = predictor.predict( + { + "dataset": DATA_DIR / "dataset_demo.csv", + "mol_dir": DATA_DIR / "mol_convert", + }, + save_path=ROOT / "pred_property_20", +) + +print(y_pred) diff --git a/deepmd/deepmd_property_tools/pyproject.toml b/deepmd/deepmd_property_tools/pyproject.toml new file mode 100644 index 0000000000..6d3fd9b0b6 --- /dev/null +++ b/deepmd/deepmd_property_tools/pyproject.toml @@ -0,0 +1,47 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "deepmd-property-tools" +version = "0.1.0" +description = "Uni-Mol-tools-like property training and prediction helpers for DeePMD-kit." +readme = "README.md" +requires-python = ">=3.10" +license = "LGPL-3.0-or-later" +authors = [ + {name = "DeepModeling"}, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Chemistry", +] +dependencies = [ + "deepmd-kit[torch]==3.1.3", + "dpdata", + "numpy", +] + +[project.optional-dependencies] +test = [ + "pytest", +] + +[project.scripts] +deepmd-property-tools = "deepmd_property_tools.cli:main" + +[tool.setuptools.packages.find] +where = ["."] +include = ["deepmd_property_tools*"] + +[tool.setuptools.package-data] +"deepmd_property_tools.config" = ["*.json"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] diff --git a/deepmd/deepmd_property_tools/tests/test_cli.py b/deepmd/deepmd_property_tools/tests/test_cli.py new file mode 100644 index 0000000000..f20142b5bf --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_cli.py @@ -0,0 +1,75 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) +from unittest import ( + mock, +) + +from deepmd_property_tools import ( + cli, +) + + +def test_main_prints_help_without_command(capsys) -> None: + exit_code = cli.main([]) + + captured = capsys.readouterr() + assert exit_code == 0 + assert "DeePMD molecular property training" in captured.out + + +def test_train_command_calls_property_train() -> None: + trainer = mock.Mock() + with mock.patch.object(cli, "PropertyTrain", return_value=trainer) as train_cls: + exit_code = cli.main( + [ + "train", + "--dataset", + "data.csv", + "--mol-dir", + "mol", + "--save-path", + "exp", + "--numb-steps", + "10", + "--batch-size", + "1", + ] + ) + + assert exit_code == 0 + train_cls.assert_called_once() + trainer.fit.assert_called_once_with( + {"dataset": Path("data.csv"), "mol_dir": Path("mol")} + ) + + +def test_predict_command_calls_property_predict() -> None: + predictor = mock.Mock() + predictor.predict.return_value = [[1.0]] + with mock.patch.object(cli, "PropertyPredict", return_value=predictor): + with mock.patch("builtins.print"): + exit_code = cli.main( + [ + "predict", + "--model", + "exp", + "--dataset", + "data.csv", + "--mol-dir", + "mol", + "--save-path", + "pred", + ] + ) + + assert exit_code == 0 + predictor.predict.assert_called_once_with( + {"dataset": Path("data.csv"), "mol_dir": Path("mol")}, + save_path=Path("pred"), + ) diff --git a/deepmd/deepmd_property_tools/tests/test_config.py b/deepmd/deepmd_property_tools/tests/test_config.py new file mode 100644 index 0000000000..43ec2942fb --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_config.py @@ -0,0 +1,19 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from deepmd_property_tools.config import ( + ConfigHandler, +) + + +def test_merge_deep_updates_nested_dicts() -> None: + base = {"training": {"numb_steps": 10, "data": {"batch_size": 1}}, "loss": "mae"} + updates = {"training": {"data": {"batch_size": 4}}} + + merged = ConfigHandler.merge(base, updates) + + assert merged["training"]["numb_steps"] == 10 + assert merged["training"]["data"]["batch_size"] == 4 + assert base["training"]["data"]["batch_size"] == 1 diff --git a/deepmd/deepmd_property_tools/tests/test_mol.py b/deepmd/deepmd_property_tools/tests/test_mol.py new file mode 100644 index 0000000000..2ee0fb0477 --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_mol.py @@ -0,0 +1,67 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) + +import numpy as np +from deepmd_property_tools.data.mol import ( + build_used_type_map, + has_overlapping_atoms, + parse_property_value, + read_mol_coords, + records_from_direct_data, +) + + +def test_parse_property_value_accepts_text_with_units() -> None: + assert parse_property_value("gap = -1.25 eV") == -1.25 + + +def test_overlap_detection() -> None: + coords = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], dtype=np.float32) + + assert has_overlapping_atoms(coords, 1e-6) + + +def test_type_map_uses_periodic_table_order() -> None: + assert build_used_type_map({"O", "C", "H"}) == ["H", "C", "O"] + + +def test_records_from_direct_data() -> None: + records, rows = records_from_direct_data( + { + "atoms": [["O", "H", "H"]], + "coordinates": [np.zeros((3, 3), dtype=np.float32)], + "target": [1.5], + } + ) + + assert records[0][0] == ["O", "H", "H"] + assert records[0][2] == 1.5 + assert rows == [{"sample_id": 0, "target": 1.5}] + + +def test_read_mol_coords(tmp_path: Path) -> None: + mol_path = tmp_path / "id0.mol" + mol_path.write_text( + "\n".join( + [ + "methane", + "", + "", + " 1 0 0 0 0 0 999 V2000", + " 0.0000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0", + "M END", + ] + ), + encoding="utf-8", + ) + + symbols, coords = read_mol_coords(mol_path) + + assert symbols == ["C"] + assert coords.shape == (1, 3) diff --git a/deepmd/deepmd_property_tools/tests/test_predict.py b/deepmd/deepmd_property_tools/tests/test_predict.py new file mode 100644 index 0000000000..d068c87135 --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_predict.py @@ -0,0 +1,121 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +import json +import time +from pathlib import ( + Path, +) +from unittest import ( + mock, +) + +import numpy as np +from deepmd_property_tools import ( + PropertyPredict, +) +from deepmd_property_tools.data.mol import ( + predict_records_from_data, +) + + +def _write_mol(path: Path) -> None: + path.write_text( + "\n".join( + [ + "water", + " deepmd_property_tools", + "", + " 3 2 0 0 0 0 999 V2000", + " 0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0", + " 0.9572 0.0000 0.0000 H 0 0 0 0 0 0 0 0 0 0 0 0", + " -0.2390 0.9270 0.0000 H 0 0 0 0 0 0 0 0 0 0 0 0", + "M END", + "", + ] + ), + encoding="utf-8", + ) + + +def test_predict_records_from_csv_without_property_column(tmp_path: Path) -> None: + dataset = tmp_path / "dataset.csv" + dataset.write_text("SMILES\nO\n", encoding="utf-8") + mol_dir = tmp_path / "mol" + mol_dir.mkdir() + _write_mol(mol_dir / "id0.mol") + + atoms, coords, rows = predict_records_from_data( + {"dataset": dataset, "mol_dir": mol_dir}, + property_col=None, + ) + + assert atoms == [["O", "H", "H"]] + assert coords[0].shape == (3, 3) + assert rows == [{"SMILES": "O"}] + + +def test_predict_directory_uses_latest_checkpoint(tmp_path: Path) -> None: + old_checkpoint = tmp_path / "model.ckpt-1.pt" + old_checkpoint.write_text("old", encoding="utf-8") + time.sleep(0.01) + latest_checkpoint = tmp_path / "model.ckpt-2.pt" + latest_checkpoint.write_text("new", encoding="utf-8") + (tmp_path / "property_tools_config.json").write_text( + json.dumps({"type_map": ["H", "O"], "property_name": "Property"}), + encoding="utf-8", + ) + + predictor = PropertyPredict(tmp_path) + + assert predictor.load_model == latest_checkpoint + assert predictor.type_map == ["H", "O"] + + +def test_predict_directory_prefers_frozen_model(tmp_path: Path) -> None: + frozen_model = tmp_path / "frozen_model.pth" + frozen_model.write_text("frozen", encoding="utf-8") + checkpoint = tmp_path / "model.ckpt-1.pt" + checkpoint.write_text("checkpoint", encoding="utf-8") + (tmp_path / "property_tools_config.json").write_text( + json.dumps({"type_map": ["H"], "property_name": "Property"}), + encoding="utf-8", + ) + + predictor = PropertyPredict(tmp_path) + + assert predictor.load_model == frozen_model + + +def test_predict_save_handles_single_output(tmp_path: Path) -> None: + from deepmd_property_tools import predictor as predictor_module + + class DummyModel: + def __init__(self, model_path: Path) -> None: + self.model_path = model_path + + def eval(self, *args, **kwargs): + return (np.array([[1.25]], dtype=float),) + + with mock.patch.object(predictor_module, "PropertyModel", DummyModel): + predictor = predictor_module.Predictor( + model_path=tmp_path / "model.ckpt-1.pt", + type_map=["H"], + property_name="Property", + ) + y_pred = predictor.predict( + atoms=[["H"]], + coordinates=[np.array([[0.0, 0.0, 0.0]], dtype=np.float32)], + rows=[{"SMILES": "[H]"}], + save_path=tmp_path, + ) + + assert y_pred.tolist() == [[1.25]] + assert (tmp_path / "test.predict.0.csv").read_text( + encoding="utf-8" + ).splitlines() == [ + "SMILES,predict_Property", + "[H],1.25", + ] diff --git a/deepmd/deepmd_property_tools/tests/test_train.py b/deepmd/deepmd_property_tools/tests/test_train.py new file mode 100644 index 0000000000..96095ee683 --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_train.py @@ -0,0 +1,20 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +import pytest +from deepmd_property_tools import ( + PropertyTrain, +) + + +def test_property_train_rejects_unknown_arguments() -> None: + with pytest.raises(TypeError, match="Unexpected PropertyTrain argument"): + PropertyTrain(unknown_option=True) + + +def test_epochs_to_steps() -> None: + assert PropertyTrain._epochs_to_steps(None) == 1000000 + assert PropertyTrain._epochs_to_steps(2) == 2000 + assert PropertyTrain._epochs_to_steps(0) == 1000 diff --git a/deepmd/deepmd_property_tools/tests/test_trainer.py b/deepmd/deepmd_property_tools/tests/test_trainer.py new file mode 100644 index 0000000000..a802efc60a --- /dev/null +++ b/deepmd/deepmd_property_tools/tests/test_trainer.py @@ -0,0 +1,49 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from __future__ import ( + annotations, +) + +from pathlib import ( + Path, +) +from unittest import ( + mock, +) + +from deepmd_property_tools.tasks.trainer import ( + Trainer, +) + + +def test_latest_checkpoint_prefers_newest_numbered_checkpoint(tmp_path: Path) -> None: + fallback = tmp_path / "model.ckpt.pt" + fallback.write_text("fallback", encoding="utf-8") + checkpoint = tmp_path / "model.ckpt-10.pt" + checkpoint.write_text("checkpoint", encoding="utf-8") + + trainer = Trainer(save_path=tmp_path) + + assert trainer.latest_checkpoint() == checkpoint + + +def test_torchrun_command_includes_options() -> None: + trainer = Trainer( + save_path="exp", + finetune="pretrained.pt", + nproc_per_node=2, + use_pretrain_script=True, + force_load=True, + skip_neighbor_stat=True, + model_branch="Default", + ) + + with mock.patch("subprocess.run") as run_mock: + trainer._run_torchrun(Path("input.json")) + + cmd = run_mock.call_args.args[0] + assert "--nproc_per_node=2" in cmd + assert "--finetune" in cmd + assert "--use-pretrain-script" in cmd + assert "--force-load" in cmd + assert "--skip-neighbor-stat" in cmd + assert "--model-branch" in cmd diff --git a/deepmd/deepmd_property_tools/train_property_20.py b/deepmd/deepmd_property_tools/train_property_20.py new file mode 100644 index 0000000000..db98f31ea6 --- /dev/null +++ b/deepmd/deepmd_property_tools/train_property_20.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +# SPDX-License-Identifier: LGPL-3.0-or-later +from pathlib import ( + Path, +) + +from deepmd_property_tools import ( + PropertyPredict, + PropertyTrain, +) + +ROOT = Path(__file__).resolve().parent +DATA_DIR = ROOT / "DATA" +EXP_DIR = ROOT / "exp_property_20" +PRED_DIR = ROOT / "pred_property_20" +PRETRAINED_MODEL = "DPA-3.2-5M" +TRAIN_DATA = { + "dataset": DATA_DIR / "dataset_demo.csv", + "mol_dir": DATA_DIR / "mol_convert", +} +PREDICT_DATA = { + "dataset": DATA_DIR / "dataset_demo.csv", + "mol_dir": DATA_DIR / "mol_convert", +} + +trainer = PropertyTrain( + task="regression", + data_type="molecule", + property_name="Property", + property_col="Property", + save_path=EXP_DIR, + epochs=1, + numb_steps=10, + batch_size=1, + model_name="dpa3", + model_size="5m", + freeze=False, + finetune=PRETRAINED_MODEL, + use_pretrain_script=False, + input_updates={ + "learning_rate": { + "type": "exp", + "decay_steps": 1000, + "start_lr": 1e-4, + "stop_lr": 1e-6, + "warmup_steps": 0, + } + }, +) + +trainer.fit(TRAIN_DATA) + +checkpoints = sorted( + EXP_DIR.glob("model.ckpt-*.pt"), key=lambda path: path.stat().st_mtime +) +if not checkpoints: + raise FileNotFoundError(f"No checkpoint found in {EXP_DIR}") +model_path = checkpoints[-1] +print(f"Using trained model for prediction: {model_path}") + +predictor = PropertyPredict(load_model=model_path) +y_pred = predictor.predict(PREDICT_DATA, save_path=PRED_DIR) +print(y_pred)