diff --git a/README.md b/README.md index 7f0ed9ca513b9cc01277e3a84f8f52cb6171cc13..910642836eebcfc48e50c39812bdf44c0b853ed2 100644 --- a/README.md +++ b/README.md @@ -1,165 +1,60 @@ -DeePMD-kit Manual -======== -[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases) -[![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2020.107206) -![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016) -[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases) -[![conda install](https://img.shields.io/badge/downloads-9k%20total-green.svg?style=round-square&label=conda%20install)](https://anaconda.org/deepmodeling/deepmd-kit) -[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit) -[![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit) -[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/) - -# Table of contents -- [About DeePMD-kit](#about-deepmd-kit) - - [Highlights in v2.0](#highlights-in-deepmd-kit-v2.0) - - [Highlighted features](#highlighted-features) - - [License and credits](#license-and-credits) - - [Deep Potential in a nutshell](#deep-potential-in-a-nutshell) -- [Download and install](#download-and-install) -- [Use DeePMD-kit](#use-deepmd-kit) -- [Code structure](#code-structure) -- [Troubleshooting](#troubleshooting) - -# About DeePMD-kit -DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. - -For more information, check the [documentation](https://deepmd.readthedocs.io/). - -# Highlights in DeePMD-kit v2.0 -* [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference for 4-15 times. -* [New descriptors](doc/model/overall.md). Including [`se_e2_r`](doc/model/train-se-e2-r.md) and [`se_e3`](doc/model/train-se-e3.md). -* [Hybridization of descriptors](doc/model/train-hybrid.md). Hybrid descriptor constructed from concatenation of several descriptors. -* [Atom type embedding](doc/model/train-se-e2-a-tebd.md). Enable atom type embedding to decline training complexity and refine performance. -* Training and inference the dipole (vector) and polarizability (matrix). -* Split of training and validation dataset. -* Optimized training on GPUs. - -## Highlighted features -* **interfaced with TensorFlow**, one of the most popular deep learning frameworks, making the training process highly automatic and efficient, in addition Tensorboard can be used to visualize training procedure. -* **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, i.e., LAMMPS and i-PI, respectively. -* **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc. -* **implements MPI and GPU supports**, makes it highly efficient for high performance parallel and distributed computing. -* **highly modularized**, easy to adapt to different descriptors for deep learning based potential energy models. - -## License and credits -The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE). -If you use this code in any future publications, please cite this using -``Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.`` - -## Deep Potential in a nutshell -The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called *atomic energy*. Summing up all the atomic energies gives the potential energy of the system. - -The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical *ab initio* molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations. - -Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science. - -In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details. - -# Download and install - -Please follow our [GitHub](https://github.com/deepmodeling/deepmd-kit) webpage to download the [latest released version](https://github.com/deepmodeling/deepmd-kit/tree/master) and [development version](https://github.com/deepmodeling/deepmd-kit/tree/devel). - -DeePMD-kit offers multiple installation methods. It is recommend using easily methods like [offline packages](doc/install/easy-install.md#offline-packages), [conda](doc/install/easy-install.md#with-conda) and [docker](doc/install/easy-install.md#with-docker). - -One may manually install DeePMD-kit by following the instuctions on [installing the Python interface](doc/install/install-from-source.md#install-the-python-interface) and [installing the C++ interface](doc/install/install-from-source.md#install-the-c-interface). The C++ interface is necessary when using DeePMD-kit with LAMMPS, i-PI or GROMACS. - - -# Use DeePMD-kit - -A quick-start on using DeePMD-kit can be found as follows: - -- [Prepare data with dpdata](doc/data/dpdata.md) -- [Training a model](doc/train/training.md) -- [Freeze a model](doc/freeze/freeze.md) -- [Test a model](doc/test/test.md) -- [Run MD with LAMMPS](doc/third-party/lammps.md) - -A full [document](doc/train/train-input-auto.rst) on options in the training input script is available. - -# Advanced - -- [Installation](doc/install/index.md) - - [Easy install](doc/install/easy-install.md) - - [Install from source code](doc/install/install-from-source.md) - - [Install LAMMPS](doc/install/install-lammps.md) - - [Install i-PI](doc/install/install-ipi.md) - - [Install GROMACS](doc/install/install-gromacs.md) - - [Building conda packages](doc/install/build-conda.md) -- [Data](doc/data/index.md) - - [System](doc/data/system.md) - - [Formats of a system](doc/data/data-conv.md) - - [Prepare data with dpdata](doc/data/dpdata.md) -- [Model](doc/model/index.md) - - [Overall](doc/model/overall.md) - - [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md) - - [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md) - - [Descriptor `"se_e3"`](doc/model/train-se-e3.md) - - [Descriptor `"se_atten"`](doc/model/train-se-atten.md) - - [Descriptor `"hybrid"`](doc/model/train-hybrid.md) - - [Descriptor `sel`](doc/model/sel.md) - - [Fit energy](doc/model/train-energy.md) - - [Fit `tensor` like `Dipole` and `Polarizability`](doc/model/train-fitting-tensor.md) - - [Train a Deep Potential model using `type embedding` approach](doc/model/train-se-e2-a-tebd.md) - - [Deep potential long-range](doc/model/dplr.md) - - [Deep Potential - Range Correction (DPRc)](doc/model/dprc.md) -- [Training](doc/train/index.md) - - [Training a model](doc/train/training.md) - - [Advanced options](doc/train/training-advanced.md) - - [Parallel training](doc/train/parallel-training.md) - - [TensorBoard Usage](doc/train/tensorboard.md) - - [Known limitations of using GPUs](doc/train/gpu-limitations.md) - - [Training Parameters](doc/train/train-input-auto.rst) -- [Freeze and Compress](doc/freeze/index.rst) - - [Freeze a model](doc/freeze/freeze.md) - - [Compress a model](doc/freeze/compress.md) -- [Test](doc/test/index.rst) - - [Test a model](doc/test/test.md) - - [Calculate Model Deviation](doc/test/model-deviation.md) -- [Inference](doc/inference/index.rst) - - [Python interface](doc/inference/python.md) - - [C++ interface](doc/inference/cxx.md) -- [Integrate with third-party packages](doc/third-party/index.rst) - - [Use deep potential with ASE](doc/third-party/ase.md) - - [Run MD with LAMMPS](doc/third-party/lammps.md) - - [LAMMPS commands](doc/third-party/lammps-command.md) - - [Run path-integral MD with i-PI](doc/third-party/ipi.md) - - [Run MD with GROMACS](doc/third-party/gromacs.md) - - [Interfaces out of DeePMD-kit](doc/third-party/out-of-deepmd-kit.md) -- [Use NVNMD](doc/nvnmd/index.md) - -# Code structure - -The code is organized as follows: - -* `data/raw`: tools manipulating the raw data files. -* `examples`: examples. -* `deepmd`: DeePMD-kit python modules. -* `source/api_cc`: source code of DeePMD-kit C++ API. -* `source/ipi`: source code of i-PI client. -* `source/lib`: source code of DeePMD-kit library. -* `source/lmp`: source code of Lammps module. -* `source/gmx`: source code of Gromacs plugin. -* `source/op`: tensorflow op implementation. working with library. - - -# Troubleshooting - -- [Model compatibility](doc/troubleshooting/model_compatability.md) -- [Installation](doc/troubleshooting/installation.md) -- [The temperature undulates violently during early stages of MD](doc/troubleshooting/md_energy_undulation.md) -- [MD: cannot run LAMMPS after installing a new version of DeePMD-kit](doc/troubleshooting/md_version_compatibility.md) -- [Do we need to set rcut < half boxsize?](doc/troubleshooting/howtoset_rcut.md) -- [How to set sel?](doc/troubleshooting/howtoset_sel.md) -- [How to control the number of nodes used by a job?](doc/troubleshooting/howtoset_num_nodes.md) -- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md) - - -# Contributing - -See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓 - - -[1]: https://arxiv.org/abs/1707.01478 -[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001 -[3]: https://arxiv.org/abs/1805.09003 -[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645 +# DeePMD-kit(Deep Potential Molecular Dynamics) +## 模型介绍 +DeePMD-kit是深度势能分子动力学的开源项目。分子动力学是一套分子模拟方法，该方法主要依靠牛顿力学来模拟分子体系的运动。分子动力学在微观世界模拟中起着承载作用，在物理、化学、生物、材料科学以及力学等领域都有着非常重要的应用。 + +深势科技成功将DeePMD-kit进行了并行化，首次实现了具有AIMD精度的第一性原理分子动力学模拟的商业与原子规模的应用，为分子动力学提供了革命性的高性能解决方案。 + +## 模型结构 + +DeePMD-Kit,参考https://docs.deepmodeling.com/projects/deepmd/en/master/index.html + +## DeePMD代码参考版本 + +版本：v2.1.5 + +原始代码位置：https://github.com/deepmodeling/deepmd-kit + +## 数据集 +示例中验证的数据集来自： + +wget http://pai-blade.oss-accelerate.aliyuncs.com/bladedisc_notebook_binaries/data/disc_deepmd_example/data.tar.gz +tar -xvf data.tar.gz +实际业务测试请选择合适的数据集进行测试。 + +## 训练 + +### 环境配置 +提供[光源](https://www.sourcefind.cn/#/service-details)拉取的训练的docker镜像： +* 训练镜像： + +python依赖安装： + + pip3 install -r requirement.txt + +### 安装 + +export ROCM_ROOT=/opt/dtk-22.04.2 +DP_VARIANT=rocm python3 -m pip install . -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn + +### 单卡测试 + +使用BladeDISC加速的测试命令（此处以单机单卡规模为例说明）,请根据实际使用的卡id号调整run.sh中指定的卡号： + + bash run.sh +如果需要关闭BladeDISC加速，需要注释掉main.py文件中的如下内容： + disc.enable() + +### 性能和准确率数据 + +| case | 优化前(关闭disc) | 优化后(打开disc) | 性能提升 | +| :---- | ----: | :----: | ---- | +water | 3.1s | 2.7s | 12.9% + GeTe_2_body描述符| 4.06s | 2.67s | 34.2% +| GeTe_hybrid描述符 | 18.5s | 17.8s | 3.7% | + +准确率通过对比优化前与优化后的输出文件lcurve.out输出趋势一致进行确认； + + +## 参考 +* https://github.com/deepmodeling/deepmd-kit + diff --git a/README_deepmd.md b/README_deepmd.md new file mode 100644 index 0000000000000000000000000000000000000000..7f0ed9ca513b9cc01277e3a84f8f52cb6171cc13 --- /dev/null +++ b/README_deepmd.md @@ -0,0 +1,165 @@ +DeePMD-kit Manual +======== +[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases) +[![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2020.107206) +![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016) +[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases) +[![conda install](https://img.shields.io/badge/downloads-9k%20total-green.svg?style=round-square&label=conda%20install)](https://anaconda.org/deepmodeling/deepmd-kit) +[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit) +[![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit) +[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/) + +# Table of contents +- [About DeePMD-kit](#about-deepmd-kit) + - [Highlights in v2.0](#highlights-in-deepmd-kit-v2.0) + - [Highlighted features](#highlighted-features) + - [License and credits](#license-and-credits) + - [Deep Potential in a nutshell](#deep-potential-in-a-nutshell) +- [Download and install](#download-and-install) +- [Use DeePMD-kit](#use-deepmd-kit) +- [Code structure](#code-structure) +- [Troubleshooting](#troubleshooting) + +# About DeePMD-kit +DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. + +For more information, check the [documentation](https://deepmd.readthedocs.io/). + +# Highlights in DeePMD-kit v2.0 +* [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference for 4-15 times. +* [New descriptors](doc/model/overall.md). Including [`se_e2_r`](doc/model/train-se-e2-r.md) and [`se_e3`](doc/model/train-se-e3.md). +* [Hybridization of descriptors](doc/model/train-hybrid.md). Hybrid descriptor constructed from concatenation of several descriptors. +* [Atom type embedding](doc/model/train-se-e2-a-tebd.md). Enable atom type embedding to decline training complexity and refine performance. +* Training and inference the dipole (vector) and polarizability (matrix). +* Split of training and validation dataset. +* Optimized training on GPUs. + +## Highlighted features +* **interfaced with TensorFlow**, one of the most popular deep learning frameworks, making the training process highly automatic and efficient, in addition Tensorboard can be used to visualize training procedure. +* **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, i.e., LAMMPS and i-PI, respectively. +* **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc. +* **implements MPI and GPU supports**, makes it highly efficient for high performance parallel and distributed computing. +* **highly modularized**, easy to adapt to different descriptors for deep learning based potential energy models. + +## License and credits +The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE). +If you use this code in any future publications, please cite this using +``Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.`` + +## Deep Potential in a nutshell +The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called *atomic energy*. Summing up all the atomic energies gives the potential energy of the system. + +The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical *ab initio* molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations. + +Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science. + +In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details. + +# Download and install + +Please follow our [GitHub](https://github.com/deepmodeling/deepmd-kit) webpage to download the [latest released version](https://github.com/deepmodeling/deepmd-kit/tree/master) and [development version](https://github.com/deepmodeling/deepmd-kit/tree/devel). + +DeePMD-kit offers multiple installation methods. It is recommend using easily methods like [offline packages](doc/install/easy-install.md#offline-packages), [conda](doc/install/easy-install.md#with-conda) and [docker](doc/install/easy-install.md#with-docker). + +One may manually install DeePMD-kit by following the instuctions on [installing the Python interface](doc/install/install-from-source.md#install-the-python-interface) and [installing the C++ interface](doc/install/install-from-source.md#install-the-c-interface). The C++ interface is necessary when using DeePMD-kit with LAMMPS, i-PI or GROMACS. + + +# Use DeePMD-kit + +A quick-start on using DeePMD-kit can be found as follows: + +- [Prepare data with dpdata](doc/data/dpdata.md) +- [Training a model](doc/train/training.md) +- [Freeze a model](doc/freeze/freeze.md) +- [Test a model](doc/test/test.md) +- [Run MD with LAMMPS](doc/third-party/lammps.md) + +A full [document](doc/train/train-input-auto.rst) on options in the training input script is available. + +# Advanced + +- [Installation](doc/install/index.md) + - [Easy install](doc/install/easy-install.md) + - [Install from source code](doc/install/install-from-source.md) + - [Install LAMMPS](doc/install/install-lammps.md) + - [Install i-PI](doc/install/install-ipi.md) + - [Install GROMACS](doc/install/install-gromacs.md) + - [Building conda packages](doc/install/build-conda.md) +- [Data](doc/data/index.md) + - [System](doc/data/system.md) + - [Formats of a system](doc/data/data-conv.md) + - [Prepare data with dpdata](doc/data/dpdata.md) +- [Model](doc/model/index.md) + - [Overall](doc/model/overall.md) + - [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md) + - [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md) + - [Descriptor `"se_e3"`](doc/model/train-se-e3.md) + - [Descriptor `"se_atten"`](doc/model/train-se-atten.md) + - [Descriptor `"hybrid"`](doc/model/train-hybrid.md) + - [Descriptor `sel`](doc/model/sel.md) + - [Fit energy](doc/model/train-energy.md) + - [Fit `tensor` like `Dipole` and `Polarizability`](doc/model/train-fitting-tensor.md) + - [Train a Deep Potential model using `type embedding` approach](doc/model/train-se-e2-a-tebd.md) + - [Deep potential long-range](doc/model/dplr.md) + - [Deep Potential - Range Correction (DPRc)](doc/model/dprc.md) +- [Training](doc/train/index.md) + - [Training a model](doc/train/training.md) + - [Advanced options](doc/train/training-advanced.md) + - [Parallel training](doc/train/parallel-training.md) + - [TensorBoard Usage](doc/train/tensorboard.md) + - [Known limitations of using GPUs](doc/train/gpu-limitations.md) + - [Training Parameters](doc/train/train-input-auto.rst) +- [Freeze and Compress](doc/freeze/index.rst) + - [Freeze a model](doc/freeze/freeze.md) + - [Compress a model](doc/freeze/compress.md) +- [Test](doc/test/index.rst) + - [Test a model](doc/test/test.md) + - [Calculate Model Deviation](doc/test/model-deviation.md) +- [Inference](doc/inference/index.rst) + - [Python interface](doc/inference/python.md) + - [C++ interface](doc/inference/cxx.md) +- [Integrate with third-party packages](doc/third-party/index.rst) + - [Use deep potential with ASE](doc/third-party/ase.md) + - [Run MD with LAMMPS](doc/third-party/lammps.md) + - [LAMMPS commands](doc/third-party/lammps-command.md) + - [Run path-integral MD with i-PI](doc/third-party/ipi.md) + - [Run MD with GROMACS](doc/third-party/gromacs.md) + - [Interfaces out of DeePMD-kit](doc/third-party/out-of-deepmd-kit.md) +- [Use NVNMD](doc/nvnmd/index.md) + +# Code structure + +The code is organized as follows: + +* `data/raw`: tools manipulating the raw data files. +* `examples`: examples. +* `deepmd`: DeePMD-kit python modules. +* `source/api_cc`: source code of DeePMD-kit C++ API. +* `source/ipi`: source code of i-PI client. +* `source/lib`: source code of DeePMD-kit library. +* `source/lmp`: source code of Lammps module. +* `source/gmx`: source code of Gromacs plugin. +* `source/op`: tensorflow op implementation. working with library. + + +# Troubleshooting + +- [Model compatibility](doc/troubleshooting/model_compatability.md) +- [Installation](doc/troubleshooting/installation.md) +- [The temperature undulates violently during early stages of MD](doc/troubleshooting/md_energy_undulation.md) +- [MD: cannot run LAMMPS after installing a new version of DeePMD-kit](doc/troubleshooting/md_version_compatibility.md) +- [Do we need to set rcut < half boxsize?](doc/troubleshooting/howtoset_rcut.md) +- [How to set sel?](doc/troubleshooting/howtoset_sel.md) +- [How to control the number of nodes used by a job?](doc/troubleshooting/howtoset_num_nodes.md) +- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md) + + +# Contributing + +See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓 + + +[1]: https://arxiv.org/abs/1707.01478 +[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001 +[3]: https://arxiv.org/abs/1805.09003 +[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645 diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/.ninja_deps b/_skbuild/linux-x86_64-3.6/cmake-build/.ninja_deps deleted file mode 100644 index f980e6c17e7863ac2454b37fa7da193f2df4e036..0000000000000000000000000000000000000000 Binary files a/_skbuild/linux-x86_64-3.6/cmake-build/.ninja_deps and /dev/null differ diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/.ninja_log b/_skbuild/linux-x86_64-3.6/cmake-build/.ninja_log deleted file mode 100644 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- -/* Construct a string literal encoding the version number components. */ -#elif defined(COMPILER_VERSION_MAJOR) -char const info_version[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', - COMPILER_VERSION_MAJOR, -# ifdef COMPILER_VERSION_MINOR - '.', COMPILER_VERSION_MINOR, -# ifdef COMPILER_VERSION_PATCH - '.', COMPILER_VERSION_PATCH, -# ifdef COMPILER_VERSION_TWEAK - '.', COMPILER_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; -#endif - -/* Construct a string literal encoding the internal version number. */ -#ifdef COMPILER_VERSION_INTERNAL -char const info_version_internal[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', - 'i','n','t','e','r','n','a','l','[', - COMPILER_VERSION_INTERNAL,']','\0'}; -#elif defined(COMPILER_VERSION_INTERNAL_STR) -char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; -#endif - -/* Construct a string literal encoding the version number components. */ -#ifdef SIMULATE_VERSION_MAJOR -char const info_simulate_version[] = { - 'I', 'N', 'F', 'O', ':', - 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', - SIMULATE_VERSION_MAJOR, -# ifdef SIMULATE_VERSION_MINOR - '.', SIMULATE_VERSION_MINOR, -# ifdef SIMULATE_VERSION_PATCH - '.', SIMULATE_VERSION_PATCH, -# ifdef SIMULATE_VERSION_TWEAK - '.', SIMULATE_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; 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(((__VER__) / 100)*100)) -# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) -# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) -# endif - - -/* These compilers are either not known or too old to define an - identification macro. 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This is because - the compilers do not have flags that can change the architecture, - but rather depend on which compiler is being used -*/ -#if defined(_WIN32) && defined(_MSC_VER) -# if defined(_M_IA64) -# define ARCHITECTURE_ID "IA64" - -# elif defined(_M_ARM64EC) -# define ARCHITECTURE_ID "ARM64EC" - -# elif defined(_M_X64) || defined(_M_AMD64) -# define ARCHITECTURE_ID "x64" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# elif defined(_M_ARM64) -# define ARCHITECTURE_ID "ARM64" - -# elif defined(_M_ARM) -# if _M_ARM == 4 -# define ARCHITECTURE_ID "ARMV4I" -# elif _M_ARM == 5 -# define ARCHITECTURE_ID "ARMV5I" -# else -# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) -# endif - -# elif defined(_M_MIPS) -# define ARCHITECTURE_ID "MIPS" - -# elif defined(_M_SH) -# define ARCHITECTURE_ID "SHx" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__WATCOMC__) -# if defined(_M_I86) -# define ARCHITECTURE_ID "I86" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) -# if defined(__ICCARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__ICCRX__) -# define ARCHITECTURE_ID "RX" - -# elif defined(__ICCRH850__) -# define ARCHITECTURE_ID "RH850" - -# elif defined(__ICCRL78__) -# define ARCHITECTURE_ID "RL78" - -# elif defined(__ICCRISCV__) -# define ARCHITECTURE_ID "RISCV" - -# elif defined(__ICCAVR__) -# define ARCHITECTURE_ID "AVR" - -# elif defined(__ICC430__) -# define ARCHITECTURE_ID "MSP430" - -# elif defined(__ICCV850__) -# define ARCHITECTURE_ID "V850" - -# elif defined(__ICC8051__) -# define ARCHITECTURE_ID "8051" - -# elif defined(__ICCSTM8__) -# define ARCHITECTURE_ID "STM8" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__ghs__) -# if defined(__PPC64__) -# define ARCHITECTURE_ID "PPC64" - -# elif defined(__ppc__) -# define ARCHITECTURE_ID "PPC" - -# elif defined(__ARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__x86_64__) -# define ARCHITECTURE_ID "x64" - -# elif defined(__i386__) -# define ARCHITECTURE_ID "X86" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__TI_COMPILER_VERSION__) -# if defined(__TI_ARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__MSP430__) -# define ARCHITECTURE_ID "MSP430" - -# elif defined(__TMS320C28XX__) -# define ARCHITECTURE_ID "TMS320C28x" - -# elif defined(__TMS320C6X__) || defined(_TMS320C6X) -# define ARCHITECTURE_ID "TMS320C6x" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -# elif defined(__ADSPSHARC__) -# define ARCHITECTURE_ID "SHARC" - -# elif defined(__ADSPBLACKFIN__) -# define ARCHITECTURE_ID "Blackfin" - -#elif defined(__TASKING__) - -# if defined(__CTC__) || defined(__CPTC__) -# define ARCHITECTURE_ID "TriCore" - -# elif defined(__CMCS__) -# define ARCHITECTURE_ID "MCS" - -# elif defined(__CARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__CARC__) -# define ARCHITECTURE_ID "ARC" - -# elif defined(__C51__) -# define ARCHITECTURE_ID "8051" - -# elif defined(__CPCP__) -# define ARCHITECTURE_ID "PCP" - -# else -# define ARCHITECTURE_ID "" -# endif - -#else -# define ARCHITECTURE_ID -#endif - -/* Convert integer to decimal digit literals. */ -#define DEC(n) \ - ('0' + (((n) / 10000000)%10)), \ - ('0' + (((n) / 1000000)%10)), \ - ('0' + (((n) / 100000)%10)), \ - ('0' + (((n) / 10000)%10)), \ - ('0' + (((n) / 1000)%10)), \ - ('0' + (((n) / 100)%10)), \ - ('0' + (((n) / 10)%10)), \ - ('0' + ((n) % 10)) - -/* Convert integer to hex digit literals. */ -#define HEX(n) \ - ('0' + ((n)>>28 & 0xF)), \ - ('0' + ((n)>>24 & 0xF)), \ - ('0' + ((n)>>20 & 0xF)), \ - ('0' + ((n)>>16 & 0xF)), \ - ('0' + ((n)>>12 & 0xF)), \ - ('0' + ((n)>>8 & 0xF)), \ - ('0' + ((n)>>4 & 0xF)), \ - ('0' + ((n) & 0xF)) - -/* Construct a string literal encoding the version number. */ -#ifdef COMPILER_VERSION -char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; 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/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake - DESC = Installing only the local directory... - pool = console - restat = 1 - -build config/install/local: phony config/CMakeFiles/install/local.util - - -############################################# -# Utility command for install/strip - -build config/CMakeFiles/install/strip.util: CUSTOM_COMMAND config/all - COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/config && /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake - DESC = Installing the project stripped... - pool = console - restat = 1 - -build config/install/strip: phony config/CMakeFiles/install/strip.util - -# ============================================================================= -# Target aliases. - -build deepmd: phony lib/libdeepmd.so - -build libdeepmd.so: phony lib/libdeepmd.so - -build libop_abi.so: phony op/libop_abi.so - -build libop_grads.so: phony op/libop_grads.so - -build op_abi: phony op/libop_abi.so - -build op_grads: phony op/libop_grads.so - -# ============================================================================= -# Folder targets. - -# ============================================================================= - -############################################# -# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build - -build all: phony op/all lib/all config/all - -# ============================================================================= - -############################################# -# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/config - -build config/all: phony - -# ============================================================================= - -############################################# -# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lib - -build lib/all: phony lib/libdeepmd.so - -# ============================================================================= - -############################################# -# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/op - -build op/all: phony op/libop_abi.so op/libop_grads.so - -# ============================================================================= -# Built-in targets - - -############################################# -# Re-run CMake if any of its inputs changed. - -build build.ninja: RERUN_CMAKE | /root/deepmd-kit/source/CMakeLists.txt /root/deepmd-kit/source/cmake/Findtensorflow.cmake /root/deepmd-kit/source/cmake/cmake_lammps.cmake.in /root/deepmd-kit/source/cmake/cmake_uninstall.cmake.in /root/deepmd-kit/source/cmake/tf_version.cpp /root/deepmd-kit/source/config/CMakeLists.txt /root/deepmd-kit/source/config/run_config.ini /root/deepmd-kit/source/lib/CMakeLists.txt /root/deepmd-kit/source/op/CMakeLists.txt /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCCompiler.cmake.in /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCCompilerABI.c /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCInformation.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCXXCompiler.cmake.in /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCXXCompilerABI.cpp /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCXXInformation.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/CMakeCommonLanguageInclude.cmake 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/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/FindPackageMessage.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/FindThreads.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Internal/CheckSourceCompiles.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Internal/FeatureTesting.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux-Determine-CXX.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux-GNU-C.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux-GNU-CXX.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux-GNU.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/UnixPaths.cmake CMakeCache.txt CMakeFiles/3.25.2/CMakeCCompiler.cmake CMakeFiles/3.25.2/CMakeCXXCompiler.cmake CMakeFiles/3.25.2/CMakeSystem.cmake - pool = console - - -############################################# -# A missing CMake input file is not an error. - -build /root/deepmd-kit/source/CMakeLists.txt /root/deepmd-kit/source/cmake/Findtensorflow.cmake /root/deepmd-kit/source/cmake/cmake_lammps.cmake.in /root/deepmd-kit/source/cmake/cmake_uninstall.cmake.in /root/deepmd-kit/source/cmake/tf_version.cpp /root/deepmd-kit/source/config/CMakeLists.txt /root/deepmd-kit/source/config/run_config.ini 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/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux-GNU.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/Linux.cmake /tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/cmake/data/share/cmake-3.25/Modules/Platform/UnixPaths.cmake CMakeCache.txt CMakeFiles/3.25.2/CMakeCCompiler.cmake CMakeFiles/3.25.2/CMakeCXXCompiler.cmake CMakeFiles/3.25.2/CMakeSystem.cmake: phony - - -############################################# -# Clean all the built files. - -build clean: CLEAN - - -############################################# -# Print all primary targets available. - -build help: HELP - - -############################################# -# Make the all target the default. - -default all diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_install.cmake b/_skbuild/linux-x86_64-3.6/cmake-build/cmake_install.cmake deleted file mode 100644 index 5826cf1467faf74b414ca4fc009573989ae87c6b..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_install.cmake +++ /dev/null @@ -1,62 +0,0 @@ -# Install script for directory: /root/deepmd-kit/source - -# Set the install prefix -if(NOT DEFINED CMAKE_INSTALL_PREFIX) - set(CMAKE_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-install") -endif() -string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") - -# Set the install configuration name. -if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) - if(BUILD_TYPE) - string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" - CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") - else() - set(CMAKE_INSTALL_CONFIG_NAME "Release") - endif() - message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") -endif() - -# Set the component getting installed. -if(NOT CMAKE_INSTALL_COMPONENT) - if(COMPONENT) - message(STATUS "Install component: \"${COMPONENT}\"") - set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") - else() - set(CMAKE_INSTALL_COMPONENT) - endif() -endif() - -# Install shared libraries without execute permission? -if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE) - set(CMAKE_INSTALL_SO_NO_EXE "1") -endif() - -# Is this installation the result of a crosscompile? -if(NOT DEFINED CMAKE_CROSSCOMPILING) - set(CMAKE_CROSSCOMPILING "FALSE") -endif() - -# Set default install directory permissions. -if(NOT DEFINED CMAKE_OBJDUMP) - set(CMAKE_OBJDUMP "/usr/bin/objdump") -endif() - -if(NOT CMAKE_INSTALL_LOCAL_ONLY) - # Include the install script for each subdirectory. - include("/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/op/cmake_install.cmake") - include("/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lib/cmake_install.cmake") - include("/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/config/cmake_install.cmake") - -endif() - -if(CMAKE_INSTALL_COMPONENT) - set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt") -else() - set(CMAKE_INSTALL_MANIFEST "install_manifest.txt") -endif() - -string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT - "${CMAKE_INSTALL_MANIFEST_FILES}") -file(WRITE "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/${CMAKE_INSTALL_MANIFEST}" - "${CMAKE_INSTALL_MANIFEST_CONTENT}") diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_lammps.cmake b/_skbuild/linux-x86_64-3.6/cmake-build/cmake_lammps.cmake deleted file mode 100644 index 684b8df52c99af395de763d5fa8e2509233787e2..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_lammps.cmake +++ /dev/null @@ -1,28 +0,0 @@ -set (LMP_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/USER-DEEPMD") -file(READ "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lmp/lammps_install_list.txt" files) -string(REGEX REPLACE "\n" "" files "${files}") - -foreach (cur_file ${files}) - file ( - INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" - TYPE FILE - FILES "${cur_file}" - ) -endforeach () - -file ( - INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" - TYPE FILE - FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lmp/env.sh" -) -file ( - INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" - TYPE FILE - FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lmp/env_low.sh" -) - -file ( - INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" - TYPE FILE - FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/lmp/pair_deepmd.h" -) diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_uninstall.cmake b/_skbuild/linux-x86_64-3.6/cmake-build/cmake_uninstall.cmake deleted file mode 100644 index a38afa15a9895087e3a006da10791618457cda8b..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/cmake_uninstall.cmake +++ /dev/null @@ -1,21 +0,0 @@ -if(NOT EXISTS "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt") - message(FATAL_ERROR "Cannot find install manifest: 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a/_skbuild/linux-x86_64-3.6/cmake-build/op/libop_grads.so and /dev/null differ diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__about__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__about__.py deleted file mode 100644 index 840624bec554d26de81a37b9df46ecc95361c0e5..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__about__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = 'unknown' diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__init__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__init__.py deleted file mode 100644 index c9e4149517159e206add8fb4936ee455493b1cd6..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Root of the deepmd package, exposes all public classes and submodules.""" - -try: - from importlib import metadata -except ImportError: # for Python<3.8 - import importlib_metadata as metadata -import deepmd.utils.network as network - -from . import cluster, descriptor, fit, loss, utils, nvnmd -from .env import set_mkl -from .infer import DeepEval, DeepPotential -from .infer.data_modifier import DipoleChargeModifier - -set_mkl() - -try: - from ._version import version as __version__ -except ImportError: - from .__about__ import __version__ - -# load third-party plugins -for ep in metadata.entry_points().get('deepmd', []): - ep.load() - -__all__ = [ - "descriptor", - "fit", - "loss", - "utils", - "cluster", - "network", - "DeepEval", - "DeepPotential", - "DipoleChargeModifier", - "nvnmd", -] diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__main__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__main__.py deleted file mode 100644 index ca814493674c4fcc14d1868e28f832e0ca41c857..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/__main__.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Package dp entry point.""" - -from .entrypoints.main import main - -if __name__ == '__main__': - main() diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/_version.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/_version.py deleted file mode 100644 index b095e94640c21cb94a6e0e74d6160144c855a92c..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/_version.py +++ /dev/null @@ -1,4 +0,0 @@ -# coding: utf-8 -# file generated by setuptools_scm -# don't change, don't track in version control -version = '2.1.6.dev0+ng6e3d4a62.d20230313' diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/calculator.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/calculator.py deleted file mode 100644 index 38c56b72b261ff2ac9d974aefbfa604331db8e46..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/calculator.py +++ /dev/null @@ -1,119 +0,0 @@ -"""ASE calculator interface module.""" - -from pathlib import Path -from typing import TYPE_CHECKING, Dict, List, Optional, Union - -from ase.calculators.calculator import ( - Calculator, all_changes, PropertyNotImplementedError -) - -from deepmd import DeepPotential - -if TYPE_CHECKING: - from ase import Atoms - -__all__ = ["DP"] - - -class DP(Calculator): - """Implementation of ASE deepmd calculator. - - Implemented propertie are `energy`, `forces` and `stress` - - Parameters - ---------- - model : Union[str, Path] - path to the model - label : str, optional - calculator label, by default "DP" - type_dict : Dict[str, int], optional - mapping of element types and their numbers, best left None and the calculator - will infer this information from model, by default None - - Examples - -------- - Compute potential energy - - >>> from ase import Atoms - >>> from deepmd.calculator import DP - >>> water = Atoms('H2O', - >>> positions=[(0.7601, 1.9270, 1), - >>> (1.9575, 1, 1), - >>> (1., 1., 1.)], - >>> cell=[100, 100, 100], - >>> calculator=DP(model="frozen_model.pb")) - >>> print(water.get_potential_energy()) - >>> print(water.get_forces()) - - Run BFGS structure optimization - - >>> from ase.optimize import BFGS - >>> dyn = BFGS(water) - >>> dyn.run(fmax=1e-6) - >>> print(water.get_positions()) - """ - - name = "DP" - implemented_properties = ["energy", "free_energy", "forces", "virial", "stress"] - - def __init__( - self, - model: Union[str, "Path"], - label: str = "DP", - type_dict: Dict[str, int] = None, - **kwargs - ) -> None: - Calculator.__init__(self, label=label, **kwargs) - self.dp = DeepPotential(str(Path(model).resolve())) - if type_dict: - self.type_dict = type_dict - else: - self.type_dict = dict( - zip(self.dp.get_type_map(), range(self.dp.get_ntypes())) - ) - - def calculate( - self, - atoms: Optional["Atoms"] = None, - properties: List[str] = ["energy", "forces", "virial"], - system_changes: List[str] = all_changes, - ): - """Run calculation with deepmd model. - - Parameters - ---------- - atoms : Optional[Atoms], optional - atoms object to run the calculation on, by default None - properties : List[str], optional - unused, only for function signature compatibility, - by default ["energy", "forces", "stress"] - system_changes : List[str], optional - unused, only for function signature compatibility, by default all_changes - """ - if atoms is not None: - self.atoms = atoms.copy() - - coord = self.atoms.get_positions().reshape([1, -1]) - if sum(self.atoms.get_pbc()) > 0: - cell = self.atoms.get_cell().reshape([1, -1]) - else: - cell = None - symbols = self.atoms.get_chemical_symbols() - atype = [self.type_dict[k] for k in symbols] - e, f, v = self.dp.eval(coords=coord, cells=cell, atom_types=atype) - self.results['energy'] = e[0][0] - # see https://gitlab.com/ase/ase/-/merge_requests/2485 - self.results['free_energy'] = e[0][0] - self.results['forces'] = f[0] - self.results['virial'] = v[0].reshape(3, 3) - - # convert virial into stress for lattice relaxation - if "stress" in properties: - if sum(atoms.get_pbc()) > 0: - # the usual convention (tensile stress is positive) - # stress = -virial / volume - stress = -0.5 * (v[0].copy() + v[0].copy().T) / atoms.get_volume() - # Voigt notation - self.results['stress'] = stress.flat[[0, 4, 8, 5, 2, 1]] - else: - raise PropertyNotImplementedError diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/__init__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/__init__.py deleted file mode 100644 index 7007208d4b7dec61dd4ec7651c5f9d5e362d8a2e..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Module that reads node resources, auto detects if running local or on SLURM.""" - -from .local import get_resource as get_local_res -from .slurm import get_resource as get_slurm_res -import os -from typing import List, Tuple, Optional - -__all__ = ["get_resource"] - - -def get_resource() -> Tuple[str, List[str], Optional[List[int]]]: - """Get local or slurm resources: nodename, nodelist, and gpus. - - Returns - ------- - Tuple[str, List[str], Optional[List[int]]] - nodename, nodelist, and gpus - """ - if "SLURM_JOB_NODELIST" in os.environ: - return get_slurm_res() - else: - return get_local_res() diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/local.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/local.py deleted file mode 100644 index 9b7f622975881aa0b514dc4d708581cd42e92fc5..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/local.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Get local GPU resources.""" - -import os -import socket -import subprocess as sp -import sys - -from deepmd.env import tf -from typing import List, Tuple, Optional - - -__all__ = ["get_gpus", "get_resource"] - - -def get_gpus(): - """Get available IDs of GPU cards at local. - These IDs are valid when used as the TensorFlow device ID. - - Returns - ------- - Optional[List[int]] - List of available GPU IDs. Otherwise, None. - """ - test_cmd = 'from tensorflow.python.client import device_lib; ' \ - 'devices = device_lib.list_local_devices(); ' \ - 'gpus = [d.name for d in devices if d.device_type == "GPU"]; ' \ - 'print(len(gpus))' - with sp.Popen([sys.executable, "-c", test_cmd], stderr=sp.PIPE, stdout=sp.PIPE) as p: - stdout, stderr = p.communicate() - if p.returncode != 0: - decoded = stderr.decode('UTF-8') - raise RuntimeError('Failed to detect availbe GPUs due to:\n%s' % decoded) - decoded = stdout.decode('UTF-8').strip() - num_gpus = int(decoded) - return list(range(num_gpus)) if num_gpus > 0 else None - - -def get_resource() -> Tuple[str, List[str], Optional[List[int]]]: - """Get local resources: nodename, nodelist, and gpus. - - Returns - ------- - Tuple[str, List[str], Optional[List[int]]] - nodename, nodelist, and gpus - """ - nodename = socket.gethostname() - nodelist = [nodename] - gpus = get_gpus() - return nodename, nodelist, gpus diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/slurm.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/slurm.py deleted file mode 100644 index 6334f4a8f6ef2936bed8b705bd5cc0282948c419..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/cluster/slurm.py +++ /dev/null @@ -1,51 +0,0 @@ -"""MOdule to get resources on SLURM cluster. - -References ----------- -https://github.com/deepsense-ai/tensorflow_on_slurm #### -""" - -import hostlist -import os - -from deepmd.cluster import local -from typing import List, Tuple, Optional - -__all__ = ["get_resource"] - - -def get_resource() -> Tuple[str, List[str], Optional[List[int]]]: - """Get SLURM resources: nodename, nodelist, and gpus. - - Returns - ------- - Tuple[str, List[str], Optional[List[int]]] - nodename, nodelist, and gpus - - Raises - ------ - RuntimeError - if number of nodes could not be retrieved - ValueError - list of nodes is not of the same length sa number of nodes - ValueError - if current nodename is not found in node list - """ - nodelist = hostlist.expand_hostlist(os.environ["SLURM_JOB_NODELIST"]) - nodename = os.environ["SLURMD_NODENAME"] - num_nodes_env = os.getenv("SLURM_JOB_NUM_NODES") - if num_nodes_env: - num_nodes = int(num_nodes_env) - else: - raise RuntimeError("Could not get SLURM number of nodes") - - if len(nodelist) != num_nodes: - raise ValueError( - f"Number of slurm nodes {len(nodelist)} not equal to {num_nodes}" - ) - if nodename not in nodelist: - raise ValueError( - f"Nodename({nodename}) not in nodelist({nodelist}). This should not happen!" - ) - gpus = local.get_gpus() - return nodename, nodelist, gpus diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/common.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/common.py deleted file mode 100644 index b868038b3d346ea716e336741e7ade531848ac6a..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/common.py +++ /dev/null @@ -1,579 +0,0 @@ -"""Collection of functions and classes used throughout the whole package.""" - -import json -import warnings -import tensorflow -from functools import wraps -from pathlib import Path -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Dict, - List, - Optional, - Tuple, - TypeVar, - Union, -) - -import numpy as np -import yaml - -from deepmd.env import op_module, tf -from tensorflow.python.framework import tensor_util -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION, GLOBAL_NP_FLOAT_PRECISION -from deepmd.utils.sess import run_sess -from deepmd.utils.errors import GraphWithoutTensorError -from deepmd.utils.path import DPPath - -if TYPE_CHECKING: - _DICT_VAL = TypeVar("_DICT_VAL") - _OBJ = TypeVar("_OBJ") - try: - from typing import Literal # python >3.6 - except ImportError: - from typing_extensions import Literal # type: ignore - _ACTIVATION = Literal["relu", "relu6", "softplus", "sigmoid", "tanh", "gelu", "gelu_tf"] - _PRECISION = Literal["default", "float16", "float32", "float64"] - -# define constants -PRECISION_DICT = { - "default": GLOBAL_TF_FLOAT_PRECISION, - "float16": tf.float16, - "float32": tf.float32, - "float64": tf.float64, -} - - -def gelu(x: tf.Tensor) -> tf.Tensor: - """Gaussian Error Linear Unit. - - This is a smoother version of the RELU, implemented by custom operator. - - Parameters - ---------- - x : tf.Tensor - float Tensor to perform activation - - Returns - ------- - tf.Tensor - `x` with the GELU activation applied - - References - ---------- - Original paper - https://arxiv.org/abs/1606.08415 - """ - return op_module.gelu_custom(x) - - -def gelu_tf(x: tf.Tensor) -> tf.Tensor: - """Gaussian Error Linear Unit. - - This is a smoother version of the RELU, implemented by TF. - - Parameters - ---------- - x : tf.Tensor - float Tensor to perform activation - - Returns - ------- - tf.Tensor - `x` with the GELU activation applied - - References - ---------- - Original paper - https://arxiv.org/abs/1606.08415 - """ - def gelu_wrapper(x): - try: - return tensorflow.nn.gelu(x, approximate=True) - except AttributeError: - warnings.warn("TensorFlow does not provide an implementation of gelu, please upgrade your TensorFlow version. Fallback to the custom gelu operator.") - return op_module.gelu_custom(x) - return (lambda x: gelu_wrapper(x))(x) - -# TODO this is not a good way to do things. This is some global variable to which -# TODO anyone can write and there is no good way to keep track of the changes -data_requirement = {} - -ACTIVATION_FN_DICT = { - "relu": tf.nn.relu, - "relu6": tf.nn.relu6, - "softplus": tf.nn.softplus, - "sigmoid": tf.sigmoid, - "tanh": tf.nn.tanh, - "gelu": gelu, - "gelu_tf": gelu_tf, -} - - -def add_data_requirement( - key: str, - ndof: int, - atomic: bool = False, - must: bool = False, - high_prec: bool = False, - type_sel: bool = None, - repeat: int = 1, - default: float = 0., -): - """Specify data requirements for training. - - Parameters - ---------- - key : str - type of data stored in corresponding `*.npy` file e.g. `forces` or `energy` - ndof : int - number of the degrees of freedom, this is tied to `atomic` parameter e.g. forces - have `atomic=True` and `ndof=3` - atomic : bool, optional - specifies whwther the `ndof` keyworrd applies to per atom quantity or not, - by default False - must : bool, optional - specifi if the `*.npy` data file must exist, by default False - high_prec : bool, optional - if tru load data to `np.float64` else `np.float32`, by default False - type_sel : bool, optional - select only certain type of atoms, by default None - repeat : int, optional - if specify repaeat data `repeat` times, by default 1 - default : float, optional, default=0. - default value of data - """ - data_requirement[key] = { - "ndof": ndof, - "atomic": atomic, - "must": must, - "high_prec": high_prec, - "type_sel": type_sel, - "repeat": repeat, - "default": default, - } - - -def select_idx_map( - atom_types: np.ndarray, select_types: np.ndarray -) -> np.ndarray: - """Build map of indices for element supplied element types from all atoms list. - - Parameters - ---------- - atom_types : np.ndarray - array specifing type for each atoms as integer - select_types : np.ndarray - types of atoms you want to find indices for - - Returns - ------- - np.ndarray - indices of types of atoms defined by `select_types` in `atom_types` array - - Warnings - -------- - `select_types` array will be sorted before finding indices in `atom_types` - """ - sort_select_types = np.sort(select_types) - idx_map = np.array([], dtype=int) - for ii in sort_select_types: - idx_map = np.append(idx_map, np.where(atom_types == ii)) - return idx_map - - -# TODO not really sure if the docstring is right the purpose of this is a bit unclear -def make_default_mesh( - test_box: np.ndarray, cell_size: float = 3.0 -) -> np.ndarray: - """Get number of cells of size=`cell_size` fit into average box. - - Parameters - ---------- - test_box : np.ndarray - numpy array with cells of shape Nx9 - cell_size : float, optional - length of one cell, by default 3.0 - - Returns - ------- - np.ndarray - mesh for supplied boxes, how many cells fit in each direction - """ - cell_lengths = np.linalg.norm(test_box.reshape([-1, 3, 3]), axis=2) - avg_cell_lengths = np.average(cell_lengths, axis=0) - ncell = (avg_cell_lengths / cell_size).astype(np.int32) - ncell[ncell < 2] = 2 - default_mesh = np.zeros(6, dtype=np.int32) - default_mesh[3:6] = ncell - return default_mesh - - -# TODO not an ideal approach, every class uses this to parse arguments on its own, json -# TODO should be parsed once and the parsed result passed to all objects that need it -class ClassArg: - """Class that take care of input json/yaml parsing. - - The rules for parsing are defined by the `add` method, than `parse` is called to - process the supplied dict - - Attributes - ---------- - arg_dict: Dict[str, Any] - dictionary containing parsing rules - alias_map: Dict[str, Any] - dictionary with keyword aliases - """ - - def __init__(self) -> None: - self.arg_dict = {} - self.alias_map = {} - - def add( - self, - key: str, - types_: Union[type, List[type]], - alias: Optional[Union[str, List[str]]] = None, - default: Any = None, - must: bool = False, - ) -> "ClassArg": - """Add key to be parsed. - - Parameters - ---------- - key : str - key name - types_ : Union[type, List[type]] - list of allowed key types - alias : Optional[Union[str, List[str]]], optional - alias for the key, by default None - default : Any, optional - default value for the key, by default None - must : bool, optional - if the key is mandatory, by default False - - Returns - ------- - ClassArg - instance with added key - """ - if not isinstance(types_, list): - types = [types_] - else: - types = types_ - if alias is not None: - if not isinstance(alias, list): - alias_ = [alias] - else: - alias_ = alias - else: - alias_ = [] - - self.arg_dict[key] = { - "types": types, - "alias": alias_, - "value": default, - "must": must, - } - for ii in alias_: - self.alias_map[ii] = key - - return self - - def _add_single(self, key: str, data: Any): - vtype = type(data) - if data is None: - return data - if not (vtype in self.arg_dict[key]["types"]): - for tp in self.arg_dict[key]["types"]: - try: - vv = tp(data) - except TypeError: - pass - else: - break - else: - raise TypeError( - f"cannot convert provided key {key} to type(s) " - f'{self.arg_dict[key]["types"]} ' - ) - else: - vv = data - self.arg_dict[key]["value"] = vv - - def _check_must(self): - for kk in self.arg_dict: - if self.arg_dict[kk]["must"] and self.arg_dict[kk]["value"] is None: - raise RuntimeError(f"key {kk} must be provided") - - def parse(self, jdata: Dict[str, Any]) -> Dict[str, Any]: - """Parse input dictionary, use the rules defined by add method. - - Parameters - ---------- - jdata : Dict[str, Any] - loaded json/yaml data - - Returns - ------- - Dict[str, Any] - parsed dictionary - """ - for kk in jdata.keys(): - if kk in self.arg_dict: - key = kk - self._add_single(key, jdata[kk]) - else: - if kk in self.alias_map: - key = self.alias_map[kk] - self._add_single(key, jdata[kk]) - self._check_must() - return self.get_dict() - - def get_dict(self) -> Dict[str, Any]: - """Get dictionary built from rules defined by add method. - - Returns - ------- - Dict[str, Any] - settings dictionary with default values - """ - ret = {} - for kk in self.arg_dict.keys(): - ret[kk] = self.arg_dict[kk]["value"] - return ret - - -# TODO maybe rename this to j_deprecated and only warn about deprecated keys, -# TODO if the deprecated_key argument is left empty function puppose is only custom -# TODO error since dict[key] already raises KeyError when the key is missing -def j_must_have( - jdata: Dict[str, "_DICT_VAL"], key: str, deprecated_key: List[str] = [] -) -> "_DICT_VAL": - """Assert that supplied dictionary conaines specified key. - - Returns - ------- - _DICT_VAL - value that was store unde supplied key - - Raises - ------ - RuntimeError - if the key is not present - """ - if key not in jdata.keys(): - for ii in deprecated_key: - if ii in jdata.keys(): - warnings.warn(f"the key {ii} is deprecated, please use {key} instead") - return jdata[ii] - else: - raise RuntimeError(f"json database must provide key {key}") - else: - return jdata[key] - - -def j_loader(filename: Union[str, Path]) -> Dict[str, Any]: - """Load yaml or json settings file. - - Parameters - ---------- - filename : Union[str, Path] - path to file - - Returns - ------- - Dict[str, Any] - loaded dictionary - - Raises - ------ - TypeError - if the supplied file is of unsupported type - """ - filepath = Path(filename) - if filepath.suffix.endswith("json"): - with filepath.open() as fp: - return json.load(fp) - elif filepath.suffix.endswith(("yml", "yaml")): - with filepath.open() as fp: - return yaml.safe_load(fp) - else: - raise TypeError("config file must be json, or yaml/yml") - - -def get_activation_func( - activation_fn: Union["_ACTIVATION", None], -) -> Union[Callable[[tf.Tensor], tf.Tensor], None]: - """Get activation function callable based on string name. - - Parameters - ---------- - activation_fn : _ACTIVATION - one of the defined activation functions - - Returns - ------- - Callable[[tf.Tensor], tf.Tensor] - correspondingg TF callable - - Raises - ------ - RuntimeError - if unknown activation function is specified - """ - if activation_fn is None or activation_fn in ['none', 'None']: - return None - if activation_fn not in ACTIVATION_FN_DICT: - raise RuntimeError(f"{activation_fn} is not a valid activation function") - return ACTIVATION_FN_DICT[activation_fn] - - -def get_precision(precision: "_PRECISION") -> Any: - """Convert str to TF DType constant. - - Parameters - ---------- - precision : _PRECISION - one of the allowed precisions - - Returns - ------- - tf.python.framework.dtypes.DType - appropriate TF constant - - Raises - ------ - RuntimeError - if supplied precision string does not have acorresponding TF constant - """ - if precision not in PRECISION_DICT: - raise RuntimeError(f"{precision} is not a valid precision") - return PRECISION_DICT[precision] - - -# TODO port completely to pathlib when all callers are ported -def expand_sys_str(root_dir: Union[str, Path]) -> List[str]: - """Recursively iterate over directories taking those that contain `type.raw` file. - - Parameters - ---------- - root_dir : Union[str, Path] - starting directory - - Returns - ------- - List[str] - list of string pointing to system directories - """ - root_dir = DPPath(root_dir) - matches = [str(d) for d in root_dir.rglob("*") if (d / "type.raw").is_file()] - if (root_dir / "type.raw").is_file(): - matches.append(str(root_dir)) - return matches - - -def get_np_precision(precision: "_PRECISION") -> np.dtype: - """Get numpy precision constant from string. - - Parameters - ---------- - precision : _PRECISION - string name of numpy constant or default - - Returns - ------- - np.dtype - numpy presicion constant - - Raises - ------ - RuntimeError - if string is invalid - """ - if precision == "default": - return GLOBAL_NP_FLOAT_PRECISION - elif precision == "float16": - return np.float16 - elif precision == "float32": - return np.float32 - elif precision == "float64": - return np.float64 - else: - raise RuntimeError(f"{precision} is not a valid precision") - - -def safe_cast_tensor(input: tf.Tensor, - from_precision: tf.DType, - to_precision: tf.DType) -> tf.Tensor: - """Convert a Tensor from a precision to another precision. - - If input is not a Tensor or without the specific precision, the method will not - cast it. - - Parameters - ---------- - input: tf.Tensor - input tensor - precision : tf.DType - Tensor data type that casts to - - Returns - ------- - tf.Tensor - casted Tensor - """ - if tensor_util.is_tensor(input) and input.dtype == from_precision: - return tf.cast(input, to_precision) - return input - - -def cast_precision(func: Callable) -> Callable: - """A decorator that casts and casts back the input - and output tensor of a method. - - The decorator should be used in a classmethod. - - The decorator will do the following thing: - (1) It casts input Tensors from `GLOBAL_TF_FLOAT_PRECISION` - to precision defined by property `precision`. - (2) It casts output Tensors from `precision` to - `GLOBAL_TF_FLOAT_PRECISION`. - (3) It checks inputs and outputs and only casts when - input or output is a Tensor and its dtype matches - `GLOBAL_TF_FLOAT_PRECISION` and `precision`, respectively. - If it does not match (e.g. it is an integer), the decorator - will do nothing on it. - - Returns - ------- - Callable - a decorator that casts and casts back the input and - output tensor of a method - - Examples - -------- - >>> class A: - ... @property - ... def precision(self): - ... return tf.float32 - ... - ... @cast_precision - ... def f(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor: - ... return x ** 2 + y - """ - @wraps(func) - def wrapper(self, *args, **kwargs): - # only convert tensors - returned_tensor = func( - self, - *[safe_cast_tensor(vv, GLOBAL_TF_FLOAT_PRECISION, self.precision) for vv in args], - **{kk: safe_cast_tensor(vv, GLOBAL_TF_FLOAT_PRECISION, self.precision) for kk, vv in kwargs.items()}, - ) - if isinstance(returned_tensor, tuple): - return tuple((safe_cast_tensor(vv, self.precision, GLOBAL_TF_FLOAT_PRECISION) for vv in returned_tensor)) - else: - return safe_cast_tensor(returned_tensor, self.precision, GLOBAL_TF_FLOAT_PRECISION) - return wrapper diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/__init__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/__init__.py deleted file mode 100644 index c5025a74831deee1573a0e6a794b5ba2cfb1e69e..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from .descriptor import Descriptor -from .hybrid import DescrptHybrid -from .se_a import DescrptSeA -from .se_r import DescrptSeR -from .se_t import DescrptSeT -from .se_a_ebd import DescrptSeAEbd -from .se_a_ef import DescrptSeAEf -from .se_a_ef import DescrptSeAEfLower -from .loc_frame import DescrptLocFrame -from .se_atten import DescrptSeAtten diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/descriptor.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/descriptor.py deleted file mode 100644 index 0c40013f884d15915a90501865e4537b7053a210..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/descriptor.py +++ /dev/null @@ -1,408 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Any, Dict, List, Tuple - -import numpy as np -from deepmd.env import tf -from deepmd.utils import Plugin, PluginVariant - - -class Descriptor(PluginVariant): - r"""The abstract class for descriptors. All specific descriptors should - be based on this class. - - The descriptor :math:`\mathcal{D}` describes the environment of an atom, - which should be a function of coordinates and types of its neighbour atoms. - - Examples - -------- - >>> descript = Descriptor(type="se_e2_a", rcut=6., rcut_smth=0.5, sel=[50]) - >>> type(descript) - - - Notes - ----- - Only methods and attributes defined in this class are generally public, - that can be called by other classes. - """ - - __plugins = Plugin() - - @staticmethod - def register(key: str) -> "Descriptor": - """Regiester a descriptor plugin. - - Parameters - ---------- - key : str - the key of a descriptor - - Returns - ------- - Descriptor - the regiestered descriptor - - Examples - -------- - >>> @Descriptor.register("some_descrpt") - class SomeDescript(Descriptor): - pass - """ - return Descriptor.__plugins.register(key) - - def __new__(cls, *args, **kwargs): - if cls is Descriptor: - try: - descrpt_type = kwargs['type'] - except KeyError: - raise KeyError('the type of descriptor should be set by `type`') - if descrpt_type in Descriptor.__plugins.plugins: - cls = Descriptor.__plugins.plugins[descrpt_type] - else: - raise RuntimeError('Unknown descriptor type: ' + descrpt_type) - return super().__new__(cls) - - @abstractmethod - def get_rcut(self) -> float: - """ - Returns the cut-off radius. - - Returns - ------- - float - the cut-off radius - - Notes - ----- - This method must be implemented, as it's called by other classes. - """ - - @abstractmethod - def get_ntypes(self) -> int: - """ - Returns the number of atom types. - - Returns - ------- - int - the number of atom types - - Notes - ----- - This method must be implemented, as it's called by other classes. - """ - - @abstractmethod - def get_dim_out(self) -> int: - """ - Returns the output dimension of this descriptor. - - Returns - ------- - int - the output dimension of this descriptor - - Notes - ----- - This method must be implemented, as it's called by other classes. - """ - - def get_dim_rot_mat_1(self) -> int: - """ - Returns the first dimension of the rotation matrix. The rotation is of shape - dim_1 x 3 - - Returns - ------- - int - the first dimension of the rotation matrix - """ - # TODO: I think this method should be implemented as it's called by dipole and - # polar fitting network. However, currently not all descriptors have this - # method. - raise NotImplementedError - - def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """ - Returns neighbor information. - - Returns - ------- - nlist : tf.Tensor - Neighbor list - rij : tf.Tensor - The relative distance between the neighbor and the center atom. - sel_a : list[int] - The number of neighbors with full information - sel_r : list[int] - The number of neighbors with only radial information - """ - # TODO: I think this method should be implemented as it's called by energy - # model. However, se_ar and hybrid doesn't have this method. - raise NotImplementedError - - @abstractmethod - def compute_input_stats(self, - data_coord: List[np.ndarray], - data_box: List[np.ndarray], - data_atype: List[np.ndarray], - natoms_vec: List[np.ndarray], - mesh: List[np.ndarray], - input_dict: Dict[str, List[np.ndarray]] - ) -> None: - """ - Compute the statisitcs (avg and std) of the training data. The input will be - normalized by the statistics. - - Parameters - ---------- - data_coord : list[np.ndarray] - The coordinates. Can be generated by - :meth:`deepmd.model.model_stat.make_stat_input` - data_box : list[np.ndarray] - The box. Can be generated by - :meth:`deepmd.model.model_stat.make_stat_input` - data_atype : list[np.ndarray] - The atom types. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input` - natoms_vec : list[np.ndarray] - The vector for the number of atoms of the system and different types of - atoms. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input` - mesh : list[np.ndarray] - The mesh for neighbor searching. Can be generated by - :meth:`deepmd.model.model_stat.make_stat_input` - input_dict : dict[str, list[np.ndarray]] - Dictionary for additional input - - Notes - ----- - This method must be implemented, as it's called by other classes. - """ - - @abstractmethod - def build(self, - coord_: tf.Tensor, - atype_: tf.Tensor, - natoms: tf.Tensor, - box_: tf.Tensor, - mesh: tf.Tensor, - input_dict: Dict[str, Any], - reuse: bool = None, - suffix: str = '', - ) -> tf.Tensor: - """ - Build the computational graph for the descriptor. - - Parameters - ---------- - coord_ : tf.Tensor - The coordinate of atoms - atype_ : tf.Tensor - The type of atoms - natoms : tf.Tensor - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - box : tf.Tensor - The box of frames - mesh : tf.Tensor - For historical reasons, only the length of the Tensor matters. - if size of mesh == 6, pbc is assumed. - if size of mesh == 0, no-pbc is assumed. - input_dict : dict[str, Any] - Dictionary for additional inputs - reuse : bool, optional - The weights in the networks should be reused when get the variable. - suffix : str, optional - Name suffix to identify this descriptor - - Returns - ------- - descriptor: tf.Tensor - The output descriptor - - Notes - ----- - This method must be implemented, as it's called by other classes. - """ - - def enable_compression(self, - min_nbor_dist: float, - model_file: str = 'frozon_model.pb', - table_extrapolate: float = 5., - table_stride_1: float = 0.01, - table_stride_2: float = 0.1, - check_frequency: int = -1, - suffix: str = "", - ) -> None: - """ - Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the - training data. - - Parameters - ---------- - min_nbor_dist : float - The nearest distance between atoms - model_file : str, default: 'frozon_model.pb' - The original frozen model, which will be compressed by the program - table_extrapolate : float, default: 5. - The scale of model extrapolation - table_stride_1 : float, default: 0.01 - The uniform stride of the first table - table_stride_2 : float, default: 0.1 - The uniform stride of the second table - check_frequency : int, default: -1 - The overflow check frequency - suffix : str, optional - The suffix of the scope - - Notes - ----- - This method is called by others when the descriptor supported compression. - """ - raise NotImplementedError( - "Descriptor %s doesn't support compression!" % type(self).__name__) - - def enable_mixed_precision(self, mixed_prec: dict = None) -> None: - """ - Reveive the mixed precision setting. - - Parameters - ---------- - mixed_prec - The mixed precision setting used in the embedding net - - Notes - ----- - This method is called by others when the descriptor supported compression. - """ - raise NotImplementedError( - "Descriptor %s doesn't support mixed precision training!" - % type(self).__name__ - ) - - @abstractmethod - def prod_force_virial(self, - atom_ener: tf.Tensor, - natoms: tf.Tensor - ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: - """ - Compute force and virial. - - Parameters - ---------- - atom_ener : tf.Tensor - The atomic energy - natoms : tf.Tensor - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - - Returns - ------- - force : tf.Tensor - The force on atoms - virial : tf.Tensor - The total virial - atom_virial : tf.Tensor - The atomic virial - """ - - def get_feed_dict(self, - coord_: tf.Tensor, - atype_: tf.Tensor, - natoms: tf.Tensor, - box: tf.Tensor, - mesh: tf.Tensor - ) -> Dict[str, tf.Tensor]: - """ - Generate the feed_dict for current descriptor - - Parameters - ---------- - coord_ : tf.Tensor - The coordinate of atoms - atype_ : tf.Tensor - The type of atoms - natoms : tf.Tensor - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - box : tf.Tensor - The box. Can be generated by deepmd.model.make_stat_input - mesh : tf.Tensor - For historical reasons, only the length of the Tensor matters. - if size of mesh == 6, pbc is assumed. - if size of mesh == 0, no-pbc is assumed. - - Returns - ------- - feed_dict : dict[str, tf.Tensor] - The output feed_dict of current descriptor - """ - feed_dict = { - 't_coord:0' :coord_, - 't_type:0' :atype_, - 't_natoms:0' :natoms, - 't_box:0' :box, - 't_mesh:0' :mesh - } - return feed_dict - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix : str = "", - ) -> None: - """ - Init the embedding net variables with the given dict - - Parameters - ---------- - graph : tf.Graph - The input frozen model graph - graph_def : tf.GraphDef - The input frozen model graph_def - suffix : str, optional - The suffix of the scope - - Notes - ----- - This method is called by others when the descriptor supported initialization from the given variables. - """ - raise NotImplementedError( - "Descriptor %s doesn't support initialization from the given variables!" % type(self).__name__) - - def get_tensor_names(self, suffix : str = "") -> Tuple[str]: - """Get names of tensors. - - Parameters - ---------- - suffix : str - The suffix of the scope - - Returns - ------- - Tuple[str] - Names of tensors - """ - raise NotImplementedError("Descriptor %s doesn't support this property!" % type(self).__name__) - - def pass_tensors_from_frz_model(self, - *tensors : tf.Tensor, - ) -> None: - """ - Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def - - Parameters - ---------- - *tensors : tf.Tensor - passed tensors - - Notes - ----- - The number of parameters in the method must be equal to the numbers of returns in - :meth:`get_tensor_names`. - """ - raise NotImplementedError("Descriptor %s doesn't support this method!" % type(self).__name__) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/hybrid.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/hybrid.py deleted file mode 100644 index e577b2ef17040a891a8e44cfe0cce5f0b9d9ca82..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/hybrid.py +++ /dev/null @@ -1,334 +0,0 @@ -import numpy as np -from typing import Tuple, List - -from deepmd.env import tf -from deepmd.common import ClassArg -from deepmd.env import op_module -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -# from deepmd.descriptor import DescrptLocFrame -# from deepmd.descriptor import DescrptSeA -# from deepmd.descriptor import DescrptSeT -# from deepmd.descriptor import DescrptSeAEbd -# from deepmd.descriptor import DescrptSeAEf -# from deepmd.descriptor import DescrptSeR -from .descriptor import Descriptor -from .se_a import DescrptSeA -from .se_r import DescrptSeR -from .se_t import DescrptSeT -from .se_a_ebd import DescrptSeAEbd -from .se_a_ef import DescrptSeAEf -from .loc_frame import DescrptLocFrame - -@Descriptor.register("hybrid") -class DescrptHybrid (Descriptor): - """Concate a list of descriptors to form a new descriptor. - - Parameters - ---------- - list : list - Build a descriptor from the concatenation of the list of descriptors. - """ - def __init__ (self, - list : list - ) -> None : - """ - Constructor - """ - # warning: list is conflict with built-in list - descrpt_list = list - if descrpt_list == [] or descrpt_list is None: - raise RuntimeError('cannot build descriptor from an empty list of descriptors.') - formatted_descript_list = [] - for ii in descrpt_list: - if isinstance(ii, Descriptor): - formatted_descript_list.append(ii) - elif isinstance(ii, dict): - formatted_descript_list.append(Descriptor(**ii)) - else: - raise NotImplementedError - # args = ClassArg()\ - # .add('list', list, must = True) - # class_data = args.parse(jdata) - # dict_list = class_data['list'] - self.descrpt_list = formatted_descript_list - self.numb_descrpt = len(self.descrpt_list) - for ii in range(1, self.numb_descrpt): - assert(self.descrpt_list[ii].get_ntypes() == - self.descrpt_list[ 0].get_ntypes()), \ - f'number of atom types in {ii}th descrptor does not match others' - - - def get_rcut (self) -> float: - """ - Returns the cut-off radius - """ - all_rcut = [ii.get_rcut() for ii in self.descrpt_list] - return np.max(all_rcut) - - - def get_ntypes (self) -> int: - """ - Returns the number of atom types - """ - return self.descrpt_list[0].get_ntypes() - - - def get_dim_out (self) -> int: - """ - Returns the output dimension of this descriptor - """ - all_dim_out = [ii.get_dim_out() for ii in self.descrpt_list] - return sum(all_dim_out) - - - def get_nlist_i(self, - ii : int - ) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """Get the neighbor information of the ii-th descriptor - - Parameters - ---------- - ii : int - The index of the descriptor - - Returns - ------- - nlist - Neighbor list - rij - The relative distance between the neighbor and the center atom. - sel_a - The number of neighbors with full information - sel_r - The number of neighbors with only radial information - """ - return self.descrpt_list[ii].nlist, self.descrpt_list[ii].rij, self.descrpt_list[ii].sel_a, self.descrpt_list[ii].sel_r - - - def compute_input_stats (self, - data_coord : list, - data_box : list, - data_atype : list, - natoms_vec : list, - mesh : list, - input_dict : dict - ) -> None : - """ - Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics. - - Parameters - ---------- - data_coord - The coordinates. Can be generated by deepmd.model.make_stat_input - data_box - The box. Can be generated by deepmd.model.make_stat_input - data_atype - The atom types. Can be generated by deepmd.model.make_stat_input - natoms_vec - The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input - mesh - The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input - input_dict - Dictionary for additional input - """ - for ii in self.descrpt_list: - ii.compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict) - - - def build (self, - coord_ : tf.Tensor, - atype_ : tf.Tensor, - natoms : tf.Tensor, - box_ : tf.Tensor, - mesh : tf.Tensor, - input_dict : dict, - reuse : bool = None, - suffix : str = '' - ) -> tf.Tensor: - """ - Build the computational graph for the descriptor - - Parameters - ---------- - coord_ - The coordinate of atoms - atype_ - The type of atoms - natoms - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - mesh - For historical reasons, only the length of the Tensor matters. - if size of mesh == 6, pbc is assumed. - if size of mesh == 0, no-pbc is assumed. - input_dict - Dictionary for additional inputs - reuse - The weights in the networks should be reused when get the variable. - suffix - Name suffix to identify this descriptor - - Returns - ------- - descriptor - The output descriptor - """ - with tf.variable_scope('descrpt_attr' + suffix, reuse = reuse) : - t_rcut = tf.constant(self.get_rcut(), - name = 'rcut', - dtype = GLOBAL_TF_FLOAT_PRECISION) - t_ntypes = tf.constant(self.get_ntypes(), - name = 'ntypes', - dtype = tf.int32) - all_dout = [] - for idx,ii in enumerate(self.descrpt_list): - dout = ii.build(coord_, atype_, natoms, box_, mesh, input_dict, suffix=suffix+f'_{idx}', reuse=reuse) - dout = tf.reshape(dout, [-1, ii.get_dim_out()]) - all_dout.append(dout) - dout = tf.concat(all_dout, axis = 1) - dout = tf.reshape(dout, [-1, natoms[0], self.get_dim_out()]) - return dout - - - def prod_force_virial(self, - atom_ener : tf.Tensor, - natoms : tf.Tensor - ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: - """ - Compute force and virial - - Parameters - ---------- - atom_ener - The atomic energy - natoms - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - - Returns - ------- - force - The force on atoms - virial - The total virial - atom_virial - The atomic virial - """ - for idx,ii in enumerate(self.descrpt_list): - ff, vv, av = ii.prod_force_virial(atom_ener, natoms) - if idx == 0: - force = ff - virial = vv - atom_virial = av - else: - force += ff - virial += vv - atom_virial += av - return force, virial, atom_virial - - def enable_compression(self, - min_nbor_dist: float, - model_file: str = 'frozon_model.pb', - table_extrapolate: float = 5., - table_stride_1: float = 0.01, - table_stride_2: float = 0.1, - check_frequency: int = -1, - suffix: str = "" - ) -> None: - """ - Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the - training data. - - Parameters - ---------- - min_nbor_dist : float - The nearest distance between atoms - model_file : str, default: 'frozon_model.pb' - The original frozen model, which will be compressed by the program - table_extrapolate : float, default: 5. - The scale of model extrapolation - table_stride_1 : float, default: 0.01 - The uniform stride of the first table - table_stride_2 : float, default: 0.1 - The uniform stride of the second table - check_frequency : int, default: -1 - The overflow check frequency - suffix : str, optional - The suffix of the scope - """ - for idx, ii in enumerate(self.descrpt_list): - ii.enable_compression(min_nbor_dist, model_file, table_extrapolate, table_stride_1, table_stride_2, check_frequency, suffix=f"{suffix}_{idx}") - - - def enable_mixed_precision(self, mixed_prec : dict = None) -> None: - """ - Reveive the mixed precision setting. - - Parameters - ---------- - mixed_prec - The mixed precision setting used in the embedding net - """ - for idx, ii in enumerate(self.descrpt_list): - ii.enable_mixed_precision(mixed_prec) - - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix : str = "", - ) -> None: - """ - Init the embedding net variables with the given dict - - Parameters - ---------- - graph : tf.Graph - The input frozen model graph - graph_def : tf.GraphDef - The input frozen model graph_def - suffix : str, optional - The suffix of the scope - """ - for idx, ii in enumerate(self.descrpt_list): - ii.init_variables(graph, graph_def, suffix=f"{suffix}_{idx}") - - def get_tensor_names(self, suffix : str = "") -> Tuple[str]: - """Get names of tensors. - - Parameters - ---------- - suffix : str - The suffix of the scope - - Returns - ------- - Tuple[str] - Names of tensors - """ - tensor_names = [] - for idx, ii in enumerate(self.descrpt_list): - tensor_names.extend(ii.get_tensor_names(suffix=f"{suffix}_{idx}")) - return tuple(tensor_names) - - def pass_tensors_from_frz_model(self, - *tensors : tf.Tensor, - ) -> None: - """ - Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def - - Parameters - ---------- - *tensors : tf.Tensor - passed tensors - """ - jj = 0 - for ii in self.descrpt_list: - n_tensors = len(ii.get_tensor_names()) - ii.pass_tensors_from_frz_model(*tensors[jj:jj+n_tensors]) - jj += n_tensors diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/loc_frame.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/loc_frame.py deleted file mode 100644 index 81a8f684e948d9ac741fcc56126343cc7f641969..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/loc_frame.py +++ /dev/null @@ -1,389 +0,0 @@ -import numpy as np -from typing import Tuple, List - -from deepmd.env import tf -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.env import op_module -from deepmd.env import default_tf_session_config -from deepmd.utils.sess import run_sess -from .descriptor import Descriptor -from deepmd.utils.graph import get_tensor_by_name_from_graph - -@Descriptor.register("loc_frame") -class DescrptLocFrame (Descriptor) : - """Defines a local frame at each atom, and the compute the descriptor as local - coordinates under this frame. - - Parameters - ---------- - rcut - The cut-off radius - sel_a : list[str] - The length of the list should be the same as the number of atom types in the system. - `sel_a[i]` gives the selected number of type-i neighbors. - The full relative coordinates of the neighbors are used by the descriptor. - sel_r : list[str] - The length of the list should be the same as the number of atom types in the system. - `sel_r[i]` gives the selected number of type-i neighbors. - Only relative distance of the neighbors are used by the descriptor. - sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. - axis_rule: list[int] - The length should be 6 times of the number of types. - - axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\ - - axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.\n\n\ - - axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.\n\n\ - - axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\ - - axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.\n\n\ - - axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance. - """ - def __init__(self, - rcut: float, - sel_a : List[int], - sel_r : List[int], - axis_rule : List[int] - ) -> None: - """ - Constructor - """ - # args = ClassArg()\ - # .add('sel_a', list, must = True) \ - # .add('sel_r', list, must = True) \ - # .add('rcut', float, default = 6.0) \ - # .add('axis_rule',list, must = True) - # class_data = args.parse(jdata) - self.sel_a = sel_a - self.sel_r = sel_r - self.axis_rule = axis_rule - self.rcut_r = rcut - # ntypes and rcut_a === -1 - self.ntypes = len(self.sel_a) - assert(self.ntypes == len(self.sel_r)) - self.rcut_a = -1 - # numb of neighbors and numb of descrptors - self.nnei_a = np.cumsum(self.sel_a)[-1] - self.nnei_r = np.cumsum(self.sel_r)[-1] - self.nnei = self.nnei_a + self.nnei_r - self.ndescrpt_a = self.nnei_a * 4 - self.ndescrpt_r = self.nnei_r * 1 - self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r - self.davg = None - self.dstd = None - - self.place_holders = {} - avg_zero = np.zeros([self.ntypes,self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) - std_ones = np.ones ([self.ntypes,self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) - sub_graph = tf.Graph() - with sub_graph.as_default(): - name_pfx = 'd_lf_' - for ii in ['coord', 'box']: - self.place_holders[ii] = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None], name = name_pfx+'t_'+ii) - self.place_holders['type'] = tf.placeholder(tf.int32, [None, None], name=name_pfx+'t_type') - self.place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes+2], name=name_pfx+'t_natoms') - self.place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name=name_pfx+'t_mesh') - self.stat_descrpt, descrpt_deriv, rij, nlist, axis, rot_mat \ - = op_module.descrpt (self.place_holders['coord'], - self.place_holders['type'], - self.place_holders['natoms_vec'], - self.place_holders['box'], - self.place_holders['default_mesh'], - tf.constant(avg_zero), - tf.constant(std_ones), - rcut_a = self.rcut_a, - rcut_r = self.rcut_r, - sel_a = self.sel_a, - sel_r = self.sel_r, - axis_rule = self.axis_rule) - self.sub_sess = tf.Session(graph = sub_graph, config=default_tf_session_config) - - - def get_rcut (self) -> float: - """ - Returns the cut-off radisu - """ - return self.rcut_r - - def get_ntypes (self) -> int: - """ - Returns the number of atom types - """ - return self.ntypes - - def get_dim_out (self) -> int: - """ - Returns the output dimension of this descriptor - """ - return self.ndescrpt - - def get_nlist (self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """ - Returns - ------- - nlist - Neighbor list - rij - The relative distance between the neighbor and the center atom. - sel_a - The number of neighbors with full information - sel_r - The number of neighbors with only radial information - """ - return self.nlist, self.rij, self.sel_a, self.sel_r - - def compute_input_stats (self, - data_coord : list, - data_box : list, - data_atype : list, - natoms_vec : list, - mesh : list, - input_dict : dict - ) -> None : - """ - Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics. - - Parameters - ---------- - data_coord - The coordinates. Can be generated by deepmd.model.make_stat_input - data_box - The box. Can be generated by deepmd.model.make_stat_input - data_atype - The atom types. Can be generated by deepmd.model.make_stat_input - natoms_vec - The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input - mesh - The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input - input_dict - Dictionary for additional input - """ - all_davg = [] - all_dstd = [] - if True: - sumv = [] - sumn = [] - sumv2 = [] - for cc,bb,tt,nn,mm in zip(data_coord,data_box,data_atype,natoms_vec,mesh) : - sysv,sysv2,sysn \ - = self._compute_dstats_sys_nonsmth(cc,bb,tt,nn,mm) - sumv.append(sysv) - sumn.append(sysn) - sumv2.append(sysv2) - sumv = np.sum(sumv, axis = 0) - sumn = np.sum(sumn, axis = 0) - sumv2 = np.sum(sumv2, axis = 0) - for type_i in range(self.ntypes) : - davg = sumv[type_i] / sumn[type_i] - dstd = self._compute_std(sumv2[type_i], sumv[type_i], sumn[type_i]) - for ii in range (len(dstd)) : - if (np.abs(dstd[ii]) < 1e-2) : - dstd[ii] = 1e-2 - all_davg.append(davg) - all_dstd.append(dstd) - self.davg = np.array(all_davg) - self.dstd = np.array(all_dstd) - - - def build (self, - coord_ : tf.Tensor, - atype_ : tf.Tensor, - natoms : tf.Tensor, - box_ : tf.Tensor, - mesh : tf.Tensor, - input_dict : dict, - reuse : bool = None, - suffix : str = '' - ) -> tf.Tensor: - """ - Build the computational graph for the descriptor - - Parameters - ---------- - coord_ - The coordinate of atoms - atype_ - The type of atoms - natoms - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - mesh - For historical reasons, only the length of the Tensor matters. - if size of mesh == 6, pbc is assumed. - if size of mesh == 0, no-pbc is assumed. - input_dict - Dictionary for additional inputs - reuse - The weights in the networks should be reused when get the variable. - suffix - Name suffix to identify this descriptor - - Returns - ------- - descriptor - The output descriptor - """ - davg = self.davg - dstd = self.dstd - with tf.variable_scope('descrpt_attr' + suffix, reuse = reuse) : - if davg is None: - davg = np.zeros([self.ntypes, self.ndescrpt]) - if dstd is None: - dstd = np.ones ([self.ntypes, self.ndescrpt]) - t_rcut = tf.constant(np.max([self.rcut_r, self.rcut_a]), - name = 'rcut', - dtype = GLOBAL_TF_FLOAT_PRECISION) - t_ntypes = tf.constant(self.ntypes, - name = 'ntypes', - dtype = tf.int32) - self.t_avg = tf.get_variable('t_avg', - davg.shape, - dtype = GLOBAL_TF_FLOAT_PRECISION, - trainable = False, - initializer = tf.constant_initializer(davg)) - self.t_std = tf.get_variable('t_std', - dstd.shape, - dtype = GLOBAL_TF_FLOAT_PRECISION, - trainable = False, - initializer = tf.constant_initializer(dstd)) - - coord = tf.reshape (coord_, [-1, natoms[1] * 3]) - box = tf.reshape (box_, [-1, 9]) - atype = tf.reshape (atype_, [-1, natoms[1]]) - - self.descrpt, self.descrpt_deriv, self.rij, self.nlist, self.axis, self.rot_mat \ - = op_module.descrpt (coord, - atype, - natoms, - box, - mesh, - self.t_avg, - self.t_std, - rcut_a = self.rcut_a, - rcut_r = self.rcut_r, - sel_a = self.sel_a, - sel_r = self.sel_r, - axis_rule = self.axis_rule) - self.descrpt = tf.reshape(self.descrpt, [-1, self.ndescrpt]) - tf.summary.histogram('descrpt', self.descrpt) - tf.summary.histogram('rij', self.rij) - tf.summary.histogram('nlist', self.nlist) - - return self.descrpt - - def get_rot_mat(self) -> tf.Tensor: - """ - Get rotational matrix - """ - return self.rot_mat - - def prod_force_virial(self, - atom_ener : tf.Tensor, - natoms : tf.Tensor - ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: - """ - Compute force and virial - - Parameters - ---------- - atom_ener - The atomic energy - natoms - The number of atoms. This tensor has the length of Ntypes + 2 - natoms[0]: number of local atoms - natoms[1]: total number of atoms held by this processor - natoms[i]: 2 <= i < Ntypes+2, number of type i atoms - - Returns - ------- - force - The force on atoms - virial - The total virial - atom_virial - The atomic virial - """ - [net_deriv] = tf.gradients (atom_ener, self.descrpt) - tf.summary.histogram('net_derivative', net_deriv) - net_deriv_reshape = tf.reshape (net_deriv, [np.cast['int64'](-1), natoms[0] * np.cast['int64'](self.ndescrpt)]) - force = op_module.prod_force (net_deriv_reshape, - self.descrpt_deriv, - self.nlist, - self.axis, - natoms, - n_a_sel = self.nnei_a, - n_r_sel = self.nnei_r) - virial, atom_virial \ - = op_module.prod_virial (net_deriv_reshape, - self.descrpt_deriv, - self.rij, - self.nlist, - self.axis, - natoms, - n_a_sel = self.nnei_a, - n_r_sel = self.nnei_r) - tf.summary.histogram('force', force) - tf.summary.histogram('virial', virial) - tf.summary.histogram('atom_virial', atom_virial) - - return force, virial, atom_virial - - - def _compute_dstats_sys_nonsmth (self, - data_coord, - data_box, - data_atype, - natoms_vec, - mesh) : - dd_all \ - = run_sess(self.sub_sess, self.stat_descrpt, - feed_dict = { - self.place_holders['coord']: data_coord, - self.place_holders['type']: data_atype, - self.place_holders['natoms_vec']: natoms_vec, - self.place_holders['box']: data_box, - self.place_holders['default_mesh']: mesh, - }) - natoms = natoms_vec - dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]]) - start_index = 0 - sysv = [] - sysn = [] - sysv2 = [] - for type_i in range(self.ntypes): - end_index = start_index + self.ndescrpt * natoms[2+type_i] - dd = dd_all[:, start_index:end_index] - dd = np.reshape(dd, [-1, self.ndescrpt]) - start_index = end_index - # compute - sumv = np.sum(dd, axis = 0) - sumn = dd.shape[0] - sumv2 = np.sum(np.multiply(dd,dd), axis = 0) - sysv.append(sumv) - sysn.append(sumn) - sysv2.append(sumv2) - return sysv, sysv2, sysn - - - def _compute_std (self,sumv2, sumv, sumn) : - return np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn)) - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix : str = "", - ) -> None: - """ - Init the embedding net variables with the given dict - - Parameters - ---------- - graph : tf.Graph - The input frozen model graph - graph_def : tf.GraphDef - The input frozen model graph_def - suffix : str, optional - The suffix of the scope - """ - self.davg = get_tensor_by_name_from_graph(graph, 'descrpt_attr%s/t_avg' % suffix) - self.dstd = get_tensor_by_name_from_graph(graph, 'descrpt_attr%s/t_std' % suffix) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se.py deleted file mode 100644 index 09bb57137cf2c3d7b5b87cce1c4aa6651c29b2b0..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se.py +++ /dev/null @@ -1,118 +0,0 @@ -from typing import Tuple, List - -from deepmd.env import tf -from deepmd.utils.graph import get_embedding_net_variables_from_graph_def, get_tensor_by_name_from_graph -from .descriptor import Descriptor - - -class DescrptSe (Descriptor): - """A base class for smooth version of descriptors. - - Notes - ----- - All of these descriptors have an environmental matrix and an - embedding network (:meth:`deepmd.utils.network.embedding_net`), so - they can share some similiar methods without defining them twice. - - Attributes - ---------- - embedding_net_variables : dict - initial embedding network variables - descrpt_reshape : tf.Tensor - the reshaped descriptor - descrpt_deriv : tf.Tensor - the descriptor derivative - rij : tf.Tensor - distances between two atoms - nlist : tf.Tensor - the neighbor list - - """ - def _identity_tensors(self, suffix : str = "") -> None: - """Identify tensors which are expected to be stored and restored. - - Notes - ----- - These tensors will be indentitied: - self.descrpt_reshape : o_rmat - self.descrpt_deriv : o_rmat_deriv - self.rij : o_rij - self.nlist : o_nlist - Thus, this method should be called during building the descriptor and - after these tensors are initialized. - - Parameters - ---------- - suffix : str - The suffix of the scope - """ - self.descrpt_reshape = tf.identity(self.descrpt_reshape, name = 'o_rmat' + suffix) - self.descrpt_deriv = tf.identity(self.descrpt_deriv, name = 'o_rmat_deriv' + suffix) - self.rij = tf.identity(self.rij, name = 'o_rij' + suffix) - self.nlist = tf.identity(self.nlist, name = 'o_nlist' + suffix) - - def get_tensor_names(self, suffix : str = "") -> Tuple[str]: - """Get names of tensors. - - Parameters - ---------- - suffix : str - The suffix of the scope - - Returns - ------- - Tuple[str] - Names of tensors - """ - return (f'o_rmat{suffix}:0', f'o_rmat_deriv{suffix}:0', f'o_rij{suffix}:0', f'o_nlist{suffix}:0') - - def pass_tensors_from_frz_model(self, - descrpt_reshape : tf.Tensor, - descrpt_deriv : tf.Tensor, - rij : tf.Tensor, - nlist : tf.Tensor - ): - """ - Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def - - Parameters - ---------- - descrpt_reshape - The passed descrpt_reshape tensor - descrpt_deriv - The passed descrpt_deriv tensor - rij - The passed rij tensor - nlist - The passed nlist tensor - """ - self.rij = rij - self.nlist = nlist - self.descrpt_deriv = descrpt_deriv - self.descrpt_reshape = descrpt_reshape - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix : str = "", - ) -> None: - """ - Init the embedding net variables with the given dict - - Parameters - ---------- - graph : tf.Graph - The input frozen model graph - graph_def : tf.GraphDef - The input frozen model graph_def - suffix : str, optional - The suffix of the scope - """ - self.embedding_net_variables = get_embedding_net_variables_from_graph_def(graph_def, suffix = suffix) - self.davg = get_tensor_by_name_from_graph(graph, 'descrpt_attr%s/t_avg' % suffix) - self.dstd = get_tensor_by_name_from_graph(graph, 'descrpt_attr%s/t_std' % suffix) - - @property - def precision(self) -> tf.DType: - """Precision of filter network.""" - return self.filter_precision diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se_a.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se_a.py deleted file mode 100644 index de83f5926e7cc9e3252d37b85579caae9fea617e..0000000000000000000000000000000000000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/descriptor/se_a.py +++ /dev/null @@ -1,924 +0,0 @@ -import math -import numpy as np -from typing import Tuple, List, Dict, Any - -from deepmd.env import tf -from deepmd.common import get_activation_func, get_precision, cast_precision -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.env import op_module -from deepmd.env import default_tf_session_config -from deepmd.utils.network import embedding_net, embedding_net_rand_seed_shift -from deepmd.utils.tabulate import DPTabulate -from deepmd.utils.type_embed import embed_atom_type -from deepmd.utils.sess import run_sess -from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph, get_tensor_by_name -from deepmd.utils.errors import GraphWithoutTensorError -from .descriptor import Descriptor -from .se import DescrptSe - -from deepmd.nvnmd.descriptor.se_a import descrpt2r4, build_davg_dstd, build_op_descriptor, filter_lower_R42GR, filter_GR2D -from deepmd.nvnmd.utils.config import nvnmd_cfg - -@Descriptor.register("se_e2_a") -@Descriptor.register("se_a") -class DescrptSeA (DescrptSe): - r"""DeepPot-SE constructed from all information (both angular and radial) of - atomic configurations. The embedding takes the distance between atoms as input. - - The descriptor :math:`\mathcal{D}^i \in \mathcal{R}^{M_1 \times M_2}` is given by [1]_ - - .. math:: - \mathcal{D}^i = (\mathcal{G}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \mathcal{G}^i_< - - where :math:`\mathcal{R}^i \in \mathbb{R}^{N \times 4}` is the coordinate - matrix, and each row of :math:`\mathcal{R}^i` can be constructed as follows - - .. math:: - (\mathcal{R}^i)_j = [ - \begin{array}{c} - s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} - \end{array} - ] - - where :math:`\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})` is - the relative coordinate and :math:`r_{ji}=\lVert \mathbf{R}_{ji} \lVert` is its norm. - The switching function :math:`s(r)` is defined as: - - .. math:: - s(r)= - \begin{cases} - \frac{1}{r}, & r