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deepmd/__pycache__/__init__.cpython-37.pyc create mode 100644 deepmd/__pycache__/env.cpython-37.pyc create mode 100644 deepmd/utils/__pycache__/__init__.cpython-37.pyc create mode 100644 deepmd/utils/__pycache__/data.cpython-37.pyc create mode 100644 input_v2_compat.json create mode 100644 main.py create mode 100644 run.sh create mode 100644 source/CMakeLists.txt diff --git a/README.md b/README.md index 7f0ed9c..9106428 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 0000000..7f0ed9c --- /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 GIT binary patch literal 0 HcmV?d00001 literal 332304 zcmeF42b>$l_4pSxd6a!~4U}Ns&h6~unz2J(w!65HRd#ClOS>=kc!8WG%W_mZh zcS7&ICG_5FAR(cbg#Y*LYIQoDbf-frIlufTpL~&gnwdBC&3iLDv%6G;&F~`KZ$#r8 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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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- return 0; -} - - -Determining if the function pthread_create exists in the pthreads failed with the following output: -Change Dir: /public/home/zhangqha/sourcecode/source/examples/TensorFlow/Train/new/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-MeJDCi - -Run Build Command(s):/tmp/pip-build-env-6r5leccz/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_e25c5 && [1/2] Building C object CMakeFiles/cmTC_e25c5.dir/CheckFunctionExists.c.o -[2/2] Linking C executable cmTC_e25c5 -FAILED: cmTC_e25c5 -: && /usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTC_e25c5.dir/CheckFunctionExists.c.o -o cmTC_e25c5 -lpthreads && : -/usr/bin/ld: cannot find -lpthreads -collect2: error: ld returned 1 exit status -ninja: build stopped: subcommand failed. - - - -Performing C SOURCE FILE Test CMAKE_HAVE_LIBC_PTHREAD failed with the following output: -Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-maaLfJ - 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5826cf1..0000000 --- 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 684b8df..0000000 --- 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 a38afa1..0000000 --- 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: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt") -endif(NOT EXISTS "/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt") - -file(READ 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b/_skbuild/linux-x86_64-3.6/cmake-build/config/cmake_install.cmake deleted file mode 100644 index fffc069..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/config/cmake_install.cmake +++ /dev/null @@ -1,48 +0,0 @@ -# Install script for directory: /root/deepmd-kit/source/config - -# 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: 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a/_skbuild/linux-x86_64-3.6/cmake-build/config/run_config.ini +++ /dev/null @@ -1,12 +0,0 @@ -[CONFIG] -INSTALL_PREFIX = /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-install -GIT_SUMM = v2.1.5-dirty -GIT_HASH = 6e3d4a62 -GIT_DATE = 2022-09-23 16:10:28 +0800 -GIT_BRANCH = HEAD -TF_INCLUDE_DIR = /usr/local/lib/python3.6/dist-packages/tensorflow/include;/usr/local/lib/python3.6/dist-packages/tensorflow/include -TF_LIBS = -TF_VERSION = 2.4.0 -TF_CXX11_ABI_FLAG = 0 -MODEL_VERSION=1.1 -DP_VARIANT=cpu diff --git a/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt b/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt deleted file mode 100644 index aab3ddd..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/install_manifest.txt +++ /dev/null @@ -1,19 +0,0 @@ -/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/op/libop_abi.so -/root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/op/libop_grads.so 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b/_skbuild/linux-x86_64-3.6/cmake-build/lib/cmake_install.cmake deleted file mode 100644 index b8ebdc1..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-build/lib/cmake_install.cmake +++ /dev/null @@ -1,67 +0,0 @@ -# Install script for directory: /root/deepmd-kit/source/lib - -# 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}\"") - 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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 ca81449..0000000 --- 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 b095e94..0000000 --- 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 38c56b7..0000000 --- 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 7007208..0000000 --- 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 9b7f622..0000000 --- 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 6334f4a..0000000 --- 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 b868038..0000000 --- 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 c5025a7..0000000 --- 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 0c40013..0000000 --- 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 e577b2e..0000000 --- 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 81a8f68..0000000 --- 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 09bb571..0000000 --- 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 de83f59..0000000 --- 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)6#?1v!#}$2m=c9mlvktpVLX1b3}4q;9Cbj^rjwRmBS>>T7R9gD zkRqBC;^SE0mI*sKQhmel)u`pjhxx2CGfaHfP0zdf7rkk5R2|SfECUxu)#9qxd}~?! 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/usr/local/lib/python3.6/dist-packages/tensorflow/include;/usr/local/lib/python3.6/dist-packages/tensorflow/include -TF_LIBS = -TF_VERSION = 2.4.0 -TF_CXX11_ABI_FLAG = 0 -MODEL_VERSION=1.1 -DP_VARIANT=cpu diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/__init__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/__init__.py deleted file mode 100644 index d3f5a12..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/run_options.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/run_options.py deleted file mode 100644 index ce5815e..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/run_options.py +++ /dev/null @@ -1,233 +0,0 @@ -"""Module taking care of important package constants.""" - -import logging -import os -from pathlib import Path -from typing import TYPE_CHECKING, Dict, List, Optional, Tuple - -import numpy as np -from deepmd.cluster import get_resource -from deepmd.env import get_tf_default_nthreads, tf, GLOBAL_CONFIG, global_float_prec -from deepmd.loggers import set_log_handles - -if TYPE_CHECKING: - import horovod.tensorflow as HVD - - -__all__ = [ - "WELCOME", - "CITATION", - "BUILD", - "RunOptions", -] - -log = logging.getLogger(__name__) - - -# http://patorjk.com/software/taag. Font:Big" -WELCOME = ( # noqa - " _____ _____ __ __ _____ _ _ _ ", - "| __ \ | __ \ | \/ || __ \ | | (_)| | ", - "| | | | ___ ___ | |__) || \ / || | | | ______ | | __ _ | |_ ", - "| | | | / _ \ / _ \| ___/ | |\/| || | | ||______|| |/ /| || __|", - "| |__| || __/| __/| | | | | || |__| | | < | || |_ ", - "|_____/ \___| \___||_| |_| |_||_____/ |_|\_\|_| \__|", -) - -CITATION = ( - "Please read and cite:", - "Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018)", -) - -_sep = "\n " -BUILD = ( - f"installed to: {GLOBAL_CONFIG['install_prefix']}", - f"source : {GLOBAL_CONFIG['git_summ']}", - f"source brach: {GLOBAL_CONFIG['git_branch']}", - f"source commit: {GLOBAL_CONFIG['git_hash']}", - f"source commit at: {GLOBAL_CONFIG['git_date']}", - f"build float prec: {global_float_prec}", - f"build variant: {GLOBAL_CONFIG['dp_variant']}", - f"build with tf inc: {GLOBAL_CONFIG['tf_include_dir']}", - f"build with tf lib: {GLOBAL_CONFIG['tf_libs'].replace(';', _sep)}" # noqa -) - - -class RunOptions: - """Class with inf oon how to run training (cluster, MPI and GPU config). - - Attributes - ---------- - gpus: Optional[List[int]] - list of GPUs if any are present else None - is_chief: bool - in distribured training it is true for tha main MPI process in serail it is - always true - world_size: int - total worker count - my_rank: int - index of the MPI task - nodename: str - name of the node - node_list_ : List[str] - the list of nodes of the current mpirun - my_device: str - deviice type - gpu or cpu - """ - - gpus: Optional[List[int]] - world_size: int - my_rank: int - nodename: str - nodelist: List[int] - my_device: str - - _HVD: Optional["HVD"] - _log_handles_already_set: bool = False - - def __init__( - self, - init_model: Optional[str] = None, - init_frz_model: Optional[str] = None, - restart: Optional[str] = None, - log_path: Optional[str] = None, - log_level: int = 0, - mpi_log: str = "master" - ): - self._try_init_distrib() - - if all((init_model, restart)): - raise RuntimeError( - "--init-model and --restart should not be set at the same time" - ) - - # model init options - self.restart = restart - self.init_model = init_model - self.init_mode = "init_from_scratch" - - if restart is not None: - self.restart = os.path.abspath(restart) - self.init_mode = "restart" - elif init_model is not None: - self.init_model = os.path.abspath(init_model) - self.init_mode = "init_from_model" - elif init_frz_model is not None: - self.init_frz_model = os.path.abspath(init_frz_model) - self.init_mode = "init_from_frz_model" - - self._setup_logger(Path(log_path) if log_path else None, log_level, mpi_log) - - @property - def is_chief(self): - """Whether my rank is 0.""" - return self.my_rank == 0 - - def print_resource_summary(self): - """Print build and current running cluster configuration summary.""" - log.info("---Summary of the training---------------------------------------") - if self.is_distrib: - log.info("distributed") - log.info(f"world size: {self.world_size}") - log.info(f"my rank: {self.my_rank}") - log.info(f"node list: {self.nodelist}") - log.info(f"running on: {self.nodename}") - log.info(f"computing device: {self.my_device}") - env_value = os.environ.get('CUDA_VISIBLE_DEVICES', 'unset') - log.info(f"CUDA_VISIBLE_DEVICES: {env_value}") - log.info(f"Count of visible GPU: {len(self.gpus or [])}") - intra, inter = get_tf_default_nthreads() - log.info(f"num_intra_threads: {intra:d}") - log.info(f"num_inter_threads: {inter:d}") - log.info("-----------------------------------------------------------------") - - def _setup_logger( - self, - log_path: Optional[Path], - log_level: int, - mpi_log: Optional[str], - ): - """Set up package loggers. - - Parameters - ---------- - log_level: int - logging level - log_path: Optional[str] - path to log file, if None logs will be send only to console. If the parent - directory does not exist it will be automatically created, by default None - mpi_log : Optional[str], optional - mpi log type. Has three options. `master` will output logs to file and - console only from rank==0. `collect` will write messages from all ranks to - one file opened under rank==0 and to console. `workers` will open one log - file for each worker designated by its rank, console behaviour is the same - as for `collect`. - """ - if not self._log_handles_already_set: - if not self._HVD: - mpi_log = None - set_log_handles(log_level, log_path, mpi_log=mpi_log) - self._log_handles_already_set = True - log.debug("Log handles were successfully set") - else: - log.warning( - f"Log handles have already been set. It is not advisable to " - f"reset them{', especially when runnig with MPI!' if self._HVD else ''}" - ) - - def _try_init_distrib(self): - try: - import horovod.tensorflow as HVD - HVD.init() - self.is_distrib = HVD.size() > 1 - except ImportError: - log.warning("Switch to serial execution due to lack of horovod module.") - self.is_distrib = False - - # Do real intialization - if self.is_distrib: - self._init_distributed(HVD) - self._HVD = HVD - else: - self._init_serial() - self._HVD = None - - def _init_distributed(self, HVD: "HVD"): - """Initialize settings for distributed training. - - Parameters - ---------- - HVD : HVD - horovod object - """ - nodename, nodelist, gpus = get_resource() - self.nodename = nodename - self.nodelist = nodelist - self.gpus = gpus - self.my_rank = HVD.rank() - self.world_size = HVD.size() - - if gpus is not None: - gpu_idx = HVD.local_rank() - if gpu_idx >= len(gpus): - raise RuntimeError('Count of local processes is larger than that of available GPUs!') - self.my_device = f"gpu:{gpu_idx:d}" - else: - self.my_device = "cpu:0" - - def _init_serial(self): - """Initialize setting for serial training.""" - nodename, _, gpus = get_resource() - - self.gpus = gpus - self.world_size = 1 - self.my_rank = 0 - self.nodename = nodename - self.nodelist = [nodename] - - if gpus is not None: - self.my_device = "gpu:0" - else: - self.my_device = "cpu:0" - - self._HVD = None diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/trainer.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/trainer.py deleted file mode 100644 index e2f1e39..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/train/trainer.py +++ /dev/null @@ -1,744 +0,0 @@ -#!/usr/bin/env python3 -from deepmd.descriptor.descriptor import Descriptor -import logging -import os -import glob -import platform -import time -import shutil -import google.protobuf.message -import numpy as np -from packaging.version import Version - -from deepmd.env import tf, tfv2 -from deepmd.env import get_tf_session_config -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.env import GLOBAL_ENER_FLOAT_PRECISION -from deepmd.fit import EnerFitting, WFCFitting, PolarFittingLocFrame, PolarFittingSeA, GlobalPolarFittingSeA, DipoleFittingSeA -from deepmd.descriptor import Descriptor -from deepmd.model import EnerModel, WFCModel, DipoleModel, PolarModel, GlobalPolarModel -from deepmd.loss import EnerStdLoss, EnerDipoleLoss, TensorLoss -from deepmd.utils.errors import GraphTooLargeError -from deepmd.utils.learning_rate import LearningRateExp -from deepmd.utils.neighbor_stat import NeighborStat -from deepmd.utils.sess import run_sess -from deepmd.utils.type_embed import TypeEmbedNet -from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph -from deepmd.utils.argcheck import type_embedding_args - -from tensorflow.python.client import timeline -from deepmd.env import op_module, TF_VERSION -from deepmd.utils.errors import GraphWithoutTensorError - -# load grad of force module -import deepmd.op - -from deepmd.common import j_must_have, ClassArg, data_requirement, get_precision - -log = logging.getLogger(__name__) - -# nvnmd -from deepmd.nvnmd.utils.config import nvnmd_cfg - -def _is_subdir(path, directory): - path = os.path.realpath(path) - directory = os.path.realpath(directory) - if path == directory: - return False - relative = os.path.relpath(path, directory) + os.sep - return not relative.startswith(os.pardir + os.sep) - - -class DPTrainer (object): - def __init__(self, - jdata, - run_opt, - is_compress = False): - self.run_opt = run_opt - self._init_param(jdata) - self.is_compress = is_compress - - def _init_param(self, jdata): - # model config - model_param = j_must_have(jdata, 'model') - descrpt_param = j_must_have(model_param, 'descriptor') - fitting_param = j_must_have(model_param, 'fitting_net') - typeebd_param = model_param.get('type_embedding', None) - self.model_param = model_param - self.descrpt_param = descrpt_param - - # nvnmd - self.nvnmd_param = jdata.get('nvnmd', {}) - nvnmd_cfg.init_from_jdata(self.nvnmd_param) - if nvnmd_cfg.enable: - nvnmd_cfg.init_from_deepmd_input(model_param) - nvnmd_cfg.disp_message() - nvnmd_cfg.save() - - # descriptor - try: - descrpt_type = descrpt_param['type'] - self.descrpt_type = descrpt_type - except KeyError: - raise KeyError('the type of descriptor should be set by `type`') - - if descrpt_param['type'] in ['se_atten']: - descrpt_param['ntypes'] = len(model_param['type_map']) - self.descrpt = Descriptor(**descrpt_param) - - # fitting net - fitting_type = fitting_param.get('type', 'ener') - self.fitting_type = fitting_type - fitting_param.pop('type', None) - fitting_param['descrpt'] = self.descrpt - if fitting_type == 'ener': - self.fitting = EnerFitting(**fitting_param) - # elif fitting_type == 'wfc': - # self.fitting = WFCFitting(fitting_param, self.descrpt) - elif fitting_type == 'dipole': - if descrpt_type == 'se_e2_a': - self.fitting = DipoleFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting dipole only supports descrptors: se_e2_a') - elif fitting_type == 'polar': - # if descrpt_type == 'loc_frame': - # self.fitting = PolarFittingLocFrame(fitting_param, self.descrpt) - if descrpt_type == 'se_e2_a': - self.fitting = PolarFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting polar only supports descrptors: loc_frame and se_e2_a') - elif fitting_type == 'global_polar': - if descrpt_type == 'se_e2_a': - self.fitting = GlobalPolarFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting global_polar only supports descrptors: loc_frame and se_e2_a') - else : - raise RuntimeError('unknow fitting type ' + fitting_type) - - # type embedding - padding = False - if descrpt_type == 'se_atten': - padding = True - if typeebd_param is not None: - self.typeebd = TypeEmbedNet( - neuron=typeebd_param['neuron'], - resnet_dt=typeebd_param['resnet_dt'], - activation_function=typeebd_param['activation_function'], - precision=typeebd_param['precision'], - trainable=typeebd_param['trainable'], - seed=typeebd_param['seed'], - padding=padding - ) - elif descrpt_type == 'se_atten': - default_args = type_embedding_args() - default_args_dict = {i.name: i.default for i in default_args} - self.typeebd = TypeEmbedNet( - neuron=default_args_dict['neuron'], - resnet_dt=default_args_dict['resnet_dt'], - activation_function=None, - precision=default_args_dict['precision'], - trainable=default_args_dict['trainable'], - seed=default_args_dict['seed'], - padding=padding - ) - else: - self.typeebd = None - - # init model - # infer model type by fitting_type - if fitting_type == 'ener': - self.model = EnerModel( - self.descrpt, - self.fitting, - self.typeebd, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2), - model_param.get('use_srtab'), - model_param.get('smin_alpha'), - model_param.get('sw_rmin'), - model_param.get('sw_rmax') - ) - # elif fitting_type == 'wfc': - # self.model = WFCModel(model_param, self.descrpt, self.fitting) - elif fitting_type == 'dipole': - self.model = DipoleModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - elif fitting_type == 'polar': - self.model = PolarModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - elif fitting_type == 'global_polar': - self.model = GlobalPolarModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - else : - raise RuntimeError('get unknown fitting type when building model') - - # learning rate - lr_param = j_must_have(jdata, 'learning_rate') - scale_by_worker = lr_param.get('scale_by_worker', 'linear') - if scale_by_worker == 'linear': - self.scale_lr_coef = float(self.run_opt.world_size) - elif scale_by_worker == 'sqrt': - self.scale_lr_coef = np.sqrt(self.run_opt.world_size).real - else: - self.scale_lr_coef = 1. - lr_type = lr_param.get('type', 'exp') - if lr_type == 'exp': - self.lr = LearningRateExp(lr_param['start_lr'], - lr_param['stop_lr'], - lr_param['decay_steps']) - else : - raise RuntimeError('unknown learning_rate type ' + lr_type) - - # loss - # infer loss type by fitting_type - loss_param = jdata.get('loss', None) - loss_type = loss_param.get('type', 'ener') - - if fitting_type == 'ener': - loss_param.pop('type', None) - loss_param['starter_learning_rate'] = self.lr.start_lr() - if loss_type == 'ener': - self.loss = EnerStdLoss(**loss_param) - elif loss_type == 'ener_dipole': - self.loss = EnerDipoleLoss(**loss_param) - else: - raise RuntimeError('unknow loss type') - elif fitting_type == 'wfc': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'wfc', - tensor_size = self.model.get_out_size(), - label_name = 'wfc') - elif fitting_type == 'dipole': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'dipole', - tensor_size = 3, - label_name = 'dipole') - elif fitting_type == 'polar': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'polar', - tensor_size = 9, - label_name = 'polarizability') - elif fitting_type == 'global_polar': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'global_polar', - tensor_size = 9, - atomic = False, - label_name = 'polarizability') - else : - raise RuntimeError('get unknown fitting type when building loss function') - - # training - tr_data = jdata['training'] - self.disp_file = tr_data.get('disp_file', 'lcurve.out') - self.disp_freq = tr_data.get('disp_freq', 1000) - self.save_freq = tr_data.get('save_freq', 1000) - self.save_ckpt = tr_data.get('save_ckpt', 'model.ckpt') - self.display_in_training = tr_data.get('disp_training', True) - self.timing_in_training = tr_data.get('time_training', True) - self.profiling = self.run_opt.is_chief and tr_data.get('profiling', False) - self.profiling_file = tr_data.get('profiling_file', 'timeline.json') - self.enable_profiler = tr_data.get('enable_profiler', False) - self.tensorboard = self.run_opt.is_chief and tr_data.get('tensorboard', False) - self.tensorboard_log_dir = tr_data.get('tensorboard_log_dir', 'log') - self.tensorboard_freq = tr_data.get('tensorboard_freq', 1) - self.mixed_prec = tr_data.get('mixed_precision', None) - if self.mixed_prec is not None: - if (self.mixed_prec['compute_prec'] != 'float16' or self.mixed_prec['output_prec'] != 'float32'): - raise RuntimeError( - "Unsupported mixed precision option [output_prec, compute_prec]: [%s, %s], " - " Supported: [float32, float16], Please set mixed precision option correctly!" - % (self.mixed_prec['output_prec'], self.mixed_prec['compute_prec'])) - # self.sys_probs = tr_data['sys_probs'] - # self.auto_prob_style = tr_data['auto_prob'] - self.useBN = False - if fitting_type == 'ener' and self.fitting.get_numb_fparam() > 0 : - self.numb_fparam = self.fitting.get_numb_fparam() - else : - self.numb_fparam = 0 - - if tr_data.get("validation_data", None) is not None: - self.valid_numb_batch = tr_data["validation_data"].get("numb_btch", 1) - else: - self.valid_numb_batch = 1 - - # if init the graph with the frozen model - self.frz_model = None - self.model_type = None - - - def build (self, - data = None, - stop_batch = 0, - suffix = "") : - self.ntypes = self.model.get_ntypes() - self.stop_batch = stop_batch - - if not self.is_compress and data.mixed_type: - assert self.descrpt_type in ['se_atten'], 'Data in mixed_type format must use attention descriptor!' - assert self.fitting_type in ['ener'], 'Data in mixed_type format must use ener fitting!' - - if self.numb_fparam > 0 : - log.info("training with %d frame parameter(s)" % self.numb_fparam) - else: - log.info("training without frame parameter") - - if not self.is_compress: - # Usually, the type number of the model should be equal to that of the data - # However, nt_model > nt_data should be allowed, since users may only want to - # train using a dataset that only have some of elements - if self.ntypes < data.get_ntypes(): - raise ValueError( - "The number of types of the training data is %d, but that of the " - "model is only %d. The latter must be no less than the former. " - "You may need to reset one or both of them. Usually, the former " - "is given by `model/type_map` in the training parameter (if set) " - "or the maximum number in the training data. The latter is given " - "by `model/descriptor/sel` in the training parameter." % ( - data.get_ntypes(), self.ntypes - )) - self.type_map = data.get_type_map() - self.batch_size = data.get_batch_size() - if self.run_opt.init_mode not in ('init_from_model', 'restart', 'init_from_frz_model'): - # self.saver.restore (in self._init_session) will restore avg and std variables, so data_stat is useless - # init_from_frz_model will restore data_stat variables in `init_variables` method - log.info("data stating... (this step may take long time)") - self.model.data_stat(data) - - # config the init_frz_model command - if self.run_opt.init_mode == 'init_from_frz_model': - self._init_from_frz_model() - - # neighbor_stat is moved to train.py as duplicated - # TODO: this is a simple fix but we should have a clear - # architecture to call neighbor stat - else : - graph, graph_def = load_graph_def(self.model_param['compress']['model_file']) - self.descrpt.enable_compression(self.model_param['compress']["min_nbor_dist"], self.model_param['compress']['model_file'], self.model_param['compress']['table_config'][0], self.model_param['compress']['table_config'][1], self.model_param['compress']['table_config'][2], self.model_param['compress']['table_config'][3]) - self.fitting.init_variables(graph, graph_def) - # for fparam or aparam settings in 'ener' type fitting net - if self.fitting_type == 'ener': - self.fitting.enable_compression(self.model_param['compress']['model_file']) - - if self.is_compress or self.model_type == 'compressed_model': - tf.constant("compressed_model", name = 'model_type', dtype = tf.string) - else: - tf.constant("original_model", name = 'model_type', dtype = tf.string) - - if self.mixed_prec is not None: - self.descrpt.enable_mixed_precision(self.mixed_prec) - self.fitting.enable_mixed_precision(self.mixed_prec) - - self._build_lr() - self._build_network(data, suffix) - self._build_training() - - - def _build_lr(self): - self._extra_train_ops = [] - self.global_step = tf.train.get_or_create_global_step() - self.learning_rate = self.lr.build(self.global_step, self.stop_batch) - log.info("built lr") - - def _build_network(self, data, suffix=""): - self.place_holders = {} - if self.is_compress : - for kk in ['coord', 'box']: - self.place_holders[kk] = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], 't_' + kk) - self._get_place_horders(data_requirement) - else : - self._get_place_horders(data.get_data_dict()) - - self.place_holders['type'] = tf.placeholder(tf.int32, [None], name='t_type') - self.place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes+2], name='t_natoms') - self.place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name='t_mesh') - self.place_holders['is_training'] = tf.placeholder(tf.bool) - self.model_pred\ - = self.model.build (self.place_holders['coord'], - self.place_holders['type'], - self.place_holders['natoms_vec'], - self.place_holders['box'], - self.place_holders['default_mesh'], - self.place_holders, - self.frz_model, - suffix = suffix, - reuse = False) - - self.l2_l, self.l2_more\ - = self.loss.build (self.learning_rate, - self.place_holders['natoms_vec'], - self.model_pred, - self.place_holders, - suffix = "test") - - if self.mixed_prec is not None: - self.l2_l = tf.cast(self.l2_l, get_precision(self.mixed_prec['output_prec'])) - log.info("built network") - - def _build_training(self): - trainable_variables = tf.trainable_variables() - if self.run_opt.is_distrib: - if self.scale_lr_coef > 1.: - log.info('Scale learning rate by coef: %f', self.scale_lr_coef) - optimizer = tf.train.AdamOptimizer(self.learning_rate*self.scale_lr_coef) - else: - optimizer = tf.train.AdamOptimizer(self.learning_rate) - optimizer = self.run_opt._HVD.DistributedOptimizer(optimizer) - else: - optimizer = tf.train.AdamOptimizer(learning_rate = self.learning_rate) - if self.mixed_prec is not None: - _TF_VERSION = Version(TF_VERSION) - # check the TF_VERSION, when TF < 1.12, mixed precision is not allowed - if _TF_VERSION < Version('1.14.0'): - raise RuntimeError("TensorFlow version %s is not compatible with the mixed precision setting. Please consider upgrading your TF version!" % TF_VERSION) - elif _TF_VERSION < Version('2.4.0'): - optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) - else: - optimizer = tf.mixed_precision.enable_mixed_precision_graph_rewrite(optimizer) - apply_op = optimizer.minimize(loss=self.l2_l, - global_step=self.global_step, - var_list=trainable_variables, - name='train_step') - train_ops = [apply_op] + self._extra_train_ops - self.train_op = tf.group(*train_ops) - log.info("built training") - - def _init_session(self): - config = get_tf_session_config() - device, idx = self.run_opt.my_device.split(":", 1) - if device == "gpu": - config.gpu_options.visible_device_list = idx - self.sess = tf.Session(config=config) - - # Initializes or restore global variables - init_op = tf.global_variables_initializer() - if self.run_opt.is_chief: - self.saver = tf.train.Saver(save_relative_paths=True) - if self.run_opt.init_mode == 'init_from_scratch' : - log.info("initialize model from scratch") - run_sess(self.sess, init_op) - if not self.is_compress: - fp = open(self.disp_file, "w") - fp.close () - elif self.run_opt.init_mode == 'init_from_model' : - log.info("initialize from model %s" % self.run_opt.init_model) - run_sess(self.sess, init_op) - self.saver.restore (self.sess, self.run_opt.init_model) - run_sess(self.sess, self.global_step.assign(0)) - fp = open(self.disp_file, "w") - fp.close () - elif self.run_opt.init_mode == 'restart' : - log.info("restart from model %s" % self.run_opt.restart) - run_sess(self.sess, init_op) - self.saver.restore (self.sess, self.run_opt.restart) - elif self.run_opt.init_mode == 'init_from_frz_model' : - log.info("initialize training from the frozen model") - run_sess(self.sess, init_op) - fp = open(self.disp_file, "w") - fp.close () - else : - raise RuntimeError ("unkown init mode") - else: - run_sess(self.sess, init_op) - self.saver = None - - # Ensure variable consistency among tasks when training starts - if self.run_opt.is_distrib: - bcast_op = self.run_opt._HVD.broadcast_global_variables(0) - if self.run_opt.is_chief: - log.info('broadcast global variables to other tasks') - else: - log.info('receive global variables from task#0') - run_sess(self.sess, bcast_op) - - def train (self, train_data = None, valid_data=None) : - - # if valid_data is None: # no validation set specified. - # valid_data = train_data # using training set as validation set. - - stop_batch = self.stop_batch - self._init_session() - - # Before data shard is enabled, only cheif do evaluation and record it - # self.print_head() - fp = None - if self.run_opt.is_chief : - fp = open(self.disp_file, "a") - - cur_batch = run_sess(self.sess, self.global_step) - is_first_step = True - self.cur_batch = cur_batch - log.info("start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e" % - (run_sess(self.sess, self.learning_rate), - self.lr.value(cur_batch), - self.lr.decay_steps_, - self.lr.decay_rate_, - self.lr.value(stop_batch)) - ) - - prf_options = None - prf_run_metadata = None - if self.profiling: - prf_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - prf_run_metadata = tf.RunMetadata() - - # set tensorboard execution environment - if self.tensorboard: - summary_merged_op = tf.summary.merge_all() - # Remove TB old logging directory from previous run - try: - shutil.rmtree(self.tensorboard_log_dir) - except FileNotFoundError: - pass # directory does not exist, this is OK - except Exception as e: - # general error when removing directory, warn user - log.exception( - f"Could not remove old tensorboard logging directory: " - f"{self.tensorboard_log_dir}. Error: {e}" - ) - else: - log.debug("Removing old tensorboard log directory.") - tb_train_writer = tf.summary.FileWriter(self.tensorboard_log_dir + '/train', self.sess.graph) - tb_valid_writer = tf.summary.FileWriter(self.tensorboard_log_dir + '/test') - else: - tb_train_writer = None - tb_valid_writer = None - if self.enable_profiler: - # https://www.tensorflow.org/guide/profiler - tfv2.profiler.experimental.start(self.tensorboard_log_dir) - - train_time = 0 - - while cur_batch < stop_batch : - - # first round validation: - train_batch = train_data.get_batch() - if self.display_in_training and is_first_step: - if self.run_opt.is_chief: - valid_batches = [valid_data.get_batch() for ii in range(self.valid_numb_batch)] if valid_data is not None else None - self.valid_on_the_fly(fp, [train_batch], valid_batches, print_header=True) - is_first_step = False - - if self.timing_in_training: tic = time.time() - train_feed_dict = self.get_feed_dict(train_batch, is_training=True) - # use tensorboard to visualize the training of deepmd-kit - # it will takes some extra execution time to generate the tensorboard data - if self.tensorboard and (cur_batch % self.tensorboard_freq == 0): - summary, _ = run_sess(self.sess, [summary_merged_op, self.train_op], feed_dict=train_feed_dict, - options=prf_options, run_metadata=prf_run_metadata) - tb_train_writer.add_summary(summary, cur_batch) - else: - run_sess(self.sess, [self.train_op], feed_dict=train_feed_dict, - options=prf_options, run_metadata=prf_run_metadata) - if self.timing_in_training: toc = time.time() - if self.timing_in_training: train_time += toc - tic - cur_batch = run_sess(self.sess, self.global_step) - self.cur_batch = cur_batch - - # on-the-fly validation - if self.display_in_training and (cur_batch % self.disp_freq == 0): - if self.timing_in_training: - tic = time.time() - if self.run_opt.is_chief: - valid_batches = [valid_data.get_batch() for ii in range(self.valid_numb_batch)] if valid_data is not None else None - self.valid_on_the_fly(fp, [train_batch], valid_batches) - if self.timing_in_training: - toc = time.time() - test_time = toc - tic - log.info("batch %7d training time %.2f s, testing time %.2f s" - % (cur_batch, train_time, test_time)) - train_time = 0 - if self.save_freq > 0 and cur_batch % self.save_freq == 0 and self.saver is not None: - self.save_checkpoint(cur_batch) - if (self.save_freq == 0 or cur_batch == 0 or cur_batch % self.save_freq != 0) and self.saver is not None: - self.save_checkpoint(cur_batch) - if self.run_opt.is_chief: - fp.close () - if self.profiling and self.run_opt.is_chief : - fetched_timeline = timeline.Timeline(prf_run_metadata.step_stats) - chrome_trace = fetched_timeline.generate_chrome_trace_format() - with open(self.profiling_file, 'w') as f: - f.write(chrome_trace) - if self.enable_profiler and self.run_opt.is_chief: - tfv2.profiler.experimental.stop() - - def save_checkpoint(self, cur_batch: int): - try: - ckpt_prefix = self.saver.save (self.sess, os.path.join(os.getcwd(), self.save_ckpt), global_step=cur_batch) - except google.protobuf.message.DecodeError as e: - raise GraphTooLargeError( - "The graph size exceeds 2 GB, the hard limitation of protobuf." - " Then a DecodeError was raised by protobuf. You should " - "reduce the size of your model." - ) from e - # make symlinks from prefix with step to that without step to break nothing - # get all checkpoint files - original_files = glob.glob(ckpt_prefix + ".*") - for ori_ff in original_files: - new_ff = self.save_ckpt + ori_ff[len(ckpt_prefix):] - try: - # remove old one - os.remove(new_ff) - except OSError: - pass - if platform.system() != 'Windows': - # by default one does not have access to create symlink on Windows - os.symlink(ori_ff, new_ff) - else: - shutil.copyfile(ori_ff, new_ff) - log.info("saved checkpoint %s" % self.save_ckpt) - - def get_feed_dict(self, batch, is_training): - feed_dict = {} - for kk in batch.keys(): - if kk == 'find_type' or kk == 'type' or kk == 'real_natoms_vec': - continue - if 'find_' in kk: - feed_dict[self.place_holders[kk]] = batch[kk] - else: - feed_dict[self.place_holders[kk]] = np.reshape(batch[kk], [-1]) - for ii in ['type']: - feed_dict[self.place_holders[ii]] = np.reshape(batch[ii], [-1]) - for ii in ['natoms_vec', 'default_mesh']: - feed_dict[self.place_holders[ii]] = batch[ii] - feed_dict[self.place_holders['is_training']] = is_training - return feed_dict - - def get_global_step(self): - return run_sess(self.sess, self.global_step) - - # def print_head (self) : # depreciated - # if self.run_opt.is_chief: - # fp = open(self.disp_file, "a") - # print_str = "# %5s" % 'batch' - # print_str += self.loss.print_header() - # print_str += ' %8s\n' % 'lr' - # fp.write(print_str) - # fp.close () - - def valid_on_the_fly(self, - fp, - train_batches, - valid_batches, - print_header=False): - train_results = self.get_evaluation_results(train_batches) - valid_results = self.get_evaluation_results(valid_batches) - - cur_batch = self.cur_batch - current_lr = run_sess(self.sess, self.learning_rate) - if print_header: - self.print_header(fp, train_results, valid_results) - self.print_on_training(fp, train_results, valid_results, cur_batch, current_lr) - - @staticmethod - def print_header(fp, train_results, valid_results): - print_str = '' - print_str += "# %5s" % 'step' - if valid_results is not None: - prop_fmt = ' %11s %11s' - for k in train_results.keys(): - print_str += prop_fmt % (k + '_val', k + '_trn') - else: - prop_fmt = ' %11s' - for k in train_results.keys(): - print_str += prop_fmt % (k + '_trn') - print_str += ' %8s\n' % 'lr' - fp.write(print_str) - fp.flush() - - @staticmethod - def print_on_training(fp, train_results, valid_results, cur_batch, cur_lr): - print_str = '' - print_str += "%7d" % cur_batch - if valid_results is not None: - prop_fmt = " %11.2e %11.2e" - for k in valid_results.keys(): - # assert k in train_results.keys() - print_str += prop_fmt % (valid_results[k], train_results[k]) - else: - prop_fmt = " %11.2e" - for k in train_results.keys(): - print_str += prop_fmt % (train_results[k]) - print_str += " %8.1e\n" % cur_lr - fp.write(print_str) - fp.flush() - - def get_evaluation_results(self, batch_list): - if batch_list is None: return None - numb_batch = len(batch_list) - - sum_results = {} # sum of losses on all atoms - sum_natoms = 0 - for i in range(numb_batch): - batch = batch_list[i] - natoms = batch["natoms_vec"] - feed_dict = self.get_feed_dict(batch, is_training=False) - results = self.loss.eval(self.sess, feed_dict, natoms) - - for k, v in results.items(): - if k == "natoms": - sum_natoms += v - else: - sum_results[k] = sum_results.get(k, 0.) + v * results["natoms"] - avg_results = {k: v / sum_natoms for k, v in sum_results.items() if not k == "natoms"} - return avg_results - - def save_compressed(self): - """ - Save the compressed graph - """ - self._init_session() - if self.is_compress: - self.saver.save (self.sess, os.path.join(os.getcwd(), self.save_ckpt)) - - def _get_place_horders(self, data_dict): - for kk in data_dict.keys(): - if kk == 'type': - continue - prec = GLOBAL_TF_FLOAT_PRECISION - if data_dict[kk]['high_prec'] : - prec = GLOBAL_ENER_FLOAT_PRECISION - self.place_holders[kk] = tf.placeholder(prec, [None], name = 't_' + kk) - self.place_holders['find_' + kk] = tf.placeholder(tf.float32, name = 't_find_' + kk) - - def _init_from_frz_model(self): - try: - graph, graph_def = load_graph_def(self.run_opt.init_frz_model) - except FileNotFoundError as e: - # throw runtime error if there's no frozen model - raise RuntimeError( - "The input frozen model %s (%s) does not exist! Please check the path of the frozen model. " % (self.run_opt.init_frz_model, os.path.abspath(self.run_opt.init_frz_model)) - ) from e - # get the model type from the frozen model(self.run_opt.init_frz_model) - try: - t_model_type = get_tensor_by_name_from_graph(graph, 'model_type') - except GraphWithoutTensorError as e: - # throw runtime error if the frozen_model has no model type information... - raise RuntimeError( - "The input frozen model: %s has no 'model_type' information, " - "which is not supported by the 'dp train init-frz-model' interface. " % self.run_opt.init_frz_model - ) from e - else: - self.model_type = bytes.decode(t_model_type) - if self.model_type == 'compressed_model': - self.frz_model = self.run_opt.init_frz_model - self.model.init_variables(graph, graph_def, model_type=self.model_type) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/__init__.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/__init__.py deleted file mode 100644 index 2646df2..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -from .data import DeepmdData -from .data_system import DeepmdDataSystem - -# out-of-dated -from .data import DataSets -from .data_system import DataSystem -from .pair_tab import PairTab -from .learning_rate import LearningRateExp -from .plugin import Plugin, PluginVariant diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/argcheck.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/argcheck.py deleted file mode 100644 index 3f81f31..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/argcheck.py +++ /dev/null @@ -1,789 +0,0 @@ -from typing import List, Callable - -from dargs import dargs, Argument, Variant, ArgumentEncoder -from deepmd import descriptor -from deepmd.common import ACTIVATION_FN_DICT, PRECISION_DICT -from deepmd.utils.plugin import Plugin -import json - -from deepmd.nvnmd.utils.argcheck import nvnmd_args - -def list_to_doc(xx): - items = [] - for ii in xx: - if len(items) == 0: - items.append(f'"{ii}"') - else: - items.append(f', "{ii}"') - items.append('.') - return ''.join(items) - - -def make_link(content, ref_key): - return f'`{content} <{ref_key}_>`_' if not dargs.RAW_ANCHOR \ - else f'`{content} <#{ref_key}>`_' - - -def type_embedding_args(): - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_seed = 'Random seed for parameter initialization' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - - return [ - Argument("neuron", list, optional = True, default = [8], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, default = None, doc = doc_seed), - ] - - -# --- Descriptor configurations: --- # - -class ArgsPlugin: - def __init__(self) -> None: - self.__plugin = Plugin() - - def register(self, name : str, alias : List[str] = None) -> Callable[[], List[Argument]]: - """Regiester a descriptor argument plugin. - - Parameters - ---------- - name : str - the name of a descriptor - alias : List[str], optional - the list of aliases of this descriptor - - Returns - ------- - Callable[[], List[Argument]] - the regiestered descriptor argument method - - Examples - -------- - >>> some_plugin = ArgsPlugin() - >>> @some_plugin.register("some_descrpt") - def descrpt_some_descrpt_args(): - return [] - """ - # convert alias to hashed item - if isinstance(alias, list): - alias = tuple(alias) - return self.__plugin.register((name, alias)) - - def get_all_argument(self, exclude_hybrid: bool = False) -> List[Argument]: - """Get all arguments. - - Parameters - ---------- - exclude_hybrid : bool - exclude hybrid descriptor to prevent circular calls - - Returns - ------- - List[Argument] - all arguments - """ - arguments = [] - for (name, alias), metd in self.__plugin.plugins.items(): - if exclude_hybrid and name == "hybrid": - continue - arguments.append(Argument(name=name, dtype=dict, sub_fields=metd(), alias=alias)) - return arguments - - -descrpt_args_plugin = ArgsPlugin() - -@descrpt_args_plugin.register("loc_frame") -def descrpt_local_frame_args (): - doc_sel_a = 'A list of integers. 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.' - doc_sel_r = 'A list of integers. 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.' - doc_rcut = 'The cut-off radius. The default value is 6.0' - doc_axis_rule = 'A list of integers. The length should be 6 times of the number of types. \n\n\ -- 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.' - - return [ - Argument("sel_a", list, optional = False, doc = doc_sel_a), - Argument("sel_r", list, optional = False, doc = doc_sel_r), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("axis_rule", list, optional = False, doc = doc_axis_rule) - ] - - -@descrpt_args_plugin.register("se_e2_a", alias=["se_a"]) -def descrpt_se_a_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net is trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("axis_neuron", int, optional = True, default = 4, alias = ['n_axis_neuron'], doc = doc_axis_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - -@descrpt_args_plugin.register("se_e3", alias=['se_at', 'se_a_3be', 'se_t']) -def descrpt_se_t_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - doc_seed = 'Random seed for parameter initialization' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - - -@descrpt_args_plugin.register("se_a_tpe", alias=['se_a_ebd']) -def descrpt_se_a_tpe_args(): - doc_type_nchanl = 'number of channels for type embedding' - doc_type_nlayer = 'number of hidden layers of type embedding net' - doc_numb_aparam = 'dimension of atomic parameter. if set to a value > 0, the atomic parameters are embedded.' - - return descrpt_se_a_args() + [ - Argument("type_nchanl", int, optional = True, default = 4, doc = doc_type_nchanl), - Argument("type_nlayer", int, optional = True, default = 2, doc = doc_type_nlayer), - Argument("numb_aparam", int, optional = True, default = 0, doc = doc_numb_aparam) - ] - - -@descrpt_args_plugin.register("se_e2_r", alias=['se_r']) -def descrpt_se_r_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - -@descrpt_args_plugin.register("hybrid") -def descrpt_hybrid_args(): - doc_list = f'A list of descriptor definitions' - - return [ - Argument("list", list, optional = False, doc = doc_list) - ] - - -@descrpt_args_plugin.register("se_atten") -def descrpt_se_atten_args(): - doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Whether to consider the information from only one side or both sides.' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net is trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - doc_attn = 'The length of hidden vectors in attention layers' - doc_attn_layer = 'The number of attention layers' - doc_attn_dotr = 'Whether to do dot product with the normalized relative coordinates' - doc_attn_mask = 'Whether to do mask on the diagonal in the attention matrix' - - return [ - Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), - Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), - Argument("rcut_smth", float, optional=True, default=0.5, doc=doc_rcut_smth), - Argument("neuron", list, optional=True, default=[10, 20, 40], doc=doc_neuron), - Argument("axis_neuron", int, optional=True, default=4, alias=['n_axis_neuron'], doc=doc_axis_neuron), - Argument("activation_function", str, optional=True, default='tanh', doc=doc_activation_function), - Argument("resnet_dt", bool, optional=True, default=False, doc=doc_resnet_dt), - Argument("type_one_side", bool, optional=True, default=False, doc=doc_type_one_side), - Argument("precision", str, optional=True, default="default", doc=doc_precision), - Argument("trainable", bool, optional=True, default=True, doc=doc_trainable), - Argument("seed", [int, None], optional=True, doc=doc_seed), - Argument("exclude_types", list, optional=True, default=[], doc=doc_exclude_types), - Argument("set_davg_zero", bool, optional=True, default=False, doc=doc_set_davg_zero), - Argument("attn", int, optional=True, default=128, doc=doc_attn), - Argument("attn_layer", int, optional=True, default=2, doc=doc_attn_layer), - Argument("attn_dotr", bool, optional=True, default=True, doc=doc_attn_dotr), - Argument("attn_mask", bool, optional=True, default=False, doc=doc_attn_mask) - ] - -def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant: - link_lf = make_link('loc_frame', 'model/descriptor[loc_frame]') - link_se_e2_a = make_link('se_e2_a', 'model/descriptor[se_e2_a]') - link_se_e2_r = make_link('se_e2_r', 'model/descriptor[se_e2_r]') - link_se_e3 = make_link('se_e3', 'model/descriptor[se_e3]') - link_se_a_tpe = make_link('se_a_tpe', 'model/descriptor[se_a_tpe]') - link_hybrid = make_link('hybrid', 'model/descriptor[hybrid]') - link_se_atten = make_link('se_atten', 'model/descriptor[se_atten]') - doc_descrpt_type = f'The type of the descritpor. See explanation below. \n\n\ -- `loc_frame`: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.\n\n\ -- `se_e2_a`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.\n\n\ -- `se_e2_r`: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.\n\n\ -- `se_e3`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.\n\n\ -- `se_a_tpe`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.\n\n\ -- `se_atten`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.\n\n\ -- `hybrid`: Concatenate of a list of descriptors as a new descriptor.' - - return Variant("type", descrpt_args_plugin.get_all_argument(), doc = doc_descrpt_type) - - -# --- Fitting net configurations: --- # -def fitting_ener(): - doc_numb_fparam = 'The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams.' - doc_numb_aparam = 'The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams.' - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_trainable = 'Whether the parameters in the fitting net are trainable. This option can be\n\n\ -- bool: True if all parameters of the fitting net are trainable, False otherwise.\n\n\ -- list of bool: Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of tihs list should be equal to len(`neuron`)+1.' - doc_rcond = 'The condition number used to determine the inital energy shift for each type of atoms.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - doc_atom_ener = 'Specify the atomic energy in vacuum for each type' - - return [ - Argument("numb_fparam", int, optional = True, default = 0, doc = doc_numb_fparam), - Argument("numb_aparam", int, optional = True, default = 0, doc = doc_numb_aparam), - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("trainable", [list,bool], optional = True, default = True, doc = doc_trainable), - Argument("rcond", float, optional = True, default = 1e-3, doc = doc_rcond), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("atom_ener", list, optional = True, default = [], doc = doc_atom_ener) - ] - - -def fitting_polar(): - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_scale = 'The output of the fitting net (polarizability matrix) will be scaled by ``scale``' - #doc_diag_shift = 'The diagonal part of the polarizability matrix will be shifted by ``diag_shift``. The shift operation is carried out after ``scale``.' - doc_fit_diag = 'Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.' - doc_sel_type = 'The atom types for which the atomic polarizability will be provided. If not set, all types will be selected.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - - # YWolfeee: user can decide whether to use shift diag - doc_shift_diag = 'Whether to shift the diagonal of polar, which is beneficial to training. Default is true.' - - return [ - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("fit_diag", bool, optional = True, default = True, doc = doc_fit_diag), - Argument("scale", [list,float], optional = True, default = 1.0, doc = doc_scale), - #Argument("diag_shift", [list,float], optional = True, default = 0.0, doc = doc_diag_shift), - Argument("shift_diag", bool, optional = True, default = True, doc = doc_shift_diag), - Argument("sel_type", [list,int,None], optional = True, alias = ['pol_type'], doc = doc_sel_type), - Argument("seed", [int,None], optional = True, doc = doc_seed) - ] - - -#def fitting_global_polar(): -# return fitting_polar() - - -def fitting_dipole(): - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_sel_type = 'The atom types for which the atomic dipole will be provided. If not set, all types will be selected.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - return [ - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("sel_type", [list,int,None], optional = True, alias = ['dipole_type'], doc = doc_sel_type), - Argument("seed", [int,None], optional = True, doc = doc_seed) - ] - -# YWolfeee: Delete global polar mode, merge it into polar mode and use loss setting to support. -def fitting_variant_type_args(): - doc_descrpt_type = 'The type of the fitting. See explanation below. \n\n\ -- `ener`: Fit an energy model (potential energy surface).\n\n\ -- `dipole`: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter.\n\n\ -- `polar`: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file eith has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter.\n\n' - - return Variant("type", [Argument("ener", dict, fitting_ener()), - Argument("dipole", dict, fitting_dipole()), - Argument("polar", dict, fitting_polar()), - ], - optional = True, - default_tag = 'ener', - doc = doc_descrpt_type) - - -# --- Modifier configurations: --- # -def modifier_dipole_charge(): - doc_model_name = "The name of the frozen dipole model file." - doc_model_charge_map = f"The charge of the WFCC. The list length should be the same as the {make_link('sel_type', 'model/fitting_net[dipole]/sel_type')}. " - doc_sys_charge_map = f"The charge of real atoms. The list length should be the same as the {make_link('type_map', 'model/type_map')}" - doc_ewald_h = f"The grid spacing of the FFT grid. Unit is A" - doc_ewald_beta = f"The splitting parameter of Ewald sum. Unit is A^{-1}" - - return [ - Argument("model_name", str, optional = False, doc = doc_model_name), - Argument("model_charge_map", list, optional = False, doc = doc_model_charge_map), - Argument("sys_charge_map", list, optional = False, doc = doc_sys_charge_map), - Argument("ewald_beta", float, optional = True, default = 0.4, doc = doc_ewald_beta), - Argument("ewald_h", float, optional = True, default = 1.0, doc = doc_ewald_h), - ] - - -def modifier_variant_type_args(): - doc_modifier_type = "The type of modifier. See explanation below.\n\n\ --`dipole_charge`: Use WFCC to model the electronic structure of the system. Correct the long-range interaction" - return Variant("type", - [ - Argument("dipole_charge", dict, modifier_dipole_charge()), - ], - optional = False, - doc = doc_modifier_type) - -# --- model compression configurations: --- # -def model_compression(): - doc_model_file = f"The input model file, which will be compressed by the DeePMD-kit." - doc_table_config = f"The arguments of model compression, including extrapolate(scale of model extrapolation), stride(uniform stride of tabulation's first and second table), and frequency(frequency of tabulation overflow check)." - doc_min_nbor_dist = f"The nearest distance between neighbor atoms saved in the frozen model." - - return [ - Argument("model_file", str, optional = False, doc = doc_model_file), - Argument("table_config", list, optional = False, doc = doc_table_config), - Argument("min_nbor_dist", float, optional = False, doc = doc_min_nbor_dist), - ] - -# --- model compression configurations: --- # -def model_compression_type_args(): - doc_compress_type = "The type of model compression, which should be consistent with the descriptor type." - - return Variant("type", [ - Argument("se_e2_a", dict, model_compression(), alias = ['se_a']) - ], - optional = True, - default_tag = 'se_e2_a', - doc = doc_compress_type) - - -def model_args (): - doc_type_map = 'A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment.' - doc_data_stat_nbatch = 'The model determines the normalization from the statistics of the data. This key specifies the number of `frames` in each `system` used for statistics.' - doc_data_stat_protect = 'Protect parameter for atomic energy regression.' - doc_type_embedding = "The type embedding." - doc_descrpt = 'The descriptor of atomic environment.' - doc_fitting = 'The fitting of physical properties.' - doc_modifier = 'The modifier of model output.' - doc_use_srtab = 'The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.' - doc_smin_alpha = 'The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when `use_srtab` is provided.' - doc_sw_rmin = 'The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.' - doc_sw_rmax = 'The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.' - doc_compress_config = 'Model compression configurations' - - ca = Argument("model", dict, - [Argument("type_map", list, optional = True, doc = doc_type_map), - Argument("data_stat_nbatch", int, optional = True, default = 10, doc = doc_data_stat_nbatch), - Argument("data_stat_protect", float, optional = True, default = 1e-2, doc = doc_data_stat_protect), - Argument("use_srtab", str, optional = True, doc = doc_use_srtab), - Argument("smin_alpha", float, optional = True, doc = doc_smin_alpha), - Argument("sw_rmin", float, optional = True, doc = doc_sw_rmin), - Argument("sw_rmax", float, optional = True, doc = doc_sw_rmax), - Argument("type_embedding", dict, type_embedding_args(), [], optional = True, doc = doc_type_embedding), - Argument("descriptor", dict, [], [descrpt_variant_type_args()], doc = doc_descrpt), - Argument("fitting_net", dict, [], [fitting_variant_type_args()], doc = doc_fitting), - Argument("modifier", dict, [], [modifier_variant_type_args()], optional = True, doc = doc_modifier), - Argument("compress", dict, [], [model_compression_type_args()], optional = True, doc = doc_compress_config) - ]) - # print(ca.gen_doc()) - return ca - - -# --- Learning rate configurations: --- # -def learning_rate_exp(): - doc_start_lr = 'The learning rate the start of the training.' - doc_stop_lr = 'The desired learning rate at the end of the training.' - doc_decay_steps = 'The learning rate is decaying every this number of training steps.' - - args = [ - Argument("start_lr", float, optional = True, default = 1e-3, doc = doc_start_lr), - Argument("stop_lr", float, optional = True, default = 1e-8, doc = doc_stop_lr), - Argument("decay_steps", int, optional = True, default = 5000, doc = doc_decay_steps) - ] - return args - - -def learning_rate_variant_type_args(): - doc_lr = 'The type of the learning rate.' - - return Variant("type", - [Argument("exp", dict, learning_rate_exp())], - optional = True, - default_tag = 'exp', - doc = doc_lr) - - -def learning_rate_args(): - doc_scale_by_worker = 'When parallel training or batch size scaled, how to alter learning rate. Valid values are `linear`(default), `sqrt` or `none`.' - doc_lr = "The definitio of learning rate" - return Argument("learning_rate", dict, - [Argument("scale_by_worker", str, optional=True, default='linear', doc=doc_scale_by_worker)], - [learning_rate_variant_type_args()], - doc = doc_lr) - - -# --- Loss configurations: --- # -def start_pref(item): - return f'The prefactor of {item} loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the {item} label should be provided by file {item}.npy in each data system. If both start_pref_{item} and limit_pref_{item} are set to 0, then the {item} will be ignored.' - - -def limit_pref(item): - return f'The prefactor of {item} loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.' - - -def loss_ener(): - doc_start_pref_e = start_pref('energy') - doc_limit_pref_e = limit_pref('energy') - doc_start_pref_f = start_pref('force') - doc_limit_pref_f = limit_pref('force') - doc_start_pref_v = start_pref('virial') - doc_limit_pref_v = limit_pref('virial') - doc_start_pref_ae = start_pref('atom_ener') - doc_limit_pref_ae = limit_pref('atom_ener') - doc_start_pref_pf = start_pref('atom_pref') - doc_limit_pref_pf = limit_pref('atom_pref') - doc_relative_f = 'If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by `relative_f`, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.' - doc_enable_atom_ener_coeff = "If true, the energy will be computed as \sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it's 1." - return [ - Argument("start_pref_e", [float,int], optional = True, default = 0.02, doc = doc_start_pref_e), - Argument("limit_pref_e", [float,int], optional = True, default = 1.00, doc = doc_limit_pref_e), - Argument("start_pref_f", [float,int], optional = True, default = 1000, doc = doc_start_pref_f), - Argument("limit_pref_f", [float,int], optional = True, default = 1.00, doc = doc_limit_pref_f), - Argument("start_pref_v", [float,int], optional = True, default = 0.00, doc = doc_start_pref_v), - Argument("limit_pref_v", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_v), - Argument("start_pref_ae", [float,int], optional = True, default = 0.00, doc = doc_start_pref_ae), - Argument("limit_pref_ae", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_ae), - Argument("start_pref_pf", [float,int], optional = True, default = 0.00, doc = doc_start_pref_pf), - Argument("limit_pref_pf", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_pf), - Argument("relative_f", [float,None], optional = True, doc = doc_relative_f), - Argument("enable_atom_ener_coeff", [bool], optional=True, default=False, doc=doc_enable_atom_ener_coeff), - ] - -# YWolfeee: Modified to support tensor type of loss args. -def loss_tensor(): - #doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If only `pref` is provided or both are not provided, training will be global mode, i.e. the shape of 'polarizability.npy` or `dipole.npy` should be #frams x [9 or 3]." - #doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If only `pref_atomic` is provided, training will be atomic mode, i.e. the shape of `polarizability.npy` or `dipole.npy` should be #frames x ([9 or 3] x #selected atoms). If both `pref` and `pref_atomic` are provided, training will be combined mode, and atomic label should be provided as well." - doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included." - doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #selected atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0." - return [ - Argument("pref", [float,int], optional = False, default = None, doc = doc_global_weight), - Argument("pref_atomic", [float,int], optional = False, default = None, doc = doc_local_weight), - ] - - -def loss_variant_type_args(): - doc_loss = 'The type of the loss. When the fitting type is `ener`, the loss type should be set to `ener` or left unset. When the fitting type is `dipole` or `polar`, the loss type should be set to `tensor`. \n\.' - - - return Variant("type", - [Argument("ener", dict, loss_ener()), - Argument("tensor", dict, loss_tensor()), - #Argument("polar", dict, loss_tensor()), - #Argument("global_polar", dict, loss_tensor("global")) - ], - optional = True, - default_tag = 'ener', - doc = doc_loss) - - -def loss_args(): - doc_loss = 'The definition of loss function. The loss type should be set to `tensor`, `ener` or left unset.\n\.' - ca = Argument('loss', dict, [], - [loss_variant_type_args()], - optional = True, - doc = doc_loss) - return ca - - -# --- Training configurations: --- # -def training_data_args(): # ! added by Ziyao: new specification style for data systems. - link_sys = make_link("systems", "training/training_data/systems") - doc_systems = 'The data systems for training. ' \ - 'This key can be provided with a list that specifies the systems, or be provided with a string ' \ - 'by which the prefix of all systems are given and the list of the systems is automatically generated.' - doc_set_prefix = f'The prefix of the sets in the {link_sys}.' - doc_batch_size = f'This key can be \n\n\ -- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\ -- int: all {link_sys} use the same batch size.\n\n\ -- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\ -- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.' - doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\ -- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\ -- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\ -- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.' - doc_sys_probs = "A list of float if specified. " \ - "Should be of the same length as `systems`, " \ - "specifying the probability of each system." - - - args = [ - Argument("systems", [list, str], optional=False, default=".", doc=doc_systems), - Argument("set_prefix", str, optional=True, default='set', doc=doc_set_prefix), - Argument("batch_size", [list, int, str], optional=True, default='auto', doc=doc_batch_size), - Argument("auto_prob", str, optional=True, default="prob_sys_size", - doc=doc_auto_prob_style, alias=["auto_prob_style",]), - Argument("sys_probs", list, optional=True, default=None, doc=doc_sys_probs, alias=["sys_weights"]), - ] - - doc_training_data = "Configurations of training data." - return Argument("training_data", dict, optional=False, - sub_fields=args, sub_variants=[], doc=doc_training_data) - - -def validation_data_args(): # ! added by Ziyao: new specification style for data systems. - link_sys = make_link("systems", "training/validation_data/systems") - doc_systems = 'The data systems for validation. ' \ - 'This key can be provided with a list that specifies the systems, or be provided with a string ' \ - 'by which the prefix of all systems are given and the list of the systems is automatically generated.' - doc_set_prefix = f'The prefix of the sets in the {link_sys}.' - doc_batch_size = f'This key can be \n\n\ -- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\ -- int: all {link_sys} use the same batch size.\n\n\ -- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\ -- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.' - doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\ -- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\ -- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\ -- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.' - doc_sys_probs = "A list of float if specified. " \ - "Should be of the same length as `systems`, " \ - "specifying the probability of each system." - doc_numb_btch = "An integer that specifies the number of systems to be sampled for each validation period." - - args = [ - Argument("systems", [list, str], optional=False, default=".", doc=doc_systems), - Argument("set_prefix", str, optional=True, default='set', doc=doc_set_prefix), - Argument("batch_size", [list, int, str], optional=True, default='auto', doc=doc_batch_size), - Argument("auto_prob", str, optional=True, default="prob_sys_size", - doc=doc_auto_prob_style, alias=["auto_prob_style", ]), - Argument("sys_probs", list, optional=True, default=None, doc=doc_sys_probs, alias=["sys_weights"]), - Argument("numb_btch", int, optional=True, default=1, doc=doc_numb_btch, alias=["numb_batch", ]) - ] - - doc_validation_data = "Configurations of validation data. Similar to that of training data, " \ - "except that a `numb_btch` argument may be configured" - return Argument("validation_data", dict, optional=True, default=None, - sub_fields=args, sub_variants=[], doc=doc_validation_data) - - -def mixed_precision_args(): # ! added by Denghui. - doc_output_prec = 'The precision for mixed precision params. " \ - "The trainable variables precision during the mixed precision training process, " \ - "supported options are float32 only currently.' - doc_compute_prec = 'The precision for mixed precision compute. " \ - "The compute precision during the mixed precision training process, "" \ - "supported options are float16 only currently.' - - args = [ - Argument("output_prec", str, optional=True, default="float32", doc=doc_output_prec), - Argument("compute_prec", str, optional=False, default="float16", doc=doc_compute_prec), - ] - - doc_mixed_precision = "Configurations of mixed precision." - return Argument("mixed_precision", dict, optional=True, - sub_fields=args, sub_variants=[], doc=doc_mixed_precision) - - -def training_args(): # ! modified by Ziyao: data configuration isolated. - doc_numb_steps = 'Number of training batch. Each training uses one batch of data.' - doc_seed = 'The random seed for getting frames from the training data set.' - doc_disp_file = 'The file for printing learning curve.' - doc_disp_freq = 'The frequency of printing learning curve.' - doc_save_freq = 'The frequency of saving check point.' - doc_save_ckpt = 'The file name of saving check point.' - doc_disp_training = 'Displaying verbose information during training.' - doc_time_training = 'Timing durining training.' - doc_profiling = 'Profiling during training.' - doc_profiling_file = 'Output file for profiling.' - doc_enable_profiler = 'Enable TensorFlow Profiler (available in TensorFlow 2.3) to analyze performance. The log will be saved to `tensorboard_log_dir`.' - doc_tensorboard = 'Enable tensorboard' - doc_tensorboard_log_dir = 'The log directory of tensorboard outputs' - doc_tensorboard_freq = 'The frequency of writing tensorboard events.' - - arg_training_data = training_data_args() - arg_validation_data = validation_data_args() - mixed_precision_data = mixed_precision_args() - - args = [ - arg_training_data, - arg_validation_data, - mixed_precision_data, - Argument("numb_steps", int, optional=False, doc=doc_numb_steps, alias=["stop_batch"]), - Argument("seed", [int,None], optional=True, doc=doc_seed), - Argument("disp_file", str, optional=True, default='lcurve.out', doc=doc_disp_file), - Argument("disp_freq", int, optional=True, default=1000, doc=doc_disp_freq), - Argument("save_freq", int, optional=True, default=1000, doc=doc_save_freq), - Argument("save_ckpt", str, optional=True, default='model.ckpt', doc=doc_save_ckpt), - Argument("disp_training", bool, optional=True, default=True, doc=doc_disp_training), - Argument("time_training", bool, optional=True, default=True, doc=doc_time_training), - Argument("profiling", bool, optional=True, default=False, doc=doc_profiling), - Argument("profiling_file", str, optional=True, default='timeline.json', doc=doc_profiling_file), - Argument("enable_profiler", bool, optional=True, default=False, doc=doc_enable_profiler), - Argument("tensorboard", bool, optional=True, default=False, doc=doc_tensorboard), - Argument("tensorboard_log_dir", str, optional=True, default='log', doc=doc_tensorboard_log_dir), - Argument("tensorboard_freq", int, optional=True, default=1, doc=doc_tensorboard_freq), - ] - - doc_training = 'The training options.' - return Argument("training", dict, args, [], doc = doc_training) - - -def make_index(keys): - ret = [] - for ii in keys: - ret.append(make_link(ii, ii)) - return ', '.join(ret) - - -def gen_doc(*, make_anchor=True, make_link=True, **kwargs): - if make_link: - make_anchor = True - ma = model_args() - lra = learning_rate_args() - la = loss_args() - ta = training_args() - nvnmda = nvnmd_args() - ptr = [] - ptr.append(ma.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(la.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(lra.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(ta.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(nvnmda.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - - key_words = [] - for ii in "\n\n".join(ptr).split('\n'): - if 'argument path' in ii: - key_words.append(ii.split(':')[1].replace('`','').strip()) - #ptr.insert(0, make_index(key_words)) - - return "\n\n".join(ptr) - -def gen_json(**kwargs): - return json.dumps(( - model_args(), - learning_rate_args(), - loss_args(), - training_args(), - nvnmd_args(), - ), cls=ArgumentEncoder) - -def normalize_hybrid_list(hy_list): - new_list = [] - base = Argument("base", dict, [], [descrpt_variant_type_args()], doc = "") - for ii in range(len(hy_list)): - data = base.normalize_value(hy_list[ii], trim_pattern="_*") - base.check_value(data, strict=True) - new_list.append(data) - return new_list - - -def normalize(data): - if "hybrid" == data["model"]["descriptor"]["type"]: - data["model"]["descriptor"]["list"] \ - = normalize_hybrid_list(data["model"]["descriptor"]["list"]) - - ma = model_args() - lra = learning_rate_args() - la = loss_args() - ta = training_args() - nvnmda = nvnmd_args() - - base = Argument("base", dict, [ma, lra, la, ta, nvnmda]) - data = base.normalize_value(data, trim_pattern="_*") - base.check_value(data, strict=True) - - return data - - -if __name__ == '__main__': - gen_doc() - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/batch_size.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/batch_size.py deleted file mode 100644 index aa724de..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/batch_size.py +++ /dev/null @@ -1,129 +0,0 @@ -import logging -from typing import Callable, Tuple - -import numpy as np - -from deepmd.utils.errors import OutOfMemoryError - -class AutoBatchSize: - """This class allows DeePMD-kit to automatically decide the maximum - batch size that will not cause an OOM error. - - Notes - ----- - We assume all OOM error will raise :class:`OutOfMemoryError`. - - Parameters - ---------- - initial_batch_size : int, default: 1024 - initial batch size (number of total atoms) - factor : float, default: 2. - increased factor - - Attributes - ---------- - current_batch_size : int - current batch size (number of total atoms) - maximum_working_batch_size : int - maximum working batch size - minimal_not_working_batch_size : int - minimal not working batch size - """ - def __init__(self, initial_batch_size: int = 1024, factor: float = 2.) -> None: - # See also PyTorchLightning/pytorch-lightning#1638 - # TODO: discuss a proper initial batch size - self.current_batch_size = initial_batch_size - self.maximum_working_batch_size = 0 - self.minimal_not_working_batch_size = 2**31 - self.factor = factor - - def execute(self, callable: Callable, start_index: int, natoms: int) -> Tuple[int, tuple]: - """Excuate a method with given batch size. - - Parameters - ---------- - callable : Callable - The method should accept the batch size and start_index as parameters, - and returns executed batch size and data. - start_index : int - start index - natoms : int - natoms - - Returns - ------- - int - executed batch size * number of atoms - tuple - result from callable, None if failing to execute - - Raises - ------ - OutOfMemoryError - OOM when batch size is 1 - """ - try: - n_batch, result = callable(max(self.current_batch_size // natoms, 1), start_index) - except OutOfMemoryError as e: - # TODO: it's very slow to catch OOM error; I don't know what TF is doing here - # but luckily we only need to catch once - self.minimal_not_working_batch_size = min(self.minimal_not_working_batch_size, self.current_batch_size) - if self.maximum_working_batch_size >= self.minimal_not_working_batch_size: - self.maximum_working_batch_size = int(self.minimal_not_working_batch_size / self.factor) - if self.minimal_not_working_batch_size <= natoms: - raise OutOfMemoryError("The callable still throws an out-of-memory (OOM) error even when batch size is 1!") from e - # adjust the next batch size - self._adjust_batch_size(1./self.factor) - return 0, None - else: - n_tot = n_batch * natoms - self.maximum_working_batch_size = max(self.maximum_working_batch_size, n_tot) - # adjust the next batch size - if n_tot + natoms > self.current_batch_size and self.current_batch_size * self.factor < self.minimal_not_working_batch_size: - self._adjust_batch_size(self.factor) - return n_batch, result - - def _adjust_batch_size(self, factor: float): - old_batch_size = self.current_batch_size - self.current_batch_size = int(self.current_batch_size * factor) - logging.info("Adjust batch size from %d to %d" % (old_batch_size, self.current_batch_size)) - - def execute_all(self, callable: Callable, total_size: int, natoms: int, *args, **kwargs) -> Tuple[np.ndarray]: - """Excuate a method with all given data. - - Parameters - ---------- - callable : Callable - The method should accept *args and **kwargs as input and return the similiar array. - total_size : int - Total size - natoms : int - The number of atoms - **kwargs - If 2D np.ndarray, assume the first axis is batch; otherwise do nothing. - """ - def execute_with_batch_size(batch_size: int, start_index: int) -> Tuple[int, Tuple[np.ndarray]]: - end_index = start_index + batch_size - end_index = min(end_index, total_size) - return (end_index - start_index), callable( - *[(vv[start_index:end_index] if isinstance(vv, np.ndarray) and vv.ndim > 1 else vv) for vv in args], - **{kk: (vv[start_index:end_index] if isinstance(vv, np.ndarray) and vv.ndim > 1 else vv) for kk, vv in kwargs.items()}, - ) - - index = 0 - results = [] - while index < total_size: - n_batch, result = self.execute(execute_with_batch_size, index, natoms) - if not isinstance(result, tuple): - result = (result,) - index += n_batch - if n_batch: - for rr in result: - rr.reshape((n_batch, -1)) - results.append(result) - - r = tuple([np.concatenate(r, axis=0) for r in zip(*results)]) - if len(r) == 1: - # avoid returning tuple if callable doesn't return tuple - r = r[0] - return r diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/compat.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/compat.py deleted file mode 100644 index 1d012a7..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/compat.py +++ /dev/null @@ -1,376 +0,0 @@ -"""Module providing compatibility between `0.x.x` and `1.x.x` input versions.""" - -import json -import warnings -from pathlib import Path -from typing import Any, Dict, Optional, Sequence, Union - -import numpy as np -from deepmd.common import j_must_have - - -def convert_input_v0_v1( - jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, Path]] = None -) -> Dict[str, Any]: - """Convert input from v0 format to v1. - - Parameters - ---------- - jdata : Dict[str, Any] - loaded json/yaml file - warning : bool, optional - whether to show deprecation warning, by default True - dump : Optional[Union[str, Path]], optional - whether to dump converted file, by default None - - Returns - ------- - Dict[str, Any] - converted output - """ - - output = {} - output["model"] = _model(jdata, jdata["use_smooth"]) - output["learning_rate"] = _learning_rate(jdata) - output["loss"] = _loss(jdata) - output["training"] = _training(jdata) - if warning: - _warning_input_v0_v1(dump) - if dump is not None: - with open(dump, "w") as fp: - json.dump(output, fp, indent=4) - return output - - -def _warning_input_v0_v1(fname: Optional[Union[str, Path]]): - msg = "It seems that you are using a deepmd-kit input of version 0.x.x, " \ - "which is deprecated. we have converted the input to >2.0.0 compatible" - if fname is not None: - msg += f", and output it to file {fname}" - warnings.warn(msg) - - -def _model(jdata: Dict[str, Any], smooth: bool) -> Dict[str, Dict[str, Any]]: - """Convert data to v1 input for non-smooth model. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - smooth : bool - whether to use smooth or non-smooth descriptor version - - Returns - ------- - Dict[str, Dict[str, Any]] - dictionary with model input parameters and sub-dictionaries for descriptor and - fitting net - """ - model = {} - model["descriptor"] = ( - _smth_descriptor(jdata) if smooth else _nonsmth_descriptor(jdata) - ) - model["fitting_net"] = _fitting_net(jdata) - return model - - -def _nonsmth_descriptor(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for non-smooth descriptor. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with descriptor parameters - """ - descriptor = {} - descriptor["type"] = "loc_frame" - _jcopy(jdata, descriptor, ("sel_a", "sel_r", "rcut", "axis_rule")) - return descriptor - - -def _smth_descriptor(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for smooth descriptor. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with descriptor parameters - """ - descriptor = {} - seed = jdata.get("seed", None) - if seed is not None: - descriptor["seed"] = seed - descriptor["type"] = "se_a" - descriptor["sel"] = jdata["sel_a"] - _jcopy(jdata, descriptor, ("rcut", )) - descriptor["rcut_smth"] = jdata.get("rcut_smth", descriptor["rcut"]) - descriptor["neuron"] = j_must_have(jdata, "filter_neuron") - descriptor["axis_neuron"] = j_must_have(jdata, "axis_neuron", ["n_axis_neuron"]) - descriptor["resnet_dt"] = False - if "resnet_dt" in jdata: - descriptor["resnet_dt"] = jdata["filter_resnet_dt"] - - return descriptor - - -def _fitting_net(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for fitting net. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with fitting net parameters - """ - fitting_net = {} - - seed = jdata.get("seed", None) - if seed is not None: - fitting_net["seed"] = seed - fitting_net["neuron"] = j_must_have(jdata, "fitting_neuron", ["n_neuron"]) - fitting_net["resnet_dt"] = True - if "resnet_dt" in jdata: - fitting_net["resnet_dt"] = jdata["resnet_dt"] - if "fitting_resnet_dt" in jdata: - fitting_net["resnet_dt"] = jdata["fitting_resnet_dt"] - return fitting_net - - -def _learning_rate(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for learning rate section. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with learning rate parameters - """ - learning_rate = {} - learning_rate["type"] = "exp" - _jcopy(jdata, learning_rate, ("decay_steps", "decay_rate", "start_lr")) - return learning_rate - - -def _loss(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for loss function. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with loss function parameters - """ - loss: Dict[str, Any] = {} - _jcopy( - jdata, - loss, - ( - "start_pref_e", - "limit_pref_e", - "start_pref_f", - "limit_pref_f", - "start_pref_v", - "limit_pref_v", - ), - ) - if "start_pref_ae" in jdata: - loss["start_pref_ae"] = jdata["start_pref_ae"] - if "limit_pref_ae" in jdata: - loss["limit_pref_ae"] = jdata["limit_pref_ae"] - return loss - - -def _training(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for training. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with training parameters - """ - training = {} - seed = jdata.get("seed", None) - if seed is not None: - training["seed"] = seed - - _jcopy(jdata, training, ("systems", "set_prefix", "stop_batch", "batch_size")) - training["disp_file"] = "lcurve.out" - if "disp_file" in jdata: - training["disp_file"] = jdata["disp_file"] - training["disp_freq"] = j_must_have(jdata, "disp_freq") - training["numb_test"] = j_must_have(jdata, "numb_test") - training["save_freq"] = j_must_have(jdata, "save_freq") - training["save_ckpt"] = j_must_have(jdata, "save_ckpt") - training["disp_training"] = j_must_have(jdata, "disp_training") - training["time_training"] = j_must_have(jdata, "time_training") - if "profiling" in jdata: - training["profiling"] = jdata["profiling"] - if training["profiling"]: - training["profiling_file"] = j_must_have(jdata, "profiling_file") - return training - - -def _jcopy(src: Dict[str, Any], dst: Dict[str, Any], keys: Sequence[str]): - """Copy specified keys from one dict to another. - - Parameters - ---------- - src : Dict[str, Any] - source dictionary - dst : Dict[str, Any] - destination dictionary, will be modified in place - keys : Sequence[str] - list of keys to copy - must_have : bool - ensure that the source dictionary contains the copyyied keys - """ - for k in keys: - dst[k] = src[k] - - -def remove_decay_rate(jdata: Dict[str, Any]): - """convert decay_rate to stop_lr. - - Parameters - ---------- - jdata: Dict[str, Any] - input data - """ - lr = jdata["learning_rate"] - if "decay_rate" in lr: - decay_rate = lr["decay_rate"] - start_lr = lr["start_lr"] - stop_step = jdata["training"]["stop_batch"] - decay_steps = lr["decay_steps"] - stop_lr = np.exp(np.log(decay_rate) * (stop_step / decay_steps)) * start_lr - lr["stop_lr"] = stop_lr - lr.pop("decay_rate") - - -def convert_input_v1_v2(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - - tr_cfg = jdata["training"] - tr_data_keys = { - "systems", - "set_prefix", - "batch_size", - "sys_prob", - "auto_prob", - # alias included - "sys_weights", - "auto_prob_style" - } - - tr_data_cfg = {k: v for k, v in tr_cfg.items() if k in tr_data_keys} - new_tr_cfg = {k: v for k, v in tr_cfg.items() if k not in tr_data_keys} - new_tr_cfg["training_data"] = tr_data_cfg - - jdata["training"] = new_tr_cfg - - # remove deprecated arguments - remove_decay_rate(jdata) - - if warning: - _warning_input_v1_v2(dump) - if dump is not None: - with open(dump, "w") as fp: - json.dump(jdata, fp, indent=4) - - return jdata - - -def _warning_input_v1_v2(fname: Optional[Union[str, Path]]): - msg = "It seems that you are using a deepmd-kit input of version 1.x.x, " \ - "which is deprecated. we have converted the input to >2.0.0 compatible" - if fname is not None: - msg += f", and output it to file {fname}" - warnings.warn(msg) - - -def deprecate_numb_test(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - """Deprecate `numb_test` since v2.1. It has taken no effect since v2.0. - - See `#1243 `_. - - Parameters - ---------- - jdata : Dict[str, Any] - loaded json/yaml file - warning : bool, optional - whether to show deprecation warning, by default True - dump : Optional[Union[str, Path]], optional - whether to dump converted file, by default None - - Returns - ------- - Dict[str, Any] - converted output - """ - try: - jdata.get("training", {}).pop("numb_test") - except KeyError: - pass - else: - if warning: - warnings.warn( - "The argument training->numb_test has been deprecated since v2.0.0. " - "Use training->validation_data->batch_size instead." - ) - - if dump is not None: - with open(dump, "w") as fp: - json.dump(jdata, fp, indent=4) - return jdata - - -def update_deepmd_input(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - def is_deepmd_v0_input(jdata): - return "model" not in jdata.keys() - - def is_deepmd_v1_input(jdata): - return "systems" in j_must_have(jdata, "training").keys() - - if is_deepmd_v0_input(jdata): - jdata = convert_input_v0_v1(jdata, warning, None) - jdata = convert_input_v1_v2(jdata, False, None) - jdata = deprecate_numb_test(jdata, False, dump) - elif is_deepmd_v1_input(jdata): - jdata = convert_input_v1_v2(jdata, warning, None) - jdata = deprecate_numb_test(jdata, False, dump) - else: - jdata = deprecate_numb_test(jdata, warning, dump) - - return jdata diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/convert.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/convert.py deleted file mode 100644 index 6688708..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/convert.py +++ /dev/null @@ -1,389 +0,0 @@ -import os -import textwrap -from deepmd.env import tf -from google.protobuf import text_format - - -def convert_13_to_21(input_model: str, output_model: str): - """Convert DP 1.3 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_13_to_21(input_model: str, output_model: str): - """Convert DP 1.3 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_12_to_21(input_model: str, output_model: str): - """Convert DP 1.2 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_10_to_21(input_model: str, output_model: str): - """Convert DP 1.0 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp10_to_dp11('frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_012_to_21(input_model: str, output_model: str): - """Convert DP 0.12 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp012_to_dp10('frozen_model.pbtxt') - convert_dp10_to_dp11('frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_20_to_21(input_model: str, output_model: str): - """Convert DP 2.0 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - -def convert_pb_to_pbtxt(pbfile: str, pbtxtfile: str): - """Convert DP graph to graph text. - - Parameters - ---------- - pbfile : str - filename of the input graph - pbtxtfile : str - filename of the output graph text - """ - with tf.gfile.GFile(pbfile, 'rb') as f: - graph_def = tf.GraphDef() - graph_def.ParseFromString(f.read()) - tf.import_graph_def(graph_def, name='') - tf.train.write_graph(graph_def, './', pbtxtfile, as_text=True) - -def convert_pbtxt_to_pb(pbtxtfile: str, pbfile: str): - """Convert DP graph text to graph. - - Parameters - ---------- - pbtxtfile : str - filename of the input graph text - pbfile : str - filename of the output graph - """ - with tf.gfile.GFile(pbtxtfile, 'r') as f: - graph_def = tf.GraphDef() - file_content = f.read() - # Merges the human-readable string in `file_content` into `graph_def`. - text_format.Merge(file_content, graph_def) - tf.train.write_graph(graph_def, './', pbfile, as_text=False) - - -def convert_dp012_to_dp10(file: str): - """Convert DP 1.0 graph text to 1.1 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(file) as fp: - file_content = fp.read() - # note: atom_energy must be put before energy, - # otherwise atom_energy_test -> atom_o_energy - file_content = file_content\ - .replace('DescrptNorot', 'DescrptSeA') \ - .replace('ProdForceNorot', 'ProdForceSeA') \ - .replace('ProdVirialNorot', 'ProdVirialSeA') \ - .replace('t_rcut', 'descrpt_attr/rcut') \ - .replace('t_ntypes', 'descrpt_attr/ntypes') \ - .replace('atom_energy_test', 'o_atom_energy') \ - .replace('atom_virial_test', 'o_atom_virial') \ - .replace('energy_test', 'o_energy') \ - .replace('force_test', 'o_force') \ - .replace('virial_test', 'o_virial') - file_content += textwrap.dedent("""\ - node { - name: "fitting_attr/dfparam" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 0 - } - } - } - } - """) - file_content += textwrap.dedent("""\ - node { - name: "model_attr/model_type" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "ener" - } - } - } - } - """) - with open(file, 'w') as fp: - fp.write(file_content) - - -def convert_dp10_to_dp11(file: str): - """Convert DP 1.0 graph text to 1.1 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(file, 'a') as f: - f.write(textwrap.dedent("""\ - node { - name: "fitting_attr/daparam" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 0 - } - } } - } - """)) - - -def convert_dp12_to_dp13(file: str): - """Convert DP 1.2 graph text to 1.3 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - file_data = "" - with open(file, "r", encoding="utf-8") as f: - ii = 0 - lines = f.readlines() - while (ii < len(lines)): - line = lines[ii] - file_data += line - ii+=1 - if 'name' in line and ('DescrptSeA' in line or 'ProdForceSeA' in line or 'ProdVirialSeA' in line): - while not('attr' in lines[ii] and '{' in lines[ii]): - file_data += lines[ii] - ii+=1 - file_data += ' attr {\n' - file_data += ' key: \"T\"\n' - file_data += ' value {\n' - file_data += ' type: DT_DOUBLE\n' - file_data += ' }\n' - file_data += ' }\n' - with open(file, "w", encoding="utf-8") as f: - f.write(file_data) - - -def convert_dp13_to_dp20(fname: str): - """Convert DP 1.3 graph text to 2.0 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(fname) as fp: - file_content = fp.read() - file_content += textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.0" - } - } - } - } - """) - file_content = file_content\ - .replace('DescrptSeA', 'ProdEnvMatA')\ - .replace('DescrptSeR', 'ProdEnvMatR') - with open(fname, 'w') as fp: - fp.write(file_content) - -def convert_dp20_to_dp21(fname: str): - with open(fname) as fp: - file_content = fp.read() - old_model_version_node = textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.0" - } - } - } - } - """) - new_model_version_node = textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.1" - } - } - } - } - """) - file_content = file_content\ - .replace(old_model_version_node, new_model_version_node)\ - .replace('TabulateFusion', 'TabulateFusionSeA')\ - .replace('TabulateFusionGrad', 'TabulateFusionSeAGrad')\ - .replace('TabulateFusionGradGrad', 'TabulateFusionSeAGradGrad') - with open(fname, 'w') as fp: - fp.write(file_content) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data.py deleted file mode 100644 index f5d2cd9..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data.py +++ /dev/null @@ -1,827 +0,0 @@ -#!/usr/bin/env python3 - -import time -import glob -import numpy as np -import os.path -from typing import Tuple, List -import logging - -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.env import GLOBAL_ENER_FLOAT_PRECISION -from deepmd.utils import random as dp_random -from deepmd.utils.path import DPPath - -log = logging.getLogger(__name__) - -class DeepmdData() : - """ - Class for a data system. - - It loads data from hard disk, and mantains the data as a `data_dict` - - Parameters - ---------- - sys_path - Path to the data system - set_prefix - Prefix for the directories of different sets - shuffle_test - If the test data are shuffled - type_map - Gives the name of different atom types - modifier - Data modifier that has the method `modify_data` - trn_all_set - Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test. - """ - def __init__ (self, - sys_path : str, - set_prefix : str = 'set', - shuffle_test : bool = True, - type_map : List[str] = None, - modifier = None, - trn_all_set : bool = False) : - """ - Constructor - """ - root = DPPath(sys_path) - self.dirs = root.glob(set_prefix + ".*") - self.dirs.sort() - self.mixed_type = self._check_mode(self.dirs[0]) # mixed_type format only has one set - # load atom type - self.atom_type = self._load_type(root) - self.natoms = len(self.atom_type) - if self.mixed_type: - # nframes x natoms - self.atom_type_mix = self._load_type_mix(self.dirs[0]) - # load atom type map - self.type_map = self._load_type_map(root) - if self.type_map is not None: - assert(len(self.type_map) >= max(self.atom_type)+1) - # check pbc - self.pbc = self._check_pbc(root) - # enforce type_map if necessary - if type_map is not None and self.type_map is not None: - if not self.mixed_type: - atom_type_ = [type_map.index(self.type_map[ii]) for ii in self.atom_type] - self.atom_type = np.array(atom_type_, dtype = np.int32) - else: - sorter = np.argsort(type_map) - type_idx_map = sorter[np.searchsorted(type_map, self.type_map, sorter=sorter)] - try: - atom_type_mix_ = np.array(type_idx_map)[self.atom_type_mix].astype(np.int32) - except RuntimeError as e: - raise RuntimeError("some types in 'real_atom_types.npy' of sys {} are not contained in {} types!" - .format(self.dirs[0], self.get_ntypes())) from e - self.atom_type_mix = atom_type_mix_ - self.type_map = type_map - if type_map is None and self.type_map is None and self.mixed_type: - raise RuntimeError('mixed_type format must have type_map!') - # make idx map - self.idx_map = self._make_idx_map(self.atom_type) - # train dirs - self.test_dir = self.dirs[-1] - if trn_all_set: - self.train_dirs = self.dirs - else: - if len(self.dirs) == 1 : - self.train_dirs = self.dirs - else : - self.train_dirs = self.dirs[:-1] - self.data_dict = {} - # add box and coord - self.add('box', 9, must = self.pbc) - self.add('coord', 3, atomic = True, must = True) - # set counters - self.set_count = 0 - self.iterator = 0 - self.shuffle_test = shuffle_test - # set modifier - self.modifier = modifier - - - def add(self, - key : str, - ndof : int, - atomic : bool = False, - must : bool = False, - high_prec : bool = False, - type_sel : List[int] = None, - repeat : int = 1, - default: float=0., - ) : - """ - Add a data item that to be loaded - - Parameters - ---------- - key - The key of the item. The corresponding data is stored in `sys_path/set.*/key.npy` - ndof - The number of dof - atomic - The item is an atomic property. - If False, the size of the data should be nframes x ndof - If True, the size of data should be nframes x natoms x ndof - must - The data file `sys_path/set.*/key.npy` must exist. - If must is False and the data file does not exist, the `data_dict[find_key]` is set to 0.0 - high_prec - Load the data and store in float64, otherwise in float32 - type_sel - Select certain type of atoms - repeat - The data will be repeated `repeat` times. - default : float, default=0. - default value of data - """ - self.data_dict[key] = {'ndof': ndof, - 'atomic': atomic, - 'must': must, - 'high_prec': high_prec, - 'type_sel': type_sel, - 'repeat': repeat, - 'reduce': None, - 'default': default, - } - return self - - - def reduce(self, - key_out : str, - key_in : str - ) : - """ - Generate a new item from the reduction of another atom - - Parameters - ---------- - key_out - The name of the reduced item - key_in - The name of the data item to be reduced - """ - assert (key_in in self.data_dict), 'cannot find input key' - assert (self.data_dict[key_in]['atomic']), 'reduced property should be atomic' - assert (not(key_out in self.data_dict)), 'output key should not have been added' - assert (self.data_dict[key_in]['repeat'] == 1), 'reduced proerties should not have been repeated' - - self.data_dict[key_out] = {'ndof': self.data_dict[key_in]['ndof'], - 'atomic': False, - 'must': True, - 'high_prec': True, - 'type_sel': None, - 'repeat': 1, - 'reduce': key_in, - } - return self - - def get_data_dict(self) -> dict: - """ - Get the `data_dict` - """ - return self.data_dict - - def check_batch_size (self, batch_size) : - """ - Check if the system can get a batch of data with `batch_size` frames. - """ - for ii in self.train_dirs : - if self.data_dict['coord']['high_prec'] : - tmpe = (ii / "coord.npy").load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - tmpe = (ii / "coord.npy").load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if tmpe.ndim == 1: - tmpe = tmpe.reshape([1,-1]) - if tmpe.shape[0] < batch_size : - return ii, tmpe.shape[0] - return None - - def check_test_size (self, test_size) : - """ - Check if the system can get a test dataset with `test_size` frames. - """ - if self.data_dict['coord']['high_prec'] : - tmpe = (self.test_dir / "coord.npy").load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - tmpe = (self.test_dir / "coord.npy").load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if tmpe.ndim == 1: - tmpe = tmpe.reshape([1,-1]) - if tmpe.shape[0] < test_size : - return self.test_dir, tmpe.shape[0] - else : - return None - - def get_batch(self, - batch_size : int - ) -> dict : - """ - Get a batch of data with `batch_size` frames. The frames are randomly picked from the data system. - - Parameters - ---------- - batch_size - size of the batch - """ - if hasattr(self, 'batch_set') : - set_size = self.batch_set["coord"].shape[0] - else : - set_size = 0 - if self.iterator + batch_size > set_size : - self._load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - self.set_count += 1 - set_size = self.batch_set["coord"].shape[0] - if self.modifier is not None: - self.modifier.modify_data(self.batch_set) - iterator_1 = self.iterator + batch_size - if iterator_1 >= set_size : - iterator_1 = set_size - idx = np.arange (self.iterator, iterator_1) - self.iterator += batch_size - ret = self._get_subdata(self.batch_set, idx) - return ret - - def get_test (self, - ntests : int = -1 - ) -> dict: - """ - Get the test data with `ntests` frames. - - Parameters - ---------- - ntests - Size of the test data set. If `ntests` is -1, all test data will be get. - """ - if not hasattr(self, 'test_set') : - self._load_test_set(self.test_dir, self.shuffle_test) - if ntests == -1: - idx = None - else : - ntests_ = ntests if ntests < self.test_set['type'].shape[0] else self.test_set['type'].shape[0] - # print('ntest', self.test_set['type'].shape[0], ntests, ntests_) - idx = np.arange(ntests_) - ret = self._get_subdata(self.test_set, idx = idx) - if self.modifier is not None: - self.modifier.modify_data(ret) - return ret - - def get_ntypes(self) -> int: - """ - Number of atom types in the system - """ - if self.type_map is not None: - return len(self.type_map) - else: - return max(self.get_atom_type()) + 1 - - def get_type_map(self) -> List[str]: - """ - Get the type map - """ - return self.type_map - - def get_atom_type(self) -> List[int]: - """ - Get atom types - """ - return self.atom_type - - def get_numb_set (self) -> int: - """ - Get number of training sets - """ - return len (self.train_dirs) - - def get_numb_batch (self, - batch_size : int, - set_idx : int - ) -> int: - """ - Get the number of batches in a set. - """ - data = self._load_set(self.train_dirs[set_idx]) - ret = data["coord"].shape[0] // batch_size - if ret == 0: - ret = 1 - return ret - - def get_sys_numb_batch (self, - batch_size : int - ) -> int: - """ - Get the number of batches in the data system. - """ - ret = 0 - for ii in range(len(self.train_dirs)) : - ret += self.get_numb_batch(batch_size, ii) - return ret - - def get_natoms (self) : - """ - Get number of atoms - """ - return len(self.atom_type) - - def get_natoms_vec (self, - ntypes : int) : - """ - Get number of atoms and number of atoms in different types - - Parameters - ---------- - ntypes - Number of types (may be larger than the actual number of types in the system). - - Returns - ------- - natoms - 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 - """ - natoms, natoms_vec = self._get_natoms_2 (ntypes) - tmp = [natoms, natoms] - tmp = np.append (tmp, natoms_vec) - return tmp.astype(np.int32) - - def avg(self, key) : - """ - Return the average value of an item. - """ - if key not in self.data_dict.keys() : - raise RuntimeError('key %s has not been added' % key) - info = self.data_dict[key] - ndof = info['ndof'] - eners = np.array([]) - for ii in self.train_dirs: - data = self._load_set(ii) - ei = data[key].reshape([-1, ndof]) - if eners.size == 0 : - eners = ei - else : - eners = np.concatenate((eners, ei), axis = 0) - if eners.size == 0 : - return 0 - else : - return np.average(eners, axis = 0) - - def _idx_map_sel(self, atom_type, type_sel) : - new_types = [] - for ii in atom_type : - if ii in type_sel: - new_types.append(ii) - new_types = np.array(new_types, dtype = int) - natoms = new_types.shape[0] - idx = np.arange(natoms) - idx_map = np.lexsort((idx, new_types)) - return idx_map - - def _get_natoms_2 (self, ntypes) : - sample_type = self.atom_type - natoms = len(sample_type) - natoms_vec = np.zeros (ntypes).astype(int) - for ii in range (ntypes) : - natoms_vec[ii] = np.count_nonzero(sample_type == ii) - return natoms, natoms_vec - - def _get_subdata(self, data, idx = None) : - new_data = {} - for ii in data: - dd = data[ii] - if 'find_' in ii: - new_data[ii] = dd - else: - if idx is not None: - new_data[ii] = dd[idx] - else : - new_data[ii] = dd - return new_data - - def _load_batch_set (self, - set_name: DPPath) : - self.batch_set = self._load_set(set_name) - self.batch_set, _ = self._shuffle_data(self.batch_set) - self.reset_get_batch() - - def reset_get_batch(self): - self.iterator = 0 - - def _load_test_set (self, - set_name: DPPath, - shuffle_test) : - self.test_set = self._load_set(set_name) - if shuffle_test : - self.test_set, _ = self._shuffle_data(self.test_set) - - def _shuffle_data (self, - data) : - ret = {} - nframes = data['coord'].shape[0] - idx = np.arange (nframes) - dp_random.shuffle(idx) - for kk in data : - if type(data[kk]) == np.ndarray and \ - len(data[kk].shape) == 2 and \ - data[kk].shape[0] == nframes and \ - not('find_' in kk): - ret[kk] = data[kk][idx] - else : - ret[kk] = data[kk] - return ret, idx - - def _load_set(self, set_name: DPPath) : - # get nframes - if not isinstance(set_name, DPPath): - set_name = DPPath(set_name) - path = set_name / "coord.npy" - if self.data_dict['coord']['high_prec'] : - coord = path.load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - coord = path.load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if coord.ndim == 1: - coord = coord.reshape([1,-1]) - nframes = coord.shape[0] - assert(coord.shape[1] == self.data_dict['coord']['ndof'] * self.natoms) - # load keys - data = {} - for kk in self.data_dict.keys(): - if self.data_dict[kk]['reduce'] is None : - data['find_'+kk], data[kk] \ - = self._load_data(set_name, - kk, - nframes, - self.data_dict[kk]['ndof'], - atomic = self.data_dict[kk]['atomic'], - high_prec = self.data_dict[kk]['high_prec'], - must = self.data_dict[kk]['must'], - type_sel = self.data_dict[kk]['type_sel'], - repeat = self.data_dict[kk]['repeat'], - default=self.data_dict[kk]['default'], - ) - for kk in self.data_dict.keys(): - if self.data_dict[kk]['reduce'] is not None : - k_in = self.data_dict[kk]['reduce'] - ndof = self.data_dict[kk]['ndof'] - data['find_'+kk] = data['find_'+k_in] - tmp_in = data[k_in].astype(GLOBAL_ENER_FLOAT_PRECISION) - data[kk] = np.sum(np.reshape(tmp_in, [nframes, self.natoms, ndof]), axis = 1) - - if self.mixed_type: - real_type = self.atom_type_mix.reshape([nframes, self.natoms]) - data['type'] = real_type - natoms = data['type'].shape[1] - # nframes x ntypes - atom_type_nums = np.array([(real_type == i).sum(axis=-1) for i in range(self.get_ntypes())], - dtype=np.int32).T - assert (atom_type_nums.sum(axis=-1) == natoms).all(), \ - "some types in 'real_atom_types.npy' of sys {} are not contained in {} types!" \ - .format(self.dirs[0], self.get_ntypes()) - data['real_natoms_vec'] = np.concatenate((np.tile(np.array([natoms, natoms], dtype=np.int32), (nframes, 1)), - atom_type_nums), axis=-1) - else: - data['type'] = np.tile(self.atom_type[self.idx_map], (nframes, 1)) - - return data - - - def _load_data(self, set_name, key, nframes, ndof_, atomic = False, must = True, repeat = 1, high_prec = False, type_sel = None, default: float=0.): - if atomic: - natoms = self.natoms - idx_map = self.idx_map - # if type_sel, then revise natoms and idx_map - if type_sel is not None: - natoms = 0 - for jj in type_sel : - natoms += np.sum(self.atom_type == jj) - idx_map = self._idx_map_sel(self.atom_type, type_sel) - ndof = ndof_ * natoms - else: - ndof = ndof_ - path = set_name / (key+".npy") - if path.is_file() : - if high_prec : - data = path.load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - data = path.load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - try: # YWolfeee: deal with data shape error - if atomic : - data = data.reshape([nframes, natoms, -1]) - data = data[:,idx_map,:] - data = data.reshape([nframes, -1]) - data = np.reshape(data, [nframes, ndof]) - except ValueError as err_message: - explanation = "This error may occur when your label mismatch it's name, i.e. you might store global tensor in `atomic_tensor.npy` or atomic tensor in `tensor.npy`." - log.error(str(err_message)) - log.error(explanation) - raise ValueError(str(err_message) + ". " + explanation) - if repeat != 1: - data = np.repeat(data, repeat).reshape([nframes, -1]) - return np.float32(1.0), data - elif must: - raise RuntimeError("%s not found!" % path) - else: - if high_prec : - data = np.full([nframes, ndof], default, dtype=GLOBAL_ENER_FLOAT_PRECISION) - else : - data = np.full([nframes, ndof], default, dtype=GLOBAL_NP_FLOAT_PRECISION) - if repeat != 1: - data = np.repeat(data, repeat).reshape([nframes, -1]) - return np.float32(0.0), data - - - def _load_type (self, sys_path: DPPath) : - atom_type = (sys_path / "type.raw").load_txt(dtype=np.int32, ndmin=1) - return atom_type - - def _load_type_mix(self, set_name: DPPath): - type_path = set_name / "real_atom_types.npy" - real_type = type_path.load_numpy().astype(np.int32).reshape([-1, self.natoms]) - return real_type - - def _make_idx_map(self, atom_type): - natoms = atom_type.shape[0] - idx = np.arange (natoms) - idx_map = np.lexsort ((idx, atom_type)) - return idx_map - - def _load_type_map(self, sys_path: DPPath) : - fname = sys_path / 'type_map.raw' - if fname.is_file() : - return fname.load_txt(dtype=str, ndmin=1).tolist() - else : - return None - - def _check_pbc(self, sys_path: DPPath): - pbc = True - if (sys_path / 'nopbc').is_file() : - pbc = False - return pbc - - def _check_mode(self, set_path: DPPath): - return (set_path / 'real_atom_types.npy').is_file() - - -class DataSets (object): - """ - Outdated class for one data system. - - .. deprecated:: 2.0.0 - This class is not maintained any more. - """ - def __init__ (self, - sys_path, - set_prefix, - seed = None, - shuffle_test = True) : - self.dirs = glob.glob (os.path.join(sys_path, set_prefix + ".*")) - self.dirs.sort() - # load atom type - self.atom_type, self.idx_map, self.idx3_map = self.load_type (sys_path) - # load atom type map - self.type_map = self.load_type_map(sys_path) - if self.type_map is not None: - assert(len(self.type_map) >= max(self.atom_type)+1) - # train dirs - self.test_dir = self.dirs[-1] - if len(self.dirs) == 1 : - self.train_dirs = self.dirs - else : - self.train_dirs = self.dirs[:-1] - # check fparam - has_fparam = [ os.path.isfile(os.path.join(ii, 'fparam.npy')) for ii in self.dirs ] - if any(has_fparam) and (not all(has_fparam)) : - raise RuntimeError("system %s: if any set has frame parameter, then all sets should have frame parameter" % sys_path) - if all(has_fparam) : - self.has_fparam = 0 - else : - self.has_fparam = -1 - # check aparam - has_aparam = [ os.path.isfile(os.path.join(ii, 'aparam.npy')) for ii in self.dirs ] - if any(has_aparam) and (not all(has_aparam)) : - raise RuntimeError("system %s: if any set has frame parameter, then all sets should have frame parameter" % sys_path) - if all(has_aparam) : - self.has_aparam = 0 - else : - self.has_aparam = -1 - # energy norm - self.eavg = self.stats_energy() - # load sets - self.set_count = 0 - self.load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - self.load_test_set (self.test_dir, shuffle_test) - - def check_batch_size (self, batch_size) : - for ii in self.train_dirs : - tmpe = np.load(os.path.join(ii, "coord.npy")) - if tmpe.shape[0] < batch_size : - return ii, tmpe.shape[0] - return None - - def check_test_size (self, test_size) : - tmpe = np.load(os.path.join(self.test_dir, "coord.npy")) - if tmpe.shape[0] < test_size : - return self.test_dir, tmpe.shape[0] - else : - return None - - def load_type (self, sys_path) : - atom_type = np.loadtxt (os.path.join(sys_path, "type.raw"), dtype=np.int32, ndmin=1) - natoms = atom_type.shape[0] - idx = np.arange (natoms) - idx_map = np.lexsort ((idx, atom_type)) - atom_type3 = np.repeat(atom_type, 3) - idx3 = np.arange (natoms * 3) - idx3_map = np.lexsort ((idx3, atom_type3)) - return atom_type, idx_map, idx3_map - - def load_type_map(self, sys_path) : - fname = os.path.join(sys_path, 'type_map.raw') - if os.path.isfile(fname) : - with open(os.path.join(sys_path, 'type_map.raw')) as fp: - return fp.read().split() - else : - return None - - def get_type_map(self) : - return self.type_map - - def get_numb_set (self) : - return len (self.train_dirs) - - def stats_energy (self) : - eners = np.array([]) - for ii in self.train_dirs: - ener_file = os.path.join(ii, "energy.npy") - if os.path.isfile(ener_file) : - ei = np.load(ener_file) - eners = np.append(eners, ei) - if eners.size == 0 : - return 0 - else : - return np.average(eners) - - def load_energy(self, - set_name, - nframes, - nvalues, - energy_file, - atom_energy_file) : - """ - return : coeff_ener, ener, coeff_atom_ener, atom_ener - """ - # load atom_energy - coeff_atom_ener, atom_ener = self.load_data(set_name, atom_energy_file, [nframes, nvalues], False) - # ignore energy_file - if coeff_atom_ener == 1: - ener = np.sum(atom_ener, axis = 1) - coeff_ener = 1 - # load energy_file - else: - coeff_ener, ener = self.load_data(set_name, energy_file, [nframes], False) - return coeff_ener, ener, coeff_atom_ener, atom_ener - - def load_data(self, set_name, data_name, shape, is_necessary = True): - path = os.path.join(set_name, data_name+".npy") - if os.path.isfile (path) : - data = np.load(path) - data = np.reshape(data, shape) - if is_necessary: - return data - return 1, data - elif is_necessary: - raise OSError("%s not found!" % path) - else: - data = np.zeros(shape) - return 0, data - - def load_set(self, set_name, shuffle = True): - data = {} - data["box"] = self.load_data(set_name, "box", [-1, 9]) - nframe = data["box"].shape[0] - data["coord"] = self.load_data(set_name, "coord", [nframe, -1]) - ncoord = data["coord"].shape[1] - if self.has_fparam >= 0: - data["fparam"] = self.load_data(set_name, "fparam", [nframe, -1]) - if self.has_fparam == 0 : - self.has_fparam = data["fparam"].shape[1] - else : - assert self.has_fparam == data["fparam"].shape[1] - if self.has_aparam >= 0: - data["aparam"] = self.load_data(set_name, "aparam", [nframe, -1]) - if self.has_aparam == 0 : - self.has_aparam = data["aparam"].shape[1] // (ncoord//3) - else : - assert self.has_aparam == data["aparam"].shape[1] // (ncoord//3) - data["prop_c"] = np.zeros(5) - data["prop_c"][0], data["energy"], data["prop_c"][3], data["atom_ener"] \ - = self.load_energy (set_name, nframe, ncoord // 3, "energy", "atom_ener") - data["prop_c"][1], data["force"] = self.load_data(set_name, "force", [nframe, ncoord], False) - data["prop_c"][2], data["virial"] = self.load_data(set_name, "virial", [nframe, 9], False) - data["prop_c"][4], data["atom_pref"] = self.load_data(set_name, "atom_pref", [nframe, ncoord//3], False) - data["atom_pref"] = np.repeat(data["atom_pref"], 3, axis=1) - # shuffle data - if shuffle: - idx = np.arange (nframe) - dp_random.shuffle(idx) - for ii in data: - if ii != "prop_c": - data[ii] = data[ii][idx] - data["type"] = np.tile (self.atom_type, (nframe, 1)) - # sort according to type - for ii in ["type", "atom_ener"]: - data[ii] = data[ii][:, self.idx_map] - for ii in ["coord", "force", "atom_pref"]: - data[ii] = data[ii][:, self.idx3_map] - return data - - def load_batch_set (self, - set_name) : - self.batch_set = self.load_set(set_name, True) - self.reset_iter () - - def load_test_set (self, - set_name, - shuffle_test) : - self.test_set = self.load_set(set_name, shuffle_test) - - def reset_iter (self) : - self.iterator = 0 - self.set_count += 1 - - def get_set(self, data, idx = None) : - new_data = {} - for ii in data: - dd = data[ii] - if ii == "prop_c": - new_data[ii] = dd.astype(np.float32) - else: - if idx is not None: - dd = dd[idx] - if ii == "type": - new_data[ii] = dd - else: - new_data[ii] = dd.astype(GLOBAL_NP_FLOAT_PRECISION) - return new_data - - def get_test (self) : - """ - returned property prefector [4] in order: - energy, force, virial, atom_ener - """ - return self.get_set(self.test_set) - - def get_batch (self, - batch_size) : - """ - returned property prefector [4] in order: - energy, force, virial, atom_ener - """ - set_size = self.batch_set["energy"].shape[0] - # assert (batch_size <= set_size), "batch size should be no more than set size" - if self.iterator + batch_size > set_size : - self.load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - set_size = self.batch_set["energy"].shape[0] - # print ("%d %d %d" % (self.iterator, self.iterator + batch_size, set_size)) - iterator_1 = self.iterator + batch_size - if iterator_1 >= set_size : - iterator_1 = set_size - idx = np.arange (self.iterator, iterator_1) - self.iterator += batch_size - return self.get_set(self.batch_set, idx) - - def get_natoms (self) : - sample_type = self.batch_set["type"][0] - natoms = len(sample_type) - return natoms - - def get_natoms_2 (self, ntypes) : - sample_type = self.batch_set["type"][0] - natoms = len(sample_type) - natoms_vec = np.zeros (ntypes).astype(int) - for ii in range (ntypes) : - natoms_vec[ii] = np.count_nonzero(sample_type == ii) - return natoms, natoms_vec - - def get_natoms_vec (self, ntypes) : - natoms, natoms_vec = self.get_natoms_2 (ntypes) - tmp = [natoms, natoms] - tmp = np.append (tmp, natoms_vec) - return tmp.astype(np.int32) - - def set_numb_batch (self, - batch_size) : - return self.batch_set["energy"].shape[0] // batch_size - - def get_sys_numb_batch (self, batch_size) : - return self.set_numb_batch(batch_size) * self.get_numb_set() - - def get_ener (self) : - return self.eavg - - def numb_fparam(self) : - return self.has_fparam - - def numb_aparam(self) : - return self.has_aparam - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data_system.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data_system.py deleted file mode 100644 index 398db54..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/data_system.py +++ /dev/null @@ -1,745 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import os -import collections -import warnings -import numpy as np -from typing import Tuple, List - -from deepmd.utils import random as dp_random -from deepmd.utils.data import DataSets -from deepmd.utils.data import DeepmdData - -log = logging.getLogger(__name__) - - -class DeepmdDataSystem() : - """ - Class for manipulating many data systems. - - It is implemented with the help of DeepmdData - """ - def __init__ (self, - systems : List[str], - batch_size : int, - test_size : int, - rcut : float, - set_prefix : str = 'set', - shuffle_test : bool = True, - type_map : List[str] = None, - modifier = None, - trn_all_set = False, - sys_probs = None, - auto_prob_style ="prob_sys_size") : - """ - Constructor - - Parameters - ---------- - systems - Specifying the paths to systems - batch_size - The batch size - test_size - The size of test data - rcut - The cut-off radius - set_prefix - Prefix for the directories of different sets - shuffle_test - If the test data are shuffled - type_map - Gives the name of different atom types - modifier - Data modifier that has the method `modify_data` - trn_all_set - Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test. - sys_probs: list of float - The probabilitis of systems to get the batch. - Summation of positive elements of this list should be no greater than 1. - Element of this list can be negative, the probability of the corresponding system is determined - automatically by the number of batches in the system. - auto_prob_style: str - Determine the probability of systems automatically. The method is assigned by this key and can be - - "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems() - - "prob_sys_size" : the probability of a system is proportional to the number of batches in the system - - "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : - the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, - where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, - the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional - to the number of batches in the system.""" - # init data - self.rcut = rcut - self.system_dirs = systems - self.nsystems = len(self.system_dirs) - self.data_systems = [] - for ii in self.system_dirs : - self.data_systems.append( - DeepmdData( - ii, - set_prefix=set_prefix, - shuffle_test=shuffle_test, - type_map = type_map, - modifier = modifier, - trn_all_set = trn_all_set - )) - # check mix_type format - error_format_msg = "if one of the system is of mixed_type format, " \ - "then all of the systems should be of mixed_type format!" - if self.data_systems[0].mixed_type: - for data_sys in self.data_systems[1:]: - assert data_sys.mixed_type, error_format_msg - self.mixed_type = True - else: - for data_sys in self.data_systems[1:]: - assert not data_sys.mixed_type, error_format_msg - self.mixed_type = False - # batch size - self.batch_size = batch_size - if isinstance(self.batch_size, int): - self.batch_size = self.batch_size * np.ones(self.nsystems, dtype=int) - elif isinstance(self.batch_size, str): - words = self.batch_size.split(':') - if 'auto' == words[0] : - rule = 32 - if len(words) == 2 : - rule = int(words[1]) - else: - raise RuntimeError('unknown batch_size rule ' + words[0]) - self.batch_size = self._make_auto_bs(rule) - elif isinstance(self.batch_size, list): - pass - else : - raise RuntimeError('invalid batch_size') - assert(isinstance(self.batch_size, (list,np.ndarray))) - assert(len(self.batch_size) == self.nsystems) - - # natoms, nbatches - ntypes = [] - for ii in self.data_systems : - ntypes.append(ii.get_ntypes()) - self.sys_ntypes = max(ntypes) - self.natoms = [] - self.natoms_vec = [] - self.nbatches = [] - type_map_list = [] - for ii in range(self.nsystems) : - self.natoms.append(self.data_systems[ii].get_natoms()) - self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(self.sys_ntypes).astype(int)) - self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(self.batch_size[ii])) - type_map_list.append(self.data_systems[ii].get_type_map()) - self.type_map = self._check_type_map_consistency(type_map_list) - - # ! altered by Marián Rynik - # test size - # now test size can be set as a percentage of systems data or test size - # can be set for each system individualy in the same manner as batch - # size. This enables one to use systems with diverse number of - # structures and different number of atoms. - self.test_size = test_size - if isinstance(self.test_size, int): - self.test_size = self.test_size * np.ones(self.nsystems, dtype=int) - elif isinstance(self.test_size, str): - words = self.test_size.split('%') - try: - percent = int(words[0]) - except ValueError: - raise RuntimeError('unknown test_size rule ' + words[0]) - self.test_size = self._make_auto_ts(percent) - elif isinstance(self.test_size, list): - pass - else : - raise RuntimeError('invalid test_size') - assert(isinstance(self.test_size, (list,np.ndarray))) - assert(len(self.test_size) == self.nsystems) - - # prob of batch, init pick idx - self.prob_nbatches = [ float(i) for i in self.nbatches] / np.sum(self.nbatches) - self.pick_idx = 0 - - # derive system probabilities - self.sys_probs = None - self.set_sys_probs(sys_probs, auto_prob_style) - - # check batch and test size - for ii in range(self.nsystems) : - chk_ret = self.data_systems[ii].check_batch_size(self.batch_size[ii]) - if chk_ret is not None : - warnings.warn("system %s required batch size is larger than the size of the dataset %s (%d > %d)" % \ - (self.system_dirs[ii], chk_ret[0], self.batch_size[ii], chk_ret[1])) - chk_ret = self.data_systems[ii].check_test_size(self.test_size[ii]) - if chk_ret is not None : - warnings.warn("system %s required test size is larger than the size of the dataset %s (%d > %d)" % \ - (self.system_dirs[ii], chk_ret[0], self.test_size[ii], chk_ret[1])) - - - def _load_test(self, ntests = -1): - self.test_data = collections.defaultdict(list) - for ii in range(self.nsystems) : - test_system_data = self.data_systems[ii].get_test(ntests = ntests) - for nn in test_system_data: - self.test_data[nn].append(test_system_data[nn]) - - - def _make_default_mesh(self): - self.default_mesh = [] - cell_size = np.max (self.rcut) - for ii in range(self.nsystems) : - if self.data_systems[ii].pbc : - test_system_data = self.data_systems[ii].get_batch(self.batch_size[ii]) - self.data_systems[ii].reset_get_batch() - # test_system_data = self.data_systems[ii].get_test() - avg_box = np.average (test_system_data["box"], axis = 0) - avg_box = np.reshape (avg_box, [3,3]) - ncell = (np.linalg.norm(avg_box, axis=1)/ cell_size).astype(np.int32) - ncell[ncell < 2] = 2 - default_mesh = np.zeros (6, dtype = np.int32) - default_mesh[3:6] = ncell - self.default_mesh.append(default_mesh) - else: - self.default_mesh.append(np.array([], dtype = np.int32)) - - - def compute_energy_shift(self, rcond = 1e-3, key = 'energy') : - sys_ener = np.array([]) - for ss in self.data_systems : - sys_ener = np.append(sys_ener, ss.avg(key)) - sys_tynatom = np.array(self.natoms_vec, dtype = float) - sys_tynatom = np.reshape(sys_tynatom, [self.nsystems,-1]) - sys_tynatom = sys_tynatom[:,2:] - energy_shift,resd,rank,s_value \ - = np.linalg.lstsq(sys_tynatom, sys_ener, rcond = rcond) - return energy_shift - - def add_dict(self, adict: dict) -> None: - """ - Add items to the data system by a `dict`. - `adict` should have items like - .. code-block:: python - - adict[key] = { - 'ndof': ndof, - 'atomic': atomic, - 'must': must, - 'high_prec': high_prec, - 'type_sel': type_sel, - 'repeat': repeat, - } - - For the explaination of the keys see `add` - """ - for kk in adict : - self.add(kk, - adict[kk]['ndof'], - atomic=adict[kk]['atomic'], - must=adict[kk]['must'], - high_prec=adict[kk]['high_prec'], - type_sel=adict[kk]['type_sel'], - repeat=adict[kk]['repeat'], - default=adict[kk]['default'], - ) - - def add(self, - key : str, - ndof : int, - atomic : bool = False, - must : bool = False, - high_prec : bool = False, - type_sel : List[int] = None, - repeat : int = 1, - default: float=0., - ) : - """ - Add a data item that to be loaded - - Parameters - ---------- - key - The key of the item. The corresponding data is stored in `sys_path/set.*/key.npy` - ndof - The number of dof - atomic - The item is an atomic property. - If False, the size of the data should be nframes x ndof - If True, the size of data should be nframes x natoms x ndof - must - The data file `sys_path/set.*/key.npy` must exist. - If must is False and the data file does not exist, the `data_dict[find_key]` is set to 0.0 - high_prec - Load the data and store in float64, otherwise in float32 - type_sel - Select certain type of atoms - repeat - The data will be repeated `repeat` times. - default, default=0. - Default value of data - """ - for ii in self.data_systems: - ii.add(key, ndof, atomic=atomic, must=must, high_prec=high_prec, repeat=repeat, type_sel=type_sel, default=default) - - def reduce(self, key_out, key_in): - """ - Generate a new item from the reduction of another atom - - Parameters - ---------- - key_out - The name of the reduced item - key_in - The name of the data item to be reduced - """ - for ii in self.data_systems: - ii.reduce(key_out, key_in) - - def get_data_dict(self, ii: int = 0) -> dict: - return self.data_systems[ii].get_data_dict() - - def set_sys_probs(self, sys_probs=None, - auto_prob_style: str = "prob_sys_size"): - if sys_probs is None : - if auto_prob_style == "prob_uniform": - prob_v = 1./float(self.nsystems) - probs = [prob_v for ii in range(self.nsystems)] - elif auto_prob_style == "prob_sys_size": - probs = self.prob_nbatches - elif auto_prob_style[:14] == "prob_sys_size;": - probs = self._prob_sys_size_ext(auto_prob_style) - else: - raise RuntimeError("Unknown auto prob style: " + auto_prob_style) - else: - probs = self._process_sys_probs(sys_probs) - self.sys_probs = probs - - def _get_sys_probs(self, - sys_probs, - auto_prob_style) : # depreciated - if sys_probs is None : - if auto_prob_style == "prob_uniform" : - prob_v = 1./float(self.nsystems) - prob = [prob_v for ii in range(self.nsystems)] - elif auto_prob_style == "prob_sys_size" : - prob = self.prob_nbatches - elif auto_prob_style[:14] == "prob_sys_size;" : - prob = self._prob_sys_size_ext(auto_prob_style) - else : - raise RuntimeError("unkown style " + auto_prob_style ) - else : - prob = self._process_sys_probs(sys_probs) - return prob - - def get_batch(self, sys_idx : int = None): - # batch generation style altered by Ziyao Li: - # one should specify the "sys_prob" and "auto_prob_style" params - # via set_sys_prob() function. The sys_probs this function uses is - # defined as a private variable, self.sys_probs, initialized in __init__(). - # This is to optimize the (vain) efforts in evaluating sys_probs every batch. - """ - Get a batch of data from the data systems - - Parameters - ---------- - sys_idx: int - The index of system from which the batch is get. - If sys_idx is not None, `sys_probs` and `auto_prob_style` are ignored - If sys_idx is None, automatically determine the system according to `sys_probs` or `auto_prob_style`, see the following. - """ - if not hasattr(self, 'default_mesh') : - self._make_default_mesh() - if sys_idx is not None : - self.pick_idx = sys_idx - else : - # prob = self._get_sys_probs(sys_probs, auto_prob_style) - self.pick_idx = dp_random.choice(np.arange(self.nsystems), p=self.sys_probs) - b_data = self.data_systems[self.pick_idx].get_batch(self.batch_size[self.pick_idx]) - b_data["natoms_vec"] = self.natoms_vec[self.pick_idx] - b_data["default_mesh"] = self.default_mesh[self.pick_idx] - return b_data - - # ! altered by Marián Rynik - def get_test (self, - sys_idx : int = None, - n_test : int = -1) : # depreciated - """ - Get test data from the the data systems. - - Parameters - ---------- - sys_idx - The test dat of system with index `sys_idx` will be returned. - If is None, the currently selected system will be returned. - n_test - Number of test data. If set to -1 all test data will be get. - """ - if not hasattr(self, 'default_mesh') : - self._make_default_mesh() - if not hasattr(self, 'test_data') : - self._load_test(ntests = n_test) - if sys_idx is not None : - idx = sys_idx - else : - idx = self.pick_idx - - test_system_data = {} - for nn in self.test_data: - test_system_data[nn] = self.test_data[nn][idx] - test_system_data["natoms_vec"] = self.natoms_vec[idx] - test_system_data["default_mesh"] = self.default_mesh[idx] - return test_system_data - - def get_sys_ntest(self, sys_idx=None): - """ - Get number of tests for the currently selected system, - or one defined by sys_idx. - """ - if sys_idx is not None : - return self.test_size[sys_idx] - else : - return self.test_size[self.pick_idx] - - def get_type_map(self) -> List[str]: - """ - Get the type map - """ - return self.type_map - - def get_nbatches (self) -> int: - """ - Get the total number of batches - """ - return self.nbatches - - def get_ntypes (self) -> int: - """ - Get the number of types - """ - return self.sys_ntypes - - def get_nsystems (self) -> int: - """ - Get the number of data systems - """ - return self.nsystems - - def get_sys (self, idx : int) -> DeepmdData: - """ - Get a certain data system - """ - return self.data_systems[idx] - - def get_batch_size(self) -> int: - """ - Get the batch size - """ - return self.batch_size - - def _format_name_length(self, name, width) : - if len(name) <= width: - return '{: >{}}'.format(name, width) - else : - name = name[-(width-3):] - name = '-- ' + name - return name - - def print_summary(self, name) : - # width 65 - sys_width = 42 - log.info(f"---Summary of DataSystem: {name:13s}-----------------------------------------------") - log.info("found %d system(s):" % self.nsystems) - log.info(("%s " % self._format_name_length('system', sys_width)) + - ("%6s %6s %6s %5s %3s" % ('natoms', 'bch_sz', 'n_bch', 'prob', 'pbc'))) - for ii in range(self.nsystems) : - log.info("%s %6d %6d %6d %5.3f %3s" % - (self._format_name_length(self.system_dirs[ii], sys_width), - self.natoms[ii], - # TODO batch size * nbatches = number of structures - self.batch_size[ii], - self.nbatches[ii], - self.sys_probs[ii], - "T" if self.data_systems[ii].pbc else "F" - ) ) - log.info("--------------------------------------------------------------------------------------") - - def _make_auto_bs(self, rule) : - bs = [] - for ii in self.data_systems: - ni = ii.get_natoms() - bsi = rule // ni - if bsi * ni < rule: - bsi += 1 - bs.append(bsi) - return bs - - # ! added by Marián Rynik - def _make_auto_ts(self, percent): - ts = [] - for ii in range(self.nsystems): - ni = self.batch_size[ii] * self.nbatches[ii] - tsi = int(ni * percent / 100) - ts.append(tsi) - - return ts - - def _check_type_map_consistency(self, type_map_list): - ret = [] - for ii in type_map_list: - if ii is not None: - min_len = min([len(ii), len(ret)]) - for idx in range(min_len) : - if ii[idx] != ret[idx] : - raise RuntimeError('inconsistent type map: %s %s' % (str(ret), str(ii))) - if len(ii) > len(ret) : - ret = ii - return ret - - def _process_sys_probs(self, sys_probs) : - sys_probs = np.array(sys_probs) - type_filter = sys_probs >= 0 - assigned_sum_prob = np.sum(type_filter * sys_probs) - # 1e-8 is to handle floating point error; See #1917 - assert assigned_sum_prob <= 1. + 1e-8, "the sum of assigned probability should be less than 1" - rest_sum_prob = 1. - assigned_sum_prob - if not np.isclose(rest_sum_prob, 0): - rest_nbatch = (1 - type_filter) * self.nbatches - rest_prob = rest_sum_prob * rest_nbatch / np.sum(rest_nbatch) - ret_prob = rest_prob + type_filter * sys_probs - else : - ret_prob = sys_probs - assert np.isclose(np.sum(ret_prob), 1), "sum of probs should be 1" - return ret_prob - - def _prob_sys_size_ext(self, keywords): - block_str = keywords.split(';')[1:] - block_stt = [] - block_end = [] - block_weights = [] - for ii in block_str: - stt = int(ii.split(':')[0]) - end = int(ii.split(':')[1]) - weight = float(ii.split(':')[2]) - assert(weight >= 0), "the weight of a block should be no less than 0" - block_stt.append(stt) - block_end.append(end) - block_weights.append(weight) - nblocks = len(block_str) - block_probs = np.array(block_weights) / np.sum(block_weights) - sys_probs = np.zeros([self.get_nsystems()]) - for ii in range(nblocks): - nbatch_block = self.nbatches[block_stt[ii]:block_end[ii]] - tmp_prob = [float(i) for i in nbatch_block] / np.sum(nbatch_block) - sys_probs[block_stt[ii]:block_end[ii]] = tmp_prob * block_probs[ii] - return sys_probs - - - -class DataSystem (object) : - """ - Outdated class for the data systems. - - .. deprecated:: 2.0.0 - This class is not maintained any more. - """ - def __init__ (self, - systems, - set_prefix, - batch_size, - test_size, - rcut, - run_opt = None) : - self.system_dirs = systems - self.nsystems = len(self.system_dirs) - self.batch_size = batch_size - if isinstance(self.batch_size, int) : - self.batch_size = self.batch_size * np.ones(self.nsystems, dtype=int) - assert(isinstance(self.batch_size, (list,np.ndarray))) - assert(len(self.batch_size) == self.nsystems) - self.data_systems = [] - self.ntypes = [] - self.natoms = [] - self.natoms_vec = [] - self.nbatches = [] - for ii in self.system_dirs : - self.data_systems.append(DataSets(ii, set_prefix)) - sys_all_types = np.loadtxt(os.path.join(ii, "type.raw")).astype(int) - self.ntypes.append(np.max(sys_all_types) + 1) - self.sys_ntypes = max(self.ntypes) - type_map = [] - for ii in range(self.nsystems) : - self.natoms.append(self.data_systems[ii].get_natoms()) - self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(self.sys_ntypes).astype(int)) - self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(self.batch_size[ii])) - type_map.append(self.data_systems[ii].get_type_map()) - self.type_map = self.check_type_map_consistency(type_map) - - # check frame parameters - has_fparam = [ii.numb_fparam() for ii in self.data_systems] - for ii in has_fparam : - if ii != has_fparam[0] : - raise RuntimeError("if any system has frame parameter, then all systems should have the same number of frame parameter") - self.has_fparam = has_fparam[0] - - # check the size of data if they satisfy the requirement of batch and test - for ii in range(self.nsystems) : - chk_ret = self.data_systems[ii].check_batch_size(self.batch_size[ii]) - if chk_ret is not None : - raise RuntimeError ("system %s required batch size %d is larger than the size %d of the dataset %s" % \ - (self.system_dirs[ii], self.batch_size[ii], chk_ret[1], chk_ret[0])) - chk_ret = self.data_systems[ii].check_test_size(test_size) - if chk_ret is not None : - print("WARNNING: system %s required test size %d is larger than the size %d of the dataset %s" % \ - (self.system_dirs[ii], test_size, chk_ret[1], chk_ret[0])) - - if run_opt is not None: - self.print_summary(run_opt) - - self.prob_nbatches = [ float(i) for i in self.nbatches] / np.sum(self.nbatches) - - self.test_data = collections.defaultdict(list) - self.default_mesh = [] - for ii in range(self.nsystems) : - test_system_data = self.data_systems[ii].get_test () - for nn in test_system_data: - self.test_data[nn].append(test_system_data[nn]) - cell_size = np.max (rcut) - avg_box = np.average (test_system_data["box"], axis = 0) - avg_box = np.reshape (avg_box, [3,3]) - ncell = (np.linalg.norm(avg_box, axis=1)/ cell_size).astype(np.int32) - ncell[ncell < 2] = 2 - default_mesh = np.zeros (6, dtype = np.int32) - default_mesh[3:6] = ncell - self.default_mesh.append(default_mesh) - self.pick_idx = 0 - - - def check_type_map_consistency(self, type_map_list): - ret = [] - for ii in type_map_list: - if ii is not None: - min_len = min([len(ii), len(ret)]) - for idx in range(min_len) : - if ii[idx] != ret[idx] : - raise RuntimeError('inconsistent type map: %s %s' % (str(ret), str(ii))) - if len(ii) > len(ret) : - ret = ii - return ret - - - def get_type_map(self): - return self.type_map - - - def format_name_length(self, name, width) : - if len(name) <= width: - return '{: >{}}'.format(name, width) - else : - name = name[-(width-3):] - name = '-- ' + name - return name - - def print_summary(self) : - tmp_msg = "" - # width 65 - sys_width = 42 - tmp_msg += "---Summary of DataSystem-----------------------------------------\n" - tmp_msg += "find %d system(s):\n" % self.nsystems - tmp_msg += "%s " % self.format_name_length('system', sys_width) - tmp_msg += "%s %s %s\n" % ('natoms', 'bch_sz', 'n_bch') - for ii in range(self.nsystems) : - tmp_msg += ("%s %6d %6d %5d\n" % - (self.format_name_length(self.system_dirs[ii], sys_width), - self.natoms[ii], - self.batch_size[ii], - self.nbatches[ii]) ) - tmp_msg += "-----------------------------------------------------------------\n" - log.info(tmp_msg) - - def compute_energy_shift(self) : - sys_ener = np.array([]) - for ss in self.data_systems : - sys_ener = np.append(sys_ener, ss.get_ener()) - sys_tynatom = np.array(self.natoms_vec, dtype = float) - sys_tynatom = np.reshape(sys_tynatom, [self.nsystems,-1]) - sys_tynatom = sys_tynatom[:,2:] - energy_shift,resd,rank,s_value \ - = np.linalg.lstsq(sys_tynatom, sys_ener, rcond = 1e-3) - return energy_shift - - def process_sys_weights(self, sys_weights) : - sys_weights = np.array(sys_weights) - type_filter = sys_weights >= 0 - assigned_sum_prob = np.sum(type_filter * sys_weights) - assert assigned_sum_prob <= 1, "the sum of assigned probability should be less than 1" - rest_sum_prob = 1. - assigned_sum_prob - rest_nbatch = (1 - type_filter) * self.nbatches - rest_prob = rest_sum_prob * rest_nbatch / np.sum(rest_nbatch) - ret_prob = rest_prob + type_filter * sys_weights - assert np.sum(ret_prob) == 1, "sum of probs should be 1" - return ret_prob - - def get_batch (self, - sys_idx = None, - sys_weights = None, - style = "prob_sys_size") : - if sys_idx is not None : - self.pick_idx = sys_idx - else : - if sys_weights is None : - if style == "prob_sys_size" : - prob = self.prob_nbatches - elif style == "prob_uniform" : - prob = None - else : - raise RuntimeError("unkown get_batch style") - else : - prob = self.process_sys_weights(sys_weights) - self.pick_idx = dp_random.choice(np.arange(self.nsystems), p=prob) - b_data = self.data_systems[self.pick_idx].get_batch(self.batch_size[self.pick_idx]) - b_data["natoms_vec"] = self.natoms_vec[self.pick_idx] - b_data["default_mesh"] = self.default_mesh[self.pick_idx] - return b_data - - def get_test (self, - sys_idx = None) : - if sys_idx is not None : - idx = sys_idx - else : - idx = self.pick_idx - test_system_data = {} - for nn in self.test_data: - test_system_data[nn] = self.test_data[nn][idx] - test_system_data["natoms_vec"] = self.natoms_vec[idx] - test_system_data["default_mesh"] = self.default_mesh[idx] - return test_system_data - - def get_nbatches (self) : - return self.nbatches - - def get_ntypes (self) : - return self.sys_ntypes - - def get_nsystems (self) : - return self.nsystems - - def get_sys (self, sys_idx) : - return self.data_systems[sys_idx] - - def get_batch_size(self) : - return self.batch_size - - def numb_fparam(self) : - return self.has_fparam - -def _main () : - sys = ['/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/20', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/30', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/38', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/MoS2', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/Pt_cluster'] - set_prefix = 'set' - ds = DataSystem (sys, set_prefix, 4, 6) - r = ds.get_batch() - print(r[1][0]) - -if __name__ == '__main__': - _main() - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/errors.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/errors.py deleted file mode 100644 index 231a9e6..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/errors.py +++ /dev/null @@ -1,8 +0,0 @@ -class GraphTooLargeError(Exception): - """The graph is too large, exceeding protobuf's hard limit of 2GB.""" - -class GraphWithoutTensorError(Exception): - pass - -class OutOfMemoryError(Exception): - """This error is caused by out-of-memory (OOM).""" \ No newline at end of file diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/graph.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/graph.py deleted file mode 100644 index 2cd2e59..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/graph.py +++ /dev/null @@ -1,453 +0,0 @@ -import re -import numpy as np -from typing import Tuple, Dict -from deepmd.env import tf, EMBEDDING_NET_PATTERN, FITTING_NET_PATTERN, TYPE_EMBEDDING_PATTERN, ATTENTION_LAYER_PATTERN -from deepmd.utils.sess import run_sess -from deepmd.utils.errors import GraphWithoutTensorError - -# TODO (JZ): I think in this file we can merge some duplicated lines into one method... -def load_graph_def(model_file: str) -> Tuple[tf.Graph, tf.GraphDef]: - """ - Load graph as well as the graph_def from the frozen model(model_file) - - Parameters - ---------- - model_file : str - The input frozen model path - - Returns - ------- - tf.Graph - The graph loaded from the frozen model - tf.GraphDef - The graph_def loaded from the frozen model - """ - graph_def = tf.GraphDef() - with open(model_file, "rb") as f: - graph_def.ParseFromString(f.read()) - with tf.Graph().as_default() as graph: - tf.import_graph_def(graph_def, name = "") - return graph, graph_def - - -def get_tensor_by_name_from_graph(graph: tf.Graph, - tensor_name: str) -> tf.Tensor: - """ - Load tensor value from the given tf.Graph object - - Parameters - ---------- - graph : tf.Graph - The input TensorFlow graph - tensor_name : str - Indicates which tensor which will be loaded from the frozen model - - Returns - ------- - tf.Tensor - The tensor which was loaded from the frozen model - - Raises - ------ - GraphWithoutTensorError - Whether the tensor_name is within the frozen model - """ - try: - tensor = graph.get_tensor_by_name(tensor_name + ':0') - except KeyError as e: - raise GraphWithoutTensorError() from e - with tf.Session(graph=graph) as sess: - tensor = run_sess(sess, tensor) - return tensor - - -def get_tensor_by_name(model_file: str, - tensor_name: str) -> tf.Tensor: - """ - Load tensor value from the frozen model(model_file) - - Parameters - ---------- - model_file : str - The input frozen model path - tensor_name : str - Indicates which tensor which will be loaded from the frozen model - - Returns - ------- - tf.Tensor - The tensor which was loaded from the frozen model - - Raises - ------ - GraphWithoutTensorError - Whether the tensor_name is within the frozen model - """ - graph, _ = load_graph_def(model_file) - return get_tensor_by_name_from_graph(graph, tensor_name) - - -def get_tensor_by_type(node, - data_type : np.dtype) -> tf.Tensor: - """ - Get the tensor value within the given node according to the input data_type - - Parameters - ---------- - node - The given tensorflow graph node - data_type - The data type of the node - - Returns - ---------- - tf.Tensor - The tensor value of the given node - """ - if data_type == np.float64: - tensor = np.array(node.double_val) - elif data_type == np.float32: - tensor = np.array(node.float_val) - else: - raise RuntimeError('model compression does not support the half precision') - return tensor - - -def get_pattern_nodes_from_graph_def(graph_def: tf.GraphDef, pattern: str) -> Dict: - """ - Get the pattern nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - pattern - The node pattern within the graph_def - - Returns - ---------- - Dict - The fitting net nodes within the given tf.GraphDef object - """ - nodes = {} - pattern = re.compile(pattern) - for node in graph_def.node: - if re.fullmatch(pattern, node.name) != None: - nodes[node.name] = node.attr["value"].tensor - return nodes - - -def get_embedding_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the embedding net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The embedding net nodes within the given tf.GraphDef object - """ - # embedding_net_pattern = f"filter_type_\d+{suffix}/matrix_\d+_\d+|filter_type_\d+{suffix}/bias_\d+_\d+|filter_type_\d+{suffix}/idt_\d+_\d+|filter_type_all{suffix}/matrix_\d+_\d+|filter_type_all{suffix}/matrix_\d+_\d+_\d+|filter_type_all{suffix}/bias_\d+_\d+|filter_type_all{suffix}/bias_\d+_\d+_\d+|filter_type_all{suffix}/idt_\d+_\d+" - if suffix != "": - embedding_net_pattern = EMBEDDING_NET_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - embedding_net_pattern = EMBEDDING_NET_PATTERN - - embedding_net_nodes = get_pattern_nodes_from_graph_def(graph_def, embedding_net_pattern) - for key in embedding_net_nodes.keys(): - assert key.find('bias') > 0 or key.find( - 'matrix') > 0, "currently, only support weight matrix and bias matrix at the tabulation op!" - return embedding_net_nodes - - -def get_embedding_net_nodes(model_file: str, suffix: str = "") -> Dict: - """ - Get the embedding net nodes with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net nodes with the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - - -def get_embedding_net_variables_from_graph_def(graph_def : tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the embedding net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given tf.GraphDef object - """ - embedding_net_variables = {} - embedding_net_nodes = get_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in embedding_net_nodes: - node = embedding_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - embedding_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return embedding_net_variables - -def get_embedding_net_variables(model_file : str, suffix: str = "") -> Dict: - """ - Get the embedding net variables with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_embedding_net_variables_from_graph_def(graph_def, suffix=suffix) - - -def get_fitting_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the fitting net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net nodes within the given tf.GraphDef object - """ - if suffix != "": - fitting_net_pattern = FITTING_NET_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - fitting_net_pattern = FITTING_NET_PATTERN - fitting_net_nodes = get_pattern_nodes_from_graph_def(graph_def, fitting_net_pattern) - for key in fitting_net_nodes.keys(): - assert key.find('bias') > 0 or key.find('matrix') > 0 or key.find( - 'idt') > 0, "currently, only support weight matrix, bias and idt at the model compression process!" - return fitting_net_nodes - - -def get_fitting_net_nodes(model_file : str) -> Dict: - """ - Get the fitting net nodes with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - - Returns - ---------- - Dict - The fitting net nodes with the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_fitting_net_nodes_from_graph_def(graph_def) - - -def get_fitting_net_variables_from_graph_def(graph_def : tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the fitting net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net variables within the given tf.GraphDef object - """ - fitting_net_variables = {} - fitting_net_nodes = get_fitting_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in fitting_net_nodes: - node = fitting_net_nodes[item] - dtype= tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - fitting_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return fitting_net_variables - -def get_fitting_net_variables(model_file : str, suffix: str = "") -> Dict: - """ - Get the fitting net variables with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net variables within the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_fitting_net_variables_from_graph_def(graph_def, suffix=suffix) - - -def get_type_embedding_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the type embedding net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The type embedding net nodes within the given tf.GraphDef object - """ - if suffix != "": - type_embedding_net_pattern = TYPE_EMBEDDING_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - type_embedding_net_pattern = TYPE_EMBEDDING_PATTERN - - type_embedding_net_nodes = get_pattern_nodes_from_graph_def(graph_def, type_embedding_net_pattern) - return type_embedding_net_nodes - - -def get_type_embedding_net_variables_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the type embedding net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def : tf.GraphDef - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given tf.GraphDef object - """ - type_embedding_net_variables = {} - type_embedding_net_nodes = get_type_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in type_embedding_net_nodes: - node = type_embedding_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - type_embedding_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return type_embedding_net_variables - - -def get_attention_layer_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the attention layer nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The attention layer nodes within the given tf.GraphDef object - """ - if suffix != "": - attention_layer_pattern = ATTENTION_LAYER_PATTERN \ - .replace('/c_query', suffix + '/c_query') \ - .replace('/c_key', suffix + '/c_key') \ - .replace('/c_value', suffix + '/c_value') \ - .replace('/c_out', suffix + '/c_out') \ - .replace('/layer_normalization', suffix + '/layer_normalization') - else: - attention_layer_pattern = ATTENTION_LAYER_PATTERN - - attention_layer_nodes = get_pattern_nodes_from_graph_def(graph_def, attention_layer_pattern) - return attention_layer_nodes - - -def get_attention_layer_variables_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the attention layer variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def : tf.GraphDef - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The attention layer variables within the given tf.GraphDef object - """ - attention_layer_variables = {} - attention_layer_net_nodes = get_attention_layer_nodes_from_graph_def(graph_def, suffix=suffix) - for item in attention_layer_net_nodes: - node = attention_layer_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - attention_layer_variables[item] = np.reshape(tensor_value, tensor_shape) - return attention_layer_variables diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/learning_rate.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/learning_rate.py deleted file mode 100644 index 685eb44..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/learning_rate.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from deepmd.env import tf -from deepmd.common import ClassArg - -class LearningRateExp (object) : - r""" - The exponentially decaying learning rate. - - The learning rate at step :math:`t` is given by - - .. math:: - - \alpha(t) = \alpha_0 \lambda ^ { t / \tau } - - where :math:`\alpha` is the learning rate, :math:`\alpha_0` is the starting learning rate, - :math:`\lambda` is the decay rate, and :math:`\tau` is the decay steps. - - Parameters - ---------- - start_lr - Starting learning rate :math:`\alpha_0` - stop_lr - Stop learning rate :math:`\alpha_1` - decay_steps - Learning rate decay every this number of steps :math:`\tau` - decay_rate - The decay rate :math:`\lambda`. - If `stop_step` is provided in `build`, then it will be determined automatically and overwritten. - """ - def __init__ (self, - start_lr : float, - stop_lr : float = 5e-8, - decay_steps : int = 5000, - decay_rate : float = 0.95 - ) -> None : - """ - Constructor - """ - # args = ClassArg()\ - # .add('decay_steps', int, must = False)\ - # .add('decay_rate', float, must = False)\ - # .add('start_lr', float, must = True)\ - # .add('stop_lr', float, must = False) - # self.cd = args.parse(jdata) - self.cd = {} - self.cd['start_lr'] = start_lr - self.cd['stop_lr'] = stop_lr - self.cd['decay_steps'] = decay_steps - self.cd['decay_rate'] = decay_rate - self.start_lr_ = self.cd['start_lr'] - - def build(self, - global_step : tf.Tensor, - stop_step : int = None - ) -> tf.Tensor : - """ - Build the learning rate - - Parameters - ---------- - global_step - The tf Tensor prividing the global training step - stop_step - The stop step. If provided, the decay_rate will be determined automatically and overwritten. - - Returns - ------- - learning_rate - The learning rate - """ - if stop_step is None: - self.decay_steps_ = self.cd['decay_steps'] if self.cd['decay_steps'] is not None else 5000 - self.decay_rate_ = self.cd['decay_rate'] if self.cd['decay_rate'] is not None else 0.95 - else: - self.stop_lr_ = self.cd['stop_lr'] if self.cd['stop_lr'] is not None else 5e-8 - default_ds = 100 if stop_step // 10 > 100 else stop_step // 100 + 1 - self.decay_steps_ = self.cd['decay_steps'] if self.cd['decay_steps'] is not None else default_ds - if self.decay_steps_ >= stop_step: - self.decay_steps_ = default_ds - self.decay_rate_ = np.exp(np.log(self.stop_lr_ / self.start_lr_) / (stop_step / self.decay_steps_)) - - return tf.train.exponential_decay(self.start_lr_, - global_step, - self.decay_steps_, - self.decay_rate_, - staircase=True) - def start_lr(self) -> float: - """ - Get the start lr - """ - return self.start_lr_ - - def value (self, - step : int - ) -> float: - """ - Get the lr at a certain step - """ - return self.start_lr_ * np.power (self.decay_rate_, (step // self.decay_steps_)) - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/neighbor_stat.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/neighbor_stat.py deleted file mode 100644 index 79a988e..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/neighbor_stat.py +++ /dev/null @@ -1,127 +0,0 @@ -import math -import logging -import numpy as np -from deepmd.env import tf -from typing import Tuple, List -from deepmd.env import op_module -from deepmd.env import default_tf_session_config -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.utils.data_system import DeepmdDataSystem -from deepmd.utils.parallel_op import ParallelOp - -log = logging.getLogger(__name__) - -class NeighborStat(): - """ - Class for getting training data information. - - It loads data from DeepmdData object, and measures the data info, including neareest nbor distance between atoms, max nbor size of atoms and the output data range of the environment matrix. - - Parameters - ---------- - ntypes - The num of atom types - rcut - The cut-off radius - one_type : bool, optional, default=False - Treat all types as a single type. - """ - def __init__(self, - ntypes : int, - rcut: float, - one_type : bool = False, - ) -> None: - """ - Constructor - """ - self.rcut = rcut - self.ntypes = ntypes - self.one_type = one_type - sub_graph = tf.Graph() - - def builder(): - place_holders = {} - for ii in ['coord', 'box']: - place_holders[ii] = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None], name='t_'+ii) - place_holders['type'] = tf.placeholder(tf.int32, [None, None], name='t_type') - place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes+2], name='t_natoms') - place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name='t_mesh') - t_type = place_holders['type'] - t_natoms = place_holders['natoms_vec'] - if self.one_type: - # all types = 0, natoms_vec = [natoms, natoms, natoms] - t_type = tf.zeros_like(t_type, dtype=tf.int32) - t_natoms = tf.repeat(t_natoms[0], 3) - - _max_nbor_size, _min_nbor_dist \ - = op_module.neighbor_stat(place_holders['coord'], - t_type, - t_natoms, - place_holders['box'], - place_holders['default_mesh'], - rcut = self.rcut) - place_holders['dir'] = tf.placeholder(tf.string) - return place_holders, (_max_nbor_size, _min_nbor_dist, place_holders['dir']) - - with sub_graph.as_default(): - self.p = ParallelOp(builder, config=default_tf_session_config) - - self.sub_sess = tf.Session(graph = sub_graph, config=default_tf_session_config) - - def get_stat(self, - data : DeepmdDataSystem) -> Tuple[float, List[int]]: - """ - get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms - - Parameters - ---------- - data - Class for manipulating many data systems. It is implemented with the help of DeepmdData. - - Returns - ------- - min_nbor_dist - The nearest distance between neighbor atoms - max_nbor_size - A list with ntypes integers, denotes the actual achieved max sel - """ - self.min_nbor_dist = 100.0 - self.max_nbor_size = [0] - if not self.one_type: - self.max_nbor_size *= self.ntypes - - def feed(): - for ii in range(len(data.system_dirs)): - for jj in data.data_systems[ii].dirs: - data_set = data.data_systems[ii]._load_set(jj) - for kk in range(np.array(data_set['type']).shape[0]): - yield { - 'coord': np.array(data_set['coord'])[kk].reshape([-1, data.natoms[ii] * 3]), - 'type': np.array(data_set['type'])[kk].reshape([-1, data.natoms[ii]]), - 'natoms_vec': np.array(data.natoms_vec[ii]), - 'box': np.array(data_set['box'])[kk].reshape([-1, 9]), - 'default_mesh': np.array(data.default_mesh[ii]), - 'dir': str(jj), - } - - for mn, dt, jj in self.p.generate(self.sub_sess, feed()): - if dt.size != 0: - dt = np.min(dt) - else: - dt = self.rcut - log.warning("Atoms with no neighbors found in %s. Please make sure it's what you expected." % jj) - if dt < self.min_nbor_dist: - if math.isclose(dt, 0., rel_tol=1e-6): - # it's unexpected that the distance between two atoms is zero - # zero distance will cause nan (#874) - raise RuntimeError( - "Some atoms are overlapping in %s. Please check your" - " training data to remove duplicated atoms." % jj - ) - self.min_nbor_dist = dt - var = np.max(mn, axis=0) - self.max_nbor_size = np.maximum(var, self.max_nbor_size) - - log.info('training data with min nbor dist: ' + str(self.min_nbor_dist)) - log.info('training data with max nbor size: ' + str(self.max_nbor_size)) - return self.min_nbor_dist, self.max_nbor_size diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/network.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/network.py deleted file mode 100644 index aa3dabb..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/network.py +++ /dev/null @@ -1,265 +0,0 @@ -import numpy as np - -from deepmd.env import tf -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.common import get_precision - -def one_layer_rand_seed_shift(): - return 3 - -def one_layer(inputs, - outputs_size, - activation_fn=tf.nn.tanh, - precision = GLOBAL_TF_FLOAT_PRECISION, - stddev=1.0, - bavg=0.0, - name='linear', - scope='', - reuse=None, - seed=None, - use_timestep = False, - trainable = True, - useBN = False, - uniform_seed = False, - initial_variables = None, - mixed_prec = None, - final_layer = False): - # For good accuracy, the last layer of the fitting network uses a higher precision neuron network. - if mixed_prec is not None and final_layer: - inputs = tf.cast(inputs, get_precision(mixed_prec['output_prec'])) - with tf.variable_scope(name, reuse=reuse): - shape = inputs.get_shape().as_list() - w_initializer = tf.random_normal_initializer( - stddev=stddev / np.sqrt(shape[1] + outputs_size), - seed=seed if (seed is None or uniform_seed) else seed + 0) - b_initializer = tf.random_normal_initializer( - stddev=stddev, - mean=bavg, - seed=seed if (seed is None or uniform_seed) else seed + 1) - if initial_variables is not None: - w_initializer = tf.constant_initializer(initial_variables[scope + name + '/matrix']) - b_initializer = tf.constant_initializer(initial_variables[scope + name + '/bias']) - w = tf.get_variable('matrix', - [shape[1], outputs_size], - precision, - w_initializer, - trainable = trainable) - variable_summaries(w, 'matrix') - b = tf.get_variable('bias', - [outputs_size], - precision, - b_initializer, - trainable = trainable) - variable_summaries(b, 'bias') - - if mixed_prec is not None and not final_layer: - inputs = tf.cast(inputs, get_precision(mixed_prec['compute_prec'])) - w = tf.cast(w, get_precision(mixed_prec['compute_prec'])) - b = tf.cast(b, get_precision(mixed_prec['compute_prec'])) - - hidden = tf.nn.bias_add(tf.matmul(inputs, w), b) - if activation_fn != None and use_timestep : - idt_initializer = tf.random_normal_initializer( - stddev=0.001, - mean=0.1, - seed=seed if (seed is None or uniform_seed) else seed + 2) - if initial_variables is not None: - idt_initializer = tf.constant_initializer(initial_variables[scope + name + '/idt']) - idt = tf.get_variable('idt', - [outputs_size], - precision, - idt_initializer, - trainable = trainable) - variable_summaries(idt, 'idt') - if activation_fn != None: - if useBN: - None - # hidden_bn = self._batch_norm(hidden, name=name+'_normalization', reuse=reuse) - # return activation_fn(hidden_bn) - else: - if use_timestep : - if mixed_prec is not None and not final_layer: - idt = tf.cast(idt, get_precision(mixed_prec['compute_prec'])) - hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size]) * idt - else : - hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size]) - - if mixed_prec is not None: - hidden = tf.cast(hidden, get_precision(mixed_prec['output_prec'])) - return hidden - - -def embedding_net_rand_seed_shift( - network_size -): - shift = 3 * (len(network_size) + 1) - return shift - -def embedding_net(xx, - network_size, - precision, - activation_fn = tf.nn.tanh, - resnet_dt = False, - name_suffix = '', - stddev = 1.0, - bavg = 0.0, - seed = None, - trainable = True, - uniform_seed = False, - initial_variables = None, - mixed_prec = None): - r"""The embedding network. - - The embedding network function :math:`\mathcal{N}` is constructed by is the - composition of multiple layers :math:`\mathcal{L}^{(i)}`: - - .. math:: - \mathcal{N} = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} - \circ \cdots \circ \mathcal{L}^{(1)} - - A layer :math:`\mathcal{L}` is given by one of the following forms, - depending on the number of nodes: [1]_ - - .. math:: - \mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= - \begin{cases} - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + \mathbf{x}, & N_2=N_1 \\ - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + (\mathbf{x}, \mathbf{x}), & N_2 = 2N_1\\ - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\ - \end{cases} - - where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` - is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and - :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively, - both of which are trainable if `trainable` is `True`. :math:`\boldsymbol{\phi}` - is the activation function. - - Parameters - ---------- - xx : Tensor - Input tensor :math:`\mathbf{x}` of shape [-1,1] - network_size: list of int - Size of the embedding network. For example [16,32,64] - precision: - Precision of network weights. For example, tf.float64 - activation_fn: - Activation function :math:`\boldsymbol{\phi}` - resnet_dt: boolean - Using time-step in the ResNet construction - name_suffix: str - The name suffix append to each variable. - stddev: float - Standard deviation of initializing network parameters - bavg: float - Mean of network intial bias - seed: int - Random seed for initializing network parameters - trainable: boolean - If the network is trainable - uniform_seed : boolean - Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed - initial_variables : dict - The input dict which stores the embedding net variables - mixed_prec - The input dict which stores the mixed precision setting for the embedding net - - - References - ---------- - .. [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identitymappings - in deep residual networks. InComputer Vision – ECCV 2016,pages 630–645. Springer - International Publishing, 2016. - """ - input_shape = xx.get_shape().as_list() - outputs_size = [input_shape[1]] + network_size - - for ii in range(1, len(outputs_size)): - w_initializer = tf.random_normal_initializer( - stddev=stddev/np.sqrt(outputs_size[ii]+outputs_size[ii-1]), - seed = seed if (seed is None or uniform_seed) else seed + ii*3+0 - ) - b_initializer = tf.random_normal_initializer( - stddev=stddev, - mean = bavg, - seed = seed if (seed is None or uniform_seed) else seed + 3*ii+1 - ) - if initial_variables is not None: - scope = tf.get_variable_scope().name - w_initializer = tf.constant_initializer(initial_variables[scope+'/matrix_'+str(ii)+name_suffix]) - b_initializer = tf.constant_initializer(initial_variables[scope+'/bias_'+str(ii)+name_suffix]) - w = tf.get_variable('matrix_'+str(ii)+name_suffix, - [outputs_size[ii - 1], outputs_size[ii]], - precision, - w_initializer, - trainable = trainable) - variable_summaries(w, 'matrix_'+str(ii)+name_suffix) - - b = tf.get_variable('bias_'+str(ii)+name_suffix, - [outputs_size[ii]], - precision, - b_initializer, - trainable = trainable) - variable_summaries(b, 'bias_'+str(ii)+name_suffix) - - if mixed_prec is not None: - xx = tf.cast(xx, get_precision(mixed_prec['compute_prec'])) - w = tf.cast(w, get_precision(mixed_prec['compute_prec'])) - b = tf.cast(b, get_precision(mixed_prec['compute_prec'])) - if activation_fn is not None: - hidden = tf.reshape(activation_fn(tf.nn.bias_add(tf.matmul(xx, w), b)), [-1, outputs_size[ii]]) - else: - hidden = tf.reshape(tf.nn.bias_add(tf.matmul(xx, w), b), [-1, outputs_size[ii]]) - if resnet_dt : - idt_initializer = tf.random_normal_initializer( - stddev=0.001, - mean = 1.0, - seed = seed if (seed is None or uniform_seed) else seed + 3*ii+2 - ) - if initial_variables is not None: - scope = tf.get_variable_scope().name - idt_initializer = tf.constant_initializer(initial_variables[scope+'/idt_'+str(ii)+name_suffix]) - idt = tf.get_variable('idt_'+str(ii)+name_suffix, - [1, outputs_size[ii]], - precision, - idt_initializer, - trainable = trainable) - variable_summaries(idt, 'idt_'+str(ii)+name_suffix) - if mixed_prec is not None: - idt = tf.cast(idt, get_precision(mixed_prec['compute_prec'])) - - if outputs_size[ii] == outputs_size[ii-1]: - if resnet_dt : - xx += hidden * idt - else : - xx += hidden - elif outputs_size[ii] == outputs_size[ii-1] * 2: - if resnet_dt : - xx = tf.concat([xx,xx], 1) + hidden * idt - else : - xx = tf.concat([xx,xx], 1) + hidden - else: - xx = hidden - if mixed_prec is not None: - xx = tf.cast(xx, get_precision(mixed_prec['output_prec'])) - return xx - -def variable_summaries(var: tf.Variable, name: str): - """Attach a lot of summaries to a Tensor (for TensorBoard visualization). - - Parameters - ---------- - var : tf.Variable - [description] - name : str - variable name - """ - with tf.name_scope(name): - mean = tf.reduce_mean(var) - tf.summary.scalar('mean', mean) - - with tf.name_scope('stddev'): - stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) - tf.summary.scalar('stddev', stddev) - tf.summary.scalar('max', tf.reduce_max(var)) - tf.summary.scalar('min', tf.reduce_min(var)) - tf.summary.histogram('histogram', var) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/pair_tab.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/pair_tab.py deleted file mode 100644 index d9919bd..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/pair_tab.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np -from typing import Tuple, List - -from scipy.interpolate import CubicSpline - -class PairTab (object): - """ - - Parameters - ---------- - filename - File name for the short-range tabulated potential. - The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. - The first colume is the distance between atoms. - The second to the last columes are energies for pairs of certain types. - For example we have two atom types, 0 and 1. - The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly. - """ - def __init__(self, - filename : str - ) -> None: - """ - Constructor - """ - self.reinit(filename) - - def reinit(self, - filename : str - ) -> None: - """ - Initialize the tabulated interaction - - Parameters - ---------- - filename - File name for the short-range tabulated potential. - The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. - The first colume is the distance between atoms. - The second to the last columes are energies for pairs of certain types. - For example we have two atom types, 0 and 1. - The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly. - """ - self.vdata = np.loadtxt(filename) - self.rmin = self.vdata[0][0] - self.hh = self.vdata[1][0] - self.vdata[0][0] - self.nspline = self.vdata.shape[0] - 1 - ncol = self.vdata.shape[1] - 1 - n0 = (-1 + np.sqrt(1 + 8 * ncol)) * 0.5 - self.ntypes = int(n0 + 0.1) - assert(self.ntypes * (self.ntypes+1) // 2 == ncol),\ - "number of volumes provided in %s does not match guessed number of types %d" % (filename, self.ntypes) - self.tab_info = np.array([self.rmin, self.hh, self.nspline, self.ntypes]) - self.tab_data = self._make_data() - - def get(self) -> Tuple[np.array, np.array]: - """ - Get the serialized table. - """ - return self.tab_info, self.tab_data - - def _make_data(self) : - data = np.zeros([self.ntypes * self.ntypes * 4 * self.nspline]) - stride = 4 * self.nspline - idx_iter = 0 - xx = self.vdata[:,0] - for t0 in range(self.ntypes) : - for t1 in range(t0, self.ntypes) : - vv = self.vdata[:,1+idx_iter] - cs = CubicSpline(xx, vv) - dd = cs(xx, 1) - dd *= self.hh - dtmp = np.zeros(stride) - for ii in range(self.nspline) : - dtmp[ii*4+0] = 2 * vv[ii] - 2 * vv[ii+1] + dd[ii] + dd[ii+1] - dtmp[ii*4+1] =-3 * vv[ii] + 3 * vv[ii+1] - 2 * dd[ii] - dd[ii+1] - dtmp[ii*4+2] = dd[ii] - dtmp[ii*4+3] = vv[ii] - data[(t0 * self.ntypes + t1) * stride : (t0 * self.ntypes + t1) * stride + stride] \ - = dtmp - data[(t1 * self.ntypes + t0) * stride : (t1 * self.ntypes + t0) * stride + stride] \ - = dtmp - idx_iter += 1 - return data diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/parallel_op.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/parallel_op.py deleted file mode 100644 index 6752724..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/parallel_op.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import Callable, Generator, Tuple, Dict, Any - -from deepmd.env import tf -from deepmd.utils.sess import run_sess - - -class ParallelOp: - """Run an op with data parallelism. - - Parameters - ---------- - builder : Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]] - returns two objects: a dict which stores placeholders by key, and a tuple with the final op(s) - nthreads : int, optional - the number of threads - config : tf.ConfigProto, optional - tf.ConfigProto - - Examples - -------- - >>> from deepmd.env import tf - >>> from deepmd.utils.parallel_op import ParallelOp - >>> def builder(): - ... x = tf.placeholder(tf.int32, [1]) - ... return {"x": x}, (x + 1) - ... - >>> p = ParallelOp(builder, nthreads=4) - >>> def feed(): - ... for ii in range(10): - ... yield {"x": [ii]} - ... - >>> print(*p.generate(tf.Session(), feed())) - [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] - """ - def __init__(self, builder: Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]], nthreads: int = None, config: tf.ConfigProto = None) -> None: - if nthreads is not None: - self.nthreads = nthreads - elif config is not None: - self.nthreads = max(config.inter_op_parallelism_threads, 1) - else: - self.nthreads = 1 - - self.placeholders = [] - self.ops = [] - for ii in range(self.nthreads): - with tf.name_scope("task_%d" % ii) as scope: - placeholder, op = builder() - self.placeholders.append(placeholder) - self.ops.append(op) - - def generate(self, sess: tf.Session, feed: Generator[Dict[str, Any], None, None]) -> Generator[Tuple, None, None]: - """Returns a generator. - - Parameters - ---------- - feed : Generator[dict, None, None] - generator which yields feed_dict - - Yields - ------ - Generator[Tuple, None, None] - generator which yields session returns - """ - nn = self.nthreads - while True: - feed_dict = {} - for ii in range(self.nthreads): - try: - fd = next(feed) - except StopIteration: - if ii == 0: - return - nn = ii - break - for kk, vv in fd.items(): - feed_dict[self.placeholders[ii][kk]] = vv - ops = self.ops[:nn] - for yy in run_sess(sess, ops, feed_dict=feed_dict): - yield yy - diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/path.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/path.py deleted file mode 100644 index 30f3e9b..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/path.py +++ /dev/null @@ -1,338 +0,0 @@ -import os -from abc import ABC, abstractmethod -from typing import List -from pathlib import Path -from functools import lru_cache - -import numpy as np -import h5py -from wcmatch.glob import globfilter - -class DPPath(ABC): - """The path class to data system (DeepmdData). - - Parameters - ---------- - path : str - path - """ - def __new__(cls, path: str): - if cls is DPPath: - if os.path.isdir(path): - return super().__new__(DPOSPath) - elif os.path.isfile(path.split("#")[0]): - # assume h5 if it is not dir - # TODO: check if it is a real h5? or just check suffix? - return super().__new__(DPH5Path) - raise FileNotFoundError("%s not found" % path) - return super().__new__(cls) - - @abstractmethod - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - - @abstractmethod - def load_txt(self, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - - @abstractmethod - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - - @abstractmethod - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - - @abstractmethod - def is_file(self) -> bool: - """Check if self is file.""" - - @abstractmethod - def is_dir(self) -> bool: - """Check if self is directory.""" - - @abstractmethod - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - - @abstractmethod - def __lt__(self, other: "DPPath") -> bool: - """whether this DPPath is less than other for sorting""" - - @abstractmethod - def __str__(self) -> str: - """Represent string""" - - def __repr__(self) -> str: - return "%s (%s)" % (type(self), str(self)) - - def __eq__(self, other) -> bool: - return str(self) == str(other) - - def __hash__(self): - return hash(str(self)) - - -class DPOSPath(DPPath): - """The OS path class to data system (DeepmdData) for real directories. - - Parameters - ---------- - path : str - path - """ - def __init__(self, path: str) -> None: - super().__init__() - if isinstance(path, Path): - self.path = path - else: - self.path = Path(path) - - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return np.load(str(self.path)) - - def load_txt(self, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return np.loadtxt(str(self.path), **kwargs) - - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - # currently DPOSPath will only derivative DPOSPath - # TODO: discuss if we want to mix DPOSPath and DPH5Path? - return list([type(self)(p) for p in self.path.glob(pattern)]) - - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - return list([type(self)(p) for p in self.path.rglob(pattern)]) - - def is_file(self) -> bool: - """Check if self is file.""" - return self.path.is_file() - - def is_dir(self) -> bool: - """Check if self is directory.""" - return self.path.is_dir() - - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - return type(self)(self.path / key) - - def __lt__(self, other: "DPOSPath") -> bool: - """whether this DPPath is less than other for sorting""" - return self.path < other.path - - def __str__(self) -> str: - """Represent string""" - return str(self.path) - - -class DPH5Path(DPPath): - """The path class to data system (DeepmdData) for HDF5 files. - - Notes - ----- - OS - HDF5 relationship: - directory - Group - file - Dataset - - Parameters - ---------- - path : str - path - """ - def __init__(self, path: str) -> None: - super().__init__() - # we use "#" to split path - # so we do not support file names containing #... - s = path.split("#") - self.root_path = s[0] - self.root = self._load_h5py(s[0]) - # h5 path: default is the root path - self.name = s[1] if len(s) > 1 else "/" - - @classmethod - @lru_cache(None) - def _load_h5py(cls, path: str) -> h5py.File: - """Load hdf5 file. - - Parameters - ---------- - path : str - path to hdf5 file - """ - # this method has cache to avoid duplicated - # loading from different DPH5Path - # However the file will be never closed? - return h5py.File(path, 'r') - - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return self.root[self.name][:] - - def load_txt(self, dtype: np.dtype = None, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - arr = self.load_numpy() - if dtype: - arr = arr.astype(dtype) - return arr - - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - # got paths starts with current path first, which is faster - subpaths = [ii for ii in self._keys if ii.startswith(self.name)] - return list([type(self)("%s#%s"%(self.root_path, pp)) for pp in globfilter(subpaths, self._connect_path(pattern))]) - - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - return self.glob("**" + pattern) - - @property - def _keys(self) -> List[str]: - """Walk all groups and dataset""" - return self._file_keys(self.root) - - @classmethod - @lru_cache(None) - def _file_keys(cls, file: h5py.File) -> List[str]: - """Walk all groups and dataset""" - l = [] - file.visit(lambda x: l.append("/" + x)) - return l - - def is_file(self) -> bool: - """Check if self is file.""" - if self.name not in self._keys: - return False - return isinstance(self.root[self.name], h5py.Dataset) - - def is_dir(self) -> bool: - """Check if self is directory.""" - if self.name not in self._keys: - return False - return isinstance(self.root[self.name], h5py.Group) - - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - return type(self)("%s#%s" % (self.root_path, self._connect_path(key))) - - def _connect_path(self, path: str) -> str: - """Connect self with path""" - if self.name.endswith("/"): - return "%s%s" % (self.name, path) - return "%s/%s" % (self.name, path) - - def __lt__(self, other: "DPH5Path") -> bool: - """whether this DPPath is less than other for sorting""" - if self.root_path == other.root_path: - return self.name < other.name - return self.root_path < other.root_path - - def __str__(self) -> str: - """returns path of self""" - return "%s#%s" % (self.root_path, self.name) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/plugin.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/plugin.py deleted file mode 100644 index a0799f8..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/plugin.py +++ /dev/null @@ -1,84 +0,0 @@ - -"""Base of plugin systems.""" -# copied from https://github.com/deepmodeling/dpdata/blob/a3e76d75de53f6076254de82d18605a010dc3b00/dpdata/plugin.py - -from abc import ABCMeta -from typing import Callable - - -class Plugin: - """A class to register and restore plugins. - - Attributes - ---------- - plugins : Dict[str, object] - plugins - - Examples - -------- - >>> plugin = Plugin() - >>> @plugin.register("xx") - def xxx(): - pass - >>> print(plugin.plugins['xx']) - """ - def __init__(self): - self.plugins = {} - - def __add__(self, other) -> "Plugin": - self.plugins.update(other.plugins) - return self - - def register(self, key : str) -> Callable[[object], object]: - """Register a plugin. - - Parameters - ---------- - key : str - key of the plugin - - Returns - ------- - Callable[[object], object] - decorator - """ - def decorator(object : object) -> object: - self.plugins[key] = object - return object - return decorator - - def get_plugin(self, key) -> object: - """Visit a plugin by key. - - Parameters - ---------- - key : str - key of the plugin - - Returns - ------- - object - the plugin - """ - return self.plugins[key] - -class VariantMeta: - def __call__(cls, *args, **kwargs): - """Remove `type` and keys that starts with underline.""" - obj = cls.__new__(cls, *args, **kwargs) - kwargs.pop('type', None) - to_pop = [] - for kk in kwargs: - if kk[0] == '_': - to_pop.append(kk) - for kk in to_pop: - kwargs.pop(kk, None) - obj.__init__(*args, **kwargs) - return obj - -class VariantABCMeta(VariantMeta, ABCMeta): - pass - -class PluginVariant(metaclass=VariantABCMeta): - """A class to remove `type` from input arguments.""" - pass \ No newline at end of file diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/random.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/random.py deleted file mode 100644 index 565f28c..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/random.py +++ /dev/null @@ -1,63 +0,0 @@ -import numpy as np - - -_RANDOM_GENERATOR = np.random.RandomState() - - -def choice(a: np.ndarray, p: np.ndarray = None): - """Generates a random sample from a given 1-D array. - - Parameters - ---------- - a : np.ndarray - A random sample is generated from its elements. - p : np.ndarray - The probabilities associated with each entry in a. - - Returns - ------- - np.ndarray - arrays with results and their shapes - """ - return _RANDOM_GENERATOR.choice(a, p=p) - - -def random(size=None): - """Return random floats in the half-open interval [0.0, 1.0). - - Parameters - ---------- - size - Output shape. - - Returns - ------- - np.ndarray - Arrays with results and their shapes. - """ - return _RANDOM_GENERATOR.random_sample(size) - - -def seed(val: int = None): - """Seed the generator. - - Parameters - ---------- - val : int - Seed. - """ - _RANDOM_GENERATOR.seed(val) - - -def shuffle(x: np.ndarray): - """Modify a sequence in-place by shuffling its contents. - - Parameters - ---------- - x : np.ndarray - The array or list to be shuffled. - """ - _RANDOM_GENERATOR.shuffle(x) - - -__all__ = ['choice', 'random', 'seed', 'shuffle'] diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/sess.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/sess.py deleted file mode 100644 index 829ba74..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/sess.py +++ /dev/null @@ -1,39 +0,0 @@ -import os - -from deepmd.env import tf -from deepmd.utils.errors import OutOfMemoryError - - -def run_sess(sess: tf.Session, *args, **kwargs): - """Run session with erorrs caught. - - Parameters - ---------- - sess: tf.Session - TensorFlow Session - - Returns - ------- - the result of sess.run() - """ - try: - # https://www.tensorflow.org/api_docs/python/tf/compat/v1/Session#run - return sess.run(*args, **kwargs) - except tf.errors.ResourceExhaustedError as e: - MESSAGE = ( - "Your memory may be not enough, thus an error has been raised " - "above. You need to take the following actions:\n" - "1. Check if the network size of the model is too large.\n" - "2. Check if the batch size of training or testing is too large." - " You can set the training batch size to `auto`.\n" - "3. Check if the number of atoms is too large.\n" - ) - if tf.test.is_built_with_cuda(): - MESSAGE += ( - "4. Check if another program is using the same GPU by " - "execuating `nvidia-smi`. The usage of GPUs is " - "controlled by `CUDA_VISIBLE_DEVICES` environment " - "variable (current value: %s).\n" % ( - os.getenv("CUDA_VISIBLE_DEVICES", None), - )) - raise OutOfMemoryError(MESSAGE) from e diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/tabulate.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/tabulate.py deleted file mode 100644 index 8c9159e..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/tabulate.py +++ /dev/null @@ -1,496 +0,0 @@ -import logging -import numpy as np -import deepmd -from typing import Callable -from typing import Tuple, List, Dict -from functools import lru_cache -from scipy.special import comb -from deepmd.env import tf -from deepmd.env import op_module -from deepmd.common import ACTIVATION_FN_DICT -from deepmd.utils.graph import get_tensor_by_name_from_graph, load_graph_def -from deepmd.utils.graph import get_embedding_net_nodes_from_graph_def -from deepmd.descriptor import Descriptor - -log = logging.getLogger(__name__) - -class DPTabulate(): - """ - Class for tabulation. - - Compress a model, which including tabulating the embedding-net. - The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the stride(parameter) as it\'s uniform stride, while the second table takes 10 * stride as it\s uniform stride - The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table\'s upper boundary(upper) to the extrapolate(parameter) * upper. - - Parameters - ---------- - descrpt - Descriptor of the original model - neuron - Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` - model_file - The frozen model - type_one_side - Try to build N_types tables. Otherwise, building N_types^2 tables - exclude_types : List[List[int]] - The excluded pairs of types which have no interaction with each other. - For example, `[[0, 1]]` means no interaction between type 0 and type 1. - activation_function - The activation function in the embedding net. Supported options are {"tanh","gelu"} in common.ACTIVATION_FN_DICT. - suffix : str, optional - The suffix of the scope - """ - def __init__(self, - descrpt : Descriptor, - neuron : List[int], - model_file : str, - type_one_side : bool = False, - exclude_types : List[List[int]] = [], - activation_fn : Callable[[tf.Tensor], tf.Tensor] = tf.nn.tanh, - suffix : str = "", - ) -> None: - """ - Constructor - """ - self.descrpt = descrpt - self.neuron = neuron - self.model_file = model_file - self.type_one_side = type_one_side - self.exclude_types = exclude_types - self.suffix = suffix - - # functype - if activation_fn == ACTIVATION_FN_DICT["tanh"]: - self.functype = 1 - elif activation_fn == ACTIVATION_FN_DICT["gelu"]: - self.functype = 2 - elif activation_fn == ACTIVATION_FN_DICT["relu"]: - self.functype = 3 - elif activation_fn == ACTIVATION_FN_DICT["relu6"]: - self.functype = 4 - elif activation_fn == ACTIVATION_FN_DICT["softplus"]: - self.functype = 5 - elif activation_fn == ACTIVATION_FN_DICT["sigmoid"]: - self.functype = 6 - else: - raise RuntimeError("Unknown actication function type!") - self.activation_fn = activation_fn - - self.graph, self.graph_def = load_graph_def(self.model_file) - #self.sess = tf.Session(graph = self.graph) - - self.sub_graph, self.sub_graph_def = self._load_sub_graph() - self.sub_sess = tf.Session(graph = self.sub_graph) - - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - self.sel_a = self.descrpt.sel_r - self.rcut = self.descrpt.rcut - self.rcut_smth = self.descrpt.rcut_smth - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - self.sel_a = self.descrpt.sel_a - self.rcut = self.descrpt.rcut_r - self.rcut_smth = self.descrpt.rcut_r_smth - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - self.sel_a = self.descrpt.sel_a - self.rcut = self.descrpt.rcut_r - self.rcut_smth = self.descrpt.rcut_r_smth - else: - raise RuntimeError("Unsupported descriptor") - - self.davg = get_tensor_by_name_from_graph(self.graph, f'descrpt_attr{self.suffix}/t_avg') - self.dstd = get_tensor_by_name_from_graph(self.graph, f'descrpt_attr{self.suffix}/t_std') - self.ntypes = get_tensor_by_name_from_graph(self.graph, 'descrpt_attr/ntypes') - - self.embedding_net_nodes = get_embedding_net_nodes_from_graph_def(self.graph_def, suffix=self.suffix) - - # move it to the descriptor class - # for tt in self.exclude_types: - # if (tt[0] not in range(self.ntypes)) or (tt[1] not in range(self.ntypes)): - # raise RuntimeError("exclude types" + str(tt) + " must within the number of atomic types " + str(self.ntypes) + "!") - # if (self.ntypes * self.ntypes - len(self.exclude_types) == 0): - # raise RuntimeError("empty embedding-net are not supported in model compression!") - self.layer_size = self._get_layer_size() - self.table_size = self._get_table_size() - - self.bias = self._get_bias() - self.matrix = self._get_matrix() - - self.data_type = self._get_data_type() - self.last_layer_size = self._get_last_layer_size() - - self.data = {} - - self.upper = {} - self.lower = {} - - - def build(self, - min_nbor_dist : float, - extrapolate : float, - stride0 : float, - stride1 : float) -> Tuple[Dict[str, int], Dict[str, int]]: - """ - Build the tables for model compression - - Parameters - ---------- - min_nbor_dist - The nearest distance between neighbor atoms - extrapolate - The scale of model extrapolation - stride0 - The uniform stride of the first table - stride1 - The uniform stride of the second table - neuron - Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` - - Returns - ---------- - lower : dict[str, int] - The lower boundary of environment matrix by net - upper : dict[str, int] - The upper boundary of environment matrix by net - """ - # tabulate range [lower, upper] with stride0 'stride0' - lower, upper = self._get_env_mat_range(min_nbor_dist) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - for ii in range(self.table_size): - if (self.type_one_side and not self._all_excluded(ii)) or (not self.type_one_side and (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types): - if self.type_one_side: - net = "filter_-1_net_" + str(ii) - # upper and lower should consider all types which are not excluded and sel>0 - idx = [(type_i, ii) not in self.exclude_types and self.sel_a[type_i] > 0 for type_i in range(self.ntypes)] - uu = np.max(upper[idx]) - ll = np.min(lower[idx]) - else: - ielement = ii // self.ntypes - net = "filter_" + str(ielement) + "_net_" + str(ii % self.ntypes) - uu = upper[ielement] - ll = lower[ielement] - xx = np.arange(ll, uu, stride0, dtype = self.data_type) - xx = np.append(xx, np.arange(uu, extrapolate * uu, stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * uu], dtype = self.data_type)) - nspline = ((uu - ll) / stride0 + (extrapolate * uu - uu) / stride1).astype(int) - self._build_lower(net, xx, ii, uu, ll, stride0, stride1, extrapolate, nspline) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - xx_all = [] - for ii in range(self.ntypes): - xx = np.arange(extrapolate * lower[ii], lower[ii], stride1, dtype = self.data_type) - xx = np.append(xx, np.arange(lower[ii], upper[ii], stride0, dtype = self.data_type)) - xx = np.append(xx, np.arange(upper[ii], extrapolate * upper[ii], stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * upper[ii]], dtype = self.data_type)) - xx_all.append(xx) - nspline = ((upper - lower) / stride0 + 2 * ((extrapolate * upper - upper) / stride1)).astype(int) - idx = 0 - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - net = "filter_" + str(ii) + "_net_" + str(jj) - self._build_lower(net, xx_all[ii], idx, upper[ii], lower[ii], stride0, stride1, extrapolate, nspline[ii]) - idx += 1 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - for ii in range(self.table_size): - if (self.type_one_side and not self._all_excluded(ii)) or (not self.type_one_side and (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types): - if self.type_one_side: - net = "filter_-1_net_" + str(ii) - # upper and lower should consider all types which are not excluded and sel>0 - idx = [(type_i, ii) not in self.exclude_types and self.sel_a[type_i] > 0 for type_i in range(self.ntypes)] - uu = np.max(upper[idx]) - ll = np.min(lower[idx]) - else: - ielement = ii // self.ntypes - net = "filter_" + str(ielement) + "_net_" + str(ii % self.ntypes) - uu = upper[ielement] - ll = lower[ielement] - xx = np.arange(ll, uu, stride0, dtype = self.data_type) - xx = np.append(xx, np.arange(uu, extrapolate * uu, stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * uu], dtype = self.data_type)) - nspline = ((uu - ll) / stride0 + (extrapolate * uu - uu) / stride1).astype(int) - self._build_lower(net, xx, ii, uu, ll, stride0, stride1, extrapolate, nspline) - else: - raise RuntimeError("Unsupported descriptor") - self._convert_numpy_to_tensor() - - return self.lower, self.upper - - def _build_lower(self, net, xx, idx, upper, lower, stride0, stride1, extrapolate, nspline): - vv, dd, d2 = self._make_data(xx, idx) - self.data[net] = np.zeros([nspline, 6 * self.last_layer_size], dtype = self.data_type) - - # tt.shape: [nspline, self.last_layer_size] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[:int((upper - lower) / stride0), :] = stride0 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[int((lower - extrapolate * lower) / stride1) + 1:(int((lower - extrapolate * lower) / stride1) + int((upper - lower) / stride0)), :] = stride0 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[:int((upper - lower) / stride0), :] = stride0 - else: - raise RuntimeError("Unsupported descriptor") - - # hh.shape: [nspline, self.last_layer_size] - hh = vv[1:nspline+1, :self.last_layer_size] - vv[:nspline, :self.last_layer_size] - - self.data[net][:, :6 * self.last_layer_size:6] = vv[:nspline, :self.last_layer_size] - self.data[net][:, 1:6 * self.last_layer_size:6] = dd[:nspline, :self.last_layer_size] - self.data[net][:, 2:6 * self.last_layer_size:6] = 0.5 * d2[:nspline, :self.last_layer_size] - self.data[net][:, 3:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt)) * (20 * hh - (8 * dd[1:nspline+1, :self.last_layer_size] + 12 * dd[:nspline, :self.last_layer_size]) * tt - (3 * d2[:nspline, :self.last_layer_size] - d2[1:nspline+1, :self.last_layer_size]) * tt * tt) - self.data[net][:, 4:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt * tt)) * (-30 * hh + (14 * dd[1:nspline+1, :self.last_layer_size] + 16 * dd[:nspline, :self.last_layer_size]) * tt + (3 * d2[:nspline, :self.last_layer_size] - 2 * d2[1:nspline+1, :self.last_layer_size]) * tt * tt) - self.data[net][:, 5:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt * tt * tt)) * (12 * hh - 6 * (dd[1:nspline+1, :self.last_layer_size] + dd[:nspline, :self.last_layer_size]) * tt + (d2[1:nspline+1, :self.last_layer_size] - d2[:nspline, :self.last_layer_size]) * tt * tt) - - self.upper[net] = upper - self.lower[net] = lower - - def _load_sub_graph(self): - sub_graph_def = tf.GraphDef() - with tf.Graph().as_default() as sub_graph: - tf.import_graph_def(sub_graph_def, name = "") - return sub_graph, sub_graph_def - - def _get_bias(self): - bias = {} - for layer in range(1, self.layer_size + 1): - bias["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/bias_{layer}_{ii % self.ntypes}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}_{jj}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/bias_{layer}_{ii % self.ntypes}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - raise RuntimeError("Unsupported descriptor") - return bias - - def _get_matrix(self): - matrix = {} - for layer in range(1, self.layer_size + 1): - matrix["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/matrix_{layer}_{ii % self.ntypes}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}_{jj}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/matrix_{layer}_{ii % self.ntypes}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - raise RuntimeError("Unsupported descriptor") - - return matrix - - # one-by-one executions - def _make_data(self, xx, idx): - with self.sub_graph.as_default(): - with self.sub_sess.as_default(): - xx = tf.reshape(xx, [xx.size, -1]) - for layer in range(self.layer_size): - if layer == 0: - xbar = tf.matmul( - xx, self.matrix["layer_" + str(layer + 1)][idx]) + self.bias["layer_" + str(layer + 1)][idx] - if self.neuron[0] == 1: - yy = self._layer_0( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) + xx - dy = op_module.unaggregated_dy_dx_s( - yy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) + tf.ones([1, 1], yy.dtype) - dy2 = op_module.unaggregated_dy2_dx_s( - yy, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - elif self.neuron[0] == 2: - tt, yy = self._layer_1( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dy = op_module.unaggregated_dy_dx_s( - yy - tt, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) + tf.ones([1, 2], yy.dtype) - dy2 = op_module.unaggregated_dy2_dx_s( - yy - tt, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - else: - yy = self._layer_0( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dy = op_module.unaggregated_dy_dx_s( - yy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - dy2 = op_module.unaggregated_dy2_dx_s( - yy, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - else: - ybar = tf.matmul( - yy, self.matrix["layer_" + str(layer + 1)][idx]) + self.bias["layer_" + str(layer + 1)][idx] - tt, zz = self._layer_1( - yy, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dz = op_module.unaggregated_dy_dx( - zz - tt, self.matrix["layer_" + str(layer + 1)][idx], dy, ybar, tf.constant(self.functype)) - dy2 = op_module.unaggregated_dy2_dx( - zz - tt, self.matrix["layer_" + str(layer + 1)][idx], dy, dy2, ybar, tf.constant(self.functype)) - dy = dz - yy = zz - - vv = zz.eval() - dd = dy.eval() - d2 = dy2.eval() - return vv, dd, d2 - - def _layer_0(self, x, w, b): - return self.activation_fn(tf.matmul(x, w) + b) - - def _layer_1(self, x, w, b): - t = tf.concat([x, x], axis=1) - return t, self.activation_fn(tf.matmul(x, w) + b) + t - - # Change the embedding net range to sw / min_nbor_dist - def _get_env_mat_range(self, - min_nbor_dist): - sw = self._spline5_switch(min_nbor_dist, self.rcut_smth, self.rcut) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - lower = -self.davg[:, 0] / self.dstd[:, 0] - upper = ((1 / min_nbor_dist) * sw - self.davg[:, 0]) / self.dstd[:, 0] - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - var = np.square(sw / (min_nbor_dist * self.dstd[:, 1:4])) - lower = np.min(-var, axis=1) - upper = np.max(var, axis=1) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - lower = -self.davg[:, 0] / self.dstd[:, 0] - upper = ((1 / min_nbor_dist) * sw - self.davg[:, 0]) / self.dstd[:, 0] - else: - raise RuntimeError("Unsupported descriptor") - log.info('training data with lower boundary: ' + str(lower)) - log.info('training data with upper boundary: ' + str(upper)) - # returns element-wise lower and upper - return np.floor(lower), np.ceil(upper) - - def _spline5_switch(self, - xx, - rmin, - rmax): - if xx < rmin: - vv = 1 - elif xx < rmax: - uu = (xx - rmin) / (rmax - rmin) - vv = uu*uu*uu * (-6 * uu*uu + 15 * uu - 10) + 1 - else: - vv = 0 - return vv - - def _get_layer_size(self): - layer_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - layer_size = len(self.embedding_net_nodes) // ((self.ntypes * self.ntypes - len(self.exclude_types)) * 2) - if self.type_one_side : - layer_size = len(self.embedding_net_nodes) // ((self.ntypes - self._n_all_excluded) * 2) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - layer_size = len(self.embedding_net_nodes) // int(comb(self.ntypes + 1, 2) * 2) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - layer_size = len(self.embedding_net_nodes) // ((self.ntypes * self.ntypes - len(self.exclude_types)) * 2) - if self.type_one_side : - layer_size = len(self.embedding_net_nodes) // ((self.ntypes - self._n_all_excluded) * 2) - else: - raise RuntimeError("Unsupported descriptor") - return layer_size - - @property - @lru_cache() - def _n_all_excluded(self) -> int: - """Then number of types excluding all types.""" - return sum((int(self._all_excluded(ii)) for ii in range(0, self.ntypes))) - - @lru_cache() - def _all_excluded(self, ii: int) -> bool: - """Check if type ii excluds all types. - - Parameters - ---------- - ii : int - type index - - Returns - ------- - bool - if type ii excluds all types - """ - return all([(ii, type_i) in self.exclude_types for type_i in range(self.ntypes)]) - - def _get_table_size(self): - table_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - table_size = self.ntypes * self.ntypes - if self.type_one_side : - table_size = self.ntypes - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - table_size = int(comb(self.ntypes + 1, 2)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - table_size = self.ntypes * self.ntypes - if self.type_one_side : - table_size = self.ntypes - else: - raise RuntimeError("Unsupported descriptor") - return table_size - - def _get_data_type(self): - for item in self.matrix["layer_" + str(self.layer_size)]: - if len(item) != 0: - return type(item[0][0]) - return None - - def _get_last_layer_size(self): - for item in self.matrix["layer_" + str(self.layer_size)]: - if len(item) != 0: - return item.shape[1] - return 0 - - def _convert_numpy_to_tensor(self): - """Convert self.data from np.ndarray to tf.Tensor.""" - for ii in self.data: - self.data[ii] = tf.constant(self.data[ii]) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/type_embed.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/type_embed.py deleted file mode 100644 index c2712bc..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/type_embed.py +++ /dev/null @@ -1,168 +0,0 @@ -import numpy as np -from typing import Tuple, List, Union - -from deepmd.env import tf -from deepmd.utils.network import one_layer -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 - -from deepmd.utils.graph import get_type_embedding_net_variables_from_graph_def -from deepmd.common import get_activation_func, get_precision - - -def embed_atom_type( - ntypes : int, - natoms : tf.Tensor, - type_embedding : tf.Tensor, -): - """ - Make the embedded type for the atoms in system. - The atoms are assumed to be sorted according to the type, - thus their types are described by a `tf.Tensor` natoms, see explanation below. - - Parameters - ---------- - ntypes: - Number of types. - 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 - type_embedding: - The type embedding. - It has the shape of [ntypes, embedding_dim] - - Returns - ------- - atom_embedding - The embedded type of each atom. - It has the shape of [numb_atoms, embedding_dim] - """ - te_out_dim = type_embedding.get_shape().as_list()[-1] - atype = [] - for ii in range(ntypes): - atype.append(tf.tile([ii], [natoms[2+ii]])) - atype = tf.concat(atype, axis = 0) - atm_embed = tf.nn.embedding_lookup(type_embedding,tf.cast(atype,dtype=tf.int32)) #(nf*natom)*nchnl - atm_embed = tf.reshape(atm_embed,[-1,te_out_dim]) - return atm_embed - - -class TypeEmbedNet(): - """ - - Parameters - ---------- - neuron : list[int] - Number of neurons in each hidden layers of the embedding net - resnet_dt - Time-step `dt` in the resnet construction: - y = x + dt * \phi (Wx + b) - activation_function - The activation function in the embedding net. Supported options are |ACTIVATION_FN| - precision - The precision of the embedding net parameters. Supported options are |PRECISION| - trainable - If the weights of embedding net are trainable. - seed - Random seed for initializing the network parameters. - uniform_seed - Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed - padding - Concat the zero padding to the output, as the default embedding of empty type. - """ - def __init__( - self, - neuron: List[int]=[], - resnet_dt: bool = False, - activation_function: Union[str, None] = 'tanh', - precision: str = 'default', - trainable: bool = True, - seed: int = None, - uniform_seed: bool = False, - padding: bool = False, - )->None: - """ - Constructor - """ - self.neuron = neuron - self.seed = seed - self.filter_resnet_dt = resnet_dt - self.filter_precision = get_precision(precision) - self.filter_activation_fn = get_activation_func(activation_function) - self.trainable = trainable - self.uniform_seed = uniform_seed - self.type_embedding_net_variables = None - self.padding = padding - - - def build( - self, - ntypes: int, - reuse = None, - suffix = '', - ): - """ - Build the computational graph for the descriptor - - Parameters - ---------- - ntypes - Number of atom types. - reuse - The weights in the networks should be reused when get the variable. - suffix - Name suffix to identify this descriptor - - Returns - ------- - embedded_types - The computational graph for embedded types - """ - types = tf.convert_to_tensor( - [ii for ii in range(ntypes)], - dtype = tf.int32 - ) - ebd_type = tf.cast(tf.one_hot(tf.cast(types,dtype=tf.int32),int(ntypes)), self.filter_precision) - ebd_type = tf.reshape(ebd_type, [-1, ntypes]) - name = 'type_embed_net' + suffix - with tf.variable_scope(name, reuse=reuse): - ebd_type = embedding_net( - ebd_type, - self.neuron, - activation_fn = self.filter_activation_fn, - precision = self.filter_precision, - resnet_dt = self.filter_resnet_dt, - seed = self.seed, - trainable = self.trainable, - initial_variables = self.type_embedding_net_variables, - uniform_seed = self.uniform_seed) - ebd_type = tf.reshape(ebd_type, [-1, self.neuron[-1]]) # ntypes * neuron[-1] - if self.padding: - last_type = tf.cast(tf.zeros([1, self.neuron[-1]]), self.filter_precision) - ebd_type = tf.concat([ebd_type, last_type], 0) # (ntypes + 1) * neuron[-1] - self.ebd_type = tf.identity(ebd_type, name ='t_typeebd') - return self.ebd_type - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix = '', - ) -> None: - """ - Init the type 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 - Name suffix to identify this descriptor - """ - self.type_embedding_net_variables = get_type_embedding_net_variables_from_graph_def(graph_def, suffix = suffix) diff --git a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/weight_avg.py b/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/weight_avg.py deleted file mode 100644 index c58c72d..0000000 --- a/_skbuild/linux-x86_64-3.6/cmake-install/deepmd/utils/weight_avg.py +++ /dev/null @@ -1,34 +0,0 @@ -from typing import TYPE_CHECKING, List, Dict, Optional, Tuple -import numpy as np - - -def weighted_average( - errors: List[Dict[str, Tuple[float, float]]] -) -> Dict: - """Compute wighted average of prediction errors for model. - - Parameters - ---------- - errors : List[Dict[str, Tuple[float, float]]] - List: the error of systems - Dict: the error of quantities, name given by the key - Tuple: (error, weight) - - Returns - ------- - Dict - weighted averages - """ - sum_err = {} - sum_siz = {} - for err in errors: - for kk, (ee, ss) in err.items(): - if kk in sum_err: - sum_err[kk] += ee * ee * ss - sum_siz[kk] += ss - else : - sum_err[kk] = ee * ee * ss - sum_siz[kk] = ss - for kk in sum_err.keys(): - sum_err[kk] = np.sqrt(sum_err[kk] / sum_siz[kk]) - return sum_err diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__about__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__about__.py deleted file mode 100644 index 840624b..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__about__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = 'unknown' diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__init__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__init__.py deleted file mode 100644 index c9e4149..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/__main__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/__main__.py deleted file mode 100644 index ca81449..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/_version.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/_version.py deleted file mode 100644 index b095e94..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/calculator.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/calculator.py deleted file mode 100644 index 38c56b7..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/cluster/__init__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/cluster/__init__.py deleted file mode 100644 index 7007208..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/cluster/local.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/cluster/local.py deleted file mode 100644 index 9b7f622..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/cluster/slurm.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/cluster/slurm.py deleted file mode 100644 index 6334f4a..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/common.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/common.py deleted file mode 100644 index b868038..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/__init__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/__init__.py deleted file mode 100644 index c5025a7..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/descriptor.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/descriptor.py deleted file mode 100644 index 0c40013..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/hybrid.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/hybrid.py deleted file mode 100644 index e577b2e..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/loc_frame.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/loc_frame.py deleted file mode 100644 index 81a8f68..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/se.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/se.py deleted file mode 100644 index 09bb571..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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/setuptools/lib/deepmd/descriptor/se_a.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/descriptor/se_a.py deleted file mode 100644 index de83f59..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/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)6#?1v!#}$2m=c9mlvktpVLX1b3}4q;9Cbj^rjwRmBS>>T7R9gD zkRqBC;^SE0mI*sKQhmel)u`pjhxx2CGfaHfP0zdf7rkk5R2|SfECUxu)#9qxd}~?! 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/usr/local/lib/python3.6/dist-packages/tensorflow/include;/usr/local/lib/python3.6/dist-packages/tensorflow/include -TF_LIBS = -TF_VERSION = 2.4.0 -TF_CXX11_ABI_FLAG = 0 -MODEL_VERSION=1.1 -DP_VARIANT=cpu diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/__init__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/__init__.py deleted file mode 100644 index d3f5a12..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/run_options.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/run_options.py deleted file mode 100644 index ce5815e..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/run_options.py +++ /dev/null @@ -1,233 +0,0 @@ -"""Module taking care of important package constants.""" - -import logging -import os -from pathlib import Path -from typing import TYPE_CHECKING, Dict, List, Optional, Tuple - -import numpy as np -from deepmd.cluster import get_resource -from deepmd.env import get_tf_default_nthreads, tf, GLOBAL_CONFIG, global_float_prec -from deepmd.loggers import set_log_handles - -if TYPE_CHECKING: - import horovod.tensorflow as HVD - - -__all__ = [ - "WELCOME", - "CITATION", - "BUILD", - "RunOptions", -] - -log = logging.getLogger(__name__) - - -# http://patorjk.com/software/taag. Font:Big" -WELCOME = ( # noqa - " _____ _____ __ __ _____ _ _ _ ", - "| __ \ | __ \ | \/ || __ \ | | (_)| | ", - "| | | | ___ ___ | |__) || \ / || | | | ______ | | __ _ | |_ ", - "| | | | / _ \ / _ \| ___/ | |\/| || | | ||______|| |/ /| || __|", - "| |__| || __/| __/| | | | | || |__| | | < | || |_ ", - "|_____/ \___| \___||_| |_| |_||_____/ |_|\_\|_| \__|", -) - -CITATION = ( - "Please read and cite:", - "Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018)", -) - -_sep = "\n " -BUILD = ( - f"installed to: {GLOBAL_CONFIG['install_prefix']}", - f"source : {GLOBAL_CONFIG['git_summ']}", - f"source brach: {GLOBAL_CONFIG['git_branch']}", - f"source commit: {GLOBAL_CONFIG['git_hash']}", - f"source commit at: {GLOBAL_CONFIG['git_date']}", - f"build float prec: {global_float_prec}", - f"build variant: {GLOBAL_CONFIG['dp_variant']}", - f"build with tf inc: {GLOBAL_CONFIG['tf_include_dir']}", - f"build with tf lib: {GLOBAL_CONFIG['tf_libs'].replace(';', _sep)}" # noqa -) - - -class RunOptions: - """Class with inf oon how to run training (cluster, MPI and GPU config). - - Attributes - ---------- - gpus: Optional[List[int]] - list of GPUs if any are present else None - is_chief: bool - in distribured training it is true for tha main MPI process in serail it is - always true - world_size: int - total worker count - my_rank: int - index of the MPI task - nodename: str - name of the node - node_list_ : List[str] - the list of nodes of the current mpirun - my_device: str - deviice type - gpu or cpu - """ - - gpus: Optional[List[int]] - world_size: int - my_rank: int - nodename: str - nodelist: List[int] - my_device: str - - _HVD: Optional["HVD"] - _log_handles_already_set: bool = False - - def __init__( - self, - init_model: Optional[str] = None, - init_frz_model: Optional[str] = None, - restart: Optional[str] = None, - log_path: Optional[str] = None, - log_level: int = 0, - mpi_log: str = "master" - ): - self._try_init_distrib() - - if all((init_model, restart)): - raise RuntimeError( - "--init-model and --restart should not be set at the same time" - ) - - # model init options - self.restart = restart - self.init_model = init_model - self.init_mode = "init_from_scratch" - - if restart is not None: - self.restart = os.path.abspath(restart) - self.init_mode = "restart" - elif init_model is not None: - self.init_model = os.path.abspath(init_model) - self.init_mode = "init_from_model" - elif init_frz_model is not None: - self.init_frz_model = os.path.abspath(init_frz_model) - self.init_mode = "init_from_frz_model" - - self._setup_logger(Path(log_path) if log_path else None, log_level, mpi_log) - - @property - def is_chief(self): - """Whether my rank is 0.""" - return self.my_rank == 0 - - def print_resource_summary(self): - """Print build and current running cluster configuration summary.""" - log.info("---Summary of the training---------------------------------------") - if self.is_distrib: - log.info("distributed") - log.info(f"world size: {self.world_size}") - log.info(f"my rank: {self.my_rank}") - log.info(f"node list: {self.nodelist}") - log.info(f"running on: {self.nodename}") - log.info(f"computing device: {self.my_device}") - env_value = os.environ.get('CUDA_VISIBLE_DEVICES', 'unset') - log.info(f"CUDA_VISIBLE_DEVICES: {env_value}") - log.info(f"Count of visible GPU: {len(self.gpus or [])}") - intra, inter = get_tf_default_nthreads() - log.info(f"num_intra_threads: {intra:d}") - log.info(f"num_inter_threads: {inter:d}") - log.info("-----------------------------------------------------------------") - - def _setup_logger( - self, - log_path: Optional[Path], - log_level: int, - mpi_log: Optional[str], - ): - """Set up package loggers. - - Parameters - ---------- - log_level: int - logging level - log_path: Optional[str] - path to log file, if None logs will be send only to console. If the parent - directory does not exist it will be automatically created, by default None - mpi_log : Optional[str], optional - mpi log type. Has three options. `master` will output logs to file and - console only from rank==0. `collect` will write messages from all ranks to - one file opened under rank==0 and to console. `workers` will open one log - file for each worker designated by its rank, console behaviour is the same - as for `collect`. - """ - if not self._log_handles_already_set: - if not self._HVD: - mpi_log = None - set_log_handles(log_level, log_path, mpi_log=mpi_log) - self._log_handles_already_set = True - log.debug("Log handles were successfully set") - else: - log.warning( - f"Log handles have already been set. It is not advisable to " - f"reset them{', especially when runnig with MPI!' if self._HVD else ''}" - ) - - def _try_init_distrib(self): - try: - import horovod.tensorflow as HVD - HVD.init() - self.is_distrib = HVD.size() > 1 - except ImportError: - log.warning("Switch to serial execution due to lack of horovod module.") - self.is_distrib = False - - # Do real intialization - if self.is_distrib: - self._init_distributed(HVD) - self._HVD = HVD - else: - self._init_serial() - self._HVD = None - - def _init_distributed(self, HVD: "HVD"): - """Initialize settings for distributed training. - - Parameters - ---------- - HVD : HVD - horovod object - """ - nodename, nodelist, gpus = get_resource() - self.nodename = nodename - self.nodelist = nodelist - self.gpus = gpus - self.my_rank = HVD.rank() - self.world_size = HVD.size() - - if gpus is not None: - gpu_idx = HVD.local_rank() - if gpu_idx >= len(gpus): - raise RuntimeError('Count of local processes is larger than that of available GPUs!') - self.my_device = f"gpu:{gpu_idx:d}" - else: - self.my_device = "cpu:0" - - def _init_serial(self): - """Initialize setting for serial training.""" - nodename, _, gpus = get_resource() - - self.gpus = gpus - self.world_size = 1 - self.my_rank = 0 - self.nodename = nodename - self.nodelist = [nodename] - - if gpus is not None: - self.my_device = "gpu:0" - else: - self.my_device = "cpu:0" - - self._HVD = None diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/trainer.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/trainer.py deleted file mode 100644 index e2f1e39..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/train/trainer.py +++ /dev/null @@ -1,744 +0,0 @@ -#!/usr/bin/env python3 -from deepmd.descriptor.descriptor import Descriptor -import logging -import os -import glob -import platform -import time -import shutil -import google.protobuf.message -import numpy as np -from packaging.version import Version - -from deepmd.env import tf, tfv2 -from deepmd.env import get_tf_session_config -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.env import GLOBAL_ENER_FLOAT_PRECISION -from deepmd.fit import EnerFitting, WFCFitting, PolarFittingLocFrame, PolarFittingSeA, GlobalPolarFittingSeA, DipoleFittingSeA -from deepmd.descriptor import Descriptor -from deepmd.model import EnerModel, WFCModel, DipoleModel, PolarModel, GlobalPolarModel -from deepmd.loss import EnerStdLoss, EnerDipoleLoss, TensorLoss -from deepmd.utils.errors import GraphTooLargeError -from deepmd.utils.learning_rate import LearningRateExp -from deepmd.utils.neighbor_stat import NeighborStat -from deepmd.utils.sess import run_sess -from deepmd.utils.type_embed import TypeEmbedNet -from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph -from deepmd.utils.argcheck import type_embedding_args - -from tensorflow.python.client import timeline -from deepmd.env import op_module, TF_VERSION -from deepmd.utils.errors import GraphWithoutTensorError - -# load grad of force module -import deepmd.op - -from deepmd.common import j_must_have, ClassArg, data_requirement, get_precision - -log = logging.getLogger(__name__) - -# nvnmd -from deepmd.nvnmd.utils.config import nvnmd_cfg - -def _is_subdir(path, directory): - path = os.path.realpath(path) - directory = os.path.realpath(directory) - if path == directory: - return False - relative = os.path.relpath(path, directory) + os.sep - return not relative.startswith(os.pardir + os.sep) - - -class DPTrainer (object): - def __init__(self, - jdata, - run_opt, - is_compress = False): - self.run_opt = run_opt - self._init_param(jdata) - self.is_compress = is_compress - - def _init_param(self, jdata): - # model config - model_param = j_must_have(jdata, 'model') - descrpt_param = j_must_have(model_param, 'descriptor') - fitting_param = j_must_have(model_param, 'fitting_net') - typeebd_param = model_param.get('type_embedding', None) - self.model_param = model_param - self.descrpt_param = descrpt_param - - # nvnmd - self.nvnmd_param = jdata.get('nvnmd', {}) - nvnmd_cfg.init_from_jdata(self.nvnmd_param) - if nvnmd_cfg.enable: - nvnmd_cfg.init_from_deepmd_input(model_param) - nvnmd_cfg.disp_message() - nvnmd_cfg.save() - - # descriptor - try: - descrpt_type = descrpt_param['type'] - self.descrpt_type = descrpt_type - except KeyError: - raise KeyError('the type of descriptor should be set by `type`') - - if descrpt_param['type'] in ['se_atten']: - descrpt_param['ntypes'] = len(model_param['type_map']) - self.descrpt = Descriptor(**descrpt_param) - - # fitting net - fitting_type = fitting_param.get('type', 'ener') - self.fitting_type = fitting_type - fitting_param.pop('type', None) - fitting_param['descrpt'] = self.descrpt - if fitting_type == 'ener': - self.fitting = EnerFitting(**fitting_param) - # elif fitting_type == 'wfc': - # self.fitting = WFCFitting(fitting_param, self.descrpt) - elif fitting_type == 'dipole': - if descrpt_type == 'se_e2_a': - self.fitting = DipoleFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting dipole only supports descrptors: se_e2_a') - elif fitting_type == 'polar': - # if descrpt_type == 'loc_frame': - # self.fitting = PolarFittingLocFrame(fitting_param, self.descrpt) - if descrpt_type == 'se_e2_a': - self.fitting = PolarFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting polar only supports descrptors: loc_frame and se_e2_a') - elif fitting_type == 'global_polar': - if descrpt_type == 'se_e2_a': - self.fitting = GlobalPolarFittingSeA(**fitting_param) - else : - raise RuntimeError('fitting global_polar only supports descrptors: loc_frame and se_e2_a') - else : - raise RuntimeError('unknow fitting type ' + fitting_type) - - # type embedding - padding = False - if descrpt_type == 'se_atten': - padding = True - if typeebd_param is not None: - self.typeebd = TypeEmbedNet( - neuron=typeebd_param['neuron'], - resnet_dt=typeebd_param['resnet_dt'], - activation_function=typeebd_param['activation_function'], - precision=typeebd_param['precision'], - trainable=typeebd_param['trainable'], - seed=typeebd_param['seed'], - padding=padding - ) - elif descrpt_type == 'se_atten': - default_args = type_embedding_args() - default_args_dict = {i.name: i.default for i in default_args} - self.typeebd = TypeEmbedNet( - neuron=default_args_dict['neuron'], - resnet_dt=default_args_dict['resnet_dt'], - activation_function=None, - precision=default_args_dict['precision'], - trainable=default_args_dict['trainable'], - seed=default_args_dict['seed'], - padding=padding - ) - else: - self.typeebd = None - - # init model - # infer model type by fitting_type - if fitting_type == 'ener': - self.model = EnerModel( - self.descrpt, - self.fitting, - self.typeebd, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2), - model_param.get('use_srtab'), - model_param.get('smin_alpha'), - model_param.get('sw_rmin'), - model_param.get('sw_rmax') - ) - # elif fitting_type == 'wfc': - # self.model = WFCModel(model_param, self.descrpt, self.fitting) - elif fitting_type == 'dipole': - self.model = DipoleModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - elif fitting_type == 'polar': - self.model = PolarModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - elif fitting_type == 'global_polar': - self.model = GlobalPolarModel( - self.descrpt, - self.fitting, - model_param.get('type_map'), - model_param.get('data_stat_nbatch', 10), - model_param.get('data_stat_protect', 1e-2) - ) - else : - raise RuntimeError('get unknown fitting type when building model') - - # learning rate - lr_param = j_must_have(jdata, 'learning_rate') - scale_by_worker = lr_param.get('scale_by_worker', 'linear') - if scale_by_worker == 'linear': - self.scale_lr_coef = float(self.run_opt.world_size) - elif scale_by_worker == 'sqrt': - self.scale_lr_coef = np.sqrt(self.run_opt.world_size).real - else: - self.scale_lr_coef = 1. - lr_type = lr_param.get('type', 'exp') - if lr_type == 'exp': - self.lr = LearningRateExp(lr_param['start_lr'], - lr_param['stop_lr'], - lr_param['decay_steps']) - else : - raise RuntimeError('unknown learning_rate type ' + lr_type) - - # loss - # infer loss type by fitting_type - loss_param = jdata.get('loss', None) - loss_type = loss_param.get('type', 'ener') - - if fitting_type == 'ener': - loss_param.pop('type', None) - loss_param['starter_learning_rate'] = self.lr.start_lr() - if loss_type == 'ener': - self.loss = EnerStdLoss(**loss_param) - elif loss_type == 'ener_dipole': - self.loss = EnerDipoleLoss(**loss_param) - else: - raise RuntimeError('unknow loss type') - elif fitting_type == 'wfc': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'wfc', - tensor_size = self.model.get_out_size(), - label_name = 'wfc') - elif fitting_type == 'dipole': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'dipole', - tensor_size = 3, - label_name = 'dipole') - elif fitting_type == 'polar': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'polar', - tensor_size = 9, - label_name = 'polarizability') - elif fitting_type == 'global_polar': - self.loss = TensorLoss(loss_param, - model = self.model, - tensor_name = 'global_polar', - tensor_size = 9, - atomic = False, - label_name = 'polarizability') - else : - raise RuntimeError('get unknown fitting type when building loss function') - - # training - tr_data = jdata['training'] - self.disp_file = tr_data.get('disp_file', 'lcurve.out') - self.disp_freq = tr_data.get('disp_freq', 1000) - self.save_freq = tr_data.get('save_freq', 1000) - self.save_ckpt = tr_data.get('save_ckpt', 'model.ckpt') - self.display_in_training = tr_data.get('disp_training', True) - self.timing_in_training = tr_data.get('time_training', True) - self.profiling = self.run_opt.is_chief and tr_data.get('profiling', False) - self.profiling_file = tr_data.get('profiling_file', 'timeline.json') - self.enable_profiler = tr_data.get('enable_profiler', False) - self.tensorboard = self.run_opt.is_chief and tr_data.get('tensorboard', False) - self.tensorboard_log_dir = tr_data.get('tensorboard_log_dir', 'log') - self.tensorboard_freq = tr_data.get('tensorboard_freq', 1) - self.mixed_prec = tr_data.get('mixed_precision', None) - if self.mixed_prec is not None: - if (self.mixed_prec['compute_prec'] != 'float16' or self.mixed_prec['output_prec'] != 'float32'): - raise RuntimeError( - "Unsupported mixed precision option [output_prec, compute_prec]: [%s, %s], " - " Supported: [float32, float16], Please set mixed precision option correctly!" - % (self.mixed_prec['output_prec'], self.mixed_prec['compute_prec'])) - # self.sys_probs = tr_data['sys_probs'] - # self.auto_prob_style = tr_data['auto_prob'] - self.useBN = False - if fitting_type == 'ener' and self.fitting.get_numb_fparam() > 0 : - self.numb_fparam = self.fitting.get_numb_fparam() - else : - self.numb_fparam = 0 - - if tr_data.get("validation_data", None) is not None: - self.valid_numb_batch = tr_data["validation_data"].get("numb_btch", 1) - else: - self.valid_numb_batch = 1 - - # if init the graph with the frozen model - self.frz_model = None - self.model_type = None - - - def build (self, - data = None, - stop_batch = 0, - suffix = "") : - self.ntypes = self.model.get_ntypes() - self.stop_batch = stop_batch - - if not self.is_compress and data.mixed_type: - assert self.descrpt_type in ['se_atten'], 'Data in mixed_type format must use attention descriptor!' - assert self.fitting_type in ['ener'], 'Data in mixed_type format must use ener fitting!' - - if self.numb_fparam > 0 : - log.info("training with %d frame parameter(s)" % self.numb_fparam) - else: - log.info("training without frame parameter") - - if not self.is_compress: - # Usually, the type number of the model should be equal to that of the data - # However, nt_model > nt_data should be allowed, since users may only want to - # train using a dataset that only have some of elements - if self.ntypes < data.get_ntypes(): - raise ValueError( - "The number of types of the training data is %d, but that of the " - "model is only %d. The latter must be no less than the former. " - "You may need to reset one or both of them. Usually, the former " - "is given by `model/type_map` in the training parameter (if set) " - "or the maximum number in the training data. The latter is given " - "by `model/descriptor/sel` in the training parameter." % ( - data.get_ntypes(), self.ntypes - )) - self.type_map = data.get_type_map() - self.batch_size = data.get_batch_size() - if self.run_opt.init_mode not in ('init_from_model', 'restart', 'init_from_frz_model'): - # self.saver.restore (in self._init_session) will restore avg and std variables, so data_stat is useless - # init_from_frz_model will restore data_stat variables in `init_variables` method - log.info("data stating... (this step may take long time)") - self.model.data_stat(data) - - # config the init_frz_model command - if self.run_opt.init_mode == 'init_from_frz_model': - self._init_from_frz_model() - - # neighbor_stat is moved to train.py as duplicated - # TODO: this is a simple fix but we should have a clear - # architecture to call neighbor stat - else : - graph, graph_def = load_graph_def(self.model_param['compress']['model_file']) - self.descrpt.enable_compression(self.model_param['compress']["min_nbor_dist"], self.model_param['compress']['model_file'], self.model_param['compress']['table_config'][0], self.model_param['compress']['table_config'][1], self.model_param['compress']['table_config'][2], self.model_param['compress']['table_config'][3]) - self.fitting.init_variables(graph, graph_def) - # for fparam or aparam settings in 'ener' type fitting net - if self.fitting_type == 'ener': - self.fitting.enable_compression(self.model_param['compress']['model_file']) - - if self.is_compress or self.model_type == 'compressed_model': - tf.constant("compressed_model", name = 'model_type', dtype = tf.string) - else: - tf.constant("original_model", name = 'model_type', dtype = tf.string) - - if self.mixed_prec is not None: - self.descrpt.enable_mixed_precision(self.mixed_prec) - self.fitting.enable_mixed_precision(self.mixed_prec) - - self._build_lr() - self._build_network(data, suffix) - self._build_training() - - - def _build_lr(self): - self._extra_train_ops = [] - self.global_step = tf.train.get_or_create_global_step() - self.learning_rate = self.lr.build(self.global_step, self.stop_batch) - log.info("built lr") - - def _build_network(self, data, suffix=""): - self.place_holders = {} - if self.is_compress : - for kk in ['coord', 'box']: - self.place_holders[kk] = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], 't_' + kk) - self._get_place_horders(data_requirement) - else : - self._get_place_horders(data.get_data_dict()) - - self.place_holders['type'] = tf.placeholder(tf.int32, [None], name='t_type') - self.place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes+2], name='t_natoms') - self.place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name='t_mesh') - self.place_holders['is_training'] = tf.placeholder(tf.bool) - self.model_pred\ - = self.model.build (self.place_holders['coord'], - self.place_holders['type'], - self.place_holders['natoms_vec'], - self.place_holders['box'], - self.place_holders['default_mesh'], - self.place_holders, - self.frz_model, - suffix = suffix, - reuse = False) - - self.l2_l, self.l2_more\ - = self.loss.build (self.learning_rate, - self.place_holders['natoms_vec'], - self.model_pred, - self.place_holders, - suffix = "test") - - if self.mixed_prec is not None: - self.l2_l = tf.cast(self.l2_l, get_precision(self.mixed_prec['output_prec'])) - log.info("built network") - - def _build_training(self): - trainable_variables = tf.trainable_variables() - if self.run_opt.is_distrib: - if self.scale_lr_coef > 1.: - log.info('Scale learning rate by coef: %f', self.scale_lr_coef) - optimizer = tf.train.AdamOptimizer(self.learning_rate*self.scale_lr_coef) - else: - optimizer = tf.train.AdamOptimizer(self.learning_rate) - optimizer = self.run_opt._HVD.DistributedOptimizer(optimizer) - else: - optimizer = tf.train.AdamOptimizer(learning_rate = self.learning_rate) - if self.mixed_prec is not None: - _TF_VERSION = Version(TF_VERSION) - # check the TF_VERSION, when TF < 1.12, mixed precision is not allowed - if _TF_VERSION < Version('1.14.0'): - raise RuntimeError("TensorFlow version %s is not compatible with the mixed precision setting. Please consider upgrading your TF version!" % TF_VERSION) - elif _TF_VERSION < Version('2.4.0'): - optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) - else: - optimizer = tf.mixed_precision.enable_mixed_precision_graph_rewrite(optimizer) - apply_op = optimizer.minimize(loss=self.l2_l, - global_step=self.global_step, - var_list=trainable_variables, - name='train_step') - train_ops = [apply_op] + self._extra_train_ops - self.train_op = tf.group(*train_ops) - log.info("built training") - - def _init_session(self): - config = get_tf_session_config() - device, idx = self.run_opt.my_device.split(":", 1) - if device == "gpu": - config.gpu_options.visible_device_list = idx - self.sess = tf.Session(config=config) - - # Initializes or restore global variables - init_op = tf.global_variables_initializer() - if self.run_opt.is_chief: - self.saver = tf.train.Saver(save_relative_paths=True) - if self.run_opt.init_mode == 'init_from_scratch' : - log.info("initialize model from scratch") - run_sess(self.sess, init_op) - if not self.is_compress: - fp = open(self.disp_file, "w") - fp.close () - elif self.run_opt.init_mode == 'init_from_model' : - log.info("initialize from model %s" % self.run_opt.init_model) - run_sess(self.sess, init_op) - self.saver.restore (self.sess, self.run_opt.init_model) - run_sess(self.sess, self.global_step.assign(0)) - fp = open(self.disp_file, "w") - fp.close () - elif self.run_opt.init_mode == 'restart' : - log.info("restart from model %s" % self.run_opt.restart) - run_sess(self.sess, init_op) - self.saver.restore (self.sess, self.run_opt.restart) - elif self.run_opt.init_mode == 'init_from_frz_model' : - log.info("initialize training from the frozen model") - run_sess(self.sess, init_op) - fp = open(self.disp_file, "w") - fp.close () - else : - raise RuntimeError ("unkown init mode") - else: - run_sess(self.sess, init_op) - self.saver = None - - # Ensure variable consistency among tasks when training starts - if self.run_opt.is_distrib: - bcast_op = self.run_opt._HVD.broadcast_global_variables(0) - if self.run_opt.is_chief: - log.info('broadcast global variables to other tasks') - else: - log.info('receive global variables from task#0') - run_sess(self.sess, bcast_op) - - def train (self, train_data = None, valid_data=None) : - - # if valid_data is None: # no validation set specified. - # valid_data = train_data # using training set as validation set. - - stop_batch = self.stop_batch - self._init_session() - - # Before data shard is enabled, only cheif do evaluation and record it - # self.print_head() - fp = None - if self.run_opt.is_chief : - fp = open(self.disp_file, "a") - - cur_batch = run_sess(self.sess, self.global_step) - is_first_step = True - self.cur_batch = cur_batch - log.info("start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e" % - (run_sess(self.sess, self.learning_rate), - self.lr.value(cur_batch), - self.lr.decay_steps_, - self.lr.decay_rate_, - self.lr.value(stop_batch)) - ) - - prf_options = None - prf_run_metadata = None - if self.profiling: - prf_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - prf_run_metadata = tf.RunMetadata() - - # set tensorboard execution environment - if self.tensorboard: - summary_merged_op = tf.summary.merge_all() - # Remove TB old logging directory from previous run - try: - shutil.rmtree(self.tensorboard_log_dir) - except FileNotFoundError: - pass # directory does not exist, this is OK - except Exception as e: - # general error when removing directory, warn user - log.exception( - f"Could not remove old tensorboard logging directory: " - f"{self.tensorboard_log_dir}. Error: {e}" - ) - else: - log.debug("Removing old tensorboard log directory.") - tb_train_writer = tf.summary.FileWriter(self.tensorboard_log_dir + '/train', self.sess.graph) - tb_valid_writer = tf.summary.FileWriter(self.tensorboard_log_dir + '/test') - else: - tb_train_writer = None - tb_valid_writer = None - if self.enable_profiler: - # https://www.tensorflow.org/guide/profiler - tfv2.profiler.experimental.start(self.tensorboard_log_dir) - - train_time = 0 - - while cur_batch < stop_batch : - - # first round validation: - train_batch = train_data.get_batch() - if self.display_in_training and is_first_step: - if self.run_opt.is_chief: - valid_batches = [valid_data.get_batch() for ii in range(self.valid_numb_batch)] if valid_data is not None else None - self.valid_on_the_fly(fp, [train_batch], valid_batches, print_header=True) - is_first_step = False - - if self.timing_in_training: tic = time.time() - train_feed_dict = self.get_feed_dict(train_batch, is_training=True) - # use tensorboard to visualize the training of deepmd-kit - # it will takes some extra execution time to generate the tensorboard data - if self.tensorboard and (cur_batch % self.tensorboard_freq == 0): - summary, _ = run_sess(self.sess, [summary_merged_op, self.train_op], feed_dict=train_feed_dict, - options=prf_options, run_metadata=prf_run_metadata) - tb_train_writer.add_summary(summary, cur_batch) - else: - run_sess(self.sess, [self.train_op], feed_dict=train_feed_dict, - options=prf_options, run_metadata=prf_run_metadata) - if self.timing_in_training: toc = time.time() - if self.timing_in_training: train_time += toc - tic - cur_batch = run_sess(self.sess, self.global_step) - self.cur_batch = cur_batch - - # on-the-fly validation - if self.display_in_training and (cur_batch % self.disp_freq == 0): - if self.timing_in_training: - tic = time.time() - if self.run_opt.is_chief: - valid_batches = [valid_data.get_batch() for ii in range(self.valid_numb_batch)] if valid_data is not None else None - self.valid_on_the_fly(fp, [train_batch], valid_batches) - if self.timing_in_training: - toc = time.time() - test_time = toc - tic - log.info("batch %7d training time %.2f s, testing time %.2f s" - % (cur_batch, train_time, test_time)) - train_time = 0 - if self.save_freq > 0 and cur_batch % self.save_freq == 0 and self.saver is not None: - self.save_checkpoint(cur_batch) - if (self.save_freq == 0 or cur_batch == 0 or cur_batch % self.save_freq != 0) and self.saver is not None: - self.save_checkpoint(cur_batch) - if self.run_opt.is_chief: - fp.close () - if self.profiling and self.run_opt.is_chief : - fetched_timeline = timeline.Timeline(prf_run_metadata.step_stats) - chrome_trace = fetched_timeline.generate_chrome_trace_format() - with open(self.profiling_file, 'w') as f: - f.write(chrome_trace) - if self.enable_profiler and self.run_opt.is_chief: - tfv2.profiler.experimental.stop() - - def save_checkpoint(self, cur_batch: int): - try: - ckpt_prefix = self.saver.save (self.sess, os.path.join(os.getcwd(), self.save_ckpt), global_step=cur_batch) - except google.protobuf.message.DecodeError as e: - raise GraphTooLargeError( - "The graph size exceeds 2 GB, the hard limitation of protobuf." - " Then a DecodeError was raised by protobuf. You should " - "reduce the size of your model." - ) from e - # make symlinks from prefix with step to that without step to break nothing - # get all checkpoint files - original_files = glob.glob(ckpt_prefix + ".*") - for ori_ff in original_files: - new_ff = self.save_ckpt + ori_ff[len(ckpt_prefix):] - try: - # remove old one - os.remove(new_ff) - except OSError: - pass - if platform.system() != 'Windows': - # by default one does not have access to create symlink on Windows - os.symlink(ori_ff, new_ff) - else: - shutil.copyfile(ori_ff, new_ff) - log.info("saved checkpoint %s" % self.save_ckpt) - - def get_feed_dict(self, batch, is_training): - feed_dict = {} - for kk in batch.keys(): - if kk == 'find_type' or kk == 'type' or kk == 'real_natoms_vec': - continue - if 'find_' in kk: - feed_dict[self.place_holders[kk]] = batch[kk] - else: - feed_dict[self.place_holders[kk]] = np.reshape(batch[kk], [-1]) - for ii in ['type']: - feed_dict[self.place_holders[ii]] = np.reshape(batch[ii], [-1]) - for ii in ['natoms_vec', 'default_mesh']: - feed_dict[self.place_holders[ii]] = batch[ii] - feed_dict[self.place_holders['is_training']] = is_training - return feed_dict - - def get_global_step(self): - return run_sess(self.sess, self.global_step) - - # def print_head (self) : # depreciated - # if self.run_opt.is_chief: - # fp = open(self.disp_file, "a") - # print_str = "# %5s" % 'batch' - # print_str += self.loss.print_header() - # print_str += ' %8s\n' % 'lr' - # fp.write(print_str) - # fp.close () - - def valid_on_the_fly(self, - fp, - train_batches, - valid_batches, - print_header=False): - train_results = self.get_evaluation_results(train_batches) - valid_results = self.get_evaluation_results(valid_batches) - - cur_batch = self.cur_batch - current_lr = run_sess(self.sess, self.learning_rate) - if print_header: - self.print_header(fp, train_results, valid_results) - self.print_on_training(fp, train_results, valid_results, cur_batch, current_lr) - - @staticmethod - def print_header(fp, train_results, valid_results): - print_str = '' - print_str += "# %5s" % 'step' - if valid_results is not None: - prop_fmt = ' %11s %11s' - for k in train_results.keys(): - print_str += prop_fmt % (k + '_val', k + '_trn') - else: - prop_fmt = ' %11s' - for k in train_results.keys(): - print_str += prop_fmt % (k + '_trn') - print_str += ' %8s\n' % 'lr' - fp.write(print_str) - fp.flush() - - @staticmethod - def print_on_training(fp, train_results, valid_results, cur_batch, cur_lr): - print_str = '' - print_str += "%7d" % cur_batch - if valid_results is not None: - prop_fmt = " %11.2e %11.2e" - for k in valid_results.keys(): - # assert k in train_results.keys() - print_str += prop_fmt % (valid_results[k], train_results[k]) - else: - prop_fmt = " %11.2e" - for k in train_results.keys(): - print_str += prop_fmt % (train_results[k]) - print_str += " %8.1e\n" % cur_lr - fp.write(print_str) - fp.flush() - - def get_evaluation_results(self, batch_list): - if batch_list is None: return None - numb_batch = len(batch_list) - - sum_results = {} # sum of losses on all atoms - sum_natoms = 0 - for i in range(numb_batch): - batch = batch_list[i] - natoms = batch["natoms_vec"] - feed_dict = self.get_feed_dict(batch, is_training=False) - results = self.loss.eval(self.sess, feed_dict, natoms) - - for k, v in results.items(): - if k == "natoms": - sum_natoms += v - else: - sum_results[k] = sum_results.get(k, 0.) + v * results["natoms"] - avg_results = {k: v / sum_natoms for k, v in sum_results.items() if not k == "natoms"} - return avg_results - - def save_compressed(self): - """ - Save the compressed graph - """ - self._init_session() - if self.is_compress: - self.saver.save (self.sess, os.path.join(os.getcwd(), self.save_ckpt)) - - def _get_place_horders(self, data_dict): - for kk in data_dict.keys(): - if kk == 'type': - continue - prec = GLOBAL_TF_FLOAT_PRECISION - if data_dict[kk]['high_prec'] : - prec = GLOBAL_ENER_FLOAT_PRECISION - self.place_holders[kk] = tf.placeholder(prec, [None], name = 't_' + kk) - self.place_holders['find_' + kk] = tf.placeholder(tf.float32, name = 't_find_' + kk) - - def _init_from_frz_model(self): - try: - graph, graph_def = load_graph_def(self.run_opt.init_frz_model) - except FileNotFoundError as e: - # throw runtime error if there's no frozen model - raise RuntimeError( - "The input frozen model %s (%s) does not exist! Please check the path of the frozen model. " % (self.run_opt.init_frz_model, os.path.abspath(self.run_opt.init_frz_model)) - ) from e - # get the model type from the frozen model(self.run_opt.init_frz_model) - try: - t_model_type = get_tensor_by_name_from_graph(graph, 'model_type') - except GraphWithoutTensorError as e: - # throw runtime error if the frozen_model has no model type information... - raise RuntimeError( - "The input frozen model: %s has no 'model_type' information, " - "which is not supported by the 'dp train init-frz-model' interface. " % self.run_opt.init_frz_model - ) from e - else: - self.model_type = bytes.decode(t_model_type) - if self.model_type == 'compressed_model': - self.frz_model = self.run_opt.init_frz_model - self.model.init_variables(graph, graph_def, model_type=self.model_type) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/__init__.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/__init__.py deleted file mode 100644 index 2646df2..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -from .data import DeepmdData -from .data_system import DeepmdDataSystem - -# out-of-dated -from .data import DataSets -from .data_system import DataSystem -from .pair_tab import PairTab -from .learning_rate import LearningRateExp -from .plugin import Plugin, PluginVariant diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/argcheck.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/argcheck.py deleted file mode 100644 index 3f81f31..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/argcheck.py +++ /dev/null @@ -1,789 +0,0 @@ -from typing import List, Callable - -from dargs import dargs, Argument, Variant, ArgumentEncoder -from deepmd import descriptor -from deepmd.common import ACTIVATION_FN_DICT, PRECISION_DICT -from deepmd.utils.plugin import Plugin -import json - -from deepmd.nvnmd.utils.argcheck import nvnmd_args - -def list_to_doc(xx): - items = [] - for ii in xx: - if len(items) == 0: - items.append(f'"{ii}"') - else: - items.append(f', "{ii}"') - items.append('.') - return ''.join(items) - - -def make_link(content, ref_key): - return f'`{content} <{ref_key}_>`_' if not dargs.RAW_ANCHOR \ - else f'`{content} <#{ref_key}>`_' - - -def type_embedding_args(): - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_seed = 'Random seed for parameter initialization' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - - return [ - Argument("neuron", list, optional = True, default = [8], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, default = None, doc = doc_seed), - ] - - -# --- Descriptor configurations: --- # - -class ArgsPlugin: - def __init__(self) -> None: - self.__plugin = Plugin() - - def register(self, name : str, alias : List[str] = None) -> Callable[[], List[Argument]]: - """Regiester a descriptor argument plugin. - - Parameters - ---------- - name : str - the name of a descriptor - alias : List[str], optional - the list of aliases of this descriptor - - Returns - ------- - Callable[[], List[Argument]] - the regiestered descriptor argument method - - Examples - -------- - >>> some_plugin = ArgsPlugin() - >>> @some_plugin.register("some_descrpt") - def descrpt_some_descrpt_args(): - return [] - """ - # convert alias to hashed item - if isinstance(alias, list): - alias = tuple(alias) - return self.__plugin.register((name, alias)) - - def get_all_argument(self, exclude_hybrid: bool = False) -> List[Argument]: - """Get all arguments. - - Parameters - ---------- - exclude_hybrid : bool - exclude hybrid descriptor to prevent circular calls - - Returns - ------- - List[Argument] - all arguments - """ - arguments = [] - for (name, alias), metd in self.__plugin.plugins.items(): - if exclude_hybrid and name == "hybrid": - continue - arguments.append(Argument(name=name, dtype=dict, sub_fields=metd(), alias=alias)) - return arguments - - -descrpt_args_plugin = ArgsPlugin() - -@descrpt_args_plugin.register("loc_frame") -def descrpt_local_frame_args (): - doc_sel_a = 'A list of integers. 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.' - doc_sel_r = 'A list of integers. 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.' - doc_rcut = 'The cut-off radius. The default value is 6.0' - doc_axis_rule = 'A list of integers. The length should be 6 times of the number of types. \n\n\ -- 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.' - - return [ - Argument("sel_a", list, optional = False, doc = doc_sel_a), - Argument("sel_r", list, optional = False, doc = doc_sel_r), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("axis_rule", list, optional = False, doc = doc_axis_rule) - ] - - -@descrpt_args_plugin.register("se_e2_a", alias=["se_a"]) -def descrpt_se_a_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net is trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("axis_neuron", int, optional = True, default = 4, alias = ['n_axis_neuron'], doc = doc_axis_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - -@descrpt_args_plugin.register("se_e3", alias=['se_at', 'se_a_3be', 'se_t']) -def descrpt_se_t_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - doc_seed = 'Random seed for parameter initialization' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - - -@descrpt_args_plugin.register("se_a_tpe", alias=['se_a_ebd']) -def descrpt_se_a_tpe_args(): - doc_type_nchanl = 'number of channels for type embedding' - doc_type_nlayer = 'number of hidden layers of type embedding net' - doc_numb_aparam = 'dimension of atomic parameter. if set to a value > 0, the atomic parameters are embedded.' - - return descrpt_se_a_args() + [ - Argument("type_nchanl", int, optional = True, default = 4, doc = doc_type_nchanl), - Argument("type_nlayer", int, optional = True, default = 2, doc = doc_type_nlayer), - Argument("numb_aparam", int, optional = True, default = 0, doc = doc_numb_aparam) - ] - - -@descrpt_args_plugin.register("se_e2_r", alias=['se_r']) -def descrpt_se_r_args(): - doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net are trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - - return [ - Argument("sel", [list,str], optional = True, default = "auto", doc = doc_sel), - Argument("rcut", float, optional = True, default = 6.0, doc = doc_rcut), - Argument("rcut_smth", float, optional = True, default = 0.5, doc = doc_rcut_smth), - Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt), - Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side), - Argument("precision", str, optional = True, default = "default", doc = doc_precision), - Argument("trainable", bool, optional = True, default = True, doc = doc_trainable), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types), - Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero) - ] - - -@descrpt_args_plugin.register("hybrid") -def descrpt_hybrid_args(): - doc_list = f'A list of descriptor definitions' - - return [ - Argument("list", list, optional = False, doc = doc_list) - ] - - -@descrpt_args_plugin.register("se_atten") -def descrpt_se_atten_args(): - doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ - - `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".' - doc_rcut = 'The cut-off radius.' - doc_rcut_smth = 'Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`' - doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.' - doc_axis_neuron = 'Size of the submatrix of G (embedding matrix).' - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_type_one_side = 'Whether to consider the information from only one side or both sides.' - doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_trainable = 'If the parameters in the embedding net is trainable' - doc_seed = 'Random seed for parameter initialization' - doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.' - doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used' - doc_attn = 'The length of hidden vectors in attention layers' - doc_attn_layer = 'The number of attention layers' - doc_attn_dotr = 'Whether to do dot product with the normalized relative coordinates' - doc_attn_mask = 'Whether to do mask on the diagonal in the attention matrix' - - return [ - Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), - Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), - Argument("rcut_smth", float, optional=True, default=0.5, doc=doc_rcut_smth), - Argument("neuron", list, optional=True, default=[10, 20, 40], doc=doc_neuron), - Argument("axis_neuron", int, optional=True, default=4, alias=['n_axis_neuron'], doc=doc_axis_neuron), - Argument("activation_function", str, optional=True, default='tanh', doc=doc_activation_function), - Argument("resnet_dt", bool, optional=True, default=False, doc=doc_resnet_dt), - Argument("type_one_side", bool, optional=True, default=False, doc=doc_type_one_side), - Argument("precision", str, optional=True, default="default", doc=doc_precision), - Argument("trainable", bool, optional=True, default=True, doc=doc_trainable), - Argument("seed", [int, None], optional=True, doc=doc_seed), - Argument("exclude_types", list, optional=True, default=[], doc=doc_exclude_types), - Argument("set_davg_zero", bool, optional=True, default=False, doc=doc_set_davg_zero), - Argument("attn", int, optional=True, default=128, doc=doc_attn), - Argument("attn_layer", int, optional=True, default=2, doc=doc_attn_layer), - Argument("attn_dotr", bool, optional=True, default=True, doc=doc_attn_dotr), - Argument("attn_mask", bool, optional=True, default=False, doc=doc_attn_mask) - ] - -def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant: - link_lf = make_link('loc_frame', 'model/descriptor[loc_frame]') - link_se_e2_a = make_link('se_e2_a', 'model/descriptor[se_e2_a]') - link_se_e2_r = make_link('se_e2_r', 'model/descriptor[se_e2_r]') - link_se_e3 = make_link('se_e3', 'model/descriptor[se_e3]') - link_se_a_tpe = make_link('se_a_tpe', 'model/descriptor[se_a_tpe]') - link_hybrid = make_link('hybrid', 'model/descriptor[hybrid]') - link_se_atten = make_link('se_atten', 'model/descriptor[se_atten]') - doc_descrpt_type = f'The type of the descritpor. See explanation below. \n\n\ -- `loc_frame`: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.\n\n\ -- `se_e2_a`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.\n\n\ -- `se_e2_r`: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.\n\n\ -- `se_e3`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.\n\n\ -- `se_a_tpe`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.\n\n\ -- `se_atten`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.\n\n\ -- `hybrid`: Concatenate of a list of descriptors as a new descriptor.' - - return Variant("type", descrpt_args_plugin.get_all_argument(), doc = doc_descrpt_type) - - -# --- Fitting net configurations: --- # -def fitting_ener(): - doc_numb_fparam = 'The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams.' - doc_numb_aparam = 'The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams.' - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_trainable = 'Whether the parameters in the fitting net are trainable. This option can be\n\n\ -- bool: True if all parameters of the fitting net are trainable, False otherwise.\n\n\ -- list of bool: Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of tihs list should be equal to len(`neuron`)+1.' - doc_rcond = 'The condition number used to determine the inital energy shift for each type of atoms.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - doc_atom_ener = 'Specify the atomic energy in vacuum for each type' - - return [ - Argument("numb_fparam", int, optional = True, default = 0, doc = doc_numb_fparam), - Argument("numb_aparam", int, optional = True, default = 0, doc = doc_numb_aparam), - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("trainable", [list,bool], optional = True, default = True, doc = doc_trainable), - Argument("rcond", float, optional = True, default = 1e-3, doc = doc_rcond), - Argument("seed", [int,None], optional = True, doc = doc_seed), - Argument("atom_ener", list, optional = True, default = [], doc = doc_atom_ener) - ] - - -def fitting_polar(): - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_scale = 'The output of the fitting net (polarizability matrix) will be scaled by ``scale``' - #doc_diag_shift = 'The diagonal part of the polarizability matrix will be shifted by ``diag_shift``. The shift operation is carried out after ``scale``.' - doc_fit_diag = 'Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.' - doc_sel_type = 'The atom types for which the atomic polarizability will be provided. If not set, all types will be selected.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - - # YWolfeee: user can decide whether to use shift diag - doc_shift_diag = 'Whether to shift the diagonal of polar, which is beneficial to training. Default is true.' - - return [ - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("fit_diag", bool, optional = True, default = True, doc = doc_fit_diag), - Argument("scale", [list,float], optional = True, default = 1.0, doc = doc_scale), - #Argument("diag_shift", [list,float], optional = True, default = 0.0, doc = doc_diag_shift), - Argument("shift_diag", bool, optional = True, default = True, doc = doc_shift_diag), - Argument("sel_type", [list,int,None], optional = True, alias = ['pol_type'], doc = doc_sel_type), - Argument("seed", [int,None], optional = True, doc = doc_seed) - ] - - -#def fitting_global_polar(): -# return fitting_polar() - - -def fitting_dipole(): - doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.' - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' - doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' - doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.' - doc_sel_type = 'The atom types for which the atomic dipole will be provided. If not set, all types will be selected.' - doc_seed = 'Random seed for parameter initialization of the fitting net' - return [ - Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron), - Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function), - Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt), - Argument("precision", str, optional = True, default = 'default', doc = doc_precision), - Argument("sel_type", [list,int,None], optional = True, alias = ['dipole_type'], doc = doc_sel_type), - Argument("seed", [int,None], optional = True, doc = doc_seed) - ] - -# YWolfeee: Delete global polar mode, merge it into polar mode and use loss setting to support. -def fitting_variant_type_args(): - doc_descrpt_type = 'The type of the fitting. See explanation below. \n\n\ -- `ener`: Fit an energy model (potential energy surface).\n\n\ -- `dipole`: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter.\n\n\ -- `polar`: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file eith has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter.\n\n' - - return Variant("type", [Argument("ener", dict, fitting_ener()), - Argument("dipole", dict, fitting_dipole()), - Argument("polar", dict, fitting_polar()), - ], - optional = True, - default_tag = 'ener', - doc = doc_descrpt_type) - - -# --- Modifier configurations: --- # -def modifier_dipole_charge(): - doc_model_name = "The name of the frozen dipole model file." - doc_model_charge_map = f"The charge of the WFCC. The list length should be the same as the {make_link('sel_type', 'model/fitting_net[dipole]/sel_type')}. " - doc_sys_charge_map = f"The charge of real atoms. The list length should be the same as the {make_link('type_map', 'model/type_map')}" - doc_ewald_h = f"The grid spacing of the FFT grid. Unit is A" - doc_ewald_beta = f"The splitting parameter of Ewald sum. Unit is A^{-1}" - - return [ - Argument("model_name", str, optional = False, doc = doc_model_name), - Argument("model_charge_map", list, optional = False, doc = doc_model_charge_map), - Argument("sys_charge_map", list, optional = False, doc = doc_sys_charge_map), - Argument("ewald_beta", float, optional = True, default = 0.4, doc = doc_ewald_beta), - Argument("ewald_h", float, optional = True, default = 1.0, doc = doc_ewald_h), - ] - - -def modifier_variant_type_args(): - doc_modifier_type = "The type of modifier. See explanation below.\n\n\ --`dipole_charge`: Use WFCC to model the electronic structure of the system. Correct the long-range interaction" - return Variant("type", - [ - Argument("dipole_charge", dict, modifier_dipole_charge()), - ], - optional = False, - doc = doc_modifier_type) - -# --- model compression configurations: --- # -def model_compression(): - doc_model_file = f"The input model file, which will be compressed by the DeePMD-kit." - doc_table_config = f"The arguments of model compression, including extrapolate(scale of model extrapolation), stride(uniform stride of tabulation's first and second table), and frequency(frequency of tabulation overflow check)." - doc_min_nbor_dist = f"The nearest distance between neighbor atoms saved in the frozen model." - - return [ - Argument("model_file", str, optional = False, doc = doc_model_file), - Argument("table_config", list, optional = False, doc = doc_table_config), - Argument("min_nbor_dist", float, optional = False, doc = doc_min_nbor_dist), - ] - -# --- model compression configurations: --- # -def model_compression_type_args(): - doc_compress_type = "The type of model compression, which should be consistent with the descriptor type." - - return Variant("type", [ - Argument("se_e2_a", dict, model_compression(), alias = ['se_a']) - ], - optional = True, - default_tag = 'se_e2_a', - doc = doc_compress_type) - - -def model_args (): - doc_type_map = 'A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment.' - doc_data_stat_nbatch = 'The model determines the normalization from the statistics of the data. This key specifies the number of `frames` in each `system` used for statistics.' - doc_data_stat_protect = 'Protect parameter for atomic energy regression.' - doc_type_embedding = "The type embedding." - doc_descrpt = 'The descriptor of atomic environment.' - doc_fitting = 'The fitting of physical properties.' - doc_modifier = 'The modifier of model output.' - doc_use_srtab = 'The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.' - doc_smin_alpha = 'The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when `use_srtab` is provided.' - doc_sw_rmin = 'The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.' - doc_sw_rmax = 'The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.' - doc_compress_config = 'Model compression configurations' - - ca = Argument("model", dict, - [Argument("type_map", list, optional = True, doc = doc_type_map), - Argument("data_stat_nbatch", int, optional = True, default = 10, doc = doc_data_stat_nbatch), - Argument("data_stat_protect", float, optional = True, default = 1e-2, doc = doc_data_stat_protect), - Argument("use_srtab", str, optional = True, doc = doc_use_srtab), - Argument("smin_alpha", float, optional = True, doc = doc_smin_alpha), - Argument("sw_rmin", float, optional = True, doc = doc_sw_rmin), - Argument("sw_rmax", float, optional = True, doc = doc_sw_rmax), - Argument("type_embedding", dict, type_embedding_args(), [], optional = True, doc = doc_type_embedding), - Argument("descriptor", dict, [], [descrpt_variant_type_args()], doc = doc_descrpt), - Argument("fitting_net", dict, [], [fitting_variant_type_args()], doc = doc_fitting), - Argument("modifier", dict, [], [modifier_variant_type_args()], optional = True, doc = doc_modifier), - Argument("compress", dict, [], [model_compression_type_args()], optional = True, doc = doc_compress_config) - ]) - # print(ca.gen_doc()) - return ca - - -# --- Learning rate configurations: --- # -def learning_rate_exp(): - doc_start_lr = 'The learning rate the start of the training.' - doc_stop_lr = 'The desired learning rate at the end of the training.' - doc_decay_steps = 'The learning rate is decaying every this number of training steps.' - - args = [ - Argument("start_lr", float, optional = True, default = 1e-3, doc = doc_start_lr), - Argument("stop_lr", float, optional = True, default = 1e-8, doc = doc_stop_lr), - Argument("decay_steps", int, optional = True, default = 5000, doc = doc_decay_steps) - ] - return args - - -def learning_rate_variant_type_args(): - doc_lr = 'The type of the learning rate.' - - return Variant("type", - [Argument("exp", dict, learning_rate_exp())], - optional = True, - default_tag = 'exp', - doc = doc_lr) - - -def learning_rate_args(): - doc_scale_by_worker = 'When parallel training or batch size scaled, how to alter learning rate. Valid values are `linear`(default), `sqrt` or `none`.' - doc_lr = "The definitio of learning rate" - return Argument("learning_rate", dict, - [Argument("scale_by_worker", str, optional=True, default='linear', doc=doc_scale_by_worker)], - [learning_rate_variant_type_args()], - doc = doc_lr) - - -# --- Loss configurations: --- # -def start_pref(item): - return f'The prefactor of {item} loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the {item} label should be provided by file {item}.npy in each data system. If both start_pref_{item} and limit_pref_{item} are set to 0, then the {item} will be ignored.' - - -def limit_pref(item): - return f'The prefactor of {item} loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.' - - -def loss_ener(): - doc_start_pref_e = start_pref('energy') - doc_limit_pref_e = limit_pref('energy') - doc_start_pref_f = start_pref('force') - doc_limit_pref_f = limit_pref('force') - doc_start_pref_v = start_pref('virial') - doc_limit_pref_v = limit_pref('virial') - doc_start_pref_ae = start_pref('atom_ener') - doc_limit_pref_ae = limit_pref('atom_ener') - doc_start_pref_pf = start_pref('atom_pref') - doc_limit_pref_pf = limit_pref('atom_pref') - doc_relative_f = 'If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by `relative_f`, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.' - doc_enable_atom_ener_coeff = "If true, the energy will be computed as \sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it's 1." - return [ - Argument("start_pref_e", [float,int], optional = True, default = 0.02, doc = doc_start_pref_e), - Argument("limit_pref_e", [float,int], optional = True, default = 1.00, doc = doc_limit_pref_e), - Argument("start_pref_f", [float,int], optional = True, default = 1000, doc = doc_start_pref_f), - Argument("limit_pref_f", [float,int], optional = True, default = 1.00, doc = doc_limit_pref_f), - Argument("start_pref_v", [float,int], optional = True, default = 0.00, doc = doc_start_pref_v), - Argument("limit_pref_v", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_v), - Argument("start_pref_ae", [float,int], optional = True, default = 0.00, doc = doc_start_pref_ae), - Argument("limit_pref_ae", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_ae), - Argument("start_pref_pf", [float,int], optional = True, default = 0.00, doc = doc_start_pref_pf), - Argument("limit_pref_pf", [float,int], optional = True, default = 0.00, doc = doc_limit_pref_pf), - Argument("relative_f", [float,None], optional = True, doc = doc_relative_f), - Argument("enable_atom_ener_coeff", [bool], optional=True, default=False, doc=doc_enable_atom_ener_coeff), - ] - -# YWolfeee: Modified to support tensor type of loss args. -def loss_tensor(): - #doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If only `pref` is provided or both are not provided, training will be global mode, i.e. the shape of 'polarizability.npy` or `dipole.npy` should be #frams x [9 or 3]." - #doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If only `pref_atomic` is provided, training will be atomic mode, i.e. the shape of `polarizability.npy` or `dipole.npy` should be #frames x ([9 or 3] x #selected atoms). If both `pref` and `pref_atomic` are provided, training will be combined mode, and atomic label should be provided as well." - doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included." - doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #selected atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0." - return [ - Argument("pref", [float,int], optional = False, default = None, doc = doc_global_weight), - Argument("pref_atomic", [float,int], optional = False, default = None, doc = doc_local_weight), - ] - - -def loss_variant_type_args(): - doc_loss = 'The type of the loss. When the fitting type is `ener`, the loss type should be set to `ener` or left unset. When the fitting type is `dipole` or `polar`, the loss type should be set to `tensor`. \n\.' - - - return Variant("type", - [Argument("ener", dict, loss_ener()), - Argument("tensor", dict, loss_tensor()), - #Argument("polar", dict, loss_tensor()), - #Argument("global_polar", dict, loss_tensor("global")) - ], - optional = True, - default_tag = 'ener', - doc = doc_loss) - - -def loss_args(): - doc_loss = 'The definition of loss function. The loss type should be set to `tensor`, `ener` or left unset.\n\.' - ca = Argument('loss', dict, [], - [loss_variant_type_args()], - optional = True, - doc = doc_loss) - return ca - - -# --- Training configurations: --- # -def training_data_args(): # ! added by Ziyao: new specification style for data systems. - link_sys = make_link("systems", "training/training_data/systems") - doc_systems = 'The data systems for training. ' \ - 'This key can be provided with a list that specifies the systems, or be provided with a string ' \ - 'by which the prefix of all systems are given and the list of the systems is automatically generated.' - doc_set_prefix = f'The prefix of the sets in the {link_sys}.' - doc_batch_size = f'This key can be \n\n\ -- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\ -- int: all {link_sys} use the same batch size.\n\n\ -- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\ -- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.' - doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\ -- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\ -- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\ -- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.' - doc_sys_probs = "A list of float if specified. " \ - "Should be of the same length as `systems`, " \ - "specifying the probability of each system." - - - args = [ - Argument("systems", [list, str], optional=False, default=".", doc=doc_systems), - Argument("set_prefix", str, optional=True, default='set', doc=doc_set_prefix), - Argument("batch_size", [list, int, str], optional=True, default='auto', doc=doc_batch_size), - Argument("auto_prob", str, optional=True, default="prob_sys_size", - doc=doc_auto_prob_style, alias=["auto_prob_style",]), - Argument("sys_probs", list, optional=True, default=None, doc=doc_sys_probs, alias=["sys_weights"]), - ] - - doc_training_data = "Configurations of training data." - return Argument("training_data", dict, optional=False, - sub_fields=args, sub_variants=[], doc=doc_training_data) - - -def validation_data_args(): # ! added by Ziyao: new specification style for data systems. - link_sys = make_link("systems", "training/validation_data/systems") - doc_systems = 'The data systems for validation. ' \ - 'This key can be provided with a list that specifies the systems, or be provided with a string ' \ - 'by which the prefix of all systems are given and the list of the systems is automatically generated.' - doc_set_prefix = f'The prefix of the sets in the {link_sys}.' - doc_batch_size = f'This key can be \n\n\ -- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\ -- int: all {link_sys} use the same batch size.\n\n\ -- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\ -- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.' - doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\ -- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\ -- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\ -- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.' - doc_sys_probs = "A list of float if specified. " \ - "Should be of the same length as `systems`, " \ - "specifying the probability of each system." - doc_numb_btch = "An integer that specifies the number of systems to be sampled for each validation period." - - args = [ - Argument("systems", [list, str], optional=False, default=".", doc=doc_systems), - Argument("set_prefix", str, optional=True, default='set', doc=doc_set_prefix), - Argument("batch_size", [list, int, str], optional=True, default='auto', doc=doc_batch_size), - Argument("auto_prob", str, optional=True, default="prob_sys_size", - doc=doc_auto_prob_style, alias=["auto_prob_style", ]), - Argument("sys_probs", list, optional=True, default=None, doc=doc_sys_probs, alias=["sys_weights"]), - Argument("numb_btch", int, optional=True, default=1, doc=doc_numb_btch, alias=["numb_batch", ]) - ] - - doc_validation_data = "Configurations of validation data. Similar to that of training data, " \ - "except that a `numb_btch` argument may be configured" - return Argument("validation_data", dict, optional=True, default=None, - sub_fields=args, sub_variants=[], doc=doc_validation_data) - - -def mixed_precision_args(): # ! added by Denghui. - doc_output_prec = 'The precision for mixed precision params. " \ - "The trainable variables precision during the mixed precision training process, " \ - "supported options are float32 only currently.' - doc_compute_prec = 'The precision for mixed precision compute. " \ - "The compute precision during the mixed precision training process, "" \ - "supported options are float16 only currently.' - - args = [ - Argument("output_prec", str, optional=True, default="float32", doc=doc_output_prec), - Argument("compute_prec", str, optional=False, default="float16", doc=doc_compute_prec), - ] - - doc_mixed_precision = "Configurations of mixed precision." - return Argument("mixed_precision", dict, optional=True, - sub_fields=args, sub_variants=[], doc=doc_mixed_precision) - - -def training_args(): # ! modified by Ziyao: data configuration isolated. - doc_numb_steps = 'Number of training batch. Each training uses one batch of data.' - doc_seed = 'The random seed for getting frames from the training data set.' - doc_disp_file = 'The file for printing learning curve.' - doc_disp_freq = 'The frequency of printing learning curve.' - doc_save_freq = 'The frequency of saving check point.' - doc_save_ckpt = 'The file name of saving check point.' - doc_disp_training = 'Displaying verbose information during training.' - doc_time_training = 'Timing durining training.' - doc_profiling = 'Profiling during training.' - doc_profiling_file = 'Output file for profiling.' - doc_enable_profiler = 'Enable TensorFlow Profiler (available in TensorFlow 2.3) to analyze performance. The log will be saved to `tensorboard_log_dir`.' - doc_tensorboard = 'Enable tensorboard' - doc_tensorboard_log_dir = 'The log directory of tensorboard outputs' - doc_tensorboard_freq = 'The frequency of writing tensorboard events.' - - arg_training_data = training_data_args() - arg_validation_data = validation_data_args() - mixed_precision_data = mixed_precision_args() - - args = [ - arg_training_data, - arg_validation_data, - mixed_precision_data, - Argument("numb_steps", int, optional=False, doc=doc_numb_steps, alias=["stop_batch"]), - Argument("seed", [int,None], optional=True, doc=doc_seed), - Argument("disp_file", str, optional=True, default='lcurve.out', doc=doc_disp_file), - Argument("disp_freq", int, optional=True, default=1000, doc=doc_disp_freq), - Argument("save_freq", int, optional=True, default=1000, doc=doc_save_freq), - Argument("save_ckpt", str, optional=True, default='model.ckpt', doc=doc_save_ckpt), - Argument("disp_training", bool, optional=True, default=True, doc=doc_disp_training), - Argument("time_training", bool, optional=True, default=True, doc=doc_time_training), - Argument("profiling", bool, optional=True, default=False, doc=doc_profiling), - Argument("profiling_file", str, optional=True, default='timeline.json', doc=doc_profiling_file), - Argument("enable_profiler", bool, optional=True, default=False, doc=doc_enable_profiler), - Argument("tensorboard", bool, optional=True, default=False, doc=doc_tensorboard), - Argument("tensorboard_log_dir", str, optional=True, default='log', doc=doc_tensorboard_log_dir), - Argument("tensorboard_freq", int, optional=True, default=1, doc=doc_tensorboard_freq), - ] - - doc_training = 'The training options.' - return Argument("training", dict, args, [], doc = doc_training) - - -def make_index(keys): - ret = [] - for ii in keys: - ret.append(make_link(ii, ii)) - return ', '.join(ret) - - -def gen_doc(*, make_anchor=True, make_link=True, **kwargs): - if make_link: - make_anchor = True - ma = model_args() - lra = learning_rate_args() - la = loss_args() - ta = training_args() - nvnmda = nvnmd_args() - ptr = [] - ptr.append(ma.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(la.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(lra.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(ta.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - ptr.append(nvnmda.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) - - key_words = [] - for ii in "\n\n".join(ptr).split('\n'): - if 'argument path' in ii: - key_words.append(ii.split(':')[1].replace('`','').strip()) - #ptr.insert(0, make_index(key_words)) - - return "\n\n".join(ptr) - -def gen_json(**kwargs): - return json.dumps(( - model_args(), - learning_rate_args(), - loss_args(), - training_args(), - nvnmd_args(), - ), cls=ArgumentEncoder) - -def normalize_hybrid_list(hy_list): - new_list = [] - base = Argument("base", dict, [], [descrpt_variant_type_args()], doc = "") - for ii in range(len(hy_list)): - data = base.normalize_value(hy_list[ii], trim_pattern="_*") - base.check_value(data, strict=True) - new_list.append(data) - return new_list - - -def normalize(data): - if "hybrid" == data["model"]["descriptor"]["type"]: - data["model"]["descriptor"]["list"] \ - = normalize_hybrid_list(data["model"]["descriptor"]["list"]) - - ma = model_args() - lra = learning_rate_args() - la = loss_args() - ta = training_args() - nvnmda = nvnmd_args() - - base = Argument("base", dict, [ma, lra, la, ta, nvnmda]) - data = base.normalize_value(data, trim_pattern="_*") - base.check_value(data, strict=True) - - return data - - -if __name__ == '__main__': - gen_doc() - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/batch_size.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/batch_size.py deleted file mode 100644 index aa724de..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/batch_size.py +++ /dev/null @@ -1,129 +0,0 @@ -import logging -from typing import Callable, Tuple - -import numpy as np - -from deepmd.utils.errors import OutOfMemoryError - -class AutoBatchSize: - """This class allows DeePMD-kit to automatically decide the maximum - batch size that will not cause an OOM error. - - Notes - ----- - We assume all OOM error will raise :class:`OutOfMemoryError`. - - Parameters - ---------- - initial_batch_size : int, default: 1024 - initial batch size (number of total atoms) - factor : float, default: 2. - increased factor - - Attributes - ---------- - current_batch_size : int - current batch size (number of total atoms) - maximum_working_batch_size : int - maximum working batch size - minimal_not_working_batch_size : int - minimal not working batch size - """ - def __init__(self, initial_batch_size: int = 1024, factor: float = 2.) -> None: - # See also PyTorchLightning/pytorch-lightning#1638 - # TODO: discuss a proper initial batch size - self.current_batch_size = initial_batch_size - self.maximum_working_batch_size = 0 - self.minimal_not_working_batch_size = 2**31 - self.factor = factor - - def execute(self, callable: Callable, start_index: int, natoms: int) -> Tuple[int, tuple]: - """Excuate a method with given batch size. - - Parameters - ---------- - callable : Callable - The method should accept the batch size and start_index as parameters, - and returns executed batch size and data. - start_index : int - start index - natoms : int - natoms - - Returns - ------- - int - executed batch size * number of atoms - tuple - result from callable, None if failing to execute - - Raises - ------ - OutOfMemoryError - OOM when batch size is 1 - """ - try: - n_batch, result = callable(max(self.current_batch_size // natoms, 1), start_index) - except OutOfMemoryError as e: - # TODO: it's very slow to catch OOM error; I don't know what TF is doing here - # but luckily we only need to catch once - self.minimal_not_working_batch_size = min(self.minimal_not_working_batch_size, self.current_batch_size) - if self.maximum_working_batch_size >= self.minimal_not_working_batch_size: - self.maximum_working_batch_size = int(self.minimal_not_working_batch_size / self.factor) - if self.minimal_not_working_batch_size <= natoms: - raise OutOfMemoryError("The callable still throws an out-of-memory (OOM) error even when batch size is 1!") from e - # adjust the next batch size - self._adjust_batch_size(1./self.factor) - return 0, None - else: - n_tot = n_batch * natoms - self.maximum_working_batch_size = max(self.maximum_working_batch_size, n_tot) - # adjust the next batch size - if n_tot + natoms > self.current_batch_size and self.current_batch_size * self.factor < self.minimal_not_working_batch_size: - self._adjust_batch_size(self.factor) - return n_batch, result - - def _adjust_batch_size(self, factor: float): - old_batch_size = self.current_batch_size - self.current_batch_size = int(self.current_batch_size * factor) - logging.info("Adjust batch size from %d to %d" % (old_batch_size, self.current_batch_size)) - - def execute_all(self, callable: Callable, total_size: int, natoms: int, *args, **kwargs) -> Tuple[np.ndarray]: - """Excuate a method with all given data. - - Parameters - ---------- - callable : Callable - The method should accept *args and **kwargs as input and return the similiar array. - total_size : int - Total size - natoms : int - The number of atoms - **kwargs - If 2D np.ndarray, assume the first axis is batch; otherwise do nothing. - """ - def execute_with_batch_size(batch_size: int, start_index: int) -> Tuple[int, Tuple[np.ndarray]]: - end_index = start_index + batch_size - end_index = min(end_index, total_size) - return (end_index - start_index), callable( - *[(vv[start_index:end_index] if isinstance(vv, np.ndarray) and vv.ndim > 1 else vv) for vv in args], - **{kk: (vv[start_index:end_index] if isinstance(vv, np.ndarray) and vv.ndim > 1 else vv) for kk, vv in kwargs.items()}, - ) - - index = 0 - results = [] - while index < total_size: - n_batch, result = self.execute(execute_with_batch_size, index, natoms) - if not isinstance(result, tuple): - result = (result,) - index += n_batch - if n_batch: - for rr in result: - rr.reshape((n_batch, -1)) - results.append(result) - - r = tuple([np.concatenate(r, axis=0) for r in zip(*results)]) - if len(r) == 1: - # avoid returning tuple if callable doesn't return tuple - r = r[0] - return r diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/compat.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/compat.py deleted file mode 100644 index 1d012a7..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/compat.py +++ /dev/null @@ -1,376 +0,0 @@ -"""Module providing compatibility between `0.x.x` and `1.x.x` input versions.""" - -import json -import warnings -from pathlib import Path -from typing import Any, Dict, Optional, Sequence, Union - -import numpy as np -from deepmd.common import j_must_have - - -def convert_input_v0_v1( - jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, Path]] = None -) -> Dict[str, Any]: - """Convert input from v0 format to v1. - - Parameters - ---------- - jdata : Dict[str, Any] - loaded json/yaml file - warning : bool, optional - whether to show deprecation warning, by default True - dump : Optional[Union[str, Path]], optional - whether to dump converted file, by default None - - Returns - ------- - Dict[str, Any] - converted output - """ - - output = {} - output["model"] = _model(jdata, jdata["use_smooth"]) - output["learning_rate"] = _learning_rate(jdata) - output["loss"] = _loss(jdata) - output["training"] = _training(jdata) - if warning: - _warning_input_v0_v1(dump) - if dump is not None: - with open(dump, "w") as fp: - json.dump(output, fp, indent=4) - return output - - -def _warning_input_v0_v1(fname: Optional[Union[str, Path]]): - msg = "It seems that you are using a deepmd-kit input of version 0.x.x, " \ - "which is deprecated. we have converted the input to >2.0.0 compatible" - if fname is not None: - msg += f", and output it to file {fname}" - warnings.warn(msg) - - -def _model(jdata: Dict[str, Any], smooth: bool) -> Dict[str, Dict[str, Any]]: - """Convert data to v1 input for non-smooth model. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - smooth : bool - whether to use smooth or non-smooth descriptor version - - Returns - ------- - Dict[str, Dict[str, Any]] - dictionary with model input parameters and sub-dictionaries for descriptor and - fitting net - """ - model = {} - model["descriptor"] = ( - _smth_descriptor(jdata) if smooth else _nonsmth_descriptor(jdata) - ) - model["fitting_net"] = _fitting_net(jdata) - return model - - -def _nonsmth_descriptor(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for non-smooth descriptor. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with descriptor parameters - """ - descriptor = {} - descriptor["type"] = "loc_frame" - _jcopy(jdata, descriptor, ("sel_a", "sel_r", "rcut", "axis_rule")) - return descriptor - - -def _smth_descriptor(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for smooth descriptor. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with descriptor parameters - """ - descriptor = {} - seed = jdata.get("seed", None) - if seed is not None: - descriptor["seed"] = seed - descriptor["type"] = "se_a" - descriptor["sel"] = jdata["sel_a"] - _jcopy(jdata, descriptor, ("rcut", )) - descriptor["rcut_smth"] = jdata.get("rcut_smth", descriptor["rcut"]) - descriptor["neuron"] = j_must_have(jdata, "filter_neuron") - descriptor["axis_neuron"] = j_must_have(jdata, "axis_neuron", ["n_axis_neuron"]) - descriptor["resnet_dt"] = False - if "resnet_dt" in jdata: - descriptor["resnet_dt"] = jdata["filter_resnet_dt"] - - return descriptor - - -def _fitting_net(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for fitting net. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with fitting net parameters - """ - fitting_net = {} - - seed = jdata.get("seed", None) - if seed is not None: - fitting_net["seed"] = seed - fitting_net["neuron"] = j_must_have(jdata, "fitting_neuron", ["n_neuron"]) - fitting_net["resnet_dt"] = True - if "resnet_dt" in jdata: - fitting_net["resnet_dt"] = jdata["resnet_dt"] - if "fitting_resnet_dt" in jdata: - fitting_net["resnet_dt"] = jdata["fitting_resnet_dt"] - return fitting_net - - -def _learning_rate(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for learning rate section. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with learning rate parameters - """ - learning_rate = {} - learning_rate["type"] = "exp" - _jcopy(jdata, learning_rate, ("decay_steps", "decay_rate", "start_lr")) - return learning_rate - - -def _loss(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for loss function. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with loss function parameters - """ - loss: Dict[str, Any] = {} - _jcopy( - jdata, - loss, - ( - "start_pref_e", - "limit_pref_e", - "start_pref_f", - "limit_pref_f", - "start_pref_v", - "limit_pref_v", - ), - ) - if "start_pref_ae" in jdata: - loss["start_pref_ae"] = jdata["start_pref_ae"] - if "limit_pref_ae" in jdata: - loss["limit_pref_ae"] = jdata["limit_pref_ae"] - return loss - - -def _training(jdata: Dict[str, Any]) -> Dict[str, Any]: - """Convert data to v1 input for training. - - Parameters - ---------- - jdata : Dict[str, Any] - parsed input json/yaml data - - Returns - ------- - Dict[str, Any] - dict with training parameters - """ - training = {} - seed = jdata.get("seed", None) - if seed is not None: - training["seed"] = seed - - _jcopy(jdata, training, ("systems", "set_prefix", "stop_batch", "batch_size")) - training["disp_file"] = "lcurve.out" - if "disp_file" in jdata: - training["disp_file"] = jdata["disp_file"] - training["disp_freq"] = j_must_have(jdata, "disp_freq") - training["numb_test"] = j_must_have(jdata, "numb_test") - training["save_freq"] = j_must_have(jdata, "save_freq") - training["save_ckpt"] = j_must_have(jdata, "save_ckpt") - training["disp_training"] = j_must_have(jdata, "disp_training") - training["time_training"] = j_must_have(jdata, "time_training") - if "profiling" in jdata: - training["profiling"] = jdata["profiling"] - if training["profiling"]: - training["profiling_file"] = j_must_have(jdata, "profiling_file") - return training - - -def _jcopy(src: Dict[str, Any], dst: Dict[str, Any], keys: Sequence[str]): - """Copy specified keys from one dict to another. - - Parameters - ---------- - src : Dict[str, Any] - source dictionary - dst : Dict[str, Any] - destination dictionary, will be modified in place - keys : Sequence[str] - list of keys to copy - must_have : bool - ensure that the source dictionary contains the copyyied keys - """ - for k in keys: - dst[k] = src[k] - - -def remove_decay_rate(jdata: Dict[str, Any]): - """convert decay_rate to stop_lr. - - Parameters - ---------- - jdata: Dict[str, Any] - input data - """ - lr = jdata["learning_rate"] - if "decay_rate" in lr: - decay_rate = lr["decay_rate"] - start_lr = lr["start_lr"] - stop_step = jdata["training"]["stop_batch"] - decay_steps = lr["decay_steps"] - stop_lr = np.exp(np.log(decay_rate) * (stop_step / decay_steps)) * start_lr - lr["stop_lr"] = stop_lr - lr.pop("decay_rate") - - -def convert_input_v1_v2(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - - tr_cfg = jdata["training"] - tr_data_keys = { - "systems", - "set_prefix", - "batch_size", - "sys_prob", - "auto_prob", - # alias included - "sys_weights", - "auto_prob_style" - } - - tr_data_cfg = {k: v for k, v in tr_cfg.items() if k in tr_data_keys} - new_tr_cfg = {k: v for k, v in tr_cfg.items() if k not in tr_data_keys} - new_tr_cfg["training_data"] = tr_data_cfg - - jdata["training"] = new_tr_cfg - - # remove deprecated arguments - remove_decay_rate(jdata) - - if warning: - _warning_input_v1_v2(dump) - if dump is not None: - with open(dump, "w") as fp: - json.dump(jdata, fp, indent=4) - - return jdata - - -def _warning_input_v1_v2(fname: Optional[Union[str, Path]]): - msg = "It seems that you are using a deepmd-kit input of version 1.x.x, " \ - "which is deprecated. we have converted the input to >2.0.0 compatible" - if fname is not None: - msg += f", and output it to file {fname}" - warnings.warn(msg) - - -def deprecate_numb_test(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - """Deprecate `numb_test` since v2.1. It has taken no effect since v2.0. - - See `#1243 `_. - - Parameters - ---------- - jdata : Dict[str, Any] - loaded json/yaml file - warning : bool, optional - whether to show deprecation warning, by default True - dump : Optional[Union[str, Path]], optional - whether to dump converted file, by default None - - Returns - ------- - Dict[str, Any] - converted output - """ - try: - jdata.get("training", {}).pop("numb_test") - except KeyError: - pass - else: - if warning: - warnings.warn( - "The argument training->numb_test has been deprecated since v2.0.0. " - "Use training->validation_data->batch_size instead." - ) - - if dump is not None: - with open(dump, "w") as fp: - json.dump(jdata, fp, indent=4) - return jdata - - -def update_deepmd_input(jdata: Dict[str, Any], - warning: bool = True, - dump: Optional[Union[str, Path]] = None) -> Dict[str, Any]: - def is_deepmd_v0_input(jdata): - return "model" not in jdata.keys() - - def is_deepmd_v1_input(jdata): - return "systems" in j_must_have(jdata, "training").keys() - - if is_deepmd_v0_input(jdata): - jdata = convert_input_v0_v1(jdata, warning, None) - jdata = convert_input_v1_v2(jdata, False, None) - jdata = deprecate_numb_test(jdata, False, dump) - elif is_deepmd_v1_input(jdata): - jdata = convert_input_v1_v2(jdata, warning, None) - jdata = deprecate_numb_test(jdata, False, dump) - else: - jdata = deprecate_numb_test(jdata, warning, dump) - - return jdata diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/convert.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/convert.py deleted file mode 100644 index 6688708..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/convert.py +++ /dev/null @@ -1,389 +0,0 @@ -import os -import textwrap -from deepmd.env import tf -from google.protobuf import text_format - - -def convert_13_to_21(input_model: str, output_model: str): - """Convert DP 1.3 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_13_to_21(input_model: str, output_model: str): - """Convert DP 1.3 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_12_to_21(input_model: str, output_model: str): - """Convert DP 1.2 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_10_to_21(input_model: str, output_model: str): - """Convert DP 1.0 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp10_to_dp11('frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_012_to_21(input_model: str, output_model: str): - """Convert DP 0.12 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp012_to_dp10('frozen_model.pbtxt') - convert_dp10_to_dp11('frozen_model.pbtxt') - convert_dp12_to_dp13('frozen_model.pbtxt') - convert_dp13_to_dp20('frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - - -def convert_20_to_21(input_model: str, output_model: str): - """Convert DP 2.0 graph to 2.1 graph. - - Parameters - ---------- - input_model : str - filename of the input graph - output_model : str - filename of the output graph - """ - convert_pb_to_pbtxt(input_model, 'frozen_model.pbtxt') - convert_dp20_to_dp21('frozen_model.pbtxt') - convert_pbtxt_to_pb('frozen_model.pbtxt', output_model) - if os.path.isfile('frozen_model.pbtxt'): - os.remove('frozen_model.pbtxt') - print("the converted output model (2.1 support) is saved in %s" % output_model) - -def convert_pb_to_pbtxt(pbfile: str, pbtxtfile: str): - """Convert DP graph to graph text. - - Parameters - ---------- - pbfile : str - filename of the input graph - pbtxtfile : str - filename of the output graph text - """ - with tf.gfile.GFile(pbfile, 'rb') as f: - graph_def = tf.GraphDef() - graph_def.ParseFromString(f.read()) - tf.import_graph_def(graph_def, name='') - tf.train.write_graph(graph_def, './', pbtxtfile, as_text=True) - -def convert_pbtxt_to_pb(pbtxtfile: str, pbfile: str): - """Convert DP graph text to graph. - - Parameters - ---------- - pbtxtfile : str - filename of the input graph text - pbfile : str - filename of the output graph - """ - with tf.gfile.GFile(pbtxtfile, 'r') as f: - graph_def = tf.GraphDef() - file_content = f.read() - # Merges the human-readable string in `file_content` into `graph_def`. - text_format.Merge(file_content, graph_def) - tf.train.write_graph(graph_def, './', pbfile, as_text=False) - - -def convert_dp012_to_dp10(file: str): - """Convert DP 1.0 graph text to 1.1 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(file) as fp: - file_content = fp.read() - # note: atom_energy must be put before energy, - # otherwise atom_energy_test -> atom_o_energy - file_content = file_content\ - .replace('DescrptNorot', 'DescrptSeA') \ - .replace('ProdForceNorot', 'ProdForceSeA') \ - .replace('ProdVirialNorot', 'ProdVirialSeA') \ - .replace('t_rcut', 'descrpt_attr/rcut') \ - .replace('t_ntypes', 'descrpt_attr/ntypes') \ - .replace('atom_energy_test', 'o_atom_energy') \ - .replace('atom_virial_test', 'o_atom_virial') \ - .replace('energy_test', 'o_energy') \ - .replace('force_test', 'o_force') \ - .replace('virial_test', 'o_virial') - file_content += textwrap.dedent("""\ - node { - name: "fitting_attr/dfparam" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 0 - } - } - } - } - """) - file_content += textwrap.dedent("""\ - node { - name: "model_attr/model_type" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "ener" - } - } - } - } - """) - with open(file, 'w') as fp: - fp.write(file_content) - - -def convert_dp10_to_dp11(file: str): - """Convert DP 1.0 graph text to 1.1 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(file, 'a') as f: - f.write(textwrap.dedent("""\ - node { - name: "fitting_attr/daparam" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 0 - } - } } - } - """)) - - -def convert_dp12_to_dp13(file: str): - """Convert DP 1.2 graph text to 1.3 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - file_data = "" - with open(file, "r", encoding="utf-8") as f: - ii = 0 - lines = f.readlines() - while (ii < len(lines)): - line = lines[ii] - file_data += line - ii+=1 - if 'name' in line and ('DescrptSeA' in line or 'ProdForceSeA' in line or 'ProdVirialSeA' in line): - while not('attr' in lines[ii] and '{' in lines[ii]): - file_data += lines[ii] - ii+=1 - file_data += ' attr {\n' - file_data += ' key: \"T\"\n' - file_data += ' value {\n' - file_data += ' type: DT_DOUBLE\n' - file_data += ' }\n' - file_data += ' }\n' - with open(file, "w", encoding="utf-8") as f: - f.write(file_data) - - -def convert_dp13_to_dp20(fname: str): - """Convert DP 1.3 graph text to 2.0 graph text. - - Parameters - ---------- - file : str - filename of the graph text - """ - with open(fname) as fp: - file_content = fp.read() - file_content += textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.0" - } - } - } - } - """) - file_content = file_content\ - .replace('DescrptSeA', 'ProdEnvMatA')\ - .replace('DescrptSeR', 'ProdEnvMatR') - with open(fname, 'w') as fp: - fp.write(file_content) - -def convert_dp20_to_dp21(fname: str): - with open(fname) as fp: - file_content = fp.read() - old_model_version_node = textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.0" - } - } - } - } - """) - new_model_version_node = textwrap.dedent("""\ - node { - name: "model_attr/model_version" - op: "Const" - attr { - key: "dtype" - value { - type: DT_STRING - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.1" - } - } - } - } - """) - file_content = file_content\ - .replace(old_model_version_node, new_model_version_node)\ - .replace('TabulateFusion', 'TabulateFusionSeA')\ - .replace('TabulateFusionGrad', 'TabulateFusionSeAGrad')\ - .replace('TabulateFusionGradGrad', 'TabulateFusionSeAGradGrad') - with open(fname, 'w') as fp: - fp.write(file_content) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data.py deleted file mode 100644 index f5d2cd9..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data.py +++ /dev/null @@ -1,827 +0,0 @@ -#!/usr/bin/env python3 - -import time -import glob -import numpy as np -import os.path -from typing import Tuple, List -import logging - -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.env import GLOBAL_ENER_FLOAT_PRECISION -from deepmd.utils import random as dp_random -from deepmd.utils.path import DPPath - -log = logging.getLogger(__name__) - -class DeepmdData() : - """ - Class for a data system. - - It loads data from hard disk, and mantains the data as a `data_dict` - - Parameters - ---------- - sys_path - Path to the data system - set_prefix - Prefix for the directories of different sets - shuffle_test - If the test data are shuffled - type_map - Gives the name of different atom types - modifier - Data modifier that has the method `modify_data` - trn_all_set - Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test. - """ - def __init__ (self, - sys_path : str, - set_prefix : str = 'set', - shuffle_test : bool = True, - type_map : List[str] = None, - modifier = None, - trn_all_set : bool = False) : - """ - Constructor - """ - root = DPPath(sys_path) - self.dirs = root.glob(set_prefix + ".*") - self.dirs.sort() - self.mixed_type = self._check_mode(self.dirs[0]) # mixed_type format only has one set - # load atom type - self.atom_type = self._load_type(root) - self.natoms = len(self.atom_type) - if self.mixed_type: - # nframes x natoms - self.atom_type_mix = self._load_type_mix(self.dirs[0]) - # load atom type map - self.type_map = self._load_type_map(root) - if self.type_map is not None: - assert(len(self.type_map) >= max(self.atom_type)+1) - # check pbc - self.pbc = self._check_pbc(root) - # enforce type_map if necessary - if type_map is not None and self.type_map is not None: - if not self.mixed_type: - atom_type_ = [type_map.index(self.type_map[ii]) for ii in self.atom_type] - self.atom_type = np.array(atom_type_, dtype = np.int32) - else: - sorter = np.argsort(type_map) - type_idx_map = sorter[np.searchsorted(type_map, self.type_map, sorter=sorter)] - try: - atom_type_mix_ = np.array(type_idx_map)[self.atom_type_mix].astype(np.int32) - except RuntimeError as e: - raise RuntimeError("some types in 'real_atom_types.npy' of sys {} are not contained in {} types!" - .format(self.dirs[0], self.get_ntypes())) from e - self.atom_type_mix = atom_type_mix_ - self.type_map = type_map - if type_map is None and self.type_map is None and self.mixed_type: - raise RuntimeError('mixed_type format must have type_map!') - # make idx map - self.idx_map = self._make_idx_map(self.atom_type) - # train dirs - self.test_dir = self.dirs[-1] - if trn_all_set: - self.train_dirs = self.dirs - else: - if len(self.dirs) == 1 : - self.train_dirs = self.dirs - else : - self.train_dirs = self.dirs[:-1] - self.data_dict = {} - # add box and coord - self.add('box', 9, must = self.pbc) - self.add('coord', 3, atomic = True, must = True) - # set counters - self.set_count = 0 - self.iterator = 0 - self.shuffle_test = shuffle_test - # set modifier - self.modifier = modifier - - - def add(self, - key : str, - ndof : int, - atomic : bool = False, - must : bool = False, - high_prec : bool = False, - type_sel : List[int] = None, - repeat : int = 1, - default: float=0., - ) : - """ - Add a data item that to be loaded - - Parameters - ---------- - key - The key of the item. The corresponding data is stored in `sys_path/set.*/key.npy` - ndof - The number of dof - atomic - The item is an atomic property. - If False, the size of the data should be nframes x ndof - If True, the size of data should be nframes x natoms x ndof - must - The data file `sys_path/set.*/key.npy` must exist. - If must is False and the data file does not exist, the `data_dict[find_key]` is set to 0.0 - high_prec - Load the data and store in float64, otherwise in float32 - type_sel - Select certain type of atoms - repeat - The data will be repeated `repeat` times. - default : float, default=0. - default value of data - """ - self.data_dict[key] = {'ndof': ndof, - 'atomic': atomic, - 'must': must, - 'high_prec': high_prec, - 'type_sel': type_sel, - 'repeat': repeat, - 'reduce': None, - 'default': default, - } - return self - - - def reduce(self, - key_out : str, - key_in : str - ) : - """ - Generate a new item from the reduction of another atom - - Parameters - ---------- - key_out - The name of the reduced item - key_in - The name of the data item to be reduced - """ - assert (key_in in self.data_dict), 'cannot find input key' - assert (self.data_dict[key_in]['atomic']), 'reduced property should be atomic' - assert (not(key_out in self.data_dict)), 'output key should not have been added' - assert (self.data_dict[key_in]['repeat'] == 1), 'reduced proerties should not have been repeated' - - self.data_dict[key_out] = {'ndof': self.data_dict[key_in]['ndof'], - 'atomic': False, - 'must': True, - 'high_prec': True, - 'type_sel': None, - 'repeat': 1, - 'reduce': key_in, - } - return self - - def get_data_dict(self) -> dict: - """ - Get the `data_dict` - """ - return self.data_dict - - def check_batch_size (self, batch_size) : - """ - Check if the system can get a batch of data with `batch_size` frames. - """ - for ii in self.train_dirs : - if self.data_dict['coord']['high_prec'] : - tmpe = (ii / "coord.npy").load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - tmpe = (ii / "coord.npy").load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if tmpe.ndim == 1: - tmpe = tmpe.reshape([1,-1]) - if tmpe.shape[0] < batch_size : - return ii, tmpe.shape[0] - return None - - def check_test_size (self, test_size) : - """ - Check if the system can get a test dataset with `test_size` frames. - """ - if self.data_dict['coord']['high_prec'] : - tmpe = (self.test_dir / "coord.npy").load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - tmpe = (self.test_dir / "coord.npy").load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if tmpe.ndim == 1: - tmpe = tmpe.reshape([1,-1]) - if tmpe.shape[0] < test_size : - return self.test_dir, tmpe.shape[0] - else : - return None - - def get_batch(self, - batch_size : int - ) -> dict : - """ - Get a batch of data with `batch_size` frames. The frames are randomly picked from the data system. - - Parameters - ---------- - batch_size - size of the batch - """ - if hasattr(self, 'batch_set') : - set_size = self.batch_set["coord"].shape[0] - else : - set_size = 0 - if self.iterator + batch_size > set_size : - self._load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - self.set_count += 1 - set_size = self.batch_set["coord"].shape[0] - if self.modifier is not None: - self.modifier.modify_data(self.batch_set) - iterator_1 = self.iterator + batch_size - if iterator_1 >= set_size : - iterator_1 = set_size - idx = np.arange (self.iterator, iterator_1) - self.iterator += batch_size - ret = self._get_subdata(self.batch_set, idx) - return ret - - def get_test (self, - ntests : int = -1 - ) -> dict: - """ - Get the test data with `ntests` frames. - - Parameters - ---------- - ntests - Size of the test data set. If `ntests` is -1, all test data will be get. - """ - if not hasattr(self, 'test_set') : - self._load_test_set(self.test_dir, self.shuffle_test) - if ntests == -1: - idx = None - else : - ntests_ = ntests if ntests < self.test_set['type'].shape[0] else self.test_set['type'].shape[0] - # print('ntest', self.test_set['type'].shape[0], ntests, ntests_) - idx = np.arange(ntests_) - ret = self._get_subdata(self.test_set, idx = idx) - if self.modifier is not None: - self.modifier.modify_data(ret) - return ret - - def get_ntypes(self) -> int: - """ - Number of atom types in the system - """ - if self.type_map is not None: - return len(self.type_map) - else: - return max(self.get_atom_type()) + 1 - - def get_type_map(self) -> List[str]: - """ - Get the type map - """ - return self.type_map - - def get_atom_type(self) -> List[int]: - """ - Get atom types - """ - return self.atom_type - - def get_numb_set (self) -> int: - """ - Get number of training sets - """ - return len (self.train_dirs) - - def get_numb_batch (self, - batch_size : int, - set_idx : int - ) -> int: - """ - Get the number of batches in a set. - """ - data = self._load_set(self.train_dirs[set_idx]) - ret = data["coord"].shape[0] // batch_size - if ret == 0: - ret = 1 - return ret - - def get_sys_numb_batch (self, - batch_size : int - ) -> int: - """ - Get the number of batches in the data system. - """ - ret = 0 - for ii in range(len(self.train_dirs)) : - ret += self.get_numb_batch(batch_size, ii) - return ret - - def get_natoms (self) : - """ - Get number of atoms - """ - return len(self.atom_type) - - def get_natoms_vec (self, - ntypes : int) : - """ - Get number of atoms and number of atoms in different types - - Parameters - ---------- - ntypes - Number of types (may be larger than the actual number of types in the system). - - Returns - ------- - natoms - 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 - """ - natoms, natoms_vec = self._get_natoms_2 (ntypes) - tmp = [natoms, natoms] - tmp = np.append (tmp, natoms_vec) - return tmp.astype(np.int32) - - def avg(self, key) : - """ - Return the average value of an item. - """ - if key not in self.data_dict.keys() : - raise RuntimeError('key %s has not been added' % key) - info = self.data_dict[key] - ndof = info['ndof'] - eners = np.array([]) - for ii in self.train_dirs: - data = self._load_set(ii) - ei = data[key].reshape([-1, ndof]) - if eners.size == 0 : - eners = ei - else : - eners = np.concatenate((eners, ei), axis = 0) - if eners.size == 0 : - return 0 - else : - return np.average(eners, axis = 0) - - def _idx_map_sel(self, atom_type, type_sel) : - new_types = [] - for ii in atom_type : - if ii in type_sel: - new_types.append(ii) - new_types = np.array(new_types, dtype = int) - natoms = new_types.shape[0] - idx = np.arange(natoms) - idx_map = np.lexsort((idx, new_types)) - return idx_map - - def _get_natoms_2 (self, ntypes) : - sample_type = self.atom_type - natoms = len(sample_type) - natoms_vec = np.zeros (ntypes).astype(int) - for ii in range (ntypes) : - natoms_vec[ii] = np.count_nonzero(sample_type == ii) - return natoms, natoms_vec - - def _get_subdata(self, data, idx = None) : - new_data = {} - for ii in data: - dd = data[ii] - if 'find_' in ii: - new_data[ii] = dd - else: - if idx is not None: - new_data[ii] = dd[idx] - else : - new_data[ii] = dd - return new_data - - def _load_batch_set (self, - set_name: DPPath) : - self.batch_set = self._load_set(set_name) - self.batch_set, _ = self._shuffle_data(self.batch_set) - self.reset_get_batch() - - def reset_get_batch(self): - self.iterator = 0 - - def _load_test_set (self, - set_name: DPPath, - shuffle_test) : - self.test_set = self._load_set(set_name) - if shuffle_test : - self.test_set, _ = self._shuffle_data(self.test_set) - - def _shuffle_data (self, - data) : - ret = {} - nframes = data['coord'].shape[0] - idx = np.arange (nframes) - dp_random.shuffle(idx) - for kk in data : - if type(data[kk]) == np.ndarray and \ - len(data[kk].shape) == 2 and \ - data[kk].shape[0] == nframes and \ - not('find_' in kk): - ret[kk] = data[kk][idx] - else : - ret[kk] = data[kk] - return ret, idx - - def _load_set(self, set_name: DPPath) : - # get nframes - if not isinstance(set_name, DPPath): - set_name = DPPath(set_name) - path = set_name / "coord.npy" - if self.data_dict['coord']['high_prec'] : - coord = path.load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - coord = path.load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - if coord.ndim == 1: - coord = coord.reshape([1,-1]) - nframes = coord.shape[0] - assert(coord.shape[1] == self.data_dict['coord']['ndof'] * self.natoms) - # load keys - data = {} - for kk in self.data_dict.keys(): - if self.data_dict[kk]['reduce'] is None : - data['find_'+kk], data[kk] \ - = self._load_data(set_name, - kk, - nframes, - self.data_dict[kk]['ndof'], - atomic = self.data_dict[kk]['atomic'], - high_prec = self.data_dict[kk]['high_prec'], - must = self.data_dict[kk]['must'], - type_sel = self.data_dict[kk]['type_sel'], - repeat = self.data_dict[kk]['repeat'], - default=self.data_dict[kk]['default'], - ) - for kk in self.data_dict.keys(): - if self.data_dict[kk]['reduce'] is not None : - k_in = self.data_dict[kk]['reduce'] - ndof = self.data_dict[kk]['ndof'] - data['find_'+kk] = data['find_'+k_in] - tmp_in = data[k_in].astype(GLOBAL_ENER_FLOAT_PRECISION) - data[kk] = np.sum(np.reshape(tmp_in, [nframes, self.natoms, ndof]), axis = 1) - - if self.mixed_type: - real_type = self.atom_type_mix.reshape([nframes, self.natoms]) - data['type'] = real_type - natoms = data['type'].shape[1] - # nframes x ntypes - atom_type_nums = np.array([(real_type == i).sum(axis=-1) for i in range(self.get_ntypes())], - dtype=np.int32).T - assert (atom_type_nums.sum(axis=-1) == natoms).all(), \ - "some types in 'real_atom_types.npy' of sys {} are not contained in {} types!" \ - .format(self.dirs[0], self.get_ntypes()) - data['real_natoms_vec'] = np.concatenate((np.tile(np.array([natoms, natoms], dtype=np.int32), (nframes, 1)), - atom_type_nums), axis=-1) - else: - data['type'] = np.tile(self.atom_type[self.idx_map], (nframes, 1)) - - return data - - - def _load_data(self, set_name, key, nframes, ndof_, atomic = False, must = True, repeat = 1, high_prec = False, type_sel = None, default: float=0.): - if atomic: - natoms = self.natoms - idx_map = self.idx_map - # if type_sel, then revise natoms and idx_map - if type_sel is not None: - natoms = 0 - for jj in type_sel : - natoms += np.sum(self.atom_type == jj) - idx_map = self._idx_map_sel(self.atom_type, type_sel) - ndof = ndof_ * natoms - else: - ndof = ndof_ - path = set_name / (key+".npy") - if path.is_file() : - if high_prec : - data = path.load_numpy().astype(GLOBAL_ENER_FLOAT_PRECISION) - else: - data = path.load_numpy().astype(GLOBAL_NP_FLOAT_PRECISION) - try: # YWolfeee: deal with data shape error - if atomic : - data = data.reshape([nframes, natoms, -1]) - data = data[:,idx_map,:] - data = data.reshape([nframes, -1]) - data = np.reshape(data, [nframes, ndof]) - except ValueError as err_message: - explanation = "This error may occur when your label mismatch it's name, i.e. you might store global tensor in `atomic_tensor.npy` or atomic tensor in `tensor.npy`." - log.error(str(err_message)) - log.error(explanation) - raise ValueError(str(err_message) + ". " + explanation) - if repeat != 1: - data = np.repeat(data, repeat).reshape([nframes, -1]) - return np.float32(1.0), data - elif must: - raise RuntimeError("%s not found!" % path) - else: - if high_prec : - data = np.full([nframes, ndof], default, dtype=GLOBAL_ENER_FLOAT_PRECISION) - else : - data = np.full([nframes, ndof], default, dtype=GLOBAL_NP_FLOAT_PRECISION) - if repeat != 1: - data = np.repeat(data, repeat).reshape([nframes, -1]) - return np.float32(0.0), data - - - def _load_type (self, sys_path: DPPath) : - atom_type = (sys_path / "type.raw").load_txt(dtype=np.int32, ndmin=1) - return atom_type - - def _load_type_mix(self, set_name: DPPath): - type_path = set_name / "real_atom_types.npy" - real_type = type_path.load_numpy().astype(np.int32).reshape([-1, self.natoms]) - return real_type - - def _make_idx_map(self, atom_type): - natoms = atom_type.shape[0] - idx = np.arange (natoms) - idx_map = np.lexsort ((idx, atom_type)) - return idx_map - - def _load_type_map(self, sys_path: DPPath) : - fname = sys_path / 'type_map.raw' - if fname.is_file() : - return fname.load_txt(dtype=str, ndmin=1).tolist() - else : - return None - - def _check_pbc(self, sys_path: DPPath): - pbc = True - if (sys_path / 'nopbc').is_file() : - pbc = False - return pbc - - def _check_mode(self, set_path: DPPath): - return (set_path / 'real_atom_types.npy').is_file() - - -class DataSets (object): - """ - Outdated class for one data system. - - .. deprecated:: 2.0.0 - This class is not maintained any more. - """ - def __init__ (self, - sys_path, - set_prefix, - seed = None, - shuffle_test = True) : - self.dirs = glob.glob (os.path.join(sys_path, set_prefix + ".*")) - self.dirs.sort() - # load atom type - self.atom_type, self.idx_map, self.idx3_map = self.load_type (sys_path) - # load atom type map - self.type_map = self.load_type_map(sys_path) - if self.type_map is not None: - assert(len(self.type_map) >= max(self.atom_type)+1) - # train dirs - self.test_dir = self.dirs[-1] - if len(self.dirs) == 1 : - self.train_dirs = self.dirs - else : - self.train_dirs = self.dirs[:-1] - # check fparam - has_fparam = [ os.path.isfile(os.path.join(ii, 'fparam.npy')) for ii in self.dirs ] - if any(has_fparam) and (not all(has_fparam)) : - raise RuntimeError("system %s: if any set has frame parameter, then all sets should have frame parameter" % sys_path) - if all(has_fparam) : - self.has_fparam = 0 - else : - self.has_fparam = -1 - # check aparam - has_aparam = [ os.path.isfile(os.path.join(ii, 'aparam.npy')) for ii in self.dirs ] - if any(has_aparam) and (not all(has_aparam)) : - raise RuntimeError("system %s: if any set has frame parameter, then all sets should have frame parameter" % sys_path) - if all(has_aparam) : - self.has_aparam = 0 - else : - self.has_aparam = -1 - # energy norm - self.eavg = self.stats_energy() - # load sets - self.set_count = 0 - self.load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - self.load_test_set (self.test_dir, shuffle_test) - - def check_batch_size (self, batch_size) : - for ii in self.train_dirs : - tmpe = np.load(os.path.join(ii, "coord.npy")) - if tmpe.shape[0] < batch_size : - return ii, tmpe.shape[0] - return None - - def check_test_size (self, test_size) : - tmpe = np.load(os.path.join(self.test_dir, "coord.npy")) - if tmpe.shape[0] < test_size : - return self.test_dir, tmpe.shape[0] - else : - return None - - def load_type (self, sys_path) : - atom_type = np.loadtxt (os.path.join(sys_path, "type.raw"), dtype=np.int32, ndmin=1) - natoms = atom_type.shape[0] - idx = np.arange (natoms) - idx_map = np.lexsort ((idx, atom_type)) - atom_type3 = np.repeat(atom_type, 3) - idx3 = np.arange (natoms * 3) - idx3_map = np.lexsort ((idx3, atom_type3)) - return atom_type, idx_map, idx3_map - - def load_type_map(self, sys_path) : - fname = os.path.join(sys_path, 'type_map.raw') - if os.path.isfile(fname) : - with open(os.path.join(sys_path, 'type_map.raw')) as fp: - return fp.read().split() - else : - return None - - def get_type_map(self) : - return self.type_map - - def get_numb_set (self) : - return len (self.train_dirs) - - def stats_energy (self) : - eners = np.array([]) - for ii in self.train_dirs: - ener_file = os.path.join(ii, "energy.npy") - if os.path.isfile(ener_file) : - ei = np.load(ener_file) - eners = np.append(eners, ei) - if eners.size == 0 : - return 0 - else : - return np.average(eners) - - def load_energy(self, - set_name, - nframes, - nvalues, - energy_file, - atom_energy_file) : - """ - return : coeff_ener, ener, coeff_atom_ener, atom_ener - """ - # load atom_energy - coeff_atom_ener, atom_ener = self.load_data(set_name, atom_energy_file, [nframes, nvalues], False) - # ignore energy_file - if coeff_atom_ener == 1: - ener = np.sum(atom_ener, axis = 1) - coeff_ener = 1 - # load energy_file - else: - coeff_ener, ener = self.load_data(set_name, energy_file, [nframes], False) - return coeff_ener, ener, coeff_atom_ener, atom_ener - - def load_data(self, set_name, data_name, shape, is_necessary = True): - path = os.path.join(set_name, data_name+".npy") - if os.path.isfile (path) : - data = np.load(path) - data = np.reshape(data, shape) - if is_necessary: - return data - return 1, data - elif is_necessary: - raise OSError("%s not found!" % path) - else: - data = np.zeros(shape) - return 0, data - - def load_set(self, set_name, shuffle = True): - data = {} - data["box"] = self.load_data(set_name, "box", [-1, 9]) - nframe = data["box"].shape[0] - data["coord"] = self.load_data(set_name, "coord", [nframe, -1]) - ncoord = data["coord"].shape[1] - if self.has_fparam >= 0: - data["fparam"] = self.load_data(set_name, "fparam", [nframe, -1]) - if self.has_fparam == 0 : - self.has_fparam = data["fparam"].shape[1] - else : - assert self.has_fparam == data["fparam"].shape[1] - if self.has_aparam >= 0: - data["aparam"] = self.load_data(set_name, "aparam", [nframe, -1]) - if self.has_aparam == 0 : - self.has_aparam = data["aparam"].shape[1] // (ncoord//3) - else : - assert self.has_aparam == data["aparam"].shape[1] // (ncoord//3) - data["prop_c"] = np.zeros(5) - data["prop_c"][0], data["energy"], data["prop_c"][3], data["atom_ener"] \ - = self.load_energy (set_name, nframe, ncoord // 3, "energy", "atom_ener") - data["prop_c"][1], data["force"] = self.load_data(set_name, "force", [nframe, ncoord], False) - data["prop_c"][2], data["virial"] = self.load_data(set_name, "virial", [nframe, 9], False) - data["prop_c"][4], data["atom_pref"] = self.load_data(set_name, "atom_pref", [nframe, ncoord//3], False) - data["atom_pref"] = np.repeat(data["atom_pref"], 3, axis=1) - # shuffle data - if shuffle: - idx = np.arange (nframe) - dp_random.shuffle(idx) - for ii in data: - if ii != "prop_c": - data[ii] = data[ii][idx] - data["type"] = np.tile (self.atom_type, (nframe, 1)) - # sort according to type - for ii in ["type", "atom_ener"]: - data[ii] = data[ii][:, self.idx_map] - for ii in ["coord", "force", "atom_pref"]: - data[ii] = data[ii][:, self.idx3_map] - return data - - def load_batch_set (self, - set_name) : - self.batch_set = self.load_set(set_name, True) - self.reset_iter () - - def load_test_set (self, - set_name, - shuffle_test) : - self.test_set = self.load_set(set_name, shuffle_test) - - def reset_iter (self) : - self.iterator = 0 - self.set_count += 1 - - def get_set(self, data, idx = None) : - new_data = {} - for ii in data: - dd = data[ii] - if ii == "prop_c": - new_data[ii] = dd.astype(np.float32) - else: - if idx is not None: - dd = dd[idx] - if ii == "type": - new_data[ii] = dd - else: - new_data[ii] = dd.astype(GLOBAL_NP_FLOAT_PRECISION) - return new_data - - def get_test (self) : - """ - returned property prefector [4] in order: - energy, force, virial, atom_ener - """ - return self.get_set(self.test_set) - - def get_batch (self, - batch_size) : - """ - returned property prefector [4] in order: - energy, force, virial, atom_ener - """ - set_size = self.batch_set["energy"].shape[0] - # assert (batch_size <= set_size), "batch size should be no more than set size" - if self.iterator + batch_size > set_size : - self.load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()]) - set_size = self.batch_set["energy"].shape[0] - # print ("%d %d %d" % (self.iterator, self.iterator + batch_size, set_size)) - iterator_1 = self.iterator + batch_size - if iterator_1 >= set_size : - iterator_1 = set_size - idx = np.arange (self.iterator, iterator_1) - self.iterator += batch_size - return self.get_set(self.batch_set, idx) - - def get_natoms (self) : - sample_type = self.batch_set["type"][0] - natoms = len(sample_type) - return natoms - - def get_natoms_2 (self, ntypes) : - sample_type = self.batch_set["type"][0] - natoms = len(sample_type) - natoms_vec = np.zeros (ntypes).astype(int) - for ii in range (ntypes) : - natoms_vec[ii] = np.count_nonzero(sample_type == ii) - return natoms, natoms_vec - - def get_natoms_vec (self, ntypes) : - natoms, natoms_vec = self.get_natoms_2 (ntypes) - tmp = [natoms, natoms] - tmp = np.append (tmp, natoms_vec) - return tmp.astype(np.int32) - - def set_numb_batch (self, - batch_size) : - return self.batch_set["energy"].shape[0] // batch_size - - def get_sys_numb_batch (self, batch_size) : - return self.set_numb_batch(batch_size) * self.get_numb_set() - - def get_ener (self) : - return self.eavg - - def numb_fparam(self) : - return self.has_fparam - - def numb_aparam(self) : - return self.has_aparam - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data_system.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data_system.py deleted file mode 100644 index 398db54..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/data_system.py +++ /dev/null @@ -1,745 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import os -import collections -import warnings -import numpy as np -from typing import Tuple, List - -from deepmd.utils import random as dp_random -from deepmd.utils.data import DataSets -from deepmd.utils.data import DeepmdData - -log = logging.getLogger(__name__) - - -class DeepmdDataSystem() : - """ - Class for manipulating many data systems. - - It is implemented with the help of DeepmdData - """ - def __init__ (self, - systems : List[str], - batch_size : int, - test_size : int, - rcut : float, - set_prefix : str = 'set', - shuffle_test : bool = True, - type_map : List[str] = None, - modifier = None, - trn_all_set = False, - sys_probs = None, - auto_prob_style ="prob_sys_size") : - """ - Constructor - - Parameters - ---------- - systems - Specifying the paths to systems - batch_size - The batch size - test_size - The size of test data - rcut - The cut-off radius - set_prefix - Prefix for the directories of different sets - shuffle_test - If the test data are shuffled - type_map - Gives the name of different atom types - modifier - Data modifier that has the method `modify_data` - trn_all_set - Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test. - sys_probs: list of float - The probabilitis of systems to get the batch. - Summation of positive elements of this list should be no greater than 1. - Element of this list can be negative, the probability of the corresponding system is determined - automatically by the number of batches in the system. - auto_prob_style: str - Determine the probability of systems automatically. The method is assigned by this key and can be - - "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems() - - "prob_sys_size" : the probability of a system is proportional to the number of batches in the system - - "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : - the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, - where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, - the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional - to the number of batches in the system.""" - # init data - self.rcut = rcut - self.system_dirs = systems - self.nsystems = len(self.system_dirs) - self.data_systems = [] - for ii in self.system_dirs : - self.data_systems.append( - DeepmdData( - ii, - set_prefix=set_prefix, - shuffle_test=shuffle_test, - type_map = type_map, - modifier = modifier, - trn_all_set = trn_all_set - )) - # check mix_type format - error_format_msg = "if one of the system is of mixed_type format, " \ - "then all of the systems should be of mixed_type format!" - if self.data_systems[0].mixed_type: - for data_sys in self.data_systems[1:]: - assert data_sys.mixed_type, error_format_msg - self.mixed_type = True - else: - for data_sys in self.data_systems[1:]: - assert not data_sys.mixed_type, error_format_msg - self.mixed_type = False - # batch size - self.batch_size = batch_size - if isinstance(self.batch_size, int): - self.batch_size = self.batch_size * np.ones(self.nsystems, dtype=int) - elif isinstance(self.batch_size, str): - words = self.batch_size.split(':') - if 'auto' == words[0] : - rule = 32 - if len(words) == 2 : - rule = int(words[1]) - else: - raise RuntimeError('unknown batch_size rule ' + words[0]) - self.batch_size = self._make_auto_bs(rule) - elif isinstance(self.batch_size, list): - pass - else : - raise RuntimeError('invalid batch_size') - assert(isinstance(self.batch_size, (list,np.ndarray))) - assert(len(self.batch_size) == self.nsystems) - - # natoms, nbatches - ntypes = [] - for ii in self.data_systems : - ntypes.append(ii.get_ntypes()) - self.sys_ntypes = max(ntypes) - self.natoms = [] - self.natoms_vec = [] - self.nbatches = [] - type_map_list = [] - for ii in range(self.nsystems) : - self.natoms.append(self.data_systems[ii].get_natoms()) - self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(self.sys_ntypes).astype(int)) - self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(self.batch_size[ii])) - type_map_list.append(self.data_systems[ii].get_type_map()) - self.type_map = self._check_type_map_consistency(type_map_list) - - # ! altered by Marián Rynik - # test size - # now test size can be set as a percentage of systems data or test size - # can be set for each system individualy in the same manner as batch - # size. This enables one to use systems with diverse number of - # structures and different number of atoms. - self.test_size = test_size - if isinstance(self.test_size, int): - self.test_size = self.test_size * np.ones(self.nsystems, dtype=int) - elif isinstance(self.test_size, str): - words = self.test_size.split('%') - try: - percent = int(words[0]) - except ValueError: - raise RuntimeError('unknown test_size rule ' + words[0]) - self.test_size = self._make_auto_ts(percent) - elif isinstance(self.test_size, list): - pass - else : - raise RuntimeError('invalid test_size') - assert(isinstance(self.test_size, (list,np.ndarray))) - assert(len(self.test_size) == self.nsystems) - - # prob of batch, init pick idx - self.prob_nbatches = [ float(i) for i in self.nbatches] / np.sum(self.nbatches) - self.pick_idx = 0 - - # derive system probabilities - self.sys_probs = None - self.set_sys_probs(sys_probs, auto_prob_style) - - # check batch and test size - for ii in range(self.nsystems) : - chk_ret = self.data_systems[ii].check_batch_size(self.batch_size[ii]) - if chk_ret is not None : - warnings.warn("system %s required batch size is larger than the size of the dataset %s (%d > %d)" % \ - (self.system_dirs[ii], chk_ret[0], self.batch_size[ii], chk_ret[1])) - chk_ret = self.data_systems[ii].check_test_size(self.test_size[ii]) - if chk_ret is not None : - warnings.warn("system %s required test size is larger than the size of the dataset %s (%d > %d)" % \ - (self.system_dirs[ii], chk_ret[0], self.test_size[ii], chk_ret[1])) - - - def _load_test(self, ntests = -1): - self.test_data = collections.defaultdict(list) - for ii in range(self.nsystems) : - test_system_data = self.data_systems[ii].get_test(ntests = ntests) - for nn in test_system_data: - self.test_data[nn].append(test_system_data[nn]) - - - def _make_default_mesh(self): - self.default_mesh = [] - cell_size = np.max (self.rcut) - for ii in range(self.nsystems) : - if self.data_systems[ii].pbc : - test_system_data = self.data_systems[ii].get_batch(self.batch_size[ii]) - self.data_systems[ii].reset_get_batch() - # test_system_data = self.data_systems[ii].get_test() - avg_box = np.average (test_system_data["box"], axis = 0) - avg_box = np.reshape (avg_box, [3,3]) - ncell = (np.linalg.norm(avg_box, axis=1)/ cell_size).astype(np.int32) - ncell[ncell < 2] = 2 - default_mesh = np.zeros (6, dtype = np.int32) - default_mesh[3:6] = ncell - self.default_mesh.append(default_mesh) - else: - self.default_mesh.append(np.array([], dtype = np.int32)) - - - def compute_energy_shift(self, rcond = 1e-3, key = 'energy') : - sys_ener = np.array([]) - for ss in self.data_systems : - sys_ener = np.append(sys_ener, ss.avg(key)) - sys_tynatom = np.array(self.natoms_vec, dtype = float) - sys_tynatom = np.reshape(sys_tynatom, [self.nsystems,-1]) - sys_tynatom = sys_tynatom[:,2:] - energy_shift,resd,rank,s_value \ - = np.linalg.lstsq(sys_tynatom, sys_ener, rcond = rcond) - return energy_shift - - def add_dict(self, adict: dict) -> None: - """ - Add items to the data system by a `dict`. - `adict` should have items like - .. code-block:: python - - adict[key] = { - 'ndof': ndof, - 'atomic': atomic, - 'must': must, - 'high_prec': high_prec, - 'type_sel': type_sel, - 'repeat': repeat, - } - - For the explaination of the keys see `add` - """ - for kk in adict : - self.add(kk, - adict[kk]['ndof'], - atomic=adict[kk]['atomic'], - must=adict[kk]['must'], - high_prec=adict[kk]['high_prec'], - type_sel=adict[kk]['type_sel'], - repeat=adict[kk]['repeat'], - default=adict[kk]['default'], - ) - - def add(self, - key : str, - ndof : int, - atomic : bool = False, - must : bool = False, - high_prec : bool = False, - type_sel : List[int] = None, - repeat : int = 1, - default: float=0., - ) : - """ - Add a data item that to be loaded - - Parameters - ---------- - key - The key of the item. The corresponding data is stored in `sys_path/set.*/key.npy` - ndof - The number of dof - atomic - The item is an atomic property. - If False, the size of the data should be nframes x ndof - If True, the size of data should be nframes x natoms x ndof - must - The data file `sys_path/set.*/key.npy` must exist. - If must is False and the data file does not exist, the `data_dict[find_key]` is set to 0.0 - high_prec - Load the data and store in float64, otherwise in float32 - type_sel - Select certain type of atoms - repeat - The data will be repeated `repeat` times. - default, default=0. - Default value of data - """ - for ii in self.data_systems: - ii.add(key, ndof, atomic=atomic, must=must, high_prec=high_prec, repeat=repeat, type_sel=type_sel, default=default) - - def reduce(self, key_out, key_in): - """ - Generate a new item from the reduction of another atom - - Parameters - ---------- - key_out - The name of the reduced item - key_in - The name of the data item to be reduced - """ - for ii in self.data_systems: - ii.reduce(key_out, key_in) - - def get_data_dict(self, ii: int = 0) -> dict: - return self.data_systems[ii].get_data_dict() - - def set_sys_probs(self, sys_probs=None, - auto_prob_style: str = "prob_sys_size"): - if sys_probs is None : - if auto_prob_style == "prob_uniform": - prob_v = 1./float(self.nsystems) - probs = [prob_v for ii in range(self.nsystems)] - elif auto_prob_style == "prob_sys_size": - probs = self.prob_nbatches - elif auto_prob_style[:14] == "prob_sys_size;": - probs = self._prob_sys_size_ext(auto_prob_style) - else: - raise RuntimeError("Unknown auto prob style: " + auto_prob_style) - else: - probs = self._process_sys_probs(sys_probs) - self.sys_probs = probs - - def _get_sys_probs(self, - sys_probs, - auto_prob_style) : # depreciated - if sys_probs is None : - if auto_prob_style == "prob_uniform" : - prob_v = 1./float(self.nsystems) - prob = [prob_v for ii in range(self.nsystems)] - elif auto_prob_style == "prob_sys_size" : - prob = self.prob_nbatches - elif auto_prob_style[:14] == "prob_sys_size;" : - prob = self._prob_sys_size_ext(auto_prob_style) - else : - raise RuntimeError("unkown style " + auto_prob_style ) - else : - prob = self._process_sys_probs(sys_probs) - return prob - - def get_batch(self, sys_idx : int = None): - # batch generation style altered by Ziyao Li: - # one should specify the "sys_prob" and "auto_prob_style" params - # via set_sys_prob() function. The sys_probs this function uses is - # defined as a private variable, self.sys_probs, initialized in __init__(). - # This is to optimize the (vain) efforts in evaluating sys_probs every batch. - """ - Get a batch of data from the data systems - - Parameters - ---------- - sys_idx: int - The index of system from which the batch is get. - If sys_idx is not None, `sys_probs` and `auto_prob_style` are ignored - If sys_idx is None, automatically determine the system according to `sys_probs` or `auto_prob_style`, see the following. - """ - if not hasattr(self, 'default_mesh') : - self._make_default_mesh() - if sys_idx is not None : - self.pick_idx = sys_idx - else : - # prob = self._get_sys_probs(sys_probs, auto_prob_style) - self.pick_idx = dp_random.choice(np.arange(self.nsystems), p=self.sys_probs) - b_data = self.data_systems[self.pick_idx].get_batch(self.batch_size[self.pick_idx]) - b_data["natoms_vec"] = self.natoms_vec[self.pick_idx] - b_data["default_mesh"] = self.default_mesh[self.pick_idx] - return b_data - - # ! altered by Marián Rynik - def get_test (self, - sys_idx : int = None, - n_test : int = -1) : # depreciated - """ - Get test data from the the data systems. - - Parameters - ---------- - sys_idx - The test dat of system with index `sys_idx` will be returned. - If is None, the currently selected system will be returned. - n_test - Number of test data. If set to -1 all test data will be get. - """ - if not hasattr(self, 'default_mesh') : - self._make_default_mesh() - if not hasattr(self, 'test_data') : - self._load_test(ntests = n_test) - if sys_idx is not None : - idx = sys_idx - else : - idx = self.pick_idx - - test_system_data = {} - for nn in self.test_data: - test_system_data[nn] = self.test_data[nn][idx] - test_system_data["natoms_vec"] = self.natoms_vec[idx] - test_system_data["default_mesh"] = self.default_mesh[idx] - return test_system_data - - def get_sys_ntest(self, sys_idx=None): - """ - Get number of tests for the currently selected system, - or one defined by sys_idx. - """ - if sys_idx is not None : - return self.test_size[sys_idx] - else : - return self.test_size[self.pick_idx] - - def get_type_map(self) -> List[str]: - """ - Get the type map - """ - return self.type_map - - def get_nbatches (self) -> int: - """ - Get the total number of batches - """ - return self.nbatches - - def get_ntypes (self) -> int: - """ - Get the number of types - """ - return self.sys_ntypes - - def get_nsystems (self) -> int: - """ - Get the number of data systems - """ - return self.nsystems - - def get_sys (self, idx : int) -> DeepmdData: - """ - Get a certain data system - """ - return self.data_systems[idx] - - def get_batch_size(self) -> int: - """ - Get the batch size - """ - return self.batch_size - - def _format_name_length(self, name, width) : - if len(name) <= width: - return '{: >{}}'.format(name, width) - else : - name = name[-(width-3):] - name = '-- ' + name - return name - - def print_summary(self, name) : - # width 65 - sys_width = 42 - log.info(f"---Summary of DataSystem: {name:13s}-----------------------------------------------") - log.info("found %d system(s):" % self.nsystems) - log.info(("%s " % self._format_name_length('system', sys_width)) + - ("%6s %6s %6s %5s %3s" % ('natoms', 'bch_sz', 'n_bch', 'prob', 'pbc'))) - for ii in range(self.nsystems) : - log.info("%s %6d %6d %6d %5.3f %3s" % - (self._format_name_length(self.system_dirs[ii], sys_width), - self.natoms[ii], - # TODO batch size * nbatches = number of structures - self.batch_size[ii], - self.nbatches[ii], - self.sys_probs[ii], - "T" if self.data_systems[ii].pbc else "F" - ) ) - log.info("--------------------------------------------------------------------------------------") - - def _make_auto_bs(self, rule) : - bs = [] - for ii in self.data_systems: - ni = ii.get_natoms() - bsi = rule // ni - if bsi * ni < rule: - bsi += 1 - bs.append(bsi) - return bs - - # ! added by Marián Rynik - def _make_auto_ts(self, percent): - ts = [] - for ii in range(self.nsystems): - ni = self.batch_size[ii] * self.nbatches[ii] - tsi = int(ni * percent / 100) - ts.append(tsi) - - return ts - - def _check_type_map_consistency(self, type_map_list): - ret = [] - for ii in type_map_list: - if ii is not None: - min_len = min([len(ii), len(ret)]) - for idx in range(min_len) : - if ii[idx] != ret[idx] : - raise RuntimeError('inconsistent type map: %s %s' % (str(ret), str(ii))) - if len(ii) > len(ret) : - ret = ii - return ret - - def _process_sys_probs(self, sys_probs) : - sys_probs = np.array(sys_probs) - type_filter = sys_probs >= 0 - assigned_sum_prob = np.sum(type_filter * sys_probs) - # 1e-8 is to handle floating point error; See #1917 - assert assigned_sum_prob <= 1. + 1e-8, "the sum of assigned probability should be less than 1" - rest_sum_prob = 1. - assigned_sum_prob - if not np.isclose(rest_sum_prob, 0): - rest_nbatch = (1 - type_filter) * self.nbatches - rest_prob = rest_sum_prob * rest_nbatch / np.sum(rest_nbatch) - ret_prob = rest_prob + type_filter * sys_probs - else : - ret_prob = sys_probs - assert np.isclose(np.sum(ret_prob), 1), "sum of probs should be 1" - return ret_prob - - def _prob_sys_size_ext(self, keywords): - block_str = keywords.split(';')[1:] - block_stt = [] - block_end = [] - block_weights = [] - for ii in block_str: - stt = int(ii.split(':')[0]) - end = int(ii.split(':')[1]) - weight = float(ii.split(':')[2]) - assert(weight >= 0), "the weight of a block should be no less than 0" - block_stt.append(stt) - block_end.append(end) - block_weights.append(weight) - nblocks = len(block_str) - block_probs = np.array(block_weights) / np.sum(block_weights) - sys_probs = np.zeros([self.get_nsystems()]) - for ii in range(nblocks): - nbatch_block = self.nbatches[block_stt[ii]:block_end[ii]] - tmp_prob = [float(i) for i in nbatch_block] / np.sum(nbatch_block) - sys_probs[block_stt[ii]:block_end[ii]] = tmp_prob * block_probs[ii] - return sys_probs - - - -class DataSystem (object) : - """ - Outdated class for the data systems. - - .. deprecated:: 2.0.0 - This class is not maintained any more. - """ - def __init__ (self, - systems, - set_prefix, - batch_size, - test_size, - rcut, - run_opt = None) : - self.system_dirs = systems - self.nsystems = len(self.system_dirs) - self.batch_size = batch_size - if isinstance(self.batch_size, int) : - self.batch_size = self.batch_size * np.ones(self.nsystems, dtype=int) - assert(isinstance(self.batch_size, (list,np.ndarray))) - assert(len(self.batch_size) == self.nsystems) - self.data_systems = [] - self.ntypes = [] - self.natoms = [] - self.natoms_vec = [] - self.nbatches = [] - for ii in self.system_dirs : - self.data_systems.append(DataSets(ii, set_prefix)) - sys_all_types = np.loadtxt(os.path.join(ii, "type.raw")).astype(int) - self.ntypes.append(np.max(sys_all_types) + 1) - self.sys_ntypes = max(self.ntypes) - type_map = [] - for ii in range(self.nsystems) : - self.natoms.append(self.data_systems[ii].get_natoms()) - self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(self.sys_ntypes).astype(int)) - self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(self.batch_size[ii])) - type_map.append(self.data_systems[ii].get_type_map()) - self.type_map = self.check_type_map_consistency(type_map) - - # check frame parameters - has_fparam = [ii.numb_fparam() for ii in self.data_systems] - for ii in has_fparam : - if ii != has_fparam[0] : - raise RuntimeError("if any system has frame parameter, then all systems should have the same number of frame parameter") - self.has_fparam = has_fparam[0] - - # check the size of data if they satisfy the requirement of batch and test - for ii in range(self.nsystems) : - chk_ret = self.data_systems[ii].check_batch_size(self.batch_size[ii]) - if chk_ret is not None : - raise RuntimeError ("system %s required batch size %d is larger than the size %d of the dataset %s" % \ - (self.system_dirs[ii], self.batch_size[ii], chk_ret[1], chk_ret[0])) - chk_ret = self.data_systems[ii].check_test_size(test_size) - if chk_ret is not None : - print("WARNNING: system %s required test size %d is larger than the size %d of the dataset %s" % \ - (self.system_dirs[ii], test_size, chk_ret[1], chk_ret[0])) - - if run_opt is not None: - self.print_summary(run_opt) - - self.prob_nbatches = [ float(i) for i in self.nbatches] / np.sum(self.nbatches) - - self.test_data = collections.defaultdict(list) - self.default_mesh = [] - for ii in range(self.nsystems) : - test_system_data = self.data_systems[ii].get_test () - for nn in test_system_data: - self.test_data[nn].append(test_system_data[nn]) - cell_size = np.max (rcut) - avg_box = np.average (test_system_data["box"], axis = 0) - avg_box = np.reshape (avg_box, [3,3]) - ncell = (np.linalg.norm(avg_box, axis=1)/ cell_size).astype(np.int32) - ncell[ncell < 2] = 2 - default_mesh = np.zeros (6, dtype = np.int32) - default_mesh[3:6] = ncell - self.default_mesh.append(default_mesh) - self.pick_idx = 0 - - - def check_type_map_consistency(self, type_map_list): - ret = [] - for ii in type_map_list: - if ii is not None: - min_len = min([len(ii), len(ret)]) - for idx in range(min_len) : - if ii[idx] != ret[idx] : - raise RuntimeError('inconsistent type map: %s %s' % (str(ret), str(ii))) - if len(ii) > len(ret) : - ret = ii - return ret - - - def get_type_map(self): - return self.type_map - - - def format_name_length(self, name, width) : - if len(name) <= width: - return '{: >{}}'.format(name, width) - else : - name = name[-(width-3):] - name = '-- ' + name - return name - - def print_summary(self) : - tmp_msg = "" - # width 65 - sys_width = 42 - tmp_msg += "---Summary of DataSystem-----------------------------------------\n" - tmp_msg += "find %d system(s):\n" % self.nsystems - tmp_msg += "%s " % self.format_name_length('system', sys_width) - tmp_msg += "%s %s %s\n" % ('natoms', 'bch_sz', 'n_bch') - for ii in range(self.nsystems) : - tmp_msg += ("%s %6d %6d %5d\n" % - (self.format_name_length(self.system_dirs[ii], sys_width), - self.natoms[ii], - self.batch_size[ii], - self.nbatches[ii]) ) - tmp_msg += "-----------------------------------------------------------------\n" - log.info(tmp_msg) - - def compute_energy_shift(self) : - sys_ener = np.array([]) - for ss in self.data_systems : - sys_ener = np.append(sys_ener, ss.get_ener()) - sys_tynatom = np.array(self.natoms_vec, dtype = float) - sys_tynatom = np.reshape(sys_tynatom, [self.nsystems,-1]) - sys_tynatom = sys_tynatom[:,2:] - energy_shift,resd,rank,s_value \ - = np.linalg.lstsq(sys_tynatom, sys_ener, rcond = 1e-3) - return energy_shift - - def process_sys_weights(self, sys_weights) : - sys_weights = np.array(sys_weights) - type_filter = sys_weights >= 0 - assigned_sum_prob = np.sum(type_filter * sys_weights) - assert assigned_sum_prob <= 1, "the sum of assigned probability should be less than 1" - rest_sum_prob = 1. - assigned_sum_prob - rest_nbatch = (1 - type_filter) * self.nbatches - rest_prob = rest_sum_prob * rest_nbatch / np.sum(rest_nbatch) - ret_prob = rest_prob + type_filter * sys_weights - assert np.sum(ret_prob) == 1, "sum of probs should be 1" - return ret_prob - - def get_batch (self, - sys_idx = None, - sys_weights = None, - style = "prob_sys_size") : - if sys_idx is not None : - self.pick_idx = sys_idx - else : - if sys_weights is None : - if style == "prob_sys_size" : - prob = self.prob_nbatches - elif style == "prob_uniform" : - prob = None - else : - raise RuntimeError("unkown get_batch style") - else : - prob = self.process_sys_weights(sys_weights) - self.pick_idx = dp_random.choice(np.arange(self.nsystems), p=prob) - b_data = self.data_systems[self.pick_idx].get_batch(self.batch_size[self.pick_idx]) - b_data["natoms_vec"] = self.natoms_vec[self.pick_idx] - b_data["default_mesh"] = self.default_mesh[self.pick_idx] - return b_data - - def get_test (self, - sys_idx = None) : - if sys_idx is not None : - idx = sys_idx - else : - idx = self.pick_idx - test_system_data = {} - for nn in self.test_data: - test_system_data[nn] = self.test_data[nn][idx] - test_system_data["natoms_vec"] = self.natoms_vec[idx] - test_system_data["default_mesh"] = self.default_mesh[idx] - return test_system_data - - def get_nbatches (self) : - return self.nbatches - - def get_ntypes (self) : - return self.sys_ntypes - - def get_nsystems (self) : - return self.nsystems - - def get_sys (self, sys_idx) : - return self.data_systems[sys_idx] - - def get_batch_size(self) : - return self.batch_size - - def numb_fparam(self) : - return self.has_fparam - -def _main () : - sys = ['/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/20', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/30', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/38', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/MoS2', - '/home/wanghan/study/deep.md/results.01/data/mos2/only_raws/Pt_cluster'] - set_prefix = 'set' - ds = DataSystem (sys, set_prefix, 4, 6) - r = ds.get_batch() - print(r[1][0]) - -if __name__ == '__main__': - _main() - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/errors.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/errors.py deleted file mode 100644 index 231a9e6..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/errors.py +++ /dev/null @@ -1,8 +0,0 @@ -class GraphTooLargeError(Exception): - """The graph is too large, exceeding protobuf's hard limit of 2GB.""" - -class GraphWithoutTensorError(Exception): - pass - -class OutOfMemoryError(Exception): - """This error is caused by out-of-memory (OOM).""" \ No newline at end of file diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/graph.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/graph.py deleted file mode 100644 index 2cd2e59..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/graph.py +++ /dev/null @@ -1,453 +0,0 @@ -import re -import numpy as np -from typing import Tuple, Dict -from deepmd.env import tf, EMBEDDING_NET_PATTERN, FITTING_NET_PATTERN, TYPE_EMBEDDING_PATTERN, ATTENTION_LAYER_PATTERN -from deepmd.utils.sess import run_sess -from deepmd.utils.errors import GraphWithoutTensorError - -# TODO (JZ): I think in this file we can merge some duplicated lines into one method... -def load_graph_def(model_file: str) -> Tuple[tf.Graph, tf.GraphDef]: - """ - Load graph as well as the graph_def from the frozen model(model_file) - - Parameters - ---------- - model_file : str - The input frozen model path - - Returns - ------- - tf.Graph - The graph loaded from the frozen model - tf.GraphDef - The graph_def loaded from the frozen model - """ - graph_def = tf.GraphDef() - with open(model_file, "rb") as f: - graph_def.ParseFromString(f.read()) - with tf.Graph().as_default() as graph: - tf.import_graph_def(graph_def, name = "") - return graph, graph_def - - -def get_tensor_by_name_from_graph(graph: tf.Graph, - tensor_name: str) -> tf.Tensor: - """ - Load tensor value from the given tf.Graph object - - Parameters - ---------- - graph : tf.Graph - The input TensorFlow graph - tensor_name : str - Indicates which tensor which will be loaded from the frozen model - - Returns - ------- - tf.Tensor - The tensor which was loaded from the frozen model - - Raises - ------ - GraphWithoutTensorError - Whether the tensor_name is within the frozen model - """ - try: - tensor = graph.get_tensor_by_name(tensor_name + ':0') - except KeyError as e: - raise GraphWithoutTensorError() from e - with tf.Session(graph=graph) as sess: - tensor = run_sess(sess, tensor) - return tensor - - -def get_tensor_by_name(model_file: str, - tensor_name: str) -> tf.Tensor: - """ - Load tensor value from the frozen model(model_file) - - Parameters - ---------- - model_file : str - The input frozen model path - tensor_name : str - Indicates which tensor which will be loaded from the frozen model - - Returns - ------- - tf.Tensor - The tensor which was loaded from the frozen model - - Raises - ------ - GraphWithoutTensorError - Whether the tensor_name is within the frozen model - """ - graph, _ = load_graph_def(model_file) - return get_tensor_by_name_from_graph(graph, tensor_name) - - -def get_tensor_by_type(node, - data_type : np.dtype) -> tf.Tensor: - """ - Get the tensor value within the given node according to the input data_type - - Parameters - ---------- - node - The given tensorflow graph node - data_type - The data type of the node - - Returns - ---------- - tf.Tensor - The tensor value of the given node - """ - if data_type == np.float64: - tensor = np.array(node.double_val) - elif data_type == np.float32: - tensor = np.array(node.float_val) - else: - raise RuntimeError('model compression does not support the half precision') - return tensor - - -def get_pattern_nodes_from_graph_def(graph_def: tf.GraphDef, pattern: str) -> Dict: - """ - Get the pattern nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - pattern - The node pattern within the graph_def - - Returns - ---------- - Dict - The fitting net nodes within the given tf.GraphDef object - """ - nodes = {} - pattern = re.compile(pattern) - for node in graph_def.node: - if re.fullmatch(pattern, node.name) != None: - nodes[node.name] = node.attr["value"].tensor - return nodes - - -def get_embedding_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the embedding net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The embedding net nodes within the given tf.GraphDef object - """ - # embedding_net_pattern = f"filter_type_\d+{suffix}/matrix_\d+_\d+|filter_type_\d+{suffix}/bias_\d+_\d+|filter_type_\d+{suffix}/idt_\d+_\d+|filter_type_all{suffix}/matrix_\d+_\d+|filter_type_all{suffix}/matrix_\d+_\d+_\d+|filter_type_all{suffix}/bias_\d+_\d+|filter_type_all{suffix}/bias_\d+_\d+_\d+|filter_type_all{suffix}/idt_\d+_\d+" - if suffix != "": - embedding_net_pattern = EMBEDDING_NET_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - embedding_net_pattern = EMBEDDING_NET_PATTERN - - embedding_net_nodes = get_pattern_nodes_from_graph_def(graph_def, embedding_net_pattern) - for key in embedding_net_nodes.keys(): - assert key.find('bias') > 0 or key.find( - 'matrix') > 0, "currently, only support weight matrix and bias matrix at the tabulation op!" - return embedding_net_nodes - - -def get_embedding_net_nodes(model_file: str, suffix: str = "") -> Dict: - """ - Get the embedding net nodes with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net nodes with the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - - -def get_embedding_net_variables_from_graph_def(graph_def : tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the embedding net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given tf.GraphDef object - """ - embedding_net_variables = {} - embedding_net_nodes = get_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in embedding_net_nodes: - node = embedding_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - embedding_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return embedding_net_variables - -def get_embedding_net_variables(model_file : str, suffix: str = "") -> Dict: - """ - Get the embedding net variables with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_embedding_net_variables_from_graph_def(graph_def, suffix=suffix) - - -def get_fitting_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the fitting net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net nodes within the given tf.GraphDef object - """ - if suffix != "": - fitting_net_pattern = FITTING_NET_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - fitting_net_pattern = FITTING_NET_PATTERN - fitting_net_nodes = get_pattern_nodes_from_graph_def(graph_def, fitting_net_pattern) - for key in fitting_net_nodes.keys(): - assert key.find('bias') > 0 or key.find('matrix') > 0 or key.find( - 'idt') > 0, "currently, only support weight matrix, bias and idt at the model compression process!" - return fitting_net_nodes - - -def get_fitting_net_nodes(model_file : str) -> Dict: - """ - Get the fitting net nodes with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - - Returns - ---------- - Dict - The fitting net nodes with the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_fitting_net_nodes_from_graph_def(graph_def) - - -def get_fitting_net_variables_from_graph_def(graph_def : tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the fitting net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net variables within the given tf.GraphDef object - """ - fitting_net_variables = {} - fitting_net_nodes = get_fitting_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in fitting_net_nodes: - node = fitting_net_nodes[item] - dtype= tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - fitting_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return fitting_net_variables - -def get_fitting_net_variables(model_file : str, suffix: str = "") -> Dict: - """ - Get the fitting net variables with the given frozen model(model_file) - - Parameters - ---------- - model_file - The input frozen model path - suffix - suffix of the scope - - Returns - ---------- - Dict - The fitting net variables within the given frozen model - """ - _, graph_def = load_graph_def(model_file) - return get_fitting_net_variables_from_graph_def(graph_def, suffix=suffix) - - -def get_type_embedding_net_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the type embedding net nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The type embedding net nodes within the given tf.GraphDef object - """ - if suffix != "": - type_embedding_net_pattern = TYPE_EMBEDDING_PATTERN\ - .replace('/idt', suffix + '/idt')\ - .replace('/bias', suffix + '/bias')\ - .replace('/matrix', suffix + '/matrix') - else: - type_embedding_net_pattern = TYPE_EMBEDDING_PATTERN - - type_embedding_net_nodes = get_pattern_nodes_from_graph_def(graph_def, type_embedding_net_pattern) - return type_embedding_net_nodes - - -def get_type_embedding_net_variables_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the type embedding net variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def : tf.GraphDef - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The embedding net variables within the given tf.GraphDef object - """ - type_embedding_net_variables = {} - type_embedding_net_nodes = get_type_embedding_net_nodes_from_graph_def(graph_def, suffix=suffix) - for item in type_embedding_net_nodes: - node = type_embedding_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype = tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - type_embedding_net_variables[item] = np.reshape(tensor_value, tensor_shape) - return type_embedding_net_variables - - -def get_attention_layer_nodes_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the attention layer nodes with the given tf.GraphDef object - - Parameters - ---------- - graph_def - The input tf.GraphDef object - suffix : str, optional - The scope suffix - - Returns - ---------- - Dict - The attention layer nodes within the given tf.GraphDef object - """ - if suffix != "": - attention_layer_pattern = ATTENTION_LAYER_PATTERN \ - .replace('/c_query', suffix + '/c_query') \ - .replace('/c_key', suffix + '/c_key') \ - .replace('/c_value', suffix + '/c_value') \ - .replace('/c_out', suffix + '/c_out') \ - .replace('/layer_normalization', suffix + '/layer_normalization') - else: - attention_layer_pattern = ATTENTION_LAYER_PATTERN - - attention_layer_nodes = get_pattern_nodes_from_graph_def(graph_def, attention_layer_pattern) - return attention_layer_nodes - - -def get_attention_layer_variables_from_graph_def(graph_def: tf.GraphDef, suffix: str = "") -> Dict: - """ - Get the attention layer variables with the given tf.GraphDef object - - Parameters - ---------- - graph_def : tf.GraphDef - The input tf.GraphDef object - suffix : str, optional - The suffix of the scope - - Returns - ---------- - Dict - The attention layer variables within the given tf.GraphDef object - """ - attention_layer_variables = {} - attention_layer_net_nodes = get_attention_layer_nodes_from_graph_def(graph_def, suffix=suffix) - for item in attention_layer_net_nodes: - node = attention_layer_net_nodes[item] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer(node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype) - else: - tensor_value = get_tensor_by_type(node, dtype) - attention_layer_variables[item] = np.reshape(tensor_value, tensor_shape) - return attention_layer_variables diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/learning_rate.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/learning_rate.py deleted file mode 100644 index 685eb44..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/learning_rate.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from deepmd.env import tf -from deepmd.common import ClassArg - -class LearningRateExp (object) : - r""" - The exponentially decaying learning rate. - - The learning rate at step :math:`t` is given by - - .. math:: - - \alpha(t) = \alpha_0 \lambda ^ { t / \tau } - - where :math:`\alpha` is the learning rate, :math:`\alpha_0` is the starting learning rate, - :math:`\lambda` is the decay rate, and :math:`\tau` is the decay steps. - - Parameters - ---------- - start_lr - Starting learning rate :math:`\alpha_0` - stop_lr - Stop learning rate :math:`\alpha_1` - decay_steps - Learning rate decay every this number of steps :math:`\tau` - decay_rate - The decay rate :math:`\lambda`. - If `stop_step` is provided in `build`, then it will be determined automatically and overwritten. - """ - def __init__ (self, - start_lr : float, - stop_lr : float = 5e-8, - decay_steps : int = 5000, - decay_rate : float = 0.95 - ) -> None : - """ - Constructor - """ - # args = ClassArg()\ - # .add('decay_steps', int, must = False)\ - # .add('decay_rate', float, must = False)\ - # .add('start_lr', float, must = True)\ - # .add('stop_lr', float, must = False) - # self.cd = args.parse(jdata) - self.cd = {} - self.cd['start_lr'] = start_lr - self.cd['stop_lr'] = stop_lr - self.cd['decay_steps'] = decay_steps - self.cd['decay_rate'] = decay_rate - self.start_lr_ = self.cd['start_lr'] - - def build(self, - global_step : tf.Tensor, - stop_step : int = None - ) -> tf.Tensor : - """ - Build the learning rate - - Parameters - ---------- - global_step - The tf Tensor prividing the global training step - stop_step - The stop step. If provided, the decay_rate will be determined automatically and overwritten. - - Returns - ------- - learning_rate - The learning rate - """ - if stop_step is None: - self.decay_steps_ = self.cd['decay_steps'] if self.cd['decay_steps'] is not None else 5000 - self.decay_rate_ = self.cd['decay_rate'] if self.cd['decay_rate'] is not None else 0.95 - else: - self.stop_lr_ = self.cd['stop_lr'] if self.cd['stop_lr'] is not None else 5e-8 - default_ds = 100 if stop_step // 10 > 100 else stop_step // 100 + 1 - self.decay_steps_ = self.cd['decay_steps'] if self.cd['decay_steps'] is not None else default_ds - if self.decay_steps_ >= stop_step: - self.decay_steps_ = default_ds - self.decay_rate_ = np.exp(np.log(self.stop_lr_ / self.start_lr_) / (stop_step / self.decay_steps_)) - - return tf.train.exponential_decay(self.start_lr_, - global_step, - self.decay_steps_, - self.decay_rate_, - staircase=True) - def start_lr(self) -> float: - """ - Get the start lr - """ - return self.start_lr_ - - def value (self, - step : int - ) -> float: - """ - Get the lr at a certain step - """ - return self.start_lr_ * np.power (self.decay_rate_, (step // self.decay_steps_)) - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/neighbor_stat.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/neighbor_stat.py deleted file mode 100644 index 79a988e..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/neighbor_stat.py +++ /dev/null @@ -1,127 +0,0 @@ -import math -import logging -import numpy as np -from deepmd.env import tf -from typing import Tuple, List -from deepmd.env import op_module -from deepmd.env import default_tf_session_config -from deepmd.env import GLOBAL_NP_FLOAT_PRECISION -from deepmd.utils.data_system import DeepmdDataSystem -from deepmd.utils.parallel_op import ParallelOp - -log = logging.getLogger(__name__) - -class NeighborStat(): - """ - Class for getting training data information. - - It loads data from DeepmdData object, and measures the data info, including neareest nbor distance between atoms, max nbor size of atoms and the output data range of the environment matrix. - - Parameters - ---------- - ntypes - The num of atom types - rcut - The cut-off radius - one_type : bool, optional, default=False - Treat all types as a single type. - """ - def __init__(self, - ntypes : int, - rcut: float, - one_type : bool = False, - ) -> None: - """ - Constructor - """ - self.rcut = rcut - self.ntypes = ntypes - self.one_type = one_type - sub_graph = tf.Graph() - - def builder(): - place_holders = {} - for ii in ['coord', 'box']: - place_holders[ii] = tf.placeholder(GLOBAL_NP_FLOAT_PRECISION, [None, None], name='t_'+ii) - place_holders['type'] = tf.placeholder(tf.int32, [None, None], name='t_type') - place_holders['natoms_vec'] = tf.placeholder(tf.int32, [self.ntypes+2], name='t_natoms') - place_holders['default_mesh'] = tf.placeholder(tf.int32, [None], name='t_mesh') - t_type = place_holders['type'] - t_natoms = place_holders['natoms_vec'] - if self.one_type: - # all types = 0, natoms_vec = [natoms, natoms, natoms] - t_type = tf.zeros_like(t_type, dtype=tf.int32) - t_natoms = tf.repeat(t_natoms[0], 3) - - _max_nbor_size, _min_nbor_dist \ - = op_module.neighbor_stat(place_holders['coord'], - t_type, - t_natoms, - place_holders['box'], - place_holders['default_mesh'], - rcut = self.rcut) - place_holders['dir'] = tf.placeholder(tf.string) - return place_holders, (_max_nbor_size, _min_nbor_dist, place_holders['dir']) - - with sub_graph.as_default(): - self.p = ParallelOp(builder, config=default_tf_session_config) - - self.sub_sess = tf.Session(graph = sub_graph, config=default_tf_session_config) - - def get_stat(self, - data : DeepmdDataSystem) -> Tuple[float, List[int]]: - """ - get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms - - Parameters - ---------- - data - Class for manipulating many data systems. It is implemented with the help of DeepmdData. - - Returns - ------- - min_nbor_dist - The nearest distance between neighbor atoms - max_nbor_size - A list with ntypes integers, denotes the actual achieved max sel - """ - self.min_nbor_dist = 100.0 - self.max_nbor_size = [0] - if not self.one_type: - self.max_nbor_size *= self.ntypes - - def feed(): - for ii in range(len(data.system_dirs)): - for jj in data.data_systems[ii].dirs: - data_set = data.data_systems[ii]._load_set(jj) - for kk in range(np.array(data_set['type']).shape[0]): - yield { - 'coord': np.array(data_set['coord'])[kk].reshape([-1, data.natoms[ii] * 3]), - 'type': np.array(data_set['type'])[kk].reshape([-1, data.natoms[ii]]), - 'natoms_vec': np.array(data.natoms_vec[ii]), - 'box': np.array(data_set['box'])[kk].reshape([-1, 9]), - 'default_mesh': np.array(data.default_mesh[ii]), - 'dir': str(jj), - } - - for mn, dt, jj in self.p.generate(self.sub_sess, feed()): - if dt.size != 0: - dt = np.min(dt) - else: - dt = self.rcut - log.warning("Atoms with no neighbors found in %s. Please make sure it's what you expected." % jj) - if dt < self.min_nbor_dist: - if math.isclose(dt, 0., rel_tol=1e-6): - # it's unexpected that the distance between two atoms is zero - # zero distance will cause nan (#874) - raise RuntimeError( - "Some atoms are overlapping in %s. Please check your" - " training data to remove duplicated atoms." % jj - ) - self.min_nbor_dist = dt - var = np.max(mn, axis=0) - self.max_nbor_size = np.maximum(var, self.max_nbor_size) - - log.info('training data with min nbor dist: ' + str(self.min_nbor_dist)) - log.info('training data with max nbor size: ' + str(self.max_nbor_size)) - return self.min_nbor_dist, self.max_nbor_size diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/network.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/network.py deleted file mode 100644 index aa3dabb..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/network.py +++ /dev/null @@ -1,265 +0,0 @@ -import numpy as np - -from deepmd.env import tf -from deepmd.env import GLOBAL_TF_FLOAT_PRECISION -from deepmd.common import get_precision - -def one_layer_rand_seed_shift(): - return 3 - -def one_layer(inputs, - outputs_size, - activation_fn=tf.nn.tanh, - precision = GLOBAL_TF_FLOAT_PRECISION, - stddev=1.0, - bavg=0.0, - name='linear', - scope='', - reuse=None, - seed=None, - use_timestep = False, - trainable = True, - useBN = False, - uniform_seed = False, - initial_variables = None, - mixed_prec = None, - final_layer = False): - # For good accuracy, the last layer of the fitting network uses a higher precision neuron network. - if mixed_prec is not None and final_layer: - inputs = tf.cast(inputs, get_precision(mixed_prec['output_prec'])) - with tf.variable_scope(name, reuse=reuse): - shape = inputs.get_shape().as_list() - w_initializer = tf.random_normal_initializer( - stddev=stddev / np.sqrt(shape[1] + outputs_size), - seed=seed if (seed is None or uniform_seed) else seed + 0) - b_initializer = tf.random_normal_initializer( - stddev=stddev, - mean=bavg, - seed=seed if (seed is None or uniform_seed) else seed + 1) - if initial_variables is not None: - w_initializer = tf.constant_initializer(initial_variables[scope + name + '/matrix']) - b_initializer = tf.constant_initializer(initial_variables[scope + name + '/bias']) - w = tf.get_variable('matrix', - [shape[1], outputs_size], - precision, - w_initializer, - trainable = trainable) - variable_summaries(w, 'matrix') - b = tf.get_variable('bias', - [outputs_size], - precision, - b_initializer, - trainable = trainable) - variable_summaries(b, 'bias') - - if mixed_prec is not None and not final_layer: - inputs = tf.cast(inputs, get_precision(mixed_prec['compute_prec'])) - w = tf.cast(w, get_precision(mixed_prec['compute_prec'])) - b = tf.cast(b, get_precision(mixed_prec['compute_prec'])) - - hidden = tf.nn.bias_add(tf.matmul(inputs, w), b) - if activation_fn != None and use_timestep : - idt_initializer = tf.random_normal_initializer( - stddev=0.001, - mean=0.1, - seed=seed if (seed is None or uniform_seed) else seed + 2) - if initial_variables is not None: - idt_initializer = tf.constant_initializer(initial_variables[scope + name + '/idt']) - idt = tf.get_variable('idt', - [outputs_size], - precision, - idt_initializer, - trainable = trainable) - variable_summaries(idt, 'idt') - if activation_fn != None: - if useBN: - None - # hidden_bn = self._batch_norm(hidden, name=name+'_normalization', reuse=reuse) - # return activation_fn(hidden_bn) - else: - if use_timestep : - if mixed_prec is not None and not final_layer: - idt = tf.cast(idt, get_precision(mixed_prec['compute_prec'])) - hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size]) * idt - else : - hidden = tf.reshape(activation_fn(hidden), [-1, outputs_size]) - - if mixed_prec is not None: - hidden = tf.cast(hidden, get_precision(mixed_prec['output_prec'])) - return hidden - - -def embedding_net_rand_seed_shift( - network_size -): - shift = 3 * (len(network_size) + 1) - return shift - -def embedding_net(xx, - network_size, - precision, - activation_fn = tf.nn.tanh, - resnet_dt = False, - name_suffix = '', - stddev = 1.0, - bavg = 0.0, - seed = None, - trainable = True, - uniform_seed = False, - initial_variables = None, - mixed_prec = None): - r"""The embedding network. - - The embedding network function :math:`\mathcal{N}` is constructed by is the - composition of multiple layers :math:`\mathcal{L}^{(i)}`: - - .. math:: - \mathcal{N} = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} - \circ \cdots \circ \mathcal{L}^{(1)} - - A layer :math:`\mathcal{L}` is given by one of the following forms, - depending on the number of nodes: [1]_ - - .. math:: - \mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= - \begin{cases} - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + \mathbf{x}, & N_2=N_1 \\ - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + (\mathbf{x}, \mathbf{x}), & N_2 = 2N_1\\ - \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\ - \end{cases} - - where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` - is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and - :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively, - both of which are trainable if `trainable` is `True`. :math:`\boldsymbol{\phi}` - is the activation function. - - Parameters - ---------- - xx : Tensor - Input tensor :math:`\mathbf{x}` of shape [-1,1] - network_size: list of int - Size of the embedding network. For example [16,32,64] - precision: - Precision of network weights. For example, tf.float64 - activation_fn: - Activation function :math:`\boldsymbol{\phi}` - resnet_dt: boolean - Using time-step in the ResNet construction - name_suffix: str - The name suffix append to each variable. - stddev: float - Standard deviation of initializing network parameters - bavg: float - Mean of network intial bias - seed: int - Random seed for initializing network parameters - trainable: boolean - If the network is trainable - uniform_seed : boolean - Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed - initial_variables : dict - The input dict which stores the embedding net variables - mixed_prec - The input dict which stores the mixed precision setting for the embedding net - - - References - ---------- - .. [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identitymappings - in deep residual networks. InComputer Vision – ECCV 2016,pages 630–645. Springer - International Publishing, 2016. - """ - input_shape = xx.get_shape().as_list() - outputs_size = [input_shape[1]] + network_size - - for ii in range(1, len(outputs_size)): - w_initializer = tf.random_normal_initializer( - stddev=stddev/np.sqrt(outputs_size[ii]+outputs_size[ii-1]), - seed = seed if (seed is None or uniform_seed) else seed + ii*3+0 - ) - b_initializer = tf.random_normal_initializer( - stddev=stddev, - mean = bavg, - seed = seed if (seed is None or uniform_seed) else seed + 3*ii+1 - ) - if initial_variables is not None: - scope = tf.get_variable_scope().name - w_initializer = tf.constant_initializer(initial_variables[scope+'/matrix_'+str(ii)+name_suffix]) - b_initializer = tf.constant_initializer(initial_variables[scope+'/bias_'+str(ii)+name_suffix]) - w = tf.get_variable('matrix_'+str(ii)+name_suffix, - [outputs_size[ii - 1], outputs_size[ii]], - precision, - w_initializer, - trainable = trainable) - variable_summaries(w, 'matrix_'+str(ii)+name_suffix) - - b = tf.get_variable('bias_'+str(ii)+name_suffix, - [outputs_size[ii]], - precision, - b_initializer, - trainable = trainable) - variable_summaries(b, 'bias_'+str(ii)+name_suffix) - - if mixed_prec is not None: - xx = tf.cast(xx, get_precision(mixed_prec['compute_prec'])) - w = tf.cast(w, get_precision(mixed_prec['compute_prec'])) - b = tf.cast(b, get_precision(mixed_prec['compute_prec'])) - if activation_fn is not None: - hidden = tf.reshape(activation_fn(tf.nn.bias_add(tf.matmul(xx, w), b)), [-1, outputs_size[ii]]) - else: - hidden = tf.reshape(tf.nn.bias_add(tf.matmul(xx, w), b), [-1, outputs_size[ii]]) - if resnet_dt : - idt_initializer = tf.random_normal_initializer( - stddev=0.001, - mean = 1.0, - seed = seed if (seed is None or uniform_seed) else seed + 3*ii+2 - ) - if initial_variables is not None: - scope = tf.get_variable_scope().name - idt_initializer = tf.constant_initializer(initial_variables[scope+'/idt_'+str(ii)+name_suffix]) - idt = tf.get_variable('idt_'+str(ii)+name_suffix, - [1, outputs_size[ii]], - precision, - idt_initializer, - trainable = trainable) - variable_summaries(idt, 'idt_'+str(ii)+name_suffix) - if mixed_prec is not None: - idt = tf.cast(idt, get_precision(mixed_prec['compute_prec'])) - - if outputs_size[ii] == outputs_size[ii-1]: - if resnet_dt : - xx += hidden * idt - else : - xx += hidden - elif outputs_size[ii] == outputs_size[ii-1] * 2: - if resnet_dt : - xx = tf.concat([xx,xx], 1) + hidden * idt - else : - xx = tf.concat([xx,xx], 1) + hidden - else: - xx = hidden - if mixed_prec is not None: - xx = tf.cast(xx, get_precision(mixed_prec['output_prec'])) - return xx - -def variable_summaries(var: tf.Variable, name: str): - """Attach a lot of summaries to a Tensor (for TensorBoard visualization). - - Parameters - ---------- - var : tf.Variable - [description] - name : str - variable name - """ - with tf.name_scope(name): - mean = tf.reduce_mean(var) - tf.summary.scalar('mean', mean) - - with tf.name_scope('stddev'): - stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) - tf.summary.scalar('stddev', stddev) - tf.summary.scalar('max', tf.reduce_max(var)) - tf.summary.scalar('min', tf.reduce_min(var)) - tf.summary.histogram('histogram', var) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/pair_tab.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/pair_tab.py deleted file mode 100644 index d9919bd..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/pair_tab.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np -from typing import Tuple, List - -from scipy.interpolate import CubicSpline - -class PairTab (object): - """ - - Parameters - ---------- - filename - File name for the short-range tabulated potential. - The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. - The first colume is the distance between atoms. - The second to the last columes are energies for pairs of certain types. - For example we have two atom types, 0 and 1. - The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly. - """ - def __init__(self, - filename : str - ) -> None: - """ - Constructor - """ - self.reinit(filename) - - def reinit(self, - filename : str - ) -> None: - """ - Initialize the tabulated interaction - - Parameters - ---------- - filename - File name for the short-range tabulated potential. - The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. - The first colume is the distance between atoms. - The second to the last columes are energies for pairs of certain types. - For example we have two atom types, 0 and 1. - The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly. - """ - self.vdata = np.loadtxt(filename) - self.rmin = self.vdata[0][0] - self.hh = self.vdata[1][0] - self.vdata[0][0] - self.nspline = self.vdata.shape[0] - 1 - ncol = self.vdata.shape[1] - 1 - n0 = (-1 + np.sqrt(1 + 8 * ncol)) * 0.5 - self.ntypes = int(n0 + 0.1) - assert(self.ntypes * (self.ntypes+1) // 2 == ncol),\ - "number of volumes provided in %s does not match guessed number of types %d" % (filename, self.ntypes) - self.tab_info = np.array([self.rmin, self.hh, self.nspline, self.ntypes]) - self.tab_data = self._make_data() - - def get(self) -> Tuple[np.array, np.array]: - """ - Get the serialized table. - """ - return self.tab_info, self.tab_data - - def _make_data(self) : - data = np.zeros([self.ntypes * self.ntypes * 4 * self.nspline]) - stride = 4 * self.nspline - idx_iter = 0 - xx = self.vdata[:,0] - for t0 in range(self.ntypes) : - for t1 in range(t0, self.ntypes) : - vv = self.vdata[:,1+idx_iter] - cs = CubicSpline(xx, vv) - dd = cs(xx, 1) - dd *= self.hh - dtmp = np.zeros(stride) - for ii in range(self.nspline) : - dtmp[ii*4+0] = 2 * vv[ii] - 2 * vv[ii+1] + dd[ii] + dd[ii+1] - dtmp[ii*4+1] =-3 * vv[ii] + 3 * vv[ii+1] - 2 * dd[ii] - dd[ii+1] - dtmp[ii*4+2] = dd[ii] - dtmp[ii*4+3] = vv[ii] - data[(t0 * self.ntypes + t1) * stride : (t0 * self.ntypes + t1) * stride + stride] \ - = dtmp - data[(t1 * self.ntypes + t0) * stride : (t1 * self.ntypes + t0) * stride + stride] \ - = dtmp - idx_iter += 1 - return data diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/parallel_op.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/parallel_op.py deleted file mode 100644 index 6752724..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/parallel_op.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import Callable, Generator, Tuple, Dict, Any - -from deepmd.env import tf -from deepmd.utils.sess import run_sess - - -class ParallelOp: - """Run an op with data parallelism. - - Parameters - ---------- - builder : Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]] - returns two objects: a dict which stores placeholders by key, and a tuple with the final op(s) - nthreads : int, optional - the number of threads - config : tf.ConfigProto, optional - tf.ConfigProto - - Examples - -------- - >>> from deepmd.env import tf - >>> from deepmd.utils.parallel_op import ParallelOp - >>> def builder(): - ... x = tf.placeholder(tf.int32, [1]) - ... return {"x": x}, (x + 1) - ... - >>> p = ParallelOp(builder, nthreads=4) - >>> def feed(): - ... for ii in range(10): - ... yield {"x": [ii]} - ... - >>> print(*p.generate(tf.Session(), feed())) - [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] - """ - def __init__(self, builder: Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]], nthreads: int = None, config: tf.ConfigProto = None) -> None: - if nthreads is not None: - self.nthreads = nthreads - elif config is not None: - self.nthreads = max(config.inter_op_parallelism_threads, 1) - else: - self.nthreads = 1 - - self.placeholders = [] - self.ops = [] - for ii in range(self.nthreads): - with tf.name_scope("task_%d" % ii) as scope: - placeholder, op = builder() - self.placeholders.append(placeholder) - self.ops.append(op) - - def generate(self, sess: tf.Session, feed: Generator[Dict[str, Any], None, None]) -> Generator[Tuple, None, None]: - """Returns a generator. - - Parameters - ---------- - feed : Generator[dict, None, None] - generator which yields feed_dict - - Yields - ------ - Generator[Tuple, None, None] - generator which yields session returns - """ - nn = self.nthreads - while True: - feed_dict = {} - for ii in range(self.nthreads): - try: - fd = next(feed) - except StopIteration: - if ii == 0: - return - nn = ii - break - for kk, vv in fd.items(): - feed_dict[self.placeholders[ii][kk]] = vv - ops = self.ops[:nn] - for yy in run_sess(sess, ops, feed_dict=feed_dict): - yield yy - diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/path.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/path.py deleted file mode 100644 index 30f3e9b..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/path.py +++ /dev/null @@ -1,338 +0,0 @@ -import os -from abc import ABC, abstractmethod -from typing import List -from pathlib import Path -from functools import lru_cache - -import numpy as np -import h5py -from wcmatch.glob import globfilter - -class DPPath(ABC): - """The path class to data system (DeepmdData). - - Parameters - ---------- - path : str - path - """ - def __new__(cls, path: str): - if cls is DPPath: - if os.path.isdir(path): - return super().__new__(DPOSPath) - elif os.path.isfile(path.split("#")[0]): - # assume h5 if it is not dir - # TODO: check if it is a real h5? or just check suffix? - return super().__new__(DPH5Path) - raise FileNotFoundError("%s not found" % path) - return super().__new__(cls) - - @abstractmethod - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - - @abstractmethod - def load_txt(self, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - - @abstractmethod - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - - @abstractmethod - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - - @abstractmethod - def is_file(self) -> bool: - """Check if self is file.""" - - @abstractmethod - def is_dir(self) -> bool: - """Check if self is directory.""" - - @abstractmethod - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - - @abstractmethod - def __lt__(self, other: "DPPath") -> bool: - """whether this DPPath is less than other for sorting""" - - @abstractmethod - def __str__(self) -> str: - """Represent string""" - - def __repr__(self) -> str: - return "%s (%s)" % (type(self), str(self)) - - def __eq__(self, other) -> bool: - return str(self) == str(other) - - def __hash__(self): - return hash(str(self)) - - -class DPOSPath(DPPath): - """The OS path class to data system (DeepmdData) for real directories. - - Parameters - ---------- - path : str - path - """ - def __init__(self, path: str) -> None: - super().__init__() - if isinstance(path, Path): - self.path = path - else: - self.path = Path(path) - - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return np.load(str(self.path)) - - def load_txt(self, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return np.loadtxt(str(self.path), **kwargs) - - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - # currently DPOSPath will only derivative DPOSPath - # TODO: discuss if we want to mix DPOSPath and DPH5Path? - return list([type(self)(p) for p in self.path.glob(pattern)]) - - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - return list([type(self)(p) for p in self.path.rglob(pattern)]) - - def is_file(self) -> bool: - """Check if self is file.""" - return self.path.is_file() - - def is_dir(self) -> bool: - """Check if self is directory.""" - return self.path.is_dir() - - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - return type(self)(self.path / key) - - def __lt__(self, other: "DPOSPath") -> bool: - """whether this DPPath is less than other for sorting""" - return self.path < other.path - - def __str__(self) -> str: - """Represent string""" - return str(self.path) - - -class DPH5Path(DPPath): - """The path class to data system (DeepmdData) for HDF5 files. - - Notes - ----- - OS - HDF5 relationship: - directory - Group - file - Dataset - - Parameters - ---------- - path : str - path - """ - def __init__(self, path: str) -> None: - super().__init__() - # we use "#" to split path - # so we do not support file names containing #... - s = path.split("#") - self.root_path = s[0] - self.root = self._load_h5py(s[0]) - # h5 path: default is the root path - self.name = s[1] if len(s) > 1 else "/" - - @classmethod - @lru_cache(None) - def _load_h5py(cls, path: str) -> h5py.File: - """Load hdf5 file. - - Parameters - ---------- - path : str - path to hdf5 file - """ - # this method has cache to avoid duplicated - # loading from different DPH5Path - # However the file will be never closed? - return h5py.File(path, 'r') - - def load_numpy(self) -> np.ndarray: - """Load NumPy array. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - return self.root[self.name][:] - - def load_txt(self, dtype: np.dtype = None, **kwargs) -> np.ndarray: - """Load NumPy array from text. - - Returns - ------- - np.ndarray - loaded NumPy array - """ - arr = self.load_numpy() - if dtype: - arr = arr.astype(dtype) - return arr - - def glob(self, pattern: str) -> List["DPPath"]: - """Search path using the glob pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - # got paths starts with current path first, which is faster - subpaths = [ii for ii in self._keys if ii.startswith(self.name)] - return list([type(self)("%s#%s"%(self.root_path, pp)) for pp in globfilter(subpaths, self._connect_path(pattern))]) - - def rglob(self, pattern: str) -> List["DPPath"]: - """This is like calling :meth:`DPPath.glob()` with `**/` added in front - of the given relative pattern. - - Parameters - ---------- - pattern : str - glob pattern - - Returns - ------- - List[DPPath] - list of paths - """ - return self.glob("**" + pattern) - - @property - def _keys(self) -> List[str]: - """Walk all groups and dataset""" - return self._file_keys(self.root) - - @classmethod - @lru_cache(None) - def _file_keys(cls, file: h5py.File) -> List[str]: - """Walk all groups and dataset""" - l = [] - file.visit(lambda x: l.append("/" + x)) - return l - - def is_file(self) -> bool: - """Check if self is file.""" - if self.name not in self._keys: - return False - return isinstance(self.root[self.name], h5py.Dataset) - - def is_dir(self) -> bool: - """Check if self is directory.""" - if self.name not in self._keys: - return False - return isinstance(self.root[self.name], h5py.Group) - - def __truediv__(self, key: str) -> "DPPath": - """Used for / operator.""" - return type(self)("%s#%s" % (self.root_path, self._connect_path(key))) - - def _connect_path(self, path: str) -> str: - """Connect self with path""" - if self.name.endswith("/"): - return "%s%s" % (self.name, path) - return "%s/%s" % (self.name, path) - - def __lt__(self, other: "DPH5Path") -> bool: - """whether this DPPath is less than other for sorting""" - if self.root_path == other.root_path: - return self.name < other.name - return self.root_path < other.root_path - - def __str__(self) -> str: - """returns path of self""" - return "%s#%s" % (self.root_path, self.name) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/plugin.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/plugin.py deleted file mode 100644 index a0799f8..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/plugin.py +++ /dev/null @@ -1,84 +0,0 @@ - -"""Base of plugin systems.""" -# copied from https://github.com/deepmodeling/dpdata/blob/a3e76d75de53f6076254de82d18605a010dc3b00/dpdata/plugin.py - -from abc import ABCMeta -from typing import Callable - - -class Plugin: - """A class to register and restore plugins. - - Attributes - ---------- - plugins : Dict[str, object] - plugins - - Examples - -------- - >>> plugin = Plugin() - >>> @plugin.register("xx") - def xxx(): - pass - >>> print(plugin.plugins['xx']) - """ - def __init__(self): - self.plugins = {} - - def __add__(self, other) -> "Plugin": - self.plugins.update(other.plugins) - return self - - def register(self, key : str) -> Callable[[object], object]: - """Register a plugin. - - Parameters - ---------- - key : str - key of the plugin - - Returns - ------- - Callable[[object], object] - decorator - """ - def decorator(object : object) -> object: - self.plugins[key] = object - return object - return decorator - - def get_plugin(self, key) -> object: - """Visit a plugin by key. - - Parameters - ---------- - key : str - key of the plugin - - Returns - ------- - object - the plugin - """ - return self.plugins[key] - -class VariantMeta: - def __call__(cls, *args, **kwargs): - """Remove `type` and keys that starts with underline.""" - obj = cls.__new__(cls, *args, **kwargs) - kwargs.pop('type', None) - to_pop = [] - for kk in kwargs: - if kk[0] == '_': - to_pop.append(kk) - for kk in to_pop: - kwargs.pop(kk, None) - obj.__init__(*args, **kwargs) - return obj - -class VariantABCMeta(VariantMeta, ABCMeta): - pass - -class PluginVariant(metaclass=VariantABCMeta): - """A class to remove `type` from input arguments.""" - pass \ No newline at end of file diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/random.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/random.py deleted file mode 100644 index 565f28c..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/random.py +++ /dev/null @@ -1,63 +0,0 @@ -import numpy as np - - -_RANDOM_GENERATOR = np.random.RandomState() - - -def choice(a: np.ndarray, p: np.ndarray = None): - """Generates a random sample from a given 1-D array. - - Parameters - ---------- - a : np.ndarray - A random sample is generated from its elements. - p : np.ndarray - The probabilities associated with each entry in a. - - Returns - ------- - np.ndarray - arrays with results and their shapes - """ - return _RANDOM_GENERATOR.choice(a, p=p) - - -def random(size=None): - """Return random floats in the half-open interval [0.0, 1.0). - - Parameters - ---------- - size - Output shape. - - Returns - ------- - np.ndarray - Arrays with results and their shapes. - """ - return _RANDOM_GENERATOR.random_sample(size) - - -def seed(val: int = None): - """Seed the generator. - - Parameters - ---------- - val : int - Seed. - """ - _RANDOM_GENERATOR.seed(val) - - -def shuffle(x: np.ndarray): - """Modify a sequence in-place by shuffling its contents. - - Parameters - ---------- - x : np.ndarray - The array or list to be shuffled. - """ - _RANDOM_GENERATOR.shuffle(x) - - -__all__ = ['choice', 'random', 'seed', 'shuffle'] diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/sess.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/sess.py deleted file mode 100644 index 829ba74..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/sess.py +++ /dev/null @@ -1,39 +0,0 @@ -import os - -from deepmd.env import tf -from deepmd.utils.errors import OutOfMemoryError - - -def run_sess(sess: tf.Session, *args, **kwargs): - """Run session with erorrs caught. - - Parameters - ---------- - sess: tf.Session - TensorFlow Session - - Returns - ------- - the result of sess.run() - """ - try: - # https://www.tensorflow.org/api_docs/python/tf/compat/v1/Session#run - return sess.run(*args, **kwargs) - except tf.errors.ResourceExhaustedError as e: - MESSAGE = ( - "Your memory may be not enough, thus an error has been raised " - "above. You need to take the following actions:\n" - "1. Check if the network size of the model is too large.\n" - "2. Check if the batch size of training or testing is too large." - " You can set the training batch size to `auto`.\n" - "3. Check if the number of atoms is too large.\n" - ) - if tf.test.is_built_with_cuda(): - MESSAGE += ( - "4. Check if another program is using the same GPU by " - "execuating `nvidia-smi`. The usage of GPUs is " - "controlled by `CUDA_VISIBLE_DEVICES` environment " - "variable (current value: %s).\n" % ( - os.getenv("CUDA_VISIBLE_DEVICES", None), - )) - raise OutOfMemoryError(MESSAGE) from e diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/tabulate.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/tabulate.py deleted file mode 100644 index 8c9159e..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/tabulate.py +++ /dev/null @@ -1,496 +0,0 @@ -import logging -import numpy as np -import deepmd -from typing import Callable -from typing import Tuple, List, Dict -from functools import lru_cache -from scipy.special import comb -from deepmd.env import tf -from deepmd.env import op_module -from deepmd.common import ACTIVATION_FN_DICT -from deepmd.utils.graph import get_tensor_by_name_from_graph, load_graph_def -from deepmd.utils.graph import get_embedding_net_nodes_from_graph_def -from deepmd.descriptor import Descriptor - -log = logging.getLogger(__name__) - -class DPTabulate(): - """ - Class for tabulation. - - Compress a model, which including tabulating the embedding-net. - The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the stride(parameter) as it\'s uniform stride, while the second table takes 10 * stride as it\s uniform stride - The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table\'s upper boundary(upper) to the extrapolate(parameter) * upper. - - Parameters - ---------- - descrpt - Descriptor of the original model - neuron - Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` - model_file - The frozen model - type_one_side - Try to build N_types tables. Otherwise, building N_types^2 tables - exclude_types : List[List[int]] - The excluded pairs of types which have no interaction with each other. - For example, `[[0, 1]]` means no interaction between type 0 and type 1. - activation_function - The activation function in the embedding net. Supported options are {"tanh","gelu"} in common.ACTIVATION_FN_DICT. - suffix : str, optional - The suffix of the scope - """ - def __init__(self, - descrpt : Descriptor, - neuron : List[int], - model_file : str, - type_one_side : bool = False, - exclude_types : List[List[int]] = [], - activation_fn : Callable[[tf.Tensor], tf.Tensor] = tf.nn.tanh, - suffix : str = "", - ) -> None: - """ - Constructor - """ - self.descrpt = descrpt - self.neuron = neuron - self.model_file = model_file - self.type_one_side = type_one_side - self.exclude_types = exclude_types - self.suffix = suffix - - # functype - if activation_fn == ACTIVATION_FN_DICT["tanh"]: - self.functype = 1 - elif activation_fn == ACTIVATION_FN_DICT["gelu"]: - self.functype = 2 - elif activation_fn == ACTIVATION_FN_DICT["relu"]: - self.functype = 3 - elif activation_fn == ACTIVATION_FN_DICT["relu6"]: - self.functype = 4 - elif activation_fn == ACTIVATION_FN_DICT["softplus"]: - self.functype = 5 - elif activation_fn == ACTIVATION_FN_DICT["sigmoid"]: - self.functype = 6 - else: - raise RuntimeError("Unknown actication function type!") - self.activation_fn = activation_fn - - self.graph, self.graph_def = load_graph_def(self.model_file) - #self.sess = tf.Session(graph = self.graph) - - self.sub_graph, self.sub_graph_def = self._load_sub_graph() - self.sub_sess = tf.Session(graph = self.sub_graph) - - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - self.sel_a = self.descrpt.sel_r - self.rcut = self.descrpt.rcut - self.rcut_smth = self.descrpt.rcut_smth - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - self.sel_a = self.descrpt.sel_a - self.rcut = self.descrpt.rcut_r - self.rcut_smth = self.descrpt.rcut_r_smth - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - self.sel_a = self.descrpt.sel_a - self.rcut = self.descrpt.rcut_r - self.rcut_smth = self.descrpt.rcut_r_smth - else: - raise RuntimeError("Unsupported descriptor") - - self.davg = get_tensor_by_name_from_graph(self.graph, f'descrpt_attr{self.suffix}/t_avg') - self.dstd = get_tensor_by_name_from_graph(self.graph, f'descrpt_attr{self.suffix}/t_std') - self.ntypes = get_tensor_by_name_from_graph(self.graph, 'descrpt_attr/ntypes') - - self.embedding_net_nodes = get_embedding_net_nodes_from_graph_def(self.graph_def, suffix=self.suffix) - - # move it to the descriptor class - # for tt in self.exclude_types: - # if (tt[0] not in range(self.ntypes)) or (tt[1] not in range(self.ntypes)): - # raise RuntimeError("exclude types" + str(tt) + " must within the number of atomic types " + str(self.ntypes) + "!") - # if (self.ntypes * self.ntypes - len(self.exclude_types) == 0): - # raise RuntimeError("empty embedding-net are not supported in model compression!") - self.layer_size = self._get_layer_size() - self.table_size = self._get_table_size() - - self.bias = self._get_bias() - self.matrix = self._get_matrix() - - self.data_type = self._get_data_type() - self.last_layer_size = self._get_last_layer_size() - - self.data = {} - - self.upper = {} - self.lower = {} - - - def build(self, - min_nbor_dist : float, - extrapolate : float, - stride0 : float, - stride1 : float) -> Tuple[Dict[str, int], Dict[str, int]]: - """ - Build the tables for model compression - - Parameters - ---------- - min_nbor_dist - The nearest distance between neighbor atoms - extrapolate - The scale of model extrapolation - stride0 - The uniform stride of the first table - stride1 - The uniform stride of the second table - neuron - Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}` - - Returns - ---------- - lower : dict[str, int] - The lower boundary of environment matrix by net - upper : dict[str, int] - The upper boundary of environment matrix by net - """ - # tabulate range [lower, upper] with stride0 'stride0' - lower, upper = self._get_env_mat_range(min_nbor_dist) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - for ii in range(self.table_size): - if (self.type_one_side and not self._all_excluded(ii)) or (not self.type_one_side and (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types): - if self.type_one_side: - net = "filter_-1_net_" + str(ii) - # upper and lower should consider all types which are not excluded and sel>0 - idx = [(type_i, ii) not in self.exclude_types and self.sel_a[type_i] > 0 for type_i in range(self.ntypes)] - uu = np.max(upper[idx]) - ll = np.min(lower[idx]) - else: - ielement = ii // self.ntypes - net = "filter_" + str(ielement) + "_net_" + str(ii % self.ntypes) - uu = upper[ielement] - ll = lower[ielement] - xx = np.arange(ll, uu, stride0, dtype = self.data_type) - xx = np.append(xx, np.arange(uu, extrapolate * uu, stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * uu], dtype = self.data_type)) - nspline = ((uu - ll) / stride0 + (extrapolate * uu - uu) / stride1).astype(int) - self._build_lower(net, xx, ii, uu, ll, stride0, stride1, extrapolate, nspline) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - xx_all = [] - for ii in range(self.ntypes): - xx = np.arange(extrapolate * lower[ii], lower[ii], stride1, dtype = self.data_type) - xx = np.append(xx, np.arange(lower[ii], upper[ii], stride0, dtype = self.data_type)) - xx = np.append(xx, np.arange(upper[ii], extrapolate * upper[ii], stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * upper[ii]], dtype = self.data_type)) - xx_all.append(xx) - nspline = ((upper - lower) / stride0 + 2 * ((extrapolate * upper - upper) / stride1)).astype(int) - idx = 0 - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - net = "filter_" + str(ii) + "_net_" + str(jj) - self._build_lower(net, xx_all[ii], idx, upper[ii], lower[ii], stride0, stride1, extrapolate, nspline[ii]) - idx += 1 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - for ii in range(self.table_size): - if (self.type_one_side and not self._all_excluded(ii)) or (not self.type_one_side and (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types): - if self.type_one_side: - net = "filter_-1_net_" + str(ii) - # upper and lower should consider all types which are not excluded and sel>0 - idx = [(type_i, ii) not in self.exclude_types and self.sel_a[type_i] > 0 for type_i in range(self.ntypes)] - uu = np.max(upper[idx]) - ll = np.min(lower[idx]) - else: - ielement = ii // self.ntypes - net = "filter_" + str(ielement) + "_net_" + str(ii % self.ntypes) - uu = upper[ielement] - ll = lower[ielement] - xx = np.arange(ll, uu, stride0, dtype = self.data_type) - xx = np.append(xx, np.arange(uu, extrapolate * uu, stride1, dtype = self.data_type)) - xx = np.append(xx, np.array([extrapolate * uu], dtype = self.data_type)) - nspline = ((uu - ll) / stride0 + (extrapolate * uu - uu) / stride1).astype(int) - self._build_lower(net, xx, ii, uu, ll, stride0, stride1, extrapolate, nspline) - else: - raise RuntimeError("Unsupported descriptor") - self._convert_numpy_to_tensor() - - return self.lower, self.upper - - def _build_lower(self, net, xx, idx, upper, lower, stride0, stride1, extrapolate, nspline): - vv, dd, d2 = self._make_data(xx, idx) - self.data[net] = np.zeros([nspline, 6 * self.last_layer_size], dtype = self.data_type) - - # tt.shape: [nspline, self.last_layer_size] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[:int((upper - lower) / stride0), :] = stride0 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[int((lower - extrapolate * lower) / stride1) + 1:(int((lower - extrapolate * lower) / stride1) + int((upper - lower) / stride0)), :] = stride0 - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - tt = np.full((nspline, self.last_layer_size), stride1) - tt[:int((upper - lower) / stride0), :] = stride0 - else: - raise RuntimeError("Unsupported descriptor") - - # hh.shape: [nspline, self.last_layer_size] - hh = vv[1:nspline+1, :self.last_layer_size] - vv[:nspline, :self.last_layer_size] - - self.data[net][:, :6 * self.last_layer_size:6] = vv[:nspline, :self.last_layer_size] - self.data[net][:, 1:6 * self.last_layer_size:6] = dd[:nspline, :self.last_layer_size] - self.data[net][:, 2:6 * self.last_layer_size:6] = 0.5 * d2[:nspline, :self.last_layer_size] - self.data[net][:, 3:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt)) * (20 * hh - (8 * dd[1:nspline+1, :self.last_layer_size] + 12 * dd[:nspline, :self.last_layer_size]) * tt - (3 * d2[:nspline, :self.last_layer_size] - d2[1:nspline+1, :self.last_layer_size]) * tt * tt) - self.data[net][:, 4:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt * tt)) * (-30 * hh + (14 * dd[1:nspline+1, :self.last_layer_size] + 16 * dd[:nspline, :self.last_layer_size]) * tt + (3 * d2[:nspline, :self.last_layer_size] - 2 * d2[1:nspline+1, :self.last_layer_size]) * tt * tt) - self.data[net][:, 5:6 * self.last_layer_size:6] = (1 / (2 * tt * tt * tt * tt * tt)) * (12 * hh - 6 * (dd[1:nspline+1, :self.last_layer_size] + dd[:nspline, :self.last_layer_size]) * tt + (d2[1:nspline+1, :self.last_layer_size] - d2[:nspline, :self.last_layer_size]) * tt * tt) - - self.upper[net] = upper - self.lower[net] = lower - - def _load_sub_graph(self): - sub_graph_def = tf.GraphDef() - with tf.Graph().as_default() as sub_graph: - tf.import_graph_def(sub_graph_def, name = "") - return sub_graph, sub_graph_def - - def _get_bias(self): - bias = {} - for layer in range(1, self.layer_size + 1): - bias["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/bias_{layer}_{ii % self.ntypes}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}_{jj}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/bias_{layer}_{ii}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/bias_{layer}_{ii % self.ntypes}"] - bias["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - bias["layer_" + str(layer)].append(np.array([])) - else: - raise RuntimeError("Unsupported descriptor") - return bias - - def _get_matrix(self): - matrix = {} - for layer in range(1, self.layer_size + 1): - matrix["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/matrix_{layer}_{ii % self.ntypes}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - for ii in range(self.ntypes): - for jj in range(ii, self.ntypes): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}_{jj}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - if self.type_one_side: - for ii in range(0, self.ntypes): - if not self._all_excluded(ii): - node = self.embedding_net_nodes[f"filter_type_all{self.suffix}/matrix_{layer}_{ii}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - for ii in range(0, self.ntypes * self.ntypes): - if (ii // self.ntypes, ii % self.ntypes) not in self.exclude_types: - node = self.embedding_net_nodes[f"filter_type_{ii // self.ntypes}{self.suffix}/matrix_{layer}_{ii % self.ntypes}"] - matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) - else: - matrix["layer_" + str(layer)].append(np.array([])) - else: - raise RuntimeError("Unsupported descriptor") - - return matrix - - # one-by-one executions - def _make_data(self, xx, idx): - with self.sub_graph.as_default(): - with self.sub_sess.as_default(): - xx = tf.reshape(xx, [xx.size, -1]) - for layer in range(self.layer_size): - if layer == 0: - xbar = tf.matmul( - xx, self.matrix["layer_" + str(layer + 1)][idx]) + self.bias["layer_" + str(layer + 1)][idx] - if self.neuron[0] == 1: - yy = self._layer_0( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) + xx - dy = op_module.unaggregated_dy_dx_s( - yy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) + tf.ones([1, 1], yy.dtype) - dy2 = op_module.unaggregated_dy2_dx_s( - yy, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - elif self.neuron[0] == 2: - tt, yy = self._layer_1( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dy = op_module.unaggregated_dy_dx_s( - yy - tt, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) + tf.ones([1, 2], yy.dtype) - dy2 = op_module.unaggregated_dy2_dx_s( - yy - tt, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - else: - yy = self._layer_0( - xx, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dy = op_module.unaggregated_dy_dx_s( - yy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - dy2 = op_module.unaggregated_dy2_dx_s( - yy, dy, self.matrix["layer_" + str(layer + 1)][idx], xbar, tf.constant(self.functype)) - else: - ybar = tf.matmul( - yy, self.matrix["layer_" + str(layer + 1)][idx]) + self.bias["layer_" + str(layer + 1)][idx] - tt, zz = self._layer_1( - yy, self.matrix["layer_" + str(layer + 1)][idx], self.bias["layer_" + str(layer + 1)][idx]) - dz = op_module.unaggregated_dy_dx( - zz - tt, self.matrix["layer_" + str(layer + 1)][idx], dy, ybar, tf.constant(self.functype)) - dy2 = op_module.unaggregated_dy2_dx( - zz - tt, self.matrix["layer_" + str(layer + 1)][idx], dy, dy2, ybar, tf.constant(self.functype)) - dy = dz - yy = zz - - vv = zz.eval() - dd = dy.eval() - d2 = dy2.eval() - return vv, dd, d2 - - def _layer_0(self, x, w, b): - return self.activation_fn(tf.matmul(x, w) + b) - - def _layer_1(self, x, w, b): - t = tf.concat([x, x], axis=1) - return t, self.activation_fn(tf.matmul(x, w) + b) + t - - # Change the embedding net range to sw / min_nbor_dist - def _get_env_mat_range(self, - min_nbor_dist): - sw = self._spline5_switch(min_nbor_dist, self.rcut_smth, self.rcut) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - lower = -self.davg[:, 0] / self.dstd[:, 0] - upper = ((1 / min_nbor_dist) * sw - self.davg[:, 0]) / self.dstd[:, 0] - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - var = np.square(sw / (min_nbor_dist * self.dstd[:, 1:4])) - lower = np.min(-var, axis=1) - upper = np.max(var, axis=1) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - lower = -self.davg[:, 0] / self.dstd[:, 0] - upper = ((1 / min_nbor_dist) * sw - self.davg[:, 0]) / self.dstd[:, 0] - else: - raise RuntimeError("Unsupported descriptor") - log.info('training data with lower boundary: ' + str(lower)) - log.info('training data with upper boundary: ' + str(upper)) - # returns element-wise lower and upper - return np.floor(lower), np.ceil(upper) - - def _spline5_switch(self, - xx, - rmin, - rmax): - if xx < rmin: - vv = 1 - elif xx < rmax: - uu = (xx - rmin) / (rmax - rmin) - vv = uu*uu*uu * (-6 * uu*uu + 15 * uu - 10) + 1 - else: - vv = 0 - return vv - - def _get_layer_size(self): - layer_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - layer_size = len(self.embedding_net_nodes) // ((self.ntypes * self.ntypes - len(self.exclude_types)) * 2) - if self.type_one_side : - layer_size = len(self.embedding_net_nodes) // ((self.ntypes - self._n_all_excluded) * 2) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - layer_size = len(self.embedding_net_nodes) // int(comb(self.ntypes + 1, 2) * 2) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - layer_size = len(self.embedding_net_nodes) // ((self.ntypes * self.ntypes - len(self.exclude_types)) * 2) - if self.type_one_side : - layer_size = len(self.embedding_net_nodes) // ((self.ntypes - self._n_all_excluded) * 2) - else: - raise RuntimeError("Unsupported descriptor") - return layer_size - - @property - @lru_cache() - def _n_all_excluded(self) -> int: - """Then number of types excluding all types.""" - return sum((int(self._all_excluded(ii)) for ii in range(0, self.ntypes))) - - @lru_cache() - def _all_excluded(self, ii: int) -> bool: - """Check if type ii excluds all types. - - Parameters - ---------- - ii : int - type index - - Returns - ------- - bool - if type ii excluds all types - """ - return all([(ii, type_i) in self.exclude_types for type_i in range(self.ntypes)]) - - def _get_table_size(self): - table_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): - table_size = self.ntypes * self.ntypes - if self.type_one_side : - table_size = self.ntypes - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeT): - table_size = int(comb(self.ntypes + 1, 2)) - elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeR): - table_size = self.ntypes * self.ntypes - if self.type_one_side : - table_size = self.ntypes - else: - raise RuntimeError("Unsupported descriptor") - return table_size - - def _get_data_type(self): - for item in self.matrix["layer_" + str(self.layer_size)]: - if len(item) != 0: - return type(item[0][0]) - return None - - def _get_last_layer_size(self): - for item in self.matrix["layer_" + str(self.layer_size)]: - if len(item) != 0: - return item.shape[1] - return 0 - - def _convert_numpy_to_tensor(self): - """Convert self.data from np.ndarray to tf.Tensor.""" - for ii in self.data: - self.data[ii] = tf.constant(self.data[ii]) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/type_embed.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/type_embed.py deleted file mode 100644 index c2712bc..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/type_embed.py +++ /dev/null @@ -1,168 +0,0 @@ -import numpy as np -from typing import Tuple, List, Union - -from deepmd.env import tf -from deepmd.utils.network import one_layer -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 - -from deepmd.utils.graph import get_type_embedding_net_variables_from_graph_def -from deepmd.common import get_activation_func, get_precision - - -def embed_atom_type( - ntypes : int, - natoms : tf.Tensor, - type_embedding : tf.Tensor, -): - """ - Make the embedded type for the atoms in system. - The atoms are assumed to be sorted according to the type, - thus their types are described by a `tf.Tensor` natoms, see explanation below. - - Parameters - ---------- - ntypes: - Number of types. - 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 - type_embedding: - The type embedding. - It has the shape of [ntypes, embedding_dim] - - Returns - ------- - atom_embedding - The embedded type of each atom. - It has the shape of [numb_atoms, embedding_dim] - """ - te_out_dim = type_embedding.get_shape().as_list()[-1] - atype = [] - for ii in range(ntypes): - atype.append(tf.tile([ii], [natoms[2+ii]])) - atype = tf.concat(atype, axis = 0) - atm_embed = tf.nn.embedding_lookup(type_embedding,tf.cast(atype,dtype=tf.int32)) #(nf*natom)*nchnl - atm_embed = tf.reshape(atm_embed,[-1,te_out_dim]) - return atm_embed - - -class TypeEmbedNet(): - """ - - Parameters - ---------- - neuron : list[int] - Number of neurons in each hidden layers of the embedding net - resnet_dt - Time-step `dt` in the resnet construction: - y = x + dt * \phi (Wx + b) - activation_function - The activation function in the embedding net. Supported options are |ACTIVATION_FN| - precision - The precision of the embedding net parameters. Supported options are |PRECISION| - trainable - If the weights of embedding net are trainable. - seed - Random seed for initializing the network parameters. - uniform_seed - Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed - padding - Concat the zero padding to the output, as the default embedding of empty type. - """ - def __init__( - self, - neuron: List[int]=[], - resnet_dt: bool = False, - activation_function: Union[str, None] = 'tanh', - precision: str = 'default', - trainable: bool = True, - seed: int = None, - uniform_seed: bool = False, - padding: bool = False, - )->None: - """ - Constructor - """ - self.neuron = neuron - self.seed = seed - self.filter_resnet_dt = resnet_dt - self.filter_precision = get_precision(precision) - self.filter_activation_fn = get_activation_func(activation_function) - self.trainable = trainable - self.uniform_seed = uniform_seed - self.type_embedding_net_variables = None - self.padding = padding - - - def build( - self, - ntypes: int, - reuse = None, - suffix = '', - ): - """ - Build the computational graph for the descriptor - - Parameters - ---------- - ntypes - Number of atom types. - reuse - The weights in the networks should be reused when get the variable. - suffix - Name suffix to identify this descriptor - - Returns - ------- - embedded_types - The computational graph for embedded types - """ - types = tf.convert_to_tensor( - [ii for ii in range(ntypes)], - dtype = tf.int32 - ) - ebd_type = tf.cast(tf.one_hot(tf.cast(types,dtype=tf.int32),int(ntypes)), self.filter_precision) - ebd_type = tf.reshape(ebd_type, [-1, ntypes]) - name = 'type_embed_net' + suffix - with tf.variable_scope(name, reuse=reuse): - ebd_type = embedding_net( - ebd_type, - self.neuron, - activation_fn = self.filter_activation_fn, - precision = self.filter_precision, - resnet_dt = self.filter_resnet_dt, - seed = self.seed, - trainable = self.trainable, - initial_variables = self.type_embedding_net_variables, - uniform_seed = self.uniform_seed) - ebd_type = tf.reshape(ebd_type, [-1, self.neuron[-1]]) # ntypes * neuron[-1] - if self.padding: - last_type = tf.cast(tf.zeros([1, self.neuron[-1]]), self.filter_precision) - ebd_type = tf.concat([ebd_type, last_type], 0) # (ntypes + 1) * neuron[-1] - self.ebd_type = tf.identity(ebd_type, name ='t_typeebd') - return self.ebd_type - - def init_variables(self, - graph: tf.Graph, - graph_def: tf.GraphDef, - suffix = '', - ) -> None: - """ - Init the type 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 - Name suffix to identify this descriptor - """ - self.type_embedding_net_variables = get_type_embedding_net_variables_from_graph_def(graph_def, suffix = suffix) diff --git a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/weight_avg.py b/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/weight_avg.py deleted file mode 100644 index c58c72d..0000000 --- a/_skbuild/linux-x86_64-3.6/setuptools/lib/deepmd/utils/weight_avg.py +++ /dev/null @@ -1,34 +0,0 @@ -from typing import TYPE_CHECKING, List, Dict, Optional, Tuple -import numpy as np - - -def weighted_average( - errors: List[Dict[str, Tuple[float, float]]] -) -> Dict: - """Compute wighted average of prediction errors for model. - - Parameters - ---------- - errors : List[Dict[str, Tuple[float, float]]] - List: the error of systems - Dict: the error of quantities, name given by the key - Tuple: (error, weight) - - Returns - ------- - Dict - weighted averages - """ - sum_err = {} - sum_siz = {} - for err in errors: - for kk, (ee, ss) in err.items(): - if kk in sum_err: - sum_err[kk] += ee * ee * ss - sum_siz[kk] += ss - else : - sum_err[kk] = ee * ee * ss - sum_siz[kk] = ss - for kk in sum_err.keys(): - sum_err[kk] = np.sqrt(sum_err[kk] / sum_siz[kk]) - return 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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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list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-7cc9b2.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - 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list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-859d98.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - 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list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-88713d.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - 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list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-683c11.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - 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list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-30aac3.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - 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"/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/backward" - ignoring duplicate directory "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include" - ignoring duplicate directory "/usr/local/include" - ignoring duplicate directory "/usr/include" - #include "..." search starts here: - #include <...> search starts here: - /opt/dtk-22.10/include - /opt/dtk-22.10/llvm/include - . - /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7 - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/x86_64-redhat-linux - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/backward - /opt/dtk-22.10/llvm/lib/clang/14.0.0/include - /usr/local/include - /usr/include - End of search list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-bb7696.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - - kind: "try_run-v1" - backtrace: - - "cmake/Findtensorflow.cmake:169 (try_run)" - - "CMakeLists.txt:88 (find_package)" - directories: - source: 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CMakeFiles/cmTC_d8e6c.dir/tf_version.cpp.o -c /public/home/zhangqha/deepmd-kit/source/cmake/tf_version.cpp - [2/2] : && /opt/rh/devtoolset-7/root/usr/bin/c++ CMakeFiles/cmTC_d8e6c.dir/tf_version.cpp.o -o cmTC_d8e6c && : - - exitCode: 0 - runResult: - variable: "TENSORFLOW_VERSION_RUN_RESULT_VAR" - cached: true - stdout: | - 2.7.0 - exitCode: 0 -... - ---- -events: - - - kind: "message-v1" - backtrace: - - "/tmp/pip-build-env-80htwqaa/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:17 (message)" - - "/tmp/pip-build-env-80htwqaa/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" - - "/tmp/pip-build-env-80htwqaa/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineHIPCompiler.cmake:74 (CMAKE_DETERMINE_COMPILER_ID)" - - "/opt/dtk-22.10/hip/cmake/FindHIP.cmake:190 (include)" - - "cmake/FindROCM.cmake:65 (find_package)" 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default target x86_64-unknown-linux-gnu - ignoring nonexistent directory "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../x86_64-redhat-linux/include" - ignoring nonexistent directory "/include" - ignoring nonexistent directory "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../x86_64-redhat-linux/include" - ignoring nonexistent directory "/include" - ignoring duplicate directory "/opt/dtk-22.10/include" - ignoring duplicate directory "/opt/dtk-22.10/llvm/include" - ignoring duplicate directory "/opt/dtk-22.10/include" - ignoring duplicate directory "/opt/dtk-22.10/llvm/include" - ignoring duplicate directory "/opt/dtk-22.10/include" - ignoring duplicate directory "/opt/dtk-22.10/llvm/include" - ignoring duplicate directory "/opt/dtk-22.10/include" - ignoring duplicate directory "/opt/dtk-22.10/llvm/include" - ignoring duplicate directory "/opt/dtk-22.10/include" - ignoring duplicate directory 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/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/backward - /opt/dtk-22.10/llvm/lib/clang/14.0.0/include - /usr/local/include - /usr/include - End of search list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-08a178.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - 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/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7 - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/x86_64-redhat-linux - /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../include/c++/7/backward - /opt/dtk-22.10/llvm/lib/clang/14.0.0/include - /usr/local/include - /usr/include - End of search list. - "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../bin/ld" --hash-style=gnu --eh-frame-hdr -m elf_x86_64 -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o a.out /lib/../lib64/crt1.o /lib/../lib64/crti.o /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtbegin.o -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7 -L/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/dtk-22.10/llvm/bin/../lib -L/lib -L/usr/lib /tmp/CMakeHIPCompilerId-2c5c52.o -lstdc++ -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/crtend.o /lib/../lib64/crtn.o - - - Compilation of the HIP compiler identification source "CMakeHIPCompilerId.hip" produced "a.out" - - The HIP compiler identification is Clang, found in: - /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/3.26.0/CompilerIdHIP/a.out - - - kind: "try_run-v1" + kind: "try_compile-v1" backtrace: - - "cmake/Findtensorflow.cmake:169 (try_run)" - - "CMakeLists.txt:88 (find_package)" + - "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/Internal/CheckSourceCompiles.cmake:101 (try_compile)" + - "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CheckCSourceCompiles.cmake:76 (cmake_check_source_compiles)" + - "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/FindThreads.cmake:97 (CHECK_C_SOURCE_COMPILES)" + - "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/FindThreads.cmake:163 (_threads_check_libc)" + - "CMakeLists.txt:109 (find_package)" + checks: + - "Performing Test CMAKE_HAVE_LIBC_PTHREAD" directories: - source: "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/tf_version/CMakeFiles/CMakeTmp" - binary: "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/tf_version/CMakeFiles/CMakeTmp" + source: "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/CMakeScratch/TryCompile-FnSHZr" + binary: "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/CMakeScratch/TryCompile-FnSHZr" cmakeVariables: - CMAKE_CXX_FLAGS: "" - CMAKE_CXX_FLAGS_DEBUG: "-g" + CMAKE_C_FLAGS: "" + CMAKE_C_FLAGS_DEBUG: "-g" CMAKE_EXE_LINKER_FLAGS: "" - CMAKE_HIP_ARCHITECTURES: "gfx906" - CMAKE_MODULE_PATH: "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/skbuild/resources/cmake;/public/home/zhangqha/deepmd-kit/source/cmake/;/opt/dtk-22.10/hip/cmake" + CMAKE_MODULE_PATH: "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/skbuild/resources/cmake;/root/deepmd-kit/source/cmake/;/opt/dtk-22.04.2/hip/cmake" buildResult: - variable: "TENSORFLOW_VERSION_COMPILE_RESULT_VAR" + variable: "CMAKE_HAVE_LIBC_PTHREAD" cached: true stdout: | - Change Dir: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/tf_version/CMakeFiles/CMakeTmp + Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/CMakeScratch/TryCompile-FnSHZr - Run Build Command(s):/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja -v cmTC_b3590 && [1/2] /opt/rh/devtoolset-7/root/usr/bin/c++ -I/usr/local/lib/python3.7/site-packages/tensorflow/include -o CMakeFiles/cmTC_b3590.dir/tf_version.cpp.o -c /public/home/zhangqha/deepmd-kit/source/cmake/tf_version.cpp - [2/2] : && /opt/rh/devtoolset-7/root/usr/bin/c++ CMakeFiles/cmTC_b3590.dir/tf_version.cpp.o -o cmTC_b3590 && : + Run Build Command(s):/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja -v cmTC_1bda6 && [1/2] /opt/rh/devtoolset-7/root/usr/bin/cc -DCMAKE_HAVE_LIBC_PTHREAD -o CMakeFiles/cmTC_1bda6.dir/src.c.o -c /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/CMakeScratch/TryCompile-FnSHZr/src.c + [2/2] : && /opt/rh/devtoolset-7/root/usr/bin/cc CMakeFiles/cmTC_1bda6.dir/src.c.o -o cmTC_1bda6 && : + FAILED: cmTC_1bda6 + : && /opt/rh/devtoolset-7/root/usr/bin/cc CMakeFiles/cmTC_1bda6.dir/src.c.o -o cmTC_1bda6 && : + CMakeFiles/cmTC_1bda6.dir/src.c.o: In function `main': + src.c:(.text+0x2d): undefined reference to `pthread_create' + src.c:(.text+0x39): undefined reference to `pthread_detach' + src.c:(.text+0x45): undefined reference to `pthread_cancel' + src.c:(.text+0x56): undefined reference to `pthread_join' + src.c:(.text+0x6a): undefined reference to `pthread_atfork' + collect2: error: ld returned 1 exit status + ninja: build stopped: subcommand failed. - exitCode: 0 - runResult: - variable: "TENSORFLOW_VERSION_RUN_RESULT_VAR" - cached: true - stdout: | - 2.9.0 - exitCode: 0 -... - ---- -events: + exitCode: 1 - - kind: "message-v1" + kind: "try_compile-v1" backtrace: - - "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:17 (message)" - - "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:64 (__determine_compiler_id_test)" - - "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineHIPCompiler.cmake:74 (CMAKE_DETERMINE_COMPILER_ID)" - - "/opt/dtk-22.10/hip/cmake/FindHIP.cmake:190 (include)" - 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+/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/list_install_components.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/install.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/install/local.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/install/strip.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/edit_cache.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/rebuild_cache.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/list_install_components.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/install.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/install/local.dir +/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/CMakeFiles/install/strip.dir diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/rules.ninja b/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/rules.ninja index 7b18715..424152d 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/rules.ninja +++ b/_skbuild/linux-x86_64-3.7/cmake-build/CMakeFiles/rules.ninja @@ -80,7 +80,7 @@ rule CXX_SHARED_LIBRARY_LINKER__deepmd_Release # Rule for linking CXX shared library. rule CXX_SHARED_LIBRARY_LINKER__deepmd_op_rocm_Release - command = $PRE_LINK && /opt/rh/devtoolset-7/root/usr/bin/c++ -fPIC $LANGUAGE_COMPILE_FLAGS $ARCH_FLAGS $LINK_FLAGS -shared $SONAME_FLAG$SONAME -o $TARGET_FILE $in $LINK_PATH $LINK_LIBRARIES && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E __run_co_compile --lwyu="ldd;-u;-r" --source=lib/src/rocm/libdeepmd_op_rocm.so && $POST_BUILD + command = $PRE_LINK && /opt/rh/devtoolset-7/root/usr/bin/c++ -fPIC $LANGUAGE_COMPILE_FLAGS $ARCH_FLAGS $LINK_FLAGS -shared $SONAME_FLAG$SONAME -o $TARGET_FILE $in $LINK_PATH $LINK_LIBRARIES && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E __run_co_compile --lwyu="ldd;-u;-r" --source=lib/src/rocm/libdeepmd_op_rocm.so && $POST_BUILD description = Linking CXX shared library $TARGET_FILE restat = $RESTAT @@ -89,7 +89,7 @@ rule CXX_SHARED_LIBRARY_LINKER__deepmd_op_rocm_Release # Rule for re-running cmake. rule RERUN_CMAKE - command = /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/public/home/zhangqha/deepmd-kit/source -B/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build + command = /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/root/deepmd-kit/source -B/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build description = Re-running CMake... generator = 1 @@ -98,7 +98,7 @@ rule RERUN_CMAKE # Rule for cleaning all built files. rule CLEAN - command = /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja $FILE_ARG -t clean $TARGETS + command = /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja $FILE_ARG -t clean $TARGETS description = Cleaning all built files... @@ -106,6 +106,6 @@ rule CLEAN # Rule for printing all primary targets available. rule HELP - command = /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja -t targets + command = /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/ninja/data/bin/ninja -t targets description = All primary targets available: diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/CMakeSpec.json b/_skbuild/linux-x86_64-3.7/cmake-build/CMakeSpec.json index 08f7507..c0cb044 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/CMakeSpec.json +++ b/_skbuild/linux-x86_64-3.7/cmake-build/CMakeSpec.json @@ -1 +1 @@ -{"args": ["/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake", "-DTENSORFLOW_ROOT:STRING=/usr/local/lib/python3.7/site-packages/tensorflow", "-DBUILD_PY_IF:BOOL=TRUE", "-DBUILD_CPP_IF:BOOL=FALSE", "-DUSE_ROCM_TOOLKIT:BOOL=TRUE", "-DCMAKE_BUILD_TYPE:STRING=Release"], "version": "3.26.0", "environment": {"PYTHONNOUSERSITE": "1", "PYTHONPATH": "/tmp/pip-build-env-77v541be/site"}} \ No newline at end of file +{"args": ["/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake", "-DTENSORFLOW_ROOT:STRING=/usr/local/lib/python3.7/site-packages/tensorflow", "-DBUILD_PY_IF:BOOL=TRUE", "-DBUILD_CPP_IF:BOOL=FALSE", "-DUSE_ROCM_TOOLKIT:BOOL=TRUE", "-DROCM_ROOT:STRING=/opt/dtk-22.04.2", "-DCMAKE_BUILD_TYPE:STRING=Release"], "version": "3.26.3", "environment": {"PYTHONNOUSERSITE": "1", "PYTHONPATH": "/tmp/pip-build-env-lhxr9b79/site"}} \ No newline at end of file diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/build.ninja b/_skbuild/linux-x86_64-3.7/cmake-build/build.ninja index e2c6013..3998e1d 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/build.ninja +++ b/_skbuild/linux-x86_64-3.7/cmake-build/build.ninja @@ -39,7 +39,7 @@ include CMakeFiles/rules.ninja ############################################# # Logical path to working directory; prefix for absolute paths. -cmake_ninja_workdir = /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/ +cmake_ninja_workdir = /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/ ############################################# # Utility command for uninstall @@ -57,7 +57,7 @@ build lammps: phony CMakeFiles/lammps # Utility command for edit_cache build CMakeFiles/edit_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. 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DESC = No interactive CMake dialog available... restat = 1 @@ -782,7 +905,7 @@ build lib/edit_cache: phony lib/CMakeFiles/edit_cache.util # Utility command for rebuild_cache build lib/CMakeFiles/rebuild_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/public/home/zhangqha/deepmd-kit/source -B/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/root/deepmd-kit/source -B/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build DESC = Running CMake to regenerate build system... pool = console restat = 1 @@ -800,7 +923,7 @@ build lib/list_install_components: phony # Utility command for install build lib/CMakeFiles/install.util: CUSTOM_COMMAND lib/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake DESC = Install the project... pool = console restat = 1 @@ -812,7 +935,7 @@ build lib/install: phony lib/CMakeFiles/install.util # Utility command for install/local build lib/CMakeFiles/install/local.util: CUSTOM_COMMAND lib/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/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 @@ -824,7 +947,7 @@ build lib/install/local: phony lib/CMakeFiles/install/local.util # Utility command for install/strip build lib/CMakeFiles/install/strip.util: CUSTOM_COMMAND lib/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake DESC = Installing the project stripped... pool = console restat = 1 @@ -833,7 +956,7 @@ build lib/install/strip: phony lib/CMakeFiles/install/strip.util # ============================================================================= # Write statements declared in CMakeLists.txt: -# /public/home/zhangqha/deepmd-kit/source/lib/CMakeLists.txt +# /root/deepmd-kit/source/lib/CMakeLists.txt # ============================================================================= # ============================================================================= @@ -861,15 +984,16 @@ build lib/src/rocm/libdeepmd_op_rocm.so: CXX_SHARED_LIBRARY_LINKER__deepmd_op_ro PRE_LINK = : SONAME = libdeepmd_op_rocm.so SONAME_FLAG = -Wl,-soname, + TARGET_COMPILE_PDB = lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/ TARGET_FILE = lib/src/rocm/libdeepmd_op_rocm.so - TARGET_PDB = deepmd_op_rocm.so.dbg + TARGET_PDB = lib/src/rocm/libdeepmd_op_rocm.pdb ############################################# # Utility command for edit_cache build lib/src/rocm/CMakeFiles/edit_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. DESC = No interactive CMake dialog available... restat = 1 @@ -880,7 +1004,7 @@ build lib/src/rocm/edit_cache: phony lib/src/rocm/CMakeFiles/edit_cache.util # Utility command for rebuild_cache build lib/src/rocm/CMakeFiles/rebuild_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/public/home/zhangqha/deepmd-kit/source -B/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/root/deepmd-kit/source -B/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build DESC = Running CMake to regenerate build system... pool = console restat = 1 @@ -898,7 +1022,7 @@ build lib/src/rocm/list_install_components: phony # Utility command for install build lib/src/rocm/CMakeFiles/install.util: CUSTOM_COMMAND lib/src/rocm/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake DESC = Install the project... pool = console restat = 1 @@ -910,7 +1034,7 @@ build lib/src/rocm/install: phony lib/src/rocm/CMakeFiles/install.util # Utility command for install/local build lib/src/rocm/CMakeFiles/install/local.util: CUSTOM_COMMAND lib/src/rocm/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/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 @@ -922,7 +1046,7 @@ build lib/src/rocm/install/local: phony lib/src/rocm/CMakeFiles/install/local.ut # Utility command for install/strip build lib/src/rocm/CMakeFiles/install/strip.util: CUSTOM_COMMAND lib/src/rocm/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake DESC = Installing the project stripped... pool = console restat = 1 @@ -933,8 +1057,8 @@ build lib/src/rocm/install/strip: phony lib/src/rocm/CMakeFiles/install/strip.ut ############################################# # Custom command for lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o -build lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o | ${cmake_ninja_workdir}lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o: CUSTOM_COMMAND /public/home/zhangqha/deepmd-kit/source/lib/src/rocm/coord.hip.cu /opt/dtk-22.10/hip/include/hip/hip_common.h /opt/dtk-22.10/hip/include/hip/hip_runtime.h /opt/dtk-22.10/hip/include/hip/hip_runtime_api.h /opt/dtk-22.10/hip/include/hip/hip_texture_types.h /opt/dtk-22.10/hip/include/hip/hip_vector_types.h /opt/dtk-22.10/hip/include/hip/hip_version.h /opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h /opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h /opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h /opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp /opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h /opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h /opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp /opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h /opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h /opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h /opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h /opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h /opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h /opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h /opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h /opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h /opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h /opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h /opt/dtk-22.10/hip/include/hip/library_types.h /opt/dtk-22.10/hip/include/hip/surface_types.h /opt/dtk-22.10/hip/include/hip/texture_types.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h /opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h /opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm /opt/rh/devtoolset-7/root/usr/include/c++/7/array /opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h /opt/rh/devtoolset-7/root/usr/include/c++/7/backward/binders.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/algorithmfwd.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/alloc_traits.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/allocated_ptr.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/allocator.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/atomic_base.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/atomic_lockfree_defines.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/basic_ios.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/basic_ios.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/basic_string.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/basic_string.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/char_traits.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/concept_check.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/cpp_type_traits.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/cxxabi_forced.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/cxxabi_init_exception.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/exception.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/exception_defines.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/exception_ptr.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/functexcept.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/functional_hash.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/hash_bytes.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/invoke.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/ios_base.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/istream.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/locale_classes.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/locale_classes.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/locale_facets.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/locale_facets.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/localefwd.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/memoryfwd.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/move.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/nested_exception.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/ostream.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/ostream_insert.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/parse_numbers.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/postypes.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/predefined_ops.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/ptr_traits.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/range_access.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/refwrap.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/shared_ptr.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/shared_ptr_atomic.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/shared_ptr_base.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/sstream.tcc /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/std_abs.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_algo.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_algobase.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_bvector.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_construct.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_function.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_heap.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_iterator.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_iterator_base_funcs.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_iterator_base_types.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_pair.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_raw_storage_iter.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_relops.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_tempbuf.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_uninitialized.h /opt/rh/devtoolset-7/root/usr/include/c++/7/bits/stl_vector.h 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/usr/include/sys/select.h /usr/include/sys/sysmacros.h /usr/include/sys/types.h /usr/include/time.h /usr/include/wchar.h /usr/include/wctype.h /usr/include/xlocale.h lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E make_directory /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//. && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -D verbose:BOOL=OFF -D build_configuration:STRING=RELEASE -D generated_file:STRING=/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//./deepmd_op_rocm_generated_tabulate.hip.cu.o -P /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake DESC = Building HIPCC object lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o restat = 1 # ============================================================================= # Write statements declared in CMakeLists.txt: -# /public/home/zhangqha/deepmd-kit/source/CMakeLists.txt +# /root/deepmd-kit/source/CMakeLists.txt # ============================================================================= @@ -1029,7 +1153,7 @@ build lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabula # Utility command for edit_cache build config/CMakeFiles/edit_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -E echo No\ interactive\ CMake\ dialog\ available. DESC = No interactive CMake dialog available... restat = 1 @@ -1040,7 +1164,7 @@ build config/edit_cache: phony config/CMakeFiles/edit_cache.util # Utility command for rebuild_cache build config/CMakeFiles/rebuild_cache.util: CUSTOM_COMMAND - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/public/home/zhangqha/deepmd-kit/source -B/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake --regenerate-during-build -S/root/deepmd-kit/source -B/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build DESC = Running CMake to regenerate build system... pool = console restat = 1 @@ -1058,7 +1182,7 @@ build config/list_install_components: phony # Utility command for install build config/CMakeFiles/install.util: CUSTOM_COMMAND config/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -P cmake_install.cmake DESC = Install the project... pool = console restat = 1 @@ -1070,7 +1194,7 @@ build config/install: phony config/CMakeFiles/install.util # Utility command for install/local build config/CMakeFiles/install/local.util: CUSTOM_COMMAND config/all - COMMAND = cd /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_LOCAL_ONLY=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/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 @@ -1082,7 +1206,7 @@ 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 /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake + COMMAND = cd /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config && /tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake DESC = Installing the project stripped... pool = console restat = 1 @@ -1114,35 +1238,35 @@ build op_grads: phony op/libop_grads.so # ============================================================================= ############################################# -# Folder: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build +# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build build all: phony op/all lib/all config/all # ============================================================================= ############################################# -# Folder: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config +# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config build config/all: phony # ============================================================================= ############################################# -# Folder: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib +# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib build lib/all: phony lib/libdeepmd.so lib/src/rocm/all # ============================================================================= ############################################# -# Folder: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm +# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm build lib/src/rocm/all: phony lib/src/rocm/libdeepmd_op_rocm.so # ============================================================================= ############################################# -# Folder: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/op +# Folder: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/op build op/all: phony op/libop_abi.so op/libop_grads.so @@ -1153,14 +1277,14 @@ build op/all: phony op/libop_abi.so op/libop_grads.so ############################################# # Re-run CMake if any of its inputs changed. -build build.ninja: RERUN_CMAKE | /opt/dtk-22.10/hip/cmake/FindHIP.cmake 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/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeCheckCompilerFlagCommonPatterns.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeCommonLanguageInclude.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeCompilerIdDetection.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompiler.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineHIPCompiler.cmake /tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeFindBinUtils.cmake 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100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/cmake_install.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/cmake_install.cmake @@ -1,8 +1,8 @@ -# Install script for directory: /public/home/zhangqha/deepmd-kit/source +# Install script for directory: /root/deepmd-kit/source # Set the install prefix if(NOT DEFINED CMAKE_INSTALL_PREFIX) - set(CMAKE_INSTALL_PREFIX "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") + set(CMAKE_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") endif() string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") @@ -44,9 +44,9 @@ endif() if(NOT CMAKE_INSTALL_LOCAL_ONLY) # Include the install script for each subdirectory. - include("/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/op/cmake_install.cmake") - include("/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/cmake_install.cmake") - include("/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake") + include("/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/op/cmake_install.cmake") + include("/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/cmake_install.cmake") + include("/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake") endif() @@ -58,5 +58,5 @@ endif() string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT "${CMAKE_INSTALL_MANIFEST_FILES}") -file(WRITE "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/${CMAKE_INSTALL_MANIFEST}" +file(WRITE "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/${CMAKE_INSTALL_MANIFEST}" "${CMAKE_INSTALL_MANIFEST_CONTENT}") diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/cmake_lammps.cmake b/_skbuild/linux-x86_64-3.7/cmake-build/cmake_lammps.cmake index 7952a3d..e5579ee 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/cmake_lammps.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/cmake_lammps.cmake @@ -1,5 +1,5 @@ -set (LMP_INSTALL_PREFIX "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/USER-DEEPMD") -file(READ "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/lammps_install_list.txt" files) +set (LMP_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/USER-DEEPMD") +file(READ "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/lammps_install_list.txt" files) string(REGEX REPLACE "\n" "" files "${files}") foreach (cur_file ${files}) @@ -13,16 +13,16 @@ endforeach () file ( INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" TYPE FILE - FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/env.sh" + FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/env.sh" ) file ( INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" TYPE FILE - FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/env_low.sh" + FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/env_low.sh" ) file ( INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" TYPE FILE - FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/pair_deepmd.h" + FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lmp/pair_deepmd.h" ) diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/cmake_uninstall.cmake b/_skbuild/linux-x86_64-3.7/cmake-build/cmake_uninstall.cmake index 5bc7825..b3845b5 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/cmake_uninstall.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/cmake_uninstall.cmake @@ -1,14 +1,14 @@ -if(NOT EXISTS "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") - message(FATAL_ERROR "Cannot find install manifest: /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") -endif(NOT EXISTS "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") +if(NOT EXISTS "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") + message(FATAL_ERROR "Cannot find install manifest: /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") +endif(NOT EXISTS "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt") -file(READ "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt" files) +file(READ "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt" files) string(REGEX REPLACE "\n" ";" files "${files}") foreach(file ${files}) message(STATUS "Uninstalling $ENV{DESTDIR}${file}") if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}") exec_program( - "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake" ARGS "-E remove \"$ENV{DESTDIR}${file}\"" + "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake" ARGS "-E remove \"$ENV{DESTDIR}${file}\"" OUTPUT_VARIABLE rm_out RETURN_VALUE rm_retval ) diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake b/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake index 91b15bc..4e52532 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/config/cmake_install.cmake @@ -1,8 +1,8 @@ -# Install script for directory: /public/home/zhangqha/deepmd-kit/source/config +# Install script for directory: /root/deepmd-kit/source/config # Set the install prefix if(NOT DEFINED CMAKE_INSTALL_PREFIX) - set(CMAKE_INSTALL_PREFIX "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") + set(CMAKE_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") endif() string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") @@ -43,6 +43,6 @@ if(NOT DEFINED CMAKE_OBJDUMP) endif() if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT) - file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/deepmd/pkg_config" TYPE FILE FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini") + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/deepmd/pkg_config" TYPE FILE FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini") endif() diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini b/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini index 2251b27..2033fe9 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini +++ b/_skbuild/linux-x86_64-3.7/cmake-build/config/run_config.ini @@ -1,12 +1,12 @@ [CONFIG] -INSTALL_PREFIX = /public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install -GIT_SUMM = v2.1.5-dirty -GIT_HASH = 6e3d4a6 -GIT_DATE = 2022-09-23 16:10:28 +0800 -GIT_BRANCH = HEAD +INSTALL_PREFIX = /root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install +GIT_SUMM = +GIT_HASH = 187c0ae +GIT_DATE = 2023-03-14 01:42:38 +0000 +GIT_BRANCH = main TF_INCLUDE_DIR = /usr/local/lib/python3.7/site-packages/tensorflow/include;/usr/local/lib/python3.7/site-packages/tensorflow/include TF_LIBS = -TF_VERSION = 2.9.0 +TF_VERSION = 2.7.0 TF_CXX11_ABI_FLAG = 0 MODEL_VERSION=1.1 DP_VARIANT=rocm diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt b/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt index 335eb5d..effa008 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt +++ b/_skbuild/linux-x86_64-3.7/cmake-build/install_manifest.txt @@ -1,21 +1,21 @@ -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/libop_abi.so -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/libop_grads.so -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_gelu.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_map_nvnmd_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_matmul_nvnmd_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_force_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_force_se_a_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_force_se_r_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_virial_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_virial_se_a_grad.py -/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install/deepmd/op/_prod_virial_se_r_grad.py 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zQEPn5ldk`hqW@}vNb_$CuGIcB`M>>X#=j5kQ^&tT#1E>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_coord.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/coord.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_coord.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/coord.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o.depend index 75c660f..bba25e2 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_coord.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,13 +170,18 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/coord.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/region.cuh" - "/public/home/zhangqha/deepmd-kit/source/lib/include/region.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/coord.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/coord.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/region.cuh" + "/root/deepmd-kit/source/lib/include/region.h" + "/root/deepmd-kit/source/lib/src/rocm/coord.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_gelu.hip.cu.o 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z{Hz9SV%0yU^j}P6I&u4q`3SJn-~R>4v0X7j|EuWY`mFzhYX7x#-!Q_m;%D&khfYc_ zhJHtl3;h{T>EO*W!|iY}x6k*#q59B2%798cmHu2G`fs9(>odNBFl<${4dl@w)xJgD zTzzInOk&smuxh_jF`|9MPuBLG#^0-6Fj8T#m^&@1Er>*$pgYw5YaRc?N`G!oa*wyq z_gShhWLXCAejEE?C|Mrc?aVvHMKTGXfbq8NajsJEU9}w!>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_gelu.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/gelu.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_gelu.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/gelu.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_gelu.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_gelu.hip.cu.o.depend index bba56cb..382a2f7 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_gelu.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_gelu.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,11 +170,16 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gelu.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/gelu.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + 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zLiN<+(N(2pv_kpG+tW^SKDo)NV9UuutG^XRLD`%FXHSk#JIDFt4Oc^6&#R2~8pX(J zbm*p=8F)rp rW?x^6q!4~a;LGjil-`&SOo!%n-FgwNsdQ=Zh#vN11_%`BJgNT=>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/neighbor_list.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/neighbor_list.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend index 8464c09..cfd3b61 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_neighbor_list.hip.cu.o.depend @@ -1,206 +1,182 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_bfloat16.h" - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_fp16.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - 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"/opt/dtk-22.04.2/include/rocprim/detail/match_result_type.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/various.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/config_types.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_histogram.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_partition.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_reduce_by_key.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_scan_lookback.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_scan_reduce_then_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_segmented_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_segmented_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_transform.hpp" + 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"/opt/dtk-22.04.2/include/rocprim/device/device_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_scan_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_segmented_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_segmented_radix_sort_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_segmented_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_select.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_select_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_transform.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_transform_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/specialization/device_radix_merge_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/specialization/device_radix_single_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/functional.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/atomic.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/bit.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/thread.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/warp.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/warp_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/arg_index_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/constant_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/counting_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/discard_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/texture_cache_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/transform_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/zip_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/type_traits.hpp" + "/opt/dtk-22.04.2/include/rocprim/types.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/double_buffer.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/future_value.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/integer_sequence.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/key_value_pair.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/tuple.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_crosslane.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_dpp.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_shared_mem.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_crosslane.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_dpp.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_shared_mem.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_segment_bounds.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/warp_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/warp_scan.hpp" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/atomic" @@ -351,14 +327,19 @@ SET(HIP_HIPCC_DEPEND 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b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake index e4fa365..cda17da 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -46,10 +46,10 @@ set(HIP_NVCC_FLAGS_DEBUG ) set(HIP_NVCC_FLAGS_MINSIZEREL ) set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope -set(HIP_HIPCC_INCLUDE_ARGS -I/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/../../include/) # list +set(HIP_HIPCC_INCLUDE_ARGS -I/root/deepmd-kit/source/lib/src/rocm/../../include/) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake.pre-gen b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake.pre-gen index 3009d7b..c3da4bb 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake.pre-gen +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.cmake.pre-gen @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;$<$>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend index 0d62322..72530b3 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_env_mat.hip.cu.o.depend @@ -1,206 +1,182 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_bfloat16.h" - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_fp16.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_discontinuity.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_exchange.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_histogram.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_load.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_load_func.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_merge_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_radix_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_reduce.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_scan.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_store.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/block/block_store_func.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_histogram.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_radix_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_reduce.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_run_length_encode.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_scan.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_segmented_radix_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_segmented_reduce.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_segmented_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/device/device_select.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/hipcub.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/arg_index_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/cache_modified_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/cache_modified_output_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/constant_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/counting_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/discard_output_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/tex_obj_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/tex_ref_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/iterator/transform_input_iterator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_load.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_operators.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_reduce.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_scan.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_search.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/thread/thread_store.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/util_allocator.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/util_math.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/util_ptx.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/util_type.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_exchange.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_load.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_merge_sort.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_reduce.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_scan.hpp" - "/opt/dtk-22.10/include/hipcub/backend/rocprim/warp/warp_store.hpp" - "/opt/dtk-22.10/include/hipcub/config.hpp" - "/opt/dtk-22.10/include/hipcub/hipcub.hpp" - "/opt/dtk-22.10/include/hipcub/hipcub_version.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_discontinuity.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_exchange.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_histogram.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_load.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_load_func.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_radix_sort.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_scan.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_store.hpp" - "/opt/dtk-22.10/include/rocprim/block/block_store_func.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_histogram_atomic.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_histogram_sort.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_reduce_raking_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_reduce_warp_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_scan_reduce_then_scan.hpp" - "/opt/dtk-22.10/include/rocprim/block/detail/block_scan_warp_scan.hpp" - "/opt/dtk-22.10/include/rocprim/config.hpp" - "/opt/dtk-22.10/include/rocprim/detail/all_true.hpp" - "/opt/dtk-22.10/include/rocprim/detail/binary_op_wrappers.hpp" - "/opt/dtk-22.10/include/rocprim/detail/match_result_type.hpp" - "/opt/dtk-22.10/include/rocprim/detail/radix_sort.hpp" - "/opt/dtk-22.10/include/rocprim/detail/various.hpp" - "/opt/dtk-22.10/include/rocprim/device/config_types.hpp" - "/opt/dtk-22.10/include/rocprim/device/detail/device_histogram.hpp" - "/opt/dtk-22.10/include/rocprim/device/detail/device_partition.hpp" - 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"/opt/dtk-22.10/include/rocprim/device/device_histogram.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_histogram_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_partition.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_radix_sort.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_radix_sort_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_reduce_by_key.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_reduce_by_key_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_reduce_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_run_length_encode.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_run_length_encode_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_scan.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_scan_by_key.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_scan_by_key_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_scan_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_segmented_radix_sort.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_segmented_radix_sort_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_segmented_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_select.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_select_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_transform.hpp" - "/opt/dtk-22.10/include/rocprim/device/device_transform_config.hpp" - "/opt/dtk-22.10/include/rocprim/device/specialization/device_radix_merge_sort.hpp" - "/opt/dtk-22.10/include/rocprim/device/specialization/device_radix_single_sort.hpp" - "/opt/dtk-22.10/include/rocprim/functional.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics/atomic.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics/bit.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics/thread.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics/warp.hpp" - "/opt/dtk-22.10/include/rocprim/intrinsics/warp_shuffle.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/arg_index_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/constant_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/counting_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/discard_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/texture_cache_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/transform_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/iterator/zip_iterator.hpp" - "/opt/dtk-22.10/include/rocprim/type_traits.hpp" - "/opt/dtk-22.10/include/rocprim/types.hpp" - "/opt/dtk-22.10/include/rocprim/types/double_buffer.hpp" - "/opt/dtk-22.10/include/rocprim/types/future_value.hpp" - "/opt/dtk-22.10/include/rocprim/types/integer_sequence.hpp" - "/opt/dtk-22.10/include/rocprim/types/key_value_pair.hpp" - "/opt/dtk-22.10/include/rocprim/types/tuple.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_reduce_crosslane.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_reduce_dpp.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_reduce_shared_mem.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_reduce_shuffle.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_scan_crosslane.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_scan_dpp.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_scan_shared_mem.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_scan_shuffle.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_segment_bounds.hpp" - "/opt/dtk-22.10/include/rocprim/warp/detail/warp_sort_shuffle.hpp" - "/opt/dtk-22.10/include/rocprim/warp/warp_exchange.hpp" - "/opt/dtk-22.10/include/rocprim/warp/warp_load.hpp" - "/opt/dtk-22.10/include/rocprim/warp/warp_reduce.hpp" - "/opt/dtk-22.10/include/rocprim/warp/warp_scan.hpp" - "/opt/dtk-22.10/include/rocprim/warp/warp_sort.hpp" - 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"/opt/dtk-22.04.2/include/rocprim/block/block_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/block_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/block_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/block_store.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/block_store_func.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_histogram_atomic.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_histogram_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_reduce_raking_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_reduce_warp_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_scan_reduce_then_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/block/detail/block_scan_warp_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/config.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/all_true.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/binary_op_wrappers.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/match_result_type.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/detail/various.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/config_types.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_histogram.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_partition.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_reduce_by_key.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_scan_lookback.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_scan_reduce_then_scan.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_segmented_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_segmented_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/device_transform.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/lookback_scan_state.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/ordered_block_id.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/detail/uint_fast_div.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_histogram.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_histogram_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_partition.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_radix_sort.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_radix_sort_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_reduce_by_key.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_reduce_by_key_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_reduce_config.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_run_length_encode.hpp" + "/opt/dtk-22.04.2/include/rocprim/device/device_run_length_encode_config.hpp" + 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"/opt/dtk-22.04.2/include/rocprim/intrinsics/bit.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/thread.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/warp.hpp" + "/opt/dtk-22.04.2/include/rocprim/intrinsics/warp_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/arg_index_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/constant_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/counting_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/discard_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/texture_cache_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/transform_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/iterator/zip_iterator.hpp" + "/opt/dtk-22.04.2/include/rocprim/type_traits.hpp" + "/opt/dtk-22.04.2/include/rocprim/types.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/double_buffer.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/future_value.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/integer_sequence.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/key_value_pair.hpp" + "/opt/dtk-22.04.2/include/rocprim/types/tuple.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_crosslane.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_dpp.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_shared_mem.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_reduce_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_crosslane.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_dpp.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_shared_mem.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_scan_shuffle.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/detail/warp_segment_bounds.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/warp_reduce.hpp" + "/opt/dtk-22.04.2/include/rocprim/warp/warp_scan.hpp" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/atomic" @@ -351,16 +327,21 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/SimulationRegion.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/fmt_nlist.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/neighbor_list.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/prod_env_mat.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/region.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/utilities.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/SimulationRegion.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/fmt_nlist.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/neighbor_list.h" + "/root/deepmd-kit/source/lib/include/prod_env_mat.h" + "/root/deepmd-kit/source/lib/include/region.h" + "/root/deepmd-kit/source/lib/include/utilities.h" + "/root/deepmd-kit/source/lib/src/rocm/prod_env_mat.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" 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o|KI%+Bc?qo1*%WMEdPH2LDSOB@!l5w-;DW%0@Z85W9I+=0sHOv%m4rY diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake index b33eb5f..c28dcdb 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -46,10 +46,10 @@ set(HIP_NVCC_FLAGS_DEBUG ) set(HIP_NVCC_FLAGS_MINSIZEREL ) set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope -set(HIP_HIPCC_INCLUDE_ARGS -I/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/../../include/) # list +set(HIP_HIPCC_INCLUDE_ARGS -I/root/deepmd-kit/source/lib/src/rocm/../../include/) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake.pre-gen b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake.pre-gen index d97c104..86e93f4 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake.pre-gen +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.cmake.pre-gen @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;$<$>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.depend index a4675fc..21a3488 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,11 +170,16 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/prod_force.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/prod_force.h" + "/root/deepmd-kit/source/lib/src/rocm/prod_force.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o index 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set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -46,10 +46,10 @@ set(HIP_NVCC_FLAGS_DEBUG ) set(HIP_NVCC_FLAGS_MINSIZEREL ) set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope -set(HIP_HIPCC_INCLUDE_ARGS -I/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/../../include/) # list +set(HIP_HIPCC_INCLUDE_ARGS -I/root/deepmd-kit/source/lib/src/rocm/../../include/) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.cmake.pre-gen b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.cmake.pre-gen index 31e3e61..ae7b9ec 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.cmake.pre-gen +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.cmake.pre-gen @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;$<$>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend index 8043edd..9d4af3b 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_force_grad.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. 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"/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - 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"/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,11 +170,16 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/prod_force_grad.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/prod_force_grad.h" + "/root/deepmd-kit/source/lib/src/rocm/prod_force_grad.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o index 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set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_virial.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_virial.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend index 24a9c9f..5eabe24 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,11 +170,16 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/prod_virial.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_virial.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/prod_virial.h" + "/root/deepmd-kit/source/lib/src/rocm/prod_virial.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o index 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a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -46,10 +46,10 @@ set(HIP_NVCC_FLAGS_DEBUG ) set(HIP_NVCC_FLAGS_MINSIZEREL ) set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope -set(HIP_HIPCC_INCLUDE_ARGS -I/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/../../include/) # list +set(HIP_HIPCC_INCLUDE_ARGS -I/root/deepmd-kit/source/lib/src/rocm/../../include/) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_virial_grad.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_virial_grad.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake.pre-gen b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake.pre-gen index a21d986..a1fa75b 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake.pre-gen +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.cmake.pre-gen @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;$<$>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/prod_virial_grad.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/prod_virial_grad.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend index 94a0fb6..1fbb52f 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_prod_virial_grad.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + 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zX8-0^uH-fl@Xct$Np`x_HSYR#b42X9dtSMKSIj8=^y5Y>T>>*iYgL$ zlD>%wyXpTJ>yP)BgHZoEx}5s|4cG77fAq2AH=?jXqQ5|1{ug%XKYY(2)-1hsvWZVUvmC zBV7Lk@MLs9`+CLmw{5B)@HVTNx#X%{>?7>cb>|7yxJ3A`qG`R|=B_QUu3Qm=$pa{bQ!4|Dx8PNYt0 zkEC}Hpkz75UzY6`Wk$3e{U_x`u9yDzxaf~_Euy!Czwzk*-K>8z*Drz8FXd~p$`X#-V3d0s* z0XS?d=!leu{yXS$>fcTnOy#Wq93@3jtLyr4l=YYTPzK~o|Hfngzt6>f5Gm!wu4i)pNjp^~^fANrUoQZM=xdOK z%gFE&*E#z0;@xIyHtyuTL6?`uyd}mkm+y0QtmrR{*HXS)|Iu4i6urO|qW`XF>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_region.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/region.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_region.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/region.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_region.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_region.hip.cu.o.depend index 1c936e8..c2d14e9 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_region.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_region.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,12 +170,17 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/region.cuh" - "/public/home/zhangqha/deepmd-kit/source/lib/include/region.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/region.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/region.cuh" + "/root/deepmd-kit/source/lib/include/region.h" + "/root/deepmd-kit/source/lib/src/rocm/region.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o 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b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;-DTENSORFLOW_USE_ROCM -D__HIP_PLATFORM_HCC__ -D_GLIBCXX_USE_CXX11_ABI=0 -DTENSORFLOW_USE_ROCM -DCUB_IGNORE_DEPRECATED_CPP_DIALECT -DCUB_IGNORE_DEPRECATED_CPP_DIALECT) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -46,10 +46,10 @@ set(HIP_NVCC_FLAGS_DEBUG ) set(HIP_NVCC_FLAGS_MINSIZEREL ) set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope -set(HIP_HIPCC_INCLUDE_ARGS -I/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/../../include/) # list +set(HIP_HIPCC_INCLUDE_ARGS -I/root/deepmd-kit/source/lib/src/rocm/../../include/) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_tabulate.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_tabulate.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake.pre-gen b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake.pre-gen index b5077a7..376d6f1 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake.pre-gen +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.cmake.pre-gen @@ -21,16 +21,16 @@ if(NOT generated_file) endif() # Set these up as variables to make reading the generated file easier -set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipcc") # path -set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.10/hip/bin/hipconfig") #path +set(HIP_HIPCC_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipcc") # path +set(HIP_HIPCONFIG_EXECUTABLE "/opt/dtk-22.04.2/hip/bin/hipconfig") #path set(HIP_HOST_COMPILER "/opt/rh/devtoolset-7/root/usr/bin/c++") # path -set(CMAKE_COMMAND "/tmp/pip-build-env-77v541be/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path -set(HIP_run_make2cmake "/opt/dtk-22.10/hip/cmake/FindHIP/run_make2cmake.cmake") # path -set(HIP_CLANG_PATH "/opt/dtk-22.10/llvm/bin") #path +set(CMAKE_COMMAND "/tmp/pip-build-env-lhxr9b79/overlay/lib/python3.7/site-packages/cmake/data/bin/cmake") # path +set(HIP_run_make2cmake "/opt/dtk-22.04.2/hip/cmake/FindHIP/run_make2cmake.cmake") # path +set(HIP_CLANG_PATH "/opt/dtk-22.04.2/llvm/bin") #path set(HIP_CLANG_PARALLEL_BUILD_COMPILE_OPTIONS "") -set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++14;$<$>:-D$, -D>>) +set(HIP_HIPCC_FLAGS -fno-gpu-rdc;--amdgpu-target=gfx906;-fPIC;-O3;--std=c++11;$<$>:-D$, -D>>) set(HIP_HIPCC_FLAGS_RELEASE ) set(HIP_HIPCC_FLAGS_DEBUG ) set(HIP_HIPCC_FLAGS_MINSIZEREL ) @@ -48,8 +48,8 @@ set(HIP_NVCC_FLAGS_RELWITHDEBINFO ) #Needed to bring the HIP_HIPCC_INCLUDE_ARGS variable in scope set(HIP_HIPCC_INCLUDE_ARGS $<$>:-I$, -I>>) # list -set(cmake_dependency_file "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_tabulate.hip.cu.o.depend") # path -set(source_file "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu") # path +set(cmake_dependency_file "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir//deepmd_op_rocm_generated_tabulate.hip.cu.o.depend") # path +set(source_file "/root/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu") # path set(host_flag "FALSE") # bool # Determine compiler and compiler flags diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.depend b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.depend index 23df488..f7bf230 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.depend +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/CMakeFiles/deepmd_op_rocm.dir/deepmd_op_rocm_generated_tabulate.hip.cu.o.depend @@ -1,54 +1,47 @@ # Generated by: FindHIP.cmake. Do not edit. SET(HIP_HIPCC_DEPEND - "/opt/dtk-22.10/hip/include/hip/hip_common.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_vector_types.h" - "/opt/dtk-22.10/hip/include/hip/hip_version.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/channel_descriptor.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_ext_function.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_functions.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/device_library_decls.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/driver_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_atomic.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_ldg.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_runtime_pt_api.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_texture_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/hip_vector_types.hpp" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/host_defines.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/library_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/llvm_intrinsics.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/math_fwd.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/ockl_image.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_fetch_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_indirect_functions.h" - "/opt/dtk-22.10/hip/include/hip/hsa_detail/texture_types.h" - "/opt/dtk-22.10/hip/include/hip/library_types.h" - "/opt/dtk-22.10/hip/include/hip/surface_types.h" - "/opt/dtk-22.10/hip/include/hip/texture_types.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/__stddef_max_align_t.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/limits.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdarg.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stddef.h" - "/opt/dtk-22.10/llvm/lib/clang/14.0.0/include/stdint.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_common.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_vector_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hip_version.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/channel_descriptor.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_ext_function_.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/device_library_decls.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/driver_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_atomic.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_fp16_math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_ldg.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_runtime_api.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_surface_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/hip_vector_types.hpp" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/host_defines.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/llvm_intrinsics.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/math_fwd.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/ockl_image.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/surface_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_fetch_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_indirect_functions.h" + "/opt/dtk-22.04.2/hip/include/hip/hsa_detail/texture_types.h" + "/opt/dtk-22.04.2/hip/include/hip/library_types.h" + "/opt/dtk-22.04.2/hip/include/hip/texture_types.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_complex_builtins.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_cuda_math_forward_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_cmath.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_libdevice_declares.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_math.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/__clang_hip_runtime_wrapper.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/algorithm" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/complex" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/cuda_wrappers/new" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/limits.h" + "/opt/dtk-22.04.2/llvm/lib/clang/14.0.0/include/stdint.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/algorithm" "/opt/rh/devtoolset-7/root/usr/include/c++/7/array" "/opt/rh/devtoolset-7/root/usr/include/c++/7/backward/auto_ptr.h" @@ -177,11 +170,16 @@ SET(HIP_HIPCC_DEPEND "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr-default.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/gthr.h" "/opt/rh/devtoolset-7/root/usr/include/c++/7/x86_64-redhat-linux/bits/os_defines.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/device.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/errors.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/gpu_rocm.h" - "/public/home/zhangqha/deepmd-kit/source/lib/include/tabulate.h" - "/public/home/zhangqha/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/limits.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdarg.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stddef.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/stdint.h" + "/opt/rh/devtoolset-7/root/usr/lib/gcc/x86_64-redhat-linux/7/include/syslimits.h" + "/root/deepmd-kit/source/lib/include/device.h" + "/root/deepmd-kit/source/lib/include/errors.h" + "/root/deepmd-kit/source/lib/include/gpu_rocm.h" + "/root/deepmd-kit/source/lib/include/tabulate.h" + "/root/deepmd-kit/source/lib/src/rocm/tabulate.hip.cu" "/usr/include/_G_config.h" "/usr/include/alloca.h" "/usr/include/asm-generic/errno-base.h" diff --git a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/cmake_install.cmake b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/cmake_install.cmake index ec1d224..1969135 100644 --- a/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/cmake_install.cmake +++ b/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/cmake_install.cmake @@ -1,8 +1,8 @@ -# Install script for directory: /public/home/zhangqha/deepmd-kit/source/lib/src/rocm +# Install script for directory: /root/deepmd-kit/source/lib/src/rocm # Set the install prefix if(NOT DEFINED CMAKE_INSTALL_PREFIX) - set(CMAKE_INSTALL_PREFIX "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") + set(CMAKE_INSTALL_PREFIX "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-install") endif() string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") @@ -49,7 +49,7 @@ if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT FILE "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libdeepmd_op_rocm.so" RPATH "") endif() - file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/libdeepmd_op_rocm.so") + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/libdeepmd_op_rocm.so") if(EXISTS "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libdeepmd_op_rocm.so" AND NOT IS_SYMLINK "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libdeepmd_op_rocm.so") if(CMAKE_INSTALL_DO_STRIP) @@ -68,7 +68,7 @@ if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT FILE "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/deepmd/op/libdeepmd_op_rocm.so" RPATH "") endif() - file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/deepmd/op" TYPE SHARED_LIBRARY FILES "/public/home/zhangqha/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/libdeepmd_op_rocm.so") + file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/deepmd/op" TYPE SHARED_LIBRARY FILES "/root/deepmd-kit/_skbuild/linux-x86_64-3.7/cmake-build/lib/src/rocm/libdeepmd_op_rocm.so") if(EXISTS "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/deepmd/op/libdeepmd_op_rocm.so" AND NOT 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a/data/raw/raw_to_set.sh b/data/raw/raw_to_set.sh deleted file mode 100644 index 918d361..0000000 --- a/data/raw/raw_to_set.sh +++ /dev/null @@ -1,112 +0,0 @@ -#!/bin/bash - -nline_per_set=2000 - -if test $# -ge 1; then - nline_per_set=$1 -fi - -rm -fr set.* -echo nframe is `cat box.raw | wc -l` -echo nline per set is $nline_per_set - -split box.raw -l $nline_per_set -d -a 3 box.raw -split coord.raw -l $nline_per_set -d -a 3 coord.raw -test -f energy.raw && split energy.raw -l $nline_per_set -d -a 3 energy.raw -test -f force.raw && split force.raw -l $nline_per_set -d -a 3 force.raw -test -f virial.raw && split virial.raw -l $nline_per_set -d -a 3 virial.raw -test -f atom_ener.raw && split atom_ener.raw -l $nline_per_set -d -a 3 atom_ener.raw -test -f fparam.raw && split fparam.raw -l $nline_per_set -d -a 3 fparam.raw -test -f dipole.raw && split dipole.raw -l $nline_per_set -d -a 3 dipole.raw -test -f polarizability.raw && split polarizability.raw -l $nline_per_set -d -a 3 polarizability.raw -test -f atomic_dipole.raw && split atomic_dipole.raw -l $nline_per_set -d -a 3 atomic_dipole.raw -test -f atomic_polarizability.raw && split atomic_polarizability.raw -l $nline_per_set -d -a 3 atomic_polarizability.raw - -nset=`ls | grep box.raw[0-9] | wc -l` -nset_1=$(($nset-1)) -echo will make $nset sets - -for ii in `seq 0 $nset_1` -do - echo making set $ii ... - pi=`printf %03d $ii` - mkdir set.$pi - mv box.raw$pi set.$pi/box.raw - mv coord.raw$pi set.$pi/coord.raw - test -f energy.raw$pi && mv energy.raw$pi set.$pi/energy.raw - test -f force.raw$pi && mv force.raw$pi set.$pi/force.raw - test -f virial.raw$pi && mv virial.raw$pi set.$pi/virial.raw - test -f atom_ener.raw$pi && mv atom_ener.raw$pi set.$pi/atom_ener.raw - test -f fparam.raw$pi && mv fparam.raw$pi set.$pi/fparam.raw - test -f atomic_dipole.raw$pi && mv atomic_dipole.raw$pi set.$pi/atomic_dipole.raw - test -f atomic_polarizability.raw$pi && mv atomic_polarizability.raw$pi set.$pi/atomic_polarizability.raw - - cd set.$pi - python -c 'import numpy as np; data = np.loadtxt("box.raw" , ndmin = 2); data = data.astype (np.float32); np.save ("box", data)' - python -c 'import numpy as np; data = np.loadtxt("coord.raw" , ndmin = 2); data = data.astype (np.float32); np.save ("coord", data)' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("energy.raw"): - data = np.loadtxt("energy.raw"); - data = data.astype (np.float32); - np.save ("energy", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("force.raw" ): - data = np.loadtxt("force.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("force", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("virial.raw"): - data = np.loadtxt("virial.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("virial", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("atom_ener.raw"): - data = np.loadtxt("atom_ener.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("atom_ener", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("fparam.raw"): - data = np.loadtxt("fparam.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("fparam", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("dipole.raw"): - data = np.loadtxt("dipole.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("dipole", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("polarizability.raw"): - data = np.loadtxt("polarizability.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("polarizability", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("atomic_dipole.raw"): - data = np.loadtxt("atomic_dipole.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("atomic_dipole", data) -' - python -c \ -'import numpy as np; import os.path; -if os.path.isfile("atomic_polarizability.raw"): - data = np.loadtxt("atomic_polarizability.raw", ndmin = 2); - data = data.astype (np.float32); - np.save ("atomic_polarizability", data) -' - rm *.raw - cd ../ -done diff --git a/data/raw/shuffle_raw.py b/data/raw/shuffle_raw.py deleted file mode 100644 index 6c8e774..0000000 --- a/data/raw/shuffle_raw.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 - -import os -import numpy as np -import argparse - -def _parse_args () : - parser = argparse.ArgumentParser ( - description = "parse shuffle args" ) - parser.add_argument ("INPUT", default = ".", - help = "input dir of raw files") - parser.add_argument ("OUTPUT", default = ".", - help = "output dir of shuffled raw files") - parser.add_argument ('-r', '--raws', nargs = '+', default = [], - help = "raw files, if not set, then auto detect") - return parser.parse_args() - -def detect_raw (path) : - raws = [] - names = ["box.raw", "coord.raw", "energy.raw", "force.raw", "virial.raw"] - for ff in names : - if os.path.isfile (path + "/" + ff) : raws.append (ff) - return raws - -def _main () : - args = _parse_args () - raws = args.raws - inpath = args.INPUT - outpath = args.OUTPUT - - if not os.path.isdir (inpath): - print ("# no input dir " + inpath + ", exit") - return - - if not os.path.isdir (outpath) : - os.mkdir (outpath) - - if len(raws) == 0 : - raws = detect_raw (inpath) - - if len(raws) == 0 : - print ("# no file to shuffle, exit") - return - - assert ("box.raw" in raws) - tmp = np.loadtxt(os.path.join(inpath, "box.raw")) - tmp = np.reshape(tmp, [-1, 9]) - nframe = tmp.shape[0] - print(nframe) - - print ("# will shuffle raw files " + str(raws) + - " in dir " + inpath + - " and output to dir " + outpath) - - tmp = np.loadtxt (inpath + "/" + raws[0]) - tmp = np.reshape(tmp, [nframe, -1]) - nframe = tmp.shape[0] - idx = np.arange (nframe) - np.random.shuffle(idx) - - for ii in raws : - data = np.loadtxt(inpath + "/" + ii) - data = np.reshape(data, [nframe, -1]) - 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+endif (BUILD_CPP_IF) + +add_subdirectory (op/) +add_subdirectory (lib/) +if (BUILD_PY_IF) + add_subdirectory (config/) + # add_subdirectory (tests/) +endif (BUILD_PY_IF) +if (BUILD_CPP_IF) + add_subdirectory (api_cc/) + add_subdirectory (lmp/) + if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.8) + # add_subdirectory (md/) + add_subdirectory (ipi/) + add_subdirectory (gmx/) + endif () +endif (BUILD_CPP_IF) + +# uninstall target +configure_file( + "${CMAKE_CURRENT_SOURCE_DIR}/cmake/cmake_uninstall.cmake.in" + "${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake" + IMMEDIATE @ONLY) + +add_custom_target(uninstall + COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake) + +# lammps target +configure_file( + "${CMAKE_CURRENT_SOURCE_DIR}/cmake/cmake_lammps.cmake.in" + "${CMAKE_CURRENT_BINARY_DIR}/cmake_lammps.cmake" + IMMEDIATE @ONLY) + +add_custom_target(lammps + COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/cmake_lammps.cmake) + diff --git a/source/lib/src/rocm/CMakeLists.txt b/source/lib/src/rocm/CMakeLists.txt index 6773c60..9374666 100644 --- a/source/lib/src/rocm/CMakeLists.txt +++ b/source/lib/src/rocm/CMakeLists.txt @@ -12,7 +12,7 @@ add_definitions("-DCUB_IGNORE_DEPRECATED_CPP_DIALECT") message(STATUS "HIP major version is " ${HIP_VERSION_MAJOR}) -set (HIP_HIPCC_FLAGS ; -fno-gpu-rdc; --amdgpu-target=gfx906; -fPIC; -O3; --std=c++14) +set (HIP_HIPCC_FLAGS ; -fno-gpu-rdc; --amdgpu-target=gfx906; -fPIC; -O3; --std=c++11) file (GLOB SOURCE_FILES "*.hip.cu" ) -- GitLab

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directory: /root/deepmd-kit/source/op - -# 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") 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